feat(RP12): post-cleanup real-progression re-run on #163 + pipeline fixes #281
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@@ -16,6 +16,16 @@ search for failures, limitations, simpler alternatives, negative results
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## You MUST NOT
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do NOT advocate for the preferred approach
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## Disposition (MANDATORY — top-level field)
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Your dossier MUST carry a top-level "disposition" field, exactly one of:
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- SURVIVES — leading candidate withstands attack; no blocking gaps
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- SURVIVES_WITH_MITIGATIONS — viable with explicit required_mitigations
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- EVIDENCE_DEFICIENT — key claims unsupported → targeted OUTWARD
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- APPLICABILITY_DEFICIENT — Rune-fit was mapped wrong → APPLICABILITY
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- INNOVATION_WARRANTED — standard options killed, credible combination exists
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- NO_VIABLE_CANDIDATE — everything killed → expand the candidate space
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## Output
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Produce your stage artifact as structured text. End with:
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```
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consumed at 1788951395.044743
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consumed at 1788951877.631211
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consumed at 1788950449.0477326
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consumed at 1788950433.0232792
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consumed at 1788950467.3172934
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consumed at 1788956506.164559
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consumed at 1788955793.926691
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@@ -0,0 +1,52 @@
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{"type":"step_start","timestamp":1788953320541,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085ed5c5a001WquOCi90p","messageID":"msg_g001a085ed3dc1001E82MsfeLF","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","snapshot":"e400b0f575f7acfc46934d8ec5d67c23d9ab858d","type":"step-start"}}
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{"type":"text","timestamp":1788953472112,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085ef9e0e001b4K5gSJte","messageID":"msg_g001a085ed3dc1001E82MsfeLF","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"text","text":"I'll start by searching for a matching skill, and in parallel begin locating the code and prior research artifacts for issue #163.","time":{"start":1788953468430,"end":1788953472110}}}
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{"type":"tool_use","timestamp":1788953472117,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"skill_search","callID":"call_7eb4ad656cc7422999dd20dd","state":{"status":"completed","input":{"query":"Adversarial red-team review of research candidates against a codebase: attack leading design candidates (bitemporal graph memory, as-of queries, FTS5 entity resolution, DuckDB analytics tier) using src/rune/graph_memory.py and prior research dossier as input; output a structured adversarial.json dossier for a staged research pipeline on issue #163; audience is the research pipeline maintainer."},"output":"{\n \"status\": \"matched\",\n \"results\": [\n {\n \"skill_id\": \"firecrawl-agent\",\n \"name\": \"firecrawl-agent\",\n \"score\": 0.5973,\n \"reason\": \"The skill description matches these query terms: as, tier, structured, json, on.\"\n },\n {\n \"skill_id\": \"data-analytics\",\n \"name\": \"data-analytics\",\n \"score\": 0.3686,\n \"reason\": \"The skill description matches these query terms: design, analytic, is.\"\n },\n {\n \"skill_id\": \"super-research\",\n \"name\": \"super-research\",\n \"score\": 0.3945,\n \"reason\": \"The skill description matches these query terms: review, research, candidate, against, as, on, is.\"\n }\n ],\n \"loaded_skill_id\": null\n}","metadata":{"status":"matched","loadedSkillID":null,"truncated":false},"title":"Found 3 skills","time":{"start":1788953472111,"end":1788953472114}},"id":"prt_g001a085efab73001y7hThTOzK","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085ed3dc1001E82MsfeLF"}}
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{"type":"tool_use","timestamp":1788953472138,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"glob","callID":"call_89310c79566f46c3be861b5e","state":{"status":"completed","input":{"pattern":"**/graph_memory.py"},"output":"/mnt/workdrive/issue161-harness/src/rune/graph_memory.py","metadata":{"count":1,"truncated":false},"title":"","time":{"start":1788953472116,"end":1788953472137}},"id":"prt_g001a085efabff001iv7X7dz4Y","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085ed3dc1001E82MsfeLF"}}
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{"type":"tool_use","timestamp":1788953472159,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"glob","callID":"call_bfe3820847524762a6050a6d","state":{"status":"completed","input":{"pattern":"research/issue-163/**"},"output":"/mnt/workdrive/issue161-harness/research/issue-163/stage_ADVERSARIAL.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/stage_ADVERSARIAL.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-ADVERSARIAL-1\n/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_INNOVATION.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/innovation.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_INNOVATION.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-INNOVATION-1\n/mnt/workdrive/issue161-harness/research/issue-163/stage_APPLICABILITY.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/applicability.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_APPLICABILITY.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-APPLICABILITY-1\n/mnt/workdrive/issue161-harness/research/issue-163/stage_OUTWARD.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/outward.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_OUTWARD.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-OUTWARD-1\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-INWARD-1\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-ISSUE-1","metadata":{"count":18,"truncated":false},"title":"","time":{"start":1788953472117,"end":1788953472153}},"id":"prt_g001a085efac00001iHHFAHWby","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085ed3dc1001E82MsfeLF"}}
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{"type":"step_finish","timestamp":1788953472225,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085efac9e001dhcC0h07m","reason":"tool-calls","snapshot":"df7283f3b96387ad0f6103e49b3cbf71332c2edc","messageID":"msg_g001a085ed3dc1001E82MsfeLF","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"step-finish","tokens":{"total":33639,"input":28905,"output":156,"reasoning":4578,"cache":{"write":0,"read":0}},"cost":0}}
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{"type":"step_start","timestamp":1788953479093,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085efc7b3001hdUmUZfTJ","messageID":"msg_g001a085efad380013M8SX2T8K","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","snapshot":"8dd313d4b9b7e34c2a3cadf157a7c165f211dfa0","type":"step-start"}}
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{"type":"text","timestamp":1788953485023,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085efd4220018ibuDeM5W","messageID":"msg_g001a085efad380013M8SX2T8K","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"text","text":"The project lives at `/mnt/workdrive/issue161-harness`. No skill matched cleanly (the returned IDs are generic web/data tools, not research red-team workflows), so I'll proceed directly. Now reading the target source and the prior stage dossiers that produced the leading candidates.","time":{"start":1788953482274,"end":1788953485020}}}
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{"type":"tool_use","timestamp":1788953485035,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"read","callID":"call_cfb5fda436554d7e80b577e0","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/src/rune/graph_memory.py"},"output":"<path>/mnt/workdrive/issue161-harness/src/rune/graph_memory.py</path>\n<type>file</type>\n<content>\n1: #!/usr/bin/env python3\n2: \"\"\"Graph memory tier — concept-mediated temporal knowledge graph for the agent.\n3: \n4: Built from the 2025-2026 academic frontier (briefs in\n5: `/tmp/opencode/memory-research/agentic-memory-brief.md` and\n6: `~/.firecrawl/claim-audit-2026/brief.md`). Exceeds the entity-hub approach by\n7: following the research convergence:\n8: \n9: - CONCEPT-MEDIATED, not entity-centric (GAAMA 2603.27910): nodes are typed\n10: (person / project / concept / tool / preference / fact), which kills the\n11: \"mega-hub\" dilution Letta/Graphiti hit with one entity connected to\n12: everything.\n13: - BI-TEMPORAL edges (Graphiti 2501.13956): `valid_from/valid_until` (when\n14: the fact is true in the world) + `learned_at` (when we learned it).\n15: - INCREMENTAL construction (TG-RAG 2510.13590, iText2KG 2409.03284): merge\n16: only, no full re-index; entity resolution via aliases, never blind merges.\n17: - SUPERSEDE, never delete (Write-Time Gating 2603.15994, SSGM 2603.11768):\n18: contradicting edges are marked superseded with the new one taking over;\n19: history preserved for audit.\n20: - WRITE-TIME GATING (2603.15994, D-MEM 2603.14597): routine/salience-weak\n21: facts never touch the graph; surprises and contradictions do. The biggest\n22: token saver in the literature.\n23: - PROVENANCE on every edge (MemORAI 2605.01386): each edge carries the\n24: evidence text + source, which is exactly what the claim-audit layer needs.\n25: \n26: Storage: headless SQLite at `memory/graph/graph.sqlite` — reachable by the\n27: sleep-time curator directly (no MCP dependency). Schema below.\n28: \n29: Usage:\n30: graph_memory.py add <subj> <pred> <obj> --type subj_type --evidence \"...\"\n31: [--source X] [--confidence 0.0-1.0] [--strength N]\n32: graph_memory.py add-bulk <json-file>\n33: graph_memory.py query <entity-or-concept> [--hops N] [--latest-only]\n34: graph_memory.py resolve <name>\n35: graph_memory.py neighbors <entity>\n36: graph_memory.py stats\n37: graph_memory.py superseded-by <subj> <pred> <obj> (manual conflict flag)\n38: \"\"\"\n39: \n40: import argparse\n41: import json\n42: import os\n43: import sqlite3\n44: import sys\n45: from collections import deque\n46: from datetime import datetime, timezone\n47: \n48: _SD = os.path.dirname(os.path.abspath(__file__))\n49: if _SD not in sys.path:\n50: sys.path.insert(0, _SD)\n51: import memory_env\n52: \n53: MEM_DIR = memory_env.memory_dir()\n54: GRAPH_DIR = os.path.join(MEM_DIR, \"graph\")\n55: DB_PATH = os.path.join(GRAPH_DIR, \"graph.sqlite\")\n56: \n57: # Fixed predicate vocabulary — schema drift is a researched failure mode, so we\n58: # constrain the set of relationship types we accept. Unknown preds are refused\n59: # (the extractor maps free text onto this set).\n60: PREDICATES = {\n61: \"works_on\", \"works_at\", \"teaches\", \"manages\", \"uses\", \"prefers\",\n62: \"lives_in\", \"located_in\", \"has_role\", \"part_of\", \"leads\", \"builds\",\n63: \"develops\", \"created\", \"interested_in\", \"knows\", \"collaborates_with\",\n64: \"owned_by\", \"reports_to\", \"supports\", \"runs\", \"tracks\", \"related_to\",\n65: \"based_on\", \"happens_on\", \"started_on\", \"ended_on\", \"responds_to\",\n66: \"struggles_with\", \"likes\", \"dislikes\", \"avoided_by\", \"goal_of\",\n67: }\n68: \n69: # Node types.\n70: NODE_TYPES = {\"person\", \"project\", \"concept\", \"tool\", \"preference\",\n71: \"organization\", \"place\", \"event\", \"skill\", \"artefact\"}\n72: \n73: # Confidence: evidence-gated (EvoKG-style f(frequency, recency, source_cred)).\n74: # We fold the curator's strength (0-5) into a 0-1 confidence.\n75: DEFAULT_CONF = 0.7\n76: \n77: \n78: def now_iso():\n79: return datetime.now(timezone.utc).isoformat(timespec=\"seconds\")\n80: \n81: \n82: def connect():\n83: os.makedirs(GRAPH_DIR, exist_ok=True)\n84: db = sqlite3.connect(DB_PATH)\n85: try:\n86: db.row_factory = sqlite3.Row\n87: db.executescript(\"\"\"\n88: CREATE TABLE IF NOT EXISTS entities (\n89: id INTEGER PRIMARY KEY AUTOINCREMENT,\n90: name TEXT NOT NULL UNIQUE,\n91: type TEXT NOT NULL DEFAULT 'concept',\n92: summary TEXT DEFAULT '',\n93: created_at TEXT NOT NULL\n94: );\n95: CREATE TABLE IF NOT EXISTS aliases (\n96: entity_id INTEGER NOT NULL,\n97: alias TEXT NOT NULL,\n98: UNIQUE(entity_id, alias),\n99:Line truncated
|
||||
{"type":"tool_use","timestamp":1788953485038,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"read","callID":"call_104f69117b914e96981770f7","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/research/issue-163/outward.json"},"output":"<path>/mnt/workdrive/issue161-harness/research/issue-163/outward.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"stage\": \"OUTWARD\",\n3: \"process_id\": \"research-163-r1\",\n4: \"issue\": 163,\n5: \"round\": 1,\n6: \"subquestion\": \"Address: Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\",\n7: \"discovery\": {\n8: \"queries\": [\n9: {\n10: \"id\": \"Q1\",\n11: \"tool\": \"webfetch (DuckDuckGo HTML)\",\n12: \"query\": \"neo4j vs falkordb graph database benchmark comparison\",\n13: \"receipt\": \"fetched OK; 10 results returned inline\"\n14: },\n15: {\n16: \"id\": \"Q2\",\n17: \"tool\": \"webfetch (DuckDuckGo HTML)\",\n18: \"query\": \"graphiti zep temporal knowledge graph agent memory neo4j falkordb\",\n19: \"receipt\": \"fetched OK; 10 results returned inline\"\n20: },\n21: {\n22: \"id\": \"Q3\",\n23: \"tool\": \"webfetch (DuckDuckGo HTML)\",\n24: \"query\": \"ladybugdb graph database kuzu fork\",\n25: \"receipt\": \"fetched OK; 10 results returned inline\"\n26: },\n27: {\n28: \"id\": \"Q4\",\n29: \"tool\": \"webfetch (DuckDuckGo HTML)\",\n30: \"query\": \"falkordb redisgraph successor cypher features github\",\n31: \"receipt\": \"fetched OK; 10 results returned inline\"\n32: },\n33: {\n34: \"id\": \"Q5\",\n35: \"tool\": \"webfetch (DuckDuckGo HTML)\",\n36: \"query\": \"neo4j temporal versioning bitemporal cypher valid time\",\n37: \"receipt\": \"fetched OK; 10 results returned inline\"\n38: }\n39: ],\n40: \"sources_discovered\": [\n41: { \"id\": \"O1\", \"url\": \"https://aimultiple.com/graph-databases\", \"class\": \"external (independent benchmark, updated 2026-04-15)\", \"note\": \"SELECTED and read\" },\n42: { \"id\": \"O2\", \"url\": \"https://www.falkordb.com/blog/graph-database-performance-benchmarks-falkordb-vs-neo4j/\", \"class\": \"external (vendor blog, pub 2024-12-02, updated 2026-08-23)\", \"note\": \"SELECTED and read\" },\n43: { \"id\": \"O3\", \"url\": \"https://www.puppygraph.com/blog/falkordb-vs-neo4j\", \"class\": \"external (competing-vendor comparison)\", \"note\": \"not selected — competing vendor has stake in the comparison\" },\n44: { \"id\": \"O4\", \"url\": \"https://knodegraph.com/blog/falkordb-vs-neo4j/\", \"class\": \"external (third-party blog w/ 2026 benchmarks)\", \"note\": \"not selected — O1 covers the independent-benchmark slot with fuller methodology\" },\n45: { \"id\": \"O5\", \"url\": \"https://github.com/FalkorDB/benchmark\", \"class\": \"repo (vendor benchmark harness)\", \"note\": \"SELECTED; README 404 on master and main; repo metadata read via GitHub API instead\" },\n46: { \"id\": \"O6\", \"url\": \"https://www.plushcap.com/content/falkordb/blog/falkordb-falkordb-vs-neo4j-graph-database-performance-benchmarks\", \"class\": \"external (mirror of O2)\", \"note\": \"not selected — duplicate\" },\n47: { \"id\": \"O7\", \"url\": \"https://inferensys.com/differences/neuro-symbolic-ai-frameworks/knowledge-graph-construction-tools/neo4j-vs-falkordb\", \"class\": \"external (aggregator comparison)\", \"note\": \"not selected\" },\n48: { \"id\": \"O8\", \"url\": \"https://inferensys.com/differences/ai-software-factory-platforms/code-knowledge-graph-systems/neo4j-vs-falkordb-for-code-graphs\", \"class\": \"external (aggregator comparison)\", \"note\": \"not selected\" },\n49: { \"id\": \"O9\", \"url\": \"https://3nuggets.io/compare-neo4j-vs-falkordb\", \"class\": \"external (practitioner fit comparison)\", \"note\": \"not selected\" },\n50: { \"id\": \"O10\", \"url\": \"https://github.com/getzep/graphiti\", \"class\": \"repo (Graphiti primary)\", \"note\": \"SELECTED and read (README via raw.githubusercontent.com/main)\" },\n51: { \"id\": \"O11\", \"url\": \"https://neo4j.com/blog/developer/graphiti-knowledge-graph-memory/\", \"class\": \"external (Neo4j official blog on Graphiti)\", \"note\": \"not selected — O10 is primary\" },\n52: { \"id\": \"O12\", \"url\": \"https://docs.falkordb.com/agentic-memory/graphiti\", \"class\": \"external (FalkorDB vendor docs)\", \"note\": \"not selected\" },\n53: { \"id\": \"O13\", \"url\": \"https://github.com/Memory-Agents/graphiti/\", \"class\": \"repo (mirror)\", \"note\": \"not selected — duplicate of O10\" },\n54: { \"id\": \"O14\", \"url\": \"https://codex.danielvaughan.com/2026/03/30/graphiti-agent-memory-store/\", \"class\": \"external (practitioner Line truncated
|
||||
{"type":"tool_use","timestamp":1788953485040,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"read","callID":"call_5d82ba99eff74391b4ea244f","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/research/issue-163/applicability.json"},"output":"<path>/mnt/workdrive/issue161-harness/research/issue-163/applicability.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"stage\": \"APPLICABILITY\",\n3: \"issue\": 163,\n4: \"round\": 1,\n5: \"process_id\": \"research-163-r1\",\n6: \"grounding\": {\n7: \"inward_claims_used\": [\"I3\", \"I1\", \"I4\", \"I5\"],\n8: \"rune_reality\": \"src/rune/graph_memory.py is Rune's entire graph surface: a headless SQLite tier at memory/graph/graph.sqlite (no external graph DB dependency). Schema (lines 101-116) already implements: bi-temporal edges (valid_from/valid_until + learned_at), supersede-never-delete with status/superseded_by, per-edge provenance (evidence/source), confidence-gated writes (strength<3 gated out), typed concept-mediated nodes, and alias-based entity resolution. Query is name/alias/substring entry point + BFS over status='active' edges.\",\n9: \"load_bearing_fact\": \"The dossier's repo_grounding_note and Graphiti (E17) confirm temporal semantics live at the framework layer above any backend. Rune already occupies that layer over SQLite. Neo4j and FalkorDB would be backends BELOW a layer Rune already owns, and neither offers native temporal versioning (E27, E28) - Rune's schema is already ahead of both on the spike's titular dimension.\"\n10: },\n11: \"candidates\": [\n12: {\n13: \"id\": \"C1\",\n14: \"candidate\": \"Neo4j as replacement graph backend\",\n15: \"source_ids\": [\"O1\", \"O2\", \"O38\", \"O41\"],\n16: \"classification\": \"CONFLICTS_WITH_RUNE\",\n17: \"integration_point\": \"Would replace src/rune/graph_memory.py's SQLite tier (memory/graph/graph.sqlite) with a server-backed Neo4j deployment reachable from curator.py / sleep_time.py.\",\n18: \"benefit\": \"Deepest ecosystem, best planner on heavy aggregations (E3), mature tooling (E8).\",\n19: \"cost\": \"Server + JVM process to operate; ~2.7GB heap for a 381K-node graph, ~5.2GB process RSS (E6) - two orders of magnitude above Rune's sub-1M-entity regime; 90ms cold start vs embedded alternatives (E4).\",\n20: \"architectural_fit\": \"Poor. Rune is embedded/local-first with zero external service dependencies (I3: 'reachable by the sleep-time curator directly, no MCP dependency'). A networked JVM server reverses that posture. No source benchmarks Rune's single-node regime (dossier limitation #2).\",\n21: \"authority_implications\": \"Introduces an external service and credentials into the memory tier; curator/sleep-time write authority would route through a network daemon instead of a local file.\",\n22: \"risks\": \"Licensing (Community vs Enterprise constraints unevidenced, dossier limitation #5); no native temporal versioning - the spike's core dimension (E28-E30); migration cost for zero evidenced benefit at Rune's scale.\",\n23: \"required_adaptation\": \"None - rejection is the correct disposition.\",\n24: \"evidence\": [\"E2\", \"E3\", \"E4\", \"E6\", \"E8\", \"E27\", \"E28\", \"E29\"]\n25: },\n26: {\n27: \"id\": \"C2\",\n28: \"candidate\": \"FalkorDB as replacement graph backend\",\n29: \"source_ids\": [\"O1\", \"O2\", \"O5\", \"O29\", \"O10\"],\n30: \"classification\": \"CONFLICTS_WITH_RUNE\",\n31: \"integration_point\": \"Would replace the SQLite tier; only embedded path is Graphiti's falkordblite extra (redislite-based, Python 3.12+) per E16.\",\n32: \"benefit\": \"Strong concurrent throughput and low latency at scale (E2, E3, E9), implicit indexing (E5), GraphRAG positioning (E10).\",\n33: \"cost\": \"Server-shaped: Redis 7.4 module, own container/ports, min 4GB RAM / 16GB recommended (E12, E25, E26). SSPLv1 core license (E25). Rust core rewrite (E11) resets part of the maturity story (dossier contradiction #3).\",\n34: \"architectural_fit\": \"Poor. The headline advantages (6.7x throughput, p50/p99 wins) were measured on 381K-804K-element graphs under 8-32 thread concurrency (E1, E9) - not Rune's regime (single-node, sub-1M entities, agent-local). Vendor magnitudes are marketing-adjacent (dossier contradiction #1; O5 benchmark harness README unverifiable, E13).\",\n35: \"authority_implications\": \"A Redis server in the memory path; even falkordblite drags in a redislite dependency and a Python 3.12 floor for a lateral move from SQLite.\",\n36: \"risks\": \"RedisGraph lineage EOL'd Jan 2025 (dossier contradiction #3); no evidenced FalkorDB-native temporal features (dossier limitation #4); license incompatibility with Rune's MIT-family posture.\",\n37: \"required_adaptation\": \"None - rejection is the correct disposition.\",\n38: \"evidence\": [\Line truncated
|
||||
{"type":"step_finish","timestamp":1788953485067,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085efdef0001I7iUA24Cj","reason":"tool-calls","snapshot":"7f44c901fbeaeaeffe91ef6a64bd139fc86a06a3","messageID":"msg_g001a085efad380013M8SX2T8K","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"step-finish","tokens":{"total":33491,"input":4348,"output":139,"reasoning":140,"cache":{"write":0,"read":28864}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788953491295,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085eff75d001MqxPOtMMv","messageID":"msg_g001a085efdf2c001f54mSaXmz","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","snapshot":"3e9cf3c444aed3a716a631814ecd2cd1b15f9d80","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788953649717,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085f25394001Lu1reehSy","messageID":"msg_g001a085efdf2c001f54mSaXmz","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"text","text":"I have the source and the two prior dossiers. The applicability stage's `next_stage_hint` explicitly asks ADVERSARIAL to attack C5's interval semantics and C6's write amplification — I'll go further than that. Now I need the INNOVATION stage output, the task tree, and the repo's constraint documents (authority map, rebuildable-projections, P0/P1, memory_selection ownership) to attack against.","time":{"start":1788953645972,"end":1788953649715}}}
|
||||
{"type":"tool_use","timestamp":1788953649731,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"read","callID":"call_7dfad59741464e799a1ae5f0","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json"},"output":"<path>/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"issue\": 163,\n3: \"round\": 1,\n4: \"current_stage\": \"ADVERSARIAL\",\n5: \"completed_stages\": [\n6: \"ISSUE\",\n7: \"INWARD\",\n8: \"OUTWARD\",\n9: \"APPLICABILITY\",\n10: \"INNOVATION\"\n11: ],\n12: \"artifacts\": {\n13: \"ISSUE\": {\n14: \"contract\": {\n15: \"issue\": 163,\n16: \"title\": \"Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\",\n17: \"question_type\": \"mixed\",\n18: \"sub_questions\": [\n19: \"Address: Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\"\n20: ],\n21: \"required_stages\": [\n22: \"ISSUE\",\n23: \"INWARD\",\n24: \"OUTWARD\",\n25: \"ADVERSARIAL\",\n26: \"INNOVATION\",\n27: \"SYNTHESIS\",\n28: \"QUALIFY\"\n29: ],\n30: \"skipped_stages\": {\n31: \"APPLICABILITY\": \"No OUTWARD research to ground against Rune.\"\n32: },\n33: \"evidence_classes\": [\n34: \"repo\",\n35: \"external\"\n36: ],\n37: \"sufficient_evidence\": \"\\u22652 code-path citations for INWARD; \\u22652 unique URLs for OUTWARD (if required)\",\n38: \"expected_artifact\": \"synthesis.json\",\n39: \"ambiguous\": false,\n40: \"needs_mimo\": false,\n41: \"source\": \"deterministic\"\n42: },\n43: \"deterministic\": true\n44: },\n45: \"INWARD\": {\n46: \"context\": {\n47: \"keywords\": [\n48: \"temporal\",\n49: \"graph\",\n50: \"backend\",\n51: \"spike\",\n52: \"neo\",\n53: \"falkordb\",\n54: \"ladybugdb\",\n55: \"experimental\",\n56: \"graphiti\",\n57: \"reference\",\n58: \"plain\",\n59: \"summary\",\n60: \"architecture_frontier_handoff\",\n61: \"compare\",\n62: \"graph\",\n63: \"backends\",\n64: \"rune\",\n65: \"structural\",\n66: \"projection\",\n67: \"episodes\"\n68: ],\n69: \"umbrella_matches\": [\n70: {\n71: \"umbrella\": \"memory\",\n72: \"module\": \"graph_memory\",\n73: \"keyword\": \"graph\"\n74: },\n75: {\n76: \"umbrella\": \"memory\",\n77: \"module\": \"graph_shard_bridge\",\n78: \"keyword\": \"graph\"\n79: },\n80: {\n81: \"umbrella\": \"orchestration\",\n82: \"module\": \"rune_operator\",\n83: \"keyword\": \"rune\"\n84: },\n85: {\n86: \"umbrella\": \"orchestration\",\n87: \"module\": \"rune_tui\",\n88: \"keyword\": \"rune\"\n89: },\n90: {\n91: \"umbrella\": \"orchestration\",\n92: \"module\": \"rune_graphic\",\n93: \"keyword\": \"graph\"\n94: },\n95: {\n96: \"umbrella\": \"orchestration\",\n97: \"module\": \"rune_spinner\",\n98: \"keyword\": \"rune\"\n99: },\n100: {\n101: \"umbrella\": \"orchestration\",\n102: \"module\": \"rune_profiler\",\n103: \"keyword\": \"rune\"\n104: }\n105: ],\n106: \"candidate_files\": [\n107: \"src/rune/curator.py\",\n108: \"src/rune/rune_tui.py\",\n109: \"src/rune/graph_memory.py\",\n110: \"src/rune/sleep_time.py\",\n111: \"src/rune/elastic_cognition.py\",\n112: \"src/rune/rune_operator.py\",\n113: \"src/rune/rune_profiler.py\",\n114: \"src/rune/warm_router.py\",\n115: \"src/rune/abstraction.py\",\n116: \"src/rune/canary_native.py\",\n117: \"src/rune/canary_p0_04.py\",\n118: \"src/rune/canary_p1_07.py\",\n119: \"src/rune/complexity_scorer.py\",\n120: \"src/rune/identity_anchors.py\",\n121: \"src/rune/memory_consolidation.py\"\n122: ],\n123: \"candidate_tests\": [\n124: \"tests/test_retrieval_benchmark.py\",\n125: \"tests/test_orchestration_memory_synthesis.py\",\n126: \"tests/test_p1_03_memory_selection.py\",\n127: \"tests/test_p1_06_model_routing.py\",\n128: \"tests/test_p1_07_runtime_objects.py\",\n129: \"tests/test_version_converLine truncated
|
||||
{"type":"tool_use","timestamp":1788953649733,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"read","callID":"call_f93f8f24940c48c8a259428e","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/research/issue-163/innovation.json"},"output":"<path>/mnt/workdrive/issue161-harness/research/issue-163/innovation.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"stage\": \"INNOVATION\",\n3: \"issue\": 163,\n4: \"round\": 1,\n5: \"process_id\": \"research-163-r1\",\n6: \"route\": \"INNOVATION_WARRANTED\",\n7: \"conclusion\": \"ADAPT\",\n8: \"conclusion_rationale\": \"The standard mechanism (SQLite graph tier queried via status='active' only, exact/alias/substring entry resolution) is NOT sufficient: it cannot read the bi-temporal history Rune already persists (every superseded edge is stored but unreachable through query()), and its entry point caps practical recall on paraphrase/typo. No standard drop-in closes either gap. However, the warranted innovation is strictly adapted-and-compositional, not novel-for-its-own-sake: NEP-001's AS-OF WHERE-convention (E28-E30) and Graphiti's hybrid-retrieval entry tier (E18) are adapted onto Rune's existing framework-over-SQLite layer (which already implements the semantic model 1:1, E14/E17 — C4), producing a 'temporal+lexical query layer over graph.sqlite'. Backend migration is explicitly out of scope (C1/C2/C3 rejected or deferred at APPLICABILITY; C9 tracked, not proposed).\",\n9: \"candidates\": [\n10: {\n11: \"id\": \"INN-1\",\n12: \"category\": \"ADAPT\",\n13: \"proposal\": \"A unified temporal+lexical query layer over the existing graph.sqlite, inside src/rune/graph_memory.py, with zero schema or backend change. Two read-layer components that share one integration surface (query(), lines 217-284): (1) TEMPORAL — an --as-of <iso8601> parameter that replaces the status='active' filter with valid_from <= t AND (valid_until IS NULL OR t < valid_until) AND learned_at <= t, applied identically to the outgoing BFS frontier (line 254) and the incoming-edge query (line 275); BFS structure unchanged; default behavior (no flag) keeps the current active-only fast path. (2) LEXICAL — a SQLite FTS5 virtual table over entities(name, summary) and aliases(alias), inserted as a fallback tier in entity resolution (lines 224-237) with lookup order exact -> alias -> FTS5 BM25 -> substring-LIKE; index maintained on the existing entity/alias write path (resolve_entity, add_edge) in the same DB file. Full embedding tier deliberately omitted — model dependencies are disproportionate to Rune's sub-1M regime.\",\n14: \"derived_from\": [\"E28\", \"E29\", \"E30\", \"E18\", \"E14\", \"E17\", \"E27\"],\n15: \"source_ids\": [\"O38\", \"O41\", \"O10\", \"O1\"],\n16: \"composition_of\": [\n17: \"C5 (NEP-001 Phase 1 'AS OF' syntactic sugar, APPLICABLE_WITH_ADAPTATION)\",\n18: \"C6 (Graphiti hybrid retrieval, reduced to its lexical BM25 tier, APPLICABLE_WITH_ADAPTATION)\",\n19: \"Rune's existing bi-temporal edges schema + supersede-not-delete history (already integrated per C4)\"\n20: ],\n21: \"novelty\": \"compositional\",\n22: \"expected_gain\": \"Two material capabilities the status quo lacks, both read-only on data already on disk: (a) time-travel queries — every contradiction ever superseded becomes queryable as-of any past timestamp, closing the exact gap NEP-001 documents (E29: hand-written temporal WHERE clauses are error-prone and do not compose; E30: Phase 1 is low complexity and delivers most of the value); (b) recall lift at the entry point — paraphrase/typo/alternate-naming terms that currently fail resolution fall through to BM25, which Graphiti credits for its sub-second retrieval (E18), without any new service or dependency (FTS5 is in-process SQLite). Combined, the graph's stored history and practical recall stop being capped by the two read-layer shortcuts.\",\n23: \"risks\": \"(1) learned_at is a transaction-time proxy, not true bitemporality (E30 defers the real thing at 'Very high' complexity) — must be documented as as-of-knowledge approximation. (2) Half-open-interval and NULL-as-open-ended pitfalls are the documented failure mode of exactly this convention (E29) — they become a mandatory test checklist, not an afterthought. (3) FTS5 write amplification on add-bulk paths and index/tokenizer decisions. (4) BM25 ranking quality is evidenced only via Graphiti's claim, not measured at Rune's regime (dossier limitation #7) — hence the kill test below. (5) No Rune-regime benchmark exists (dossier limitation #2), so gains are predicted, not measured, until the kill test runs.\",\n24: \"kill_test\": \"Rune-local benchmark on the real curator vocabulary and a seeded history: (a) TEMPORAL — if querying as-of realistic historical timestamps returns fact sets identical to the active-only set for every t Line truncated
|
||||
{"type":"tool_use","timestamp":1788953649735,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"read","callID":"call_1ecfaf351dc944319a96a433","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/src/rune/memory_env.py"},"output":"<path>/mnt/workdrive/issue161-harness/src/rune/memory_env.py</path>\n<type>file</type>\n<content>\n1: #!/usr/bin/env python3\n2: \"\"\"memory_env.py — portable path resolution for the memory system.\n3: \n4: Every path in the package is derived from a small set of roots that can be\n5: overridden via environment variables, so the same code runs on ANY machine:\n6: \n7: RUNE_HOME Rune home directory (default /mnt/workdrive/rune)\n8: RUNE_MEMORY_DIR memory data root (default <home>/memory)\n9: RUNE_SCRIPTS_DIR where the scripts live (default <home>/src/rune)\n10: RUNE_STORE_DB explicit store path (default <memory>/store/memory.db)\n11: \n12: Rules:\n13: - Never hardcode a user path; always go through these helpers.\n14: - Scripts invoked as `python <script>` will use sys.executable for child\n15: processes (same venv), which is portable by construction.\n16: \"\"\"\n17: \n18: import os\n19: import shutil\n20: import sys\n21: \n22: \n23: def expand(p):\n24: \"\"\"expanduser + expandvars, for env values that may contain ~ or $HOME.\"\"\"\n25: if not p:\n26: return p\n27: return os.path.expanduser(os.path.expandvars(p))\n28: \n29: \n30: def rune_home():\n31: \"\"\"Return the Rune home directory.\"\"\"\n32: return expand(os.environ.get(\"RUNE_HOME\", \"/mnt/workdrive/rune\"))\n33: \n34: \n35: def memory_dir():\n36: \"\"\"Return the memory data directory.\"\"\"\n37: return expand(os.environ.get(\n38: \"RUNE_MEMORY_DIR\",\n39: os.path.join(rune_home(), \"memory\")))\n40: \n41: \n42: def scripts_dir():\n43: \"\"\"Return the scripts directory.\"\"\"\n44: return expand(os.environ.get(\n45: \"RUNE_SCRIPTS_DIR\",\n46: os.path.dirname(os.path.abspath(__file__))))\n47: \n48: \n49: def store_db():\n50: \"\"\"Return the path to the memory store database.\"\"\"\n51: return expand(os.environ.get(\n52: \"RUNE_STORE_DB\",\n53: os.path.join(memory_dir(), \"store\", \"memory.db\")))\n54: \n55: \n56: def config_dir():\n57: \"\"\"Return the config directory.\"\"\"\n58: return expand(os.environ.get(\n59: \"RUNE_CONFIG_DIR\",\n60: os.path.join(rune_home(), \"config\")))\n61: \n62: \n63: def python_bin():\n64: \"\"\"Return the Python interpreter path.\"\"\"\n65: return sys.executable or shutil.which(\"python3\") or \"python3\"\n66: \n67: \n68: # Plugin metadata for microkernel discovery\n69: PLUGIN = {\n70: \"name\": \"memory_env\",\n71: \"version\": \"1.0.0\",\n72: \"description\": \"Portable path resolution for the memory system\",\n73: \"capabilities\": [\"memory_env\"],\n74: \"hooks\": [],\n75: }\n76: \n77: \n78: def handle_hook(event, data=None):\n79: \"\"\"Hook handler for microkernel integration.\"\"\"\n80: return None\n\n(End of file - total 80 lines)\n</content>","metadata":{"preview":"#!/usr/bin/env python3\n\"\"\"memory_env.py — portable path resolution for the memory system.\n\nEvery path in the package is derived from a small set of roots that can be\noverridden via environment variables, so the same code runs on ANY machine:\n\n RUNE_HOME Rune home directory (default /mnt/workdrive/rune)\n RUNE_MEMORY_DIR memory data root (default <home>/memory)\n RUNE_SCRIPTS_DIR where the scripts live (default <home>/src/rune)\n RUNE_STORE_DB explicit store path (default <memory>/store/memory.db)\n\nRules:\n - Never hardcode a user path; always go through these helpers.\n - Scripts invoked as `python <script>` will use sys.executable for child\n processes (same venv), which is portable by construction.\n\"\"\"\n\nimport os\nimport shutil\nimport sys","truncated":false,"loaded":[]},"title":"src/rune/memory_env.py","time":{"start":1788953649718,"end":1788953649734}},"id":"prt_g001a085f26204001YBegcfUjf","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085efdf2c001f54mSaXmz"}}
|
||||
{"type":"tool_use","timestamp":1788953649752,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"grep","callID":"call_6de7fe5fa5f2429f9553fd6f","state":{"status":"completed","input":{"path":"/mnt/workdrive/issue161-harness","pattern":"memory_selection|rebuildable|authority map|P0/P1"},"output":"Found 84 matches\n/mnt/workdrive/issue161-harness/research/issue-163/stage_ADVERSARIAL.events.jsonl:\n Line 15: {\"type\":\"text\",\"timestamp\":1788953649717,\"sessionID\":\"ses_-ffe5f7a12c34effeSzvjfsvWr\",\"part\":{\"id\":\"prt_g001a085f25394001Lu1reehSy\",\"messageID\":\"msg_g001a085efdf2c001f54mSaXmz\",\"sessionID\":\"ses_-ffe5f7a12c34effeSzvjfsvWr\",\"type\":\"text\",\"text\":\"I have the source and the two prior dossiers. The applicability stage's `next_stage_hint` explicitly asks ADVERSARIAL to attack C5's interval semantics and C6's write amplification — I'll go further than that. Now I need the INNOVATION stage output, the task tree, and the repo's constraint documents (authority map, rebuildable-projections, P0/P1, memory_selection ownership) to attack against.\",\"time\":{\"start\":1788953645972,\"end\":1788953649715}}}\n\n\n/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json:\n Line 126: \"tests/test_p1_03_memory_selection.py\",\n\n\n/mnt/workdrive/issue161-harness/scripts/research_stage_executor.py:\n Line 659: \"- Rune constraint violations: authority map, rebuildable-projections rule,\",\n\n Line 660: \" P0/P1 boundaries, memory_selection ownership.\",\n\n\n/mnt/workdrive/issue161-harness/research/issue-163/stage_APPLICABILITY.events.jsonl:\n Line 9: {\"type\":\"tool_use\",\"timestamp\":1788951418913,\"sessionID\":\"ses_-ffe5f7a2fff97ffeShe3jrJEB\",\"part\":{\"type\":\"tool\",\"tool\":\"read\",\"callID\":\"call_9f981917f1c7420084d6cfeb\",\"state\":{\"status\":\"completed\",\"input\":{\"file_path\":\"research/issue-163/task_tree.json\"},\"output\":\"<path>/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json</path>\\n<type>file</type>\\n<content>\\n1: {\\n2: \\\"issue\\\": 163,\\n3: \\\"round\\\": 1,\\n4: \\\"current_stage\\\": \\\"APPLICABILITY\\\",\\n5: \\\"completed_stages\\\": [\\n6: \\\"ISSUE\\\",\\n7: \\\"INWARD\\\",\\n8: \\\"OUTWARD\\\"\\n9: ],\\n10: \\\"artifacts\\\": {\\n11: \\\"ISSUE\\\": {\\n12: \\\"contract\\\": {\\n13: \\\"issue\\\": 163,\\n14: \\\"title\\\": \\\"Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\\\",\\n15: \\\"question_type\\\": \\\"mixed\\\",\\n16: \\\"sub_questions\\\": [\\n17: \\\"Address: Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\\\"\\n18: ],\\n19: \\\"required_stages\\\": [\\n20: \\\"ISSUE\\\",\\n21: \\\"INWARD\\\",\\n22: \\\"OUTWARD\\\",\\n23: \\\"ADVERSARIAL\\\",\\n24: \\\"INNOVATION\\\",\\n25: \\\"SYNTHESIS\\\",\\n26: \\\"QUALIFY\\\"\\n27: ],\\n28: \\\"skipped_stages\\\": {\\n29: \\\"APPLICABILITY\\\": \\\"No OUTWARD research to ground against Rune.\\\"\\n30: },\\n31: \\\"evidence_classes\\\": [\\n32: \\\"repo\\\",\\n33: \\\"external\\\"\\n34: ],\\n35: \\\"sufficient_evidence\\\": \\\"\\\\u22652 code-path citations for INWARD; \\\\u22652 unique URLs for OUTWARD (if required)\\\",\\n36: \\\"expected_artifact\\\": \\\"synthesis.json\\\",\\n37: \\\"ambiguous\\\": false,\\n38: \\\"needs_mimo\\\": false,\\n39: \\\"source\\\": \\\"deterministic\\\"\\n40: },\\n41: \\\"deterministic\\\": true\\n42: },\\n43: \\\"INWARD\\\": {\\n44: \\\"context\\\": {\\n45: \\\"keywords\\\": [\\n46: \\\"temporal\\\",\\n47: \\\"graph\\\",\\n48: ...\n\n/mnt/workdrive/issue161-harness/research/issue-163/stage_OUTWARD.events.jsonl:\n Line 20: {\"type\":\"tool_use\",\"timestamp\":1788950613864,\"sessionID\":\"ses_-ffe5f7a3e2805ffeYaOOBiQE1\",\"part\":{\"type\":\"tool\",\"tool\":\"bash\",\"callID\":\"call_4b5521c9a4ca454caa492b08\",\"state\":{\"status\":\"completed\",\"input\":{\"command\":\"python3 -c \\\"\\nimport json\\nt=json.load(open('/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json'))\\nprint(json.dumps(t, indent=1)[:4000])\\n\\\"\",\"description\":\"Read issue-163 task tree JSON\"},\"output\":\"{\\n \\\"issue\\\": 163,\\n \\\"round\\\": 1,\\n \\\"current_stage\\\": \\\"OUTWARD\\\",\\n \\\"completed_stages\\\": [\\n \\\"ISSUE\\\",\\n \\\"INWARD\\\"\\n ],\\n \\\"artifacts\\\": {\\n \\\"ISSUE\\\": {\\n \\\"contract\\\": {\\n \\\"issue\\\": 163,\\n \\\"title\\\": \\\"Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\\\",\\n \\\"question_type\\\": \\\"mixed\\\",\\n \\\"sub_questions\\\": [\\n \\\"AddresLine truncated
|
||||
{"type":"step_finish","timestamp":1788953649780,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085f26257001noPhTBQkN","reason":"tool-calls","snapshot":"77ec50e9da7dbc6de01cc65349af49e83ee34f6d","messageID":"msg_g001a085efdf2c001f54mSaXmz","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"step-finish","tokens":{"total":56895,"input":19068,"output":210,"reasoning":4465,"cache":{"write":0,"read":33152}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788953672525,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085f2bb4b001O1nhtvG30","messageID":"msg_g001a085f262af001rZ0ImFwSv","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","snapshot":"1006aee06248f9e11ad0817d04533645799530e5","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788953842871,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085f54129001oy5mloUME","messageID":"msg_g001a085f262af001rZ0ImFwSv","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"text","text":"I now have the full candidate chain (C4/C5/C6/C9 → INN-1) and the OUTWARD dossier with its receipts. Several things already look attackable in the source (`valid_until` appears write-dead; supersession keys only on identical triples). Before writing the dossier I need to verify: who actually calls `graph_memory`, whether anything ever writes `valid_until`/`expired`, what `memory_store.py` already does with FTS5 (duplicate-authority check), and the authority-map / rebuildable-projections constraints.","time":{"start":1788953837865,"end":1788953842862}}}
|
||||
{"type":"tool_use","timestamp":1788953842885,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"grep","callID":"call_129013ab350745d9ac5c4b7d","state":{"status":"completed","input":{"path":"/mnt/workdrive/issue161-harness/src/rune","pattern":"graph_memory"},"output":"Found 38 matches\n/mnt/workdrive/issue161-harness/src/rune/rune_tui.py:\n Line 63: import graph_memory\n\n Line 1840: \"graph_memory\",\n\n Line 2553: db = graph_memory.connect()\n\n Line 2555: stats = graph_memory.stats(db)\n\n\n/mnt/workdrive/issue161-harness/src/rune/warm_router.py:\n Line 732: \"graph_memory.py\"),\n\n Line 739: # AUDIT FIX 2026-08-16: graph_memory.query returns a LIST of facts ([] for\n\n\n/mnt/workdrive/issue161-harness/src/rune/rune_operator.py:\n Line 1060: - Graph memory: ~/.config/opencode/memory/graph.db (entities and relationships, query with graph_memory.resolve_entity())\n\n Line 1068: When asked about connections, I query graph_memory.resolve_entity().\n\n\n/mnt/workdrive/issue161-harness/src/rune/curator.py:\n Line 1381: \"graph_memory.py\"),\n\n\n/mnt/workdrive/issue161-harness/src/rune/canary_native.py:\n Line 37: \"graph_memory\",\n\n Line 105: \"graph_memory\": [(\"connect\", {})],\n\n\n/mnt/workdrive/issue161-harness/src/rune/memory_selector.py:\n Line 362: \"\"\"Graph source: entity BFS from the task words via graph_memory.query,\n\n Line 367: import graph_memory\n\n Line 369: import rune.graph_memory as graph_memory # type: ignore[no-redef]\n\n Line 371: db = graph_memory.connect()\n\n Line 376: result = graph_memory.query(db, word, hops=1)\n\n Line 386: out.append({\"text\": text, \"score\": score, \"kind\": \"graph\", \"source\": \"graph_memory.query\"})\n\n\n/mnt/workdrive/issue161-harness/src/rune/skills/llm-craft/full_audit.py:\n Line 44: \"memory_store.py\", \"memory_env.py\", \"warm_neuron_store.py\", \"graph_memory.py\",\n\n\n/mnt/workdrive/issue161-harness/src/rune/graph_memory.py:\n Line 30: graph_memory.py add <subj> <pred> <obj> --type subj_type --evidence \"...\"\n\n Line 32: graph_memory.py add-bulk <json-file>\n\n Line 33: graph_memory.py query <entity-or-concept> [--hops N] [--latest-only]\n\n Line 34: graph_memory.py resolve <name>\n\n Line 35: graph_memory.py neighbors <entity>\n\n Line 36: graph_memory.py stats\n\n Line 37: graph_memory.py superseded-by <subj> <pred> <obj> (manual conflict flag)\n\n Line 169: logging.getLogger(\"graph_memory\").warning(\n\n Line 176: logging.getLogger(\"graph_memory\").debug(\n\n Line 388: \"name\": \"graph_memory\",\n\n Line 403: \"graph_memory\",\n\n\n/mnt/workdrive/issue161-harness/src/rune/memory_consolidation.py:\n Line 431: import graph_memory\n\n Line 432: db = graph_memory.connect()\n\n\n/mnt/workdrive/issue161-harness/src/rune/dependency_tracker.py:\n Line 41: \"graph_memory.py\": [\n\n\n/mnt/workdrive/issue161-harness/src/rune/contracts.py:\n Line 233: name=\"graph_memory.connect returns connection\",\n\n Line 234: module=\"graph_memory\", function=\"connect\",\n\n Line 239: name=\"graph_memory.stats callable\",\n\n Line 240: module=\"graph_memory\", function=\"stats\",\n\n\n/mnt/workdrive/issue161-harness/src/rune/canary_p0_04.py:\n Line 99: \"graph_memory.resolve\",\n\n Line 118: \"graph_memory\",\n","metadata":{"matches":38,"truncated":false},"title":"graph_memory","time":{"start":1788953842869,"end":1788953842884}},"id":"prt_g001a085f553440019FgMbRWVh","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085f262af001rZ0ImFwSv"}}
|
||||
{"type":"tool_use","timestamp":1788953842887,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"grep","callID":"call_2c1758d892294c2a9ce08826","state":{"status":"completed","input":{"path":"/mnt/workdrive/issue161-harness/src/rune","pattern":"valid_until"},"output":"Found 16 matches\n/mnt/workdrive/issue161-harness/src/rune/memory_store.py:\n Line 22: topic, entities, source, method, status, valid_from, valid_until)\n\n Line 144: valid_until TEXT\n\n Line 509: valid_until=None,\n\n Line 542: \"topic, entities, source, method, status, valid_from, valid_until, \"\n\n Line 556: valid_until,\n\n Line 1263: def update_entry(db, eid, text=None, importance=None, valid_until=None):\n\n Line 1278: if valid_until is not None:\n\n Line 1279: sets.append(\"valid_until=?\")\n\n Line 1280: params.append(valid_until)\n\n\n/mnt/workdrive/issue161-harness/src/rune/graph_memory.py:\n Line 13: - BI-TEMPORAL edges (Graphiti 2501.13956): `valid_from/valid_until` (when\n\n Line 107: valid_until TEXT,\n\n Line 201: INSERT INTO edges (subj_id, pred, obj_id, valid_from, valid_until,\n\n Line 249: ed.valid_from, ed.valid_until, ed.confidence, ed.status,\n\n Line 261: \"from\": r[\"valid_from\"], \"until\": r[\"valid_until\"],\n\n Line 271: ed.valid_from, ed.valid_until, ed.confidence, ed.status, ed.source\n\n Line 281: \"from\": r[\"valid_from\"], \"until\": r[\"valid_until\"],\n","metadata":{"matches":16,"truncated":false},"title":"valid_until","time":{"start":1788953842871,"end":1788953842886}},"id":"prt_g001a085f55490001UJiyEJypD","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085f262af001rZ0ImFwSv"}}
|
||||
{"type":"tool_use","timestamp":1788953842889,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"grep","callID":"call_25922020fcd54cf08120a446","state":{"status":"completed","input":{"path":"/mnt/workdrive/issue161-harness/src/rune","pattern":"supersed"},"output":"Found 35 matches\n/mnt/workdrive/issue161-harness/src/rune/memory_store.py:\n Line 2224: # supersedes when new info replaces old; every decision is receipted.\n\n\n/mnt/workdrive/issue161-harness/src/rune/curator.py:\n Line 6: - conflict detection: a new fact superseding an old one records a\n\n Line 1132: body = body.replace(old, f\"{old} → (superseded {new})\")\n\n\n/mnt/workdrive/issue161-harness/src/rune/skills/llm-craft/knowledge.md:\n Line 319: **Pattern:** Explicit state-first memory with supersession tracking. When facts are revised, the memory system must reflect the current state, not a superseded one. StateMem achieves 1.6-1.8x improvement.\n\n\n/mnt/workdrive/issue161-harness/src/rune/tool_protocol.py:\n Line 154: status: str # \"completed\" | \"failed\" | \"superseded\" | \"cancelled\"\n\n\n/mnt/workdrive/issue161-harness/src/rune/orchestration/lifecycle.py:\n Line 4: retryable_failure, conflict, superseded, or cancelled; retryable_failure\n\n Line 23: \"superseded\",\n\n Line 27: \"retryable_failure\": frozenset({\"sending\", \"cancelled\", \"superseded\"}),\n\n Line 31: \"superseded\": frozenset(),\n\n\n/mnt/workdrive/issue161-harness/src/rune/orchestration/records.py:\n Line 110: state: str # verified | failed | cancelled | superseded\n\n Line 128: supersedes: str | None = None\n\n Line 153: state: str = \"queued\" # queued->sending->accepted | retryable_failure | conflict | cancelled | superseded\n\n Line 231: # completed|failed|cancelled|superseded. The legal edges live in\n\n Line 240: \"superseded\",\n\n\n/mnt/workdrive/issue161-harness/src/rune/orchestration/outbox.py:\n Line 29: \"superseded\",\n\n\n/mnt/workdrive/issue161-harness/src/rune/orchestration/conversation_state.py:\n Line 55: \"created\": frozenset({\"created\", \"running\", \"awaiting_approval\", \"completed\", \"failed\", \"cancelled\", \"superseded\"}),\n\n Line 56: \"running\": frozenset({\"running\", \"awaiting_approval\", \"completed\", \"failed\", \"cancelled\", \"superseded\"}),\n\n Line 57: \"awaiting_approval\": frozenset({\"awaiting_approval\", \"running\", \"completed\", \"failed\", \"cancelled\", \"superseded\"}),\n\n Line 61: \"superseded\": frozenset(),\n\n\n/mnt/workdrive/issue161-harness/src/rune/event_bus.py:\n Line 52: \"memory.superseded\": \"Memory entry superseded\",\n\n\n/mnt/workdrive/issue161-harness/src/rune/graph_memory.py:\n Line 18: contradicting edges are marked superseded with the new one taking over;\n\n Line 37: graph_memory.py superseded-by <subj> <pred> <obj> (manual conflict flag)\n\n Line 112: status TEXT NOT NULL DEFAULT 'active', -- active|superseded|expired\n\n Line 113: superseded_by INTEGER,\n\n Line 164: marked superseded and the new one wins — history preserved.\n\n Line 189: # Only supersede if new evidence is stronger or different.\n\n Line 198: # Supersede: insert new edge FIRST, then mark old as superseded\n\n Line 207: db.execute(\"UPDATE edges SET status='superseded', superseded_by=? WHERE id=?\",\n\n\n/mnt/workdrive/issue161-harness/src/rune/abstraction.py:\n Line 78: superseded_by INTEGER\n\n Line 106: superseded_by INTEGER\n\n Line 435: - Archive observations superseded by newer ones\n\n Line 451: # Archive superseded observations\n\n Line 454: WHERE superseded_by IS NOT NULL AND state='active'\n\n\n/mnt/workdrive/issue161-harness/src/rune/cache.py:\n Line 126: \"memory.superseded\": [\"retrieval\", \"warm_digest\"],\n\n\n/mnt/workdrive/issue161-harness/src/rune/behavior.py:\n Line 57: supersedes_id INTEGER\n","metadata":{"matches":35,"truncated":false},"title":"supersed","time":{"start":1788953842872,"end":1788953842889}},"id":"prt_g001a085f55494001aOavpCxxi","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085f262af001rZ0ImFwSv"}}
|
||||
{"type":"tool_use","timestamp":1788953842895,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"read","callID":"call_b3569b877ad34992b596b557","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/config/v1_authority_map.json"},"output":"<path>/mnt/workdrive/issue161-harness/config/v1_authority_map.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"schema_version\": 1,\n3: \"status\": \"decision-record-not-migration\",\n4: \"epic\": \"#16\",\n5: \"issue\": \"#17\",\n6: \"frozen_reference\": {\n7: \"tag\": \"v0.42.0\",\n8: \"commit\": \"98b8a7048455dd1a5c6fe219c114502549b8227c\",\n9: \"recoverable\": true\n10: },\n11: \"v1_branch\": \"v1/p0-01-authority-map\",\n12: \"invariant\": \"No subsystem may inject model context outside the ContextBroker\",\n13: \"dispositions\": {\n14: \"retain-authority\": \"Keep this path as the authority or a primitive owned by the named v1 authority.\",\n15: \"migrate-adapter\": \"Keep compatibility temporarily, but make it delegate to the named v1 authority.\",\n16: \"archive-candidate\": \"Remove from the live path after dependency and rollback checks.\",\n17: \"dormant-until-dependency\": \"Keep installed with zero live authority until its dependency issue activates it.\"\n18: },\n19: \"responsibilities\": [\n20: {\n21: \"id\": \"conversation_state\",\n22: \"current_authority\": \"RuneOperator task history plus ephemeral ChannelView.messages\",\n23: \"v1_owner\": \"ConversationStateRepository\",\n24: \"dependency_issue\": \"#18\",\n25: \"migration_rule\": \"Persist messages and continuation once; turn ChannelView and operator history into projections/adapters.\",\n26: \"current_paths\": [\n27: {\n28: \"path\": \"src/rune/rune_tui.py:1012-1065\",\n29: \"role\": \"Ephemeral visible channel transcript\",\n30: \"disposition\": \"migrate-adapter\",\n31: \"evidence\": \"ChannelView initializes and appends self.messages in memory.\"\n32: },\n33: {\n34: \"path\": \"src/rune/rune_operator.py:278-294\",\n35: \"role\": \"Recent conversation reconstructed from completed RuneTask records\",\n36: \"disposition\": \"migrate-adapter\",\n37: \"evidence\": \"_conversation_messages converts completed tasks into user/assistant pairs.\"\n38: },\n39: {\n40: \"path\": \"src/rune/response_adapter.py:25-45\",\n41: \"role\": \"Unused alternative response/conversation event envelope\",\n42: \"disposition\": \"dormant-until-dependency\",\n43: \"evidence\": \"RuneResponse exists but does not own the ordinary TUI turn.\"\n44: }\n45: ]\n46: },\n47: {\n48: \"id\": \"task_state\",\n49: \"current_authority\": \"RuneOperator RuneState with competing task ledgers\",\n50: \"v1_owner\": \"TaskStateRepository\",\n51: \"dependency_issue\": \"#18\",\n52: \"migration_rule\": \"One canonical task identity persists until explicit complete, cancel, replace, or amend transitions.\",\n53: \"current_paths\": [\n54: {\n55: \"path\": \"src/rune/rune_operator.py:100-177\",\n56: \"role\": \"Live RuneTask/RuneState persisted to operator.json\",\n57: \"disposition\": \"migrate-adapter\",\n58: \"evidence\": \"RuneState owns current_task, tasks, and pending approval state.\"\n59: },\n60: {\n61: \"path\": \"src/rune/task_persist.py:27-130\",\n62: \"role\": \"Separate JSON task ledger\",\n63: \"disposition\": \"migrate-adapter\",\n64: \"evidence\": \"Maintains persisted.json independently of RuneOperator.\"\n65: },\n66: {\n67: \"path\": \"src/rune/work_manager.py:59-100\",\n68: \"role\": \"Separate work-item/batch state\",\n69: \"disposition\": \"migrate-adapter\",\n70: \"evidence\": \"Defines Batch and WorkConfig with separate persistence.\"\n71: },\n72: {\n73: \"path\": \"src/rune/message_queue.py\",\n74: \"role\": \"Chat-to-task amendment queue\",\n75: \"disposition\": \"migrate-adapter\",\n76: \"evidence\": \"Used by RuneApp before ordinary model submission.\"\n77: }\n78: ]\n79: },\n80: {\n81: \"id\": \"context_construction\",\n82: \"current_authority\": \"RuneOperator prompt assembly plus parallel TUI preprocessing\",\n83: \"v1_owner\": \"ContextBroker\",\n84: \"dependency_issue\": \"#19\",\n85: \"migration_rule\": \"All selectors return typed candidates to ContextBroker; no caller appends prompt blocks directly.\",\n86: \"current_paths\": [\n87: {\n88: \"path\": \"src/rune/rune_operator.py:424-714\",\n89: \"role\": \"Actual system prompt and context block assembly\",\n90: Line truncated
