diff --git a/prompts/research-stages/adversarial.md b/prompts/research-stages/adversarial.md index 3d6d26d2..4816f7dd 100644 --- a/prompts/research-stages/adversarial.md +++ b/prompts/research-stages/adversarial.md @@ -16,6 +16,16 @@ search for failures, limitations, simpler alternatives, negative results ## You MUST NOT do NOT advocate for the preferred approach +## Disposition (MANDATORY — top-level field) + +Your dossier MUST carry a top-level "disposition" field, exactly one of: +- SURVIVES — leading candidate withstands attack; no blocking gaps +- SURVIVES_WITH_MITIGATIONS — viable with explicit required_mitigations +- EVIDENCE_DEFICIENT — key claims unsupported → targeted OUTWARD +- APPLICABILITY_DEFICIENT — Rune-fit was mapped wrong → APPLICABILITY +- INNOVATION_WARRANTED — standard options killed, credible combination exists +- NO_VIABLE_CANDIDATE — everything killed → expand the candidate space + ## Output Produce your stage artifact as structured text. End with: ``` diff --git a/research/issue-163/.consumed_issue-163-APPLICABILITY-1 b/research/issue-163/.consumed_issue-163-APPLICABILITY-1 new file mode 100644 index 00000000..1123c17a --- /dev/null +++ b/research/issue-163/.consumed_issue-163-APPLICABILITY-1 @@ -0,0 +1 @@ +consumed at 1788951395.044743 \ No newline at end of file diff --git a/research/issue-163/.consumed_issue-163-INNOVATION-1 b/research/issue-163/.consumed_issue-163-INNOVATION-1 new file mode 100644 index 00000000..7df66a57 --- /dev/null +++ b/research/issue-163/.consumed_issue-163-INNOVATION-1 @@ -0,0 +1 @@ +consumed at 1788951877.631211 \ No newline at end of file diff --git a/research/issue-163/.consumed_issue-163-INWARD-1 b/research/issue-163/.consumed_issue-163-INWARD-1 new file mode 100644 index 00000000..e7573559 --- /dev/null +++ b/research/issue-163/.consumed_issue-163-INWARD-1 @@ -0,0 +1 @@ +consumed at 1788950449.0477326 \ No newline at end of file diff --git a/research/issue-163/.consumed_issue-163-ISSUE-1 b/research/issue-163/.consumed_issue-163-ISSUE-1 new file mode 100644 index 00000000..16c319f4 --- /dev/null +++ b/research/issue-163/.consumed_issue-163-ISSUE-1 @@ -0,0 +1 @@ +consumed at 1788950433.0232792 \ No newline at end of file diff --git a/research/issue-163/.consumed_issue-163-OUTWARD-1 b/research/issue-163/.consumed_issue-163-OUTWARD-1 new file mode 100644 index 00000000..cbd5fe11 --- /dev/null +++ b/research/issue-163/.consumed_issue-163-OUTWARD-1 @@ -0,0 +1 @@ +consumed at 1788950467.3172934 \ No newline at end of file diff --git a/research/issue-163/.consumed_issue-163-QUALIFY-2 b/research/issue-163/.consumed_issue-163-QUALIFY-2 new file mode 100644 index 00000000..679ab69c --- /dev/null +++ b/research/issue-163/.consumed_issue-163-QUALIFY-2 @@ -0,0 +1 @@ +consumed at 1788956506.164559 \ No newline at end of file diff --git a/research/issue-163/.consumed_issue-163-SYNTHESIS-2 b/research/issue-163/.consumed_issue-163-SYNTHESIS-2 new file mode 100644 index 00000000..9caef355 --- /dev/null +++ b/research/issue-163/.consumed_issue-163-SYNTHESIS-2 @@ -0,0 +1 @@ +consumed at 1788955793.926691 \ No newline at end of file diff --git a/research/issue-163/stage_ADVERSARIAL.events.jsonl b/research/issue-163/stage_ADVERSARIAL.events.jsonl new file mode 100644 index 00000000..721f9d1c --- /dev/null +++ b/research/issue-163/stage_ADVERSARIAL.events.jsonl @@ -0,0 +1,52 @@ +{"type":"step_start","timestamp":1788953320541,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085ed5c5a001WquOCi90p","messageID":"msg_g001a085ed3dc1001E82MsfeLF","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","snapshot":"e400b0f575f7acfc46934d8ec5d67c23d9ab858d","type":"step-start"}} +{"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}}} +{"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"}} +{"type":"tool_use","timestamp":1788953472121,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"type":"tool","tool":"read","callID":"call_36f974d3328243b1851727db","state":{"status":"completed","input":{"file_path":"/home/nick"},"output":"/home/nick\ndirectory\n\n\n)\n\nwith open(path, w) as f:\n f.write(content)\nprint(\\\"1. Cancellation language fixed\\\")\n\"\n\n)\ncontent = content.replace(\n Cancel\n--no-pager\n,\n \n,\n 30\n.adal/\n.agents/\n.android/\n.augment/\n.bash_history\n.bash_logout\n.bash_profile\n.bashrc\n.bob/\n.bun/\n.cache/\n.cargo/\n.clap/\n.claude/\n.cmake/\n.codebuddy/\n.codeium/\n.codex/\n.commandcode/\n.config/\n.continue/\n.copilot/\n.cursor/\n.dart-tool/\n.factory/\n.firecrawl/\n.fltk/\n.flutter\n.git/\n.gitconfig\n.gnupg/\n.gradle/\n.gtkrc-2.0\n.helm-synth/\n.helm/\n.hex/\n.hindsight/\n.icons/\n.iflow/\n.ir_save\n.java/\n.junie/\n.kde-services/\n.kilocode/\n.kiro/\n.kode/\n.letta_backup/\n.letta-backup-20260619-191841/\n.letta-backups/\n.letta-blue-broken-20260617-222052/\n.letta/\n.lmstudio-home-pointer\n.local/\n.lteams/\n.masterpdfeditor/\n.mcp/\n.mcpjam/\n.mempalace/\n.mimocode/\n.mix/\n.mozilla/\n.mux/\n.n8n-mcp/\n.neovate/\n.net/\n.npm/\n.npmrc\n.nv/\n.nvidia-settings-rc\n.nvm/\n.odbc.ini\n.ollama/\n.openclaw/\n.opencode/\n.openhands/\n.pi/\n.pki/\n.plugin-backups/\n.pochi/\n.profile\n.pub-cache/\n.pulse-cookie\n.putty/\n.pytest_cache/\n.qoder/\n.qwen/\n.roo/\n.ruff_cache/\n.rune/\n.snowflake/\n.sonic-pi/\n.ssh/\n.steam/\n.steampath\n.steampid\n.tauri/\n.themes/\n.TorrHunt.id\n.trae-cn/\n.trae/\n.Transcribe!7\n.triton/\n.var/\n.venv/\n.venvs/\n.vibe/\n.viminfo\n.vscode-oss-shared/\n.vscode-oss/\n.vst/\n.vst3/\n.wezterm.lua\n.wget-hsts\n.win11.draft.txt\n.winboat/\n.wine-guitarpro8/\n.wine-ni/\n.wine/\n.wine32/\n.xinitrc\n.yeqwqauh\n.zcompdump-nicks-pc-5.9.zwc\n.zencoder/\n.zoom/\n.zsh_history\n.zsh/\n.zshenv\n.zshenv.backup.20260514-232301\n.zshrc\n.zshrc.backup.20260513-231529\n.zshrc.backup.20260514-232301\n.zynaddsubfx-bank-cache.xml\n[PL] Miss！Don't Affect my Rank_ MDAR v1.1.30/\n148881-mountIso.desktop.disabled\n30\nagent-73c6f6fe-4636-464d-9a72-9f136feb54b6.af\nai/\nalsa-scarlett-gui-debug.log\nalsactl-restore.log\nApplications/\naudio-config-backups/\naur-malware-check/\nbackups/\nbin/\nbrightbean-studio/\nbuild/\ncachyos-audit-20260831-225933.tar.gz\ncachyos-audit-20260831-225933/\ncallofcthulhu_investigatorhandbook.zip\ncallofcthulhu_keeperrulebook.zip\ncallofcthulhu_keeperscreenpack.zip\nCancel\ncomfyui/\nconductor/\nconfig.json\ndashboard.json\nDaVinci Resolve Media/\ndebtap-mod/\ndelta-green-foundry-vtt-system/\nDesktop/\ndisk-cleanup-report.sh\ndisk-cleanup-report.txt\ndisplay.json\ndocs/\nDocuments/\ndoorstodarkness.zip\nDownloads/\ndungeondraft-desktop.log\ndungeondraft-run.log\nEscape Dungeon 3/\nestimate-snapshot-cleanup-space.sh\nfcp-support/\nfeatherless_tool_trace_20260618_202454.txt\nfeatherless-models.json\nfeatherless-sync-models.sh\nfirefox-import/\nfix-letta-bash.sh\nGames/\ngo/\nGuitar-Pro-On-Linux/\ninstall_mk3_driver.sh\nisomount/\nKoikatu Party/\nletta_memory_report.md\nletta-backup-2026-06-10/\nletta-backup-2026-06-11-fresh/\nletta-backup-2026-06-18-064233-pre-fresh/\nletta-backup-fresh-20260619-191842/\nletta-bash-diagnostic.sh\nletta-bluegreen-rescue-20260617-221830/\nletta-cli-ui-rescue-20260617-190143/\nletta-code-paste-fix-backup-20260617-215641/\nletta-diag.sh\nletta-diagnose.sh\nletta-diagnostic.sh\nletta-fix-attempt.sh\nletta-fix-bash.sh\nletta-repair.sh\nletta-rescue-20260617-153624/\nlogs/\nMaya and Friends [v1.0] [Diathorn]/\nmimo_local_isolation_v3.sh\nmimo_local_repair.sh\nmimo_local_stabilize_v2.sh\nmimo-local-diagnostic-20260825-155812.txt\nmk3_color_mapper_v0.24.0.py\nmk3_color_pad_mapper_v0.26.0.py\nmk3_color_palette_tester.py\nmk3_color_test_results.json\nmk3_color_tester_v0.16.0.py\nmk3_complete_color_system_v0.27.0.py\nmk3_complete_color_system.json\nmk3_complete_driver.py\nMK3_COMPLETE_FILELIST.md\nmk3_complete_mapping_v0.20.0.py\nmk3_complete_mapping_v0.21.0.py\nmk3_complete_mapping.json\nmk3_complete_path_mapper_v0.33.0.py\nMK3_COMPLETE_SPEC.md\nmk3_complete_tester_v0.15.0.py\nMK3_DAW_ARCHITECTURE.md\nmk3_daw_driver.py\nMK3_DRIVER_FILES.md\nmk3_driver_mapping.json\nMK3_DRIVER_TODO.md\nmk3_driver.py\nmk3_drum_pad_color_finder_v0.30.0.py\nmk3_drum_pad_finder_v0.31.0.py\nmk3_efficient_mapper_v0.35.0.py\nmk3_evdev_find.py\nmk3_gui_simple.py\nmk3_indexed_color_finder_v0.17.0.py\nmk3_input_capture.py\nmk3_input_mapper.py\nmk3_input_reader.py\nmk3_led_map.py\nmk3_led_map.version\nmk3_led_mapper.py\nmk3_led_mapper.version\nmk3_led_mapping.json\nmk3_led_menu.py\nmk3_led_menu.version\nmk3_led_observe.py\nmk3_led_observe.version\nmk3_led_results_v0.3.0.json\nmk3_led_results_v0.5.0.json\nmk3_led_results.json\nmk3_led_results.version\nmk3_led_systematic.py\nmk3_led_systematic.version\nmk3_led_tester_v0.3.0.py\nmk3_led_tester_v0.4.0.py\nmk3_led_tester_v0.5.0.version\nmk3_led_tester_v0.5.3.version\nmk3_led_tester_v0.5.4.version\nmk3_led_tester_v0.6.1.py\nmk3_led_tester_v0.6.2.py\nmk3_led_tester_v0.6.2.version\nmk3_led_tester_v0.6.3.py\nmk3_led_tester_v0.6.4.py\nmk3_led_tester.version\nmk3_live_capture_fixed.py\nmk3_live_capture_v1.0.0.py\nmk3_live_capture.py\nmk3_live_input.py\nmk3_map_controls.py\nmk3_mapping_v0.22.0.py\nmk3_mapping_v0.23.0.py\nmk3_mapping_v0.23.json\nMK3_MIDI_ARCH.md\nmk3_midi_driver.py\nmk3_mido_driver.py\nmk3_morning_test.py\nmk3_off_tester_v1.0.0.py\nmk3_pad_color_map.json\nmk3_pad_color_mapper_v0.29.0.py\nmk3_pad_test.py\nmk3_pad_vs_strip_test_v0.28.0.py\nmk3_pattern_analysis_v0.34.0.py\nMK3_PERMANENT_MAPPING.md\nMK3_PREREQUISITES.md\nmk3_pressure_test.py\nMK3_PROJECT_GOAL.md\nMK3_PROJECT_SUMMARY.md\nmk3_quick_reference.py\nmk3_quick_test.py\nMK3_READY_FOR_TESTING.md\nmk3_remaining_pads_v0.25.0.py\nMK3_RESEARCH_NOTES.md\nmk3_rgb_finder_v0.10.3.py\nmk3_rgb_finder_v0.10.4.py\nmk3_rgb_finder_v0.10.5.py\nmk3_rgb_finder_v0.10.6.py\nmk3_rgb_finder_v0.10.7.py\nmk3_rgb_finder_v0.10.8.py\nmk3_rgb_finder_v0.11.0.py\nmk3_rgb_finder_v0.11.1.py\nmk3_rgb_finder_v0.11.2.py\nmk3_rgb_finder_v0.11.3.py\nmk3_rgb_finder_v0.11.4.py\nmk3_rgb_finder_v0.11.5.py\nmk3_rgb_finder_v0.11.6.py\nmk3_rgb_finder_v0.11.7.py\nmk3_rgb_finder_v0.11.8.py\nmk3_rgb_finder_v0.11.9.py\nmk3_rgb_finder_v0.12.0.py\nmk3_rgb_finder_v0.12.1.py\nmk3_rgb_finder_v0.12.2.py\nmk3_rgb_finder_v0.12.3.py\nmk3_rgb_finder_v0.12.4.py\nmk3_rgb_finder_v0.12.5.py\nmk3_rgb_finder_v0.12.6.py\nmk3_rgb_finder_v0.12.7.py\nmk3_rgb_finder_v0.13.0.py\nmk3_rgb_finder_v0.14.0.py\nmk3_rgb_finder_v0.7.0.py\nmk3_rgb_finder_v0.7.1.py\nmk3_rgb_finder_v0.7.2.py\nmk3_rgb_finder_v0.9.0.py\nmk3_rgb_mapper_v0.8.0.py\nmk3_rgb_mapper_v0.8.1.py\nmk3_rgb_mapper_v0.8.2.py\nmk3_rgb_mapper_v0.8.3.py\nmk3_rgb_mapper_v0.8.4.py\nmk3_rgb_mapping.json\nmk3_rgb_pad_final_results.json\nmk3_rgb_pad_finder_v0.19.0.py\nmk3_rgb_test_results.json\nmk3_simple_pad_test_v0.32.0.py\nmk3_smart_input.py\nmk3_systematic_explore.py\nmk3_targeted_color_finder_v0.18.0.py\nmk3_targeted_color_results.json\nmk3_test_suite.py\nmk3_tools.py\nmk3_udev_rule.rules\nmk3-check-0.2.4.py\nmk3-debug-0.0.1.py\nmk3-debug-0.0.2.py\nmk3-deep-0.3.3.py\nmk3-detach-0.1.4.py\nmk3-detach-safe-0.1.5.py\nmk3-enable-0.2.6.py\nmk3-feature-report-0.3.1.py\nmk3-hidraw-0.2.1.py\nmk3-init-bypass-0.3.0.py\nmk3-input-0.0.1.py\nmk3-input-0.0.3.py\nmk3-input-0.0.4.py\nmk3-input-0.0.5.py\nmk3-input-0.0.6.py\nmk3-input-0.0.7.py\nmk3-input-0.0.8.py\nmk3-input-0.0.9.py\nmk3-input-0.1.0.py\nmk3-input-0.1.1.py\nmk3-input-0.1.2.py\nmk3-input-0.1.3.py\nmk3-input-0.1.6.py\nmk3-input-0.1.7.py\nmk3-input-0.1.8.py\nmk3-input-0.1.9.py\nmk3-input-0.2.0.py\nmk3-input-v1.0.0.py\nmk3-midi-0.3.4.py\nmk3-scan-all-0.2.7.py\nmk3-test-0.3.2.py\nmk3-udev-0.2.3.py\nmk3-unbind-0.2.5.py\nmk3-wakeup-0.2.2.py\nmnt/\nMonster-Upscaled/\nmpv.log\nMusic/\nmy setup.qpwgraph\nMy Spotify Playlist Missing.csv\nNegligee - Love Stories/\nNightmareKnight_Chat GPT 3.5_ENG v1.03/\nnode_modules/\nnormal.carxp\nodin4.log\nodysseus.data.backup/\nodysseus/\nomz-debug.log\nopencode.json\np0-05-accept/\npackage-lock.json\npackage.json\npetersenguidetolovecraftianhorrors.zip\nPictures/\npipewire-debug.log\npnpm-lock.yaml\npnpm-workspace.yaml\npocketbase_guitar_academy_setup.py\nPRODUCT_INSTALL_LOCATION/\nprojects/\nProjects/\nPublic/\nqbittorrent-crash.log\nqbt-backup-20260330/\nqbt-restore/\nqbt-torrents/\nQueen's Glory/\nr2r_load.log\nRainyseason/\nRaspberryPi/\nRedFlag/\nRemote/\nresearch/\nrestart-discord-wayland-fix.sh\nscripts/\nset-snapshot-retention-2days.sh\nsharkord\nsnapshot-cleanup-estimate.txt\nstdout.log\nstdout.log.2026-06-26T18-39-57.tar.gz\nstereo out 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entries)\n","metadata":{"preview":"\n)\n\nwith open(path, w) as f:\n f.write(content)\nprint(\\\"1. Cancellation language fixed\\\")\n\"\n\n)\ncontent = content.replace(\n Cancel\n--no-pager\n,\n \n,\n 30\n.adal/\n.agents/\n.android/\n.augment/\n.bash_history\n.bash_logout\n.bash_profile\n.bashrc\n.bob/\n.bun/\n.cache/\n.cargo/\n.clap/\n.claude/\n.cmake/","truncated":false,"loaded":[]},"title":"../../../home/nick","time":{"start":1788953472113,"end":1788953472120}},"id":"prt_g001a085efabfe001XXsZTnNiC","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085ed3dc1001E82MsfeLF"}} +{"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"}} +{"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"}} +{"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}} +{"type":"step_start","timestamp":1788953479093,"sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","part":{"id":"prt_g001a085efc7b3001hdUmUZfTJ","messageID":"msg_g001a085efad380013M8SX2T8K","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","snapshot":"8dd313d4b9b7e34c2a3cadf157a7c165f211dfa0","type":"step-start"}} +{"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}}} +{"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":"/mnt/workdrive/issue161-harness/src/rune/graph_memory.py\nfile\n\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 --type subj_type --evidence \"...\"\n31: [--source X] [--confidence 0.0-1.0] [--strength N]\n32: graph_memory.py add-bulk \n33: graph_memory.py query [--hops N] [--latest-only]\n34: graph_memory.py resolve \n35: graph_memory.py neighbors \n36: graph_memory.py stats\n37: graph_memory.py superseded-by (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_id) REFERENCES entities(id)\n100: );\n101: CREATE TABLE IF NOT EXISTS edges (\n102: id INTEGER PRIMARY KEY AUTOINCREMENT,\n103: subj_id INTEGER NOT NULL,\n104: pred TEXT NOT NULL,\n105: obj_id INTEGER NOT NULL,\n106: valid_from TEXT,\n107: valid_until TEXT,\n108: learned_at TEXT NOT NULL,\n109: confidence REAL NOT NULL DEFAULT 0.7,\n110: evidence TEXT DEFAULT '',\n111: source TEXT DEFAULT '',\n112: status TEXT NOT NULL DEFAULT 'active', -- active|superseded|expired\n113: superseded_by INTEGER,\n114: FOREIGN KEY(subj_id) REFERENCES entities(id),\n115: FOREIGN KEY(obj_id) REFERENCES entities(id)\n116: );\n117: CREATE INDEX IF NOT EXISTS idx_edges_subj ON edges(subj_id);\n118: CREATE INDEX IF NOT EXISTS idx_edges_obj ON edges(obj_id);\n119: \"\"\")\n120: return db\n121: except Exception:\n122: db.close()\n123: raise\n124: \n125: \n126: def strength_to_conf(strength):\n127: \"\"\"Map curator strength (0-5) to a 0-1 confidence, gated so weak evidence\n128: never enters the graph (write-time gating).\"\"\"\n129: try:\n130: s = float(strength)\n131: except (TypeError, ValueError):\n132: return None\n133: if s < 3:\n134: return None # below durability threshold -> do not write\n135: return min(0.99, 0.4 + 0.12 * s) # 3->0.76, 4->0.88, 5->0.99\n136: \n137: \n138: def resolve_entity(db, name, node_type=\"concept\"):\n139: \"\"\"Find an entity by exact name or alias; create if missing. Returns id.