|
||||
{"type":"step_finish","timestamp":1788953842927,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085f554ce001hx083Xzmz","reason":"tool-calls","snapshot":"02a13192ce513083e13d1be6ed9002edd99f35aa","messageID":"msg_g001a085f262af001rZ0ImFwSv","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"step-finish","tokens":{"total":82065,"input":24822,"output":222,"reasoning":4861,"cache":{"write":0,"read":52160}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788953852522,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085f57a68001JwLjevB2j","messageID":"msg_g001a085f55516001Nd0Ui3SXT","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","snapshot":"e3485ced605474a44f1e874eb0fb56a7819349e5","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788954029660,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085f7fe11001QlBEmnZ1p","messageID":"msg_g001a085f55516001Nd0Ui3SXT","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"text","text":"The authority map confirms `ContextBroker.memory_selector` owns memory selection with `memory_store.py` as retain-authority, and `rune_operator`'s direct graph selection is already slated as archive-candidate. Two big attack threads just surfaced: `valid_until` is never written anywhere (bi-temporal claim suspect), and `status='expired'` is unreachable. Now checking the actual write paths, the GraphSource consumer, the rebuildable-projections doctrine, and — as a targeted check — whether FTS5 is even available in this runtime and whether the real graph DB contains any superseded history at all.","time":{"start":1788954017297,"end":1788954029658}}}
|
||||
{"type":"tool_use","timestamp":1788954029683,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"read","callID":"call_4912c57ae0e6469383ef20bd","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/src/rune/memory_consolidation.py","limit":60,"offset":410},"output":"<path>/mnt/workdrive/issue161-harness/src/rune/memory_consolidation.py</path>\n<type>file</type>\n<content>\n410: # 1. Consolidate warm neurons\n411: try:\n412: sys.path.insert(0, _SD)\n413: import warm_neuron_store\n414: neurons = warm_neuron_store.list_neurons()\n415: for neuron in (neurons if neuron_limit is None else neurons[:neuron_limit]):\n416: knowledge = self.consolidate_neuron(neuron)\n417: existing = [k for k in self.knowledge if k.id == knowledge.id]\n418: if not existing:\n419: self.knowledge.append(knowledge)\n420: counts[\"neurons\"] += 1\n421: elif existing and knowledge.entry_id and not existing[0].entry_id:\n422: # Update existing entry with entry_id if it was missing\n423: existing[0].entry_id = knowledge.entry_id\n424: counts[\"neurons\"] += 1\n425: except Exception as e:\n426: print(f\" neuron consolidation error: {e}\", file=sys.stderr)\n427: \n428: # 2. Consolidate graph memory\n429: try:\n430: sys.path.insert(0, _SD)\n431: import graph_memory\n432: db = graph_memory.connect()\n433: try:\n434: entities = db.execute(\n435: \"SELECT name, type, summary FROM entities WHERE type != 'alias'\"\n436: ).fetchall()\n437: for row in entities:\n438: entity = {\"name\": row[0], \"type\": row[1], \"summary\": row[2] or \"\"}\n439: knowledge = self.consolidate_graph_entity(entity)\n440: existing = [k for k in self.knowledge if k.id == knowledge.id]\n441: if not existing:\n442: self.knowledge.append(knowledge)\n443: counts[\"graph\"] += 1\n444: elif existing and knowledge.entry_id and not existing[0].entry_id:\n445: existing[0].entry_id = knowledge.entry_id\n446: counts[\"graph\"] += 1\n447: finally:\n448: db.close()\n449: except Exception as e:\n450: print(f\" graph consolidation error: {e}\", file=sys.stderr)\n451: \n452: # 3. Consolidate abstraction facts\n453: try:\n454: sys.path.insert(0, _SD)\n455: import abstraction\n456: db = abstraction.connect()\n457: try:\n458: limit_clause = f\" LIMIT {abstraction_limit}\" if abstraction_limit else \"\"\n459: facts = db.execute(\n460: f\"SELECT what, entities_json FROM facts WHERE state='active'{limit_clause}\"\n461: ).fetchall()\n462: for fact_text, entities_json in facts:\n463: knowledge = self.consolidate_abstraction_fact(fact_text, entities_json=entities_json or \"\")\n464: existing = [k for k in self.knowledge if k.id == knowledge.id]\n465: if not existing:\n466: self.knowledge.append(knowledge)\n467: counts[\"abstraction\"] += 1\n468: elif existing and knowledge.entry_id and not existing[0].entry_id:\n469: existing[0].entry_id = knowledge.entry_id\n\n(Showing lines 410-469 of 724. Use offset=470 to continue.)\n</content>","metadata":{"preview":" # 1. Consolidate warm neurons\n try:\n sys.path.insert(0, _SD)\n import warm_neuron_store\n neurons = warm_neuron_store.list_neurons()\n for neuron in (neurons if neuron_limit is None else neurons[:neuron_limit]):\n knowledge = self.consolidate_neuron(neuron)\n existing = [k for k in self.knowledge if k.id == knowledge.id]\n if not existing:\n self.knowledge.append(knowledge)\n counts[\"neurons\"] += 1\n elif existing and knowledge.entry_id and not existing[0].entry_id:\n # Update existing entry with entry_id if it was missing\n existing[0].entry_id = knowledge.entry_id\n counts[\"neurons\"] += 1\n except Exception as e:\n print(f\" neuron consolidation error: {e}\", file=sys.stderr)\n\n # 2. Consolidate graph memory\n try:","truncated":true,"loaded":[]},"title":"src/rune/memory_consolidation.py","time":{"start":1788954029659,"end":17889540296Line truncated
|
||||
{"type":"tool_use","timestamp":1788954029685,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"read","callID":"call_096d2f39affa4a63bf469324","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/src/rune/memory_selector.py","limit":70,"offset":340},"output":"<path>/mnt/workdrive/issue161-harness/src/rune/memory_selector.py</path>\n<type>file</type>\n<content>\n340: for neuron in neurons:\n341: triggers = neuron.get(\"triggers\") or []\n342: if not any(t in task_words for t in triggers):\n343: continue\n344: text = neuron.get(\"text\", \"\")\n345: score = _lex_overlap_score(text, query)\n346: if score < min_score:\n347: continue\n348: out.append(\n349: {\n350: \"text\": f\"[{neuron.get('slug', 'warm')}] {text}\",\n351: \"score\": score,\n352: \"kind\": \"neuron\",\n353: \"source\": \"warm_neuron_store.list_neurons\",\n354: }\n355: )\n356: if len(out) >= budget:\n357: break\n358: return out\n359: \n360: \n361: def _graphs_recall(query: str, types: list[str] | None, budget: int, min_score: float) -> list[dict]:\n362: \"\"\"Graph source: entity BFS from the task words via graph_memory.query,\n363: floored. The pre-P1-03 contributor called resolve_entity(), which MUTATES\n364: the graph (creates missing entities); read-only query() keeps the store\n365: side-effect-free. A term with no entity yields no candidates.\"\"\"\n366: try:\n367: import graph_memory\n368: except ImportError: # TUI top-level deploy\n369: import rune.graph_memory as graph_memory # type: ignore[no-redef]\n370: \n371: db = graph_memory.connect()\n372: out: list[dict] = []\n373: try:\n374: task_words = list(dict.fromkeys(w.lower().strip(\".,;:!?()[]{}\\\"'\") for w in query.split()))[:5]\n375: for word in task_words:\n376: result = graph_memory.query(db, word, hops=1)\n377: if not result:\n378: continue\n379: entity = result.get(\"entity\", word) if isinstance(result, dict) else str(result)\n380: facts = result.get(\"facts\", []) if isinstance(result, dict) else []\n381: lines = [f\"{f.get('subject')} {f.get('predicate', '-')} {f.get('object')}\" for f in facts]\n382: text = \"; \".join(lines) if lines else str(entity)\n383: score = _lex_overlap_score(text, query) or (1.0 / max(len(task_words), 1))\n384: if score < min_score:\n385: continue\n386: out.append({\"text\": text, \"score\": score, \"kind\": \"graph\", \"source\": \"graph_memory.query\"})\n387: if len(out) >= budget:\n388: break\n389: finally:\n390: db.close()\n391: return out\n392: \n393: \n394: def _abstraction_recall(query: str, types: list[str] | None, budget: int, min_score: float) -> list[dict]:\n395: \"\"\"Abstraction-facts source: LIKE scan over active facts, floored.\n396: \n397: The pre-P1-03 contributor injected the top-3 LIKE matches unconditionally;\n398: here a LIKE match is only a candidate — it must clear the floor.\n399: \"\"\"\n400: try:\n401: import abstraction\n402: except ImportError: # TUI top-level deploy\n403: import rune.abstraction as abstraction # type: ignore[no-redef]\n404: \n405: db = abstraction.connect()\n406: out: list[dict] = []\n407: try:\n408: task_words = [w for w in query.lower().split()[:3] if w]\n409: if not task_words:\n\n(Showing lines 340-409 of 511. Use offset=410 to continue.)\n</content>","metadata":{"preview":" for neuron in neurons:\n triggers = neuron.get(\"triggers\") or []\n if not any(t in task_words for t in triggers):\n continue\n text = neuron.get(\"text\", \"\")\n score = _lex_overlap_score(text, query)\n if score < min_score:\n continue\n out.append(\n {\n \"text\": f\"[{neuron.get('slug', 'warm')}] {text}\",\n \"score\": score,\n \"kind\": \"neuron\",\n \"source\": \"warm_neuron_store.list_neurons\",\n }\n )\n if len(out) >= budget:\n break\n return out\n","truncated":true,"loaded":[]},"title":"src/rune/memory_selector.py","time":{"start":1788954029662,"end":1788954029684}},"id":"prt_g001a085f82ded001fTZGLmcK1","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085f55516001Nd0Ui3SXT"}}
|
||||
{"type":"tool_use","timestamp":1788954029687,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"read","callID":"call_6611032b66b744a095ec7723","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/src/rune/curator.py","limit":60,"offset":1355},"output":"<path>/mnt/workdrive/issue161-harness/src/rune/curator.py</path>\n<type>file</type>\n<content>\n1355: if strength < 3:\n1356: return False\n1357: low = fact.lower()\n1358: subj = None\n1359: pred = None\n1360: obj = None\n1361: for verb, canonical in GRAPH_VERB_MAP.items():\n1362: idx = low.find(verb)\n1363: if idx > 0:\n1364: subj = fact[:idx].strip().lstrip(\"-* \").strip()\n1365: obj = fact[idx + len(verb):].strip()\n1366: obj = re.split(r\"[.,;]\", obj)[0].strip()\n1367: # Trim trailing prepositional phrases: \"teaches guitar at the\n1368: # studio\" -> object is the direct noun, not the whole clause.\n1369: obj = re.split(r\"\\s+(at|in|on|for|to|from|with|and|through|via) \",\n1370: obj)[0].strip()\n1371: pred = canonical\n1372: break\n1373: if not subj or not obj or not pred:\n1374: return False\n1375: # Subject must be a proper noun-ish or the user keyword to avoid junk triples.\n1376: if subj.lower() not in (\"user\", \"i\", \"we\") and len(subj) < 3:\n1377: return False\n1378: try:\n1379: r = subprocess.run(\n1380: [\"python3\", os.path.join(os.path.dirname(os.path.abspath(__file__)),\n1381: \"graph_memory.py\"),\n1382: \"add\", subj, pred, obj, \"--evidence\", evidence[:200],\n1383: \"--source\", source, \"--strength\", str(strength)],\n1384: capture_output=True, text=True, timeout=15)\n1385: return '\"added\": True' in (r.stdout or \"\")\n1386: except Exception:\n1387: logger.debug(\"graph_write failed\", exc_info=True)\n1388: return False\n1389: \n1390: \n1391: def apply_warm(op, journal_lines):\n1392: \"\"\"Warm-tier write: promote a fact into the neuron layer (NEW SCHEMA).\n1393: \n1394: Finds an existing neuron whose triggers match the fact and upserts the fact\n1395: into it; if none, seeds a new neuron via write_neuron. No size-based\n1396: division — neurons are atomic store entries, and firing is optimized by the\n1397: token budget at injection time, never by splitting stored content.\n1398: \"\"\"\n1399: from warm_router import list_neurons, write_neuron, _derive_triggers\n1400: fact = (op.get(\"after\") or \"\").strip()\n1401: if not fact:\n1402: return False\n1403: low = fact.lower()\n1404: best = None\n1405: best_hits = 0\n1406: for n in list_neurons():\n1407: hits = sum(1 for t in n[\"triggers\"] if t and t in low)\n1408: if hits > best_hits:\n1409: best, best_hits = n, hits\n1410: if best is not None and best_hits > 0:\n1411: # Upsert the fact into the matching neuron (auto-build, no splitting).\n1412: new_body = (best[\"body\"] + f\"\\n- {fact}\\n\")\n1413: write_neuron(best[\"slug\"], best[\"triggers\"], new_body,\n1414: importance=best[\"importance\"])\n\n(Showing lines 1355-1414 of 1612. Use offset=1415 to continue.)\n</content>","metadata":{"preview":" if strength < 3:\n return False\n low = fact.lower()\n subj = None\n pred = None\n obj = None\n for verb, canonical in GRAPH_VERB_MAP.items():\n idx = low.find(verb)\n if idx > 0:\n subj = fact[:idx].strip().lstrip(\"-* \").strip()\n obj = fact[idx + len(verb):].strip()\n obj = re.split(r\"[.,;]\", obj)[0].strip()\n # Trim trailing prepositional phrases: \"teaches guitar at the\n # studio\" -> object is the direct noun, not the whole clause.\n obj = re.split(r\"\\s+(at|in|on|for|to|from|with|and|through|via) \",\n obj)[0].strip()\n pred = canonical\n break\n if not subj or not obj or not pred:\n return False","truncated":true,"loaded":[]},"title":"src/rune/curator.py","time":{"start":1788954029661,"end":1788954029687}},"id":"prt_g001a085f813c6001Lr2L1Zdwl","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085f55516001Nd0Ui3SXT"}}
|
||||
{"type":"tool_use","timestamp":1788954029700,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"grep","callID":"call_21ab61366ef14203bcaccf23","state":{"status":"completed","input":{"include":"*.md","path":"/mnt/workdrive/issue161-harness","pattern":"projection|rebuild"},"output":"Found 93 matches\n/mnt/workdrive/issue161-harness/docs/RESEARCH_CODE_CLASSIFICATION.md:\n Line 58: projection, runner placement, refinement routing)\n\n\n/mnt/workdrive/issue161-harness/research/issue-262/.firecrawl/s-aviim.md:\n Line 53: 2. Unclean shutdown during WAL write. The WAL index is updated after a successful write. Maybe after the WAL write, the process crashed, and the index wasn’t updated. On the next start, SQLite rebuilds the index.\n\n\n/mnt/workdrive/issue161-harness/research/issue-245-research.md:\n Line 75: 3. **Unbounded chat-log widget growth**: `ChannelView.add_message` (rune_tui.py:1198-1210) mounts a `MessageBox` (src/rune/chat_bubble.py:279, a `Horizontal` wrapping `ChatBubble(Static)`) into `VerticalScroll` per message and calls `scroll_end` — no cap. Contrast: `WorkPane` is bounded at `_WORK_PANE_MAX_ENTRIES = 120` with oldest-entry eviction in `add_object` (rune_tui.py:1215, 1309-1325), and RichLog (used for command results, rune_tui.py:552) supports upstream `max_lines`. A long session accumulates thousands of mounted widgets; Textual's own history shows large scrollable containers were a real perf hazard (S2 #6317). The `self.messages` projection list (rune_tui.py:1208-1210) is the durable display record; durable state lives in ConversationStateRepository, so only DOM widgets are safe to bound.\n\n Line 129: proposal: add `_CHAT_LOG_MAX_WIDGETS = 200` beside `_WORK_PANE_MAX_ENTRIES` (rune_tui.py:1215). In `ChannelView.add_message` (rune_tui.py:1198-1210), after `log.mount(bubble)`, prune the oldest mounted `MessageBox` children beyond the cap: batch-remove `log.children[:overflow]` via `remove_children` (layout-refreshing since 6.6.0, S2 #6206), preserving `scroll_end(animate=False)`. Never touch `self.messages` — the projection list and ConversationStateRepository remain the durable record; only DOM widgets are bounded. Do NOT rewrite the log on RichLog/Line-API virtual rendering (RichLog's `max_lines` idiom — https://textual.textualize.io/widgets/rich_log/ — proves bounded retention is upstream-sanctioned, but its lines are not interactive widgets): bubbles must stay interactive (ThinkingBubble toggle chat_bubble.py:261-276; ToolCallWidget decision buttons message_widgets.py:14-41). Scroll-jump caveat: pruning while the user has scrolled up will move the view; acceptable at cap events (2-per-100-messages), and `Widget.anchor` (S2 #5950) is the upstream primitive if follow-up polish is ever needed.\n\n Line 163: f. test_chat_log_bounded: under `run_test()`, push 220 messages via `chat_channel.add_message(...)`; assert `len(log.children) <= 200` while `len(chat_channel.messages) == 220` (projection survives pruning). RED.\n\n\n/mnt/workdrive/issue161-harness/plans/2026-09-06-p1-01-capability-authority.md:\n Line 7: **Architecture:** `capability_broker.py` becomes the single selection authority (per `config/v1_authority_map.json` `capability_selection` → v1_owner `ContextBroker.capability_selector`): `select_for_job(task_description, need_tools)` returns an ordered tool-name selection with reason; the operator feeds that selection into `LocalToolRuntime(tools=...)` (both construction sites) and replaces the names-only `_contributor_tools` prompt block with the selected set; a always-exposed tiny `find_capability` native tool is the escape hatch; unknown-tool requests route to a helpful error listing `find_capability`. Cards gain `preconditions`, `side_effects`, per-tool `risk` and rebuild on empty/stale. Composition registers the broker as the single selection authority.\n\n Line 26: 4. **Card validation:** `load_cards` rebuilds when file missing OR empty OR stale (mtime older than `.microkernel.json` registry). Cards for native tools are synthesized from TOOLS metadata so selection has full coverage even with no plugins.\n\n Line 34: **Files:** modify `src/rune/tool_executor.py` (metadata on TOOLS), `src/rune/capability_broker.py` (synthesis, rebuild-on-empty/stale), test `tests/test_p1_01_capability_authority.py`.\n\n Line 36: - [ ] Step 1: failing tests — every TOOLS entry carries risk/preconditions/side_effects; `load_cards` synthesizes native cards when cards file empty; rebuild triggers on empty file and on stale mtime vs registry.\n\n Line 92: 7. **Resume mechanism.** Global constraint \"resume round-trips must use the same selected set\" is implemented by persisting an additive `exposed_tools` key into `pending_state` (selected set + any in-run discovered tools); `_runtime_for_model` rebuilds exactly that set, with deterministic re-selection from the task description as fallback for pre-#24 pending states.\n\n Line 94: 9. **Cards file rewLine truncated
|
||||
{"type":"tool_use","timestamp":1788954029737,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"bash","callID":"call_f15c70980f5a4f7bbb658223","state":{"status":"completed","input":{"command":"python3 - <<'EOF'\nimport sqlite3, os, json\n# 1) FTS5 availability in this runtime\ntry:\n c = sqlite3.connect(':memory:')\n c.execute(\"CREATE VIRTUAL TABLE t USING fts5(x)\")\n ver = sqlite3.sqlite_version\n print(\"FTS5_AVAILABLE yes, sqlite\", ver)\nexcept Exception as e:\n print(\"FTS5_AVAILABLE no:\", e)\n\n# 2) real graph DB inspection (read-only)\nfor p in [\"/mnt/workdrive/rune/memory/graph/graph.sqlite\",\n os.path.expanduser(\"~/.config/opencode/memory/graph.db\")]:\n if not os.path.exists(p):\n print(\"DB missing:\", p); continue\n print(\"DB:\", p)\n db = sqlite3.connect(f\"file:{p}?mode=ro\", uri=True)\n db.row_factory = sqlite3.Row\n for q in [\"SELECT COUNT(*) n FROM entities\",\n \"SELECT COUNT(*) n FROM aliases\",\n \"SELECT COUNT(*) n FROM edges\",\n \"SELECT status, COUNT(*) n FROM edges GROUP BY status\",\n \"SELECT COUNT(*) n FROM edges WHERE valid_until IS NOT NULL\",\n \"SELECT COUNT(*) n FROM edges WHERE valid_from <> learned_at\",\n \"SELECT COUNT(*) n FROM edges WHERE superseded_by IS NOT NULL\"]:\n try:\n r = db.execute(q).fetchone()\n print(\" \", q, \"->\", r[\"n\"] if \"n\" in r.keys() else dict(r))\n except Exception as e:\n print(\" \", q, \"ERROR\", e)\n db.close()\nEOF","description":"Check FTS5 availability and inspect real graph DB read-only"},"output":"FTS5_AVAILABLE yes, sqlite 3.53.4\nDB: /mnt/workdrive/rune/memory/graph/graph.sqlite\n SELECT COUNT(*) n FROM entities -> 0\n SELECT COUNT(*) n FROM aliases -> 0\n SELECT COUNT(*) n FROM edges -> 0\n SELECT status, COUNT(*) n FROM edges GROUP BY status ERROR 'NoneType' object has no attribute 'keys'\n SELECT COUNT(*) n FROM edges WHERE valid_until IS NOT NULL -> 0\n SELECT COUNT(*) n FROM edges WHERE valid_from <> learned_at -> 0\n SELECT COUNT(*) n FROM edges WHERE superseded_by IS NOT NULL -> 0\nDB missing: /home/nick/.config/opencode/memory/graph.db\n","metadata":{"output":"FTS5_AVAILABLE yes, sqlite 3.53.4\nDB: /mnt/workdrive/rune/memory/graph/graph.sqlite\n SELECT COUNT(*) n FROM entities -> 0\n SELECT COUNT(*) n FROM aliases -> 0\n SELECT COUNT(*) n FROM edges -> 0\n SELECT status, COUNT(*) n FROM edges GROUP BY status ERROR 'NoneType' object has no attribute 'keys'\n SELECT COUNT(*) n FROM edges WHERE valid_until IS NOT NULL -> 0\n SELECT COUNT(*) n FROM edges WHERE valid_from <> learned_at -> 0\n SELECT COUNT(*) n FROM edges WHERE superseded_by IS NOT NULL -> 0\nDB missing: /home/nick/.config/opencode/memory/graph.db\n","exit":0,"description":"Check FTS5 availability and inspect real graph DB read-only","truncated":false},"title":"Check FTS5 availability and inspect real graph DB read-only","time":{"start":1788954029731,"end":1788954029735}},"id":"prt_g001a085f82def003V7jrYqHC0","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085f55516001Nd0Ui3SXT"}}
|
||||
{"type":"step_finish","timestamp":1788954029775,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085f82ea9001LgopoQYc3","reason":"tool-calls","snapshot":"b34de943c50b3aa0b652b8e838abc5d19b1ec766","messageID":"msg_g001a085f55516001Nd0Ui3SXT","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"step-finish","tokens":{"total":92791,"input":86968,"output":634,"reasoning":5189,"cache":{"write":0,"read":0}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788954129771,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085f9b56a0013bvgM8LXs","messageID":"msg_g001a085f82f0c001SkMzNPePU","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","snapshot":"460fc91a84efbf5d87b1c516a57ae89c983091bd","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788954133134,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085f9c228002Zvj0rlhrn","messageID":"msg_g001a085f82f0c001SkMzNPePU","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"text","text":"The real production graph DB is **empty** (0 entities, 0 edges) — that's a decisive falsification datum. Two more checks to pin down the C6 benefit-chain attack (FTS5 already exists in memory_store; the selector-level score floor) and the canary/contract constraints on `connect()`:","time":{"start":1788954133032,"end":1788954133126}}}
|
||||
{"type":"tool_use","timestamp":1788954133185,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"grep","callID":"call_a52d6b8f3fea40698b935d5e","state":{"status":"completed","input":{"path":"/mnt/workdrive/issue161-harness/src/rune/memory_store.py","pattern":"fts5|bm25|sqlite-vec|vec0"},"output":"Found 36 matches\n/mnt/workdrive/issue161-harness/src/rune/memory_store.py:\n Line 5: file running sqlite-vec (dense vectors) + FTS5 (BM25) side by side.\n\n Line 10: - HYBRID RETRIEVAL: dense (vec0) + BM25 (FTS5) fused by RRF (rank-based,\n\n Line 26: Run under ~/.venvs/memory/bin/python3 (has sqlite-vec).\n\n Line 126: CREATE VIRTUAL TABLE IF NOT EXISTS entries_vec USING vec0(\n\n Line 129: CREATE VIRTUAL TABLE IF NOT EXISTS entries_fts USING fts5(\n\n Line 146: CREATE VIRTUAL TABLE IF NOT EXISTS chunks_vec USING vec0(\n\n Line 149: CREATE VIRTUAL TABLE IF NOT EXISTS chunks_fts USING fts5(\n\n Line 422: Uses vec0 KNN to find top-N nearest neighbors (O(1) query instead of O(n) scan),\n\n Line 431: # Use vec0 KNN to find top candidates instead of scanning all rows\n\n Line 443: # Fallback: if vec0 query fails, skip associations\n\n Line 450: cos = 1.0 - r[\"distance\"] # vec0 distance is 1 - cosine\n\n Line 634: # (1) Dense: exact KNN over vec0 (fine at a few thousand entries).\n\n Line 683: bm25 = []\n\n Line 686: \"SELECT rowid, bm25(entries_fts) AS score FROM entries_fts \"\n\n Line 688: \"ORDER BY bm25(entries_fts) LIMIT ?\",\n\n Line 693: bm25 = [(r[\"rowid\"], float(r[\"score\"])) for r in rows]\n\n Line 699: if not bm25 and _extra:\n\n Line 707: \"SELECT rowid, bm25(entries_fts) AS score FROM entries_fts \"\n\n Line 709: \"ORDER BY bm25(entries_fts) LIMIT 4\",\n\n Line 720: f\"SELECT rowid, bm25(entries_fts) AS score FROM entries_fts WHERE rowid IN ({_ph})\", _ids\n\n Line 722: bm25 = [(r[\"rowid\"], float(r[\"score\"])) for r in _rows]\n\n Line 735: if dense and bm25:\n\n Line 737: for idx, lst in enumerate([dense, bm25]):\n\n Line 747: elif bm25:\n\n Line 753: _min_bm = min((s for _, s in bm25), default=0.0)\n\n Line 754: _max_bm = max((s for _, s in bm25), default=0.0)\n\n Line 756: for eid, score in bm25:\n\n Line 801: if dense and bm25:\n\n Line 839: if not dense and not bm25:\n\n Line 867: if not bm25:\n\n Line 1381: db.execute(\"CREATE VIRTUAL TABLE entries_fts USING fts5(text, content='', tokenize='porter unicode61')\")\n\n Line 1522: # sqlite-vec rowid = sqlite rowid of the chunks table row.\n\n Line 1563: bm25 = []\n\n Line 1566: \"SELECT rowid FROM chunks_fts WHERE chunks_fts MATCH ? ORDER BY bm25(chunks_fts) LIMIT ?\",\n\n Line 1569: bm25 = [(r[\"rowid\"], 0.5) for r in rows]\n\n Line 1573: for lst in [dense, bm25]:\n","metadata":{"matches":36,"truncated":false},"title":"fts5|bm25|sqlite-vec|vec0","time":{"start":1788954133128,"end":1788954133184}},"id":"prt_g001a085f9c238001Inq2N9c3w","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085f82f0c001SkMzNPePU"}}
|
||||
{"type":"tool_use","timestamp":1788954133187,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"grep","callID":"call_83974c445ee24627b231ed7a","state":{"status":"completed","input":{"path":"/mnt/workdrive/issue161-harness/src/rune/memory_selector.py","pattern":"_lex_overlap_score|RECALL_MIN_SCORE|min_score"},"output":"Found 31 matches\n/mnt/workdrive/issue161-harness/src/rune/memory_selector.py:\n Line 12: - ONE relevance floor — ``memory_store.RECALL_MIN_SCORE`` (0.42) default —\n\n Line 14: calibrated gate (``min_score`` is passed down into ``recall``, which\n\n Line 88: Sources are registered callables ``(query, types, budget, min_score) ->\n\n Line 105: def __init__(self, db_path: str | None = None, min_score: float | None = None):\n\n Line 107: self.min_score = float(min_score) if min_score is not None else float(memory_store.RECALL_MIN_SCORE)\n\n Line 160: found = fn(query, types_queried, MAX_CANDIDATES, self.min_score)\n\n Line 171: # the same constant inside recall() — which applies min_score in\n\n Line 175: # against the same selector min_score.\n\n Line 176: selected = [item for item in candidates if item.get(\"store_scored\") or _score_of(item) >= self.min_score]\n\n Line 202: self.min_score,\n\n Line 209: def _source_store(self, query: str, types: list[str], budget: int, min_score: float) -> list[dict]:\n\n Line 211: return _store_recall(query, types, budget, min_score, db_path=self.db_path)\n\n Line 227: min_score: float = memory_store.RECALL_MIN_SCORE,\n\n Line 240: ``min_score`` in EVERY recall mode (lexical-only, dense-only, hybrid —\n\n Line 271: results = memory_store.recall(db, query, budget=budget, min_score=min_score, memory_types=types or None)\n\n Line 277: db, query, budget=budget, min_score=min_score, memory_types=persona_types\n\n Line 305: def _lex_overlap_score(text: str, query: str) -> float:\n\n Line 323: def _neurons_recall(query: str, types: list[str] | None, budget: int, min_score: float) -> list[dict]:\n\n Line 345: score = _lex_overlap_score(text, query)\n\n Line 346: if score < min_score:\n\n Line 361: def _graphs_recall(query: str, types: list[str] | None, budget: int, min_score: float) -> list[dict]:\n\n Line 383: score = _lex_overlap_score(text, query) or (1.0 / max(len(task_words), 1))\n\n Line 384: if score < min_score:\n\n Line 394: def _abstraction_recall(query: str, types: list[str] | None, budget: int, min_score: float) -> list[dict]:\n\n Line 419: score = _lex_overlap_score(text, query)\n\n Line 420: if score < min_score:\n\n Line 430: def _consolidated_recall(query: str, types: list[str] | None, budget: int, min_score: float) -> list[dict]:\n\n Line 454: score = _lex_overlap_score(content, query)\n\n Line 455: if score < min_score:\n\n Line 467: min_score: float,\n\n Line 486: rows = memory_store.recall(db, query, budget=budget, min_score=min_score, memory_types=sorted(wanted))\n","metadata":{"matches":31,"truncated":false},"title":"_lex_overlap_score|RECALL_MIN_SCORE|min_score","time":{"start":1788954133135,"end":1788954133186}},"id":"prt_g001a085f9c23a0013YyAy5xfw","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085f82f0c001SkMzNPePU"}}
|
||||
{"type":"tool_use","timestamp":1788954133201,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"read","callID":"call_2fb6b0a99dcb43e7bef8791e","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/src/rune/canary_p0_04.py","limit":40,"offset":90},"output":"<path>/mnt/workdrive/issue161-harness/src/rune/canary_p0_04.py</path>\n<type>file</type>\n<content>\n90: \"warm_router.route\",\n91: \"capability_broker.match\",\n92: \"memory_store.connect\",\n93: \"witness.detect_signals\",\n94: \"drift_ledger.check\",\n95: \"sleep_time.consolidate\",\n96: \"run_parallel\",\n97: \"prompt_sync\",\n98: \"abstraction.connect\",\n99: \"graph_memory.resolve\",\n100: \"time_awareness.get_time_context\",\n101: \"event_bus.emit\",\n102: )\n103: for banned in banned_ids:\n104: if banned in body:\n105: return _fail(f\"pipeline step {banned} re-added inside _run_warm_router\")\n106: return 0\n107: \n108: \n109: def check_subsystems_import() -> int:\n110: modules = (\n111: \"warm_router\",\n112: \"capability_broker\",\n113: \"memory_store\",\n114: \"witness\",\n115: \"cache\",\n116: \"prompt_sync\",\n117: \"abstraction\",\n118: \"graph_memory\",\n119: \"context_pressure\",\n120: \"event_bus\",\n121: \"task_persist\",\n122: \"time_awareness\",\n123: \"warm_neuron_store\",\n124: \"hook_engine\",\n125: \"voice_hybrid\",\n126: \"sleep_time\",\n127: \"drift_ledger\",\n128: \"auto_context\",\n129: \"auto_tasks\",\n\n(Showing lines 90-129 of 207. Use offset=130 to continue.)\n</content>","metadata":{"preview":" \"warm_router.route\",\n \"capability_broker.match\",\n \"memory_store.connect\",\n \"witness.detect_signals\",\n \"drift_ledger.check\",\n \"sleep_time.consolidate\",\n \"run_parallel\",\n \"prompt_sync\",\n \"abstraction.connect\",\n \"graph_memory.resolve\",\n \"time_awareness.get_time_context\",\n \"event_bus.emit\",\n )\n for banned in banned_ids:\n if banned in body:\n return _fail(f\"pipeline step {banned} re-added inside _run_warm_router\")\n return 0\n\n\ndef check_subsystems_import() -> int:","truncated":true,"loaded":[]},"title":"src/rune/canary_p0_04.py","time":{"start":1788954133157,"end":1788954133199}},"id":"prt_g001a085f9c282001yv7Z3JCMz","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085f82f0c001SkMzNPePU"}}
|
||||
{"type":"step_finish","timestamp":1788954133317,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085f9c2d1001KojlQl71b","reason":"tool-calls","snapshot":"e228f2fe49b636c58a8a9a7d80917dd4512b0ddf","messageID":"msg_g001a085f82f0c001SkMzNPePU","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"step-finish","tokens":{"total":97705,"input":94791,"output":2914,"reasoning":0,"cache":{"write":0,"read":0}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788954312183,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085fc7df5001L7Dji7f0h","messageID":"msg_g001a085f9c38000152JfQhd2T","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","snapshot":"95a97ac108c9bbc2b02dc206e826b626dc8d7f1f","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788954416305,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085fc9d65001EIH9tBms8","messageID":"msg_g001a085f9c38000152JfQhd2T","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"text","text":"All evidence is in hand — no external fetches were needed; the code, the authority map, and the **live production DB itself** (empty!) give decisive findings. The strongest one: INN-1's as-of filter, as specified, would return stale superseded facts alongside their replacements, because supersede never writes `valid_until`. Writing the adversarial dossier now.","time":{"start":1788954320229,"end":1788954416294}}}
|
||||
{"type":"tool_use","timestamp":1788954416311,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"write","callID":"call_9a0c7c4d4b4a48cbbcf9b7ad","state":{"status":"completed","input":{"content":"{\n \"stage\": \"ADVERSARIAL\",\n \"issue\": 163,\n \"round\": 1,\n \"process_id\": \"research-163-r1\",\n \"attacked\": [\"C4\", \"C5\", \"C6\", \"C9\", \"INN-1\"],\n \"surviving_candidates\": [\n {\n \"id\": \"C4\",\n \"disposition\": \"SURVIVES_WITH_DOWNGRADE\",\n \"note\": \"The claim 'implements E14's model 1:1' is contradicted by the code (attacks A1, A11): the tier is a transaction-time append log with same-triple supersession, NOT a bi-temporal model with automatic contradiction invalidation. The candidate survives as 'partially integrated'; downstream dossiers must not repeat the 1:1 characterization.\"\n },\n {\n \"id\": \"C5\",\n \"disposition\": \"SURVIVES_ONLY_IN_CORRECTED_FORM\",\n \"note\": \"AS-OF time-travel over preserved history is a real, low-cost capability — but the INN-1 filter formulation is falsified (attack A2). Survives only via mitigation M1: either write valid_until on supersede (one line) or implement transaction-time as-of (learned_at + superseded_by chain) and drop the valid-time half until a writer populates valid windows.\"\n },\n {\n \"id\": \"C6\",\n \"disposition\": \"SURVIVES_CONDITIONAL\",\n \"note\": \"Mechanism is sound and FTS5 is available (verified: sqlite 3.53.4 in this runtime). But the benefit chain is broken twice today: the production graph is EMPTY (attack A5) and graph recall is re-floored by memory_selector (attack A6). Survives only with the projection-rule framing (M3), selector-floor test (M4), and write-side sequencing (M5).\"\n },\n {\n \"id\": \"C9\",\n \"disposition\": \"SURVIVES_TRACK_ONLY\",\n \"note\": \"Uncontested as a future lead; attacks A9/A10 only constrain its future form: DuckDB/Arrow analytics must be a rebuildable read-only projection of graph.sqlite, never a second store, and must never acquire independent write authority.\"\n }\n ],\n \"rejected_candidates\": [\n {\n \"id\": \"INN-1#temporal-filter-as-specified\",\n \"reason\": \"REJECTED as literally specified — do NOT carry to SYNTHESIS. The filter 'valid_from <= t AND (valid_until IS NULL OR t < valid_until) AND learned_at <= t' applied to the current writer's data returns STALE SUPERSEDED FACTS as if believed: supersede (graph_memory.py:207-208) sets status/superseded_by but never valid_until, so superseded edges remain NULL-open on the valid axis and pass all three predicates for every t after their creation. An as-of query would return the superseded AND superseding edges of a contradiction simultaneously — presenting both sides of a contradiction as current knowledge, the exact failure the feature exists to prevent. C5 itself survives after rework (see surviving_candidates); this exact formulation does not. This is E29's documented NULL-as-open-ended pitfall realized in the proposal spec, not merely in hand-written Cypher.\"\n }\n ],\n \"attacks\": [\n {\n \"id\": \"A1\",\n \"target_candidate\": \"C4\",\n \"dimension\": \"contradicting_evidence + weak_assumptions\",\n \"finding\": \"The 'bi-temporal' claim is schema-only. valid_until is NEVER written anywhere in the repo (grep over src/rune: appears only in DDL line 107, INSERT writing NULL at line 201, and SELECTs); status='expired' has no writer; and valid_from and learned_at are both set to the SAME now_iso() call in the single INSERT (lines 200-204). The live production DB confirms: 0 edges where valid_until IS NOT NULL, 0 where valid_from <> learned_at. The tier is a transaction-time-only append log with supersession — the valid-time axis of E14's model is declared but dead.\",\n \"evidence\": \"graph_memory.py:107,200-204,207-208 (valid_until written NULL; valid_from=learned_at=ts); grep valid_until/superseded across src/rune (no writer); live DB receipt: read-only sqlite3 query against /mnt/workdrive/rune/memory/graph/graph.sqlite returned entities=0, aliases=0, edges=0, valid_until non-null=0, valid_from<>learned_at=0, superseded_by non-null=0\",\n \"severity\": \"high\"\n },\n {\n \"id\": \"A2\",\n \"target_candidate\": \"C5 (via INN-1)\",\n \"dimension\": \"failure_modes\",\n \"finding\": \"As-of filter as specified returns stale superseded facts (see rejected_candidates for full mechanism). Concretely: seed 'Alice works_at Acme' at t1, contradict at t2>t1; query --as-of t3>t2. Both edges have valid_from=t<=t3, valid_until=NULL, learned_at<=t3 — both are returned. The time-travel capability would be wrong on its very first contradiction.\",\n \"evidence\": \"graph_memory.py:200-204 (INSERT), :207-208 (supersede touches only status/superseded_by); INN-1 proposal filter quoted in innovation.json:13\",\n \"severity\": \"Line truncated
|
||||
{"type":"step_finish","timestamp":1788954416340,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085fe14b6001DQQ2KZNdX","reason":"tool-calls","snapshot":"21b077663966eb00b36a5ce2cd2309aa69b95237","messageID":"msg_g001a085f9c38000152JfQhd2T","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"step-finish","tokens":{"total":108355,"input":2082,"output":11425,"reasoning":0,"cache":{"write":0,"read":94848}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788954444891,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085fe84590013hH0euveQ","messageID":"msg_g001a085fe153e001IH997j2eG","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","snapshot":"0d6185b43632d1912d8553a6e3f729453b4eaf7f","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788954446629,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085fe84b4001MeJWhmbBv","messageID":"msg_g001a085fe153e001IH997j2eG","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"text","text":"Verifying the artifact is valid JSON:","time":{"start":1788954444980,"end":1788954446627}}}
|
||||
{"type":"tool_use","timestamp":1788954446653,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"bash","callID":"call_b70b6d4f316641b69dc60d2f","state":{"status":"completed","input":{"command":"python3 -c \"import json; d=json.load(open('research/issue-163/adversarial.json')); print('valid JSON;', len(d['attacks']), 'attacks,', len(d['surviving_candidates']), 'surviving,', len(d['rejected_candidates']), 'rejected,', len(d['falsification_tests']), 'tests')\"","description":"Validate adversarial.json parses","workdir":"/mnt/workdrive/issue161-harness"},"output":"valid JSON; 13 attacks, 4 surviving, 1 rejected, 6 tests\n","metadata":{"output":"valid JSON; 13 attacks, 4 surviving, 1 rejected, 6 tests\n","exit":0,"description":"Validate adversarial.json parses","truncated":false},"title":"Validate adversarial.json parses","time":{"start":1788954446649,"end":1788954446651}},"id":"prt_g001a085fe84b6001gZyUR74Kc","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085fe153e001IH997j2eG"}}
|
||||
{"type":"step_finish","timestamp":1788954446677,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085fe8b3c001P4vNHHlbI","reason":"tool-calls","snapshot":"3888c918caa95eadf804cae79759b3c2f5fccfbf","messageID":"msg_g001a085fe153e001IH997j2eG","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"step-finish","tokens":{"total":102483,"input":5396,"output":127,"reasoning":0,"cache":{"write":0,"read":96960}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788954467546,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085fedcd8001JgaqWywFl","messageID":"msg_g001a085fe8b97001CJPHbfU67","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","snapshot":"2fe30029ca4c04047e5eb0933b2327371f3a6f5e","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788954480331,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085fedcd9001e0V71tVs1","messageID":"msg_g001a085fe8b97001CJPHbfU67","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"text","text":"Adversarial dossier written to `research/issue-163/adversarial.json` (13 attacks, 4 survivors, 1 rejection, 6 falsification tests). Valid JSON, no code modified, no stage advanced.\n\n**Headline findings:**\n\n- **Critical (A2) — INN-1's as-of filter is falsified as specified.** Supersede at `graph_memory.py:207-208` sets `status`/`superseded_by` but **never writes `valid_until`** — superseded edges stay NULL-open on the valid axis. The proposed filter (`valid_from <= t AND (valid_until IS NULL OR t < valid_until) AND learned_at <= t`) therefore returns **both sides of every contradiction** as current knowledge. A time-travel query would be wrong on its first contradiction. C5 survives only in corrected form (M1: one-line valid_until-on-supersede fix, or transaction-time-only as-of via `learned_at` + `superseded_by` chain).\n\n- **High (A5) — the production graph is empty.** Read-only inspection of `/mnt/workdrive/rune/memory/graph/graph.sqlite`: 0 entities, 0 aliases, 0 edges. C6's \"capped recall\" benefit is vacuous today; the real bottleneck is write-side adoption (curator's strength<3 gate + fixed verb map at `curator.py:1355-1388`).\n\n- **High (A1/A11) — the \"bi-temporal, implements E14 1:1\" claim is schema-only.** `valid_until` has no writer anywhere in the repo; `status='expired'` is unreachable; `valid_from == learned_at` on every insert. And \"supersede-on-contradiction\" keys on identical triples only — differently-normalized restatements produce parallel active edges with no invalidation. C4 survives **with a downgrade**: downstream dossiers must call it a transaction-time append log with same-triple supersession.\n\n- **High (A6) — duplicate authority for C6.** `memory_store.py` already runs FTS5 BM25 hybrid retrieval (retain-authority, `RECALL_MIN_SCORE=0.42`), and `memory_selector._graphs_recall` re-floors every graph candidate — a typo the new FTS5 tier resolves can still be killed at the selector. INN-1 never mentions the selector. C6 survives conditionally, reframed as a rebuildable projection with an end-to-end selector-level kill test (T3).\n\n- **C9** survives track-only, constrained: any future DuckDB/Arrow sidecar must be a rebuildable read-only projection of `graph.sqlite`, never a second store.\n\nSequencing correction (A13): write-side population first, the valid_until fix second, read-layer work last — INN-1 proposed the read layer first on an empty store. Falsification tests T1–T6 are executable directly from the dossier.","time":{"start":1788954467545,"end":1788954480329}}}
|
||||
{"type":"step_finish","timestamp":1788954480359,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085ff0ecb001l2lmOAgpn","reason":"stop","snapshot":"90d23a19e19fe5462d4d9d3f62f78721df7aaaf8","messageID":"msg_g001a085fe8b97001CJPHbfU67","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","type":"step-finish","tokens":{"total":103119,"input":90,"output":629,"reasoning":0,"cache":{"write":0,"read":102400}},"cost":0}}
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@@ -0,0 +1,27 @@
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{"type":"step_start","timestamp":1788951407567,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"id":"prt_g001a085d02bcd001TH7s5ESJ5","messageID":"msg_g001a085d00191001C15RR4qji","sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","snapshot":"23f45f561ddcbbf6f5123dccdd874c04d0d387ba","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788951409819,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"id":"prt_g001a085d02c67001ZWkgahH0g","messageID":"msg_g001a085d00191001C15RR4qji","sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","type":"text","text":"I'll first check for a matching skill, then locate the research dossier and Rune's architecture docs for issue #163.","time":{"start":1788951407719,"end":1788951409811}}}
|
||||
{"type":"tool_use","timestamp":1788951409822,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"type":"tool","tool":"skill_search","callID":"call_353413a591db4f39a64ccd98","state":{"status":"completed","input":{"query":"Classify researched mechanisms against an existing codebase architecture and write a structured applicability JSON dossier with a route decision (research triage, no code changes)"},"output":"{\n \"status\": \"matched\",\n \"results\": [\n {\n \"skill_id\": \"firecrawl-agent\",\n \"name\": \"firecrawl-agent\",\n \"score\": 0.5868,\n \"reason\": \"The skill description matches these query terms: structured, json.\"\n },\n {\n \"skill_id\": \"super-research\",\n \"name\": \"super-research\",\n \"score\": 0.5981,\n \"reason\": \"The skill description matches these query terms: against, write, research, no, code.\"\n },\n {\n \"skill_id\": \"design-blueprint\",\n \"name\": \"design-blueprint\",\n \"score\": 0.5069,\n \"reason\": \"The skill description matches these query terms: existing, structured, decision, code.\"\n }\n ],\n \"loaded_skill_id\": null\n}","metadata":{"status":"matched","loadedSkillID":null,"truncated":false},"title":"Found 3 skills","time":{"start":1788951409818,"end":1788951409821}},"id":"prt_g001a085d02c6c001okn4CENYP","sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","messageID":"msg_g001a085d00191001C15RR4qji"}}
|
||||
{"type":"tool_use","timestamp":1788951409968,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"type":"tool","tool":"bash","callID":"call_2e6da9e1680347529ee1a333","state":{"status":"completed","input":{"command":"ls research/issue-163/ 2>/dev/null; ls research 2>/dev/null; ls","description":"List research directory and repo root"},"output":"outward.json\nstage_APPLICABILITY.events.jsonl\nstage_APPLICABILITY.mimo.err.log\nstage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\nbatch-processing\nbuild-mode\ncontract-testing\nfull-audit\ninterstitial-tools\nissue-161\nissue-163\nissue-245-research.md\nissue-256\nissue-260\nissue-261\nissue-262\nissue-263\nllm-craft-api\nllm-craft-deep\nquality-tools\nresearch-methodology\nrune-efficiency\nsafety-override\ntextual-craft\ntool-calling\nuncensored-models\nv1\nworkflow-audit\nAGENTS.md\nalternatives.json\nbackups\nconfig\ndocs\nhenxels_checks.py\nhenxels.yaml\nlint-baseline.json\nmemory\nplans\nPRD.md\nprompts\n__pycache__\npyproject.toml\npyright-baseline.json\npyrightconfig.json\nREADME.md\nreports\nrequirements-ci.txt\nresearch\nscripts\nshared\nspecs\nsrc\nsurvey\ntests\nwal-risks.json\n","metadata":{"output":"outward.json\nstage_APPLICABILITY.events.jsonl\nstage_APPLICABILITY.mimo.err.log\nstage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\nbatch-processing\nbuild-mode\ncontract-testing\nfull-audit\ninterstitial-tools\nissue-161\nissue-163\nissue-245-research.md\nissue-256\nissue-260\nissue-261\nissue-262\nissue-263\nllm-craft-api\nllm-craft-deep\nquality-tools\nresearch-methodology\nrune-efficiency\nsafety-override\ntextual-craft\ntool-calling\nuncensored-models\nv1\nworkflow-audit\nAGENTS.md\nalternatives.json\nbackups\nconfig\ndocs\nhenxels_checks.py\nhenxels.yaml\nlint-baseline.json\nmemory\nplans\nPRD.md\nprompts\n__pycache__\npyproject.toml\npyright-baseline.json\npyrightconfig.json\nREADME.md\nreports\nrequirements-ci.txt\nresearch\nscripts\nshared\nspecs\nsrc\nsurvey\ntests\nwal-risks.json\n","exit":0,"description":"List research directory and repo root","truncated":false},"title":"List research directory and repo root","time":{"start":1788951409961,"end":1788951409965}},"id":"prt_g001a085d030210016Hamb7x7N","sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","messageID":"msg_g001a085d00191001C15RR4qji"}}
|
||||
{"type":"step_finish","timestamp":1788951410077,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"id":"prt_g001a085d0352f001u7nzqPQCs","reason":"tool-calls","snapshot":"48539978cc6be37eb58deb52a18fb7b2e4654cf7","messageID":"msg_g001a085d00191001C15RR4qji","sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","type":"step-finish","tokens":{"total":28758,"input":28574,"output":184,"reasoning":0,"cache":{"write":0,"read":0}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788951417899,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"id":"prt_g001a085d054290014iAkJocwe","messageID":"msg_g001a085d03628001aO2kspOq2","sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","snapshot":"db849ccee7016cd8061d48c31d8891432523c3f1","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788951418900,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"id":"prt_g001a085d054d3002knCM1IxSw","messageID":"msg_g001a085d03628001aO2kspOq2","sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","type":"text","text":"No skill matches exactly — this is a bespoke research-triage pipeline step, so I'll proceed manually. Reading the accepted OUTWARD dossier and the task context now.","time":{"start":1788951418067,"end":1788951418898}}}
|
||||
{"type":"tool_use","timestamp":1788951418910,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"type":"tool","tool":"read","callID":"call_ac097ee67a37402c9fc9ea1b","state":{"status":"completed","input":{"file_path":"research/issue-163/outward.json"},"output":"<path>/mnt/workdrive/issue161-harness/research/issue-163/outward.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"stage\": \"OUTWARD\",\n3: \"process_id\": \"research-163-r1\",\n4: \"issue\": 163,\n5: \"round\": 1,\n6: \"subquestion\": \"Address: Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\",\n7: \"discovery\": {\n8: \"queries\": [\n9: {\n10: \"id\": \"Q1\",\n11: \"tool\": \"webfetch (DuckDuckGo HTML)\",\n12: \"query\": \"neo4j vs falkordb graph database benchmark comparison\",\n13: \"receipt\": \"fetched OK; 10 results returned inline\"\n14: },\n15: {\n16: \"id\": \"Q2\",\n17: \"tool\": \"webfetch (DuckDuckGo HTML)\",\n18: \"query\": \"graphiti zep temporal knowledge graph agent memory neo4j falkordb\",\n19: \"receipt\": \"fetched OK; 10 results returned inline\"\n20: },\n21: {\n22: \"id\": \"Q3\",\n23: \"tool\": \"webfetch (DuckDuckGo HTML)\",\n24: \"query\": \"ladybugdb graph database kuzu fork\",\n25: \"receipt\": \"fetched OK; 10 results returned inline\"\n26: },\n27: {\n28: \"id\": \"Q4\",\n29: \"tool\": \"webfetch (DuckDuckGo HTML)\",\n30: \"query\": \"falkordb redisgraph successor cypher features github\",\n31: \"receipt\": \"fetched OK; 10 results returned inline\"\n32: },\n33: {\n34: \"id\": \"Q5\",\n35: \"tool\": \"webfetch (DuckDuckGo HTML)\",\n36: \"query\": \"neo4j temporal versioning bitemporal cypher valid time\",\n37: \"receipt\": \"fetched OK; 10 results returned inline\"\n38: }\n39: ],\n40: \"sources_discovered\": [\n41: { \"id\": \"O1\", \"url\": \"https://aimultiple.com/graph-databases\", \"class\": \"external (independent benchmark, updated 2026-04-15)\", \"note\": \"SELECTED and read\" },\n42: { \"id\": \"O2\", \"url\": \"https://www.falkordb.com/blog/graph-database-performance-benchmarks-falkordb-vs-neo4j/\", \"class\": \"external (vendor blog, pub 2024-12-02, updated 2026-08-23)\", \"note\": \"SELECTED and read\" },\n43: { \"id\": \"O3\", \"url\": \"https://www.puppygraph.com/blog/falkordb-vs-neo4j\", \"class\": \"external (competing-vendor comparison)\", \"note\": \"not selected — competing vendor has stake in the comparison\" },\n44: { \"id\": \"O4\", \"url\": \"https://knodegraph.com/blog/falkordb-vs-neo4j/\", \"class\": \"external (third-party blog w/ 2026 benchmarks)\", \"note\": \"not selected — O1 covers the independent-benchmark slot with fuller methodology\" },\n45: { \"id\": \"O5\", \"url\": \"https://github.com/FalkorDB/benchmark\", \"class\": \"repo (vendor benchmark harness)\", \"note\": \"SELECTED; README 404 on master and main; repo metadata read via GitHub API instead\" },\n46: { \"id\": \"O6\", \"url\": \"https://www.plushcap.com/content/falkordb/blog/falkordb-falkordb-vs-neo4j-graph-database-performance-benchmarks\", \"class\": \"external (mirror of O2)\", \"note\": \"not selected — duplicate\" },\n47: { \"id\": \"O7\", \"url\": \"https://inferensys.com/differences/neuro-symbolic-ai-frameworks/knowledge-graph-construction-tools/neo4j-vs-falkordb\", \"class\": \"external (aggregator comparison)\", \"note\": \"not selected\" },\n48: { \"id\": \"O8\", \"url\": \"https://inferensys.com/differences/ai-software-factory-platforms/code-knowledge-graph-systems/neo4j-vs-falkordb-for-code-graphs\", \"class\": \"external (aggregator comparison)\", \"note\": \"not selected\" },\n49: { \"id\": \"O9\", \"url\": \"https://3nuggets.io/compare-neo4j-vs-falkordb\", \"class\": \"external (practitioner fit comparison)\", \"note\": \"not selected\" },\n50: { \"id\": \"O10\", \"url\": \"https://github.com/getzep/graphiti\", \"class\": \"repo (Graphiti primary)\", \"note\": \"SELECTED and read (README via raw.githubusercontent.com/main)\" },\n51: { \"id\": \"O11\", \"url\": \"https://neo4j.com/blog/developer/graphiti-knowledge-graph-memory/\", \"class\": \"external (Neo4j official blog on Graphiti)\", \"note\": \"not selected — O10 is primary\" },\n52: { \"id\": \"O12\", \"url\": \"https://docs.falkordb.com/agentic-memory/graphiti\", \"class\": \"external (FalkorDB vendor docs)\", \"note\": \"not selected\" },\n53: { \"id\": \"O13\", \"url\": \"https://github.com/Memory-Agents/graphiti/\", \"class\": \"repo (mirror)\", \"note\": \"not selected — duplicate of O10\" },\n54: { \"id\": \"O14\", \"url\": \"https://codex.danielvaughan.com/2026/03/30/graphiti-agent-memory-store/\", \"class\": \"external (practitioner review)\", \"note\": \"not selecLine truncated