\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: SELECT e.id, e.name, e.type FROM aliases a\n230: JOIN entities e ON a.entity_id=e.id\n231: WHERE lower(a.alias)=lower(?) LIMIT 1\"\"\", (term,)).fetchone()\n232: if not row:\n233: # substring match as last resort\n234: row = db.execute(\"SELECT id, name, type FROM entities WHERE name LIKE ? LIMIT 1\",\n235: (f\"%{term}%\",)).fetchone()\n236: if not row:\n237: return []\n238: eid, name, etype = row[\"id\"], row[\"name\"], row[\"type\"]\n239: results = []\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}\n285: \n286: \n287: def stats(db):\n288: e = db.execute(\"SELECT COUNT(*) AS n FROM entities\").fetchone()[\"n\"]\n289: a = db.execute(\"SELECT COUNT(*) AS n FROM aliases\").fetchone()[\"n\"]\n290: ed = db.execute(\"SELECT COUNT(*) AS n FROM edges\").fetchone()[\"n\"]\n291: active = db.execute(\n292: \"SELECT COUNT(*) AS n FROM edges WHERE status='active'\").fetchone()[\"n\"]\n293: by_pred = {r[\"pred\"]: r[\"n\"] for r in\n294: db.execute(\"SELECT pred, COUNT(*) AS n FROM edges GROUP BY pred\"\n295: \" ORDER BY n DESC LIMIT 8\")}\n296: return {\"entities\": e, \"aliases\": a, \"edges\": ed, \"active\": active,\n297: \"predicates\": by_pred}\n298: \n299: \n300: def main():\n301: ap = argparse.ArgumentParser()\n302: ap.add_argument(\"cmd\", choices=[\"add\", \"add-bulk\", \"query\", \"resolve\",\n303: \"neighbors\", \"stats\"])\n304: ap.add_argument(\"arg\", nargs=\"*\", default=[])\n305: ap.add_argument(\"--type\", default=\"concept\")\n306: ap.add_argument(\"--evidence\", default=\"\")\n307: ap.add_argument(\"--source\", default=\"\")\n308: ap.add_argument(\"--confidence\", type=float, default=None)\n309: ap.add_argument(\"--strength\", type=int, default=None)\n310: ap.add_argument(\"--hops\", type=int, default=1)\n311: ap.add_argument(\"--json\", action=\"store_true\")\n312: ap.add_argument(\"--latest-only\", action=\"store_true\", default=True)\n313: args = ap.parse_args()\n314: \n315: db = connect()\n316: try:\n317: if args.cmd == \"stats\":\n318: s = stats(db)\n319: print(json.dumps(s, indent=2) if args.json else\n320: f\"entities={s['entities']} aliases={s['aliases']} \"\n321: f\"edges={s['edges']} active={s['active']}\")\n322: return\n323: if args.cmd == \"add\":\n324: if len(args.arg) < 3:\n325: print(\"add needs \", file=sys.stderr)\n326: sys.exit(1)\n327: subj, pred, obj = args.arg[0], args.arg[1], args.arg[2]\n328: ok = add_edge(db, subj, pred, obj,\n329: subj_type=args.type, evidence=args.evidence,\n330: source=args.source, confidence=args.confidence,\n331: strength=args.strength)\n332: print(json.dumps({\"added\": ok}))\n333: sys.exit(0 if ok else 1)\n334: if args.cmd == \"add-bulk\":\n335: path = args.arg[0] if args.arg else None\n336: if not path:\n337: print(\"add-bulk needs \", file=sys.stderr)\n338: sys.exit(1)\n339: with open(path) as _fh:\n340: items = json.load(_fh)\n341: added = 0\n342: for it in items:\n343: ok = add_edge(db, it.get(\"subject\"), it.get(\"predicate\"),\n344: it.get(\"object\"),\n345: subj_type=it.get(\"subject_type\", \"concept\"),\n346: obj_type=it.get(\"object_type\", \"concept\"),\n347: evidence=it.get(\"evidence\", \"\"),\n348: source=it.get(\"source\", \"\"),\n349: confidence=it.get(\"confidence\"),\n350: strength=it.get(\"strength\"))\n351: if ok:\n352: added += 1\n353: print(json.dumps({\"added\": added, \"total\": len(items)}))\n354: return\n355: if args.cmd in (\"query\", \"neighbors\"):\n356: term = \" \".join(args.arg) if args.cmd == \"query\" else args.arg[0]\n357: res = query(db, term, hops=args.hops, latest_only=args.latest_only)\n358: print(json.dumps(res, indent=2) if args.json else\n359: json.dumps(res, indent=2))\n360: return\n361: if args.cmd == \"resolve\":\n362: name = \" \".join(args.arg)\n363: eid = resolve_entity(db, name, \"concept\")\n364: row = db.execute(\"SELECT name, type FROM entities WHERE id=?\",\n365: (eid,)).fetchone()\n366: aliases = [r[\"alias\"] for r in\n367: db.execute(\"SELECT alias FROM aliases WHERE entity_id=?\",\n368: (eid,))]\n369: print(json.dumps({\"id\": eid, \"name\": row[\"name\"], \"type\": row[\"type\"],\n370: \"aliases\": aliases}, indent=2))\n371: return\n372: finally:\n373: db.close()\n374: \n375: \n376: \n377: \n378: # ---------------------------------------------------------------------------\n379: # Hook handler — called by the microkernel when an event fires.\n380: # ---------------------------------------------------------------------------\n381: def handle_hook(event, data=None):\n382: \"\"\"Generic hook handler. Override in specific scripts for custom behavior.\"\"\"\n383: return {\"status\": \"ok\", \"event\": event, \"handler\": \"default\"}\n384: \n385: \n386: \n387: PLUGIN = {\n388: \"name\": \"graph_memory\",\n389: \"version\": \"1.0.0\",\n390: \"description\": \"Graph memory tier \\u2014 concept-mediated temporal knowledge graph for the agent.\",\n391: \"capabilities\": [\n392: \"skills\",\n393: \"write\",\n394: \"drift\",\n395: \"audit\",\n396: \"rag\",\n397: \"status\",\n398: \"consolidation\",\n399: \"research\",\n400: \"memory\",\n401: \"recall\",\n402: \"evidence\",\n403: \"graph_memory\",\n404: \"graph\"\n405: ],\n406: \"hooks\": {\n407: \"on_session_end\": True\n408: },\n409: \"dependencies\": [],\n410: \"provides\": [],\n411: \"priority\": 100,\n412: \"auto_activate\": True\n413: }\n414: \n415: if __name__ == \"__main__\":\n416: main()\n\n(End of file - total 416 lines)\n","metadata":{"preview":"#!/usr/bin/env python3\n\"\"\"Graph memory tier — concept-mediated temporal knowledge graph for the agent.\n\nBuilt from the 2025-2026 academic frontier (briefs in\n`/tmp/opencode/memory-research/agentic-memory-brief.md` and\n`~/.firecrawl/claim-audit-2026/brief.md`). Exceeds the entity-hub approach by\nfollowing the research convergence:\n\n - CONCEPT-MEDIATED, not entity-centric (GAAMA 2603.27910): nodes are typed\n (person / project / concept / tool / preference / fact), which kills the\n \"mega-hub\" dilution Letta/Graphiti hit with one entity connected to\n everything.\n - BI-TEMPORAL edges (Graphiti 2501.13956): `valid_from/valid_until` (when\n the fact is true in the world) + `learned_at` (when we learned it).\n - INCREMENTAL construction (TG-RAG 2510.13590, iText2KG 2409.03284): merge\n only, no full re-index; entity resolution via aliases, never blind merges.\n - SUPERSEDE, never delete (Write-Time Gating 2603.15994, SSGM 2603.11768):\n contradicting edges are marked superseded with the new one taking over;\n history preserved for audit.\n - WRITE-TIME GATING (2603.15994, D-MEM 2603.14597): routine/salience-weak","truncated":false,"loaded":[]},"title":"src/rune/graph_memory.py","time":{"start":1788953485022,"end":1788953485033}},"id":"prt_g001a085efdea6001uykReU9r8","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085efad380013M8SX2T8K"}} +{"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":"/mnt/workdrive/issue161-harness/research/issue-163/outward.json\nfile\n\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 selected\" },\n55: { \"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\" },\n56: { \"id\": \"O16\", \"url\": \"https://www.falkordb.com/blog/graphiti-falkordb-multi-agent-performance/\", \"class\": \"external (vendor integration blog)\", \"note\": \"not selected\" },\n57: { \"id\": \"O17\", \"url\": \"https://www.falkordb.com/blog/building-temporal-knowledge-graphs-graphiti/\", \"class\": \"external (vendor blog)\", \"note\": \"not selected\" },\n58: { \"id\": \"O18\", \"url\": \"https://thegtmdirectory.com/tools/graphiti\", \"class\": \"external (directory)\", \"note\": \"not selected\" },\n59: { \"id\": \"O19\", \"url\": \"https://www.getzep.com/platform/graphiti/\", \"class\": \"external (Zep platform page)\", \"note\": \"not selected\" },\n60: { \"id\": \"O20\", \"url\": \"https://ladybugdb.com/\", \"class\": \"external (official site)\", \"note\": \"not read — superseded by O21 README + O28 encyclopedia\" },\n61: { \"id\": \"O21\", \"url\": \"https://github.com/LadybugDB/ladybug\", \"class\": \"repo (LadybugDB primary)\", \"note\": \"SELECTED and read (README via raw.githubusercontent.com/main)\" },\n62: { \"id\": \"O22\", \"url\": \"https://github.com/lcretan/LadybugDB-KuzuDB.ladybug\", \"class\": \"repo (mirror)\", \"note\": \"not selected — duplicate\" },\n63: { \"id\": \"O23\", \"url\": \"https://docs.ladybugdb.com/\", \"class\": \"external (official docs)\", \"note\": \"not selected\" },\n64: { \"id\": \"O24\", \"url\": \"https://thedataquarry.com/blog/from-kuzu-to-ladybug/\", \"class\": \"external (practitioner blog by ex-Kuzu engineer, 2026-04-11)\", \"note\": \"SELECTED and read\" },\n65: { \"id\": \"O25\", \"url\": \"https://mpr.crossjam.net/wp/mpr/2025/11/kuzudb-and-ladybugdb/\", \"class\": \"external (context blog on Kuzu archival)\", \"note\": \"not selected — snippet-level evidence only\" },\n66: { \"id\": \"O26\", \"url\": \"https://gdb-engines.com/db/ladybugdb/\", \"class\": \"external (DB directory)\", \"note\": \"not selected\" },\n67: { \"id\": \"O27\", \"url\": \"https://cocoindex.io/docs-v0/targets/ladybug/\", \"class\": \"external (integration docs)\", \"note\": \"not selected\" },\n68: { \"id\": \"O28\", \"url\": \"https://dbdb.io/db/ladybugdb\", \"class\": \"external (CMU Database of Databases encyclopedia)\", \"note\": \"SELECTED and read\" },\n69: { \"id\": \"O29\", \"url\": \"https://github.com/FalkorDB/FalkorDB\", \"class\": \"repo (FalkorDB primary)\", \"note\": \"SELECTED and read (README via raw.githubusercontent.com/master)\" },\n70: { \"id\": \"O30\", \"url\": \"https://www.falkordb.com/blog/redisgraph-eol-migration-guide/\", \"class\": \"external (vendor migration guide)\", \"note\": \"not selected\" },\n71: { \"id\": \"O31\", \"url\": \"https://basekv.com/articles/redisgraph-deprecated-now-what\", \"class\": \"external (RedisGraph EOL article)\", \"note\": \"not selected — snippet-level evidence only\" },\n72: { \"id\": \"O32\", \"url\": \"https://stackharbor.com/en/knowledge-base/redis-redisgraph-module/\", \"class\": \"external (knowledge base)\", \"note\": \"not selected\" },\n73: { \"id\": \"O33\", \"url\": \"https://orchestrator.dev/blog/2025-12-11-falkordb/\", \"class\": \"external (blog)\", \"note\": \"not selected\" },\n74: { \"id\": \"O34\", \"url\": \"https://falkordb.github.io/docs-staging/\", \"class\": \"external (staging docs)\", \"note\": \"not selected\" },\n75: { \"id\": \"O35\", \"url\": \"https://www.solosoft.dev/post/falkordb-graph-database-2026/\", \"class\": \"external (blog)\", \"note\": \"not selected\" },\n76: { \"id\": \"O36\", \"url\": \"https://hydradb.com/blog/falkordb-alternatives\", \"class\": \"external (competitor listicle)\", \"note\": \"not selected\" },\n77: { \"id\": \"O37\", \"url\": \"https://groups.google.com/g/redis-db/c/aoBMFIszBRg\", \"class\": \"external (fork announcement)\", \"note\": \"not selected\" },\n78: { \"id\": \"O38\", \"url\": \"https://neo4j.com/docs/cypher-manual/current/values-and-types/temporal/\", \"class\": \"external (Neo4j official docs)\", \"note\": \"SELECTED and read (page truncated at ~40KB; temporal type table captured)\" },\n79: { \"id\": \"O39\", \"url\": \"https://dev.to/satyam_shree_087caef77512/a-practical-guide-to-temporal-versioning-in-neo4j-nodes-relationships-and-historical-graph-1m5g\", \"class\": \"external (practitioner bitemporal guide)\", \"note\": \"not selected — O41 evidences the same gap at greater authority\" },\n80: { \"id\": \"O40\", \"url\": \"https://kindatechnical.com/cypher/cypher-date-and-time-functions.html\", \"class\": \"external (tutorial)\", \"note\": \"not selected\" },\n81: { \"id\": \"O41\", \"url\": \"https://github.com/DIGITAL-FABRIC-AI/neo4j-temporal-graph\", \"class\": \"repo (NEP-001 reference implementation)\", \"note\": \"SELECTED and read (NEP-001.md via raw.githubusercontent.com/master)\" },\n82: { \"id\": \"O42\", \"url\": \"https://medium.com/neo4j/keeping-track-of-graph-changes-using-temporal-versioning-3b0f854536fa\", \"class\": \"external (Neo4j staff blog)\", \"note\": \"not selected\" },\n83: { \"id\": \"O43\", \"url\": \"https://www.cosmiclearn.com/neo4j/cypher-datetime-duration.php\", \"class\": \"external (tutorial)\", \"note\": \"not selected\" },\n84: { \"id\": \"O44\", \"url\": \"https://dev.graphacademy.neo4j.com/blog/til-replace-apoc-temporal-format\", \"class\": \"external (Neo4j academy TIL)\", \"note\": \"not selected\" },\n85: { \"id\": \"O45\", \"url\": \"https://stackoverflow.com/questions/15365387/neo4j-relationships-with-time-constraints\", \"class\": \"external (Stack Overflow)\", \"note\": \"not selected\" }\n86: ]\n87: },\n88: \"reading\": {\n89: \"sources_read\": [\n90: {\n91: \"id\": \"O1\",\n92: \"url\": \"https://aimultiple.com/graph-databases\",\n93: \"receipt\": \"full page fetched via webfetch (markdown), 2026 session turn\",\n94: \"class_note\": \"independent analyst benchmark (AIMultiple); self-declared ethical norms and no-subscriber statement; single-author (Ekrem Sarı)\",\n95: \"claims\": [\n96: { \"id\": \"E1\", \"claim\": \"Benchmark setup: Neo4j 5.26.0 Community vs FalkorDB 'latest' vs Memgraph 2.21.0 on a synthetic graph from 120,000 Amazon reviews (381K nodes, 804K edges); 12 Cypher query templates x 1,000 measurements each; ingestion at 6 batch sizes; 60-second sustained concurrent load at 1-32 threads; RunPod 8 vCPU / 32GB RAM, native installs, same machine, fixed seed=42.\", \"section\": \"methodology, environment, data\" },\n97: { \"id\": \"E2\", \"claim\": \"Concurrent throughput: FalkorDB 6,693 QPS at 8 threads = 6.7x Neo4j; FalkorDB plateaus after 8 threads (Redis single-threaded core is the ceiling); Neo4j peaks at 1,010 QPS at 16 threads and drops to 927 at 32 (thread contention).\", \"section\": \"key findings\" },\n98: { \"id\": \"E3\", \"claim\": \"FalkorDB had lowest latency on 11 of 12 queries; Neo4j won the heaviest aggregation (agg_feature_sentiment: 131ms vs 152ms) because its query optimizer produced a better execution plan.\", \"section\": \"key findings\" },\n99: { \"id\": \"E4\", \"claim\": \"Cold start: FalkorDB ready in 1.1ms (first query 0.4ms) vs Neo4j 90ms to accept first query, first warming query 274ms settling to ~34ms after ~3 queries — an 82x gap relevant to pod scale-up/serverless.\", \"section\": \"key findings\" },\n100: { \"id\": \"E5\", \"claim\": \"Index sensitivity: without indexes Neo4j's deep_feature_products query took 293ms vs 0.17ms with indexes (1,712x); FalkorDB results were roughly unchanged with/without indexes because Redis hash tables act as implicit indexes.\", \"section\": \"key findings\" },\n101: { \"id\": \"E6\", \"claim\": \"Memory for the same graph: Memgraph 415MB, FalkorDB 496MB, Neo4j 2,668MB (JMX heap; process VmRSS ~5.2GB due to 4GB JVM pre-allocation).\", \"section\": \"key findings\" },\n102: { \"id\": \"E7\", \"claim\": \"Mixed workload (80% read / 20% write, 8 threads, 60s, zero errors): FalkorDB 837 QPS, Neo4j 738 QPS, Memgraph 467 QPS; writes did not noticeably degrade reads on any engine.\", \"section\": \"key findings\" },\n103: { \"id\": \"E8\", \"claim\": \"Stated limitations: single machine/single node, no distributed or cluster benchmarking (Neo4j Enterprise clustering out of scope), synthetic data, not measured: disk persistence/recovery, full-text search, graph algorithms, write-heavy (>50% writes) workloads; different drivers used (Neo4j Python driver for Neo4j and Memgraph, falkordb-py for FalkorDB; measured overhead difference <0.5ms).\", \"section\": \"limitations\" }\n104: ]\n105: },\n106: {\n107: \"id\": \"O2\",\n108: \"url\": \"https://www.falkordb.com/blog/graph-database-performance-benchmarks-falkordb-vs-neo4j/\",\n109: \"receipt\": \"full page fetched via webfetch (markdown), 2026 session turn\",\n110: \"class_note\": \"vendor-published benchmark (FalkorDB blog, author = FalkorDB chief architect); FAQ and RAG-implications table are vendor positioning\",\n111: \"claims\": [\n112: { \"id\": \"E9\", \"claim\": \"Vendor benchmark (SNAP Pokec social network, 8 cores/16 threads, 32GB RAM, GitHub Actions runner, 11 templated queries, 82% read / 18% write): p50 55ms (FalkorDB) vs 577.5ms (Neo4j); p90 108ms vs 4,784.1ms; p99 136.2ms vs 46,923.8ms.