|
||||
{"type":"tool_use","timestamp":1788951418913,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"type":"tool","tool":"read","callID":"call_9f981917f1c7420084d6cfeb","state":{"status":"completed","input":{"file_path":"research/issue-163/task_tree.json"},"output":"<path>/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"issue\": 163,\n3: \"round\": 1,\n4: \"current_stage\": \"APPLICABILITY\",\n5: \"completed_stages\": [\n6: \"ISSUE\",\n7: \"INWARD\",\n8: \"OUTWARD\"\n9: ],\n10: \"artifacts\": {\n11: \"ISSUE\": {\n12: \"contract\": {\n13: \"issue\": 163,\n14: \"title\": \"Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\",\n15: \"question_type\": \"mixed\",\n16: \"sub_questions\": [\n17: \"Address: Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\"\n18: ],\n19: \"required_stages\": [\n20: \"ISSUE\",\n21: \"INWARD\",\n22: \"OUTWARD\",\n23: \"ADVERSARIAL\",\n24: \"INNOVATION\",\n25: \"SYNTHESIS\",\n26: \"QUALIFY\"\n27: ],\n28: \"skipped_stages\": {\n29: \"APPLICABILITY\": \"No OUTWARD research to ground against Rune.\"\n30: },\n31: \"evidence_classes\": [\n32: \"repo\",\n33: \"external\"\n34: ],\n35: \"sufficient_evidence\": \"\\u22652 code-path citations for INWARD; \\u22652 unique URLs for OUTWARD (if required)\",\n36: \"expected_artifact\": \"synthesis.json\",\n37: \"ambiguous\": false,\n38: \"needs_mimo\": false,\n39: \"source\": \"deterministic\"\n40: },\n41: \"deterministic\": true\n42: },\n43: \"INWARD\": {\n44: \"context\": {\n45: \"keywords\": [\n46: \"temporal\",\n47: \"graph\",\n48: \"backend\",\n49: \"spike\",\n50: \"neo\",\n51: \"falkordb\",\n52: \"ladybugdb\",\n53: \"experimental\",\n54: \"graphiti\",\n55: \"reference\",\n56: \"plain\",\n57: \"summary\",\n58: \"architecture_frontier_handoff\",\n59: \"compare\",\n60: \"graph\",\n61: \"backends\",\n62: \"rune\",\n63: \"structural\",\n64: \"projection\",\n65: \"episodes\"\n66: ],\n67: \"umbrella_matches\": [\n68: {\n69: \"umbrella\": \"memory\",\n70: \"module\": \"graph_memory\",\n71: \"keyword\": \"graph\"\n72: },\n73: {\n74: \"umbrella\": \"memory\",\n75: \"module\": \"graph_shard_bridge\",\n76: \"keyword\": \"graph\"\n77: },\n78: {\n79: \"umbrella\": \"orchestration\",\n80: \"module\": \"rune_operator\",\n81: \"keyword\": \"rune\"\n82: },\n83: {\n84: \"umbrella\": \"orchestration\",\n85: \"module\": \"rune_tui\",\n86: \"keyword\": \"rune\"\n87: },\n88: {\n89: \"umbrella\": \"orchestration\",\n90: \"module\": \"rune_graphic\",\n91: \"keyword\": \"graph\"\n92: },\n93: {\n94: \"umbrella\": \"orchestration\",\n95: \"module\": \"rune_spinner\",\n96: \"keyword\": \"rune\"\n97: },\n98: {\n99: \"umbrella\": \"orchestration\",\n100: \"module\": \"rune_profiler\",\n101: \"keyword\": \"rune\"\n102: }\n103: ],\n104: \"candidate_files\": [\n105: \"src/rune/curator.py\",\n106: \"src/rune/rune_tui.py\",\n107: \"src/rune/graph_memory.py\",\n108: \"src/rune/sleep_time.py\",\n109: \"src/rune/elastic_cognition.py\",\n110: \"src/rune/rune_operator.py\",\n111: \"src/rune/rune_profiler.py\",\n112: \"src/rune/warm_router.py\",\n113: \"src/rune/abstraction.py\",\n114: \"src/rune/canary_native.py\",\n115: \"src/rune/canary_p0_04.py\",\n116: \"src/rune/canary_p1_07.py\",\n117: \"src/rune/complexity_scorer.py\",\n118: \"src/rune/identity_anchors.py\",\n119: \"src/rune/memory_consolidation.py\"\n120: ],\n121: \"candidate_tests\": [\n122: \"tests/test_retrieval_benchmark.py\",\n123: \"tests/test_orchestration_memory_synthesis.py\",\n124: \"tests/test_p1_03_memory_selection.py\",\n125: \"tests/test_p1_06_model_routing.py\",\n126: \"tests/test_p1_07_runtime_objects.py\",\n127: \"tests/test_version_convergence.py\",\n128: \"tests/test_adversarial_executor.py\",\n129: \Line truncated
|
||||
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|
||||
{"type":"step_start","timestamp":1788951428962,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"id":"prt_g001a085d07f600010FQJUoXUO","messageID":"msg_g001a085d05ab7001HxOyBdecw","sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","snapshot":"a365d2ab71ac14d61f3ea9e7c28d6e9666b14327","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788951430166,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"id":"prt_g001a085d07ffc001p6yVhouLY","messageID":"msg_g001a085d05ab7001HxOyBdecw","sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","type":"text","text":"I have the dossier. Now I need to ground against Rune's actual graph memory surface (`src/rune/graph_memory.py` per the INWARD claims) to determine integration points.","time":{"start":1788951429116,"end":1788951430164}}}
|
||||
{"type":"tool_use","timestamp":1788951430228,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"type":"tool","tool":"bash","callID":"call_eb124832957949ffa92cbab3","state":{"status":"completed","input":{"command":"wc -l src/rune/graph_memory.py src/rune/curator.py src/rune/sleep_time.py src/rune/elastic_cognition.py 2>/dev/null; ls memory/graph/ 2>/dev/null","description":"Check sizes of inward-relevant files"},"output":" 416 src/rune/graph_memory.py\n 1612 src/rune/curator.py\n 899 src/rune/sleep_time.py\n 751 src/rune/elastic_cognition.py\n 3678 total\n","metadata":{"output":" 416 src/rune/graph_memory.py\n 1612 src/rune/curator.py\n 899 src/rune/sleep_time.py\n 751 src/rune/elastic_cognition.py\n 3678 total\n","exit":2,"description":"Check sizes of inward-relevant files","truncated":false},"title":"Check sizes of inward-relevant files","time":{"start":1788951430224,"end":1788951430227}},"id":"prt_g001a085d08005001N5J6eTe64","sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","messageID":"msg_g001a085d05ab7001HxOyBdecw"}}
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||||
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|
||||
{"type":"tool_use","timestamp":1788951440620,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"type":"tool","tool":"read","callID":"call_4a51679b338e4f238ce79c77","state":{"status":"completed","input":{"file_path":"src/rune/graph_memory.py"},"output":"<path>/mnt/workdrive/issue161-harness/src/rune/graph_memory.py</path>\n<type>file</type>\n<content>\n1: #!/usr/bin/env python3\n2: \"\"\"Graph memory tier — concept-mediated temporal knowledge graph for the agent.\n3: \n4: Built from the 2025-2026 academic frontier (briefs in\n5: `/tmp/opencode/memory-research/agentic-memory-brief.md` and\n6: `~/.firecrawl/claim-audit-2026/brief.md`). Exceeds the entity-hub approach by\n7: following the research convergence:\n8: \n9: - CONCEPT-MEDIATED, not entity-centric (GAAMA 2603.27910): nodes are typed\n10: (person / project / concept / tool / preference / fact), which kills the\n11: \"mega-hub\" dilution Letta/Graphiti hit with one entity connected to\n12: everything.\n13: - BI-TEMPORAL edges (Graphiti 2501.13956): `valid_from/valid_until` (when\n14: the fact is true in the world) + `learned_at` (when we learned it).\n15: - INCREMENTAL construction (TG-RAG 2510.13590, iText2KG 2409.03284): merge\n16: only, no full re-index; entity resolution via aliases, never blind merges.\n17: - SUPERSEDE, never delete (Write-Time Gating 2603.15994, SSGM 2603.11768):\n18: contradicting edges are marked superseded with the new one taking over;\n19: history preserved for audit.\n20: - WRITE-TIME GATING (2603.15994, D-MEM 2603.14597): routine/salience-weak\n21: facts never touch the graph; surprises and contradictions do. The biggest\n22: token saver in the literature.\n23: - PROVENANCE on every edge (MemORAI 2605.01386): each edge carries the\n24: evidence text + source, which is exactly what the claim-audit layer needs.\n25: \n26: Storage: headless SQLite at `memory/graph/graph.sqlite` — reachable by the\n27: sleep-time curator directly (no MCP dependency). Schema below.\n28: \n29: Usage:\n30: graph_memory.py add <subj> <pred> <obj> --type subj_type --evidence \"...\"\n31: [--source X] [--confidence 0.0-1.0] [--strength N]\n32: graph_memory.py add-bulk <json-file>\n33: graph_memory.py query <entity-or-concept> [--hops N] [--latest-only]\n34: graph_memory.py resolve <name>\n35: graph_memory.py neighbors <entity>\n36: graph_memory.py stats\n37: graph_memory.py superseded-by <subj> <pred> <obj> (manual conflict flag)\n38: \"\"\"\n39: \n40: import argparse\n41: import json\n42: import os\n43: import sqlite3\n44: import sys\n45: from collections import deque\n46: from datetime import datetime, timezone\n47: \n48: _SD = os.path.dirname(os.path.abspath(__file__))\n49: if _SD not in sys.path:\n50: sys.path.insert(0, _SD)\n51: import memory_env\n52: \n53: MEM_DIR = memory_env.memory_dir()\n54: GRAPH_DIR = os.path.join(MEM_DIR, \"graph\")\n55: DB_PATH = os.path.join(GRAPH_DIR, \"graph.sqlite\")\n56: \n57: # Fixed predicate vocabulary — schema drift is a researched failure mode, so we\n58: # constrain the set of relationship types we accept. Unknown preds are refused\n59: # (the extractor maps free text onto this set).\n60: PREDICATES = {\n61: \"works_on\", \"works_at\", \"teaches\", \"manages\", \"uses\", \"prefers\",\n62: \"lives_in\", \"located_in\", \"has_role\", \"part_of\", \"leads\", \"builds\",\n63: \"develops\", \"created\", \"interested_in\", \"knows\", \"collaborates_with\",\n64: \"owned_by\", \"reports_to\", \"supports\", \"runs\", \"tracks\", \"related_to\",\n65: \"based_on\", \"happens_on\", \"started_on\", \"ended_on\", \"responds_to\",\n66: \"struggles_with\", \"likes\", \"dislikes\", \"avoided_by\", \"goal_of\",\n67: }\n68: \n69: # Node types.\n70: NODE_TYPES = {\"person\", \"project\", \"concept\", \"tool\", \"preference\",\n71: \"organization\", \"place\", \"event\", \"skill\", \"artefact\"}\n72: \n73: # Confidence: evidence-gated (EvoKG-style f(frequency, recency, source_cred)).\n74: # We fold the curator's strength (0-5) into a 0-1 confidence.\n75: DEFAULT_CONF = 0.7\n76: \n77: \n78: def now_iso():\n79: return datetime.now(timezone.utc).isoformat(timespec=\"seconds\")\n80: \n81: \n82: def connect():\n83: os.makedirs(GRAPH_DIR, exist_ok=True)\n84: db = sqlite3.connect(DB_PATH)\n85: try:\n86: db.row_factory = sqlite3.Row\n87: db.executescript(\"\"\"\n88: CREATE TABLE IF NOT EXISTS entities (\n89: id INTEGER PRIMARY KEY AUTOINCREMENT,\n90: name TEXT NOT NULL UNIQUE,\n91: type TEXT NOT NULL DEFAULT 'concept',\n92: summary TEXT DEFAULT '',\n93: created_at TEXT NOT NULL\n94: );\n95: CREATE TABLE IF NOT EXISTS aliases (\n96: entity_id INTEGER NOT NULL,\n97: alias TEXT NOT NULL,\n98: UNIQUE(entity_id, alias),\n99: FOREIGN KEY(entity_Line truncated
|
||||
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|
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{"type":"text","timestamp":1788951660703,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"id":"prt_g001a085d25c21002cvth08Rmj","messageID":"msg_g001a085d0ad2e001SVRAIsy0z","sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","type":"text","text":"I now have everything needed: the OUTWARD dossier (evidence E1–E30, sources O1–O45) and Rune's actual graph surface (`src/rune/graph_memory.py` — an embedded SQLite bi-temporal graph that already implements Graphiti's framework-layer temporal semantics). Now I'll classify each researched mechanism against that reality and write the applicability dossier.\n\nKey grounded observations driving the classification:\n- Rune's schema (`graph_memory.py:101-116`) already has bi-temporal edges (`valid_from`/`valid_until` + `learned_at`), supersede-not-delete, provenance, and write-time gating — i.e., the load-bearing fact of the reference architecture (semantics at framework layer, not in the DB) is already Rune's design.\n- The dossier's contradiction #4 and `repo_grounding_note` show both spiked backends (Neo4j server-shaped JVM, FalkorDB Redis module/SSPL) conflict with Rune's embedded/local-first posture, and neither offers native temporal versioning (E27–E28) — Rune's own schema is already ahead of both.\n- Real gaps vs the research: Rune preserves full history but `query()` can only see the present (no AS-OF time travel, NEP-001 E28–E30), and entry-point resolution is name/alias/substring only (no hybrid lexical retrieval, E18).","time":{"start":1788951551009,"end":1788951660696}}}
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{"type":"tool_use","timestamp":1788951660708,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"type":"tool","tool":"write","callID":"call_c6a83edc308b430a9db1f9b0","state":{"status":"completed","input":{"content":"{\n \"stage\": \"APPLICABILITY\",\n \"issue\": 163,\n \"round\": 1,\n \"process_id\": \"research-163-r1\",\n \"grounding\": {\n \"inward_claims_used\": [\"I3\", \"I1\", \"I4\", \"I5\"],\n \"rune_reality\": \"src/rune/graph_memory.py is Rune's entire graph surface: a headless SQLite tier at memory/graph/graph.sqlite (no external graph DB dependency). Schema (lines 101-116) already implements: bi-temporal edges (valid_from/valid_until + learned_at), supersede-never-delete with status/superseded_by, per-edge provenance (evidence/source), confidence-gated writes (strength<3 gated out), typed concept-mediated nodes, and alias-based entity resolution. Query is name/alias/substring entry point + BFS over status='active' edges.\",\n \"load_bearing_fact\": \"The dossier's repo_grounding_note and Graphiti (E17) confirm temporal semantics live at the framework layer above any backend. Rune already occupies that layer over SQLite. Neo4j and FalkorDB would be backends BELOW a layer Rune already owns, and neither offers native temporal versioning (E27, E28) - Rune's schema is already ahead of both on the spike's titular dimension.\"\n },\n \"candidates\": [\n {\n \"id\": \"C1\",\n \"candidate\": \"Neo4j as replacement graph backend\",\n \"source_ids\": [\"O1\", \"O2\", \"O38\", \"O41\"],\n \"classification\": \"CONFLICTS_WITH_RUNE\",\n \"integration_point\": \"Would replace src/rune/graph_memory.py's SQLite tier (memory/graph/graph.sqlite) with a server-backed Neo4j deployment reachable from curator.py / sleep_time.py.\",\n \"benefit\": \"Deepest ecosystem, best planner on heavy aggregations (E3), mature tooling (E8).\",\n \"cost\": \"Server + JVM process to operate; ~2.7GB heap for a 381K-node graph, ~5.2GB process RSS (E6) - two orders of magnitude above Rune's sub-1M-entity regime; 90ms cold start vs embedded alternatives (E4).\",\n \"architectural_fit\": \"Poor. Rune is embedded/local-first with zero external service dependencies (I3: 'reachable by the sleep-time curator directly, no MCP dependency'). A networked JVM server reverses that posture. No source benchmarks Rune's single-node regime (dossier limitation #2).\",\n \"authority_implications\": \"Introduces an external service and credentials into the memory tier; curator/sleep-time write authority would route through a network daemon instead of a local file.\",\n \"risks\": \"Licensing (Community vs Enterprise constraints unevidenced, dossier limitation #5); no native temporal versioning - the spike's core dimension (E28-E30); migration cost for zero evidenced benefit at Rune's scale.\",\n \"required_adaptation\": \"None - rejection is the correct disposition.\",\n \"evidence\": [\"E2\", \"E3\", \"E4\", \"E6\", \"E8\", \"E27\", \"E28\", \"E29\"]\n },\n {\n \"id\": \"C2\",\n \"candidate\": \"FalkorDB as replacement graph backend\",\n \"source_ids\": [\"O1\", \"O2\", \"O5\", \"O29\", \"O10\"],\n \"classification\": \"CONFLICTS_WITH_RUNE\",\n \"integration_point\": \"Would replace the SQLite tier; only embedded path is Graphiti's falkordblite extra (redislite-based, Python 3.12+) per E16.\",\n \"benefit\": \"Strong concurrent throughput and low latency at scale (E2, E3, E9), implicit indexing (E5), GraphRAG positioning (E10).\",\n \"cost\": \"Server-shaped: Redis 7.4 module, own container/ports, min 4GB RAM / 16GB recommended (E12, E25, E26). SSPLv1 core license (E25). Rust core rewrite (E11) resets part of the maturity story (dossier contradiction #3).\",\n \"architectural_fit\": \"Poor. The headline advantages (6.7x throughput, p50/p99 wins) were measured on 381K-804K-element graphs under 8-32 thread concurrency (E1, E9) - not Rune's regime (single-node, sub-1M entities, agent-local). Vendor magnitudes are marketing-adjacent (dossier contradiction #1; O5 benchmark harness README unverifiable, E13).\",\n \"authority_implications\": \"A Redis server in the memory path; even falkordblite drags in a redislite dependency and a Python 3.12 floor for a lateral move from SQLite.\",\n \"risks\": \"RedisGraph lineage EOL'd Jan 2025 (dossier contradiction #3); no evidenced FalkorDB-native temporal features (dossier limitation #4); license incompatibility with Rune's MIT-family posture.\",\n \"required_adaptation\": \"None - rejection is the correct disposition.\",\n \"evidence\": [\"E1\", \"E2\", \"E3\", \"E5\", \"E9\", \"E10\", \"E11\", \"E12\", \"E13\", \"E16\", \"E25\", \"E26\"]\n },\n {\n \"id\": \"C3\",\n \"candidate\": \"LadybugDB as replacement graph backend\",\n \"source_ids\": [\"O21\", \"O24\", \"O28\"],\n \"classification\": \"INSUFFICIENT_EVIDENCE\",\n \"integration_point\"Line truncated
|
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|
||||
{"type":"tool_use","timestamp":1788951669502,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"type":"tool","tool":"bash","callID":"call_11072974bc7f418ba5b8abd5","state":{"status":"completed","input":{"command":"python3 -c \"import json; d=json.load(open('research/issue-163/applicability.json')); print('valid JSON'); print('route:', d['route']); print('candidates:', len(d['candidates'])); [print(' ', c['id'], '->', c['classification']) for c in d['candidates']]\"","description":"Validate applicability.json structure and classifications"},"output":"valid JSON\nroute: INNOVATION_WARRANTED\ncandidates: 10\n C1 -> CONFLICTS_WITH_RUNE\n C2 -> CONFLICTS_WITH_RUNE\n C3 -> INSUFFICIENT_EVIDENCE\n C4 -> DIRECTLY_APPLICABLE\n C5 -> APPLICABLE_WITH_ADAPTATION\n C6 -> APPLICABLE_WITH_ADAPTATION\n C7 -> TOO_COMPLEX_FOR_VALUE\n C8 -> TOO_COMPLEX_FOR_VALUE\n C9 -> PROMISING_FOR_INNOVATION\n C10 -> TOO_COMPLEX_FOR_VALUE\n","metadata":{"output":"valid JSON\nroute: INNOVATION_WARRANTED\ncandidates: 10\n C1 -> CONFLICTS_WITH_RUNE\n C2 -> CONFLICTS_WITH_RUNE\n C3 -> INSUFFICIENT_EVIDENCE\n C4 -> DIRECTLY_APPLICABLE\n C5 -> APPLICABLE_WITH_ADAPTATION\n C6 -> APPLICABLE_WITH_ADAPTATION\n C7 -> TOO_COMPLEX_FOR_VALUE\n C8 -> TOO_COMPLEX_FOR_VALUE\n C9 -> PROMISING_FOR_INNOVATION\n C10 -> TOO_COMPLEX_FOR_VALUE\n","exit":0,"description":"Validate applicability.json structure and classifications","truncated":false},"title":"Validate applicability.json structure and classifications","time":{"start":1788951669497,"end":1788951669501}},"id":"prt_g001a085d423dc001L5nVrusWW","sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","messageID":"msg_g001a085d40902001JzXdcSouQ"}}
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||||
{"type":"step_finish","timestamp":1788951669528,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"id":"prt_g001a085d42afe001VSTRKL7Bk","reason":"tool-calls","snapshot":"d5da261352c8097af274b8f23466cfa1ef248e13","messageID":"msg_g001a085d40902001JzXdcSouQ","sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","type":"step-finish","tokens":{"total":59443,"input":4569,"output":90,"reasoning":0,"cache":{"write":0,"read":54784}},"cost":0}}
|
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{"type":"step_start","timestamp":1788951677705,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"id":"prt_g001a085d44b07001oDf8l2FFh","messageID":"msg_g001a085d42b3c001Qr2mCmgZo","sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","snapshot":"69889b5af68548648700c57c3191028f6313d451","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788951691381,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"id":"prt_g001a085d44b100027KsH8bf6S","messageID":"msg_g001a085d42b3c001Qr2mCmgZo","sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","type":"text","text":"APPLICABILITY stage complete. Wrote `research/issue-163/applicability.json` (valid JSON, 10 candidates, route decision recorded). No code modified, no research re-run, no stage advanced.\n\n## Classification summary\n\n**Grounding**: INWARD's claims I3/I1/I4/I5, verified against `src/rune/graph_memory.py:101-116` — Rune's graph surface is a headless SQLite tier that *already* implements the reference architecture's load-bearing pattern: bi-temporal edges (`valid_from`/`valid_until` + `learned_at`), supersede-never-delete, per-edge provenance, write-time gating. The dossier's own `repo_grounding_note` and Graphiti E17 (temporal semantics at framework layer, decoupled from storage) confirm Rune already occupies that layer.\n\n**Candidates**:\n| ID | Candidate | Classification |\n|---|---|---|\n| C1 | Neo4j backend | CONFLICTS_WITH_RUNE — JVM server, ~5GB RSS (E6), no native temporal versioning (E28-E30) |\n| C2 | FalkorDB backend | CONFLICTS_WITH_RUNE — Redis module/SSPLv1, only embedded path is falkordblite (E16); benchmark advantages are outside Rune's regime |\n| C3 | LadybugDB backend | INSUFFICIENT_EVIDENCE — best *shape* fit (embedded/MIT/FTS/vector, E20) but young revival fork (E24), no Graphiti driver, zero Rune-regime benchmarks |\n| C4 | Graphiti bi-temporal model | DIRECTLY_APPLICABLE — already integrated 1:1 |\n| C5 | NEP-001 \"AS OF\" queries | APPLICABLE_WITH_ADAPTATION — time-travel over history Rune already stores but can't query (`--as-of` filter in `query()`) |\n| C6 | Hybrid retrieval entry | APPLICABLE_WITH_ADAPTATION — FTS5 lexical tier for entity resolution (currently exact/alias/substring only) |\n| C7/C8/C10 | Driver abstraction / Cypher temporal types / LLM ingestion | TOO_COMPLEX_FOR_VALUE |\n| C9 | Ladybug Memory lakehouse stack | PROMISING_FOR_INNOVATION — adjacent-field lead, not coupled to this spike |\n\n**Route: INNOVATION_WARRANTED** — the literal spike question (backend choice) resolves to standard: no swap is warranted. But combining research with Rune's architecture yields two genuinely missing capabilities that no standard drop-in provides: temporal AS-OF queries (C5) and lexical hybrid entry (C6), both staying inside the embedded, no-new-dependency posture. Notes flag what ADVERSARIAL should attack (NULL/half-open-interval semantics, FTS5 write amplification) and carry the dossier's evidence limitations forward.","time":{"start":1788951677712,"end":1788951691379}}}
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{"type":"step_finish","timestamp":1788951691405,"sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","part":{"id":"prt_g001a085d480740015iMbbvyv1","reason":"stop","snapshot":"62b57ed69b43c3ba23de24b8d830305c06ac3094","messageID":"msg_g001a085d42b3c001Qr2mCmgZo","sessionID":"ses_-ffe5f7a2fff97ffeShe3jrJEB","type":"step-finish","tokens":{"total":60174,"input":178,"output":604,"reasoning":0,"cache":{"write":0,"read":59392}},"cost":0}}
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@@ -0,0 +1,33 @@
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{"type":"step_start","timestamp":1788951891614,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085d78e9b001YKXg0dTZV","messageID":"msg_g001a085d75e2c001ddwRuEVcg","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","snapshot":"6c23a18f5e584f4ea85e3895584633cd584374bb","type":"step-start"}}
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{"type":"text","timestamp":1788951893751,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085d78f5d0019210edNCF","messageID":"msg_g001a085d75e2c001ddwRuEVcg","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","type":"text","text":"I'll start by searching for a matching skill, then examine the research context and code before forming a conclusion.","time":{"start":1788951891805,"end":1788951893748}}}
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{"type":"tool_use","timestamp":1788951893755,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"type":"tool","tool":"skill_search","callID":"call_880953f93e3e42c88c0f9b75","state":{"status":"completed","input":{"query":"Evaluate research candidates for compositional innovation against existing codebase architecture and write structured JSON verdict (conclusion ADOPT/ADAPT/BORROW/BUILD or NO_INNOVATION_WARRANTED) to research output file. Input: research stage prompt with candidate list and source code. Output: innovation.json. Audience: research pipeline."},"output":"{\n \"status\": \"matched\",\n \"results\": [\n {\n \"skill_id\": \"super-research\",\n \"name\": \"super-research\",\n \"score\": 0.6625,\n \"reason\": \"The skill description matches these query terms: research, candidate, against, write, or, no, prompt, source, code.\"\n },\n {\n \"skill_id\": \"research-paper-writing\",\n \"name\": \"research-paper-writing\",\n \"score\": 0.5854,\n \"reason\": \"The skill description matches these query terms: write, conclusion, build, or, file.\"\n },\n {\n \"skill_id\": \"mimocode-docs\",\n \"name\": \"mimocode-docs\",\n \"score\": 0.5605,\n \"reason\": \"The skill description matches these query terms: existing, json, borrow, build, or, file, prompt.\"\n }\n ],\n \"loaded_skill_id\": null\n}","metadata":{"status":"matched","loadedSkillID":null,"truncated":false},"title":"Found 3 skills","time":{"start":1788951893750,"end":1788951893752}},"id":"prt_g001a085d78f61001CtHGQd7Yv","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","messageID":"msg_g001a085d75e2c001ddwRuEVcg"}}
|
||||
{"type":"step_finish","timestamp":1788951893842,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085d796fa001NlGZP4pXS","reason":"tool-calls","snapshot":"72c67853d44dbb481bd3f6bc0fbb3851cbfbb6f1","messageID":"msg_g001a085d75e2c001ddwRuEVcg","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","type":"step-finish","tokens":{"total":28996,"input":28797,"output":199,"reasoning":0,"cache":{"write":0,"read":0}},"cost":0}}
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{"type":"step_start","timestamp":1788951903714,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085d7bde00013y3l4WsB3","messageID":"msg_g001a085d797b3001mBqbUsAJo","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","snapshot":"fa29a2ea683615719af7c44d951923a013eead31","type":"step-start"}}
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||||
{"type":"text","timestamp":1788951903903,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085d7be8e001EYTF0JWPc","messageID":"msg_g001a085d797b3001mBqbUsAJo","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","type":"text","text":"No exact skill match — this is a pipeline verdict stage, so I'll proceed directly. Let me verify the candidate claims against the actual research artifacts and code.","time":{"start":1788951903886,"end":1788951903902}}}
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{"type":"tool_use","timestamp":1788951903983,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"type":"tool","tool":"read","callID":"call_9729f3f494c74ed588d6a164","state":{"status":"error","input":{"file_path":"/home/nick/projects/rune/src/rune/graph_memory.py"},"error":"File not found: /home/nick/projects/rune/src/rune/graph_memory.py","time":{"start":1788951903904,"end":1788951903982}},"id":"prt_g001a085d7be96001TqHKYVlyZ","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","messageID":"msg_g001a085d797b3001mBqbUsAJo"}}
|
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{"type":"tool_use","timestamp":1788951904064,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"type":"tool","tool":"glob","callID":"call_fe74e78ee4174eb5bcc2926c","state":{"status":"completed","input":{"pattern":"research/issue-163/**"},"output":"/mnt/workdrive/issue161-harness/research/issue-163/stage_INNOVATION.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/stage_INNOVATION.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-INNOVATION-1\n/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_APPLICABILITY.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/applicability.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_APPLICABILITY.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-APPLICABILITY-1\n/mnt/workdrive/issue161-harness/research/issue-163/stage_OUTWARD.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/outward.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_OUTWARD.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-OUTWARD-1\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-INWARD-1\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-ISSUE-1","metadata":{"count":14,"truncated":false},"title":"","time":{"start":1788951903903,"end":1788951904063}},"id":"prt_g001a085d7be93001wZbNq3Cw0","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","messageID":"msg_g001a085d797b3001mBqbUsAJo"}}
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{"type":"step_finish","timestamp":1788951904306,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085d7bf40001N68pdLLKw","reason":"tool-calls","snapshot":"309646e87101537099d3a2da12a09febcf68ef65","messageID":"msg_g001a085d797b3001mBqbUsAJo","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","type":"step-finish","tokens":{"total":29234,"input":313,"output":121,"reasoning":0,"cache":{"write":0,"read":28800}},"cost":0}}
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{"type":"step_start","timestamp":1788951913561,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085d7e456001J5gGYDWwm","messageID":"msg_g001a085d7c175001Mhl8PCtDl","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","snapshot":"5af5483b877decd3b11d50f8cf94482b8fe25b1c","type":"step-start"}}
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{"type":"text","timestamp":1788951914578,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085d7e4f0001nvTy4ngXC","messageID":"msg_g001a085d7c175001Mhl8PCtDl","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","type":"text","text":"The research dir is at `/mnt/workdrive/issue161-harness`. Let me locate the rune repo and read the prior-stage artifacts to understand the evidence derivation.","time":{"start":1788951913712,"end":1788951914576}}}
|
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{"type":"tool_use","timestamp":1788951914705,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"type":"tool","tool":"read","callID":"call_451ebeeb22ee4aa2af07e451","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/research/issue-163/applicability.json"},"output":"<path>/mnt/workdrive/issue161-harness/research/issue-163/applicability.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"stage\": \"APPLICABILITY\",\n3: \"issue\": 163,\n4: \"round\": 1,\n5: \"process_id\": \"research-163-r1\",\n6: \"grounding\": {\n7: \"inward_claims_used\": [\"I3\", \"I1\", \"I4\", \"I5\"],\n8: \"rune_reality\": \"src/rune/graph_memory.py is Rune's entire graph surface: a headless SQLite tier at memory/graph/graph.sqlite (no external graph DB dependency). Schema (lines 101-116) already implements: bi-temporal edges (valid_from/valid_until + learned_at), supersede-never-delete with status/superseded_by, per-edge provenance (evidence/source), confidence-gated writes (strength<3 gated out), typed concept-mediated nodes, and alias-based entity resolution. Query is name/alias/substring entry point + BFS over status='active' edges.\",\n9: \"load_bearing_fact\": \"The dossier's repo_grounding_note and Graphiti (E17) confirm temporal semantics live at the framework layer above any backend. Rune already occupies that layer over SQLite. Neo4j and FalkorDB would be backends BELOW a layer Rune already owns, and neither offers native temporal versioning (E27, E28) - Rune's schema is already ahead of both on the spike's titular dimension.\"\n10: },\n11: \"candidates\": [\n12: {\n13: \"id\": \"C1\",\n14: \"candidate\": \"Neo4j as replacement graph backend\",\n15: \"source_ids\": [\"O1\", \"O2\", \"O38\", \"O41\"],\n16: \"classification\": \"CONFLICTS_WITH_RUNE\",\n17: \"integration_point\": \"Would replace src/rune/graph_memory.py's SQLite tier (memory/graph/graph.sqlite) with a server-backed Neo4j deployment reachable from curator.py / sleep_time.py.\",\n18: \"benefit\": \"Deepest ecosystem, best planner on heavy aggregations (E3), mature tooling (E8).\",\n19: \"cost\": \"Server + JVM process to operate; ~2.7GB heap for a 381K-node graph, ~5.2GB process RSS (E6) - two orders of magnitude above Rune's sub-1M-entity regime; 90ms cold start vs embedded alternatives (E4).\",\n20: \"architectural_fit\": \"Poor. Rune is embedded/local-first with zero external service dependencies (I3: 'reachable by the sleep-time curator directly, no MCP dependency'). A networked JVM server reverses that posture. No source benchmarks Rune's single-node regime (dossier limitation #2).\",\n21: \"authority_implications\": \"Introduces an external service and credentials into the memory tier; curator/sleep-time write authority would route through a network daemon instead of a local file.\",\n22: \"risks\": \"Licensing (Community vs Enterprise constraints unevidenced, dossier limitation #5); no native temporal versioning - the spike's core dimension (E28-E30); migration cost for zero evidenced benefit at Rune's scale.\",\n23: \"required_adaptation\": \"None - rejection is the correct disposition.\",\n24: \"evidence\": [\"E2\", \"E3\", \"E4\", \"E6\", \"E8\", \"E27\", \"E28\", \"E29\"]\n25: },\n26: {\n27: \"id\": \"C2\",\n28: \"candidate\": \"FalkorDB as replacement graph backend\",\n29: \"source_ids\": [\"O1\", \"O2\", \"O5\", \"O29\", \"O10\"],\n30: \"classification\": \"CONFLICTS_WITH_RUNE\",\n31: \"integration_point\": \"Would replace the SQLite tier; only embedded path is Graphiti's falkordblite extra (redislite-based, Python 3.12+) per E16.\",\n32: \"benefit\": \"Strong concurrent throughput and low latency at scale (E2, E3, E9), implicit indexing (E5), GraphRAG positioning (E10).\",\n33: \"cost\": \"Server-shaped: Redis 7.4 module, own container/ports, min 4GB RAM / 16GB recommended (E12, E25, E26). SSPLv1 core license (E25). Rust core rewrite (E11) resets part of the maturity story (dossier contradiction #3).\",\n34: \"architectural_fit\": \"Poor. The headline advantages (6.7x throughput, p50/p99 wins) were measured on 381K-804K-element graphs under 8-32 thread concurrency (E1, E9) - not Rune's regime (single-node, sub-1M entities, agent-local). Vendor magnitudes are marketing-adjacent (dossier contradiction #1; O5 benchmark harness README unverifiable, E13).\",\n35: \"authority_implications\": \"A Redis server in the memory path; even falkordblite drags in a redislite dependency and a Python 3.12 floor for a lateral move from SQLite.\",\n36: \"risks\": \"RedisGraph lineage EOL'd Jan 2025 (dossier contradiction #3); no evidenced FalkorDB-native temporal features (dossier limitation #4); license incompatibility with Rune's MIT-family posture.\",\n37: \"required_adaptation\": \"None - rejection is the correct disposition.\",\n38: \"evidence\": [\Line truncated
|
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|
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{"type":"step_finish","timestamp":1788951914897,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085d7e8d3001OXgvdbaQf","reason":"tool-calls","snapshot":"4866ca38a98c1a89dd59c8d9b327e70a8fd34008","messageID":"msg_g001a085d7c175001Mhl8PCtDl","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","type":"step-finish","tokens":{"total":29662,"input":425,"output":117,"reasoning":0,"cache":{"write":0,"read":29120}},"cost":0}}
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{"type":"step_start","timestamp":1788951923824,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085d80c6e001b4c3ks6BA","messageID":"msg_g001a085d7eae2001zeX90Xx0h","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","snapshot":"6d427c11699efc50569b4656c5222028738b4703","type":"step-start"}}
|
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{"type":"text","timestamp":1788951924292,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085d80d09001yFq3ItA5p","messageID":"msg_g001a085d7eae2001zeX90Xx0h","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","type":"text","text":"The applicability dossier is thorough. Now let me verify the candidate claims against the actual code before writing the verdict.","time":{"start":1788951923977,"end":1788951924290}}}
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{"type":"tool_use","timestamp":1788951924314,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"type":"tool","tool":"read","callID":"call_1502486cb6134575b3d4b916","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/src/rune/graph_memory.py"},"output":"<path>/mnt/workdrive/issue161-harness/src/rune/graph_memory.py</path>\n<type>file</type>\n<content>\n1: #!/usr/bin/env python3\n2: \"\"\"Graph memory tier — concept-mediated temporal knowledge graph for the agent.\n3: \n4: Built from the 2025-2026 academic frontier (briefs in\n5: `/tmp/opencode/memory-research/agentic-memory-brief.md` and\n6: `~/.firecrawl/claim-audit-2026/brief.md`). Exceeds the entity-hub approach by\n7: following the research convergence:\n8: \n9: - CONCEPT-MEDIATED, not entity-centric (GAAMA 2603.27910): nodes are typed\n10: (person / project / concept / tool / preference / fact), which kills the\n11: \"mega-hub\" dilution Letta/Graphiti hit with one entity connected to\n12: everything.\n13: - BI-TEMPORAL edges (Graphiti 2501.13956): `valid_from/valid_until` (when\n14: the fact is true in the world) + `learned_at` (when we learned it).\n15: - INCREMENTAL construction (TG-RAG 2510.13590, iText2KG 2409.03284): merge\n16: only, no full re-index; entity resolution via aliases, never blind merges.\n17: - SUPERSEDE, never delete (Write-Time Gating 2603.15994, SSGM 2603.11768):\n18: contradicting edges are marked superseded with the new one taking over;\n19: history preserved for audit.\n20: - WRITE-TIME GATING (2603.15994, D-MEM 2603.14597): routine/salience-weak\n21: facts never touch the graph; surprises and contradictions do. The biggest\n22: token saver in the literature.\n23: - PROVENANCE on every edge (MemORAI 2605.01386): each edge carries the\n24: evidence text + source, which is exactly what the claim-audit layer needs.\n25: \n26: Storage: headless SQLite at `memory/graph/graph.sqlite` — reachable by the\n27: sleep-time curator directly (no MCP dependency). Schema below.\n28: \n29: Usage:\n30: graph_memory.py add <subj> <pred> <obj> --type subj_type --evidence \"...\"\n31: [--source X] [--confidence 0.0-1.0] [--strength N]\n32: graph_memory.py add-bulk <json-file>\n33: graph_memory.py query <entity-or-concept> [--hops N] [--latest-only]\n34: graph_memory.py resolve <name>\n35: graph_memory.py neighbors <entity>\n36: graph_memory.py stats\n37: graph_memory.py superseded-by <subj> <pred> <obj> (manual conflict flag)\n38: \"\"\"\n39: \n40: import argparse\n41: import json\n42: import os\n43: import sqlite3\n44: import sys\n45: from collections import deque\n46: from datetime import datetime, timezone\n47: \n48: _SD = os.path.dirname(os.path.abspath(__file__))\n49: if _SD not in sys.path:\n50: sys.path.insert(0, _SD)\n51: import memory_env\n52: \n53: MEM_DIR = memory_env.memory_dir()\n54: GRAPH_DIR = os.path.join(MEM_DIR, \"graph\")\n55: DB_PATH = os.path.join(GRAPH_DIR, \"graph.sqlite\")\n56: \n57: # Fixed predicate vocabulary — schema drift is a researched failure mode, so we\n58: # constrain the set of relationship types we accept. Unknown preds are refused\n59: # (the extractor maps free text onto this set).\n60: PREDICATES = {\n61: \"works_on\", \"works_at\", \"teaches\", \"manages\", \"uses\", \"prefers\",\n62: \"lives_in\", \"located_in\", \"has_role\", \"part_of\", \"leads\", \"builds\",\n63: \"develops\", \"created\", \"interested_in\", \"knows\", \"collaborates_with\",\n64: \"owned_by\", \"reports_to\", \"supports\", \"runs\", \"tracks\", \"related_to\",\n65: \"based_on\", \"happens_on\", \"started_on\", \"ended_on\", \"responds_to\",\n66: \"struggles_with\", \"likes\", \"dislikes\", \"avoided_by\", \"goal_of\",\n67: }\n68: \n69: # Node types.\n70: NODE_TYPES = {\"person\", \"project\", \"concept\", \"tool\", \"preference\",\n71: \"organization\", \"place\", \"event\", \"skill\", \"artefact\"}\n72: \n73: # Confidence: evidence-gated (EvoKG-style f(frequency, recency, source_cred)).\n74: # We fold the curator's strength (0-5) into a 0-1 confidence.\n75: DEFAULT_CONF = 0.7\n76: \n77: \n78: def now_iso():\n79: return datetime.now(timezone.utc).isoformat(timespec=\"seconds\")\n80: \n81: \n82: def connect():\n83: os.makedirs(GRAPH_DIR, exist_ok=True)\n84: db = sqlite3.connect(DB_PATH)\n85: try:\n86: db.row_factory = sqlite3.Row\n87: db.executescript(\"\"\"\n88: CREATE TABLE IF NOT EXISTS entities (\n89: id INTEGER PRIMARY KEY AUTOINCREMENT,\n90: name TEXT NOT NULL UNIQUE,\n91: type TEXT NOT NULL DEFAULT 'concept',\n92: summary TEXT DEFAULT '',\n93: created_at TEXT NOT NULL\n94: );\n95: CREATE TABLE IF NOT EXISTS aliases (\n96: entity_id INTEGER NOT NULL,\n97: alias TEXT NOT NULL,\n98: UNIQUE(entity_id, alias),\n99:Line truncated
|
||||
{"type":"step_finish","timestamp":1788951924343,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085d80e590017ifIfT2rV","reason":"tool-calls","snapshot":"3be9717276dd9704685bd1067e9066cc1265e5aa","messageID":"msg_g001a085d7eae2001zeX90Xx0h","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","type":"step-finish","tokens":{"total":34410,"input":4729,"output":113,"reasoning":0,"cache":{"write":0,"read":29568}},"cost":0}}
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{"type":"step_start","timestamp":1788952011303,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085d96224001QAW50ga55","messageID":"msg_g001a085d80e96001eNZCMBkY6","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","snapshot":"29ca5db8f22bbafb44bb6d07178e4be0954e4b80","type":"step-start"}}
|
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{"type":"text","timestamp":1788952014490,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085d96e3d002RR2TJrttN","messageID":"msg_g001a085d80e96001eNZCMBkY6","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","type":"text","text":"Code verification confirms the claims: bi-temporal edges schema (`graph_memory.py:101-116`), supersede-on-contradiction with insert-before-mark ordering (`:183-208`), strength<3 write gating (`:126-135`), `status='active'`-only filtering with no as-of path (`:254`, `:275`), and exact→alias→LIKE-only entity resolution (`:224-237`). Let me spot-check the evidence ids referenced by the candidates against the prior stage artifacts.","time":{"start":1788952014397,"end":1788952014482}}}
|
||||
{"type":"tool_use","timestamp":1788952014526,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"type":"tool","tool":"grep","callID":"call_ffb3bf29d65542879fbaed09","state":{"status":"completed","input":{"path":"/mnt/workdrive/issue161-harness/research/issue-163","pattern":"\"(E28|E29|E30|E18|E14|E17|E22|E23)\""},"output":"Found 30 matches\n/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json:\n Line 934: \"E28\",\n\n Line 935: \"E29\"\n\n Line 1008: \"E14\",\n\n Line 1009: \"E17\",\n\n Line 1030: \"E28\",\n\n Line 1031: \"E29\",\n\n Line 1032: \"E30\",\n\n Line 1033: \"E14\",\n\n Line 1053: \"E18\",\n\n Line 1054: \"E14\"\n\n Line 1073: \"E17\"\n\n Line 1110: \"E22\",\n\n Line 1111: \"E23\",\n\n Line 1130: \"E18\",\n\n Line 1131: \"E14\"\n\n\n/mnt/workdrive/issue161-harness/research/issue-163/applicability.json:\n Line 24: \"evidence\": [\"E2\", \"E3\", \"E4\", \"E6\", \"E8\", \"E27\", \"E28\", \"E29\"]\n\n Line 66: \"evidence\": [\"E14\", \"E17\", \"E19\"]\n\n Line 80: \"evidence\": [\"E28\", \"E29\", \"E30\", \"E14\", \"E27\"]\n\n Line 94: \"evidence\": [\"E18\", \"E14\"]\n\n Line 108: \"evidence\": [\"E16\", \"E17\"]\n\n Line 136: \"evidence\": [\"E22\", \"E23\", \"E24\"]\n\n Line 150: \"evidence\": [\"E18\", \"E14\"]\n\n\n/mnt/workdrive/issue161-harness/research/issue-163/outward.json:\n Line 133: { \"id\": \"E14\", \"claim\": \"Graphiti models a temporal context graph where every fact (entity-relation-entity edge) carries a validity window (when it became true, when superseded); changed facts are invalidated, not deleted, preserving full temporal history; bi-temporal tracking with automatic fact invalidation is listed as a core differentiator vs GraphRAG; every derived fact traces back to episodes (raw ingested data) for provenance.\", \"section\": \"What is a Context Graph, Why Graphiti, Graphiti vs GraphRAG\" },\n\n Line 136: { \"id\": \"E17\", \"claim\": \"Zep (the managed product) runs on a proprietary 'Context Graph Engine' requiring no third-party graph database; the OSS Graphiti framework is bring-your-own backend where retrieval performance 'depends on your setup' — i.e., the reference architecture deliberately decouples temporal semantics from the storage engine.\", \"section\": \"Graphiti and Zep, Zep vs Graphiti table\" },\n\n Line 137: { \"id\": \"E18\", \"claim\": \"Ingestion is LLM-dependent: entity/edge extraction and dedup require structured (JSON) output, working best with OpenAI/Anthropic/Gemini; concurrency is throttled by SEMAPHORE_LIMIT (default 10) to avoid provider 429s; retrieval is hybrid (semantic embeddings + BM25 keyword + graph traversal) with claimed sub-second latency vs GraphRAG's seconds-to-tens-of-seconds.\", \"section\": \"Structured output notes, SEMAPHORE_LIMIT, Graphiti vs GraphRAG\" },\n\n Line 157: { \"id\": \"E22\", \"claim\": \"Ladybug Memory's platform vision combines four components: Pgembed (transactional writes from multiple agents), DuckDB (auto-migrating aging data to columnar), Icebug (zero-copy graph analytics), and LadybugDB (graph store) — a local-first unified memory substrate for AI agents; roadmap includes SparseTensors for embedding storage and GPU acceleration.\", \"section\": \"The Bigger Picture: Ladybug Memory\" },\n\n Line 158: { \"id\": \"E23\", \"claim\": \"Icebug (a fork of the networkit C++ graph analytics library optimized for columnar/Arrow data) reports a PageRank benchmark on a 115M-node Wikidata graph: 8x speed advantage over networkit and 29% less memory usage — an affiliated (not independent) benchmark.\", \"section\": \"Running Graph Algorithms with Icebug\" }\n\n Line 195: { \"id\": \"E28\", \"claim\": \"Neo4j has NO native temporal versioning: NEP-001 (draft RFC status) proposes adding 'AS OF' query semantics; its prior-art table states 'Neo4j: No native temporal support — this proposal' (likewise Apache AGE, TypeDB, Amazon Neptune: none), while SQL:2011 defines FOR SYSTEM_TIME/FOR VALID_TIME AS OF and MariaDB/Db2 implement transaction-time/bitemporal tables.\", \"section\": \"Abstract, Section 5 Prior Art\" },\n\n Line 196: { \"id\": \"E29\", \"claim\": \"Current Neo4j temporal-versioning practice is valid_from/valid_to (or effective_date/end_date) property conventions with manual WHERE filters; documented problems: verbose and error-prone (NULL-as-open-ended, <= vs <, half-open intervals), does not compose (each hop needs its own temporal filter, growing linearly with path depth), the planner cannot recognize temporal predicates (no temporal index scans or period-overlap joins), and transaction time is lost unless the application records it manually; production evidence cited: 3.1M+ tempLine truncated
|
||||
{"type":"tool_use","timestamp":1788952014545,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"type":"tool","tool":"bash","callID":"call_94770d6e52d441dabbe6c877","state":{"status":"completed","input":{"command":"ls /mnt/workdrive/issue161-harness/research/ && ls /mnt/workdrive/issue161-harness/research/issue-163/","description":"List research directory contents"},"output":"batch-processing\nbuild-mode\ncontract-testing\nfull-audit\ninterstitial-tools\nissue-161\nissue-163\nissue-245-research.md\nissue-256\nissue-260\nissue-261\nissue-262\nissue-263\nllm-craft-api\nllm-craft-deep\nquality-tools\nresearch-methodology\nrune-efficiency\nsafety-override\ntextual-craft\ntool-calling\nuncensored-models\nv1\nworkflow-audit\napplicability.json\noutward.json\nstage_APPLICABILITY.events.jsonl\nstage_APPLICABILITY.mimo.err.log\nstage_INNOVATION.events.jsonl\nstage_INNOVATION.mimo.err.log\nstage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\n","metadata":{"output":"batch-processing\nbuild-mode\ncontract-testing\nfull-audit\ninterstitial-tools\nissue-161\nissue-163\nissue-245-research.md\nissue-256\nissue-260\nissue-261\nissue-262\nissue-263\nllm-craft-api\nllm-craft-deep\nquality-tools\nresearch-methodology\nrune-efficiency\nsafety-override\ntextual-craft\ntool-calling\nuncensored-models\nv1\nworkflow-audit\napplicability.json\noutward.json\nstage_APPLICABILITY.events.jsonl\nstage_APPLICABILITY.mimo.err.log\nstage_INNOVATION.events.jsonl\nstage_INNOVATION.mimo.err.log\nstage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\n","exit":0,"description":"List research directory contents","truncated":false},"title":"List research directory contents","time":{"start":1788952014539,"end":1788952014542}},"id":"prt_g001a085d96e78001wG53V4uAX","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","messageID":"msg_g001a085d80e96001eNZCMBkY6"}}
|
||||
{"type":"step_finish","timestamp":1788952014574,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085d96ed1001fAJvqxK5T","reason":"tool-calls","snapshot":"a04f1c01034fd79492f69270a3ecf07a9ac8e09c","messageID":"msg_g001a085d80e96001eNZCMBkY6","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","type":"step-finish","tokens":{"total":42179,"input":5200,"output":2675,"reasoning":0,"cache":{"write":0,"read":34304}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788952084415,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085da7fbe001bMNODjQvt","messageID":"msg_g001a085d96f25001rtdPwqP2F","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","snapshot":"87d170a6df149f7fb93e272efb9fba10fe5e96fc","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788952133671,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085da89f70012JkM7dAHw","messageID":"msg_g001a085d96f25001rtdPwqP2F","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","type":"text","text":"All verification checks pass:\n\n- **E28/E29/E30** (NEP-001 AS-OF semantics, prior art, Phase-1 complexity) — confirmed verbatim in `outward.json`\n- **E14/E17/E18** (Graphiti bi-temporal model, framework-layer decoupling, hybrid retrieval) — confirmed\n- **E22/E23** (Ladybug Memory lakehouse, affiliated Icebug benchmark) — confirmed\n- **Code claims** — confirmed in `src/rune/graph_memory.py`: bi-temporal edges with supersede-not-delete and gated writes exist (`:101-116`, `:126-135`, `:183-208`), while `query()` filters only `status='active'` (`:254`, `:275`) and entity resolution is exact→alias→LIKE only (`:224-237`) — i.e., the history is persisted but temporally unreadable, and the entry point caps recall. C4 is genuinely already integrated; C5/C6 are real gaps.\n\nThe standard mechanism (SQLite as-is) is *not* sufficient: two evidenced, low-cost, read-layer gaps exist that no drop-in standard mechanism closes. The stage hint's merge (C5+C6 → one query-layer proposal) is the honest compositional candidate. C4 is a no-op adoption (already integrated), and C9 stays deferred per the applicability non-goals — proposing either would manufacture novelty. Conclusion: **ADAPT**. Writing the verdict now.","time":{"start":1788952087031,"end":1788952133666}}}
|
||||
{"type":"tool_use","timestamp":1788952133676,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"type":"tool","tool":"write","callID":"call_2f32ea181e1f49ccb097fb79","state":{"status":"completed","input":{"content":"{\n \"stage\": \"INNOVATION\",\n \"issue\": 163,\n \"round\": 1,\n \"process_id\": \"research-163-r1\",\n \"route\": \"INNOVATION_WARRANTED\",\n \"conclusion\": \"ADAPT\",\n \"conclusion_rationale\": \"The standard mechanism (SQLite graph tier queried via status='active' only, exact/alias/substring entry resolution) is NOT sufficient: it cannot read the bi-temporal history Rune already persists (every superseded edge is stored but unreachable through query()), and its entry point caps practical recall on paraphrase/typo. No standard drop-in closes either gap. However, the warranted innovation is strictly adapted-and-compositional, not novel-for-its-own-sake: NEP-001's AS-OF WHERE-convention (E28-E30) and Graphiti's hybrid-retrieval entry tier (E18) are adapted onto Rune's existing framework-over-SQLite layer (which already implements the semantic model 1:1, E14/E17 — C4), producing a 'temporal+lexical query layer over graph.sqlite'. Backend migration is explicitly out of scope (C1/C2/C3 rejected or deferred at APPLICABILITY; C9 tracked, not proposed).\",\n \"candidates\": [\n {\n \"id\": \"INN-1\",\n \"category\": \"ADAPT\",\n \"proposal\": \"A unified temporal+lexical query layer over the existing graph.sqlite, inside src/rune/graph_memory.py, with zero schema or backend change. Two read-layer components that share one integration surface (query(), lines 217-284): (1) TEMPORAL — an --as-of <iso8601> parameter that replaces the status='active' filter with valid_from <= t AND (valid_until IS NULL OR t < valid_until) AND learned_at <= t, applied identically to the outgoing BFS frontier (line 254) and the incoming-edge query (line 275); BFS structure unchanged; default behavior (no flag) keeps the current active-only fast path. (2) LEXICAL — a SQLite FTS5 virtual table over entities(name, summary) and aliases(alias), inserted as a fallback tier in entity resolution (lines 224-237) with lookup order exact -> alias -> FTS5 BM25 -> substring-LIKE; index maintained on the existing entity/alias write path (resolve_entity, add_edge) in the same DB file. Full embedding tier deliberately omitted — model dependencies are disproportionate to Rune's sub-1M regime.\",\n \"derived_from\": [\"E28\", \"E29\", \"E30\", \"E18\", \"E14\", \"E17\", \"E27\"],\n \"source_ids\": [\"O38\", \"O41\", \"O10\", \"O1\"],\n \"composition_of\": [\n \"C5 (NEP-001 Phase 1 'AS OF' syntactic sugar, APPLICABLE_WITH_ADAPTATION)\",\n \"C6 (Graphiti hybrid retrieval, reduced to its lexical BM25 tier, APPLICABLE_WITH_ADAPTATION)\",\n \"Rune's existing bi-temporal edges schema + supersede-not-delete history (already integrated per C4)\"\n ],\n \"novelty\": \"compositional\",\n \"expected_gain\": \"Two material capabilities the status quo lacks, both read-only on data already on disk: (a) time-travel queries — every contradiction ever superseded becomes queryable as-of any past timestamp, closing the exact gap NEP-001 documents (E29: hand-written temporal WHERE clauses are error-prone and do not compose; E30: Phase 1 is low complexity and delivers most of the value); (b) recall lift at the entry point — paraphrase/typo/alternate-naming terms that currently fail resolution fall through to BM25, which Graphiti credits for its sub-second retrieval (E18), without any new service or dependency (FTS5 is in-process SQLite). Combined, the graph's stored history and practical recall stop being capped by the two read-layer shortcuts.\",\n \"risks\": \"(1) learned_at is a transaction-time proxy, not true bitemporality (E30 defers the real thing at 'Very high' complexity) — must be documented as as-of-knowledge approximation. (2) Half-open-interval and NULL-as-open-ended pitfalls are the documented failure mode of exactly this convention (E29) — they become a mandatory test checklist, not an afterthought. (3) FTS5 write amplification on add-bulk paths and index/tokenizer decisions. (4) BM25 ranking quality is evidenced only via Graphiti's claim, not measured at Rune's regime (dossier limitation #7) — hence the kill test below. (5) No Rune-regime benchmark exists (dossier limitation #2), so gains are predicted, not measured, until the kill test runs.\",\n \"kill_test\": \"Rune-local benchmark on the real curator vocabulary and a seeded history: (a) TEMPORAL — if querying as-of realistic historical timestamps returns fact sets identical to the active-only set for every t that matters in practice (i.e., the graph accumulates negligible superseded/expired edges), the temporal component's gain is zero and it should be dropped; (b) LEXICAL — if FTS5 resolves fewer than ~30% of the paraphrase/typo terms that exact/alias/substring currently miss, oLine truncated
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{"type":"tool_use","timestamp":1788952153503,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"type":"tool","tool":"bash","callID":"call_df7c63bb410a40a78d27bdb6","state":{"status":"completed","input":{"command":"python3 -c \"import json; d=json.load(open('research/issue-163/innovation.json')); print('valid JSON, conclusion:', d['conclusion'], '| candidates:', len(d['candidates']))\"","description":"Validate innovation.json parses correctly","workdir":"/mnt/workdrive/issue161-harness"},"output":"valid JSON, conclusion: ADAPT | candidates: 1\n","metadata":{"output":"valid JSON, conclusion: ADAPT | candidates: 1\n","exit":0,"description":"Validate innovation.json parses correctly","truncated":false},"title":"Validate innovation.json parses correctly","time":{"start":1788952153498,"end":1788952153501}},"id":"prt_g001a085db8966002S4kByYyF0","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","messageID":"msg_g001a085db40c2001h6vwpr7Np"}}
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{"type":"step_finish","timestamp":1788952153525,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085db8d9e0013vnAq15yS","reason":"tool-calls","snapshot":"c79ab124dd7d49567a9c35fa4be30681f600f459","messageID":"msg_g001a085db40c2001h6vwpr7Np","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","type":"step-finish","tokens":{"total":43445,"input":2075,"output":90,"reasoning":0,"cache":{"write":0,"read":41280}},"cost":0}}
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{"type":"step_start","timestamp":1788952165183,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085dbbb3d001wdwub4CvZ","messageID":"msg_g001a085db8ddd001P3mLKHopF","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","snapshot":"07f56d6916d95306da37e4d9109430303f37b29d","type":"step-start"}}
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{"type":"text","timestamp":1788952174270,"sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","part":{"id":"prt_g001a085dbbb3e0025m9pOdODe","messageID":"msg_g001a085db8ddd001P3mLKHopF","sessionID":"ses_-ffe5f7a28a2ecffeEs822F6Ir","type":"text","text":"**Conclusion: ADAPT** — written to `research/issue-163/innovation.json` (valid JSON, verified).\n\n**One candidate: INN-1** — a unified *temporal+lexical query layer over graph.sqlite* (novelty: compositional), merging the stage-hint's C5+C6:\n\n- **Temporal half** (from C5 / NEP-001, E28–E30): `--as-of <iso8601>` replacing the `status='active'`-only filter with half-open validity-window + `learned_at <= t` predicates, defaulting to the current fast path. This unlocks the superseded-edge history Rune already persists but cannot query — verified the gap is real (`query()` at `graph_memory.py:254,275`).\n- **Lexical half** (from C6 / Graphiti E18, reduced to one tier): FTS5 BM25 fallback in entity resolution — exact → alias → FTS5 → substring — deliberately skipping the embedding tier. Gap verified (`:224-237` is LIKE-only).\n\n**What I did NOT propose, and why:**\n- **C4** (bi-temporal model) — verified as genuinely already integrated (`:101-116` schema, `:183-208` supersede ordering, `:126-135` gating); listing it as ADOPT would manufacture novelty.\n- **C9** (lakehouse) — applicability explicitly ruled \"track, don't couple\"; proposing it as BUILD would fail the evidence-derivation bar.\n\n**Kill test** (falsifiable, per-half): Rune-local benchmark — temporal half dies if as-of queries return identical fact sets to active-only at all practical `t`; lexical half dies if FTS5 resolves <~30% of currently-missed paraphrases or add-bulk latency regresses >10%. Both failing → conclusion degrades to NO_INNOVATION_WARRANTED. This operationalizes the applicability stage's own evidence constraint.\n\n**Evidence derivation verified**: E14/E17/E18/E22/E23/E28/E29/E30 confirmed verbatim in `outward.json`; no code modified, no stage advanced.","time":{"start":1788952165182,"end":1788952174268}}}
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@@ -0,0 +1,86 @@
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{"type":"step_start","timestamp":1788950482933,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c20ff1001a5JgU4KVt","messageID":"msg_g001a085c1d9b3001Jugq8c86S","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","snapshot":"c138dd4d9512a12c84ade2ed63425bb50a0b8109","type":"step-start"}}
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{"type":"text","timestamp":1788950537390,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c2c7cf001mXee3ASIq","messageID":"msg_g001a085c1d9b3001Jugq8c86S","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"text","text":"I'll start by checking for a matching skill, searching memory for prior context on this research protocol, and checking the working directory — in parallel.","time":{"start":1788950529999,"end":1788950537386}}}
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||||
{"type":"tool_use","timestamp":1788950537394,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"skill_search","callID":"call_b1c6f5b4010a4cad8c43dec5","state":{"status":"completed","input":{"query":"Execute a structured research discovery stage (\"STAGE: OUTWARD\") for a tech spike: issue real web searches to discover and classify candidate sources (GitHub repos and external docs) about temporal graph database backends (Neo4j vs FalkorDB, Graphiti as reference, LadybugDB experimental), preserve query/fetch receipts, extract evidence claims with stable source IDs, and produce an evidence report without deep synthesis. Audience: software engineer evaluating graph backends for issue #163."},"output":"{\n \"status\": \"matched\",\n \"results\": [\n {\n \"skill_id\": \"firecrawl-search\",\n \"name\": \"firecrawl-search\",\n \"score\": 0.5912,\n \"reason\": \"The skill description matches these query terms: research, real, web, discover, source, about.\"\n },\n {\n \"skill_id\": \"super-research\",\n \"name\": \"super-research\",\n \"score\": 0.6233,\n \"reason\": \"The skill description matches these query terms: research, real, candidate, source, repo, external, about, vs, as, reference, evidence, produce, without, deep.\"\n },\n {\n \"skill_id\": \"firecrawl\",\n \"name\": \"firecrawl\",\n \"score\": 0.5013,\n \"reason\": \"The skill description matches these query terms: research, real, web, external, reference, fetch.\"\n }\n ],\n \"loaded_skill_id\": null\n}","metadata":{"status":"matched","loadedSkillID":null,"truncated":false},"title":"Found 3 skills","time":{"start":1788950537389,"end":1788950537391}},"id":"prt_g001a085c2e427001IuCqPp84T","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","messageID":"msg_g001a085c1d9b3001Jugq8c86S"}}
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{"type":"tool_use","timestamp":1788950537455,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"memory","callID":"call_7c01e84ea8414e7cb96c3437","state":{"status":"completed","input":{"operation":"search","query":"temporal graph backend spike OUTWARD"},"output":"Found 10 matches (BM25-ranked, best first).\nA hit here is authoritative — use it even if a parallel/sibling query returned nothing.\nIf you need the FULL body (snippets are truncated), Read the path.\nIf you need an EXACT literal (a connection string, port, token, full command line, path) and the snippet/body only paraphrases or partially shows it, the curated memory may have dropped the precise form — query the history tool for the original message, which holds it verbatim.\n\n### /home/nick/.local/share/mimocode/memory/projects/global/MEMORY-rules-gitea-2026-09-05.md\nScope: projects/global, Type: memory, Score: 12.267\n...New issues #161 (<<backend>> harness), #162 (Qdrant <<spike>>), #163 (<<graph>> <<spike>>). FREEZE deep sqlite-vec/<<graph>> optimization unless needed for correctness or benchmark baseline. Do NOT adopt KuzuDB (archived Oct 2025). Tech...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cabf29bffeBquaPsPsG/checkpoint.md\nScope: sessions/ses_-ffe5f7cabf29bffeBquaPsPsG, Type: checkpoint, Score: 11.831\n...<<Graph>> substrate family / structural-<<graph>> evaluation** — sources [O15]; evidence [E18, E20, E22]; classification APPLICABLE_WITH_ADAPTATION (<<graph>> tier exists in <<graph>>_memory.py as headless SQLite; O15 gives no dedicated <<graph>>-<<backend>>...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7cb8a81bffe2w0dbs8qG/checkpoint.md\nScope: sessions/ses_-ffe5f7cb8a81bffe2w0dbs8qG, Type: checkpoint, Score: 10.615\n...Include verbatim quote when the user gave one._\n\nIssue the <<OUTWARD>> discovery searches for the subquestion \"Memory <<backend>> abstraction + comparative benchmark harness (SQLite FTS5/sqlite-vec vs Qdrant vs <<graph>> backends)\" — batch...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f98e46b46ffe7fdgz483s/checkpoint.md\nScope: sessions/ses_-ffe5f98e46b46ffe7fdgz483s, Type: checkpoint, Score: 10.254\n...2. **Storage-frontier sequence (P1, after #148–#152):** #161 (memory <<backend>> abstraction + comparative benchmark harness) → #162 (Qdrant <<spike>> incl. ColBERT late-interaction reranking) → #163 (<<temporal>> <<graph>> <<spike>>: Neo4j vs FalkorDB, LadybugDB experimental...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7c7b6a49ffeUetJMdQPY/checkpoint.md\nScope: sessions/ses_-ffe5f7c7b6a49ffeUetJMdQPY, Type: checkpoint, Score: 9.611\n...STANDARD_SOLUTION_SUFFICIENT — build the standard harness, register Rune's own tiers (flat hybrid memory_store.py + <<temporal>>-KG <<graph>>_memory.py) as harness backends via the existing registry; Rune's architecture...\n\n### /home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY.md\nScope: projects/add96ab3-25dd-4c38-9fa3-34c01811d081, Type: memory, Score: 9.578\n...161 <<OUTWARD>>): sqlite-vec (dense vectors) + FTS5 (BM25) in one SQLite file (`src/rune/memory_store.py`), hybrid recall accelerated by a precomputed association <<graph>> (hops=1); separate headless SQLite <<graph>> tier...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7c9d2281ffessfv3vUH4/checkpoint.md\nScope: sessions/ses_-ffe5f7c9d2281ffessfv3vUH4, Type: checkpoint, Score: 9.301\n...No source covers the 100K-1M single-node regime where Rune's decision sits; no <<graph>>-<<backend>> benchmark was found; no asyncio-integrated harness exists.\n- memory_selector.py's raw-score ranking...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f826d4b3affeptScH2iSt/notes.md\nScope: sessions/ses_-ffe5f826d4b3affeptScH2iSt, Type: free, Score: 8.261\n...stage=<<OUTWARD>>_DISCOVERY, completed=[DEFINE, INWARD]\n\nKEY FIX: gitea_api had a double-read bug (r.read() called twice, second consumed body, json.loads got empty). Fixed.\n\nCORRECTED EXECUTION MODEL: local <<backend>>...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5f7c713c17ffesxmQgDMqC/checkpoint.md\nScope: sessions/ses_-ffe5f7c713c17ffesxmQgDMqC, Type: checkpoint, Score: 7.059\n...vs live-composite delta (M2); abstention-aware headline metric (M7); <<backend>>-local PR curves only (M4); E20-E22 citation gate (M9); <<graph>> tier via read-only query() with FT6 fidelity verdict. Contains...\n\n### /home/nick/.local/share/mimocode/memory/sessions/ses_-ffe5fd9980ab3ffed2iQMMajh/checkpoint.md\nScope: sessions/ses_-ffe5fd9980ab3ffed2iQMMajh, Type: checkpoint, Score: 6.867\n...Unicode block characters (▀▄█▌▐), background colors, proper spacing/padding, and possibly Textual CSS styling on custom widgets (not RichLog).\n- **<<Backend>> availability issue**: The \"<<Backend>> not available\" message appears when `<<BACKEND>>_AVAILABLE` is FalseLine truncated
|
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{"type":"text","timestamp":1788950560951,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c33084001ALQiYQce8","messageID":"msg_g001a085c2e53a001DM7jUjThY","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"text","text":"The protocol is a structured research stage for issue #163 in this repo (`issue161-harness`). Skill search suggests `super-research` as a partial fit; before deciding, let me understand the project's own research conventions and prior stage outputs.","time":{"start":1788950556804,"end":1788950560948}}}
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{"type":"tool_use","timestamp":1788950560962,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"read","callID":"call_e4321885ca324398ada687eb","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/AGENTS.md"},"output":"<path>/mnt/workdrive/issue161-harness/AGENTS.md</path>\n<type>file</type>\n<content>\n1: <!-- henxels:begin -->\n2: \n3: ## The contract (henxels)\n4: \n5: _Auto-generated from `henxels.yaml` by `henxels sync`. Do not edit by hand._\n6: \n7: Each bullet is a **henxel** (a rule). To disobey one, change `henxels.yaml` —\n8: that is the only sanctioned escape. Run `henxels explain <path>` before creating\n9: a file to see what governs that spot.\n10: \n11: Only use `git commit --no-verify` in a genuine emergency: it bypasses the hooks that run this contract, the safety mechanism meant to protect the repository. Prefer `henxels bless <action>` or editing `henxels.yaml` — both keep the deviation visible.\n12: \n13: ### Rules\n14: \n15: - Canaries live at the repo's canary slot and are registered in CI (in ./src/rune/**/canary_*.py)\n16: - No secrets in tracked files\n17: - Plans are markdown, one per build (in ./plans)\n18: - Top-level docs are markdown or yaml references (in ./docs/*)\n19: - The known giant stays visible — file-size debt is tracked, not silent (in ./src/rune/*)\n20: - The umbrella impact map is complete and truthful — every module mapped, no phantom entries (in ./config/umbrellas.json)\n21: - Umbrella canaries exist in the repo — the propagation gate cannot reference ghosts (in ./config/umbrellas.json)\n22: - The umbrella dependency graph stays a DAG — reflexive updates cannot cycle (in ./config/umbrellas.json)\n23: - The bug-fix discharge contract is a governed doc — it exists, is markdown, and changes are deliberate (in ./docs/bugfix-contract.md)\n24: - The discharge checker ships with the repo — red-first enforcement is structural (in ./scripts)\n25: - Discharge checker and its CI wiring change together\n26: - Every workflow is registered in the pipeline registry — new pipeline machinery ships with documentation (in ./.gitea/workflows/*.yml)\n27: - Every TUI surface is registered — no silent UI growth (in ./src/rune/*tui*.py)\n28: - New rune modules ship with tests or a canary — no ungoverned code surfaces (in ./src/rune/*.py)\n29: - PRDs are living documents — their generated state sections regenerate identically from governed sources\n30: - PRD staleness is a CI failure — docs rot is silent failure (in ./scripts/gen_prd_state.py)\n31: - The adversarial review procedure is binding — dispatch templates must reference it (in ./docs/adversarial-review.md)\n32: - The fixture pipeline ships with the repo — production failure capture is structural (in ./scripts)\n33: - Production failure fixtures are a governed surface — the registry and its README exist (in ./tests/fixtures/production)\n34: - Fixture payloads and the manifest travel together — tamper-evidence needs both\n35: - The fixture manifest and its generated regression tests travel together\n36: \n37: ### Behaviours\n38: \n39: - warns when a new file looks like a near-copy of a committed one\n40: \n41: ### Custom henxels & contributing\n42: \n43: **Before writing a custom check, run `henxels catalogue` and use the built-in that\n44: matches your intent — don't reinvent one.** Never name a custom check after a built-in\n45: or a setting (e.g. `warn_about_large_files` is a setting, not a check).\n46: \n47: Need a check that genuinely doesn't exist? `henxels create-new-statement <name>` scaffolds a local check\n48: (auto-loaded from `henxels_checks.py`). **If your check is reusable** — useful in\n49: other repos, not tied to this one — contribute it upstream with `henxels contribute`.\n50: We're in the agentic era: send a ready-to-merge PR instead of opening an issue.\n51: <!-- henxels:end -->\n52: \n53: ## Bug fixes (binding)\n54: \n55: Every bug found — by gates, CI, canaries, production, or humans — is fixed per\n56: `docs/bugfix-contract.md`: red test FIRST (reproduces the cause), root-cause\n57: fix, full pipeline re-entry (suite + ratchet + ruff + canaries + henxels;\n58: gates for anything beyond a mechanical minor), regression tests frozen forever,\n59: discharge recorded finding → test → commit. Symptom patches are rejected at\n60: review. To escape: edit `henxels.yaml` or this file — visibly.\n\n(End of file - total 60 lines)\n</content>","metadata":{"preview":"<!-- henxels:begin -->\n\n## The contract (henxels)\n\n_Auto-generated from `henxels.yaml` by `henxels sync`. Do not edit by hand._\n\nEach bullet is a **henxel** (a rule). To disobey one, change `henxels.yaml` —\nthat is the only sanctioned escape. Run `henxels explain <path>` before creating\na file to see what governs that spot.\n\nOnly use `git commit --no-verify` in a genuine emergency: it bypasses the hooks that run this contract, the safety mechaniLine truncated
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{"type":"tool_use","timestamp":1788950560964,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"read","callID":"call_04ad4731f2ed491495eb422a","state":{"status":"completed","input":{"file_path":"/home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY.md"},"output":"<path>/home/nick/.local/share/mimocode/memory/projects/add96ab3-25dd-4c38-9fa3-34c01811d081/MEMORY.md</path>\n<type>file</type>\n<content>\n1: Topic: Rune research pipeline — headless structured research rounds (stages, stage artifacts, SQLite usage facts) against Gitea issues\n2: # Project memory\n3: _Durable project-level knowledge. Persists across all sessions in this project. Edit only content under italic instructions._\n4: \n5: ## Project context\n6: _What is this project? What's its goal? High-level identity._\n7: \n8: Rune project research pipeline (repo at `/mnt/workdrive/rune/`). Sessions in this project execute structured research rounds against Gitea issues, one stage at a time, dispatched as headless prompts carrying a process capsule (process_id, issue, round, current_stage, allowed_action_now, expected_completion_artifact).\n9: \n10: ## Rules\n11: _Hard constraints from user that every session must respect._\n12: \n13: - Stage prompts are authoritative: obey `allowed_action_now` strictly (e.g., DEFINE forbids codebase analysis and web research).\n14: - Stage artifacts end with the literal line `StageCompleted: <STAGE>` (or `StageFailed: <STAGE>` + `Reason:`). Never fabricate a stage outcome.\n15: \n16: ## Architecture decisions\n17: _Major design choices with rationale. The \"why\" matters more than the \"what\" for future sessions._\n18: \n19: (none yet)\n20: \n21: ## Discovered durable knowledge\n22: _Cross-task facts that survive across sessions. Promoted from session checkpoints' §7 when proven durable._\n23: \n24: - Rune research pipeline has 8 stages: DEFINE → INWARD → OUTWARD_DISCOVERY → OUTWARD_READING → ADVERSARIAL → INNOVATION → SYNTHESIS (+1 completion/validation stage). Dispatched headless via `.gitea/workflows/research-stage-headless.yml` (PRs #217/#218) + `run_agent_stage.py` (#222).\n25: - Research questions are filed in the Rune repo as `research/issue-<n>-research.md` (or under `/mnt/workdrive/rune/research/<topic>/` subdirectories). DEFINE-stage dispatches may reference \"the research question\" without restating it — locate the question file before classifying.\n26: - Stage-execution tree for research rounds lives at `/mnt/workdrive/research-stage/` (holds src/rune/ + research-executor commits, e.g. HEAD 83ea89c \"OUTWARD_DISCOVERY executor\"); distinct from the subject repo `/mnt/workdrive/rune/`.\n27: - OUTWARD_DISCOVERY technique that works here: plain webfetch of `https://duckduckgo.com/html/?q=<url-encoded query>` returns title+URL+snippet SERPs; batch ≥4 queries in parallel; snippet-level evidence satisfies the \"discover URLs, classify sources, don't read full sources\" stage contract.\n28: - OUTWARD_READING technique that works here: batch webfetch in parallel; GitHub repos read cleaner via `raw.githubusercontent.com/<owner>/<repo>/<branch>/README.md` (avoids page chrome); dev.to and sqliteforum.com (Substack-style) pages fetch OK but carry navigation noise around mid-page content; extracted claims go in a session spillover file (e.g. `checkpoint-outward-reading-sources.md`) with S-numbered source IDs so the ADVERSARIAL stage can reference claims.\n29: - Rune's SQLite usage (grounding for DB-related research questions): ~15 modules in src/rune/ each open fresh short-lived sqlite3 connections per call against a shared STORE_DB (mix of RW and `file:...?mode=ro` URI reads) across multiple OS processes; only task_gate.py sets `PRAGMA journal_mode=WAL` explicitly; sqlite-vec extension must be loaded per connection (issue #26 defect fix).\n30: - Newer pipeline runs use a 7-stage pipeline with a UNIFIED OUTWARD stage (discovery + reading merged, O-numbered source IDs, artifact ends `StageCompleted: OUTWARD`); the older 8-stage split (OUTWARD_DISCOVERY → OUTWARD_READING, S-numbered IDs) still applies to older rounds like issue-262. Deterministic DEFINE contracts may mark sub-stages \"skipped\" while the unified stage still requires \"≥2 unique URLs\".\n31: - Stage-state source of truth per round is on disk at `/mnt/workdrive/research-stage/research/issue-<n>/task_tree.json` (artifacts per stage, stage_history, expected_artifact like synthesis.json); run directories live alongside under `research/issue-<n>/runs/`.\n32: - Rune's memory stack (grounding for issue-161 OUTWARD): sqlite-vec (dense vectors) + FTS5 (BM25) in one SQLite file (`src/rune/memory_store.py`), hybrid recall accelerated by a precomputed association graph (hops=1); separate headless SQLite graph tier at `memory/graph/graph.sqlite` (`src/rune/graph_memory.py`); no Qdrant dependency exists — the \"vs Qdrant vs graph backends\" part of the question is comparative Line truncated
|
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{"type":"tool_use","timestamp":1788950560969,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"bash","callID":"call_cecdb45bf70747e7b528e38e","state":{"status":"completed","input":{"command":"ls research/ && ls prompts/ 2>/dev/null; ls research/ | head -40","description":"List research and prompts directories"},"output":"batch-processing\nbuild-mode\ncontract-testing\nfull-audit\ninterstitial-tools\nissue-161\nissue-163\nissue-245-research.md\nissue-256\nissue-260\nissue-261\nissue-262\nissue-263\nllm-craft-api\nllm-craft-deep\nquality-tools\nresearch-methodology\nrune-efficiency\nsafety-override\ntextual-craft\ntool-calling\nuncensored-models\nv1\nworkflow-audit\nresearch-round.md\nresearch-stages\nbatch-processing\nbuild-mode\ncontract-testing\nfull-audit\ninterstitial-tools\nissue-161\nissue-163\nissue-245-research.md\nissue-256\nissue-260\nissue-261\nissue-262\nissue-263\nllm-craft-api\nllm-craft-deep\nquality-tools\nresearch-methodology\nrune-efficiency\nsafety-override\ntextual-craft\ntool-calling\nuncensored-models\nv1\nworkflow-audit\n","metadata":{"output":"batch-processing\nbuild-mode\ncontract-testing\nfull-audit\ninterstitial-tools\nissue-161\nissue-163\nissue-245-research.md\nissue-256\nissue-260\nissue-261\nissue-262\nissue-263\nllm-craft-api\nllm-craft-deep\nquality-tools\nresearch-methodology\nrune-efficiency\nsafety-override\ntextual-craft\ntool-calling\nuncensored-models\nv1\nworkflow-audit\nresearch-round.md\nresearch-stages\nbatch-processing\nbuild-mode\ncontract-testing\nfull-audit\ninterstitial-tools\nissue-161\nissue-163\nissue-245-research.md\nissue-256\nissue-260\nissue-261\nissue-262\nissue-263\nllm-craft-api\nllm-craft-deep\nquality-tools\nresearch-methodology\nrune-efficiency\nsafety-override\ntextual-craft\ntool-calling\nuncensored-models\nv1\nworkflow-audit\n","exit":0,"description":"List research and prompts directories","truncated":false},"title":"List research and prompts directories","time":{"start":1788950560966,"end":1788950560968}},"id":"prt_g001a085c340460013PFW7AN41","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","messageID":"msg_g001a085c2e53a001DM7jUjThY"}}
|
||||
{"type":"step_finish","timestamp":1788950560992,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c340c900131KeblPi3","reason":"tool-calls","snapshot":"5893b28737586024b793abce57496e6705d11ed8","messageID":"msg_g001a085c2e53a001DM7jUjThY","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"step-finish","tokens":{"total":31785,"input":2847,"output":160,"reasoning":298,"cache":{"write":0,"read":28480}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788950567309,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c3598b001ka6t2Nnj3","messageID":"msg_g001a085c340fc001uGiSW8VxU","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","snapshot":"b48aec4caed0fec9fa9a402d1d92d388d306e030","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788950589361,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c39f5f0016jjmjeFFz","messageID":"msg_g001a085c340fc001uGiSW8VxU","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"text","text":"The project memory gives me the established protocol: this is the Rune research pipeline's unified OUTWARD stage (discovery + reading, O-numbered source IDs, artifact ending `StageCompleted: OUTWARD`), with stage state at `/mnt/workdrive/research-stage/research/issue-163/`. The stage dispatch is authoritative, so I'll follow it and the proven techniques from memory rather than loading a generic research skill. Let me inspect the stage state and the research question file.","time":{"start":1788950585183,"end":1788950589359}}}
|
||||
{"type":"tool_use","timestamp":1788950589371,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"bash","callID":"call_66c84886fb9645ccab722d31","state":{"status":"completed","input":{"command":"ls /mnt/workdrive/issue161-harness/research/issue-163/ 2>/dev/null; echo ---; ls /mnt/workdrive/rune/research/ 2>/dev/null | grep -i 163; echo ---; cat /mnt/workdrive/rune/research/issue-163-research.md 2>/dev/null | head -60","description":"Look for issue-163 research question file"},"output":"stage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\n---\n---\n","metadata":{"output":"stage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\n---\n---\n","exit":0,"description":"Look for issue-163 research question file","truncated":false},"title":"Look for issue-163 research question file","time":{"start":1788950589368,"end":1788950589370}},"id":"prt_g001a085c3a9cc001VHZVVFwVQ","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","messageID":"msg_g001a085c340fc001uGiSW8VxU"}}
|
||||
{"type":"tool_use","timestamp":1788950589376,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"bash","callID":"call_b76c3aef91f04c1eb0b2f42d","state":{"status":"completed","input":{"command":"ls /mnt/workdrive/research-stage/research/issue-163/ 2>/dev/null; echo ---; cat /mnt/workdrive/research-stage/research/issue-163/task_tree.json 2>/dev/null | head -100","description":"Inspect issue-163 stage state"},"output":"---\n","metadata":{"output":"---\n","exit":0,"description":"Inspect issue-163 stage state","truncated":false},"title":"Inspect issue-163 stage state","time":{"start":1788950589372,"end":1788950589374}},"id":"prt_g001a085c3a5dc0013BHmSli1B","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","messageID":"msg_g001a085c340fc001uGiSW8VxU"}}
|
||||
{"type":"step_finish","timestamp":1788950589400,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c3afbf0018zAsjwLgY","reason":"tool-calls","snapshot":"fd7e0f5cc49bdbf7e009ce9063b562b337b3131a","messageID":"msg_g001a085c340fc001uGiSW8VxU","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"step-finish","tokens":{"total":37832,"input":8341,"output":270,"reasoning":741,"cache":{"write":0,"read":28480}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788950601919,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c3e0bc001FqX7Qb6SE","messageID":"msg_g001a085c3affa001tokfuVycg","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","snapshot":"2d29dfea73e0348bd87eead1fdaf713eaadc8a2c","type":"step-start"}}
|
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{"type":"text","timestamp":1788950613838,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c4086c001cGdjFDdp5","messageID":"msg_g001a085c3affa001tokfuVycg","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"text","text":"The stage state for issue-163 lives in this repo's `research/issue-163/`. Let me read the task tree to find the research question and current stage state.","time":{"start":1788950612076,"end":1788950613835}}}
|
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{"type":"tool_use","timestamp":1788950613864,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"bash","callID":"call_4b5521c9a4ca454caa492b08","state":{"status":"completed","input":{"command":"python3 -c \"\nimport json\nt=json.load(open('/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json'))\nprint(json.dumps(t, indent=1)[:4000])\n\"","description":"Read issue-163 task tree JSON"},"output":"{\n \"issue\": 163,\n \"round\": 1,\n \"current_stage\": \"OUTWARD\",\n \"completed_stages\": [\n \"ISSUE\",\n \"INWARD\"\n ],\n \"artifacts\": {\n \"ISSUE\": {\n \"contract\": {\n \"issue\": 163,\n \"title\": \"Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\",\n \"question_type\": \"mixed\",\n \"sub_questions\": [\n \"Address: Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\"\n ],\n \"required_stages\": [\n \"ISSUE\",\n \"INWARD\",\n \"OUTWARD\",\n \"ADVERSARIAL\",\n \"INNOVATION\",\n \"SYNTHESIS\",\n \"QUALIFY\"\n ],\n \"skipped_stages\": {\n \"APPLICABILITY\": \"No OUTWARD research to ground against Rune.\"\n },\n \"evidence_classes\": [\n \"repo\",\n \"external\"\n ],\n \"sufficient_evidence\": \"\\u22652 code-path citations for INWARD; \\u22652 unique URLs for OUTWARD (if required)\",\n \"expected_artifact\": \"synthesis.json\",\n \"ambiguous\": false,\n \"needs_mimo\": false,\n \"source\": \"deterministic\"\n },\n \"deterministic\": true\n },\n \"INWARD\": {\n \"context\": {\n \"keywords\": [\n \"temporal\",\n \"graph\",\n \"backend\",\n \"spike\",\n \"neo\",\n \"falkordb\",\n \"ladybugdb\",\n \"experimental\",\n \"graphiti\",\n \"reference\",\n \"plain\",\n \"summary\",\n \"architecture_frontier_handoff\",\n \"compare\",\n \"graph\",\n \"backends\",\n \"rune\",\n \"structural\",\n \"projection\",\n \"episodes\"\n ],\n \"umbrella_matches\": [\n {\n \"umbrella\": \"memory\",\n \"module\": \"graph_memory\",\n \"keyword\": \"graph\"\n },\n {\n \"umbrella\": \"memory\",\n \"module\": \"graph_shard_bridge\",\n \"keyword\": \"graph\"\n },\n {\n \"umbrella\": \"orchestration\",\n \"module\": \"rune_operator\",\n \"keyword\": \"rune\"\n },\n {\n \"umbrella\": \"orchestration\",\n \"module\": \"rune_tui\",\n \"keyword\": \"rune\"\n },\n {\n \"umbrella\": \"orchestration\",\n \"module\": \"rune_graphic\",\n \"keyword\": \"graph\"\n },\n {\n \"umbrella\": \"orchestration\",\n \"module\": \"rune_spinner\",\n \"keyword\": \"rune\"\n },\n {\n \"umbrella\": \"orchestration\",\n \"module\": \"rune_profiler\",\n \"keyword\": \"rune\"\n }\n ],\n \"candidate_files\": [\n \"src/rune/curator.py\",\n \"src/rune/rune_tui.py\",\n \"src/rune/graph_memory.py\",\n \"src/rune/sleep_time.py\",\n \"src/rune/elastic_cognition.py\",\n \"src/rune/rune_operator.py\",\n \"src/rune/rune_profiler.py\",\n \"src/rune/warm_router.py\",\n \"src/rune/abstraction.py\",\n \"src/rune/canary_native.py\",\n \"src/rune/canary_p0_04.py\",\n \"src/rune/canary_p1_07.py\",\n \"src/rune/complexity_scorer.py\",\n \"src/rune/identity_anchors.py\",\n \"src/rune/memory_consolidation.py\"\n ],\n \"candidate_tests\": [\n \"tests/test_retrieval_benchmark.py\",\n \"tests/test_orchestration_memory_synthesis.py\",\n \"tests/test_p1_03_memory_selection.py\",\n \"tests/test_p1_06_model_routing.py\",\n \"tests/test_p1_07_runtime_objects.py\",\n \"tests/test_version_convergence.py\",\n \"tests/test_adversarial_executor.py\",\n \"tests/test_ci_lock_contract.py\",\n \"tests/test_context_broker.py\",\n \"tests/test_failure_auto_filer.py\"\n ],\n \"define_contract\": {\n \"issue\": 163,\n \"title\": \"Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\",\n \"question_type\": \"mixed\",\n \"sub_questions\": [\n \"Address: Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\"\n ],\n \"required_stages\": [\n \"ISSUE\",\n \"INWARD\",\n \"OUTWARD\",\n \"ADVERSARIAL\",\n \"INNOVATION\",\n \"SYNTHESIS\",\n \"QUALIFY\"\n ],\n \"skipped_stages\": {\n \"APPLICABILITY\": \"No OUTWARD research to ground against Rune.\"\n },\n \"evidence_classes\": [\n \"repo\",\n \"external\"\n ],\n \"sufficient_evidence\": \"\\u22652 code-path citations for INWARD; \\u22652 unique URLs for OUTWARD (if required)\",\n \"expected_arti\n","metadata":{"output":"{\n \"issue\": 163,\n \"round\": 1,\n \"current_stage\": \"OUTWARDLine truncated
|
||||
{"type":"step_finish","timestamp":1788950613884,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c40f680010TuDzZ5O8","reason":"tool-calls","snapshot":"ee6dee00f2628f10ae6321bc92a72adc25e355e0","messageID":"msg_g001a085c3affa001tokfuVycg","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"step-finish","tokens":{"total":35446,"input":3626,"output":104,"reasoning":420,"cache":{"write":0,"read":31296}},"cost":0}}
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|
||||
{"type":"tool_use","timestamp":1788950624384,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"bash","callID":"call_bd76fc3c2f2c4aa89f7b7d86","state":{"status":"completed","input":{"command":"python3 -c \"\nimport json\nt=json.load(open('/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json'))\nprint('current_stage:', t['current_stage'])\nprint('completed:', t['completed_stages'])\nprint('artifact keys:', list(t.get('artifacts',{}).keys()))\nprint('stage_history:')\nfor h in t.get('stage_history', []):\n print(' ', json.dumps(h)[:300])\nprint('expected_artifact:', t.get('expected_artifact'))\nprint('keys:', list(t.keys()))\n\"","description":"Show stage history and expected artifact"},"output":"current_stage: OUTWARD\ncompleted: ['ISSUE', 'INWARD']\nartifact keys: ['ISSUE', 'INWARD']\nstage_history:\n {\"stage\": \"ISSUE\", \"round\": 1, \"completed_at\": \"auto\", \"artifact_keys\": [\"contract\", \"deterministic\"]}\n {\"stage\": \"INWARD\", \"round\": 1, \"completed_at\": \"auto\", \"artifact_keys\": [\"context\", \"deterministic\", \"claims\"]}\nexpected_artifact: None\nkeys: ['issue', 'round', 'current_stage', 'completed_stages', 'artifacts', 'blocking_unknowns', 'status', 'stage_history']\n","metadata":{"output":"current_stage: OUTWARD\ncompleted: ['ISSUE', 'INWARD']\nartifact keys: ['ISSUE', 'INWARD']\nstage_history:\n {\"stage\": \"ISSUE\", \"round\": 1, \"completed_at\": \"auto\", \"artifact_keys\": [\"contract\", \"deterministic\"]}\n {\"stage\": \"INWARD\", \"round\": 1, \"completed_at\": \"auto\", \"artifact_keys\": [\"context\", \"deterministic\", \"claims\"]}\nexpected_artifact: None\nkeys: ['issue', 'round', 'current_stage', 'completed_stages', 'artifacts', 'blocking_unknowns', 'status', 'stage_history']\n","exit":0,"description":"Show stage history and expected artifact","truncated":false},"title":"Show stage history and expected artifact","time":{"start":1788950624379,"end":1788950624382}},"id":"prt_g001a085c4383a001Gv9FoNmP0","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","messageID":"msg_g001a085c40f98001po7c6AOS6"}}
|
||||
{"type":"step_finish","timestamp":1788950624406,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c4387f002aaJuh4ysD","reason":"tool-calls","snapshot":"c43d18dc763132198adb16de7a9217700e39e95e","messageID":"msg_g001a085c40f98001po7c6AOS6","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"step-finish","tokens":{"total":36311,"input":1294,"output":137,"reasoning":0,"cache":{"write":0,"read":34880}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788950629600,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c44cde0012MwV8ZgNF","messageID":"msg_g001a085c438ca001SLF2Qll47","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","snapshot":"25bc6b15953b73569fab88600fd1f0a47cd7ba10","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788950638490,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c46542002aQgZT3Iy4","messageID":"msg_g001a085c438ca001SLF2Qll47","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"text","text":"Issue-163 round 1, OUTWARD stage open, no artifact yet. Let me check the sibling round issue-162 (Qdrant spike, same pipeline) for the artifact format, plus the current session's stage events log and the INWARD artifact for grounding.","time":{"start":1788950635843,"end":1788950638487}}}
|
||||
{"type":"tool_use","timestamp":1788950638517,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"bash","callID":"call_b272b620893b473992e0ad60","state":{"status":"completed","input":{"command":"python3 -c \"\nimport json\nt=json.load(open('/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json'))\nprint('status:', json.dumps(t.get('status'))[:500])\nprint('blocking_unknowns:', json.dumps(t.get('blocking_unknowns'))[:500])\n\"; echo ---; ls /mnt/workdrive/issue161-harness/research/issue-162/ 2>/dev/null; echo ---; tail -5 /mnt/workdrive/issue161-harness/research/issue-163/stage_OUTWARD.events.jsonl 2>/dev/null","description":"Check status, issue-162 artifacts, events log"},"output":"status: \"ACTIVE\"\nblocking_unknowns: []\n---\n---\n{\"type\":\"step_start\",\"timestamp\":1788950624316,\"sessionID\":\"ses_-ffe5f7a3e2805ffeYaOOBiQE1\",\"part\":{\"id\":\"prt_g001a085c43838001JPQeOdanS\",\"messageID\":\"msg_g001a085c40f98001po7c6AOS6\",\"sessionID\":\"ses_-ffe5f7a3e2805ffeYaOOBiQE1\",\"snapshot\":\"e3a00ea9f0993ad2dd0aeec36c74c39932e604fe\",\"type\":\"step-start\"}}\n{\"type\":\"tool_use\",\"timestamp\":1788950624384,\"sessionID\":\"ses_-ffe5f7a3e2805ffeYaOOBiQE1\",\"part\":{\"type\":\"tool\",\"tool\":\"bash\",\"callID\":\"call_bd76fc3c2f2c4aa89f7b7d86\",\"state\":{\"status\":\"completed\",\"input\":{\"command\":\"python3 -c \\\"\\nimport json\\nt=json.load(open('/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json'))\\nprint('current_stage:', t['current_stage'])\\nprint('completed:', t['completed_stages'])\\nprint('artifact keys:', list(t.get('artifacts',{}).keys()))\\nprint('stage_history:')\\nfor h in t.get('stage_history', []):\\n print(' ', json.dumps(h)[:300])\\nprint('expected_artifact:', t.get('expected_artifact'))\\nprint('keys:', list(t.keys()))\\n\\\"\",\"description\":\"Show stage history and expected artifact\"},\"output\":\"current_stage: OUTWARD\\ncompleted: ['ISSUE', 'INWARD']\\nartifact keys: ['ISSUE', 'INWARD']\\nstage_history:\\n {\\\"stage\\\": \\\"ISSUE\\\", \\\"round\\\": 1, \\\"completed_at\\\": \\\"auto\\\", \\\"artifact_keys\\\": [\\\"contract\\\", \\\"deterministic\\\"]}\\n {\\\"stage\\\": \\\"INWARD\\\", \\\"round\\\": 1, \\\"completed_at\\\": \\\"auto\\\", \\\"artifact_keys\\\": [\\\"context\\\", \\\"deterministic\\\", \\\"claims\\\"]}\\nexpected_artifact: None\\nkeys: ['issue', 'round', 'current_stage', 'completed_stages', 'artifacts', 'blocking_unknowns', 'status', 'stage_history']\\n\",\"metadata\":{\"output\":\"current_stage: OUTWARD\\ncompleted: ['ISSUE', 'INWARD']\\nartifact keys: ['ISSUE', 'INWARD']\\nstage_history:\\n {\\\"stage\\\": \\\"ISSUE\\\", \\\"round\\\": 1, \\\"completed_at\\\": \\\"auto\\\", \\\"artifact_keys\\\": [\\\"contract\\\", \\\"deterministic\\\"]}\\n {\\\"stage\\\": \\\"INWARD\\\", \\\"round\\\": 1, \\\"completed_at\\\": \\\"auto\\\", \\\"artifact_keys\\\": [\\\"context\\\", \\\"deterministic\\\", \\\"claims\\\"]}\\nexpected_artifact: None\\nkeys: ['issue', 'round', 'current_stage', 'completed_stages', 'artifacts', 'blocking_unknowns', 'status', 'stage_history']\\n\",\"exit\":0,\"description\":\"Show stage history and expected artifact\",\"truncated\":false},\"title\":\"Show stage history and expected artifact\",\"time\":{\"start\":1788950624379,\"end\":1788950624382}},\"id\":\"prt_g001a085c4383a001Gv9FoNmP0\",\"sessionID\":\"ses_-ffe5f7a3e2805ffeYaOOBiQE1\",\"messageID\":\"msg_g001a085c40f98001po7c6AOS6\"}}\n{\"type\":\"step_finish\",\"timestamp\":1788950624406,\"sessionID\":\"ses_-ffe5f7a3e2805ffeYaOOBiQE1\",\"part\":{\"id\":\"prt_g001a085c4387f002aaJuh4ysD\",\"reason\":\"tool-calls\",\"snapshot\":\"c43d18dc763132198adb16de7a9217700e39e95e\",\"messageID\":\"msg_g001a085c40f98001po7c6AOS6\",\"sessionID\":\"ses_-ffe5f7a3e2805ffeYaOOBiQE1\",\"type\":\"step-finish\",\"tokens\":{\"total\":36311,\"input\":1294,\"output\":137,\"reasoning\":0,\"cache\":{\"write\":0,\"read\":34880}},\"cost\":0}}\n{\"type\":\"step_start\",\"timestamp\":1788950629600,\"sessionID\":\"ses_-ffe5f7a3e2805ffeYaOOBiQE1\",\"part\":{\"id\":\"prt_g001a085c44cde0012MwV8ZgNF\",\"messageID\":\"msg_g001a085c438ca001SLF2Qll47\",\"sessionID\":\"ses_-ffe5f7a3e2805ffeYaOOBiQE1\",\"snapshot\":\"25bc6b15953b73569fab88600fd1f0a47cd7ba10\",\"type\":\"step-start\"}}\n{\"type\":\"text\",\"timestamp\":1788950638490,\"sessionID\":\"ses_-ffe5f7a3e2805ffeYaOOBiQE1\",\"part\":{\"id\":\"prt_g001a085c46542002aQgZT3Iy4\",\"messageID\":\"msg_g001a085c438ca001SLF2Qll47\",\"sessionID\":\"ses_-ffe5f7a3e2805ffeYaOOBiQE1\",\"type\":\"text\",\"text\":\"Issue-163 round 1, OUTWARD stage open, no artifact yet. Let me check the sibling round issue-162 (Qdrant spike, same pipeline) for the artifact format, plus the current session's stage events log and the INWARD artifact for grounding.\",\"time\":{\"start\":1788950635843,\"end\":1788950638487}}}\n","metadata":{"output":"status: \"ACTIVE\"\nblocking_unknowns: []\n---\n---\n{\"type\":\"step_start\",\"timestamp\":17Line truncated
|
||||
{"type":"step_finish","timestamp":1788950638539,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c46fb4001Pq4CD5Dwm","reason":"tool-calls","snapshot":"0ce38fc9b124b0b8fc21d7bcad10f9ed8ca33c25","messageID":"msg_g001a085c438ca001SLF2Qll47","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"step-finish","tokens":{"total":36786,"input":293,"output":203,"reasoning":130,"cache":{"write":0,"read":36160}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788950644740,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c48802001wWeZU2kKK","messageID":"msg_g001a085c46feb001djxNg6kw3","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","snapshot":"aaa46a95d9d355c1353cdc97717d4771c99729a0","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788950651491,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c49a78001oCp2jdyy2","messageID":"msg_g001a085c46feb001djxNg6kw3","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"text","text":"The events log is just this session's live trace. Let me find a prior OUTWARD artifact from a sibling round to match format, and look at the INWARD claims for grounding.","time":{"start":1788950649464,"end":1788950651488}}}
|
||||
{"type":"tool_use","timestamp":1788950651503,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"bash","callID":"call_6783d55e47ac4363a79ecdbc","state":{"status":"completed","input":{"command":"ls /mnt/workdrive/issue161-harness/research/; echo ---; find /mnt/workdrive/issue161-harness/research -maxdepth 2 -name \"*outward*\" 2>/dev/null | head; echo ---; ls /mnt/workdrive/issue161-harness/research/issue-161 2>/dev/null","description":"Find prior outward artifacts in research dir"},"output":"batch-processing\nbuild-mode\ncontract-testing\nfull-audit\ninterstitial-tools\nissue-161\nissue-163\nissue-245-research.md\nissue-256\nissue-260\nissue-261\nissue-262\nissue-263\nllm-craft-api\nllm-craft-deep\nquality-tools\nresearch-methodology\nrune-efficiency\nsafety-override\ntextual-craft\ntool-calling\nuncensored-models\nv1\nworkflow-audit\n---\n/mnt/workdrive/issue161-harness/research/issue-262/outward_reading.json\n/mnt/workdrive/issue161-harness/research/issue-161/outward.json\n---\nadversarial.json\napplicability.json\noutward.json\nqualify.json\nsynthesis.json\ntask_tree.json\n","metadata":{"output":"batch-processing\nbuild-mode\ncontract-testing\nfull-audit\ninterstitial-tools\nissue-161\nissue-163\nissue-245-research.md\nissue-256\nissue-260\nissue-261\nissue-262\nissue-263\nllm-craft-api\nllm-craft-deep\nquality-tools\nresearch-methodology\nrune-efficiency\nsafety-override\ntextual-craft\ntool-calling\nuncensored-models\nv1\nworkflow-audit\n---\n/mnt/workdrive/issue161-harness/research/issue-262/outward_reading.json\n/mnt/workdrive/issue161-harness/research/issue-161/outward.json\n---\nadversarial.json\napplicability.json\noutward.json\nqualify.json\nsynthesis.json\ntask_tree.json\n","exit":0,"description":"Find prior outward artifacts in research dir","truncated":false},"title":"Find prior outward artifacts in research dir","time":{"start":1788950651499,"end":1788950651501}},"id":"prt_g001a085c4a2300010MYFDhAqD","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","messageID":"msg_g001a085c46feb001djxNg6kw3"}}
|
||||
{"type":"step_finish","timestamp":1788950651524,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c4a26f001s2r4W6u1J","reason":"tool-calls","snapshot":"bcd02fb0cf0c5511e106d0a8b9189a209f5ff083","messageID":"msg_g001a085c46feb001djxNg6kw3","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"step-finish","tokens":{"total":38266,"input":1562,"output":131,"reasoning":157,"cache":{"write":0,"read":36416}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788950659018,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c4bfc7001QNAFiFkVv","messageID":"msg_g001a085c4a2a5001s7rV6nuqZ","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","snapshot":"5e53bcf046a208b05de188f86e46d8ea698422db","type":"step-start"}}
|
||||
{"type":"tool_use","timestamp":1788950660723,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"bash","callID":"call_b6dcc6478d1b42a3aeed47d1","state":{"status":"completed","input":{"command":"python3 -c \"\nimport json\nd=json.load(open('/mnt/workdrive/issue161-harness/research/issue-161/outward.json'))\nprint(json.dumps(d, indent=1)[:3500])\n\"","description":"Inspect prior outward.json format"},"output":"{\n \"stage\": \"OUTWARD\",\n \"process_id\": \"research-161-r1\",\n \"issue\": 161,\n \"round\": 1,\n \"subquestion\": \"Memory backend abstraction + comparative benchmark harness (SQLite FTS5/sqlite-vec vs Qdrant vs graph backends)\",\n \"discovery\": {\n \"queries\": [\n {\n \"id\": \"Q1\",\n \"tool\": \"webfetch (DuckDuckGo HTML)\",\n \"query\": \"sqlite-vec vs qdrant benchmark vector search comparison\",\n \"receipt\": \"fetched 2026 session turn; 10 results returned inline\"\n },\n {\n \"id\": \"Q2\",\n \"tool\": \"webfetch (DuckDuckGo HTML)\",\n \"query\": \"agent memory backend benchmark harness embeddings retrieval evaluation\",\n \"receipt\": \"fetched 2026 session turn; 10 results returned inline\"\n },\n {\n \"id\": \"Q3\",\n \"tool\": \"webfetch (DuckDuckGo HTML)\",\n \"query\": \"sqlite-vec github asg017 documentation features limitations\",\n \"receipt\": \"BLOCKED: DuckDuckGo bot challenge returned instead of results; no results obtained\"\n },\n {\n \"id\": \"Q4\",\n \"tool\": \"webfetch (DuckDuckGo HTML)\",\n \"query\": \"harness the memory holistic evaluation memory substrates arxiv LongMemEval LoCoMo\",\n \"receipt\": \"BLOCKED: DuckDuckGo bot challenge returned instead of results; no results obtained\"\n }\n ],\n \"sources_discovered\": [\n {\n \"id\": \"O1\",\n \"url\": \"https://www.dreaming.press/compare/sqlite-vec-vs-qdrant\",\n \"class\": \"comparison site\",\n \"note\": \"not selected for reading \\u2014 auto-generated comparison page, unclear methodology\"\n },\n {\n \"id\": \"O2\",\n \"url\": \"https://qdrant.tech/benchmarks/\",\n \"class\": \"vendor official (Qdrant benchmark portal)\"\n },\n {\n \"id\": \"O3\",\n \"url\": \"https://www.libhunt.com/compare-sqlite-vss-vs-qdrant\",\n \"class\": \"aggregator\",\n \"note\": \"not selected \\u2014 stale sqlite-vss framing, low value\"\n },\n {\n \"id\": \"O4\",\n \"url\": \"https://github.com/mycman/sqlite-vec-benchmark\",\n \"class\": \"GitHub (independent benchmark harness)\"\n },\n {\n \"id\": \"O5\",\n \"url\": \"https://www.firecrawl.dev/blog/best-vector-databases\",\n \"class\": \"vendor blog\",\n \"note\": \"not selected \\u2014 survey listicle, no reproducible methodology\"\n },\n {\n \"id\": \"O6\",\n \"url\": \"https://encore.dev/articles/best-vector-databases\",\n \"class\": \"vendor blog\",\n \"note\": \"not selected\"\n },\n {\n \"id\": \"O7\",\n \"url\": \"https://www.reddit.com/r/vectordatabase/comments/170j6zd/\",\n \"class\": \"forum\",\n \"note\": \"not selected\"\n },\n {\n \"id\": \"O8\",\n \"url\": \"https://github.com/scriptstar/vector-db-benchmark\",\n \"class\": \"GitHub (benchmark suite: Qdrant/Milvus/Weaviate/Chroma/Pinecone/SQLite)\",\n \"note\": \"candidate; not read \\u2014 O4 covers the SQLite-vs-managed comparison more directly\"\n },\n {\n \"id\": \"O9\",\n \"url\": \"https://www.inductivee.com/blog/vector-database-performance-benchmarks-2025\",\n \"class\": \"blog (agency benchmarks)\",\n \"note\": \"not selected\"\n },\n {\n \"id\": \"O10\",\n \"url\": \"https://github.com/mem0ai/memory-benchmarks\",\n \"class\": \"GitHub (Mem0 evaluation suite)\",\n \"note\": \"candidate; not read \\u2014 Mem0-specific, O12 provides a more neutral harness\"\n },\n {\n \"id\": \"O11\",\n \"url\": \"https://agentmemorybenchmark.ai/\",\n \"class\": \"leaderboard site\",\n \"note\": \"not selected\"\n },\n {\n \"id\": \"O12\",\n \"url\": \"https://github.com/Evanyuan-builder/memory-core-eval\",\n \"class\": \"GitHub (reproducible memory-eval harness, MemoryAdapter interface)\"\n },\n {\n \"id\": \"O13\",\n \"url\": \"https://kranthib.github.io/tech-pulse/frameworks-n-pla\n","metadata":{"output":"{\n \"stage\": \"OUTWARD\",\n \"process_id\": \"research-161-r1\",\n \"issue\": 161,\n \"round\": 1,\n \"subquestion\": \"Memory backend abstraction + comparative benchmark harness (SQLite FTS5/sqlite-vec vs Qdrant vs graph backends)\",\n \"discovery\": {\n \"queries\": [\n {\n \"id\": \"Q1\",\n \"tool\": \"webfetch (DuckDuckGo HTML)\",\n \"query\": \"sqlite-vec vs qdrant benchmark vector search comparison\",\n \"receipt\": \"fetched 2026 session turn; 10 results returned inline\"\n },\n {\n \"id\": \"Q2\",\n \"tool\": \"webfetch (DuckDuckGo HTML)\",\n \"query\": \"agent memory backend benchmark harness embeddings retriLine truncated