\", \"section\": \"performance metrics\" },\n113: { \"id\": \"E10\", \"claim\": \"Vendor positioning table: FalkorDB 'native' multi-tenancy vs Neo4j 'limited'; built-in GraphRAG support vs Neo4j 'custom'; horizontal vs vertical scaling model; FAQ claims production-readiness as 'a continuation of the production-tested RedisGraph codebase'.\", \"section\": \"implications on RAG, FAQ\" },\n114: { \"id\": \"E11\", \"claim\": \"Blog update note (as of 2026-08-23 revision): FalkorDB completed a ground-up rewrite of its core engine in Rust (preview), closing 100+ open memory-safety bugs inherited from the C implementation, with no changes to existing Cypher queries, APIs, or integrations; internal benchmarks show the Rust engine matching or exceeding the C engine.\", \"section\": \"technical analysis\" },\n115: { \"id\": \"E12\", \"claim\": \"Operational facts: persistence via Redis mechanisms (RDB/AOF); Bolt protocol supported since FalkorDB 4.0; latest FalkorDB version requires Redis 7.4; minimum 4GB RAM, 16GB recommended for production.\", \"section\": \"FAQ\" }\n116: ]\n117: },\n118: {\n119: \"id\": \"O5\",\n120: \"url\": \"https://github.com/FalkorDB/benchmark\",\n121: \"receipt\": \"README.md fetch 404 on both master and main (raw.githubusercontent.com); repo metadata retrieved via api.github.com/repos/FalkorDB/benchmark (HTTP 200), 2026 session turn\",\n122: \"class_note\": \"vendor benchmark harness repo; only metadata receipt obtained — content NOT read\",\n123: \"claims\": [\n124: { \"id\": \"E13\", \"claim\": \"Repo metadata: MIT-licensed, written in Rust, active (pushed 2026-09-07), homepage benchmark.falkordb.com, 14 stars, description 'A benchmarking tool to evaluate and compare the performance of graph databases with customizable workloads and metrics'; default branch is 'master', yet README.md is not present at the raw master/main paths — headline benchmark numbers therefore rest on the vendor blog (O2) alone in this evidence set.\", \"section\": \"GitHub API metadata\" }\n125: ]\n126: },\n127: {\n128: \"id\": \"O10\",\n129: \"url\": \"https://github.com/getzep/graphiti\",\n130: \"receipt\": \"README.md fetched via raw.githubusercontent.com/getzep/graphiti/main (markdown), 2026 session turn\",\n131: \"class_note\": \"primary repo of the reference temporal-KG framework (Zep AI); Apache-2.0 per directory listings; README is maintained and detailed\",\n132: \"claims\": [\n133: { \"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\" },\n134: { \"id\": \"E15\", \"claim\": \"Supported graph backends (requirements): Neo4j 5.26, FalkorDB 1.1.2, Amazon Neptune (Database/Analytics + OpenSearch Serverless as full-text backend), and Kuzu 0.11.2 — the Kuzu driver is explicitly DEPRECATED ('upstream Kuzu project is no longer maintained. New projects should use Neo4j or FalkorDB') and emits DeprecationWarning, to be removed in a future release.\", \"section\": \"Installation requirements, Kuzu warnings\" },\n135: { \"id\": \"E16\", \"claim\": \"Graphiti uses a pluggable driver architecture (graphiti_core.driver.*: Neo4jDriver, FalkorDriver, KuzuDriver, NeptuneDriver; custom backends via CONTRIBUTING 'Adding a graph driver'); FalkorDB support installs via extra graphiti-core[falkordb]; an EMBEDDED option graphiti-core[falkordblite] exists (redislite-based AsyncFalkorDB, requires Python 3.12+) storing to a local dbfile.\", \"section\": \"Installing with FalkorDB Support, Database Configuration\" },\n136: { \"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\" },\n137: { \"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\" },\n138: { \"id\": \"E19\", \"claim\": \"The framework's reference paper is 'Zep: A Temporal Knowledge Graph Architecture for Agent Memory' (arXiv 2501.13956).\", \"section\": \"Graphiti and Zep\" }\n139: ]\n140: },\n141: {\n142: \"id\": \"O21\",\n143: \"url\": \"https://github.com/LadybugDB/ladybug\",\n144: \"receipt\": \"README.md fetched via raw.githubusercontent.com/LadybugDB/ladybug/main (markdown), 2026 session turn\",\n145: \"class_note\": \"primary repo of the Kuzu successor; README states MIT license and 'formerly known as Kuzu'\",\n146: \"claims\": [\n147: { \"id\": \"E20\", \"claim\": \"Ladybug is an embedded graph database (formerly Kuzu): flexible property graph model + Cypher; embeddable, serverless integration; native full-text search and vector index; columnar disk-based storage; CSR adjacency list/join indices; vectorized and factorized query processing; multi-core query parallelism; serializable ACID transactions; Wasm bindings; MIT license; SDKs for Python (pip install ladybug), NodeJS, Rust (lbug), Go, Swift, Java, C/C++, CLI.\", \"section\": \"README core feature list, installation table, license\" }\n148: ]\n149: },\n150: {\n151: \"id\": \"O24\",\n152: \"url\": \"https://thedataquarry.com/blog/from-kuzu-to-ladybug/\",\n153: \"receipt\": \"full page fetched via webfetch (markdown), 2026 session turn\",\n154: \"class_note\": \"practitioner blog by Prashanth Rao (ex-Kuzu employee), 2026-04-11; close discussions with LadybugDB lead maintainer disclosed — sympathetic but technically detailed\",\n155: \"claims\": [\n156: { \"id\": \"E21\", \"claim\": \"Kuzu was archived in September 2025 and the codebase went quiet; LadybugDB is a fork of Kuzu maintained by the team at Ladybug Memory, with a shifted goal: from standalone embedded graph DB to a 'graph lakehouse' interoperating with DuckDB's storage layer, reading/writing Arrow/Parquet, connecting to object stores.\", \"section\": \"intro, The Bigger Picture\" },\n157: { \"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\" },\n158: { \"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\" }\n159: ]\n160: },\n161: {\n162: \"id\": \"O28\",\n163: \"url\": \"https://dbdb.io/db/ladybugdb\",\n164: \"receipt\": \"full page fetched via webfetch (markdown), 2026 session turn\",\n165: \"class_note\": \"third-party encyclopedia entry (CMU Database Group, dbdb.io), revision #4 last updated 2026-07-17; cites repo access Sep 6, 2026, last commit Sep 5, 2026\",\n166: \"claims\": [\n167: { \"id\": \"E24\", \"claim\": \"dbdb.io classifies LadybugDB as an embedded graph-oriented DBMS (Cypher query interface, C++, MIT license, derived from Kuzu, start year 2025) tagged 'Forked Code', 'Revival Project', and 'AI-Assisted' (coding agents Claude/Copilot/Cursor); history states it 'started in 2025 as a revival fork of Kuzu after its acquisition and closure by Apple'; the repo is active (last commit Sep 5, 2026).\", \"section\": \"entry metadata + History\" }\n168: ]\n169: },\n170: {\n171: \"id\": \"O29\",\n172: \"url\": \"https://github.com/FalkorDB/FalkorDB\",\n173: \"receipt\": \"README.md fetched via raw.githubusercontent.com/FalkorDB/FalkorDB/master (markdown), 2026 session turn\",\n174: \"class_note\": \"primary repo; license section is explicit (SSPLv1)\",\n175: \"claims\": [\n176: { \"id\": \"E25\", \"claim\": \"FalkorDB is a Redis-module graph database ('hosted by Redis', loadmodule falkordb.so; Redis 7.4 required for latest version): first queryable property graph DB to use sparse matrices (GraphBLAS) for adjacency and linear algebra for querying; openCypher support with proprietary extensions; multi-tenant; LICENSED UNDER SERVER SIDE PUBLIC LICENSE v1 (SSPLv1) — not MIT/Apache; official clients: Python (MIT), Java (BSD), Node.JS (MIT), Rust (MIT), Go (BSD), C# (Apache-2.0).\", \"section\": \"Unique Features, Loading FalkorDB into Redis, License, client tables\" },\n177: { \"id\": \"E26\", \"claim\": \"Deployment shape: single Docker container (falkordb/falkordb, ports 6379 + 3000 browser UI) or compiled as a .so loaded into an existing Redis server; build requires OpenMP (libgomp).\", \"section\": \"Get Started, Compiling\" }\n178: ]\n179: },\n180: {\n181: \"id\": \"O38\",\n182: \"url\": \"https://neo4j.com/docs/cypher-manual/current/values-and-types/temporal/\",\n183: \"receipt\": \"page fetched via webfetch (markdown); output truncated at ~40KB (saved to tool-output file); temporal type table and timezone semantics captured before truncation\",\n184: \"class_note\": \"official Neo4j Cypher Manual (current, v25/5/4.4 selector)\",\n185: \"claims\": [\n186: { \"id\": \"E27\", \"claim\": \"Cypher has built-in temporal VALUE types — DATE, LOCAL TIME, ZONED TIME, LOCAL DATETIME, ZONED DATETIME (temporal instants) and DURATION (a temporal amount, not an instant) — storable as properties on nodes and relationships, with IANA-named-timezone support, UTC-internal storage, component accessors, and db.temporal.timezone configuration; this is property-level temporal typing, not graph versioning — the page describes no valid-time/transaction-time query semantics.\", \"section\": \"Temporal value types, Time zones\" }\n187: ]\n188: },\n189: {\n190: \"id\": \"O41\",\n191: \"url\": \"https://github.com/DIGITAL-FABRIC-AI/neo4j-temporal-graph\",\n192: \"receipt\": \"NEP-001.md fetched via raw.githubusercontent.com/DIGITAL-FABRIC-AI/neo4j-temporal-graph/master (markdown), 2026 session turn\",\n193: \"class_note\": \"community NEP (Neo4j Enhancement Proposal) draft RFC, 2026-03-18, author Ashley Dunfield (Digital Fabric), Apache-2.0; NOT an accepted Neo4j feature; includes a plugin reference implementation\",\n194: \"claims\": [\n195: { \"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\" },\n196: { \"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+ temporally-annotated relationships in a regulatory graph (Neo4j 2026.01.4 Community, ~15M nodes, ~30M relationships).\", \"section\": \"Section 1.2 The Current State Is Broken, Section 1.3 Evidence from Production\" },\n197: { \"id\": \"E30\", \"claim\": \"Even the proposal defers full bitemporality: transaction time (system time) requires immutable relationship versioning — new relationship versions rather than in-place updates — rated 'Very high' implementation complexity and proposed as a separate later NEP; Phase 1 ('AS OF' as parse-time syntactic sugar over the WHERE-clause convention) is rated low complexity and delivers most of the developer-experience value.\", \"section\": \"Section 2.1.4, Section 4.1, Section 7 Roadmap\" }\n198: ]\n199: }\n200: ]\n201: },\n202: \"limitations\": [\n203: \"FalkorDB/benchmark (O5) README could not be retrieved (404 on raw master and main despite default_branch='master' per GitHub API) — the vendor benchmark's methodology and raw results were not independently verified; its headline numbers enter this evidence set only via the vendor blog (O2).\",\n204: \"Both performance evidence sources are workload-specific and non-overlapping: O1 uses a 381K-node synthetic review graph on 8 vCPU; O2 uses the SNAP Pokec social network on a GitHub Actions runner. No source benchmarks Rune's regime (single-node, sub-1M entities, embedded/local-first).\",\n205: \"Neo4j temporal docs (O38) were fetched but truncated (~40KB); the temporal functions/operators pages were not read. The captured claim (E27) covers value types only.\",\n206: \"No FalkorDB-native temporal feature documentation was read (docs.falkordb.com TTL/time-function pages were not fetched) — FalkorDB's own temporal capabilities beyond Cypher property storage are unevidenced in this set.\",\n207: \"No Neo4j licensing/pricing source was read: Community-vs-Enterprise constraints (clustering, RBAC) appear only as asides inside O1/O2 and are not independently evidenced.\",\n208: \"The Kuzu-closure cause is single-sourced: dbdb.io (O28) says 'acquisition and closure by Apple'; mpr.crossjam (O25, snippet only) says 'abandoned by its creator and sponsor Kuzu Inc'; thedataquarry (O24, ex-Kuzu) says only 'archived'. The acquirer claim is unverified.\",\n209: \"Graphiti's own benchmark claims (sub-second retrieval, LongMemEval/DRIFT-style SOTA via the Zep paper) were not read from the primary paper (arXiv 2501.13956) — only the README's characterization (E18, E19).\",\n210: \"All numbers are third-party; nothing was reproduced locally, and the stage contract forbids running spikes.\"\n211: ],\n212: \"contradictions\": [\n213: {\n214: \"topic\": \"Magnitude of FalkorDB's performance advantage over Neo4j\",\n215: \"resolution\": \"Vendor benchmark (O2/E9) reports p99 136.2ms vs 46,923.8ms (~345x) and p50 55ms vs 577.5ms (~10x); the independent benchmark (O1/E2-E3) reports 6.7x concurrent throughput and 11/12 query wins, but Neo4j WINS the heaviest aggregation and FalkorDB's throughput ceiling is Redis's single-threaded core. Not strictly contradictory (different datasets, hardware, query mixes, and metrics), but the vendor number is an outlier that independent evidence does not approach; treat O2 magnitudes as marketing-adjacent until reproduced.\"\n216: },\n217: {\n218: \"topic\": \"Why Kuzu died / what LadybugDB is a revival of\",\n219: \"resolution\": \"O28 (dbdb.io): 'revival fork of Kuzu after its acquisition and closure by Apple'; O25 (snippet): 'abandoned by its creator and sponsor Kuzu Inc'; O24 (ex-Kuzu): 'Kuzu got archived' Sept 2025 without naming a cause. All agree the upstream archived in 2025 and LadybugDB is the maintained fork (active commits Sep 2026, E24); the acquirer identity is contested/unverified.\"\n220: },\n221: {\n222: \"topic\": \"FalkorDB production-readiness framing\",\n223: \"resolution\": \"O2 FAQ claims production-readiness as 'a continuation of the production-tested RedisGraph codebase' (E10), while the same evidence set records that RedisGraph itself reached EOL (support ended Jan 31, 2025 per O31 snippet; repo deprecated). Continuity of codebase is real (E25 fork lineage), but 'production-tested' inherits from a discontinued upstream; additionally the core engine is being rewritten in Rust (E11), which resets part of that maturity claim.\"\n224: },\n225: {\n226: \"topic\": \"Embedded-backend path via Graphiti\",\n227: \"resolution\": \"Graphiti supports an embedded option (falkordblite, E16) and a Kuzu driver, but the Kuzu driver is deprecated with upstream declared unmaintained (E15) — and Graphiti lists no LadybugDB driver, even though LadybugDB IS the maintained Kuzu fork (E20, E21, E24). So 'Graphiti as reference' currently endorses Neo4j or FalkorDB only; using it against LadybugDB would require a new driver (the framework documents how, E16).\"\n228: }\n229: ],\n230: \"repo_grounding_note\": \"INWARD identified src/rune/graph_memory.py (memory umbrella) as Rune's existing graph surface: a headless SQLite graph tier at memory/graph/graph.sqlite, no external graph DB dependency. The external evidence maps onto that posture as follows: FalkorDB is server-shaped (Redis module, own container/ports, E25/E26) with an embedded redislite variant only via Graphiti's falkordblite extra (E16, Python 3.12+); LadybugDB is genuinely embedded, MIT, with native FTS + vector index and ACID (E20) but is a young revival fork (E24) whose upstream died once already; Neo4j is the mature server with the deepest ecosystem (O1/E8) but JVM-scale memory (E6) and no native temporal versioning (E27, E28-E30) — the temporal semantics in this spike's reference architecture (Graphiti, E14) are implemented at the framework layer above any backend, which is the load-bearing fact for the spike question.\",\n231: \"stop_condition\": {\n232: \"external_subquestions\": [\"Address: Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\"],\n233: \"status\": \"adequate evidence\",\n234: \"note\": \"5 discovery queries issued, all returned results (receipts above); 45 unique sources discovered and classified (O1-O45); 9 sources read in full (O1, O2, O10, O21, O24, O28, O29, O38, O41) + 1 metadata-only receipt (O5) = 10 sources with fetch/read receipts; both required source classes covered (repo: O5, O10, O21, O29, O41; external: O1, O2, O24, O28, O38). Requirement was >=2 unique URLs; actual URLs read: 9 (+1 API receipt). Stopped here per stage rules: evidence requirement satisfied, remaining candidates (O3, O4, O7-O9, O11-O13, O14-O19, O20, O22, O23, O25-O27, O30-O37, O39, O40, O42-O45) recorded but not read. No synthesis, no design, no code changes performed.