|
||||
{"type":"step_finish","timestamp":1788950660744,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c4c673001VoAqv3UXu","reason":"tool-calls","snapshot":"4fce4aca3a1f94de1c8755d8cd013e3ebab48c43","messageID":"msg_g001a085c4a2a5001s7rV6nuqZ","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"step-finish","tokens":{"total":41999,"input":5135,"output":64,"reasoning":0,"cache":{"write":0,"read":36800}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788950671012,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c4eea2001BuACRFwWp","messageID":"msg_g001a085c4c6a5001BtxWpNzj8","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","snapshot":"bba3e02de5bcdc1dddcf0e48e5c6d4457afb3f3d","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788950680567,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c5063e001U4qlYffkX","messageID":"msg_g001a085c4c6a5001BtxWpNzj8","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"text","text":"Format confirmed. Let me see the tail of the prior artifact (reading/claims sections) and the issue-163 INWARD claims for grounding.","time":{"start":1788950677054,"end":1788950680564}}}
|
||||
{"type":"tool_use","timestamp":1788950680591,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"bash","callID":"call_7e6ef6ccdd224269a0eff9f8","state":{"status":"completed","input":{"command":"python3 -c \"\nimport json\nt=json.load(open('/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json'))\ninw=t['artifacts']['INWARD']\nprint(json.dumps(inw.get('claims'), indent=1)[:2500])\n\"","description":"Show INWARD claims for issue-163"},"output":"[\n {\n \"id\": \"I1\",\n \"claim\": \"Relevant file: src/rune/curator.py\",\n \"evidence\": [\n \"src/rune/curator.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I2\",\n \"claim\": \"Relevant file: src/rune/rune_tui.py\",\n \"evidence\": [\n \"src/rune/rune_tui.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I3\",\n \"claim\": \"Relevant file: src/rune/graph_memory.py\",\n \"evidence\": [\n \"src/rune/graph_memory.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I4\",\n \"claim\": \"Relevant file: src/rune/sleep_time.py\",\n \"evidence\": [\n \"src/rune/sleep_time.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I5\",\n \"claim\": \"Relevant file: src/rune/elastic_cognition.py\",\n \"evidence\": [\n \"src/rune/elastic_cognition.py\"\n ],\n \"status\": \"CURRENT\"\n }\n]\n","metadata":{"output":"[\n {\n \"id\": \"I1\",\n \"claim\": \"Relevant file: src/rune/curator.py\",\n \"evidence\": [\n \"src/rune/curator.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I2\",\n \"claim\": \"Relevant file: src/rune/rune_tui.py\",\n \"evidence\": [\n \"src/rune/rune_tui.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I3\",\n \"claim\": \"Relevant file: src/rune/graph_memory.py\",\n \"evidence\": [\n \"src/rune/graph_memory.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I4\",\n \"claim\": \"Relevant file: src/rune/sleep_time.py\",\n \"evidence\": [\n \"src/rune/sleep_time.py\"\n ],\n \"status\": \"CURRENT\"\n },\n {\n \"id\": \"I5\",\n \"claim\": \"Relevant file: src/rune/elastic_cognition.py\",\n \"evidence\": [\n \"src/rune/elastic_cognition.py\"\n ],\n \"status\": \"CURRENT\"\n }\n]\n","exit":0,"description":"Show INWARD claims for issue-163","truncated":false},"title":"Show INWARD claims for issue-163","time":{"start":1788950680588,"end":1788950680591}},"id":"prt_g001a085c51217001HuOcmvfSS","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","messageID":"msg_g001a085c4c6a5001BtxWpNzj8"}}
|
||||
{"type":"tool_use","timestamp":1788950680594,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"bash","callID":"call_5b7ecb06f606423ba952223e","state":{"status":"completed","input":{"command":"python3 -c \"\nimport json\nd=json.load(open('/mnt/workdrive/issue161-harness/research/issue-161/outward.json'))\nprint('top keys:', list(d.keys()))\nr=d.get('reading') or d.get('read_sources')\nif r: print(json.dumps(r, indent=1)[:2500])\nprint('...')\nprint('final keys/lines:', json.dumps({k:v for k,v in d.items() if k not in ('discovery','reading')}, indent=1)[:1200])\n\"","description":"Show remaining sections of prior outward.json"},"output":"top keys: ['stage', 'process_id', 'issue', 'round', 'subquestion', 'discovery', 'reading', 'limitations', 'contradictions', 'repo_grounding_note', 'stop_condition']\n{\n \"sources_read\": [\n {\n \"id\": \"O2\",\n \"url\": \"https://qdrant.tech/benchmarks/\",\n \"receipt\": \"full page fetched via webfetch (markdown)\",\n \"class_note\": \"vendor-published benchmark portal; FAQ openly discusses own bias\",\n \"claims\": [\n {\n \"id\": \"E1\",\n \"claim\": \"Qdrant's benchmarks are comparative (relative numbers, not absolute), run on the same machine for all engines, on affordable reproducible hardware, and are fully open-sourced (github.com/qdrant/vector-db-benchmark).\",\n \"section\": \"intro, principles\"\n },\n {\n \"id\": \"E2\",\n \"claim\": \"Vector-search speed must only be compared at a matched precision threshold (ANN trades precision for speed); the vendor states most published benchmarks miss this.\",\n \"section\": \"How to read the results\"\n },\n {\n \"id\": \"E3\",\n \"claim\": \"Single-node results (updated Jan/June 2024): Qdrant achieves highest RPS and lowest latency in almost all scenarios at matched precision; Elasticsearch indexes 10x slower at scale (32 min vs 5.5 h for 10M\\u00d796d); Milvus is fastest at indexing; Redis wins RPS only at low precision; Weaviate improved least.\",\n \"section\": \"Observations\"\n },\n {\n \"id\": \"E4\",\n \"claim\": \"Benchmark datasets are 1M\\u201310M vector scale: dbpedia-openai-1M (1M\\u00d71536 cosine), deep-image (10M\\u00d796), gist-960 (1M\\u00d7960), glove-100 (1.2M\\u00d7100); client/server on Azure with all engines memory-capped at 25GB.\",\n \"section\": \"Tested datasets, Setup\"\n },\n {\n \"id\": \"E5\",\n \"claim\": \"Filtered ANN search: post-filtering loses results, pre-filtering masks scale linearly, and over-filtering disconnects the HNSW graph causing accuracy collapse; Qdrant claims a filterable-HNSW + query-planning approach avoiding these.\",\n \"section\": \"Filtered search benchmark\"\n },\n {\n \"id\": \"E6\",\n \"claim\": \"The FAQ admits probable vendor bias ('Probably, yes'), excludes FAISS/Annoy as libraries rather than production engines, tests only open-source engines, and uses official Python clients for measurement.\",\n \"section\": \"Benchmarks F.A.Q.\"\n }\n ]\n },\n {\n \"id\": \"O4\",\n \"url\": \"https://github.com/mycman/sqlite-vec-benchmark\",\n \"receipt\": \"full README fetched via webfetch (markdown); results/README and JSON not fetched\",\n \"class_note\": \"independent single-author harness; 0 stars, 1 commit \\u2014 treat as illustrative, not authoritative\",\n \"claims\": [\n {\n \"id\": \"E7\",\n \"claim\": \"The harness compares SQLite\n...\nfinal keys/lines: {\n \"stage\": \"OUTWARD\",\n \"process_id\": \"research-161-r1\",\n \"issue\": 161,\n \"round\": 1,\n \"subquestion\": \"Memory backend abstraction + comparative benchmark harness (SQLite FTS5/sqlite-vec vs Qdrant vs graph backends)\",\n \"limitations\": [\n \"sqlite-vec's official maintainer docs (asg017/sqlite-vec) were never fetched \\u2014 the DuckDuckGo bot challenge blocked the targeted query (Q3) and no alternate engine was used; sqlite-vec capabilities/limits therefore rest on O4/O2 descriptions, not the primary documentation.\",\n \"O4's direct SQLite-hybrid vs Qdrant/FAISS/Weaviate numbers are unpublished (deferred to v0.2, E10) \\u2014 its headline 'matches or beats managed vector DBs' is not backed by published head-to-head rows in that repo; only the three SQLite configurations have numbers.\",\n \"O2 contains no sqlite-vec rows at all and predates it as a comparison; it is vendor-published and self-acknowledges possible bias (E6), so it cannot serve as neutral arbiter for the SQLite-vs-Qdrant question.\",\n \"No dedicated graph-backend benchmark (Kuzu, Neo4j, Memgraph, etc.) was found or read in this discovery set; graph-side evidence is indirect \\u2014 substrate-family comparisons from O15 only.\",\n\n","metadata":{"output":"top keys: ['stage', 'process_id', 'issue', 'round', 'subquestion', 'discovery', 'reading', 'limitations', 'contradictions', 'repo_grounding_note', 'stop_condition']\n{\n \"sources_read\": [\n {\n \"Line truncated
|
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{"type":"step_finish","timestamp":1788950680617,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c514110011NOyk1oqG","reason":"tool-calls","snapshot":"294fcece8de9dc800ad8caa2d7bd9aa4ca2b77c3","messageID":"msg_g001a085c4c6a5001BtxWpNzj8","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"step-finish","tokens":{"total":43477,"input":1118,"output":239,"reasoning":200,"cache":{"write":0,"read":41920}},"cost":0}}
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{"type":"step_start","timestamp":1788950687425,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c52ebf001IrG7rn0QZ","messageID":"msg_g001a085c5144c001pwjGsjfIH","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","snapshot":"86dbd3c1d59558624777f4e11b2b2c88e28597a2","type":"step-start"}}
|
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{"type":"text","timestamp":1788950738311,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c5e3f8001Q4vEH7kX9","messageID":"msg_g001a085c5144c001pwjGsjfIH","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"text","text":"Format and grounding are clear. The INWARD claims are auto-generated keyword matches (`src/rune/graph_memory.py` is the headless SQLite graph tier — the thing a graph backend would replace/extend). Now I'll execute the OUTWARD discovery: batch parallel SERP queries via the proven DuckDuckGo HTML technique, covering all four named candidates (Neo4j, FalkorDB, LadybugDB, Graphiti).","time":{"start":1788950733816,"end":1788950738308}}}
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{"type":"tool_use","timestamp":1788950739278,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"webfetch","callID":"call_c09ff4cf9fe04c6c94b0ff30","state":{"status":"completed","input":{"format":"markdown","url":"https://duckduckgo.com/html/?q=neo4j+temporal+versioning+bitemporal+cypher+valid+time"},"output":" neo4j temporal versioning bitemporal cypher valid time at DuckDuckGo \n\n[](/html/ \"DuckDuckGo\")\n\n \n\nAll Regions Argentina Australia Austria Belgium (fr) Belgium (nl) Brazil Bulgaria Canada (en) Canada (fr) Catalonia Chile China Colombia Croatia Czech Republic Denmark Estonia Finland France Germany Greece Hong Kong Hungary Iceland India (en) Indonesia (en) Ireland Israel (en) Italy Japan Korea Latvia Lithuania Malaysia (en) Mexico Netherlands New Zealand Norway Pakistan (en) Peru Philippines (en) Poland Portugal Romania Russia Saudi Arabia Singapore Slovakia Slovenia South Africa Spain (ca) Spain (es) Sweden Switzerland (de) Switzerland (fr) Taiwan Thailand (en) Turkey US (English) US (Spanish) Ukraine United Kingdom Vietnam (en)\n\nAny Time Past Day Past Week Past Month Past Year\n\n## [Temporal values - Cypher Manual - Neo4j Graph Data Platform](//duckduckgo.com/l/?uddg=https%3A%2F%2Fneo4j.com%2Fdocs%2Fcypher%2Dmanual%2Fcurrent%2Fvalues%2Dand%2Dtypes%2Ftemporal%2F&rut=8ecf8872e498541e041de04321990b831badb7c1148cc3ab52a85ee6544e122a)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fneo4j.com%2Fdocs%2Fcypher%2Dmanual%2Fcurrent%2Fvalues%2Dand%2Dtypes%2Ftemporal%2F&rut=8ecf8872e498541e041de04321990b831badb7c1148cc3ab52a85ee6544e122a)[neo4j.com/docs/cypher-manual/current/values-and-types/temporal/](//duckduckgo.com/l/?uddg=https%3A%2F%2Fneo4j.com%2Fdocs%2Fcypher%2Dmanual%2Fcurrent%2Fvalues%2Dand%2Dtypes%2Ftemporal%2F&rut=8ecf8872e498541e041de04321990b831badb7c1148cc3ab52a85ee6544e122a)\n\n[**Cypher**® has built-in support for handling **temporal** values, which can be stored as properties on nodes and relationships in **Neo4j** databases. This section will discuss how **Cypher** handles **time** zones, before exploring **temporal** values in more detail.](//duckduckgo.com/l/?uddg=https%3A%2F%2Fneo4j.com%2Fdocs%2Fcypher%2Dmanual%2Fcurrent%2Fvalues%2Dand%2Dtypes%2Ftemporal%2F&rut=8ecf8872e498541e041de04321990b831badb7c1148cc3ab52a85ee6544e122a)\n\n## [A Practical Guide to Temporal Versioning in Neo4j: Nodes, Relationships ...](//duckduckgo.com/l/?uddg=https%3A%2F%2Fdev.to%2Fsatyam_shree_087caef77512%2Fa%2Dpractical%2Dguide%2Dto%2Dtemporal%2Dversioning%2Din%2Dneo4j%2Dnodes%2Drelationships%2Dand%2Dhistorical%2Dgraph%2D1m5g&rut=8c03671afcf8f36c6f59384fc9d6b5b5a243ac3d7955a5b981a7f5a4b45ad2d5)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fdev.to%2Fsatyam_shree_087caef77512%2Fa%2Dpractical%2Dguide%2Dto%2Dtemporal%2Dversioning%2Din%2Dneo4j%2Dnodes%2Drelationships%2Dand%2Dhistorical%2Dgraph%2D1m5g&rut=8c03671afcf8f36c6f59384fc9d6b5b5a243ac3d7955a5b981a7f5a4b45ad2d5)[dev.to/satyam\\_shree\\_087caef77512/a-practical-guide-to-temporal-versioning-in-neo4j-nodes-relationships-and-historical-graph-1m5g](//duckduckgo.com/l/?uddg=https%3A%2F%2Fdev.to%2Fsatyam_shree_087caef77512%2Fa%2Dpractical%2Dguide%2Dto%2Dtemporal%2Dversioning%2Din%2Dneo4j%2Dnodes%2Drelationships%2Dand%2Dhistorical%2Dgraph%2D1m5g&rut=8c03671afcf8f36c6f59384fc9d6b5b5a243ac3d7955a5b981a7f5a4b45ad2d5) 2025-12-05T00:00:00.0000000\n\n[Modern graph databases often represent dynamic systems: applications evolving over **time**, relationships appearing and disappearing, and entities acquiring new attributes as data changes. When the underlying graph is user-facing, maintaining a complete history of nodes and relationships becomes a critical capability. This article presents a production-grade, **bitemporal** **versioning** model for **Neo4j** ...](//duckduckgo.com/l/?uddg=https%3A%2F%2Fdev.to%2Fsatyam_shree_087caef77512%2Fa%2Dpractical%2Dguide%2Dto%2Dtemporal%2Dversioning%2Din%2Dneo4j%2Dnodes%2Drelationships%2Dand%2Dhistorical%2Dgraph%2D1m5g&rut=8c03671afcf8f36c6f59384fc9d6b5b5a243ac3d7955a5b981a7f5a4b45ad2d5)\n\n## [Cypher Date and Time Functions - kindatechnical()](//duckduckgo.com/l/?uddg=https%3A%2F%2Fkindatechnical.com%2Fcypher%2Fcypher%2Ddate%2Dand%2Dtime%2Dfunctions.html&rut=908676e3b2eb4c746695be7c9616231dac8791798528b3617a745f8cac017b40)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fkindatechnical.com%2Fcypher%2Fcypher%2Ddate%2Dand%2Dtime%2Dfunctions.html&rut=908676e3b2eb4c746695be7c9616231dac8791798528b3617a745f8cac017b40)[kindatechnical.com/cypher/cypher-date-and-time-functions.html](//duckduckgo.com/l/?uddg=https%3A%2F%2Fkindatechnical.com%2Fcypher%2Fcypher%2Ddate%2Dand%2Dtime%2Dfunctions.html&rut=908676e3b2eb4c746695be7c9616231dac8791798528b3617a745f8cac017b40)\n\n[Master **Cypher** date and **time** functions in **Neo4j**, incluLine truncated
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{"type":"tool_use","timestamp":1788950739289,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"webfetch","callID":"call_94e67b5e58e847679bd75b3f","state":{"status":"completed","input":{"format":"markdown","url":"https://duckduckgo.com/html/?q=ladybugdb+graph+database+kuzu+fork"},"output":" ladybugdb graph database kuzu fork at DuckDuckGo \n\n[](/html/ \"DuckDuckGo\")\n\n \n\nAll Regions Argentina Australia Austria Belgium (fr) Belgium (nl) Brazil Bulgaria Canada (en) Canada (fr) Catalonia Chile China Colombia Croatia Czech Republic Denmark Estonia Finland France Germany Greece Hong Kong Hungary Iceland India (en) Indonesia (en) Ireland Israel (en) Italy Japan Korea Latvia Lithuania Malaysia (en) Mexico Netherlands New Zealand Norway Pakistan (en) Peru Philippines (en) Poland Portugal Romania Russia Saudi Arabia Singapore Slovakia Slovenia South Africa Spain (ca) Spain (es) Sweden Switzerland (de) Switzerland (fr) Taiwan Thailand (en) Turkey US (English) US (Spanish) Ukraine United Kingdom Vietnam (en)\n\nAny Time Past Day Past Week Past Month Past Year\n\n## [LadybugDB: DuckDB for Graphs — The KuzuDB Successor](//duckduckgo.com/l/?uddg=https%3A%2F%2Fladybugdb.com%2F&rut=39d16a40aa872ec0274642e5a5022faace352437032eff935ce0bd16e337dc4e)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fladybugdb.com%2F&rut=39d16a40aa872ec0274642e5a5022faace352437032eff935ce0bd16e337dc4e)[ladybugdb.com](//duckduckgo.com/l/?uddg=https%3A%2F%2Fladybugdb.com%2F&rut=39d16a40aa872ec0274642e5a5022faace352437032eff935ce0bd16e337dc4e)\n\n[**LadybugDB** is DuckDB for **graphs** — the embedded columnar **graph** **database** and successor to KuzuDB. Built for agentic AI in highly regulated industries. OpenAPI spec, MCP server, npm/PyPI SDKs.](//duckduckgo.com/l/?uddg=https%3A%2F%2Fladybugdb.com%2F&rut=39d16a40aa872ec0274642e5a5022faace352437032eff935ce0bd16e337dc4e)\n\n## [GitHub - LadybugDB/ladybug: LadybugDB a graph database](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2FLadybugDB%2Fladybug&rut=b8ba55b3caf5040967d438d5f3e85b53b7c3c202baa0ca7bb8f5ed9bf6233077)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2FLadybugDB%2Fladybug&rut=b8ba55b3caf5040967d438d5f3e85b53b7c3c202baa0ca7bb8f5ed9bf6233077)[github.com/LadybugDB/ladybug](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2FLadybugDB%2Fladybug&rut=b8ba55b3caf5040967d438d5f3e85b53b7c3c202baa0ca7bb8f5ed9bf6233077)\n\n[**LadybugDB** a **graph** **database**. Contribute to **LadybugDB**/ladybug development by creating an account on GitHub.](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2FLadybugDB%2Fladybug&rut=b8ba55b3caf5040967d438d5f3e85b53b7c3c202baa0ca7bb8f5ed9bf6233077)\n\n## [GitHub - lcretan/LadybugDB-KuzuDB.ladybug: LadybugDB a graph database](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2Flcretan%2FLadybugDB%2DKuzuDB.ladybug&rut=90379b855adaa5fca57f43657a3fbbc3e3361c55b5009e2b12c81584b0aeb4ad)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2Flcretan%2FLadybugDB%2DKuzuDB.ladybug&rut=90379b855adaa5fca57f43657a3fbbc3e3361c55b5009e2b12c81584b0aeb4ad)[github.com/lcretan/LadybugDB-KuzuDB.ladybug](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2Flcretan%2FLadybugDB%2DKuzuDB.ladybug&rut=90379b855adaa5fca57f43657a3fbbc3e3361c55b5009e2b12c81584b0aeb4ad)\n\n[Ladybug is an embedded **graph** **database** built for query speed and scalability. Ladybug is optimized for handling complex analytical workloads on very large **databases** and provides a set of retrieval features, such as a full text search and vector indices.](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2Flcretan%2FLadybugDB%2DKuzuDB.ladybug&rut=90379b855adaa5fca57f43657a3fbbc3e3361c55b5009e2b12c81584b0aeb4ad)\n\n## [Documentation | Ladybug](//duckduckgo.com/l/?uddg=https%3A%2F%2Fdocs.ladybugdb.com%2F&rut=31384e12b30b5f7a3e0d6ee983128f90a62a469ce0362c17d6058131168828b3)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fdocs.ladybugdb.com%2F&rut=31384e12b30b5f7a3e0d6ee983128f90a62a469ce0362c17d6058131168828b3)[docs.ladybugdb.com](//duckduckgo.com/l/?uddg=https%3A%2F%2Fdocs.ladybugdb.com%2F&rut=31384e12b30b5f7a3e0d6ee983128f90a62a469ce0362c17d6058131168828b3)\n\n[Ladybug is a state-of-the-art **graph** DBMS that came out of state-of-the-art academic research and is built by a core team of **database** experts who relentlessly focus on scalability and performance. Its founding engineers have co-authored several cutting-edge technical papers and articles on storing and querying large **graphs**.](//duckduckgo.com/l/?uddg=https%3A%2F%2Fdocs.ladybugdb.com%2F&rut=31384e12b30b5f7a3e0d6ee983128f90a62a469ce0362c17d6058131168828b3)\n\n## [From Kuzu to Ladybug: The embedded graph ecosystem powers onward](//duckduckgo.com/l/?uddg=Line truncated
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{"type":"tool_use","timestamp":1788950739291,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"webfetch","callID":"call_db61ded8edbf4ae6808ce2cf","state":{"status":"completed","input":{"format":"markdown","url":"https://duckduckgo.com/html/?q=neo4j+vs+falkordb+graph+database+benchmark+comparison"},"output":" neo4j vs falkordb graph database benchmark comparison at DuckDuckGo \n\n[](/html/ \"DuckDuckGo\")\n\n \n\nAll Regions Argentina Australia Austria Belgium (fr) Belgium (nl) Brazil Bulgaria Canada (en) Canada (fr) Catalonia Chile China Colombia Croatia Czech Republic Denmark Estonia Finland France Germany Greece Hong Kong Hungary Iceland India (en) Indonesia (en) Ireland Israel (en) Italy Japan Korea Latvia Lithuania Malaysia (en) Mexico Netherlands New Zealand Norway Pakistan (en) Peru Philippines (en) Poland Portugal Romania Russia Saudi Arabia Singapore Slovakia Slovenia South Africa Spain (ca) Spain (es) Sweden Switzerland (de) Switzerland (fr) Taiwan Thailand (en) Turkey US (English) US (Spanish) Ukraine United Kingdom Vietnam (en)\n\nAny Time Past Day Past Week Past Month Past Year\n\n## [Graph Database Benchmark: Neo4j vs FalkorDB vs Memgraph](//duckduckgo.com/l/?uddg=https%3A%2F%2Faimultiple.com%2Fgraph%2Ddatabases&rut=9a7cc6962047607446f0fff26b57d1b567c8b709fa0367b3d50af944777f4bc5)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Faimultiple.com%2Fgraph%2Ddatabases&rut=9a7cc6962047607446f0fff26b57d1b567c8b709fa0367b3d50af944777f4bc5)[aimultiple.com/graph-databases](//duckduckgo.com/l/?uddg=https%3A%2F%2Faimultiple.com%2Fgraph%2Ddatabases&rut=9a7cc6962047607446f0fff26b57d1b567c8b709fa0367b3d50af944777f4bc5) 2026-04-15T00:00:00.0000000\n\n[We benchmarked **Neo4j**, **FalkorDB**, and Memgraph on a synthetic **graph** derived from 120,000 Amazon product reviews (381K nodes, 804K edges).](//duckduckgo.com/l/?uddg=https%3A%2F%2Faimultiple.com%2Fgraph%2Ddatabases&rut=9a7cc6962047607446f0fff26b57d1b567c8b709fa0367b3d50af944777f4bc5)\n\n## [FalkorDB vs Neo4j: Graph Database Performance Benchmarks](//duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.falkordb.com%2Fblog%2Fgraph%2Ddatabase%2Dperformance%2Dbenchmarks%2Dfalkordb%2Dvs%2Dneo4j%2F&rut=84e98115c2127179c953071c0670c8040f767c50f6063572c8b5d820a2ff29ac)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.falkordb.com%2Fblog%2Fgraph%2Ddatabase%2Dperformance%2Dbenchmarks%2Dfalkordb%2Dvs%2Dneo4j%2F&rut=84e98115c2127179c953071c0670c8040f767c50f6063572c8b5d820a2ff29ac)[www.falkordb.com/blog/graph-database-performance-benchmarks-falkordb-vs-neo4j/](//duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.falkordb.com%2Fblog%2Fgraph%2Ddatabase%2Dperformance%2Dbenchmarks%2Dfalkordb%2Dvs%2Dneo4j%2F&rut=84e98115c2127179c953071c0670c8040f767c50f6063572c8b5d820a2ff29ac) 2024-12-02T00:00:00.0000000\n\n[New performance **benchmarks** results demonstrate **FalkorDB's** significant performance advantages in aggregate expansion operations compared to **Neo4j**. The data reveals consistent sub-140ms response times at p99, while **Neo4j** experiences multi-second latencies under similar conditions. The **benchmark** is composed of 11 templated queries, which represent a general usage of classic **graph** operations ...](//duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.falkordb.com%2Fblog%2Fgraph%2Ddatabase%2Dperformance%2Dbenchmarks%2Dfalkordb%2Dvs%2Dneo4j%2F&rut=84e98115c2127179c953071c0670c8040f767c50f6063572c8b5d820a2ff29ac)\n\n## [FalkorDB vs Neo4j : Key Differences - puppygraph.com](//duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.puppygraph.com%2Fblog%2Ffalkordb%2Dvs%2Dneo4j&rut=00f001e76daa6fec9322c175ac2ced7b6c3e8580b63d1f53e1b3739b35737249)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.puppygraph.com%2Fblog%2Ffalkordb%2Dvs%2Dneo4j&rut=00f001e76daa6fec9322c175ac2ced7b6c3e8580b63d1f53e1b3739b35737249)[www.puppygraph.com/blog/falkordb-vs-neo4j](//duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.puppygraph.com%2Fblog%2Ffalkordb%2Dvs%2Dneo4j&rut=00f001e76daa6fec9322c175ac2ced7b6c3e8580b63d1f53e1b3739b35737249)\n\n[Compare **FalkorDB** **vs** **Neo4j** across architecture, performance, scalability, and operational tradeoffs. Learn when to use each **graph** **database**.](//duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.puppygraph.com%2Fblog%2Ffalkordb%2Dvs%2Dneo4j&rut=00f001e76daa6fec9322c175ac2ced7b6c3e8580b63d1f53e1b3739b35737249)\n\n## [FalkorDB vs Neo4j: 2026 Benchmarks & Honest Pick](//duckduckgo.com/l/?uddg=https%3A%2F%2Fknodegraph.com%2Fblog%2Ffalkordb%2Dvs%2Dneo4j%2F&rut=11bfb2e6aa30c47ec0c600446a9612a57e45cbcf9eda6444c50aa20614c6fa15)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fknodegraph.com%2Fblog%2Ffalkordb%2Dvs%2Dneo4j%2F&rut=11bfb2e6aa30c47ec0c600446a9612a57e45cbcf9eda6444c50aa20614c6fa15)[knodegraph.comLine truncated
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{"type":"tool_use","timestamp":1788950739313,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"webfetch","callID":"call_10663d650f4849d5bb6715e4","state":{"status":"completed","input":{"format":"markdown","url":"https://duckduckgo.com/html/?q=graphiti+zep+temporal+knowledge+graph+agent+memory+neo4j+falkordb"},"output":" graphiti zep temporal knowledge graph agent memory neo4j falkordb at DuckDuckGo \n\n[](/html/ \"DuckDuckGo\")\n\n \n\nAll Regions Argentina Australia Austria Belgium (fr) Belgium (nl) Brazil Bulgaria Canada (en) Canada (fr) Catalonia Chile China Colombia Croatia Czech Republic Denmark Estonia Finland France Germany Greece Hong Kong Hungary Iceland India (en) Indonesia (en) Ireland Israel (en) Italy Japan Korea Latvia Lithuania Malaysia (en) Mexico Netherlands New Zealand Norway Pakistan (en) Peru Philippines (en) Poland Portugal Romania Russia Saudi Arabia Singapore Slovakia Slovenia South Africa Spain (ca) Spain (es) Sweden Switzerland (de) Switzerland (fr) Taiwan Thailand (en) Turkey US (English) US (Spanish) Ukraine United Kingdom Vietnam (en)\n\nAny Time Past Day Past Week Past Month Past Year\n\n## [GitHub - getzep/graphiti: Build Real-Time Knowledge Graphs for AI Agents](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2Fgetzep%2Fgraphiti&rut=e0eec4286ae13b2a2621cb77f8e63610e0a48bf4a825dd3b209363a114cc4a4a)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2Fgetzep%2Fgraphiti&rut=e0eec4286ae13b2a2621cb77f8e63610e0a48bf4a825dd3b209363a114cc4a4a)[github.com/getzep/graphiti](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2Fgetzep%2Fgraphiti&rut=e0eec4286ae13b2a2621cb77f8e63610e0a48bf4a825dd3b209363a114cc4a4a)\n\n[**Graphiti** is a framework for building and querying **temporal** context **graphs** for AI **agents**. Unlike static **knowledge** **graphs**, **Graphiti's** context **graphs** track how facts change over time, maintain provenance to source data, and support both prescribed and learned ontology — making them purpose-built for **agents** operating on evolving, real-world data.](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2Fgetzep%2Fgraphiti&rut=e0eec4286ae13b2a2621cb77f8e63610e0a48bf4a825dd3b209363a114cc4a4a)\n\n## [Graphiti: Knowledge graph memory for an agentic world](//duckduckgo.com/l/?uddg=https%3A%2F%2Fneo4j.com%2Fblog%2Fdeveloper%2Fgraphiti%2Dknowledge%2Dgraph%2Dmemory%2F&rut=f73cef9f24d804dc499123dcbad569857c129ca78b72560305f7409c3e14a0fc)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fneo4j.com%2Fblog%2Fdeveloper%2Fgraphiti%2Dknowledge%2Dgraph%2Dmemory%2F&rut=f73cef9f24d804dc499123dcbad569857c129ca78b72560305f7409c3e14a0fc)[neo4j.com/blog/developer/graphiti-knowledge-graph-memory/](//duckduckgo.com/l/?uddg=https%3A%2F%2Fneo4j.com%2Fblog%2Fdeveloper%2Fgraphiti%2Dknowledge%2Dgraph%2Dmemory%2F&rut=f73cef9f24d804dc499123dcbad569857c129ca78b72560305f7409c3e14a0fc) 2025-03-24T00:00:00.0000000\n\n[For an in-depth technical exploration of **Graphiti** and performance benchmarks, check out **Zep**: A **Temporal** **Knowledge** **Graph** Architecture for **Agent** **Memory**. A key feature is **Graphiti's** bi-**temporal** model, which tracks when an event occurred and when it was ingested.](//duckduckgo.com/l/?uddg=https%3A%2F%2Fneo4j.com%2Fblog%2Fdeveloper%2Fgraphiti%2Dknowledge%2Dgraph%2Dmemory%2F&rut=f73cef9f24d804dc499123dcbad569857c129ca78b72560305f7409c3e14a0fc)\n\n## [Graphiti - FalkorDB Docs](//duckduckgo.com/l/?uddg=https%3A%2F%2Fdocs.falkordb.com%2Fagentic%2Dmemory%2Fgraphiti&rut=5ec400c001b71ae4c58a30b0ed31d3312ab8c9d0b5ede0da1bfa788d951b2a70)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fdocs.falkordb.com%2Fagentic%2Dmemory%2Fgraphiti&rut=5ec400c001b71ae4c58a30b0ed31d3312ab8c9d0b5ede0da1bfa788d951b2a70)[docs.falkordb.com/agentic-memory/graphiti](//duckduckgo.com/l/?uddg=https%3A%2F%2Fdocs.falkordb.com%2Fagentic%2Dmemory%2Fgraphiti&rut=5ec400c001b71ae4c58a30b0ed31d3312ab8c9d0b5ede0da1bfa788d951b2a70)\n\n[Build temporally-aware, multi-tenant **knowledge** **graphs** with **Graphiti** and **FalkorDB** for AI systems with persistent **memory**, hybrid search, and real-time data integration.](//duckduckgo.com/l/?uddg=https%3A%2F%2Fdocs.falkordb.com%2Fagentic%2Dmemory%2Fgraphiti&rut=5ec400c001b71ae4c58a30b0ed31d3312ab8c9d0b5ede0da1bfa788d951b2a70)\n\n## [GitHub - Memory-Agents/graphiti](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2FMemory%2DAgents%2Fgraphiti%2F&rut=5c3656175ee42204f5fd3688a79e1f368f7276cabeac4c02d9f5d515fb9f1bc5)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2FMemory%2DAgents%2Fgraphiti%2F&rut=5c3656175ee42204f5fd3688a79e1f368f7276cabeac4c02d9f5d515fb9f1bc5)[github.com/Memory-Agents/graphiti/](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2FMemoLine truncated
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{"type":"tool_use","timestamp":1788950739643,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"webfetch","callID":"call_87ef8ff179854a0cbee1b4f8","state":{"status":"completed","input":{"format":"markdown","url":"https://duckduckgo.com/html/?q=falkordb+redisgraph+successor+cypher+features+github"},"output":" falkordb redisgraph successor cypher features github at DuckDuckGo \n\n[](/html/ \"DuckDuckGo\")\n\n \n\nAll Regions Argentina Australia Austria Belgium (fr) Belgium (nl) Brazil Bulgaria Canada (en) Canada (fr) Catalonia Chile China Colombia Croatia Czech Republic Denmark Estonia Finland France Germany Greece Hong Kong Hungary Iceland India (en) Indonesia (en) Ireland Israel (en) Italy Japan Korea Latvia Lithuania Malaysia (en) Mexico Netherlands New Zealand Norway Pakistan (en) Peru Philippines (en) Poland Portugal Romania Russia Saudi Arabia Singapore Slovakia Slovenia South Africa Spain (ca) Spain (es) Sweden Switzerland (de) Switzerland (fr) Taiwan Thailand (en) Turkey US (English) US (Spanish) Ukraine United Kingdom Vietnam (en)\n\nAny Time Past Day Past Week Past Month Past Year\n\n## [GitHub - FalkorDB/FalkorDB: A super fast Graph Database uses GraphBLAS ...](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2FFalkorDB%2FFalkorDB&rut=583fc56960fd5d777812618fe5419784a4daa7588570f13ee10f7a224917b2d7)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2FFalkorDB%2FFalkorDB&rut=583fc56960fd5d777812618fe5419784a4daa7588570f13ee10f7a224917b2d7)[github.com/FalkorDB/FalkorDB](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2FFalkorDB%2FFalkorDB&rut=583fc56960fd5d777812618fe5419784a4daa7588570f13ee10f7a224917b2d7)\n\n[Our goal is to build a high-performance Knowledge Graph tailored for Large Language Models (LLMs), prioritizing exceptionally low latency to ensure fast and efficient information delivery through our Graph Database. 🆕 **FalkorDB** is the first queryable Property Graph database to leverage sparse matrices for representing the adjacency matrix in graphs and linear algebra for querying.](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2FFalkorDB%2FFalkorDB&rut=583fc56960fd5d777812618fe5419784a4daa7588570f13ee10f7a224917b2d7)\n\n## [FalkorDB · GitHub](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2FFalkorDB&rut=475250bed7593c4bf233105810d442d46c205a1b0390e212b7ed2517611d4216)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2FFalkorDB&rut=475250bed7593c4bf233105810d442d46c205a1b0390e212b7ed2517611d4216)[github.com/FalkorDB](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2FFalkorDB&rut=475250bed7593c4bf233105810d442d46c205a1b0390e212b7ed2517611d4216)\n\n[**FalkorDB** is the first queryable Property Graphdatabase to use sparse matricesto represent the adjacency matrixin graphs and linear algebrato query the graph.](//duckduckgo.com/l/?uddg=https%3A%2F%2Fgithub.com%2FFalkorDB&rut=475250bed7593c4bf233105810d442d46c205a1b0390e212b7ed2517611d4216)\n\n## [RedisGraph EOL: FalkorDB Migration Guide](//duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.falkordb.com%2Fblog%2Fredisgraph%2Deol%2Dmigration%2Dguide%2F&rut=bc2aca3505408677678b2e697daadf7402fa91931a8287bfef3d7d68d09854ce)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.falkordb.com%2Fblog%2Fredisgraph%2Deol%2Dmigration%2Dguide%2F&rut=bc2aca3505408677678b2e697daadf7402fa91931a8287bfef3d7d68d09854ce)[www.falkordb.com/blog/redisgraph-eol-migration-guide/](//duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.falkordb.com%2Fblog%2Fredisgraph%2Deol%2Dmigration%2Dguide%2F&rut=bc2aca3505408677678b2e697daadf7402fa91931a8287bfef3d7d68d09854ce) 2025-02-16T00:00:00.0000000\n\n[**FalkorDB** maintains backward compatibility with **Cypher** queries, supporting key **features** like graph traversal, pattern matching, and filtering, making it easier for existing **RedisGraph** users to adapt.](//duckduckgo.com/l/?uddg=https%3A%2F%2Fwww.falkordb.com%2Fblog%2Fredisgraph%2Deol%2Dmigration%2Dguide%2F&rut=bc2aca3505408677678b2e697daadf7402fa91931a8287bfef3d7d68d09854ce)\n\n## [RedisGraph Is Officially Dead (EOL Jan 31, 2025): Migration Paths That ...](//duckduckgo.com/l/?uddg=https%3A%2F%2Fbasekv.com%2Farticles%2Fredisgraph%2Ddeprecated%2Dnow%2Dwhat&rut=136ffcb208bf81ea46214aedfcf8e7c0a5a31430e32b7ec48dbc368deb04b54b)\n\n [](//duckduckgo.com/l/?uddg=https%3A%2F%2Fbasekv.com%2Farticles%2Fredisgraph%2Ddeprecated%2Dnow%2Dwhat&rut=136ffcb208bf81ea46214aedfcf8e7c0a5a31430e32b7ec48dbc368deb04b54b)[basekv.com/articles/redisgraph-deprecated-now-what](//duckduckgo.com/l/?uddg=https%3A%2F%2Fbasekv.com%2Farticles%2Fredisgraph%2Ddeprecated%2Dnow%2Dwhat&rut=136ffcb208bf81ea46214aedfcf8e7c0a5a31430e32b7ec48dbc368deb04b54b) 2026-02-28T00:00:00.0000000\n\n[Yes, it is official: Redis announced **Line truncated
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{"type":"text","timestamp":1788950789490,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c6bd34001YfoYkLmEL","messageID":"msg_g001a085c5faf1001NFtYmOK3d","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"text","text":"All five discovery queries returned real SERPs — strong candidate pool across all four named candidates. Now I select sources for reading (one or more per hypothesis, balanced classes) and fetch them in parallel. GitHub repos go via `raw.githubusercontent.com` per established technique; blogs/docs via direct webfetch.","time":{"start":1788950789428,"end":1788950789487}}}
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{"type":"tool_use","timestamp":1788950789728,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"webfetch","callID":"call_49940e78c52044228f191e2a","state":{"status":"completed","input":{"format":"markdown","url":"https://thedataquarry.com/blog/from-kuzu-to-ladybug/"},"output":"From Kuzu to Ladybug: The embedded graph ecosystem powers onward • The Data Quarry \n\n[The Data Quarry](/)\n\n[Archive](/archive) [Projects](/projects) [About](/about)\n\n[Search](/search \"Search\")\n\nDark Theme Menu\n\n[\n\nBack\n\n](/archive)\n\n## TABLE OF CONTENTS\n\n- [Dataset: Who Mentored the Most Nobel Laureates?](#dataset-who-mentored-the-most-nobel-laureates)\n \n- [Preprocessing the Data](#preprocessing-the-data)\n \n- [Loading into Ladybug](#loading-into-ladybug)\n \n- [Preparing the Schema for Algorithm Results](#preparing-the-schema-for-algorithm-results)\n \n- [Running Graph Algorithms with Icebug](#running-graph-algorithms-with-icebug)\n \n- [What the Graph Tells Us](#what-the-graph-tells-us)\n \n- [The Bigger Picture: Ladybug Memory](#the-bigger-picture-ladybug-memory)\n \n- [Get Involved](#get-involved)\n \n\nApr 11, 2026\n\n10 min read\n\n[ladybug](/tags/ladybug) /\n\n[kuzu](/tags/kuzu) /\n\n[graph](/tags/graph)\n\n# From Kuzu to Ladybug: The embedded graph ecosystem powers onward\n\n> A walkthrough of building a graph algorithm workflow with Ladybug and Icebug, and an intro to the vision of Ladybug Memory\n\nIf you’ve followed any of my work for the past couple of years, you’ll know I used to work at Kuzu, where we were building an embedded graph database. My work at Kuzu was all about helping users figure out how to transform and query their data as a graph, experimenting with how to define graph data models and relationships for different domains, how to bring the power of Cypher to developers who didn’t want to spin up a server or pay for a proprietary tool just to run traversal queries.\n\nIn September 2025, Kuzu got archived, and the codebase went quiet. But the ideas were still relevant. The graph community still highly valued a permissively licensed, embedded graph storage and query engine that supported Cypher.\n\nThat’s where LadybugDB comes in. It’s a fork of Kuzu, maintained by the team at [Ladybug Memory ⤴](https://ladybugmem.ai/), but with a slightly different goal. Kuzu was built primarily as a standalone, embedded graph database. Ladybug wants to be a **graph lakehouse** — interoperating with DuckDB’s storage layer, reading and writing Arrow/Parquet, connecting to object stores. The same columnar foundation, but opened up to the broader ecosystem.\n\nOver the past few months, I’ve been in close discussions with [Arun Sharma ⤴](https://x.com/arundsharma), lead maintainer of LadybugDB. Having explored what his vision looks like in practice, I wanted to share a small project that demonstrates at least a part of the Ladybug’s vision: running fast graph algorithms natively on top of columnar graph storage.\n\nTowards the end of this post, we’ll lead into the bigger picture and what this all means for folks working with graphs.\n\nNote on the nomenclature\n\nLadybugDB refers to the database itself, while [Ladybug Memory ⤴](https://ladybugmem.ai/) refers to the collection of projects that are building towards graph-based continual learning for agents. When I say “Ladybug” in this post, I’m referring to the database.\n\n## Dataset: Who Mentored the Most Nobel Laureates?[#](#dataset-who-mentored-the-most-nobel-laureates)\n\nThe dataset we’ll be working with (see [the repo ⤴](https://github.com/prrao87/ladybug-intro) if you want to follow along) is a mentorship network of Nobel laureates, containing thousands of records that trace the academic lineages of prize winners across Physics, Chemistry, and Medicine.\n\nIt turns out that research has shown Nobel Prize committees don’t just reward isolated genius. They reward people who are already in the right networks, having received mentorship from the right teachers, who were part of the *just the right* intellectual institutions. This makes sense when you learn from the data that Niels Bohr mentored *ten* Nobel laureates in Physics. Or that J.J. Thomson, discoverer of the electron, mentored *eleven*? These aren’t just names on a list — they’re nodes in a network of knowledge transmission that spans more than a century.\n\nThe data comes in JSON format, structured as parent-child relationships. Each record has a “child” (the mentee) and one or two “parents” (the mentors). Some of those children are laureates with prize categories and years attached. Others are scholars — people who never won a Nobel but who mentored someone who did, or who mentored someone who mentored someone who did.\n\nThis data is naturally analyzed as a graph. You could flatten it into a table and run SQL queries with traditional joins, but you’d lose its inherenLine truncated
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{"type":"tool_use","timestamp":1788950789730,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"webfetch","callID":"call_0059f8547b1c42128709cfb1","state":{"status":"completed","input":{"format":"markdown","url":"https://raw.githubusercontent.com/LadybugDB/ladybug/main/README.md"},"output":"<div align=\"center\">\n <picture>\n <!-- <source srcset=\"https://ladybugdb.com/img/lbug-logo-dark.png\" media=\"(prefers-color-scheme: dark)\"> -->\n <img src=\"https://ladybugdb.com/logo.png\" height=\"100\" alt=\"Ladybug Logo\">\n </picture>\n</div>\n\n<br>\n\n<p align=\"center\">\n <a href=\"https://github.com/LadybugDB/ladybug/actions\">\n <img src=\"https://github.com/LadybugDB/ladybug/actions/workflows/ci-workflow.yml/badge.svg?branch=master\" alt=\"Github Actions Badge\"></a>\n <a href=\"https://deepwiki.com/LadybugDB/ladybug\"><img src=\"https://deepwiki.com/badge.svg\" alt=\"Ask DeepWiki\"></a>\n <a href=\"https://discord.com/invite/hXyHmvW3Vy\">\n <img src=\"https://img.shields.io/discord/1162999022819225631?logo=discord\" alt=\"discord\" /></a>\n <a href=\"https://twitter.com/lbugdb\">\n <img src=\"https://img.shields.io/badge/follow-@lbugdb-1DA1F2?logo=twitter\" alt=\"twitter\"></a>\n</p>\n\n# Ladybug\nLadybug is an embedded graph database built for query speed and scalability. Ladybug is optimized for handling complex analytical workloads\non very large databases and provides a set of retrieval features, such as a full text search and vector indices. Our core feature set includes:\n\n- Flexible Property Graph Data Model and Cypher query language\n- Embeddable, serverless integration into applications\n- Native full text search and vector index\n- Columnar disk-based storage\n- Columnar sparse row-based (CSR) adjacency list/join indices\n- Vectorized and factorized query processor\n- Novel and very fast join algorithms\n- Multi-core query parallelism\n- Serializable ACID transactions\n- Wasm (WebAssembly) bindings for fast, secure execution in the browser\n\nLadybug is being developed by [LadybugDB Developers](https://github.com/LadybugDB) and\nis available under a permissive license. So try it out and help us make it better! We welcome your feedback and feature requests.\n\nThe database was formerly known as [Kuzu](https://github.com/kuzudb/kuzu).\n\n## Installation\n\n| Language | Installation |\n| -------- |------------------------------------------------------------------------|\n| Python | `pip install ladybug` |\n| NodeJS | `npm install @ladybugdb/core` |\n| Rust | `cargo add lbug` |\n| Go | `go get github.com/LadybugDB/go-ladybug` |\n| Swift | [lbug-swift](https://github.com/LadybugDB/swift-ladybug) |\n| Java | [Maven Central](https://central.sonatype.com/artifact/com.ladybugdb/lbug) |\n| C/C++ | [precompiled binaries](https://github.com/LadybugDB/ladybug/releases/latest) |\n| CLI | [precompiled binaries](https://github.com/LadybugDB/ladybug/releases/latest) |\n\nTo learn more about installation, see our [Installation](https://docs.ladybugdb.com/installation) page.\n\n## Getting Started\n\nRefer to our [Getting Started](https://docs.ladybugdb.com/get-started/) page for your first example.\n\n## Build from Source\n\nYou can build from source using the instructions provided in the [developer guide](https://docs.ladybugdb.com/developer-guide/).\n\n## Contributing\nWe welcome contributions to Ladybug. If you are interested in contributing to Ladybug, please read our [Contributing Guide](CONTRIBUTING.md).\n\n## License\nBy contributing to Ladybug, you agree that your contributions will be licensed under the [MIT License](LICENSE).\n\n## Contact\nYou can contact us at [social@ladybugdb.com](mailto:social@ladybugdb.com) or [join our Discord community](https://discord.com/invite/hXyHmvW3Vy).\n","metadata":{"truncated":false},"title":"https://raw.githubusercontent.com/LadybugDB/ladybug/main/README.md (text/plain; charset=utf-8)","time":{"start":1788950789492,"end":1788950789729}},"id":"prt_g001a085c6bd4c001KoBmAaQFw","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","messageID":"msg_g001a085c5faf1001NFtYmOK3d"}}
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{"type":"tool_use","timestamp":1788950789995,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"webfetch","callID":"call_0efb3e3111f94ca5839a9d89","state":{"status":"completed","input":{"format":"markdown","url":"https://raw.githubusercontent.com/getzep/graphiti/main/README.md"},"output":"<p align=\"center\">\n <a href=\"https://www.getzep.com/\">\n <img src=\"https://github.com/user-attachments/assets/119c5682-9654-4257-8922-56b7cb8ffd73\" width=\"150\" alt=\"Zep Logo\">\n </a>\n</p>\n\n<h1 align=\"center\">\nGraphiti\n</h1>\n<h2 align=\"center\">A Framework for Building Temporal Knowledge Graphs</h2>\n\n<div align=\"center\">\n\n[](https://github.com/getzep/Graphiti/actions/workflows/lint.yml)\n[](https://github.com/getzep/Graphiti/actions/workflows/unit_tests.yml)\n[](https://github.com/getzep/Graphiti/actions/workflows/typecheck.yml)\n\n[](https://github.com/getzep/graphiti/stargazers)\n[](https://arxiv.org/abs/2501.13956)\n[](https://github.com/getzep/graphiti/releases)\n\n</div>\n<div align=\"center\">\n\n<a href=\"https://trendshift.io/repositories/12986\" target=\"_blank\"><img src=\"https://trendshift.io/api/badge/repositories/12986\" alt=\"getzep%2Fgraphiti | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"/></a>\n\n</div>\n\n> [!NOTE]\n> **We're Hiring!** Build context graphs that power reliable, personalized, fast production AI agents.\n> Come build with us — we're hiring Engineers and Developer Relations folks. [View open roles](https://www.getzep.com/careers/).\n\n⭐ *Help us reach more developers and grow the Graphiti community. Star this repo!*\n\n \n\nGraphiti is a framework for building and querying temporal context graphs for AI agents. Unlike static knowledge graphs,\nGraphiti's context graphs track how facts change over time, maintain provenance to source data, and support both\nprescribed and learned ontology — making them purpose-built for agents operating on evolving, real-world data.\n\nUnlike traditional retrieval-augmented generation (RAG) methods, Graphiti continuously integrates user interactions,\nstructured and unstructured enterprise data, and external information into a coherent, queryable graph. The framework\nsupports incremental data updates, efficient retrieval, and precise historical queries without requiring complete graph\nrecomputation, making it suitable for developing interactive, context-aware AI applications.\n\nUse Graphiti to:\n\n- Build temporal knowledge graphs that evolve with every interaction — tracking what's true now and what was true before.\n- Represent rich, structured context instead of flat document chunks or raw event streams.\n- Query across time, meaning, and relationships with hybrid retrieval (semantic + keyword + graph traversal).\n\n \n\n<p align=\"center\">\n <img src=\"images/graphiti-graph-intro.gif\" alt=\"Graphiti temporal walkthrough\" width=\"700px\">\n</p>\n\n \n\n## What is a Context Graph?\n\nA **context graph** is a temporal graph of entities, relationships, and facts — like *\"Kendra loves Adidas shoes (as of\nMarch 2026).\"* Unlike traditional knowledge graphs, each fact in a context graph has a validity window: when it became\ntrue, and when (if ever) it was superseded. Entities evolve over time with updated summaries. Everything traces back to\n**episodes** — the raw data that produced it.\n\nWhat makes Graphiti unique is its ability to autonomously build context graphs from unstructured and structured data,\nhandling changing relationships while preserving full temporal history.\n\nA context graph contains:\n\n| Component | What it stores |\n|-----------|---------------|\n| **Entities** (nodes) | People, products, policies, concepts — with summaries that evolve over time |\n| **Facts / Relationships** (edges) | Triplets (Entity → Relationship → Entity) with temporal validity windows |\n| **Episodes** (provenance) | Raw data as ingested — the ground truth stream. Every derived fact traces back here |\n| **Custom Types** (ontology) | Developer-defined entity and edge types via Pydantic models |\n\n## Graphiti and Zep\n\nGraphiti is the open-source framework for building temporal knowledge graphs at the core of\n[Zep's](https://www.getzep.com) context infrastructure. Zep manages context graphs at scale, providing\ngoverned, low-latency context retrieval and assembly for production deployments.\n\nUnder the hood, Line truncated
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{"type":"tool_use","timestamp":1788950790161,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"webfetch","callID":"call_0b2506b0c1674e979fa9f494","state":{"status":"completed","input":{"format":"markdown","url":"https://raw.githubusercontent.com/DIGITAL-FABRIC-AI/neo4j-temporal-graph/master/NEP-001.md"},"output":"# NEP-001: Native Bitemporal Graph Support\n**Type:** Language Enhancement + Kernel Feature\n**Author:** Ashley Dunfield\n**Status:** Draft — RFC\n**Date:** 2026-03-18\n**Reference Implementation:** https://github.com/DIGITAL-FABRIC-AI/neo4j-temporal-graph\n**Discussion:** *(to be filed at https://github.com/neo4j/neo4j/discussions)*\n\n---\n\n## Abstract\n\nThis proposal introduces native bitemporal graph support to Neo4j — the ability to\nquery the graph as it existed at any point in time using a clean Cypher syntax\nextension. Two time dimensions are made first-class: **valid time** (when a fact was\ntrue in the world) and **transaction time** (when it was recorded in the database).\nThe proposed syntax is:\n\n```cypher\nMATCH (w:Well)-[r:OWNED_BY]->(c:Company)\nAS OF date('2024-06-15')\nRETURN w.name, c.name\n```\n\nThis proposal is motivated by production experience with 3.1M+ temporally-annotated\nrelationships across a complex regulatory and operational graph. Every temporal query\nin that system is today expressed with verbose manual filtering that is fragile,\nhard to optimise, and easy to get wrong. Native support would eliminate that entire\ncategory of application complexity.\n\n---\n\n## 1. Motivation\n\n### 1.1 The Temporal Graph is Universal\n\nNearly every domain that benefits from a graph database also has temporal semantics:\n\n| Domain | Temporal fact |\n|--------|--------------|\n| Corporate ownership | \"Who owned this asset between 2018 and 2022?\" |\n| Regulatory compliance | \"What rules applied to this facility in Q3 2024?\" |\n| Supply chain | \"Which supplier was contracted at the time of this incident?\" |\n| Org charts | \"Who was this person's manager on the date the decision was made?\" |\n| Medical records | \"What medications was the patient on at admission?\" |\n| Financial | \"What was the firm's capital structure at close on this date?\" |\n\nIn every case, the graph has the right structure for the query — the challenge is\npurely about time. Relationships change: they are created, modified, and ended.\nThe naive graph models a snapshot of the present. What users need is access to\nthe full temporal history.\n\n### 1.2 The Current State Is Broken\n\nToday, teams work around missing native temporal support by storing `valid_from`\nand `valid_to` (or `effective_date` / `end_date`) directly on relationship\nproperties. This works, but it has serious problems.\n\n**The query pattern is verbose and error-prone:**\n\n```cypher\n-- \"Who owned this well on June 15, 2024?\"\n-- Current approach — every developer writes this differently\nMATCH (w:Well {wellId: $id})-[r:OWNED_BY]->(c:Company)\nWHERE (r.effective_date IS NULL OR r.effective_date <= date('2024-06-15'))\n AND (r.end_date IS NULL OR r.end_date >= date('2024-06-15'))\nRETURN c.name\n```\n\nCommon mistakes include: forgetting to handle `NULL` as \"open-ended\", using `<`\ninstead of `<=`, inconsistent property naming across relationship types, incorrect\nhandling of the half-open interval `[effective_date, end_date)`, and failing to\naccount for relationships that predate any recorded `effective_date`.\n\n**It doesn't compose:**\n\n```cypher\n-- \"Trace ownership and regulatory authority as of a given date\"\n-- Current: every hop needs its own temporal filter, duplicated everywhere\nMATCH (w:Well {wellId: $id})\n -[r1:OWNED_BY]->(c:Company)\n -[r2:SUBJECT_TO]->(a:Authority)\nWHERE (r1.effective_date IS NULL OR r1.effective_date <= $asOf)\n AND (r1.end_date IS NULL OR r1.end_date >= $asOf)\n AND (r2.effective_date IS NULL OR r2.effective_date <= $asOf)\n AND (r2.end_date IS NULL OR r2.end_date >= $asOf)\nRETURN c.name, a.name\n```\n\nAs path depth increases, the WHERE clause grows linearly. At depth 4 or 5 (common\nin regulatory traversals), the query becomes unmaintainable.\n\n**It cannot be optimised:**\n\nThe planner has no way to know that `r.effective_date <= date('2024-06-15')` is\na temporal filter — it looks like any other property predicate. A native temporal\nlayer would allow the planner to choose appropriate physical operators (temporal\nindex scans, period overlap joins) that are fundamentally more efficient.\n\n**It has no transaction time:**\n\nThe property-based approach stores only valid time (when something was true in the\nworld). Transaction time (when the database recorded it) is lost unless the\napplication explicitly adds an `_recorded_at` property to every write. Recovering\n\"what did the database know at 9am on this date\" is impossible without this.\n\n### 1.3 Evidence from Production\n\nThe following numbers come from a production Neo4Line truncated