\"\n235: }\n236: }\n\n(End of file - total 236 lines)\n","metadata":{"preview":"{\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 },","truncated":false,"loaded":[]},"title":"research/issue-163/outward.json","time":{"start":1788953485023,"end":1788953485036}},"id":"prt_g001a085efdea7001SXDEyEqi6","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085efad380013M8SX2T8K"}} +{"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":"/mnt/workdrive/issue161-harness/research/issue-163/applicability.json\nfile\n\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\": [\"E1\", \"E2\", \"E3\", \"E5\", \"E9\", \"E10\", \"E11\", \"E12\", \"E13\", \"E16\", \"E25\", \"E26\"]\n39: },\n40: {\n41: \"id\": \"C3\",\n42: \"candidate\": \"LadybugDB as replacement graph backend\",\n43: \"source_ids\": [\"O21\", \"O24\", \"O28\"],\n44: \"classification\": \"INSUFFICIENT_EVIDENCE\",\n45: \"integration_point\": \"Hypothetically the same slot as the SQLite tier (genuinely embedded, MIT, native FTS + vector index, ACID, E20).\",\n46: \"benefit\": \"Best architectural fit of the three spiked backends: embedded, MIT, Cypher, FTS+vector built in (E20, E24).\",\n47: \"cost\": \"Migration cost from a working SQLite schema for no evidenced capability gain at Rune's scale.\",\n48: \"architectural_fit\": \"Good in shape, unproven in substance: young revival fork (start year 2025, tagged 'Revival Project'/'AI-Assisted', E24) whose upstream died once already (dossier contradiction #2); no Graphiti driver exists for it (dossier contradiction #4); no benchmark anywhere near Rune's regime (dossier limitation #2).\",\n49: \"authority_implications\": \"None new (embedded, in-process) - but supply-chain risk of betting the memory tier on a single-maintainer fork.\",\n50: \"risks\": \"Upstream mortality repeated; API instability during revival; the dossier contains zero evidence LadybugDB outperforms SQLite for Rune's workload - 'experimental' per the issue title itself.\",\n51: \"required_adaptation\": \"Re-evaluate if Rune's graph ever exceeds SQLite's comfortable range or if LadybugDB matures (maintained release cadence, Graphiti/driver ecosystem support).\",\n52: \"evidence\": [\"E20\", \"E21\", \"E24\"]\n53: },\n54: {\n55: \"id\": \"C4\",\n56: \"candidate\": \"Graphiti bi-temporal semantic model (validity windows, invalidate-not-delete, episode provenance)\",\n57: \"source_ids\": [\"O10\"],\n58: \"classification\": \"DIRECTLY_APPLICABLE\",\n59: \"integration_point\": \"Already integrated: src/rune/graph_memory.py edges table (valid_from/valid_until/learned_at, status/superseded_by, evidence/source) implements E14's model 1:1, including supersede-on-contradiction (add_edge) and confidence-gated writes.\",\n60: \"benefit\": \"Confirms the current design against the reference architecture; zero further work for the core semantic model.\",\n61: \"cost\": \"Already sunk; none incremental.\",\n62: \"architectural_fit\": \"Exact. The load-bearing fact (E17: Zep's engine decouples temporal semantics from storage) validates Rune's framework-over-SQLite layering (repo_grounding_note).\",\n63: \"authority_implications\": \"None - write authority already vested in the curator via graph_memory's gated add path.\",\n64: \"risks\": \"Drift: Rune's schema must keep tracking Graphiti's semantic evolution (e.g., transaction-time versioning if NEP-001-style immutability lands).\",\n65: \"required_adaptation\": \"None for the model itself. Residual gap is query-side, not model-side (see C5).\",\n66: \"evidence\": [\"E14\", \"E17\", \"E19\"]\n67: },\n68: {\n69: \"id\": \"C5\",\n70: \"candidate\": \"NEP-001 'AS OF' temporal query semantics (Phase 1 syntactic sugar over valid_from/valid_to convention)\",\n71: \"source_ids\": [\"O41\", \"O38\", \"O10\"],\n72: \"classification\": \"APPLICABLE_WITH_ADAPTATION\",\n73: \"integration_point\": \"src/rune/graph_memory.py query() - add an --as-of parameter filtering edges by valid_from <= t < (valid_until || NULL-open) and learned_at <= t, instead of the current status='active'-only filter; BFS frontier unchanged.\",\n74: \"benefit\": \"Time-travel queries over the history Rune already preserves (superseded edges) but cannot currently read - closing the exact gap NEP-001 documents (E29: convention is error-prone and does not compose; E30: Phase 1 is low complexity and delivers most of the value).\",\n75: \"cost\": \"Low: one filter clause + CLI flag in a single 416-line module; SQLite already indexes subj_id/obj_id; half-open-interval logic is the only subtle part (NEP-001's documented pitfalls become a test checklist).\",\n76: \"architectural_fit\": \"High. Rune already stores both temporal axes (valid time + learned_at = transaction-time proxy, E14), so AS-OF is pure read-layer work on an existing schema - no backend change, consistent with the framework-layer pattern Rune already follows.\",\n77: \"authority_implications\": \"None. Read-only query-layer change; write authority untouched.\",\n78: \"risks\": \"learned_at is a transaction-time proxy, not a real bitemporal system (NEP-001 defers true bitemporality as 'Very high' complexity, E30) - document as as-of-knowledge approximation; NULL-as-open-ended and <=/< pitfalls need explicit tests (E29).\",\n79: \"required_adaptation\": \"Translate NEP-001's Cypher WHERE-convention into Python/SQLite parameterized filters over the existing edges table; keep active-status fast path as default.\",\n80: \"evidence\": [\"E28\", \"E29\", \"E30\", \"E14\", \"E27\"]\n81: },\n82: {\n83: \"id\": \"C6\",\n84: \"candidate\": \"Hybrid retrieval entry point (semantic embeddings + BM25 keyword + graph traversal)\",\n85: \"source_ids\": [\"O10\", \"O1\"],\n86: \"classification\": \"APPLICABLE_WITH_ADAPTATION\",\n87: \"integration_point\": \"src/rune/graph_memory.py query() entity resolution (lines 225-237: exact/alias/substring only) - add SQLite FTS5 index over entities(name, summary) + aliases(alias) as a lexical fallback tier before the LIKE last resort.\",\n88: \"benefit\": \"Graphiti attributes its sub-second retrieval to hybrid entry (E18); Rune's entry point currently fails on paraphrase/typo unless substring matches, which caps the graph's practical recall regardless of BFS depth.\",\n89: \"cost\": \"Moderate: FTS5 is in-process and dependency-free (fits embedded posture); requires index maintenance on entity/alias writes and a tokenizer decision. Full embedding tier deferred - would introduce model dependencies disproportionate to Rune's graph size.\",\n90: \"architectural_fit\": \"Good with adaptation: keep the lexical tier only (BM25-equivalent via FTS5), skip the embedding tier. SQLite already hosts the graph, so the index lives in the same file - no new service, consistent with 'reachable by the curator directly'.\",\n91: \"authority_implications\": \"Index writes stay inside graph_memory's own DB and its existing write path; no authority change.\",\n92: \"risks\": \"FTS5 index/write amplification on add-bulk; BM25 ranking quality unevidenced for Rune's vocabulary (evidence is Graphiti's claim, not a Rune-regime measurement - dossier limitation #7).\",\n93: \"required_adaptation\": \"Adapt from Graphiti's three-tier hybrid to a one-tier lexical hybrid over SQLite FTS5; entity lookup order becomes exact -> alias -> FTS5 BM25 -> substring.\",\n94: \"evidence\": [\"E18\", \"E14\"]\n95: },\n96: {\n97: \"id\": \"C7\",\n98: \"candidate\": \"Graphiti pluggable driver architecture (Neo4jDriver/FalkorDriver/KuzuDriver/NeptuneDriver, custom-driver recipe)\",\n99: \"source_ids\": [\"O10\"],\n100: \"classification\": \"TOO_COMPLEX_FOR_VALUE\",\n101: \"integration_point\": \"Would sit between graph_memory.py and sqlite3.\",\n102: \"benefit\": \"Backend optionality (E16).\",\n103: \"cost\": \"An abstraction layer for a single-backend system with a decided answer (C1-C3 all reject or defer backend swap).