|
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{"type":"tool_use","timestamp":1788950790707,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"webfetch","callID":"call_f5c705160ec54dce901429ca","state":{"status":"completed","input":{"format":"markdown","url":"https://aimultiple.com/graph-databases"},"output":"Graph Database Benchmark: Neo4j vs FalkorDB vs Memgraph \n\n[](https://aimultiple.com)\n\nAgentic AI\n\nCATEGORIES\n\nModel Context ProtocolAI AgentsAI in IndustriesAI HardwareGenAI ApplicationsAI ModelsAI Foundations[Browse Categories](https://aimultiple.com/ai)\n\n[Browser MCP](https://aimultiple.com/browser-mcp)[MCP Gateway](https://aimultiple.com/mcp-gateway)[Code Execution with MCP](https://aimultiple.com/code-execution-with-mcp)\n\nCybersecurity\n\nCATEGORIES\n\nData SecurityIdentity & Access ManagementSecurity ToolsThreat Detection and Response (TDR)Network Security[Browse Categories](https://aimultiple.com/cybersecurity)\n\n[Google Workspace Backup](https://aimultiple.com/google-workspace-backup)[SaaS Backup Solutions](https://aimultiple.com/saas-backup-solutions)[Backup Benchmark Results](https://aimultiple.com/backup-software)[Device Control Software](https://aimultiple.com/device-control-software)[DLP Software](https://aimultiple.com/dlp-software)[USB Blocking Software](https://aimultiple.com/usb-blocking-software)[DLP Review](https://aimultiple.com/dlp-review)[See All](https://aimultiple.com/category/data-security)\n\nData\n\nCATEGORIES\n\nWeb ProxiesWeb Data ScrapingData CollectionData ScienceSynthetic Data[Browse Categories](https://aimultiple.com/data)\n\n[Residential Proxy Providers](https://aimultiple.com/residential-proxy-providers)[Proxy Providers](https://aimultiple.com/proxy-providers)[SOCKS5 Proxies](https://aimultiple.com/socks5-proxies)[Rotating Proxy](https://aimultiple.com/rotating-proxy)[Proxy Pricing](https://aimultiple.com/proxy-pricing)[Datacenter Proxy](https://aimultiple.com/datacenter-proxy)[Geonode Proxies](https://aimultiple.com/geonode-proxies)[See All](https://aimultiple.com/category/web-proxies)\n\nEnterprise Software\n\nCATEGORIES\n\nE-CommerceWorkload AutomationIT AutomationProcess ImprovementRMMManaged File TransferHelpdesk Software[Browse Categories](https://aimultiple.com/enterprise-software)\n\n[Ecommerce Technology](https://aimultiple.com/ecommerce-technology)[Price Monitoring Tools](https://aimultiple.com/price-monitoring-tools)[Checkout Free Stores](https://aimultiple.com/checkout-free-stores)\n\n[Services](https://aimultiple.com/services)\n\n[](https://aimultiple.com/premium)\n\nAgentic AI\n\nModel Context ProtocolAI AgentsAI in IndustriesAI HardwareGenAI ApplicationsAI ModelsAI Foundations\n\nCybersecurity\n\nData SecurityIdentity & Access ManagementSecurity ToolsThreat Detection and Response (TDR)Network Security\n\nData\n\nWeb ProxiesWeb Data ScrapingData CollectionData ScienceSynthetic Data\n\nEnterprise Software\n\nE-CommerceWorkload AutomationIT AutomationProcess ImprovementRMMManaged File TransferHelpdesk Software\n\n[Services](https://aimultiple.com/services)\n\n[Premium](https://aimultiple.com/premium) \n\nBack\n\nModel Context Protocol\n\n[Browser MCP](https://aimultiple.com/browser-mcp)[MCP Gateway](https://aimultiple.com/mcp-gateway)[Code Execution with MCP](https://aimultiple.com/code-execution-with-mcp)\n\nAI Agents\n\n[AI Agent Performance](https://aimultiple.com/ai-agent-performance)[AI Agents in Marketing](https://aimultiple.com/ai-agents-in-marketing)[Agentic CRM](https://aimultiple.com/agentic-crm)[AI Lead Generation](https://aimultiple.com/ai-lead-generation)[AI Agents](https://aimultiple.com/ai-agents)[Open Source AI Agents](https://aimultiple.com/open-source-ai-agents)[Building AI Agents](https://aimultiple.com/building-ai-agents)[See All](https://aimultiple.com/category/ai-agents)\n\nAI in Industries\n\n[Sales AI](https://aimultiple.com/sales-ai)[Manufacturing AI Solutions](https://aimultiple.com/manufacturing-ai-solutions)[Drug Discovery Software](https://aimultiple.com/drug-discovery-software)[Supply Chain AI Tools](https://aimultiple.com/supply-chain-ai-tools)[Sustainability AI](https://aimultiple.com/sustainability-ai)[AI in Fashion](https://aimultiple.com/ai-in-fashion)[Logistics AI](https://aimultiple.com/logistics-ai)[See All](https://aimultiple.com/category/ai-in-industries)\n\nAI Hardware\n\n[Cloud GPU Pricing](https://aimultiple.com/cloud-gpu-pricing)[GPU Rental Index](https://aimultiple.com/gpu-index)[Cloud GPU Providers](https://aimultiple.com/cloud-gpu-providers)[AI Chip Makers](https://aimultiple.com/ai-chip-makers)[Serverless GPU](https://aimultiple.com/serverless-gpu)[Free Cloud GPU](https://aimultiple.com/free-cloud-gpu)[GPU Benchmark](https://aimultiple.com/gpu-benchmark)[See All](https://aimultiple.com/ai-hardware)\n\nGenAI Applications\n\n[Generative AI Applications](https://aimultiple.com/generative-ai-applications)[GLine truncated
|
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{"type":"tool_use","timestamp":1788950791261,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"webfetch","callID":"call_001fc0054a96423999356501","state":{"status":"completed","input":{"format":"markdown","url":"https://www.falkordb.com/blog/graph-database-performance-benchmarks-falkordb-vs-neo4j/"},"output":" FalkorDB vs Neo4j: Graph Database Performance Benchmarks \n\n \n\n[Skip to content](#content)\n\n[![FalkorDB](data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw== \"FalkorDB\")\n\n![FalkorDB](https://www.falkordb.com/wp-content/uploads/2024/06/logo-dark.svg \"FalkorDB\")\n\n](https://www.falkordb.com/)\n\nSolutions\n\n[QueryWeaver - Text2SQL](https://www.falkordb.com/queryweaver-text-to-sql/) [GraphRAG SDK](https://www.falkordb.com/graphrag-sdk/) [Agentic AI Use Cases](https://www.falkordb.com/use-cases/) [Security Graph](https://www.falkordb.com/cyber-security/)\n\nWhy FalkorDB\n\n[FalkorDB vs. Neo4j](https://www.falkordb.com/falkordb-vs-neo4j/) [FalkorDB vs. AWS Neptune](https://www.falkordb.com/falkordb-vs-aws-neptune/) [FalkorDB vs. Vector Databases](https://www.falkordb.com/blog/vector-database-vs-graph-database/) [Latest Benchmarks](https://benchmark.falkordb.com/) [Company](https://www.falkordb.com/company/)\n\nDevelopers\n\n[Documentation](https://docs.falkordb.com/) [Community Projects](https://www.falkordb.com/community-projects/) [Graph Size Calculator](https://www.falkordb.com/graph-database-graph-size-calculator/)\n\nContact Us\n\n[Book a Demo](https://www.falkordb.com/get-a-demo/) [Get in touch](https://www.falkordb.com/contact-us/) [Events & Workshops](https://luma.com/graph-rag-talks)\n\n[Pricing](https://www.falkordb.com/plans/) [Blog](https://www.falkordb.com/blog/)\n\n[Discord](https://discord.com/invite/Y2mMT9VdMy) [3.4k](https://github.com/FalkorDB/falkordb)\n\n[Log in](https://app.falkordb.cloud/login) [Start Free](https://app.falkordb.cloud/signup) Open menu\n\nMenu\n\nClose menu\n\n[Start Free](https://app.falkordb.cloud/signup)\n\n[Discord](https://discord.com/invite/Y2mMT9VdMy) [3.4k](https://github.com/FalkorDB/falkordb)\n\nSolutions\n\n[Text2SQL](https://app.queryweaver.ai) [GraphRAG SDK](https://www.falkordb.com/graphrag-sdk/) [Agentic AI Use Cases](https://www.falkordb.com/use-cases/) [Security Graph](https://www.falkordb.com/cyber-security/)\n\nWhy FalkorDB\n\n[FalkorDB vs. Neo4j](https://www.falkordb.com/falkordb-vs-neo4j/) [FalkorDB vs. AWS Neptune](https://www.falkordb.com/falkordb-vs-aws-neptune/) [FalkorDB vs. Vector Databases](https://www.falkordb.com/blog/vector-database-vs-graph-database/) [Latest Benchmarks](https://benchmark.falkordb.com/) [Company](https://www.falkordb.com/company/)\n\nDevelopers\n\n[Documentation](https://docs.falkordb.com/) [Community Projects](https://www.falkordb.com/community-projects/) [Graph Size Calculator](https://www.falkordb.com/graph-database-graph-size-calculator/)\n\nContact Us\n\n[Book a Demo](https://www.falkordb.com/get-a-demo/) [Get in touch](https://www.falkordb.com/contact-us/) [Events & Workshops](https://luma.com/graph-rag-talks)\n\n[Pricing](https://www.falkordb.com/plans/) [Blog](https://www.falkordb.com/blog/)\n\n \n\n[Back to all posts](https://www.falkordb.com/blog/)\n\n# FalkorDB vs Neo4j: Graph Database Performance Benchmarks\n\n- \n \n \n \n [Avi Avni](https://www.falkordb.com/author/aviavni/ \"Avi Avni\")\n \n\n- Date Published: December 2, 2024\n- Date Updated: August 23, 2026\n\n![FalkorDB-versus-neo4j-performance-benchmarks-results](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA5gAAAHCAQAAAACiOVuoAAAAAnRSTlMAAHaTzTgAAABJSURBVHja7cExAQAAAMKg9U9tCU+gAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADgb8voAAGq4DOeAAAAAElFTkSuQmCC \"FalkorDB versus neo4j benchmark results\")\n\n![FalkorDB-versus-neo4j-performance-benchmarks-results](https://www.falkordb.com/wp-content/uploads/elementor/thumbs/FalkorDB-versus-neo4j-benchmark-results-rqmjpqu1i6rt2jmfxcev0sp6tn41e8oiba2f5qpifo.png \"FalkorDB versus neo4j benchmark results\")\n\nNew [performance benchmarks](https://benchmark.falkordb.com/) results demonstrate FalkorDB’s significant performance advantages in aggregate expansion operations [compared to Neo4j](https://www.falkordb.com/blog/falkordb-vs-neo4j-for-ai-applications/). The data reveals consistent sub-140ms response times at p99, while Neo4j experiences multi-second latencies under similar conditions.\n\n- The benchmark is composed of 11 templated queries, which represent a general usage of classic graphLine truncated
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{"type":"tool_use","timestamp":1788950795944,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"webfetch","callID":"call_86528ea655b94ddd853ac3d5","state":{"status":"completed","input":{"format":"markdown","url":"https://neo4j.com/docs/cypher-manual/current/values-and-types/temporal/"},"output":"---\ndescription: Cypher has built-in support for handling temporal values, and the underlying database supports storing these temporal values as properties on nodes and relationships.\ntitle: Temporal values - Cypher Manual\nimage: https://dist.neo4j.com/wp-content/uploads/20240513110808/Neo4j-logo-social-preview.png\n---\n\n \n\n[Skip to content](#skip-to-content \"Skip to content\") \n\nCypher Manual \n\nProduct Version Version 25 Version 5 Version 4.4 \n\n**Is this page helpful?**\n\n[](https://neo4j.com/docs) \n\n[Raise an issue](https://github.com/neo4j/docs-cypher/issues/new/?title=Docs%20Feedback%20modules/ROOT/pages/values-and-types/temporal.adoc%20%28ref:%20cypher-25%29&body=%3E%20Do%20not%20include%20confidential%20information,%20personal%20data,%20sensitive%20data,%20or%20other%20regulated%20data.)\n\n# Temporal values\n\nCypher® has built-in support for handling temporal values, which can be stored as properties on nodes and relationships in Neo4j databases. This section will discuss how Cypher handles time zones, before exploring temporal values in more detail.\n\n| | Refer to [Temporal functions - instant types](https://neo4j.com/docs/cypher-manual/current/functions/temporal/) for information regarding temporal _functions_ allowing for the creation and manipulation of temporal values. Refer to [Temporal operators](https://neo4j.com/docs/cypher-manual/current/expressions/temporal-operators/) for information regarding temporal _operators_. Refer to [Equality, ordering, and comparison of value types](https://neo4j.com/docs/cypher-manual/current/values-and-types/ordering-equality-comparison/) for information regarding the comparison and ordering of temporal values. |\n| | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n\n## [](#%5Ftemporal%5Fvalue%5Ftypes)Temporal value types\n\nThe following table lists the temporal value types and their supported components:\n\n| Type | Date support | Time support | Time zone support |\n| -------------- | ------------ | ------------ | ----------------- |\n| DATE | | | |\n| LOCAL TIME | | | |\n| ZONED TIME | | | |\n| LOCAL DATETIME | | | |\n| ZONED DATETIME | | | |\n| DURATION | \\- | \\- | \\- |\n\n`DATE`, `LOCAL TIME`, `ZONED TIME`, `LOCAL DATETIME`, and `ZONED DATETIME` are _temporal instant_ types. A temporal instant value expresses a point in time with varying degrees of precision.\n\nBy contrast, `DURATION` is not a temporal instant type. A `DURATION` represents a temporal amount, capturing the difference in time between two instants, and can be negative. `DURATION` captures the amount of time between two instants, it does not capture a start time and end time.\n\n## [](#cypher-temporal-timezones)Time zones\n\nTime zones are represented either as an offset from UTC, or as a logical identifier of a _named time zone_ (these are based on the [IANA time zone database](https://www.iana.org/time-zones)). In either case, the time is stored as UTC internally, and the time zone offset is only applied when the time is presented. This means that temporal instants can be ordered without taking time zone into account. If, however, two times are identical in UTC, then they are ordered by timezone.\n\nWhen creating a time using a named time zone, the offset from UTC is computed from the rules in the time zone database to create a time instant in UTC, and to ensure the named time zone is a valid one.\n\nIt is possible for time zone rules to change in the IANA time zone database. For example, there could be alterations to the rules for daylight savings time in a certain area. If this occurs after the creation of a temporal instant, the presented time could differ from the originally-entered time, insofar as the local timezone is concerned. However, the absolute time in UTC would remain the same.\n\nThere are three ways oLine truncated
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{"type":"tool_use","timestamp":1788950797239,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"webfetch","callID":"call_7000c1e0b3664fed845f72b9","state":{"status":"completed","input":{"format":"markdown","url":"https://raw.githubusercontent.com/FalkorDB/FalkorDB/master/README.md"},"output":"<div align=\"center\">\n <img width=\"100\" height=\"100\" alt=\"FalkorDB Logo Square B\" src=\"https://github.com/user-attachments/assets/ec67e5e3-a060-405d-b229-1fe2badcf8ad\" />\n</div>\n<div align=\"center\">\n <h1>FalkorDB</h1>\n <h2>Ultra-fast, Multi-tenant Graph Database</h2>\n <h3>Powering Generative AI, Agent Memory, Cloud Security, and Fraud Detection</h3>\n<a href=\"https://trendshift.io/repositories/14787\" target=\"_blank\" rel=\"noopener noreferrer\"><img src=\"https://trendshift.io/api/badge/repositories/14787\" alt=\"FalkorDB%2FFalkorDB | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"/></a>\n</div>\n<div align=\"center\">\n <a href=\"https://app.falkordb.cloud/signup\">\n <img src=\"https://img.shields.io/badge/Try%20Free-FalkorDB%20Cloud-FF8101?labelColor=FDE900&style=for-the-badge\" alt=\"Try Free\" />\n </a><br>\n <a href=\"https://discord.com/invite/TJ4SaPTptf\" rel=\"nofollow\">\n <img src=\"https://img.shields.io/badge/Discord-%235865F2.svg?&logo=discord&logoColor=white\" alt=\"Discord\" />\n </a>\n <a href=\"https://hub.docker.com/r/falkordb/falkordb/\">\n <img src=\"https://img.shields.io/docker/pulls/falkordb/falkordb?label=Docker\" alt=\"Dockerhub\" />\n </a>\n <a href=\"https://discord.gg/6M4QwDXn2w\">\n <img src=\"https://img.shields.io/discord/1146782921294884966?style=flat-square\" alt=\"Discord\" />\n </a>\n <a href=\"https://codecov.io/gh/falkordb/falkordb\">\n <img src=\"https://codecov.io/gh/falkordb/falkordb/graph/badge.svg?token=0G4HBEJMW0\" alt=\"codecov\" />\n </a>\n <a href=\"https://github.com/FalkorDB/FalkorDB/actions/workflows/build.yml\">\n <img src=\"https://github.com/FalkorDB/FalkorDB/actions/workflows/build.yml/badge.svg?branch=master\" alt=\"Workflow\" />\n </a>\n</div>\n\n<div align=\"center\">\n\n\n\n</div>\n\n## UNIQUE FEATURES\n\nOur goal is to build a high-performance Knowledge Graph tailored for Large Language Models (LLMs), prioritizing exceptionally low latency to ensure fast and efficient information delivery through our Graph Database.\n\n🆕 [FalkorDB](https://www.falkordb.com/) is the first **queryable [Property Graph](https://github.com/opencypher/openCypher/blob/master/docs/property-graph-model.adoc) database to leverage sparse matrices** for representing the [adjacency matrix](https://en.wikipedia.org/wiki/Adjacency_matrix) in graphs and [linear algebra](https://en.wikipedia.org/wiki/Adjacency_matrix) for querying.\n\n### Key Features\n\n* **Sparse Matrix Representation**: Utilizes sparse matrices to represent adjacency matrices, optimizing storage and performance.\n\n\n* **Linear Algebra Querying**: Employs linear algebra for query execution, enhancing computational efficiency.\n\n\n* **Property Graph Model Compliance**: Supports nodes and relationships with attributes, adhering to the Property Graph Model.\n\n\n* **OpenCypher Support:** Compatible with [OpenCypher](ttps://github.com/opencypher/openCypher/blob/master/docs/property-graph-model.adoc) query language, including proprietary extensions for advanced querying capabilities.\n\n>Explore FalkorDB in action by visiting the [Demos](https://github.com/FalkorDB/FalkorDB/tree/master/demo).\n\n## DOCUMENTATION\n\n[Official Docs](https://docs.falkordb.com/) | [Clients](https://docs.falkordb.com/clients.html) | [Commands](https://docs.falkordb.com/commands/) | 📊 [Latest Performance Benchmarks](https://benchmark.falkordb.com/)\n\n### Community and Support\n\n* **Discussions**: Join our community discussions on [GitHub Discussions](https://github.com/FalkorDB/FalkorDB/discussions) to ask questions, share ideas, and connect with other users.\n\n* **Contributing**: We welcome contributions! Please see our [Contributing Guide](https://github.com/FalkorDB/FalkorDB/blob/master/CONTRIBUTING.md) for more details.\n\n* **License**: This project is licensed under the Server Side Public License v1 (SSPLv1). See the [LICENSE](https://github.com/FalkorDB/FalkorDB/blob/master/LICENSE.txt) file for details.\n\n## GET STARTED\n\n### Step 1\n\nTo quickly try out FalkorDB, launch an instance using docker:\n\n```\ndocker run -p 6379:6379 -p 3000:3000 -it --rm -v ./data:/var/lib/falkordb/data falkordb/falkordb\n```\n\n### Step 2\n\nThen, open your browser and navigate to `http://localhost:3000`.\n\n\nYou can also interact with FalkorDB using any of the supported [Client Libraries](https://docs.falkordb.com/clients.html)\n\n### MotoGP League Example\n\nIn this example, we'll use the [FalkorDB Python client](https://pLine truncated
|
||||
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|
||||
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{"type":"tool_use","timestamp":1788950837305,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"webfetch","callID":"call_7c6b0e86fb934a4dbe68eb3c","state":{"status":"completed","input":{"format":"markdown","url":"https://dbdb.io/db/ladybugdb"},"output":" LadybugDB · Database of Databases \n\nDBDB.io The Encyclopedia of Database Systems · Est. 2017\n\n[Login](/login/)\n\n[Database of Databases](/)\n\n[Browse](/browse) [Leaderboards](/stats) [Recent](/recent) [About](/about)\n\n[Database Entry](/db/ladybugdb)\n\n# LadybugDB\n\n---\n\n[AI-Assisted](/browse?tag=ai-assisted \"View 'AI-Assisted' Systems\") [Forked Code](/browse?tag=forked-code \"View 'Forked Code' Systems\") [Revival Project](/browse?tag=revival \"View 'Revival Project' Systems\")\n\nLadybugDB is a embedded graph-oriented DBMS.\n\n [](/db/ladybugdb/logos \"View All Logos for LadybugDB\")[Logo Versions](/db/ladybugdb/logos \"View All Logos for LadybugDB\")\n\nWebsite\n\n[https://ladybugdb.com](https://ladybugdb.com)[\\[01\\]](#citation1 \"View Citation\")\n\nSource Code\n\n[https://github.com/LadybugDB/ladybug](https://github.com/LadybugDB/ladybug)[\\[02\\]](#citation2 \"View Citation\") Accessed: Sep 6, 2026 Last Commit: Sep 5, 2026\n\nTech Docs\n\n[https://docs.ladybugdb.com](https://docs.ladybugdb.com)[\\[03\\]](#citation3 \"View Citation\")\n\nBlog\n\n[https://blog.ladybugdb.com](https://blog.ladybugdb.com)[\\[04\\]](#citation4 \"View Citation\")\n\nCountry of Origin\n\n[US](/browse?country=US \"View Systems from United States of America\")\n\nStart Year\n\n2025\n\nCoding Agents\n\n[Claude](/browse?agent=claude \"View Systems Using Claude\") [\\[06\\]](#citation6 \"View Citation\")\n\n[Copilot](/browse?agent=copilot \"View Systems Using Copilot\") [\\[07\\]](#citation7 \"View Citation\")\n\n[Cursor](/browse?agent=cursor \"View Systems Using Cursor\") [\\[08\\]](#citation8 \"View Citation\")\n\nProject Type\n\n[Open Source](/browse?project-type=open-source \"View Systems : Open Source\")\n\nWritten in\n\n[C++](/browse?programming-language=cplusplus \"View Systems Written In C++\")\n\nDerived From\n\n[Kuzu](/db/kuzu \"View Kuzu\")\n\nLicense\n\n[MIT License](/browse?license=mit \"View Systems That Use MIT License License\")\n\nTwitter\n\n[@lbugdb](https://twitter.com/lbugdb)[\\[05\\]](#citation5 \"View Citation\")\n\n [](/db/ladybugdb/logos \"View All Logos for LadybugDB\")[Logo Versions](/db/ladybugdb/logos \"View All Logos for LadybugDB\")\n\nWebsite\n\n[https://ladybugdb.com](https://ladybugdb.com)[\\[01\\]](#citation1 \"View Citation\")\n\nSource Code\n\n[https://github.com/LadybugDB/ladybug](https://github.com/LadybugDB/ladybug)[\\[02\\]](#citation2 \"View Citation\") Accessed: Sep 6, 2026 Last Commit: Sep 5, 2026\n\nTech Docs\n\n[https://docs.ladybugdb.com](https://docs.ladybugdb.com)[\\[03\\]](#citation3 \"View Citation\")\n\nBlog\n\n[https://blog.ladybugdb.com](https://blog.ladybugdb.com)[\\[04\\]](#citation4 \"View Citation\")\n\nCountry of Origin\n\n[US](/browse?country=US \"View Systems from United States of America\")\n\nStart Year\n\n2025\n\nCoding Agents\n\n[Claude](/browse?agent=claude \"View Systems Using Claude\") [\\[06\\]](#citation6 \"View Citation\")\n\n[Copilot](/browse?agent=copilot \"View Systems Using Copilot\") [\\[07\\]](#citation7 \"View Citation\")\n\n[Cursor](/browse?agent=cursor \"View Systems Using Cursor\") [\\[08\\]](#citation8 \"View Citation\")\n\nProject Type\n\n[Open Source](/browse?project-type=open-source \"View Systems : Open Source\")\n\nWritten in\n\n[C++](/browse?programming-language=cplusplus \"View Systems Written In C++\")\n\nDerived From\n\n[Kuzu](/db/kuzu \"View Kuzu\")\n\nLicense\n\n[MIT License](/browse?license=mit \"View Systems That Use MIT License License\")\n\nTwitter\n\n[@lbugdb](https://twitter.com/lbugdb)[\\[05\\]](#citation5 \"View Citation\")\n\nPeople Also Viewed\n\n [TonicDB](/db/tonicdb \"TonicDB\")[ NornicDB ](/db/nornicdb \"NornicDB\")[Stoolap](/db/stoolap \"Stoolap\") [DI discodb](/db/discodb \"discodb\")\n\n[Database Entry](/db/ladybugdb)\n\n# LadybugDB\n\n---\n\n[AI-Assisted](/browse?tag=ai-assisted \"View 'AI-Assisted' Systems\") [Forked Code](/browse?tag=forked-code \"View 'Forked Code' Systems\") [Revival Project](/browse?tag=revival \"View 'Revival Project' Systems\")\n\nLadybugDB is a embedded graph-oriented DBMS.\n\n## History\n\n---\n\nThe project started in 2025 as a revival fork of Kuzu after its acquisition and closure by Apple.\n\n## Data Model\n\n---\n\n[Graph](/browse?data-model=graph \"View Other 'Graph' Systems\")\n\n## Query Interface\n\n---\n\n[Cypher](/browse?query-interface=cypher \"View Other 'Cypher' Systems\")\n\n## System Architecture\n\n---\n\n[Embedded](/browse?system-architecture=embedded \"View Other 'Embedded' SystemLine truncated
|
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|
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{"type":"tool_use","timestamp":1788950951573,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"bash","callID":"call_79c8bd87f23b4b3a8ac77305","state":{"status":"completed","input":{"command":"tail -c 400 /mnt/workdrive/issue161-harness/research/issue-161/outward.json; echo; echo ===; python3 -c \"\nimport json\nd=json.load(open('/mnt/workdrive/issue161-harness/research/issue-161/outward.json'))\nprint('stop_condition:', json.dumps(d.get('stop_condition'))[:600])\nprint('contradictions sample:', json.dumps(d.get('contradictions'))[:400])\n\"","description":"Check artifact tail and stop_condition format"},"output":" 4 — O2, O4, O12, O15). Coverage: Qdrant-side vendor benchmarks (O2), independent SQLite-stack harness with published SQLite results (O4), a concrete adapter-protocol harness design for memory backends (O12), and multi-substrate incl. graph evaluation literature (O15). Stopped here per stage rules: evidence requirement satisfied, remaining candidates (O8, O10, O14) recorded but not read.\"\n }\n}\n\nzsh:1: == not found\n","metadata":{"output":" 4 — O2, O4, O12, O15). Coverage: Qdrant-side vendor benchmarks (O2), independent SQLite-stack harness with published SQLite results (O4), a concrete adapter-protocol harness design for memory backends (O12), and multi-substrate incl. graph evaluation literature (O15). Stopped here per stage rules: evidence requirement satisfied, remaining candidates (O8, O10, O14) recorded but not read.\"\n }\n}\n\nzsh:1: == not found\n","exit":1,"description":"Check artifact tail and stop_condition format","truncated":false},"title":"Check artifact tail and stop_condition format","time":{"start":1788950951570,"end":1788950951572}},"id":"prt_g001a085c932b50013sOVodKrg","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","messageID":"msg_g001a085c7c801001zv90Hr2IM"}}
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||||
{"type":"tool_use","timestamp":1788950966978,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"bash","callID":"call_3ccf352e242747b183094e34","state":{"status":"completed","input":{"command":"python3 -c \"\nimport json\nd=json.load(open('/mnt/workdrive/issue161-harness/research/issue-161/outward.json'))\nprint('stop_condition:', json.dumps(d.get('stop_condition'))[:700])\nprint('---contradictions:', json.dumps(d.get('contradictions'))[:600])\nprint('---process_id:', d.get('process_id'))\n\"; ls /mnt/workdrive/issue161-harness/research/issue-163/","description":"Show stop_condition, contradictions, process_id fields"},"output":"stop_condition: {\"external_subquestions\": [\"Memory backend abstraction + comparative benchmark harness (SQLite FTS5/sqlite-vec vs Qdrant vs graph backends)\"], \"status\": \"adequate evidence\", \"note\": \"\\u22652 unique URLs read (requirement: \\u22652; actual: 4 \\u2014 O2, O4, O12, O15). Coverage: Qdrant-side vendor benchmarks (O2), independent SQLite-stack harness with published SQLite results (O4), a concrete adapter-protocol harness design for memory backends (O12), and multi-substrate incl. graph evaluation literature (O15). Stopped here per stage rules: evidence requirement satisfied, remaining candidates (O8, O10, O14) recorded but not read.\"}\n---contradictions: [{\"topic\": \"SQLite-based retrieval vs dedicated vector DB competitiveness\", \"resolution\": \"O4 claims SQLite hybrid matches or beats managed vector DBs at \\u2264~100K docs (<50 qps); O2's vendor benchmarks show Qdrant fastest in 1M\\u201310M-vector client-server regimes. Not directly contradictory \\u2014 the regimes barely overlap \\u2014 but both are quoted without a head-to-head at the same scale/precision, so the crossover point is unmeasured in this evidence set (O4's own Qdrant rows are pending, E10).\"}, {\"topic\": \"what 'latency' means in the competing benchmarks\", \"resolution\": \"O4 measures\n---process_id: research-161-r1\nstage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\n","metadata":{"output":"stop_condition: {\"external_subquestions\": [\"Memory backend abstraction + comparative benchmark harness (SQLite FTS5/sqlite-vec vs Qdrant vs graph backends)\"], \"status\": \"adequate evidence\", \"note\": \"\\u22652 unique URLs read (requirement: \\u22652; actual: 4 \\u2014 O2, O4, O12, O15). Coverage: Qdrant-side vendor benchmarks (O2), independent SQLite-stack harness with published SQLite results (O4), a concrete adapter-protocol harness design for memory backends (O12), and multi-substrate incl. graph evaluation literature (O15). Stopped here per stage rules: evidence requirement satisfied, remaining candidates (O8, O10, O14) recorded but not read.\"}\n---contradictions: [{\"topic\": \"SQLite-based retrieval vs dedicated vector DB competitiveness\", \"resolution\": \"O4 claims SQLite hybrid matches or beats managed vector DBs at \\u2264~100K docs (<50 qps); O2's vendor benchmarks show Qdrant fastest in 1M\\u201310M-vector client-server regimes. Not directly contradictory \\u2014 the regimes barely overlap \\u2014 but both are quoted without a head-to-head at the same scale/precision, so the crossover point is unmeasured in this evidence set (O4's own Qdrant rows are pending, E10).\"}, {\"topic\": \"what 'latency' means in the competing benchmarks\", \"resolution\": \"O4 measures\n---process_id: research-161-r1\nstage_OUTWARD.events.jsonl\nstage_OUTWARD.mimo.err.log\ntask_tree.json\n","exit":0,"description":"Show stop_condition, contradictions, process_id fields","truncated":false},"title":"Show stop_condition, contradictions, process_id fields","time":{"start":1788950966975,"end":1788950966977}},"id":"prt_g001a085c97292001mBcQMHFfk","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","messageID":"msg_g001a085c936d7001YKHixUMNm"}}
|
||||
{"type":"step_finish","timestamp":1788950967005,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c972c2001zhNEC47fG","reason":"tool-calls","snapshot":"82090b332dac800d923d4422102fd5ac4fcc492e","messageID":"msg_g001a085c936d7001YKHixUMNm","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"step-finish","tokens":{"total":112680,"input":2416,"output":120,"reasoning":0,"cache":{"write":0,"read":110144}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788950980989,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c9a97b001K3h0xIjv3","messageID":"msg_g001a085c972fe001oi1EB7AWV","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","snapshot":"4f6da29d4e3cd399f116888eb476804ed29a6fc6","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788951128237,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085c9b51f00170BrFspeR","messageID":"msg_g001a085c972fe001oi1EB7AWV","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"text","text":"Format confirmed — pure JSON artifact with a `stop_condition` object; the executor handles the stage marker. Writing the OUTWARD artifact now.","time":{"start":1788950983967,"end":1788951128233}}}
|
||||
{"type":"tool_use","timestamp":1788951128252,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"write","callID":"call_ac753cd0eb2d4f078e63647d","state":{"status":"completed","input":{"content":"{\n \"stage\": \"OUTWARD\",\n \"process_id\": \"research-163-r1\",\n \"issue\": 163,\n \"round\": 1,\n \"subquestion\": \"Address: Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\",\n \"discovery\": {\n \"queries\": [\n {\n \"id\": \"Q1\",\n \"tool\": \"webfetch (DuckDuckGo HTML)\",\n \"query\": \"neo4j vs falkordb graph database benchmark comparison\",\n \"receipt\": \"fetched OK; 10 results returned inline\"\n },\n {\n \"id\": \"Q2\",\n \"tool\": \"webfetch (DuckDuckGo HTML)\",\n \"query\": \"graphiti zep temporal knowledge graph agent memory neo4j falkordb\",\n \"receipt\": \"fetched OK; 10 results returned inline\"\n },\n {\n \"id\": \"Q3\",\n \"tool\": \"webfetch (DuckDuckGo HTML)\",\n \"query\": \"ladybugdb graph database kuzu fork\",\n \"receipt\": \"fetched OK; 10 results returned inline\"\n },\n {\n \"id\": \"Q4\",\n \"tool\": \"webfetch (DuckDuckGo HTML)\",\n \"query\": \"falkordb redisgraph successor cypher features github\",\n \"receipt\": \"fetched OK; 10 results returned inline\"\n },\n {\n \"id\": \"Q5\",\n \"tool\": \"webfetch (DuckDuckGo HTML)\",\n \"query\": \"neo4j temporal versioning bitemporal cypher valid time\",\n \"receipt\": \"fetched OK; 10 results returned inline\"\n }\n ],\n \"sources_discovered\": [\n { \"id\": \"O1\", \"url\": \"https://aimultiple.com/graph-databases\", \"class\": \"external (independent benchmark, updated 2026-04-15)\", \"note\": \"SELECTED and read\" },\n { \"id\": \"O2\", \"url\": \"https://www.falkordb.com/blog/graph-database-performance-benchmarks-falkordb-vs-neo4j/\", \"class\": \"external (vendor blog, pub 2024-12-02, updated 2026-08-23)\", \"note\": \"SELECTED and read\" },\n { \"id\": \"O3\", \"url\": \"https://www.puppygraph.com/blog/falkordb-vs-neo4j\", \"class\": \"external (competing-vendor comparison)\", \"note\": \"not selected — competing vendor has stake in the comparison\" },\n { \"id\": \"O4\", \"url\": \"https://knodegraph.com/blog/falkordb-vs-neo4j/\", \"class\": \"external (third-party blog w/ 2026 benchmarks)\", \"note\": \"not selected — O1 covers the independent-benchmark slot with fuller methodology\" },\n { \"id\": \"O5\", \"url\": \"https://github.com/FalkorDB/benchmark\", \"class\": \"repo (vendor benchmark harness)\", \"note\": \"SELECTED; README 404 on master and main; repo metadata read via GitHub API instead\" },\n { \"id\": \"O6\", \"url\": \"https://www.plushcap.com/content/falkordb/blog/falkordb-falkordb-vs-neo4j-graph-database-performance-benchmarks\", \"class\": \"external (mirror of O2)\", \"note\": \"not selected — duplicate\" },\n { \"id\": \"O7\", \"url\": \"https://inferensys.com/differences/neuro-symbolic-ai-frameworks/knowledge-graph-construction-tools/neo4j-vs-falkordb\", \"class\": \"external (aggregator comparison)\", \"note\": \"not selected\" },\n { \"id\": \"O8\", \"url\": \"https://inferensys.com/differences/ai-software-factory-platforms/code-knowledge-graph-systems/neo4j-vs-falkordb-for-code-graphs\", \"class\": \"external (aggregator comparison)\", \"note\": \"not selected\" },\n { \"id\": \"O9\", \"url\": \"https://3nuggets.io/compare-neo4j-vs-falkordb\", \"class\": \"external (practitioner fit comparison)\", \"note\": \"not selected\" },\n { \"id\": \"O10\", \"url\": \"https://github.com/getzep/graphiti\", \"class\": \"repo (Graphiti primary)\", \"note\": \"SELECTED and read (README via raw.githubusercontent.com/main)\" },\n { \"id\": \"O11\", \"url\": \"https://neo4j.com/blog/developer/graphiti-knowledge-graph-memory/\", \"class\": \"external (Neo4j official blog on Graphiti)\", \"note\": \"not selected — O10 is primary\" },\n { \"id\": \"O12\", \"url\": \"https://docs.falkordb.com/agentic-memory/graphiti\", \"class\": \"external (FalkorDB vendor docs)\", \"note\": \"not selected\" },\n { \"id\": \"O13\", \"url\": \"https://github.com/Memory-Agents/graphiti/\", \"class\": \"repo (mirror)\", \"note\": \"not selected — duplicate of O10\" },\n { \"id\": \"O14\", \"url\": \"https://codex.danielvaughan.com/2026/03/30/graphiti-agent-memory-store/\", \"class\": \"external (practitioner review)\", \"note\": \"not selected\" },\n { \"id\": \"O15\", \"url\": \"https://memorypapers.org/papers/zep-a-temporal-knowledge-graph-architecture-for-agent-memory\", \"class\": \"external (paper summary site)\", \"note\": \"not selected — primary paper is arXiv 2501.13956, referenced from O10\" },\n { \"id\": \"O16\", \"url\": \"https://www.falkordb.com/blog/graphiti-falkordLine truncated
|
||||
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|
||||
{"type":"step_start","timestamp":1788951145199,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085cc2aed001MR70Ww0nc","messageID":"msg_g001a085cbe8f6001jaXe0kb02","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","snapshot":"7d5be098162022ec58714cb931b1aa38c1303fa1","type":"step-start"}}
|
||||
{"type":"tool_use","timestamp":1788951145257,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"type":"tool","tool":"bash","callID":"call_a8b80e7e817b43428e1e8fcb","state":{"status":"completed","input":{"command":"python3 -c \"\nimport json\nd=json.load(open('/mnt/workdrive/issue161-harness/research/issue-163/outward.json'))\nprint('valid JSON; top keys:', list(d.keys()))\nprint('queries:', len(d['discovery']['queries']))\nprint('sources:', len(d['discovery']['sources_discovered']))\nprint('read:', len(d['reading']['sources_read']))\nprint('claims total:', sum(len(s['claims']) for s in d['reading']['sources_read']))\nprint('limitations:', len(d['limitations']))\nprint('contradictions:', len(d['contradictions']))\nids=[s['id'] for s in d['discovery']['sources_discovered']]\nassert len(ids)==len(set(ids)) and ids==[f'O{i}' for i in range(1,46)], 'ID check'\neids=[c['id'] for s in d['reading']['sources_read'] for c in s['claims']]\nassert len(eids)==len(set(eids)) and eids==[f'E{i}' for i in range(1,31)], 'E check'\nprint('O1-O45 contiguous, E1-E30 contiguous: OK')\n\"","description":"Validate artifact JSON and ID continuity"},"output":"valid JSON; top keys: ['stage', 'process_id', 'issue', 'round', 'subquestion', 'discovery', 'reading', 'limitations', 'contradictions', 'repo_grounding_note', 'stop_condition']\nqueries: 5\nsources: 45\nread: 10\nclaims total: 30\nlimitations: 8\ncontradictions: 4\nO1-O45 contiguous, E1-E30 contiguous: OK\n","metadata":{"output":"valid JSON; top keys: ['stage', 'process_id', 'issue', 'round', 'subquestion', 'discovery', 'reading', 'limitations', 'contradictions', 'repo_grounding_note', 'stop_condition']\nqueries: 5\nsources: 45\nread: 10\nclaims total: 30\nlimitations: 8\ncontradictions: 4\nO1-O45 contiguous, E1-E30 contiguous: OK\n","exit":0,"description":"Validate artifact JSON and ID continuity","truncated":false},"title":"Validate artifact JSON and ID continuity","time":{"start":1788951145251,"end":1788951145256}},"id":"prt_g001a085cc2aee001rbtfZU663","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","messageID":"msg_g001a085cbe8f6001jaXe0kb02"}}
|
||||
{"type":"step_finish","timestamp":1788951145285,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085cc2b29001WndK1GTlY","reason":"tool-calls","snapshot":"2e2bf5efe4a60fb6550269a49b5bc6236d2f6ebe","messageID":"msg_g001a085cbe8f6001jaXe0kb02","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"step-finish","tokens":{"total":121904,"input":9127,"output":265,"reasoning":0,"cache":{"write":0,"read":112512}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788951152345,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085cc46d8001UrC7zcBAR","messageID":"msg_g001a085cc2b6a001zsRv7n7zV","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","snapshot":"71a5d5cd5a7aeeb7b8b26c325d245afaa8e3d870","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788951167285,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085cc4f90001aVhrOJMN0","messageID":"msg_g001a085cc2b6a001zsRv7n7zV","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"text","text":"OUTWARD stage for issue #163 is complete. Artifact written to `research/issue-163/outward.json` (validated JSON, O1–O45 and E1–E30 contiguous).\n\n## What was done\n\n**Discovery — 5 real SERP queries, all returned results** (receipts in artifact): Neo4j-vs-FalkorDB benchmarks, Graphiti/Zep, LadybugDB/Kuzu fork, FalkorDB/RedisGraph lineage, Neo4j temporal versioning. → **45 unique sources discovered and classified** (O1–O45).\n\n**Reading — 10 sources with fetch receipts** (9 full reads + 1 API-metadata receipt), covering both required classes:\n- **repo**: `getzep/graphiti` (O10), `LadybugDB/ladybug` (O21), `FalkorDB/FalkorDB` (O29), `FalkorDB/benchmark` (O5 — README 404 on both branches; GitHub API metadata only), `DIGITAL-FABRIC-AI/neo4j-temporal-graph` (O41, NEP-001)\n- **external**: AIMultiple independent benchmark (O1), FalkorDB vendor benchmark blog (O2), ex-Kuzu engineer's Ladybug writeup (O24), CMU dbdb.io entry (O28), Neo4j Cypher temporal docs (O38)\n\n**30 evidence claims (E1–E30)** extracted, including: independent benchmark (FalkorDB 6.7× throughput, 11/12 query wins, but Neo4j wins heaviest aggregation; 82× cold-start gap; 5.4× memory) vs vendor benchmark (p99 136ms vs 46.9s — treat as marketing-adjacent); FalkorDB = Redis module, SSPLv1, GraphBLAS, Rust rewrite in preview; LadybugDB = embedded MIT Cypher DB with FTS + vector index, active revival fork of archived Kuzu; Graphiti = pluggable-driver temporal framework (bi-temporal invalidation at the framework layer; Kuzu driver deprecated, no Ladybug driver); Neo4j has temporal *value types* but **no native temporal versioning** (NEP-001 draft RFC evidences the gap).\n\n**Recorded**: 8 limitations (incl. O5 README unretrievable, no FalkorDB-native temporal docs read, Apple-acquisition claim single-sourced), 4 contradictions (benchmark magnitudes by sponsor, Kuzu closure cause, \"production-tested RedisGraph\" framing, embedded-path-via-Graphiti gap), and a repo grounding note tying evidence to `src/rune/graph_memory.py`'s embedded SQLite posture.\n\nStopped per stage rules — evidence requirement (≥2 unique URLs) exceeded; no synthesis, no design, no code changes.\n\nStageCompleted: OUTWARD","time":{"start":1788951154576,"end":1788951167283}}}
|
||||
{"type":"step_finish","timestamp":1788951167313,"sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","part":{"id":"prt_g001a085cc8134001zzI3h19Si","reason":"stop","snapshot":"38cb1b15dd527c34725e003dd4c265876d62171c","messageID":"msg_g001a085cc2b6a001zsRv7n7zV","sessionID":"ses_-ffe5f7a3e2805ffeYaOOBiQE1","type":"step-finish","tokens":{"total":135675,"input":9107,"output":596,"reasoning":84,"cache":{"write":0,"read":125888}},"cost":0}}
|
||||
@@ -0,0 +1,34 @@
|
||||
{"type":"step_start","timestamp":1788956518238,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a0861e275b001IYWkR0eoE","messageID":"msg_g001a0861dfeeb001JPZ11h3vj","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","snapshot":"0d842306b88280e845c68295e9984fa094c25628","type":"step-start"}}
|
||||
{"type":"tool_use","timestamp":1788956520268,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"type":"tool","tool":"skill_search","callID":"call_d5d726efa37743ec8e41ffa7","state":{"status":"completed","input":{"query":"Review a synthesis artifact for implementation readiness (QUALIFY gate) and write a JSON verdict to research/issue-163/qualify.json; input is a research synthesis document for an issue, output is a PROCEED/REFINE verdict; audience is an orchestration pipeline"},"output":"{\n \"status\": \"matched\",\n \"results\": [\n {\n \"skill_id\": \"xlsx-official\",\n \"name\": \"xlsx-official\",\n \"score\": 0.6053,\n \"reason\": \"The skill description matches these query terms: artifact, is, pipeline.\"\n },\n {\n \"skill_id\": \"compose-next\",\n \"name\": \"compose-next\",\n \"score\": 0.5445,\n \"reason\": \"The skill description matches these query terms: review, document, orchestration.\"\n },\n {\n \"skill_id\": \"research-paper-writing\",\n \"name\": \"research-paper-writing\",\n \"score\": 0.4697,\n \"reason\": \"The skill description matches these query terms: review, write.\"\n }\n ],\n \"loaded_skill_id\": null\n}","metadata":{"status":"matched","loadedSkillID":null,"truncated":false},"title":"Found 3 skills","time":{"start":1788956520197,"end":1788956520266}},"id":"prt_g001a0861e28090014Q40dCgN0","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","messageID":"msg_g001a0861dfeeb001JPZ11h3vj"}}
|
||||
{"type":"tool_use","timestamp":1788956520397,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"type":"tool","tool":"glob","callID":"call_87723967252346cca1d076f8","state":{"status":"completed","input":{"pattern":"research/issue-163/**"},"output":"/mnt/workdrive/issue161-harness/research/issue-163/stage_QUALIFY.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/stage_QUALIFY.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-QUALIFY-2\n/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_SYNTHESIS.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/synthesis.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_SYNTHESIS.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-SYNTHESIS-2\n/mnt/workdrive/issue161-harness/research/issue-163/qualify.json\n/mnt/workdrive/issue161-harness/research/issue-163/adversarial.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_ADVERSARIAL.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/stage_ADVERSARIAL.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/stage_INNOVATION.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/innovation.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_INNOVATION.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-INNOVATION-1\n/mnt/workdrive/issue161-harness/research/issue-163/stage_APPLICABILITY.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/applicability.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_APPLICABILITY.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-APPLICABILITY-1\n/mnt/workdrive/issue161-harness/research/issue-163/stage_OUTWARD.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/outward.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_OUTWARD.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-OUTWARD-1\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-INWARD-1\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-ISSUE-1","metadata":{"count":26,"truncated":false},"title":"","time":{"start":1788956520202,"end":1788956520393}},"id":"prt_g001a0861e2e39001pIMerAr0F","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","messageID":"msg_g001a0861dfeeb001JPZ11h3vj"}}
|
||||
{"type":"step_finish","timestamp":1788956520541,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a0861e2fcd00196pbsbeYZ","reason":"tool-calls","snapshot":"5d55de6c74c6d45acc136a3a754285dfc1f04b9f","messageID":"msg_g001a0861dfeeb001JPZ11h3vj","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","type":"step-finish","tokens":{"total":28697,"input":28545,"output":152,"reasoning":0,"cache":{"write":0,"read":0}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788956529180,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a0861e521a001STCrXGO1j","messageID":"msg_g001a0861e3116001O2JuC7J3J","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","snapshot":"9624478afb62694fd6ef55e697557685308f0b6b","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788956530858,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a0861e52d3001Sv17p0aLe","messageID":"msg_g001a0861e3116001O2JuC7J3J","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","type":"text","text":"No matching skill — I'll run the QUALIFY review manually. Reading the synthesis artifact and supporting context now.","time":{"start":1788956529363,"end":1788956530856}}}
|
||||
{"type":"tool_use","timestamp":1788956530868,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"type":"tool","tool":"read","callID":"call_774c6e5c10254dfaa5664fe6","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json"},"output":"<path>/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"issue\": 163,\n3: \"round\": 1,\n4: \"current_stage\": \"QUALIFY\",\n5: \"completed_stages\": [\n6: \"ISSUE\",\n7: \"INWARD\",\n8: \"OUTWARD\",\n9: \"APPLICABILITY\",\n10: \"INNOVATION\",\n11: \"ADVERSARIAL\",\n12: \"SYNTHESIS\",\n13: \"QUALIFY\",\n14: \"SYNTHESIS\"\n15: ],\n16: \"artifacts\": {\n17: \"ISSUE\": {\n18: \"contract\": {\n19: \"issue\": 163,\n20: \"title\": \"Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\",\n21: \"question_type\": \"mixed\",\n22: \"sub_questions\": [\n23: \"Address: Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\"\n24: ],\n25: \"required_stages\": [\n26: \"ISSUE\",\n27: \"INWARD\",\n28: \"OUTWARD\",\n29: \"ADVERSARIAL\",\n30: \"INNOVATION\",\n31: \"SYNTHESIS\",\n32: \"QUALIFY\"\n33: ],\n34: \"skipped_stages\": {\n35: \"APPLICABILITY\": \"No OUTWARD research to ground against Rune.\"\n36: },\n37: \"evidence_classes\": [\n38: \"repo\",\n39: \"external\"\n40: ],\n41: \"sufficient_evidence\": \"\\u22652 code-path citations for INWARD; \\u22652 unique URLs for OUTWARD (if required)\",\n42: \"expected_artifact\": \"synthesis.json\",\n43: \"ambiguous\": false,\n44: \"needs_mimo\": false,\n45: \"source\": \"deterministic\"\n46: },\n47: \"deterministic\": true\n48: },\n49: \"INWARD\": {\n50: \"context\": {\n51: \"keywords\": [\n52: \"temporal\",\n53: \"graph\",\n54: \"backend\",\n55: \"spike\",\n56: \"neo\",\n57: \"falkordb\",\n58: \"ladybugdb\",\n59: \"experimental\",\n60: \"graphiti\",\n61: \"reference\",\n62: \"plain\",\n63: \"summary\",\n64: \"architecture_frontier_handoff\",\n65: \"compare\",\n66: \"graph\",\n67: \"backends\",\n68: \"rune\",\n69: \"structural\",\n70: \"projection\",\n71: \"episodes\"\n72: ],\n73: \"umbrella_matches\": [\n74: {\n75: \"umbrella\": \"memory\",\n76: \"module\": \"graph_memory\",\n77: \"keyword\": \"graph\"\n78: },\n79: {\n80: \"umbrella\": \"memory\",\n81: \"module\": \"graph_shard_bridge\",\n82: \"keyword\": \"graph\"\n83: },\n84: {\n85: \"umbrella\": \"orchestration\",\n86: \"module\": \"rune_operator\",\n87: \"keyword\": \"rune\"\n88: },\n89: {\n90: \"umbrella\": \"orchestration\",\n91: \"module\": \"rune_tui\",\n92: \"keyword\": \"rune\"\n93: },\n94: {\n95: \"umbrella\": \"orchestration\",\n96: \"module\": \"rune_graphic\",\n97: \"keyword\": \"graph\"\n98: },\n99: {\n100: \"umbrella\": \"orchestration\",\n101: \"module\": \"rune_spinner\",\n102: \"keyword\": \"rune\"\n103: },\n104: {\n105: \"umbrella\": \"orchestration\",\n106: \"module\": \"rune_profiler\",\n107: \"keyword\": \"rune\"\n108: }\n109: ],\n110: \"candidate_files\": [\n111: \"src/rune/curator.py\",\n112: \"src/rune/rune_tui.py\",\n113: \"src/rune/graph_memory.py\",\n114: \"src/rune/sleep_time.py\",\n115: \"src/rune/elastic_cognition.py\",\n116: \"src/rune/rune_operator.py\",\n117: \"src/rune/rune_profiler.py\",\n118: \"src/rune/warm_router.py\",\n119: \"src/rune/abstraction.py\",\n120: \"src/rune/canary_native.py\",\n121: \"src/rune/canary_p0_04.py\",\n122: \"src/rune/canary_p1_07.py\",\n123: \"src/rune/complexity_scorer.py\",\n124: \"src/rune/identity_anchors.py\",\n125: \"src/rune/memory_consolidation.py\"\n126: ],\n127: \"candidate_tests\": [\n128: \"tests/test_retrieval_benchmark.py\",\n129: \"tests/test_orchestration_memory_synthesis.py\",\n130: \"tests/test_p1_03_memory_selection.py\",\n131: \"tests/test_p1_06_model_routing.py\",\n132Line truncated
|
||||
{"type":"tool_use","timestamp":1788956530872,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"type":"tool","tool":"read","callID":"call_63225c1096c542689a03606d","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/research/issue-163/synthesis.json"},"output":"<path>/mnt/workdrive/issue161-harness/research/issue-163/synthesis.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"stage\": \"SYNTHESIS\",\n3: \"issue\": 163,\n4: \"round\": 2,\n5: \"process_id\": \"research-163-r1\",\n6: \"question\": \"Given the candidates that survived, WHAT SHOULD MIMO CODE BUILD?\",\n7: \"selected_design\": \"Corrected, re-sequenced temporal+lexical query layer over graph.sqlite (survivors C5+C6; C4 as characterization-only; C9 as a constrained track). Concretely: (1) WRITE-SIDE FIRST — M1a, the one-line fix in add_edge (graph_memory.py:206-208) that sets valid_until=now on supersede, making the documented bitemporal as-of filter correct for all future data with free backfill (the DB is empty); (2) C5 — an --as-of <iso8601> point-in-time query parameter whose input is strictly normalized to the now_iso() format before any comparison (M2), replacing the status='active' filter with valid_from <= t AND (valid_until IS NULL OR t < valid_until) AND learned_at <= t in both the outgoing and incoming edge queries, default path unchanged; (3) C6 — an FTS5 lexical entry-resolution tier built as a NON-CONTENTLESS FTS5 projection table graph_lex(name, alias, entity_id UNINDEXED) with one row per (entity_id, text) over entities.name ∪ aliases.alias, INSERT-only maintenance on the existing write path, a documented rebuild-lex command, and a recorded tokenizer decision (M3, layout committed in this round); (4) all lexical benefit measured END-TO-END at ContextBroker.memory_selector output, not graph_memory.query output (M4); (5) read-layer work sequenced behind write-side population (M5) with the T5 population gate at a pre-committed N=4 weeks and <50-edges threshold. C4 ships as a documentation characterization correction only (M6); C9 ships as nothing — it is carried solely as the M7 constraint on any future analytics sidecar.\",\n8: \"why_this_design_won\": \"C5 and C6 are the only surviving candidates that add capabilities the status quo genuinely lacks — reading the superseded history Rune already persists but cannot query temporally, and lifting entry-point recall past exact/alias/substring — and both stay inside Rune's embedded, no-new-dependency posture (in-process SQLite; FTS5 verified available at sqlite 3.53.4). INN-1's original formulation was falsified twice over: its as-of filter returns BOTH sides of a contradiction as current knowledge against the current writer (A2, critical — supersede never writes valid_until, graph_memory.py:207-208), and its benefit chain is broken at the empty graph (A5) and again at the memory_selector floor (A6). The adversarial stage's simpler-alternative analysis (A3, A13) dictated the winning shape: fix the writer first (one line, free backfill on an empty DB), then read-layer work, measured end-to-end. Within M1, option (a) wins over option (b): the valid_until-on-supersede fix makes the documented bitemporal filter correct with a single WHERE clause and avoids inventing a superseded_by chain-walk helper with terminal/cycle semantics (CA6); option (b) is retained as a documented fallback should T2 falsify M1a. C4 and C9 survive as constraints, not work: C4's 'bi-temporal E14 1:1' claim is contradicted by the code (A1, A11) and survives only as a corrected characterization (M6); C9 is track-only (A9/A10) and contributes only the M7 boundary on future analytics. Round 2 commits the three QUALIFY findings into this design — the graph_lex row layout and id mapping, a concrete N=4 weeks for the population gate, and the named alias sort key — so the handoff to the coding worker is verbatim-executable with no invented design. Nothing here resurrects INN-1-as-specified or any applicability-stage rejection.\",\n9: \"rejected_alternatives\": [\n10: {\n11: \"design\": \"INN-1#temporal-filter-as-specified — ship the as-of filter (valid_from <= t AND (valid_until IS NULL OR t < valid_until) AND learned_at <= t) against the current writer\",\n12: \"reason\": \"REJECTED at ADVERSARIAL (A2, critical): supersede (graph_memory.py:207-208) sets status/superseded_by but never valid_until, so superseded edges remain NULL-open on the valid axis and pass all three predicates for every t after creation — the feature returns the superseded AND superseding edges of a contradiction simultaneously, the exact failure it exists to prevent. Do NOT resurrect in this form.\"\n13: },\n14: {\n15: \"design\": \"M1b — transaction-time-only as-of (learned_at <= t AND (status='active' OR the superseding edge's learned_at > t), walking superseded_by to a terminal)\",\n16: \"reason\": \"Viable fallback, not selected: it rLine truncated
|
||||
{"type":"step_finish","timestamp":1788956531178,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a0861e58b7001lpk2eeKOy","reason":"tool-calls","snapshot":"53024d291d5cf651fd73b7bcf40aecfb60ffe5f9","messageID":"msg_g001a0861e3116001O2JuC7J3J","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","type":"step-finish","tokens":{"total":29559,"input":887,"output":128,"reasoning":0,"cache":{"write":0,"read":28544}},"cost":0}}
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{"type":"step_start","timestamp":1788956543701,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a0861e8ad2001MeRCG6oHm","messageID":"msg_g001a0861e5b64001ktV4slFwV","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","snapshot":"cfe0f23129992c171451fc1591234a5ae124423d","type":"step-start"}}
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{"type":"text","timestamp":1788956544806,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a0861e8c2c001nCVckiyYA","messageID":"msg_g001a0861e5b64001ktV4slFwV","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","type":"text","text":"The synthesis artifact is a round-2 dossier claiming to have resolved three prior QUALIFY findings. Before judging readiness, I'll check the prior QUALIFY verdict and verify that the cited code locations in `graph_memory.py` actually match the interfaces being specified — a coder handed these verbatim would implement against those lines.","time":{"start":1788956544044,"end":1788956544803}}}
|
||||
{"type":"tool_use","timestamp":1788956544880,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"type":"tool","tool":"read","callID":"call_fa68e4cb43bd4a3e92acbf35","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/research/issue-163/qualify.json"},"output":"<path>/mnt/workdrive/issue161-harness/research/issue-163/qualify.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"outcome\": \"REFINE_SYNTHESIS\",\n3: \"findings\": [\n4: \"INTERFACE GAP (M3/FTS5 projection — blocks verbatim handoff): the declared DDL 'CREATE VIRTUAL TABLE graph_lex USING fts5(name, alias, content='')' cannot be implemented as specified without the coder inventing a design. (a) A contentless FTS5 table has no unindexed columns, so no mapping from FTS rows back to entity ids is specified — yet the FTS5 BM25 resolution tier must return an entity id (rowid=entities.id convention vs. join-on-name are both viable and mutually incompatible). (b) The 1:N entity→aliases relation is unrepresented: the two-column DDL permits one-row-per-entity with packed aliases (packing format unspecified) or one-row-per-alias (which breaks id mapping); nothing selects between them. (c) 'Rows maintained on the existing entity/alias write path (resolve_entity, add_edge)' is not mechanically implementable for a contentless table: add_edge inserts new aliases for already-existing entities (graph_memory.py:209-212), which requires updating that entity's FTS row, and contentless FTS5 supports no UPDATE — the coder would have to invent a delete+reinsert protocol. Required fix: specify the row layout and id mapping (e.g., a non-contentless graph_lex with an entity_id column, or rowid=entities.id with one row per entity and a defined alias-packing format), or explicitly delegate the choice the way the tokenizer decision is delegated.\",\n5: \"PARAMETER GAP (T5/INV-5/AC9 — blocks test_population_gate_harness and sequence step 9): N is never committed. Step 9 says 'after N weeks of real use (N fixed in advance)', INV-5 says 'over N weeks of real use', and AC9 requires 'a pre-committed N and threshold' — but only the threshold (<50 edges) is given; N remains symbolic throughout the artifact. The coding worker cannot finalize the T5 harness or execute step 9 without inventing N, a value the artifact itself assigns to pre-commitment. Required fix: commit a concrete N (e.g., N=4 weeks) in the synthesis.\",\n6: \"MINOR GAP (M3/INV-8 — non-blocking but unpinned): the deterministic alias ordering is specified only as 'ORDER BY added, replacing the bare LIMIT 1 at :228-231' without naming the sort key. INV-8 and test_alias_resolution_deterministic pin the observable property (any stable key satisfies them), so a competent coder can proceed, but naming the key (e.g., ORDER BY a.entity_id ASC) would make the instruction verbatim.\"\n7: ]\n8: }\n\n(End of file - total 8 lines)\n</content>","metadata":{"preview":"{\n \"outcome\": \"REFINE_SYNTHESIS\",\n \"findings\": [\n \"INTERFACE GAP (M3/FTS5 projection — blocks verbatim handoff): the declared DDL 'CREATE VIRTUAL TABLE graph_lex USING fts5(name, alias, content='')' cannot be implemented as specified without the coder inventing a design. (a) A contentless FTS5 table has no unindexed columns, so no mapping from FTS rows back to entity ids is specified — yet the FTS5 BM25 resolution tier must return an entity id (rowid=entities.id convention vs. join-on-name are both viable and mutually incompatible). (b) The 1:N entity→aliases relation is unrepresented: the two-column DDL permits one-row-per-entity with packed aliases (packing format unspecified) or one-row-per-alias (which breaks id mapping); nothing selects between them. (c) 'Rows maintained on the existing entity/alias write path (resolve_entity, add_edge)' is not mechanically implementable for a contentless table: add_edge inserts new aliases for already-existing entities (graph_memory.py:209-212), which requires updating that entity's FTS row, and contentless FTS5 supports no UPDATE — the coder would have to invent a delete+reinsert protocol. Required fix: specify the row layout and id mapping (e.g., a non-contentless graph_lex with an entity_id column, or rowid=entities.id with one row per entity and a defined alias-packing format), or explicitly delegate the choice the way the tokenizer decision is delegated.\",\n \"PARAMETER GAP (T5/INV-5/AC9 — blocks test_population_gate_harness and sequence step 9): N is never committed. Step 9 says 'after N weeks of real use (N fixed in advance)', INV-5 says 'over N weeks of real use', and AC9 requires 'a pre-committed N and threshold' — but only the threshold (<50 edges) is given; N remains symbolic throughout the artifact. The coding worker cannot finalize the T5 harness or execute step 9 without inventing N, a value the artifact itself assigns to pre-commitment. Required fix: commit a concrete N (e.g., N=4 weeks) in the synthesis.\",\n \"MLine truncated
|
||||
{"type":"tool_use","timestamp":1788956544884,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"type":"tool","tool":"read","callID":"call_ad7a7168736f4076bb1e65a9","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/research/issue-163/adversarial.json","limit":120},"output":"<path>/mnt/workdrive/issue161-harness/research/issue-163/adversarial.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"stage\": \"ADVERSARIAL\",\n3: \"issue\": 163,\n4: \"round\": 1,\n5: \"process_id\": \"research-163-r1\",\n6: \"attacked\": [\n7: \"C4\",\n8: \"C5\",\n9: \"C6\",\n10: \"C9\",\n11: \"INN-1\"\n12: ],\n13: \"surviving_candidates\": [\n14: {\n15: \"id\": \"C4\",\n16: \"disposition\": \"SURVIVES_WITH_DOWNGRADE\",\n17: \"note\": \"The claim 'implements E14's model 1:1' is contradicted by the code (attacks A1, A11): the tier is a transaction-time append log with same-triple supersession, NOT a bi-temporal model with automatic contradiction invalidation. The candidate survives as 'partially integrated'; downstream dossiers must not repeat the 1:1 characterization.\"\n18: },\n19: {\n20: \"id\": \"C5\",\n21: \"disposition\": \"SURVIVES_ONLY_IN_CORRECTED_FORM\",\n22: \"note\": \"AS-OF time-travel over preserved history is a real, low-cost capability \\u2014 but the INN-1 filter formulation is falsified (attack A2). Survives only via mitigation M1: either write valid_until on supersede (one line) or implement transaction-time as-of (learned_at + superseded_by chain) and drop the valid-time half until a writer populates valid windows.\"\n23: },\n24: {\n25: \"id\": \"C6\",\n26: \"disposition\": \"SURVIVES_CONDITIONAL\",\n27: \"note\": \"Mechanism is sound and FTS5 is available (verified: sqlite 3.53.4 in this runtime). But the benefit chain is broken twice today: the production graph is EMPTY (attack A5) and graph recall is re-floored by memory_selector (attack A6). Survives only with the projection-rule framing (M3), selector-floor test (M4), and write-side sequencing (M5).\"\n28: },\n29: {\n30: \"id\": \"C9\",\n31: \"disposition\": \"SURVIVES_TRACK_ONLY\",\n32: \"note\": \"Uncontested as a future lead; attacks A9/A10 only constrain its future form: DuckDB/Arrow analytics must be a rebuildable read-only projection of graph.sqlite, never a second store, and must never acquire independent write authority.\"\n33: }\n34: ],\n35: \"rejected_candidates\": [\n36: {\n37: \"id\": \"INN-1#temporal-filter-as-specified\",\n38: \"reason\": \"REJECTED as literally specified \\u2014 do NOT carry to SYNTHESIS. The filter 'valid_from <= t AND (valid_until IS NULL OR t < valid_until) AND learned_at <= t' applied to the current writer's data returns STALE SUPERSEDED FACTS as if believed: supersede (graph_memory.py:207-208) sets status/superseded_by but never valid_until, so superseded edges remain NULL-open on the valid axis and pass all three predicates for every t after their creation. An as-of query would return the superseded AND superseding edges of a contradiction simultaneously \\u2014 presenting both sides of a contradiction as current knowledge, the exact failure the feature exists to prevent. C5 itself survives after rework (see surviving_candidates); this exact formulation does not. This is E29's documented NULL-as-open-ended pitfall realized in the proposal spec, not merely in hand-written Cypher.\"\n39: }\n40: ],\n41: \"attacks\": [\n42: {\n43: \"id\": \"A1\",\n44: \"target_candidate\": \"C4\",\n45: \"dimension\": \"contradicting_evidence + weak_assumptions\",\n46: \"finding\": \"The 'bi-temporal' claim is schema-only. valid_until is NEVER written anywhere in the repo (grep over src/rune: appears only in DDL line 107, INSERT writing NULL at line 201, and SELECTs); status='expired' has no writer; and valid_from and learned_at are both set to the SAME now_iso() call in the single INSERT (lines 200-204). The live production DB confirms: 0 edges where valid_until IS NOT NULL, 0 where valid_from <> learned_at. The tier is a transaction-time-only append log with supersession \\u2014 the valid-time axis of E14's model is declared but dead.\",\n47: \"evidence\": \"graph_memory.py:107,200-204,207-208 (valid_until written NULL; valid_from=learned_at=ts); grep valid_until/superseded across src/rune (no writer); live DB receipt: read-only sqlite3 query against /mnt/workdrive/rune/memory/graph/graph.sqlite returned entities=0, aliases=0, edges=0, valid_until non-null=0, valid_from<>learned_at=0, superseded_by non-null=0\",\n48: \"severity\": \"high\"\n49: },\n50: {\n51: \"id\": \"A2\",\n52: \"target_candidate\": \"C5 (via INN-1)\",\n53: \"dimension\": \"failure_modes\",\n54: \"finding\": \"As-of filter as specified returns stale superseded facts (see rejected_candidates for full Line truncated
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||||
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{"type":"step_start","timestamp":1788956579613,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a0861f171b0019I1Kzz6C0","messageID":"msg_g001a0861e9217001nrEPd2xoN","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","snapshot":"3ec4411eb00408d3c0a6fb071139af66fb23db23","type":"step-start"}}
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{"type":"text","timestamp":1788956582064,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a0861f1962001O0n8sUo3t","messageID":"msg_g001a0861e9217001nrEPd2xoN","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","type":"text","text":"The round-2 synthesis explicitly resolves all three prior QUALIFY findings. As a final readiness check I'll spot-verify the load-bearing code citations (supersede block, alias LIMIT 1, now_iso producer) against the actual source, since the coder will be handed these line references verbatim.","time":{"start":1788956580194,"end":1788956582062}}}
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||||
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|
||||
{"type":"tool_use","timestamp":1788956595624,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"type":"tool","tool":"read","callID":"call_2ca187c9de394e9ebf3e299b","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/src/rune/graph_memory.py","limit":100,"offset":140},"output":"<path>/mnt/workdrive/issue161-harness/src/rune/graph_memory.py</path>\n<type>file</type>\n<content>\n140: Alias-first resolution mirrors iText2KG's incremental matching.\"\"\"\n141: name = (name or \"\").strip()\n142: if not name:\n143: return None\n144: row = db.execute(\"SELECT id FROM entities WHERE name = ?\", (name,)).fetchone()\n145: if row:\n146: return row[\"id\"]\n147: row = db.execute(\"\"\"\n148: SELECT a.entity_id FROM aliases a JOIN entities e ON a.entity_id = e.id\n149: WHERE lower(a.alias) = lower(?)\"\"\", (name,)).fetchone()\n150: if row:\n151: return row[\"entity_id\"]\n152: cur = db.execute(\n153: \"INSERT INTO entities (name, type, summary, created_at) VALUES (?,?,?,?)\",\n154: (name, node_type if node_type in NODE_TYPES else \"concept\", \"\", now_iso()))\n155: return cur.lastrowid\n156: \n157: \n158: def add_edge(db, subj, pred, obj, subj_type=\"concept\", obj_type=\"concept\",\n159: evidence=\"\", source=\"\", confidence=None, strength=None):\n160: \"\"\"Add a fact to the graph with write-time gating + supersession.\n161: \n162: - If strength < 3, the fact is gated out (never touches the graph).\n163: - On a contradiction (same subj-pred-obj already active), the old edge is\n164: marked superseded and the new one wins — history preserved.\n165: \"\"\"\n166: pred = (pred or \"\").strip().lower().replace(\" \", \"_\")\n167: if pred not in PREDICATES:\n168: import logging\n169: logging.getLogger(\"graph_memory\").warning(\n170: \"unknown predicate '%s' refused (allowed: %d)\", pred, len(PREDICATES))\n171: return False\n172: if confidence is None:\n173: confidence = strength_to_conf(strength)\n174: if confidence is None:\n175: import logging\n176: logging.getLogger(\"graph_memory\").debug(\n177: \"write-gated (strength %s < 3) — skipped\", strength)\n178: return False\n179: sid = resolve_entity(db, subj, subj_type)\n180: oid = resolve_entity(db, obj, obj_type)\n181: if sid is None or oid is None or sid == oid:\n182: return False\n183: # Check for existing active edge with same (subj,pred,obj).\n184: old = db.execute(\"\"\"\n185: SELECT id, confidence, evidence FROM edges WHERE subj_id=? AND pred=? AND obj_id=?\n186: AND status='active'\"\"\", (sid, pred, oid)).fetchone()\n187: ts = now_iso()\n188: if old:\n189: # Only supersede if new evidence is stronger or different.\n190: # If same confidence and same evidence, this is a duplicate — skip.\n191: old_conf = old[\"confidence\"] or 0\n192: new_conf = confidence or 0\n193: old_evidence = (old[\"evidence\"] or \"\").strip()\n194: new_evidence = (evidence or \"\").strip()[:400]\n195: if new_conf <= old_conf and new_evidence == old_evidence:\n196: # Duplicate fact — no change needed\n197: return True\n198: # Supersede: insert new edge FIRST, then mark old as superseded\n199: # (audit fix: prevents orphaned edges on crash)\n200: cur = db.execute(\"\"\"\n201: INSERT INTO edges (subj_id, pred, obj_id, valid_from, valid_until,\n202: learned_at, confidence, evidence, source, status)\n203: VALUES (?,?,?,?,NULL,?,?,?,?,'active')\"\"\",\n204: (sid, pred, oid, ts, ts, confidence, (evidence or \"\")[:400], source or \"\"))\n205: new_id = cur.lastrowid\n206: if old:\n207: db.execute(\"UPDATE edges SET status='superseded', superseded_by=? WHERE id=?\",\n208: (new_id, old[\"id\"]))\n209: db.execute(\"INSERT OR IGNORE INTO aliases (entity_id, alias) VALUES (?,?)\",\n210: (sid, subj.strip()))\n211: db.execute(\"INSERT OR IGNORE INTO aliases (entity_id, alias) VALUES (?,?)\",\n212: (oid, obj.strip()))\n213: db.commit()\n214: return True\n215: \n216: \n217: def query(db, term, hops=1, latest_only=True):\n218: \"\"\"Find an entity by name/alias then BFS `hops` deep, returning compact\n219: facts. Follows the research: vector/lexical finds the entry node, graph\n220: traverses from it (HippoRAG-style).\"\"\"\n221: term = (term or \"\").strip()\n222: if not term:\n223: return []\n224: eid = None\n225: row = db.execute(\"SELECT id, name, type FROM entities WHERE lower(name)=lower(?)\",\n226: (term,)).fetchone()\n227: if not row:\n228: row = db.execute(\"\"\"\n229: Line truncated
|
||||
{"type":"tool_use","timestamp":1788956595629,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"type":"tool","tool":"read","callID":"call_fcff0c0f433f496797000d79","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/src/rune/graph_memory.py","limit":30,"offset":70},"output":"<path>/mnt/workdrive/issue161-harness/src/rune/graph_memory.py</path>\n<type>file</type>\n<content>\n70: NODE_TYPES = {\"person\", \"project\", \"concept\", \"tool\", \"preference\",\n71: \"organization\", \"place\", \"event\", \"skill\", \"artefact\"}\n72: \n73: # Confidence: evidence-gated (EvoKG-style f(frequency, recency, source_cred)).\n74: # We fold the curator's strength (0-5) into a 0-1 confidence.\n75: DEFAULT_CONF = 0.7\n76: \n77: \n78: def now_iso():\n79: return datetime.now(timezone.utc).isoformat(timespec=\"seconds\")\n80: \n81: \n82: def connect():\n83: os.makedirs(GRAPH_DIR, exist_ok=True)\n84: db = sqlite3.connect(DB_PATH)\n85: try:\n86: db.row_factory = sqlite3.Row\n87: db.executescript(\"\"\"\n88: CREATE TABLE IF NOT EXISTS entities (\n89: id INTEGER PRIMARY KEY AUTOINCREMENT,\n90: name TEXT NOT NULL UNIQUE,\n91: type TEXT NOT NULL DEFAULT 'concept',\n92: summary TEXT DEFAULT '',\n93: created_at TEXT NOT NULL\n94: );\n95: CREATE TABLE IF NOT EXISTS aliases (\n96: entity_id INTEGER NOT NULL,\n97: alias TEXT NOT NULL,\n98: UNIQUE(entity_id, alias),\n99: FOREIGN KEY(entity_id) REFERENCES entities(id)\n\n(Showing lines 70-99 of 416. Use offset=100 to continue.)\n</content>","metadata":{"preview":"NODE_TYPES = {\"person\", \"project\", \"concept\", \"tool\", \"preference\",\n \"organization\", \"place\", \"event\", \"skill\", \"artefact\"}\n\n# Confidence: evidence-gated (EvoKG-style f(frequency, recency, source_cred)).\n# We fold the curator's strength (0-5) into a 0-1 confidence.\nDEFAULT_CONF = 0.7\n\n\ndef now_iso():\n return datetime.now(timezone.utc).isoformat(timespec=\"seconds\")\n\n\ndef connect():\n os.makedirs(GRAPH_DIR, exist_ok=True)\n db = sqlite3.connect(DB_PATH)\n try:\n db.row_factory = sqlite3.Row\n db.executescript(\"\"\"\n CREATE TABLE IF NOT EXISTS entities (\n id INTEGER PRIMARY KEY AUTOINCREMENT,","truncated":true,"loaded":[]},"title":"src/rune/graph_memory.py","time":{"start":1788956595613,"end":1788956595626}},"id":"prt_g001a0861f51a9001t6s24bhI5","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","messageID":"msg_g001a0861f22ce001oycsoWDtI"}}
|
||||
{"type":"step_finish","timestamp":1788956595655,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a0861f55ac001nZO964t1P","reason":"tool-calls","snapshot":"b34d2d099aabe92489fd361206ced4a93f2f3309","messageID":"msg_g001a0861f22ce001oycsoWDtI","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","type":"step-finish","tokens":{"total":57919,"input":57845,"output":74,"reasoning":0,"cache":{"write":0,"read":0}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788956619819,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a0861fb429001Em13BY7ln","messageID":"msg_g001a0861f55f2001yiu3SAQvn","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","snapshot":"5bfd5ad858772d2f50d4fb88329d3a0e4e7656d2","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788956620994,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a0861fb59b001q9aiTsgzR","messageID":"msg_g001a0861f55f2001yiu3SAQvn","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","type":"text","text":"Line citations check out so far (:78-79, :147-151, :154, :187, :200-212, :217, :228-231 all match). Verifying the remaining cited spots: the outgoing/incoming edge queries (:254/:275) and the dead docstring at :37.","time":{"start":1788956620187,"end":1788956620991}}}
|
||||
{"type":"tool_use","timestamp":1788956621005,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"type":"tool","tool":"read","callID":"call_96070d79ed864d3599bcaf83","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/src/rune/graph_memory.py","limit":18,"offset":30},"output":"<path>/mnt/workdrive/issue161-harness/src/rune/graph_memory.py</path>\n<type>file</type>\n<content>\n30: graph_memory.py add <subj> <pred> <obj> --type subj_type --evidence \"...\"\n31: [--source X] [--confidence 0.0-1.0] [--strength N]\n32: graph_memory.py add-bulk <json-file>\n33: graph_memory.py query <entity-or-concept> [--hops N] [--latest-only]\n34: graph_memory.py resolve <name>\n35: graph_memory.py neighbors <entity>\n36: graph_memory.py stats\n37: graph_memory.py superseded-by <subj> <pred> <obj> (manual conflict flag)\n38: \"\"\"\n39: \n40: import argparse\n41: import json\n42: import os\n43: import sqlite3\n44: import sys\n45: from collections import deque\n46: from datetime import datetime, timezone\n47: \n\n(Showing lines 30-47 of 416. Use offset=48 to continue.)\n</content>","metadata":{"preview":" graph_memory.py add <subj> <pred> <obj> --type subj_type --evidence \"...\"\n [--source X] [--confidence 0.0-1.0] [--strength N]\n graph_memory.py add-bulk <json-file>\n graph_memory.py query <entity-or-concept> [--hops N] [--latest-only]\n graph_memory.py resolve <name>\n graph_memory.py neighbors <entity>\n graph_memory.py stats\n graph_memory.py superseded-by <subj> <pred> <obj> (manual conflict flag)\n\"\"\"\n\nimport argparse\nimport json\nimport os\nimport sqlite3\nimport sys\nfrom collections import deque\nfrom datetime import datetime, timezone\n","truncated":true,"loaded":[]},"title":"src/rune/graph_memory.py","time":{"start":1788956620992,"end":1788956621003}},"id":"prt_g001a0861fb5a7001T0osh0u24","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","messageID":"msg_g001a0861f55f2001yiu3SAQvn"}}
|
||||
{"type":"tool_use","timestamp":1788956621007,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"type":"tool","tool":"read","callID":"call_14647c4e0c2e42fc8f206933","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/src/rune/graph_memory.py","limit":45,"offset":240},"output":"<path>/mnt/workdrive/issue161-harness/src/rune/graph_memory.py</path>\n<type>file</type>\n<content>\n240: seen = {eid}\n241: frontier = deque([(eid, 0)])\n242: while frontier:\n243: cur, depth = frontier.popleft()\n244: if depth > hops:\n245: break\n246: sql = \"\"\"\n247: SELECT e.id AS sid, e.name AS sname, e.type AS stype,\n248: ed.pred, o.id AS oid, o.name AS oname, o.type AS otype,\n249: ed.valid_from, ed.valid_until, ed.confidence, ed.status,\n250: ed.source, ed.evidence\n251: FROM edges ed\n252: JOIN entities e ON ed.subj_id=e.id\n253: JOIN entities o ON ed.obj_id=o.id\n254: WHERE ed.subj_id=? AND ed.status='active'\n255: \"\"\"\n256: params = [cur]\n257: for r in db.execute(sql, params):\n258: results.append({\n259: \"subject\": r[\"sname\"], \"predicate\": r[\"pred\"],\n260: \"object\": r[\"oname\"], \"confidence\": r[\"confidence\"],\n261: \"from\": r[\"valid_from\"], \"until\": r[\"valid_until\"],\n262: \"status\": r[\"status\"], \"source\": r[\"source\"],\n263: })\n264: if r[\"oid\"] not in seen:\n265: seen.add(r[\"oid\"])\n266: if depth + 1 <= hops:\n267: frontier.append((r[\"oid\"], depth + 1))\n268: # Also include edges where this entity is the object (incoming).\n269: sql = \"\"\"\n270: SELECT s.name AS sname, ed.pred, o.name AS oname, o.type AS otype,\n271: ed.valid_from, ed.valid_until, ed.confidence, ed.status, ed.source\n272: FROM edges ed\n273: JOIN entities s ON ed.subj_id=s.id\n274: JOIN entities o ON ed.obj_id=o.id\n275: WHERE ed.obj_id=? AND ed.status='active'\n276: \"\"\"\n277: for r in db.execute(sql, (eid,)):\n278: results.append({\n279: \"subject\": r[\"sname\"], \"predicate\": r[\"pred\"],\n280: \"object\": r[\"oname\"], \"confidence\": r[\"confidence\"],\n281: \"from\": r[\"valid_from\"], \"until\": r[\"valid_until\"],\n282: \"status\": r[\"status\"], \"source\": r[\"source\"],\n283: })\n284: return {\"entity\": name, \"type\": etype, \"facts\": results}\n\n(Showing lines 240-284 of 416. Use offset=285 to continue.)\n</content>","metadata":{"preview":" seen = {eid}\n frontier = deque([(eid, 0)])\n while frontier:\n cur, depth = frontier.popleft()\n if depth > hops:\n break\n sql = \"\"\"\n SELECT e.id AS sid, e.name AS sname, e.type AS stype,\n ed.pred, o.id AS oid, o.name AS oname, o.type AS otype,\n ed.valid_from, ed.valid_until, ed.confidence, ed.status,\n ed.source, ed.evidence\n FROM edges ed\n JOIN entities e ON ed.subj_id=e.id\n JOIN entities o ON ed.obj_id=o.id\n WHERE ed.subj_id=? AND ed.status='active'\n \"\"\"\n params = [cur]\n for r in db.execute(sql, params):\n results.append({\n \"subject\": r[\"sname\"], \"predicate\": r[\"pred\"],","truncated":true,"loaded":[]},"title":"src/rune/graph_memory.py","time":{"start":1788956620994,"end":1788956621006}},"id":"prt_g001a0861fb5ab001bmq5yEXX3","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","messageID":"msg_g001a0861f55f2001yiu3SAQvn"}}
|
||||
{"type":"step_finish","timestamp":1788956621034,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a0861fb8cf001T0Tf342E1","reason":"tool-calls","snapshot":"38ba8e6e9c3a714aac1bd9c13efe573af7ae2060","messageID":"msg_g001a0861f55f2001yiu3SAQvn","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","type":"step-finish","tokens":{"total":59978,"input":1768,"output":354,"reasoning":0,"cache":{"write":0,"read":57856}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788956647072,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a086201e9e001kDbxklA4h","messageID":"msg_g001a0861fb90d001Zcrk91Ejg","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","snapshot":"6d6903e274905229bd35c444ad6d77a74d45eec6","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788956668558,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a0862021530020ldGhlZs9","messageID":"msg_g001a0861fb90d001Zcrk91Ejg","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","type":"text","text":"All line citations verified against the live source (`issue161-harness/src/rune/graph_memory.py`): `now_iso` at :78-79, unordered alias lookup at :147-151, entity creation at :152-155, supersede block at :206-208, alias `INSERT OR IGNORE` at :209-212, `query` signature at :217, bare `LIMIT 1` at :228-231, `status='active'` filters at :254/:275, dead docstring at :37 — every anchor matches. The round-2 synthesis demonstrably resolves all three round-1 QUALIFY findings. Writing the PROCEED verdict.","time":{"start":1788956647764,"end":1788956668555}}}
|
||||
{"type":"tool_use","timestamp":1788956668562,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"type":"tool","tool":"write","callID":"call_f5b2513af1484c4bb9d99453","state":{"status":"completed","input":{"content":"{\n \"outcome\": \"PROCEED\",\n \"findings\": [\n \"INTERFACE CONCRETE: the round-1 INTERFACE GAP (M3/FTS5) is resolved by committed design, not delegation — non-contentless DDL 'CREATE VIRTUAL TABLE graph_lex USING fts5(name, alias, entity_id UNINDEXED, tokenize=''unicode61'')', one row per (entity_id, text) over entities.name ∪ aliases.alias, entity_id UNINDEXED as the sole row→entity mapping, INSERT-only maintenance (name-row on entity creation at :152-155; alias-row only when INSERT OR IGNORE rowcount==1 at :209-212), and a named rebuild-lex bulk path. A coder can implement every piece without inventing structure. The rejected contentless and rowid-packed alternatives are documented with reasons, preventing silent resurrection.\",\n \"INTERFACE CONCRETE: M1a supersede fix is a verbatim UPDATE (status, superseded_by, valid_until) pinned to graph_memory.py:206-208, using the same ts from :187; the empty-DB premise for free backfill is independently verified (adversarial A1/A5 live-DB receipts: 0 entities/aliases/edges, 0 valid_until non-null).\",\n \"INTERFACE CONCRETE: normalize_as_of() is fully specified — accepted input forms ('Z' suffix, '-05:00' offset, fractional seconds), output format (UTC '+00:00', timespec='seconds' matching now_iso at :78-79), failure mode (ValueError, never lexicographic comparison of raw input), and the exact filter clause replacement at both :254 and :275.\",\n \"PARAMETER CONCRETE: the round-1 PARAMETER GAP is closed — N=4 weeks and <50-edges threshold are pre-committed in INV-5, sequence step 9, AC9, and test_population_gate_harness, with an explicit no-rechoice-after-observation rule.\",\n \"MINOR GAP CLOSED: the alias sort key is named verbatim (ORDER BY a.entity_id ASC at BOTH :228-231 and :147-151), matching the two nondeterminism sites actually present in the source; INV-8 and its test are now mechanically checkable.\",\n \"SEQUENCE IMPLEMENTABLE: steps 0-9 are in dependency order (pre-flight selector check → write-side M1a+tests → normalize_as_of → population gate check → as-of filter → FTS5 projection → kill tests → selector-floor test → docs → T5 evaluation); no step requires an artifact built by a later step, and the read-layer-before-write-side ordering hazard is explicitly gated (steps 1, 3; AC10).\",\n \"ACCEPTANCE CRITERIA OBSERVABLE: AC1-AC7 and AC9 map 1:1 to the 12 required tests, each with concrete pass/fail conditions (exact edge counts at pinned timestamps, ≥30% held-out resolution AND floor survival at memory_selector output, ≤10% latency delta with commits held constant, roundtrip row-set equality). AC8 is a documentation check with the exact replacement characterization quoted; AC10 is verifiable from change history.\",\n \"AUTHORITY BOUNDARIES RESPECTED: graph_memory remains sole writer to graph.sqlite; graph_lex lives inside the same DB as a rebuildable projection (no second store, no second writer); memory_selector._graphs_recall and curator/sleep-time write paths are explicitly non-modification targets; the M7 constraint pre-applies to any future C9 sidecar. No new authority paths introduced.\",\n \"ROLLBACK REAL: all changes are additive to a single SQLite file — remove --as-of and the FTS5 tier (default path restored exactly per INV-10, guarded by test_as_of_default_path_unchanged), DROP graph_lex (projection, no data loss), and M1a stands alone backward-compatibly (NULL valid_until remains interpretable as open-ended; no legacy backfill exists to unwind). No migration, service, or export involved.\",\n \"CODE ANCHORS VERIFIED: spot-checked all load-bearing line citations against src/rune/graph_memory.py — now_iso :78-79, alias lookup :147-151, entity insert :152-155, supersede :206-208, alias INSERT OR IGNORE :209-212, commit :213, query signature :217, LIMIT 1 :228-231, active filters :254/:275, dead docstring :37. All match; the dossier can be handed over verbatim.\"\n ],\n \"reopen\": null\n}\n","file_path":"/mnt/workdrive/issue161-harness/research/issue-163/qualify.json"},"output":"Wrote file successfully.","metadata":{"diagnostics":{},"diff":"Index: /mnt/workdrive/issue161-harness/research/issue-163/qualify.json\n===================================================================\n--- /mnt/workdrive/issue161-harness/research/issue-163/qualify.json\n+++ /mnt/workdrive/issue161-harness/research/issue-163/qualify.json\n@@ -1,8 +1,16 @@\n {\n- \"outcome\": \"REFINE_SYNTHESIS\",\n+ \"outcome\": \"PROCEED\",\n \"findings\": [\n- \"INTERFACE GAP (M3/FTS5 projection — blocks verbatim handoff): the declared DDL 'CREATE VIRTUAL TABLE graph_lex USING fts5(name, alias, content='')' cannot be implemented as specified without the coder inventing a design. (a) ALine truncated
|
||||
{"type":"step_finish","timestamp":1788956668588,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a086207292001sP3CCRuoM","reason":"tool-calls","snapshot":"2ebbbdbfea33c8d3de8341c43c1be5bf47c4ee11","messageID":"msg_g001a0861fb90d001Zcrk91Ejg","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","type":"step-finish","tokens":{"total":62301,"input":1014,"output":1639,"reasoning":0,"cache":{"write":0,"read":59648}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788956676808,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a0862092ba001PkDcUCBKN","messageID":"msg_g001a0862072d50017PXc1t7g0","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","snapshot":"cffce81fdae55b2b95f1ac24770eb692d500d13d","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788956686040,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a0862092d0002V2wl53gTb","messageID":"msg_g001a0862072d50017PXc1t7g0","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","type":"text","text":"QUALIFY review complete — verdict written to `research/issue-163/qualify.json`.\n\n**Outcome: PROCEED**\n\nWhat I checked (implementation-readiness only):\n\n1. **Round-1 findings resolved** — The prior QUALIFY verdict had 3 gaps. Round 2 closes all three by *committing* decisions rather than delegating: the FTS5 layout (non-contentless, one row per `(entity_id, text)`, `entity_id UNINDEXED` as sole mapping, INSERT-only protocol), the T5 gate parameters (N=4 weeks, <50 edges, pre-committed with a no-rechoice rule), and the alias sort key (`ORDER BY a.entity_id ASC` at both nondeterminism sites).\n\n2. **Code anchors verified against live source** — I spot-checked every load-bearing line citation in `graph_memory.py`: `now_iso` (:78-79), alias lookup (:147-151), supersede block (:206-208), alias `INSERT OR IGNORE` (:209-212), bare `LIMIT 1` (:228-231), `status='active'` filters (:254/:275), dead docstring (:37). All match — the handoff is verbatim-executable.\n\n3. **The five readiness checks:**\n - *Interfaces*: all concrete (M1a UPDATE, `normalize_as_of` with accepted/error forms, graph_lex DDL + maintenance, CLI, deterministic ordering) — no coder guesswork required.\n - *Sequence*: steps 0-9 are dependency-ordered; write-side-before-read-layer is explicitly gated (AC10).\n - *Acceptance criteria*: AC1-AC7/AC9 map 1:1 to 12 tests with measurable pass conditions; AC8/AC10 are mechanically verifiable.\n - *Authority boundaries*: respected — graph_memory stays sole writer, graph_lex is a same-DB projection, selector and curator paths are non-modification targets.\n - *Rollback*: real — additive-only changes, projection DROP is lossless, M1a backward-compatible, default path restored exactly (INV-10 guarded by a test).\n\nNo `reopen` needed — nothing new surfaced that earlier gates could have missed.","time":{"start":1788956676816,"end":1788956686038}}}
|
||||
{"type":"step_finish","timestamp":1788956686063,"sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","part":{"id":"prt_g001a08620b6d80013e1DyhZz0","reason":"stop","snapshot":"d398f1b9182b8b9b28a1fa45ec1fdf096289e963","messageID":"msg_g001a0862072d50017PXc1t7g0","sessionID":"ses_-ffe5f79e20228ffeq2voHZxjl","type":"step-finish","tokens":{"total":62270,"input":1132,"output":466,"reasoning":0,"cache":{"write":0,"read":60672}},"cost":0}}
|
||||
@@ -0,0 +1,33 @@
|
||||
{"type":"step_start","timestamp":1788955801271,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a0861336b4001LvNyLbAOr","messageID":"msg_g001a0861320ec001EMlKL2AuH","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","snapshot":"c2ce0b10c4c71c931f26e81651bc9c0cdaac581b","type":"step-start"}}
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{"type":"text","timestamp":1788956038731,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a08616c9fc001shKnAeHuA","messageID":"msg_g001a0861320ec001EMlKL2AuH","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","type":"text","text":"I'll start with the mandated skill search, and in parallel locate the prior-stage artifacts and the code files the mitigations reference, so the synthesis is grounded in the actual repo rather than guesswork.","time":{"start":1788956035581,"end":1788956038728}}}
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{"type":"tool_use","timestamp":1788956038737,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"type":"tool","tool":"skill_search","callID":"call_d85c3e346f214f41a83c3666","state":{"status":"completed","input":{"query":"Write a structured JSON design-decision dossier (synthesis stage) from settled research findings for a software issue; input: prior stage artifacts and constraints; output: research/issue-163/synthesis.json with selected_design, invariants, tests; audience: coding worker. No external research."},"output":"{\n \"status\": \"matched\",\n \"results\": [\n {\n \"skill_id\": \"design-blueprint\",\n \"name\": \"design-blueprint\",\n \"score\": 0.6083,\n \"reason\": \"The skill description matches these query terms: structured, design, decision, artifact.\"\n },\n {\n \"skill_id\": \"product-design\",\n \"name\": \"product-design\",\n \"score\": 0.5733,\n \"reason\": \"The skill description matches these query terms: design, research, selected.\"\n },\n {\n \"skill_id\": \"frontend-design\",\n \"name\": \"frontend-design\",\n \"score\": 0.5685,\n \"reason\": \"The skill description matches these query terms: design, artifact, constraint.\"\n }\n ],\n \"loaded_skill_id\": null\n}","metadata":{"status":"matched","loadedSkillID":null,"truncated":false},"title":"Found 3 skills","time":{"start":1788956038730,"end":1788956038733}},"id":"prt_g001a08616d4da001a186pbSDp","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","messageID":"msg_g001a0861320ec001EMlKL2AuH"}}
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{"type":"tool_use","timestamp":1788956038753,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"type":"tool","tool":"glob","callID":"call_30ca8d8dabaf40abafacbeda","state":{"status":"completed","input":{"pattern":"**/graph_memory.py"},"output":"/mnt/workdrive/issue161-harness/src/rune/graph_memory.py","metadata":{"count":1,"truncated":false},"title":"","time":{"start":1788956038735,"end":1788956038752}},"id":"prt_g001a08616d5d6001mCjXptUkW","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","messageID":"msg_g001a0861320ec001EMlKL2AuH"}}
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{"type":"tool_use","timestamp":1788956038761,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"type":"tool","tool":"glob","callID":"call_7934fb5dc2184b13aadbb898","state":{"status":"completed","input":{"pattern":"**/canary_p0_04.py"},"output":"/mnt/workdrive/issue161-harness/src/rune/canary_p0_04.py","metadata":{"count":1,"truncated":false},"title":"","time":{"start":1788956038736,"end":1788956038760}},"id":"prt_g001a08616d5d7002mePPFrIbH","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","messageID":"msg_g001a0861320ec001EMlKL2AuH"}}
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{"type":"tool_use","timestamp":1788956038767,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"type":"tool","tool":"glob","callID":"call_b2521579390b42b893a41eb7","state":{"status":"completed","input":{"pattern":"research/issue-163/**"},"output":"/mnt/workdrive/issue161-harness/research/issue-163/stage_SYNTHESIS.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/stage_SYNTHESIS.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-SYNTHESIS-2\n/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_QUALIFY.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/qualify.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_QUALIFY.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-QUALIFY-1\n/mnt/workdrive/issue161-harness/research/issue-163/synthesis.json\n/mnt/workdrive/issue161-harness/research/issue-163/adversarial.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_ADVERSARIAL.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/stage_ADVERSARIAL.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/stage_INNOVATION.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/innovation.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_INNOVATION.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-INNOVATION-1\n/mnt/workdrive/issue161-harness/research/issue-163/stage_APPLICABILITY.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/applicability.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_APPLICABILITY.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-APPLICABILITY-1\n/mnt/workdrive/issue161-harness/research/issue-163/stage_OUTWARD.events.jsonl\n/mnt/workdrive/issue161-harness/research/issue-163/outward.json\n/mnt/workdrive/issue161-harness/research/issue-163/stage_OUTWARD.mimo.err.log\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-OUTWARD-1\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-INWARD-1\n/mnt/workdrive/issue161-harness/research/issue-163/.consumed_issue-163-ISSUE-1","metadata":{"count":26,"truncated":false},"title":"","time":{"start":1788956038732,"end":1788956038763}},"id":"prt_g001a08616d5d40019R4JtpXiU","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","messageID":"msg_g001a0861320ec001EMlKL2AuH"}}
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||||
{"type":"step_finish","timestamp":1788956038792,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a08616d66e0012ax6SSx28","reason":"tool-calls","snapshot":"118effec57af272e83a862094721e0c094298ffc","messageID":"msg_g001a0861320ec001EMlKL2AuH","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","type":"step-finish","tokens":{"total":40012,"input":30172,"output":158,"reasoning":9618,"cache":{"write":0,"read":64}},"cost":0}}
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||||
{"type":"step_start","timestamp":1788956044255,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a08616ebdc001NCmFEfF0I","messageID":"msg_g001a08616d6ab0011Uav9RIVf","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","snapshot":"23ab9a615f7f7a03fb1d91c01f25460e3c059993","type":"step-start"}}
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{"type":"text","timestamp":1788956054024,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a08617062e001sf1UqVnZw","messageID":"msg_g001a08616d6ab0011Uav9RIVf","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","type":"text","text":"Skill search returned only visual-design skills (for slides/landing pages) — none matches a JSON design-dossier synthesis, so I'll proceed without a skill. Now reading the prior-stage artifacts and the referenced source files to ground the dossier.","time":{"start":1788956050990,"end":1788956054021}}}
|
||||
{"type":"tool_use","timestamp":1788956054036,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"type":"tool","tool":"read","callID":"call_03aaa7b7008841128ab23887","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/research/issue-163/applicability.json"},"output":"<path>/mnt/workdrive/issue161-harness/research/issue-163/applicability.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"stage\": \"APPLICABILITY\",\n3: \"issue\": 163,\n4: \"round\": 1,\n5: \"process_id\": \"research-163-r1\",\n6: \"grounding\": {\n7: \"inward_claims_used\": [\"I3\", \"I1\", \"I4\", \"I5\"],\n8: \"rune_reality\": \"src/rune/graph_memory.py is Rune's entire graph surface: a headless SQLite tier at memory/graph/graph.sqlite (no external graph DB dependency). Schema (lines 101-116) already implements: bi-temporal edges (valid_from/valid_until + learned_at), supersede-never-delete with status/superseded_by, per-edge provenance (evidence/source), confidence-gated writes (strength<3 gated out), typed concept-mediated nodes, and alias-based entity resolution. Query is name/alias/substring entry point + BFS over status='active' edges.\",\n9: \"load_bearing_fact\": \"The dossier's repo_grounding_note and Graphiti (E17) confirm temporal semantics live at the framework layer above any backend. Rune already occupies that layer over SQLite. Neo4j and FalkorDB would be backends BELOW a layer Rune already owns, and neither offers native temporal versioning (E27, E28) - Rune's schema is already ahead of both on the spike's titular dimension.\"\n10: },\n11: \"candidates\": [\n12: {\n13: \"id\": \"C1\",\n14: \"candidate\": \"Neo4j as replacement graph backend\",\n15: \"source_ids\": [\"O1\", \"O2\", \"O38\", \"O41\"],\n16: \"classification\": \"CONFLICTS_WITH_RUNE\",\n17: \"integration_point\": \"Would replace src/rune/graph_memory.py's SQLite tier (memory/graph/graph.sqlite) with a server-backed Neo4j deployment reachable from curator.py / sleep_time.py.\",\n18: \"benefit\": \"Deepest ecosystem, best planner on heavy aggregations (E3), mature tooling (E8).\",\n19: \"cost\": \"Server + JVM process to operate; ~2.7GB heap for a 381K-node graph, ~5.2GB process RSS (E6) - two orders of magnitude above Rune's sub-1M-entity regime; 90ms cold start vs embedded alternatives (E4).\",\n20: \"architectural_fit\": \"Poor. Rune is embedded/local-first with zero external service dependencies (I3: 'reachable by the sleep-time curator directly, no MCP dependency'). A networked JVM server reverses that posture. No source benchmarks Rune's single-node regime (dossier limitation #2).\",\n21: \"authority_implications\": \"Introduces an external service and credentials into the memory tier; curator/sleep-time write authority would route through a network daemon instead of a local file.\",\n22: \"risks\": \"Licensing (Community vs Enterprise constraints unevidenced, dossier limitation #5); no native temporal versioning - the spike's core dimension (E28-E30); migration cost for zero evidenced benefit at Rune's scale.\",\n23: \"required_adaptation\": \"None - rejection is the correct disposition.\",\n24: \"evidence\": [\"E2\", \"E3\", \"E4\", \"E6\", \"E8\", \"E27\", \"E28\", \"E29\"]\n25: },\n26: {\n27: \"id\": \"C2\",\n28: \"candidate\": \"FalkorDB as replacement graph backend\",\n29: \"source_ids\": [\"O1\", \"O2\", \"O5\", \"O29\", \"O10\"],\n30: \"classification\": \"CONFLICTS_WITH_RUNE\",\n31: \"integration_point\": \"Would replace the SQLite tier; only embedded path is Graphiti's falkordblite extra (redislite-based, Python 3.12+) per E16.\",\n32: \"benefit\": \"Strong concurrent throughput and low latency at scale (E2, E3, E9), implicit indexing (E5), GraphRAG positioning (E10).\",\n33: \"cost\": \"Server-shaped: Redis 7.4 module, own container/ports, min 4GB RAM / 16GB recommended (E12, E25, E26). SSPLv1 core license (E25). Rust core rewrite (E11) resets part of the maturity story (dossier contradiction #3).\",\n34: \"architectural_fit\": \"Poor. The headline advantages (6.7x throughput, p50/p99 wins) were measured on 381K-804K-element graphs under 8-32 thread concurrency (E1, E9) - not Rune's regime (single-node, sub-1M entities, agent-local). Vendor magnitudes are marketing-adjacent (dossier contradiction #1; O5 benchmark harness README unverifiable, E13).\",\n35: \"authority_implications\": \"A Redis server in the memory path; even falkordblite drags in a redislite dependency and a Python 3.12 floor for a lateral move from SQLite.\",\n36: \"risks\": \"RedisGraph lineage EOL'd Jan 2025 (dossier contradiction #3); no evidenced FalkorDB-native temporal features (dossier limitation #4); license incompatibility with Rune's MIT-family posture.\",\n37: \"required_adaptation\": \"None - rejection is the correct disposition.\",\n38: \"evidence\": [\Line truncated
|
||||
{"type":"tool_use","timestamp":1788956054038,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"type":"tool","tool":"read","callID":"call_2077d80c062240e68776d9c8","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/research/issue-163/qualify.json"},"output":"<path>/mnt/workdrive/issue161-harness/research/issue-163/qualify.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"outcome\": \"REFINE_SYNTHESIS\",\n3: \"findings\": [\n4: \"INTERFACE GAP (M3/FTS5 projection — blocks verbatim handoff): the declared DDL 'CREATE VIRTUAL TABLE graph_lex USING fts5(name, alias, content='')' cannot be implemented as specified without the coder inventing a design. (a) A contentless FTS5 table has no unindexed columns, so no mapping from FTS rows back to entity ids is specified — yet the FTS5 BM25 resolution tier must return an entity id (rowid=entities.id convention vs. join-on-name are both viable and mutually incompatible). (b) The 1:N entity→aliases relation is unrepresented: the two-column DDL permits one-row-per-entity with packed aliases (packing format unspecified) or one-row-per-alias (which breaks id mapping); nothing selects between them. (c) 'Rows maintained on the existing entity/alias write path (resolve_entity, add_edge)' is not mechanically implementable for a contentless table: add_edge inserts new aliases for already-existing entities (graph_memory.py:209-212), which requires updating that entity's FTS row, and contentless FTS5 supports no UPDATE — the coder would have to invent a delete+reinsert protocol. Required fix: specify the row layout and id mapping (e.g., a non-contentless graph_lex with an entity_id column, or rowid=entities.id with one row per entity and a defined alias-packing format), or explicitly delegate the choice the way the tokenizer decision is delegated.\",\n5: \"PARAMETER GAP (T5/INV-5/AC9 — blocks test_population_gate_harness and sequence step 9): N is never committed. Step 9 says 'after N weeks of real use (N fixed in advance)', INV-5 says 'over N weeks of real use', and AC9 requires 'a pre-committed N and threshold' — but only the threshold (<50 edges) is given; N remains symbolic throughout the artifact. The coding worker cannot finalize the T5 harness or execute step 9 without inventing N, a value the artifact itself assigns to pre-commitment. Required fix: commit a concrete N (e.g., N=4 weeks) in the synthesis.\",\n6: \"MINOR GAP (M3/INV-8 — non-blocking but unpinned): the deterministic alias ordering is specified only as 'ORDER BY added, replacing the bare LIMIT 1 at :228-231' without naming the sort key. INV-8 and test_alias_resolution_deterministic pin the observable property (any stable key satisfies them), so a competent coder can proceed, but naming the key (e.g., ORDER BY a.entity_id ASC) would make the instruction verbatim.\"\n7: ]\n8: }\n\n(End of file - total 8 lines)\n</content>","metadata":{"preview":"{\n \"outcome\": \"REFINE_SYNTHESIS\",\n \"findings\": [\n \"INTERFACE GAP (M3/FTS5 projection — blocks verbatim handoff): the declared DDL 'CREATE VIRTUAL TABLE graph_lex USING fts5(name, alias, content='')' cannot be implemented as specified without the coder inventing a design. (a) A contentless FTS5 table has no unindexed columns, so no mapping from FTS rows back to entity ids is specified — yet the FTS5 BM25 resolution tier must return an entity id (rowid=entities.id convention vs. join-on-name are both viable and mutually incompatible). (b) The 1:N entity→aliases relation is unrepresented: the two-column DDL permits one-row-per-entity with packed aliases (packing format unspecified) or one-row-per-alias (which breaks id mapping); nothing selects between them. (c) 'Rows maintained on the existing entity/alias write path (resolve_entity, add_edge)' is not mechanically implementable for a contentless table: add_edge inserts new aliases for already-existing entities (graph_memory.py:209-212), which requires updating that entity's FTS row, and contentless FTS5 supports no UPDATE — the coder would have to invent a delete+reinsert protocol. Required fix: specify the row layout and id mapping (e.g., a non-contentless graph_lex with an entity_id column, or rowid=entities.id with one row per entity and a defined alias-packing format), or explicitly delegate the choice the way the tokenizer decision is delegated.\",\n \"PARAMETER GAP (T5/INV-5/AC9 — blocks test_population_gate_harness and sequence step 9): N is never committed. Step 9 says 'after N weeks of real use (N fixed in advance)', INV-5 says 'over N weeks of real use', and AC9 requires 'a pre-committed N and threshold' — but only the threshold (<50 edges) is given; N remains symbolic throughout the artifact. The coding worker cannot finalize the T5 harness or execute step 9 without inventing N, a value the artifact itself assigns to pre-commitment. Required fix: commit a concrete N (e.g., N=4 weeks) in the synthesis.\",\n \"MLine truncated
|
||||
{"type":"tool_use","timestamp":1788956054040,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"type":"tool","tool":"read","callID":"call_b5b3ab67a1324e1db1ac67ad","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/research/issue-163/innovation.json"},"output":"<path>/mnt/workdrive/issue161-harness/research/issue-163/innovation.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"stage\": \"INNOVATION\",\n3: \"issue\": 163,\n4: \"round\": 1,\n5: \"process_id\": \"research-163-r1\",\n6: \"route\": \"INNOVATION_WARRANTED\",\n7: \"conclusion\": \"ADAPT\",\n8: \"conclusion_rationale\": \"The standard mechanism (SQLite graph tier queried via status='active' only, exact/alias/substring entry resolution) is NOT sufficient: it cannot read the bi-temporal history Rune already persists (every superseded edge is stored but unreachable through query()), and its entry point caps practical recall on paraphrase/typo. No standard drop-in closes either gap. However, the warranted innovation is strictly adapted-and-compositional, not novel-for-its-own-sake: NEP-001's AS-OF WHERE-convention (E28-E30) and Graphiti's hybrid-retrieval entry tier (E18) are adapted onto Rune's existing framework-over-SQLite layer (which already implements the semantic model 1:1, E14/E17 — C4), producing a 'temporal+lexical query layer over graph.sqlite'. Backend migration is explicitly out of scope (C1/C2/C3 rejected or deferred at APPLICABILITY; C9 tracked, not proposed).\",\n9: \"candidates\": [\n10: {\n11: \"id\": \"INN-1\",\n12: \"category\": \"ADAPT\",\n13: \"proposal\": \"A unified temporal+lexical query layer over the existing graph.sqlite, inside src/rune/graph_memory.py, with zero schema or backend change. Two read-layer components that share one integration surface (query(), lines 217-284): (1) TEMPORAL — an --as-of <iso8601> parameter that replaces the status='active' filter with valid_from <= t AND (valid_until IS NULL OR t < valid_until) AND learned_at <= t, applied identically to the outgoing BFS frontier (line 254) and the incoming-edge query (line 275); BFS structure unchanged; default behavior (no flag) keeps the current active-only fast path. (2) LEXICAL — a SQLite FTS5 virtual table over entities(name, summary) and aliases(alias), inserted as a fallback tier in entity resolution (lines 224-237) with lookup order exact -> alias -> FTS5 BM25 -> substring-LIKE; index maintained on the existing entity/alias write path (resolve_entity, add_edge) in the same DB file. Full embedding tier deliberately omitted — model dependencies are disproportionate to Rune's sub-1M regime.\",\n14: \"derived_from\": [\"E28\", \"E29\", \"E30\", \"E18\", \"E14\", \"E17\", \"E27\"],\n15: \"source_ids\": [\"O38\", \"O41\", \"O10\", \"O1\"],\n16: \"composition_of\": [\n17: \"C5 (NEP-001 Phase 1 'AS OF' syntactic sugar, APPLICABLE_WITH_ADAPTATION)\",\n18: \"C6 (Graphiti hybrid retrieval, reduced to its lexical BM25 tier, APPLICABLE_WITH_ADAPTATION)\",\n19: \"Rune's existing bi-temporal edges schema + supersede-not-delete history (already integrated per C4)\"\n20: ],\n21: \"novelty\": \"compositional\",\n22: \"expected_gain\": \"Two material capabilities the status quo lacks, both read-only on data already on disk: (a) time-travel queries — every contradiction ever superseded becomes queryable as-of any past timestamp, closing the exact gap NEP-001 documents (E29: hand-written temporal WHERE clauses are error-prone and do not compose; E30: Phase 1 is low complexity and delivers most of the value); (b) recall lift at the entry point — paraphrase/typo/alternate-naming terms that currently fail resolution fall through to BM25, which Graphiti credits for its sub-second retrieval (E18), without any new service or dependency (FTS5 is in-process SQLite). Combined, the graph's stored history and practical recall stop being capped by the two read-layer shortcuts.\",\n23: \"risks\": \"(1) learned_at is a transaction-time proxy, not true bitemporality (E30 defers the real thing at 'Very high' complexity) — must be documented as as-of-knowledge approximation. (2) Half-open-interval and NULL-as-open-ended pitfalls are the documented failure mode of exactly this convention (E29) — they become a mandatory test checklist, not an afterthought. (3) FTS5 write amplification on add-bulk paths and index/tokenizer decisions. (4) BM25 ranking quality is evidenced only via Graphiti's claim, not measured at Rune's regime (dossier limitation #7) — hence the kill test below. (5) No Rune-regime benchmark exists (dossier limitation #2), so gains are predicted, not measured, until the kill test runs.\",\n24: \"kill_test\": \"Rune-local benchmark on the real curator vocabulary and a seeded history: (a) TEMPORAL — if querying as-of realistic historical timestamps returns fact sets identical to the active-only set for every t Line truncated
|
||||
{"type":"tool_use","timestamp":1788956054049,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"type":"tool","tool":"read","callID":"call_e0ef9e81d5be4f8f9101efe6","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/research/issue-163/adversarial.json"},"output":"<path>/mnt/workdrive/issue161-harness/research/issue-163/adversarial.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"stage\": \"ADVERSARIAL\",\n3: \"issue\": 163,\n4: \"round\": 1,\n5: \"process_id\": \"research-163-r1\",\n6: \"attacked\": [\n7: \"C4\",\n8: \"C5\",\n9: \"C6\",\n10: \"C9\",\n11: \"INN-1\"\n12: ],\n13: \"surviving_candidates\": [\n14: {\n15: \"id\": \"C4\",\n16: \"disposition\": \"SURVIVES_WITH_DOWNGRADE\",\n17: \"note\": \"The claim 'implements E14's model 1:1' is contradicted by the code (attacks A1, A11): the tier is a transaction-time append log with same-triple supersession, NOT a bi-temporal model with automatic contradiction invalidation. The candidate survives as 'partially integrated'; downstream dossiers must not repeat the 1:1 characterization.\"\n18: },\n19: {\n20: \"id\": \"C5\",\n21: \"disposition\": \"SURVIVES_ONLY_IN_CORRECTED_FORM\",\n22: \"note\": \"AS-OF time-travel over preserved history is a real, low-cost capability \\u2014 but the INN-1 filter formulation is falsified (attack A2). Survives only via mitigation M1: either write valid_until on supersede (one line) or implement transaction-time as-of (learned_at + superseded_by chain) and drop the valid-time half until a writer populates valid windows.\"\n23: },\n24: {\n25: \"id\": \"C6\",\n26: \"disposition\": \"SURVIVES_CONDITIONAL\",\n27: \"note\": \"Mechanism is sound and FTS5 is available (verified: sqlite 3.53.4 in this runtime). But the benefit chain is broken twice today: the production graph is EMPTY (attack A5) and graph recall is re-floored by memory_selector (attack A6). Survives only with the projection-rule framing (M3), selector-floor test (M4), and write-side sequencing (M5).\"\n28: },\n29: {\n30: \"id\": \"C9\",\n31: \"disposition\": \"SURVIVES_TRACK_ONLY\",\n32: \"note\": \"Uncontested as a future lead; attacks A9/A10 only constrain its future form: DuckDB/Arrow analytics must be a rebuildable read-only projection of graph.sqlite, never a second store, and must never acquire independent write authority.\"\n33: }\n34: ],\n35: \"rejected_candidates\": [\n36: {\n37: \"id\": \"INN-1#temporal-filter-as-specified\",\n38: \"reason\": \"REJECTED as literally specified \\u2014 do NOT carry to SYNTHESIS. The filter 'valid_from <= t AND (valid_until IS NULL OR t < valid_until) AND learned_at <= t' applied to the current writer's data returns STALE SUPERSEDED FACTS as if believed: supersede (graph_memory.py:207-208) sets status/superseded_by but never valid_until, so superseded edges remain NULL-open on the valid axis and pass all three predicates for every t after their creation. An as-of query would return the superseded AND superseding edges of a contradiction simultaneously \\u2014 presenting both sides of a contradiction as current knowledge, the exact failure the feature exists to prevent. C5 itself survives after rework (see surviving_candidates); this exact formulation does not. This is E29's documented NULL-as-open-ended pitfall realized in the proposal spec, not merely in hand-written Cypher.\"\n39: }\n40: ],\n41: \"attacks\": [\n42: {\n43: \"id\": \"A1\",\n44: \"target_candidate\": \"C4\",\n45: \"dimension\": \"contradicting_evidence + weak_assumptions\",\n46: \"finding\": \"The 'bi-temporal' claim is schema-only. valid_until is NEVER written anywhere in the repo (grep over src/rune: appears only in DDL line 107, INSERT writing NULL at line 201, and SELECTs); status='expired' has no writer; and valid_from and learned_at are both set to the SAME now_iso() call in the single INSERT (lines 200-204). The live production DB confirms: 0 edges where valid_until IS NOT NULL, 0 where valid_from <> learned_at. The tier is a transaction-time-only append log with supersession \\u2014 the valid-time axis of E14's model is declared but dead.\",\n47: \"evidence\": \"graph_memory.py:107,200-204,207-208 (valid_until written NULL; valid_from=learned_at=ts); grep valid_until/superseded across src/rune (no writer); live DB receipt: read-only sqlite3 query against /mnt/workdrive/rune/memory/graph/graph.sqlite returned entities=0, aliases=0, edges=0, valid_until non-null=0, valid_from<>learned_at=0, superseded_by non-null=0\",\n48: \"severity\": \"high\"\n49: },\n50: {\n51: \"id\": \"A2\",\n52: \"target_candidate\": \"C5 (via INN-1)\",\n53: \"dimension\": \"failure_modes\",\n54: \"finding\": \"As-of filter as specified returns stale superseded facts (see rejected_candidates for full mechanism). Line truncated
|
||||
{"type":"step_finish","timestamp":1788956054094,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a086171220001ZtaijLzde","reason":"tool-calls","snapshot":"10ed6aa6a33aecd0eed36765cd0e2791297aa96c","messageID":"msg_g001a08616d6ab0011Uav9RIVf","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","type":"step-finish","tokens":{"total":31671,"input":31236,"output":162,"reasoning":273,"cache":{"write":0,"read":0}},"cost":0}}
|
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{"type":"step_start","timestamp":1788956060932,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a086172d02001O3hwD7oPv","messageID":"msg_g001a086171278001uhT25drcg","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","snapshot":"3bd30576c078379a2d041271efbfae1d9fabb8c9","type":"step-start"}}
|
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{"type":"text","timestamp":1788956075553,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a0861756b5001efg1BqkhS","messageID":"msg_g001a086171278001uhT25drcg","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","type":"text","text":"The process artifacts are clear: C4/C5/C6/C9 survived with mitigations M1–M7, falsification tests T1–T6, and the QUALIFY round pins three gaps (FTS5 layout spec, committed N, named sort key) that I must resolve explicitly in the synthesis. Now I'll read the actual source modules to ground interfaces, plus check the existing synthesis.json before overwriting it.","time":{"start":1788956071605,"end":1788956075551}}}
|
||||
{"type":"tool_use","timestamp":1788956075566,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"type":"tool","tool":"read","callID":"call_d660468353f94956b647def5","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/src/rune/graph_memory.py"},"output":"<path>/mnt/workdrive/issue161-harness/src/rune/graph_memory.py</path>\n<type>file</type>\n<content>\n1: #!/usr/bin/env python3\n2: \"\"\"Graph memory tier — concept-mediated temporal knowledge graph for the agent.\n3: \n4: Built from the 2025-2026 academic frontier (briefs in\n5: `/tmp/opencode/memory-research/agentic-memory-brief.md` and\n6: `~/.firecrawl/claim-audit-2026/brief.md`). Exceeds the entity-hub approach by\n7: following the research convergence:\n8: \n9: - CONCEPT-MEDIATED, not entity-centric (GAAMA 2603.27910): nodes are typed\n10: (person / project / concept / tool / preference / fact), which kills the\n11: \"mega-hub\" dilution Letta/Graphiti hit with one entity connected to\n12: everything.\n13: - BI-TEMPORAL edges (Graphiti 2501.13956): `valid_from/valid_until` (when\n14: the fact is true in the world) + `learned_at` (when we learned it).\n15: - INCREMENTAL construction (TG-RAG 2510.13590, iText2KG 2409.03284): merge\n16: only, no full re-index; entity resolution via aliases, never blind merges.\n17: - SUPERSEDE, never delete (Write-Time Gating 2603.15994, SSGM 2603.11768):\n18: contradicting edges are marked superseded with the new one taking over;\n19: history preserved for audit.\n20: - WRITE-TIME GATING (2603.15994, D-MEM 2603.14597): routine/salience-weak\n21: facts never touch the graph; surprises and contradictions do. The biggest\n22: token saver in the literature.\n23: - PROVENANCE on every edge (MemORAI 2605.01386): each edge carries the\n24: evidence text + source, which is exactly what the claim-audit layer needs.\n25: \n26: Storage: headless SQLite at `memory/graph/graph.sqlite` — reachable by the\n27: sleep-time curator directly (no MCP dependency). Schema below.\n28: \n29: Usage:\n30: graph_memory.py add <subj> <pred> <obj> --type subj_type --evidence \"...\"\n31: [--source X] [--confidence 0.0-1.0] [--strength N]\n32: graph_memory.py add-bulk <json-file>\n33: graph_memory.py query <entity-or-concept> [--hops N] [--latest-only]\n34: graph_memory.py resolve <name>\n35: graph_memory.py neighbors <entity>\n36: graph_memory.py stats\n37: graph_memory.py superseded-by <subj> <pred> <obj> (manual conflict flag)\n38: \"\"\"\n39: \n40: import argparse\n41: import json\n42: import os\n43: import sqlite3\n44: import sys\n45: from collections import deque\n46: from datetime import datetime, timezone\n47: \n48: _SD = os.path.dirname(os.path.abspath(__file__))\n49: if _SD not in sys.path:\n50: sys.path.insert(0, _SD)\n51: import memory_env\n52: \n53: MEM_DIR = memory_env.memory_dir()\n54: GRAPH_DIR = os.path.join(MEM_DIR, \"graph\")\n55: DB_PATH = os.path.join(GRAPH_DIR, \"graph.sqlite\")\n56: \n57: # Fixed predicate vocabulary — schema drift is a researched failure mode, so we\n58: # constrain the set of relationship types we accept. Unknown preds are refused\n59: # (the extractor maps free text onto this set).\n60: PREDICATES = {\n61: \"works_on\", \"works_at\", \"teaches\", \"manages\", \"uses\", \"prefers\",\n62: \"lives_in\", \"located_in\", \"has_role\", \"part_of\", \"leads\", \"builds\",\n63: \"develops\", \"created\", \"interested_in\", \"knows\", \"collaborates_with\",\n64: \"owned_by\", \"reports_to\", \"supports\", \"runs\", \"tracks\", \"related_to\",\n65: \"based_on\", \"happens_on\", \"started_on\", \"ended_on\", \"responds_to\",\n66: \"struggles_with\", \"likes\", \"dislikes\", \"avoided_by\", \"goal_of\",\n67: }\n68: \n69: # Node types.\n70: NODE_TYPES = {\"person\", \"project\", \"concept\", \"tool\", \"preference\",\n71: \"organization\", \"place\", \"event\", \"skill\", \"artefact\"}\n72: \n73: # Confidence: evidence-gated (EvoKG-style f(frequency, recency, source_cred)).\n74: # We fold the curator's strength (0-5) into a 0-1 confidence.\n75: DEFAULT_CONF = 0.7\n76: \n77: \n78: def now_iso():\n79: return datetime.now(timezone.utc).isoformat(timespec=\"seconds\")\n80: \n81: \n82: def connect():\n83: os.makedirs(GRAPH_DIR, exist_ok=True)\n84: db = sqlite3.connect(DB_PATH)\n85: try:\n86: db.row_factory = sqlite3.Row\n87: db.executescript(\"\"\"\n88: CREATE TABLE IF NOT EXISTS entities (\n89: id INTEGER PRIMARY KEY AUTOINCREMENT,\n90: name TEXT NOT NULL UNIQUE,\n91: type TEXT NOT NULL DEFAULT 'concept',\n92: summary TEXT DEFAULT '',\n93: created_at TEXT NOT NULL\n94: );\n95: CREATE TABLE IF NOT EXISTS aliases (\n96: entity_id INTEGER NOT NULL,\n97: alias TEXT NOT NULL,\n98: UNIQUE(entity_id, alias),\n99:Line truncated
|
||||
{"type":"tool_use","timestamp":1788956075568,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"type":"tool","tool":"read","callID":"call_f0ae7d6cb40a45d2a089b630","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/research/issue-163/synthesis.json"},"output":"<path>/mnt/workdrive/issue161-harness/research/issue-163/synthesis.json</path>\n<type>file</type>\n<content>\n1: {\n2: \"stage\": \"SYNTHESIS\",\n3: \"issue\": 163,\n4: \"round\": 1,\n5: \"process_id\": \"research-163-r1\",\n6: \"question\": \"Given the candidates that survived, WHAT SHOULD MIMO CODE BUILD?\",\n7: \"selected_design\": \"Corrected, re-sequenced temporal+lexical query layer over graph.sqlite (survivors C5+C6; C4 as characterization-only; C9 as a constrained track). Concretely: (1) WRITE-SIDE FIRST — M1a, the one-line fix in add_edge (graph_memory.py:206-208) that sets valid_until=now on supersede, making the documented bitemporal as-of filter correct for all future data with free backfill (the DB is empty); (2) C5 — an --as-of <iso8601> point-in-time query parameter whose input is strictly normalized to the now_iso() format before any comparison (M2), replacing the status='active' filter with valid_from <= t AND (valid_until IS NULL OR t < valid_until) AND learned_at <= t in both the outgoing and incoming edge queries, default path unchanged; (3) C6 — an FTS5 lexical entry-resolution tier framed explicitly as a rebuildable projection of entities/aliases inside graph.sqlite (M3) with deterministic alias ordering, lookup order exact -> alias -> FTS5 BM25 -> substring; (4) all lexical benefit measured END-TO-END at ContextBroker.memory_selector output, not graph_memory.query output (M4); (5) read-layer work sequenced behind write-side population (M5) with the T5 population gate. C4 ships as a documentation characterization correction only (M6); C9 ships as nothing — it is carried solely as the M7 constraint on any future analytics sidecar.\",\n8: \"why_this_design_won\": \"C5 and C6 are the only surviving candidates that add capabilities the status quo genuinely lacks — reading the superseded history Rune already persists, and lifting entry-point recall past exact/alias/substring — and both stay inside Rune's embedded, no-new-dependency posture (in-process SQLite, FTS5 verified available at sqlite 3.53.4). But INN-1's original synthesis was falsified twice over: its as-of filter returns BOTH sides of a contradiction as current knowledge against the current writer (A2, critical — supersede never writes valid_until, graph_memory.py:207-208), and its benefit chain is broken at the empty graph (A5) and again at the memory_selector floor (A6). The adversarial stage's simpler-alternative analysis (A3, A13) dictated the winning shape: fix the writer first (one line, free backfill on an empty DB), then read-layer work, measured end-to-end. Within M1, option (a) wins over option (b): the valid_until-on-supersede fix makes the documented bitemporal filter correct with a single WHERE clause and avoids inventing a superseded_by chain-walk helper with terminal/cycle semantics (CA6); option (b) is retained as a documented fallback should T2 falsify M1a. C4 and C9 survive as constraints, not work: C4's 'bi-temporal E14 1:1' claim is contradicted by the code (A1, A11) and survives only as a corrected characterization (M6); C9 is track-only (A9/A10) and contributes only the M7 boundary on future analytics. Nothing here resurrects INN-1-as-specified or any applicability-stage rejection.\",\n9: \"rejected_alternatives\": [\n10: {\n11: \"design\": \"INN-1#temporal-filter-as-specified — ship the as-of filter (valid_from <= t AND (valid_until IS NULL OR t < valid_until) AND learned_at <= t) against the current writer\",\n12: \"reason\": \"REJECTED at ADVERSARIAL (A2, critical): supersede (graph_memory.py:207-208) sets status/superseded_by but never valid_until, so superseded edges remain NULL-open on the valid axis and pass all three predicates for every t after creation — the feature returns the superseded AND superseding edges of a contradiction simultaneously, the exact failure it exists to prevent. Do NOT resurrect in this form.\"\n13: },\n14: {\n15: \"design\": \"M1b — transaction-time-only as-of (learned_at <= t AND (status='active' OR the superseding edge's learned_at > t), walking superseded_by to a terminal)\",\n16: \"reason\": \"Viable fallback, not selected: it requires a new chain-walk helper with terminal and cycle semantics (nothing asserts acyclicity, CA6) and leaves the bitemporal filter permanently unimplementable. M1a is one write-side line with free backfill on an empty DB. Escalate to M1b only if T2 falsifies M1a.\"\n17: },\n18: {\n19: \"design\": \"Read-layer-first sequencing (INN-1's original order: build as-of + FTS5 now, populate the graph later)\",\n20: \"reason\": \"REJECTED per M5/A13: the live production grLine truncated
|
||||
{"type":"tool_use","timestamp":1788956075570,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"type":"tool","tool":"read","callID":"call_c1e8b5c73a1244c0a6b05e8c","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/src/rune/memory_selector.py","limit":50,"offset":350},"output":"<path>/mnt/workdrive/issue161-harness/src/rune/memory_selector.py</path>\n<type>file</type>\n<content>\n350: \"text\": f\"[{neuron.get('slug', 'warm')}] {text}\",\n351: \"score\": score,\n352: \"kind\": \"neuron\",\n353: \"source\": \"warm_neuron_store.list_neurons\",\n354: }\n355: )\n356: if len(out) >= budget:\n357: break\n358: return out\n359: \n360: \n361: def _graphs_recall(query: str, types: list[str] | None, budget: int, min_score: float) -> list[dict]:\n362: \"\"\"Graph source: entity BFS from the task words via graph_memory.query,\n363: floored. The pre-P1-03 contributor called resolve_entity(), which MUTATES\n364: the graph (creates missing entities); read-only query() keeps the store\n365: side-effect-free. A term with no entity yields no candidates.\"\"\"\n366: try:\n367: import graph_memory\n368: except ImportError: # TUI top-level deploy\n369: import rune.graph_memory as graph_memory # type: ignore[no-redef]\n370: \n371: db = graph_memory.connect()\n372: out: list[dict] = []\n373: try:\n374: task_words = list(dict.fromkeys(w.lower().strip(\".,;:!?()[]{}\\\"'\") for w in query.split()))[:5]\n375: for word in task_words:\n376: result = graph_memory.query(db, word, hops=1)\n377: if not result:\n378: continue\n379: entity = result.get(\"entity\", word) if isinstance(result, dict) else str(result)\n380: facts = result.get(\"facts\", []) if isinstance(result, dict) else []\n381: lines = [f\"{f.get('subject')} {f.get('predicate', '-')} {f.get('object')}\" for f in facts]\n382: text = \"; \".join(lines) if lines else str(entity)\n383: score = _lex_overlap_score(text, query) or (1.0 / max(len(task_words), 1))\n384: if score < min_score:\n385: continue\n386: out.append({\"text\": text, \"score\": score, \"kind\": \"graph\", \"source\": \"graph_memory.query\"})\n387: if len(out) >= budget:\n388: break\n389: finally:\n390: db.close()\n391: return out\n392: \n393: \n394: def _abstraction_recall(query: str, types: list[str] | None, budget: int, min_score: float) -> list[dict]:\n395: \"\"\"Abstraction-facts source: LIKE scan over active facts, floored.\n396: \n397: The pre-P1-03 contributor injected the top-3 LIKE matches unconditionally;\n398: here a LIKE match is only a candidate — it must clear the floor.\n399: \"\"\"\n\n(Showing lines 350-399 of 511. Use offset=400 to continue.)\n</content>","metadata":{"preview":" \"text\": f\"[{neuron.get('slug', 'warm')}] {text}\",\n \"score\": score,\n \"kind\": \"neuron\",\n \"source\": \"warm_neuron_store.list_neurons\",\n }\n )\n if len(out) >= budget:\n break\n return out\n\n\ndef _graphs_recall(query: str, types: list[str] | None, budget: int, min_score: float) -> list[dict]:\n \"\"\"Graph source: entity BFS from the task words via graph_memory.query,\n floored. The pre-P1-03 contributor called resolve_entity(), which MUTATES\n the graph (creates missing entities); read-only query() keeps the store\n side-effect-free. A term with no entity yields no candidates.\"\"\"\n try:\n import graph_memory\n except ImportError: # TUI top-level deploy\n import rune.graph_memory as graph_memory # type: ignore[no-redef]","truncated":true,"loaded":[]},"title":"src/rune/memory_selector.py","time":{"start":1788956075555,"end":1788956075569}},"id":"prt_g001a0861765e5001jJx3gSHq7","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","messageID":"msg_g001a086171278001uhT25drcg"}}
|
||||
{"type":"tool_use","timestamp":1788956075571,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"type":"tool","tool":"read","callID":"call_95f5bdc8c0154f1598040619","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/src/rune/contracts.py","limit":30,"offset":225},"output":"<path>/mnt/workdrive/issue161-harness/src/rune/contracts.py</path>\n<type>file</type>\n<content>\n225: Contract(\n226: name=\"abstraction.pipeline_stats returns dict or None\",\n227: module=\"abstraction\", function=\"pipeline_stats\",\n228: args=(), kwargs={},\n229: ),\n230: \n231: # Graph memory\n232: Contract(\n233: name=\"graph_memory.connect returns connection\",\n234: module=\"graph_memory\", function=\"connect\",\n235: args=(), kwargs={},\n236: expect_not_none=True,\n237: ),\n238: Contract(\n239: name=\"graph_memory.stats callable\",\n240: module=\"graph_memory\", function=\"stats\",\n241: args=(), kwargs={},\n242: expect_callable=True,\n243: ),\n244: \n245: # Persona gate\n246: Contract(\n247: name=\"persona_gate.PERMISSIVE_MODE is True\",\n248: module=\"persona_gate\", function=\"PERMISSIVE_MODE\",\n249: args=(), kwargs={},\n250: ),\n251: \n252: # Voice hybrid\n253: Contract(\n254: name=\"voice_hybrid.blend returns dict or None\",\n\n(Showing lines 225-254 of 570. Use offset=255 to continue.)\n</content>","metadata":{"preview":" Contract(\n name=\"abstraction.pipeline_stats returns dict or None\",\n module=\"abstraction\", function=\"pipeline_stats\",\n args=(), kwargs={},\n ),\n\n # Graph memory\n Contract(\n name=\"graph_memory.connect returns connection\",\n module=\"graph_memory\", function=\"connect\",\n args=(), kwargs={},\n expect_not_none=True,\n ),\n Contract(\n name=\"graph_memory.stats callable\",\n module=\"graph_memory\", function=\"stats\",\n args=(), kwargs={},\n expect_callable=True,\n ),\n","truncated":true,"loaded":[]},"title":"src/rune/contracts.py","time":{"start":1788956075556,"end":1788956075570}},"id":"prt_g001a0861765e6001q1mMp60Y8","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","messageID":"msg_g001a086171278001uhT25drcg"}}
|
||||
{"type":"tool_use","timestamp":1788956075573,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"type":"tool","tool":"read","callID":"call_b43455a2c93442ba94e6398c","state":{"status":"completed","input":{"file_path":"/mnt/workdrive/issue161-harness/src/rune/canary_p0_04.py","limit":25,"offset":90},"output":"<path>/mnt/workdrive/issue161-harness/src/rune/canary_p0_04.py</path>\n<type>file</type>\n<content>\n90: \"warm_router.route\",\n91: \"capability_broker.match\",\n92: \"memory_store.connect\",\n93: \"witness.detect_signals\",\n94: \"drift_ledger.check\",\n95: \"sleep_time.consolidate\",\n96: \"run_parallel\",\n97: \"prompt_sync\",\n98: \"abstraction.connect\",\n99: \"graph_memory.resolve\",\n100: \"time_awareness.get_time_context\",\n101: \"event_bus.emit\",\n102: )\n103: for banned in banned_ids:\n104: if banned in body:\n105: return _fail(f\"pipeline step {banned} re-added inside _run_warm_router\")\n106: return 0\n107: \n108: \n109: def check_subsystems_import() -> int:\n110: modules = (\n111: \"warm_router\",\n112: \"capability_broker\",\n113: \"memory_store\",\n114: \"witness\",\n\n(Showing lines 90-114 of 207. Use offset=115 to continue.)\n</content>","metadata":{"preview":" \"warm_router.route\",\n \"capability_broker.match\",\n \"memory_store.connect\",\n \"witness.detect_signals\",\n \"drift_ledger.check\",\n \"sleep_time.consolidate\",\n \"run_parallel\",\n \"prompt_sync\",\n \"abstraction.connect\",\n \"graph_memory.resolve\",\n \"time_awareness.get_time_context\",\n \"event_bus.emit\",\n )\n for banned in banned_ids:\n if banned in body:\n return _fail(f\"pipeline step {banned} re-added inside _run_warm_router\")\n return 0\n\n\ndef check_subsystems_import() -> int:","truncated":true,"loaded":[]},"title":"src/rune/canary_p0_04.py","time":{"start":1788956075557,"end":1788956075572}},"id":"prt_g001a0861765e7002tRPrEhUZ0","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","messageID":"msg_g001a086171278001uhT25drcg"}}
|
||||
{"type":"step_finish","timestamp":1788956075600,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a086176635001Gqhv5GRxh","reason":"tool-calls","snapshot":"254fde45476e324f01a4dd008a50f0a49b3b1217","messageID":"msg_g001a086171278001uhT25drcg","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","type":"step-finish","tokens":{"total":45162,"input":44379,"output":253,"reasoning":466,"cache":{"write":0,"read":64}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788956082938,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a0861782f70013LA2A2eEt","messageID":"msg_g001a08617668a001bG8rGE3EC","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","snapshot":"9ec82ac175109c6a645338efedc4aaf0f98a6177","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788956331748,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a08619b3240010JyEz6AAr","messageID":"msg_g001a08617668a001bG8rGE3EC","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","type":"text","text":"I have everything I need. The situation is now fully clear: a round-1 synthesis.json exists, the QUALIFY stage audited it and issued three findings (an unimplementable contentless FTS5 DDL, a symbolic N, an unnamed sort key), and this is the round-2 SYNTHESIS pass. My plan: rewrite `synthesis.json` keeping the round-1 design (it matches all settled constraints) while committing concrete resolutions for all three findings, grounded in the code I just verified (`add_edge` supersede at `graph_memory.py:206-208`, alias `LIMIT 1` at `:228-231` and the same nondeterminism in `resolve_entity` at `:147-151`, `_graphs_recall` floor at `memory_selector.py:383-384`, contracts at `contracts.py:233-240`, canary bans at `canary_p0_04.py:99-105`).","time":{"start":1788956226340,"end":1788956331744}}}
|
||||
{"type":"tool_use","timestamp":1788956331783,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"type":"tool","tool":"write","callID":"call_b4ad9265408e45ea9b8cd199","state":{"status":"completed","input":{"content":"{\n \"stage\": \"SYNTHESIS\",\n \"issue\": 163,\n \"round\": 2,\n \"process_id\": \"research-163-r1\",\n \"question\": \"Given the candidates that survived, WHAT SHOULD MIMO CODE BUILD?\",\n \"selected_design\": \"Corrected, re-sequenced temporal+lexical query layer over graph.sqlite (survivors C5+C6; C4 as characterization-only; C9 as a constrained track). Concretely: (1) WRITE-SIDE FIRST — M1a, the one-line fix in add_edge (graph_memory.py:206-208) that sets valid_until=now on supersede, making the documented bitemporal as-of filter correct for all future data with free backfill (the DB is empty); (2) C5 — an --as-of <iso8601> point-in-time query parameter whose input is strictly normalized to the now_iso() format before any comparison (M2), replacing the status='active' filter with valid_from <= t AND (valid_until IS NULL OR t < valid_until) AND learned_at <= t in both the outgoing and incoming edge queries, default path unchanged; (3) C6 — an FTS5 lexical entry-resolution tier built as a NON-CONTENTLESS FTS5 projection table graph_lex(name, alias, entity_id UNINDEXED) with one row per (entity_id, text) over entities.name ∪ aliases.alias, INSERT-only maintenance on the existing write path, a documented rebuild-lex command, and a recorded tokenizer decision (M3, layout committed in this round); (4) all lexical benefit measured END-TO-END at ContextBroker.memory_selector output, not graph_memory.query output (M4); (5) read-layer work sequenced behind write-side population (M5) with the T5 population gate at a pre-committed N=4 weeks and <50-edges threshold. C4 ships as a documentation characterization correction only (M6); C9 ships as nothing — it is carried solely as the M7 constraint on any future analytics sidecar.\",\n \"why_this_design_won\": \"C5 and C6 are the only surviving candidates that add capabilities the status quo genuinely lacks — reading the superseded history Rune already persists but cannot query temporally, and lifting entry-point recall past exact/alias/substring — and both stay inside Rune's embedded, no-new-dependency posture (in-process SQLite; FTS5 verified available at sqlite 3.53.4). INN-1's original formulation was falsified twice over: its as-of filter returns BOTH sides of a contradiction as current knowledge against the current writer (A2, critical — supersede never writes valid_until, graph_memory.py:207-208), and its benefit chain is broken at the empty graph (A5) and again at the memory_selector floor (A6). The adversarial stage's simpler-alternative analysis (A3, A13) dictated the winning shape: fix the writer first (one line, free backfill on an empty DB), then read-layer work, measured end-to-end. Within M1, option (a) wins over option (b): the valid_until-on-supersede fix makes the documented bitemporal filter correct with a single WHERE clause and avoids inventing a superseded_by chain-walk helper with terminal/cycle semantics (CA6); option (b) is retained as a documented fallback should T2 falsify M1a. C4 and C9 survive as constraints, not work: C4's 'bi-temporal E14 1:1' claim is contradicted by the code (A1, A11) and survives only as a corrected characterization (M6); C9 is track-only (A9/A10) and contributes only the M7 boundary on future analytics. Round 2 commits the three QUALIFY findings into this design — the graph_lex row layout and id mapping, a concrete N=4 weeks for the population gate, and the named alias sort key — so the handoff to the coding worker is verbatim-executable with no invented design. Nothing here resurrects INN-1-as-specified or any applicability-stage rejection.\",\n \"rejected_alternatives\": [\n {\n \"design\": \"INN-1#temporal-filter-as-specified — ship the as-of filter (valid_from <= t AND (valid_until IS NULL OR t < valid_until) AND learned_at <= t) against the current writer\",\n \"reason\": \"REJECTED at ADVERSARIAL (A2, critical): supersede (graph_memory.py:207-208) sets status/superseded_by but never valid_until, so superseded edges remain NULL-open on the valid axis and pass all three predicates for every t after creation — the feature returns the superseded AND superseding edges of a contradiction simultaneously, the exact failure it exists to prevent. Do NOT resurrect in this form.\"\n },\n {\n \"design\": \"M1b — transaction-time-only as-of (learned_at <= t AND (status='active' OR the superseding edge's learned_at > t), walking superseded_by to a terminal)\",\n \"reason\": \"Viable fallback, not selected: it requires a new chain-walk helper with terminal and cycle semantics (nothing asserts acyclicity, CA6) and leaves the bitemporal filter permanently unimplementable. M1a is one write-side line with free backfill on an empty DB. Escalate to M1b onlyLine truncated
|
||||
{"type":"step_finish","timestamp":1788956331812,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a0861b4f06001Q4Jf5rOhQ","reason":"tool-calls","snapshot":"0ad22f7f663eab5e9c4704470816d0bd47d13f7c","messageID":"msg_g001a08617668a001bG8rGE3EC","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","type":"step-finish","tokens":{"total":80833,"input":46485,"output":7663,"reasoning":5565,"cache":{"write":0,"read":21120}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788956338753,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a0861b6a3f001EhWY25no4","messageID":"msg_g001a0861b4f49001gfQPaoofT","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","snapshot":"946787f3126e04fcbf707734c63d0f768b728db1","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788956343431,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a0861b6b2b002JkMDY37N4","messageID":"msg_g001a0861b4f49001gfQPaoofT","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","type":"text","text":"Now validating the written JSON parses cleanly:","time":{"start":1788956338987,"end":1788956343428}}}
|
||||
{"type":"tool_use","timestamp":1788956343516,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"type":"tool","tool":"bash","callID":"call_10663a4a66974e9497d52ef4","state":{"status":"completed","input":{"command":"python3 -c \"\nimport json\nwith open('research/issue-163/synthesis.json') as f:\n d = json.load(f)\nrequired = ['selected_design','why_this_design_won','rejected_alternatives','components_affected','authority_boundaries','interfaces','data_flow','implementation_sequence','invariants','tests_required','acceptance_criteria','failure_modes','rollback','deliberately_excluded']\nmissing = [k for k in required if k not in d]\nprint('missing keys:', missing or 'NONE')\nprint('rejected_alternatives:', len(d['rejected_alternatives']))\nprint('interfaces:', len(d['interfaces']))\nprint('invariants:', len(d['invariants']), '| tests:', len(d['tests_required']), '| ACs:', len(d['acceptance_criteria']))\nprint('sequence steps:', len(d['implementation_sequence']))\nprint('round:', d['round'], '| N committed:', 'N=4 weeks' in d['invariants'][4])\nprint('sort key named:', 'ORDER BY a.entity_id ASC' in d['interfaces'][8])\nprint('non-contentless DDL committed:', 'entity_id UNINDEXED' in d['interfaces'][4])\n\"","description":"Validate synthesis.json structure and committed resolutions","workdir":"/mnt/workdrive/issue161-harness"},"output":"missing keys: NONE\nrejected_alternatives: 10\ninterfaces: 10\ninvariants: 11 | tests: 12 | ACs: 10\nsequence steps: 10\nround: 2 | N committed: True\nsort key named: True\nnon-contentless DDL committed: True\n","metadata":{"output":"missing keys: NONE\nrejected_alternatives: 10\ninterfaces: 10\ninvariants: 11 | tests: 12 | ACs: 10\nsequence steps: 10\nround: 2 | N committed: True\nsort key named: True\nnon-contentless DDL committed: True\n","exit":0,"description":"Validate synthesis.json structure and committed resolutions","truncated":false},"title":"Validate synthesis.json structure and committed resolutions","time":{"start":1788956343507,"end":1788956343514}},"id":"prt_g001a0861b7c4a001LqFKDlCwd","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","messageID":"msg_g001a0861b4f49001gfQPaoofT"}}
|
||||
{"type":"step_finish","timestamp":1788956343543,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a0861b7cdb001ClhYKg63g","reason":"tool-calls","snapshot":"f1512fec5b5caa9350eba44da9e9350419e54d05","messageID":"msg_g001a0861b4f49001gfQPaoofT","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","type":"step-finish","tokens":{"total":65351,"input":65055,"output":287,"reasoning":9,"cache":{"write":0,"read":0}},"cost":0}}
|
||||
{"type":"step_start","timestamp":1788956349984,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a0861b961e001tQ6AgaCqM","messageID":"msg_g001a0861b7d1c001vgfk97qKo","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","snapshot":"694d3e60ee43c33ac525c6f432d27780c6133c50","type":"step-start"}}
|
||||
{"type":"text","timestamp":1788956365290,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a0861b975a001jVy6HyAoa","messageID":"msg_g001a0861b7d1c001vgfk97qKo","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","type":"text","text":"**STATUS: COMPLETE.** The synthesis dossier is written to `research/issue-163/synthesis.json` (round 2, JSON validated — all 14 required keys present).\n\n## What Mimo Code should build\n\n**Selected design:** the corrected, re-sequenced temporal+lexical query layer over `graph.sqlite` — C5+C6 with every mitigation applied, sequenced write-side-first:\n\n1. **M1a first** — one-line fix in `add_edge` (`graph_memory.py:206-208`): set `valid_until=now` on supersede. Nothing read-layer ships before this; it's what makes the as-of filter correct (the INN-1 filter as originally specified returns both sides of a contradiction — falsified at ADVERSARIAL, A2).\n2. **C5** — `--as-of` parameter with strict input normalization (`normalize_as_of`, M2), bitemporal filter on both edge queries, default path byte-identical.\n3. **C6** — FTS5 lexical entry tier as a **rebuildable projection** with deterministic alias ordering, measured end-to-end at `memory_selector` output (M4), gated by kill tests T3/T4.\n4. **C4** ships as documentation correction only (M6); **C9** ships as nothing — only the M7 constraint on any future analytics sidecar.\n\n## How the three QUALIFY REFINE findings were addressed\n\nAll three explicitly resolved and documented in a `qualify_refine_findings_resolution` section:\n\n- **INTERFACE GAP** → committed the layout rather than delegating: non-contentless `graph_lex(name, alias, entity_id UNINDEXED, tokenize='unicode61')`, one row per `(entity_id, text)`, UNINDEXED `entity_id` as the sole id mapping, **INSERT-only maintenance** (name-row on entity creation, alias-row on real inserts only) — which moots the contentless-UPDATE impossibility by construction. The contentless DDL and the packed-alias alternative were added as rejected alternatives.\n- **PARAMETER GAP** → **N=4 weeks pre-committed** in INV-5, step 9, AC9, and the harness test, with an explicit no-rechoice-after-observation clause.\n- **MINOR GAP** → sort key named: `ORDER BY a.entity_id ASC`, applied at **both** alias-resolution sites (`resolve_entity` :147-151 and `query` :228-231 — I verified both are nondeterministic in the current code, so pinning only one would leave INV-8 unverifiable).\n\nConstraints honored: no new research or fetches, no rejected candidate resurrected (INN-1-as-specified is listed with a do-not-resurrect reason), all seven mitigations and all six falsification tests converted into invariants/tests/ACs verbatim, and no new authority paths created.","time":{"start":1788956350298,"end":1788956365288}}}
|
||||
{"type":"step_finish","timestamp":1788956365319,"sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","part":{"id":"prt_g001a0861bd1e9001ekwmppkby","reason":"stop","snapshot":"1d0c07d4fa5b75b905dbf9d1054b1649d202fa52","messageID":"msg_g001a0861b7d1c001vgfk97qKo","sessionID":"ses_-ffe5f79ece03cffeFnTk9VVoS","type":"step-finish","tokens":{"total":66031,"input":387,"output":607,"reasoning":13,"cache":{"write":0,"read":65024}},"cost":0}}
|
||||
@@ -126,6 +126,8 @@ class StageExecutor:
|
||||
return self._execute_applicability(event)
|
||||
elif stage == "ADVERSARIAL":
|
||||
return self._execute_adversarial(event)
|
||||
elif stage == "INNOVATION":
|
||||
return self._execute_innovation(event)
|
||||
elif stage == "SYNTHESIS":
|
||||
return self._execute_synthesis(event)
|
||||
elif stage == "QUALIFY":
|
||||
@@ -309,10 +311,10 @@ class StageExecutor:
|
||||
return {"status": "STAGE_FAILED", "stage": "OUTWARD",
|
||||
"error": "mimo exited " + str(relay_result["returncode"])}
|
||||
|
||||
# File-channel fallback: prose-only stdout (or no stdout) with a
|
||||
# worker-written dossier file. The structured artifact IS the authority
|
||||
# (§5) — check the file whenever stdout parse yields no usable artifact.
|
||||
if not relay_result["texts"]:
|
||||
# The worker may have written the structured dossier directly to
|
||||
# the artifact file (STAGE_ARTIFACT_FILES) instead of stdout text.
|
||||
# The structured artifact IS the authority — accept either channel.
|
||||
from research_task_tree import STAGE_ARTIFACT_FILES
|
||||
artifact_file = os.path.join(
|
||||
self.artifact_dir, STAGE_ARTIFACT_FILES.get("OUTWARD", "outward.json"))
|
||||
@@ -352,6 +354,21 @@ class StageExecutor:
|
||||
return {"status": "STAGE_FAILED", "stage": "OUTWARD",
|
||||
"error": "no text output", "stderr": error_detail}
|
||||
|
||||
# Prose-only stdout with a valid dossier on disk: prefer the file.
|
||||
if relay_result["texts"] and not _re.search(r"\{.*\}", "\n\n".join(relay_result["texts"]), _re.DOTALL):
|
||||
from research_task_tree import STAGE_ARTIFACT_FILES
|
||||
_af = os.path.join(
|
||||
self.artifact_dir, STAGE_ARTIFACT_FILES.get("OUTWARD", "outward.json"))
|
||||
if os.path.exists(_af):
|
||||
try:
|
||||
with open(_af) as _f:
|
||||
_wa = json.load(_f)
|
||||
if _wa.get("discovery") or _wa.get("sources_discovered"):
|
||||
relay_result = dict(relay_result, texts=[])
|
||||
artifact_text = ""
|
||||
except (json.JSONDecodeError, OSError):
|
||||
pass
|
||||
|
||||
artifact_text = "\n\n".join(relay_result["texts"])
|
||||
url_pat = _re.compile(r"https?://[^\s)>]+")
|
||||
all_urls = url_pat.findall(artifact_text)
|
||||
@@ -728,6 +745,7 @@ class StageExecutor:
|
||||
"issue": self.issue,
|
||||
"round": self.round_no,
|
||||
"attempt": event.get("attempt", 1),
|
||||
"disposition": worker_artifact.get("disposition"),
|
||||
"surviving_candidates": worker_artifact.get("surviving_candidates", []),
|
||||
"rejected_candidates": worker_artifact.get("rejected_candidates", []),
|
||||
"attacks": attacks,
|
||||
@@ -786,6 +804,150 @@ class StageExecutor:
|
||||
f"{a.get('target_candidate')} without rebuttal")
|
||||
return errors
|
||||
|
||||
def _execute_innovation(self, event: dict) -> dict:
|
||||
"""INNOVATION (RP5/#268): conditional cross-domain/compositional stage.
|
||||
Runs only when APPLICABILITY routed INNOVATION_WARRANTED. Valid success
|
||||
outcomes include NO_INNOVATION_WARRANTED — never manufacture novelty."""
|
||||
adv = self.tree.state["artifacts"].get("ADVERSARIAL", {})
|
||||
_ = adv # innovation may run pre-adversarial; adversarial re-attacks later
|
||||
applicability = self.tree.state["artifacts"].get("APPLICABILITY", {})
|
||||
if not applicability:
|
||||
self.tree.fail("INNOVATION: no accepted APPLICABILITY artifact")
|
||||
return {"status": "STAGE_FAILED", "stage": "INNOVATION",
|
||||
"error": "APPLICABILITY artifact missing"}
|
||||
|
||||
promising = [c for c in applicability.get("candidates", [])
|
||||
if c.get("classification") in ("PROMISING_FOR_INNOVATION",
|
||||
"APPLICABLE_WITH_ADAPTATION",
|
||||
"DIRECTLY_APPLICABLE")]
|
||||
pp = [
|
||||
"STAGE: INNOVATION (conditional)",
|
||||
"ISSUE: #" + str(self.issue),
|
||||
"",
|
||||
"APPLICABILITY routed INNOVATION_WARRANTED — a credible compositional or",
|
||||
"cross-domain opportunity was identified. Your job: determine whether combining",
|
||||
"useful aspects of the research, Rune's architecture, or an adjacent field",
|
||||
"produces something MATERIALLY better than the standard mechanism.",
|
||||
"",
|
||||
"PROMISING CANDIDATES:",
|
||||
]
|
||||
for c in promising:
|
||||
pp.append(f" - {c.get('id')}: {c.get('classification')} at {c.get('integration_point', '?')}")
|
||||
pp.extend([
|
||||
"",
|
||||
"MUST answer with an explicit conclusion:",
|
||||
'- "conclusion": "NO_INNOVATION_WARRANTED" — the standard mechanism is already',
|
||||
" sufficient. This is a valid, respected result.",
|
||||
'- or "conclusion": one of ADOPT/ADAPT/BORROW/BUILD with candidates below.',
|
||||
"",
|
||||
"Each candidate: id, category (ADOPT|ADAPT|BORROW|BUILD), proposal,",
|
||||
"derived_from (evidence/claim ids), composition_of, novelty",
|
||||
"(direct|adapted|cross-domain|compositional|new), expected_gain, risks,",
|
||||
"kill_test (what would falsify the expected gain).",
|
||||
"",
|
||||
"OUTPUT: write JSON to research/issue-{n}/innovation.json.",
|
||||
"DO NOT: manufacture novelty for its own sake, propose candidates with no",
|
||||
"evidence derivation, modify code, or advance stages.",
|
||||
])
|
||||
prompt = chr(10).join(pp).replace("{n}", str(self.issue))
|
||||
|
||||
mimo_bin = os.environ.get("MIMO_BIN", "/var/lib/gitea-desktop-runner/bin/mimo")
|
||||
mimo_model = os.environ.get("MIMO_MODEL", "Verboo Code/glm-5.3-flash")
|
||||
mimo_agent = os.environ.get("MIMO_AGENT", "build")
|
||||
events_path = os.path.join(self.artifact_dir, "stage_INNOVATION.events.jsonl")
|
||||
err_path = os.path.join(self.artifact_dir, "stage_INNOVATION.mimo.err.log")
|
||||
os.makedirs(self.artifact_dir, exist_ok=True)
|
||||
|
||||
cmd = [mimo_bin, "run", "--agent", mimo_agent, "-m", mimo_model,
|
||||
"--dangerously-skip-permissions", "--format", "json", prompt]
|
||||
|
||||
from research_event_relay import ResearchEventRelay
|
||||
relay = ResearchEventRelay(self.tree, events_path, cmd, err_path)
|
||||
relay_result = relay.run()
|
||||
|
||||
if relay_result["returncode"] != 0:
|
||||
self.tree.fail("mimo exited " + str(relay_result["returncode"]))
|
||||
return {"status": "STAGE_FAILED", "stage": "INNOVATION",
|
||||
"error": "mimo exited " + str(relay_result["returncode"])}
|
||||
|
||||
worker_artifact = None
|
||||
if relay_result["texts"]:
|
||||
import re as _re
|
||||
text = "\n\n".join(relay_result["texts"])
|
||||
m = _re.search(r"\{.*\}", text, _re.DOTALL)
|
||||
if m:
|
||||
try:
|
||||
worker_artifact = json.loads(m.group(0))
|
||||
except json.JSONDecodeError:
|
||||
worker_artifact = None
|
||||
if not worker_artifact:
|
||||
f = os.path.join(self.artifact_dir, "innovation.json")
|
||||
if os.path.exists(f):
|
||||
try:
|
||||
with open(f) as fh:
|
||||
worker_artifact = json.load(fh)
|
||||
except (json.JSONDecodeError, OSError):
|
||||
pass
|
||||
if not worker_artifact:
|
||||
self.tree.fail("INNOVATION: no dossier produced (stdout or file)")
|
||||
return {"status": "STAGE_FAILED", "stage": "INNOVATION",
|
||||
"error": "no artifact produced", "stderr": self._tail_err(err_path)}
|
||||
|
||||
artifact = self._normalize_innovation_artifact(worker_artifact, relay_result, event)
|
||||
gate_errors = self._validate_innovation(artifact)
|
||||
if gate_errors:
|
||||
self.tree.fail("; ".join(gate_errors))
|
||||
attempt = event.get("attempt", 1)
|
||||
status = "GATE_FAILED" if attempt >= 2 else "GATE_RETRY"
|
||||
return {"status": status, "stage": "INNOVATION",
|
||||
"errors": gate_errors, "artifact_hash": artifact_hash(artifact)}
|
||||
|
||||
artifact_digest = artifact_hash(artifact)
|
||||
next_stage = self.tree.advance(artifact)
|
||||
print(f"\n INNOVATION completed — conclusion: {artifact.get('conclusion')}")
|
||||
print(f" Candidates: {len(artifact.get('candidates', []))} | Next: {next_stage}")
|
||||
return {"status": "STAGE_COMPLETED", "stage": "INNOVATION",
|
||||
"artifact_hash": artifact_digest, "next_stage": next_stage,
|
||||
"artifact": artifact}
|
||||
|
||||
def _normalize_innovation_artifact(self, worker_artifact: dict, relay_result: dict, event: dict) -> dict:
|
||||
conclusion = worker_artifact.get("conclusion", "NO_INNOVATION_WARRANTED")
|
||||
return {
|
||||
"stage": "INNOVATION",
|
||||
"issue": self.issue,
|
||||
"round": self.round_no,
|
||||
"attempt": event.get("attempt", 1),
|
||||
"conclusion": conclusion,
|
||||
"candidates": worker_artifact.get("candidates", []),
|
||||
"notes": worker_artifact.get("notes", ""),
|
||||
"tool_receipts": relay_result.get("receipts", []),
|
||||
"event_counts": relay_result.get("event_counts", {}),
|
||||
"elapsed": relay_result.get("elapsed", 0.0),
|
||||
}
|
||||
|
||||
def _validate_innovation(self, artifact: dict) -> list:
|
||||
"""INNOVATION gate (RP5): conclusion must be valid; every candidate has
|
||||
evidence derivation and a kill_test; novelty never 'new' without
|
||||
composition metadata."""
|
||||
from research_task_tree import INNOVATION_CONCLUSIONS
|
||||
errors = []
|
||||
conclusion = artifact.get("conclusion")
|
||||
if conclusion not in INNOVATION_CONCLUSIONS:
|
||||
errors.append(f"INNOVATION: invalid conclusion {conclusion!r} "
|
||||
f"(must be one of {sorted(INNOVATION_CONCLUSIONS)})")
|
||||
candidates = artifact.get("candidates", [])
|
||||
if conclusion != "NO_INNOVATION_WARRANTED" and not candidates:
|
||||
errors.append("INNOVATION: conclusion warrants innovation but no candidates")
|
||||
for c in candidates:
|
||||
if not c.get("derived_from"):
|
||||
errors.append(f"candidate {c.get('id', '?')}: no derived_from — "
|
||||
f"anti-decorative-research rule (RSC6)")
|
||||
if not c.get("kill_test"):
|
||||
errors.append(f"candidate {c.get('id', '?')}: no kill_test")
|
||||
if c.get("category") not in ("ADOPT", "ADAPT", "BORROW", "BUILD"):
|
||||
errors.append(f"candidate {c.get('id', '?')}: invalid category {c.get('category')!r}")
|
||||
return errors
|
||||
|
||||
def _execute_synthesis(self, event: dict) -> dict:
|
||||
"""SYNTHESIS (RP7/#270): turn ONLY adversarially surviving material
|
||||
into concrete build instructions. Inputs are already epistemically
|
||||
@@ -822,6 +984,13 @@ class StageExecutor:
|
||||
pp.append("FALSIFICATION TESTS (must convert into concrete invariants/tests/controls):")
|
||||
for ft in adv.get("falsification_tests", []):
|
||||
pp.append(f" - {ft}")
|
||||
# Round 2+: QUALIFY REFINE_SYNTHESIS findings are binding corrections
|
||||
refinements = self.tree.state.get("synthesis_refinements", [])
|
||||
if refinements:
|
||||
pp.append("QUALIFY REFINE FINDINGS (round-2 corrections — address each explicitly):")
|
||||
for r in refinements:
|
||||
pp.append(f" - {r}")
|
||||
pp.append("")
|
||||
pp.extend([
|
||||
"",
|
||||
"TASK: answer exactly one question —",
|
||||
|
||||
Reference in new issue
Block a user