\",\n104: \"architectural_fit\": \"Premature. Rune's framework-layer semantics (C4) already achieve the decoupling that matters (E17); a driver interface adds indirection with no second backend to plug.\",\n105: \"authority_implications\": \"None material.\",\n106: \"risks\": \"Interface churn; abstraction tax on a 416-line module that is currently trivially auditable.\",\n107: \"required_adaptation\": \"Revisit only if a second backend becomes live (e.g., LadybugDB maturing, C3).\",\n108: \"evidence\": [\"E16\", \"E17\"]\n109: },\n110: {\n111: \"id\": \"C8\",\n112: \"candidate\": \"Cypher temporal VALUE types (DATE/DATETIME/DURATION as property types)\",\n113: \"source_ids\": [\"O38\"],\n114: \"classification\": \"TOO_COMPLEX_FOR_VALUE\",\n115: \"integration_point\": \"Edges' valid_from/valid_until/learned_at columns.\",\n116: \"benefit\": \"Typed temporal arithmetic, timezone-aware comparisons (E27).\",\n117: \"cost\": \"Rune already stores ISO-8601 UTC strings with a fixed now_iso() producer; string comparison is correct and lexicographically ordered for UTC ISO-8601. Typing buys nothing at this scale and Cypher types are not portable to SQLite anyway.\",\n118: \"architectural_fit\": \"None - it is a Neo4j-engine feature, not a portable pattern.\",\n119: \"authority_implications\": \"None.\",\n120: \"risks\": \"None beyond wasted effort.\",\n121: \"required_adaptation\": \"Not applicable.\",\n122: \"evidence\": [\"E27\"]\n123: },\n124: {\n125: \"id\": \"C9\",\n126: \"candidate\": \"Ladybug Memory lakehouse stack (Pgembed + DuckDB columnar aging + Icebug analytics + LadybugDB store) as local-first memory substrate\",\n127: \"source_ids\": [\"O24\", \"O28\"],\n128: \"classification\": \"PROMISING_FOR_INNOVATION\",\n129: \"integration_point\": \"Future: an analytics/aging tier beside memory/graph/graph.sqlite (DuckDB/Arrow reads of the SQLite graph) rather than a replacement of it.\",\n130: \"benefit\": \"E22's vision - transactional multi-agent writes, automatic columnar migration of aging data, zero-copy graph analytics (Icebug PageRank: 8x networkit, E23, affiliated benchmark) - is a credible long-range direction for Rune's memory umbrellas (curator/sleep-time consolidation, I1/I4).\",\n131: \"cost\": \"High and undated: roadmap-stage components (SparseTensors, GPU), affiliated-only benchmarks, and it chains to C3's immature store.\",\n132: \"architectural_fit\": \"Directionally strong (local-first unified memory substrate for AI agents, E22) but architecturally premature for Rune's current sub-1M regime.\",\n133: \"authority_implications\": \"A multi-writer embedded store (Pgembed) would change the memory tier's single-curator write authority - flag for any future design.\",\n134: \"risks\": \"Vision-stage dependency; affiliated benchmarks (E23); inherits C3's fork-mortality risk.\",\n135: \"required_adaptation\": \"Track as an adjacent-field lead; do not couple to the current spike.\",\n136: \"evidence\": [\"E22\", \"E23\", \"E24\"]\n137: },\n138: {\n139: \"id\": \"C10\",\n140: \"candidate\": \"Graphiti LLM-dependent ingestion pipeline (structured-output extraction, SEMAPHORE_LIMIT throttling)\",\n141: \"source_ids\": [\"O10\"],\n142: \"classification\": \"TOO_COMPLEX_FOR_VALUE\",\n143: \"integration_point\": \"Would replace the curator's extraction feeding graph_memory add/add-bulk.\",\n144: \"benefit\": \"Automated entity/edge extraction with dedup (E18).\",\n145: \"cost\": \"Rune already has the write-side discipline the research converges on: write-time gating (strength<3, mirroring E18's own cited 2603.15994), fixed predicate vocabulary against schema drift, and alias-first resolution (C4). Importing Graphiti's pipeline duplicates this with new LLM-provider dependencies and 429-throttling complexity.\",\n146: \"architectural_fit\": \"Redundant - the extraction boundary already exists at curator.py/sleep_time.py.\",\n147: \"authority_implications\": \"Would route memory writes through LLM structured-output calls, weakening the deterministic gating Rune relies on.\",\n148: \"risks\": \"Provider coupling in the memory write path.\",\n149: \"required_adaptation\": \"Not applicable.\",\n150: \"evidence\": [\"E18\", \"E14\"]\n151: }\n152: ],\n153: \"route\": \"INNOVATION_WARRANTED\",\n154: \"notes\": {\n155: \"route_rationale\": \"The spike's literal question (which backend) resolves to STANDARD: no backend swap is warranted - Neo4j and FalkorDB conflict with Rune's embedded posture (C1, C2), LadybugDB lacks sufficient evidence (C3), and the reference architecture's load-bearing pattern (temporal semantics at the framework layer, E17) is already Rune's design (C4). However, INNOVATION is warranted on a strictly narrower front: combining researched mechanisms with Rune's existing SQLite tier yields capabilities the status quo genuinely lacks - (a) AS-OF time-travel queries over the full edge history Rune already persists but cannot query temporally (C5, NEP-001 E28-E30, low complexity per E30), and (b) a lexical FTS5 hybrid entry point fixing Rune's capped recall (C6, E18). Both are materially better than 'keep SQLite as-is', both stay inside the embedded/no-new-dependency posture, and neither exists as a standard drop-in Rune could adopt instead - which is exactly the combine-research-with-Rune's-architecture clause, not novelty for its own sake.\",\n156: \"evidence_constraints\": \"Dossier limitations carried forward: no source benchmarks Rune's regime (single-node, sub-1M entities); O2 vendor magnitudes are marketing-adjacent until reproduced (contradiction #1); FalkorDB-native temporal features unevidenced (limitation #4); Graphiti performance claims not read from the primary paper arXiv 2501.13956 (limitation #7). C5/C6 benefits should be validated with a Rune-local benchmark before synthesis hardens them into recommendations.\",\n157: \"non_goals\": \"No code modified, no research re-run, no stage advanced; backend migration explicitly NOT part of the warranted innovation scope.\",\n158: \"next_stage_hint\": \"ADVERSARIAL should attack C5's NULL/half-open-interval semantics and C6's FTS5 write amplification; INNOVATION stage may merge C5+C6 into a single 'temporal+lexical query layer over graph.sqlite' proposal.\"\n159: }\n160: }\n\n(End of file - total 160 lines)\n","metadata":{"preview":"{\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).\",","truncated":false,"loaded":[]},"title":"research/issue-163/applicability.json","time":{"start":1788953485024,"end":1788953485039}},"id":"prt_g001a085efdea8001viMP74VBn","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085efad380013M8SX2T8K"}} +{"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":"/mnt/workdrive/issue161-harness/research/issue-163/task_tree.json\nfile\n\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_convergence.py\",\n130: \"tests/test_adversarial_executor.py\",\n131: \"tests/test_ci_lock_contract.py\",\n132: \"tests/test_context_broker.py\",\n133: \"tests/test_failure_auto_filer.py\"\n134: ],\n135: \"define_contract\": {\n136: \"issue\": 163,\n137: \"title\": \"Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\",\n138: \"question_type\": \"mixed\",\n139: \"sub_questions\": [\n140: \"Address: Temporal graph backend spike: Neo4j vs FalkorDB (LadybugDB experimental; Graphiti as reference)\"\n141: ],\n142: \"required_stages\": [\n143: \"ISSUE\",\n144: \"INWARD\",\n145: \"OUTWARD\",\n146: \"ADVERSARIAL\",\n147: \"INNOVATION\",\n148: \"SYNTHESIS\",\n149: \"QUALIFY\"\n150: ],\n151: \"skipped_stages\": {\n152: \"APPLICABILITY\": \"No OUTWARD research to ground against Rune.\"\n153: },\n154: \"evidence_classes\": [\n155: \"repo\",\n156: \"external\"\n157: ],\n158: \"sufficient_evidence\": \"\\u22652 code-path citations for INWARD; \\u22652 unique URLs for OUTWARD (if required)\",\n159: \"expected_artifact\": \"synthesis.json\",\n160: \"ambiguous\": false,\n161: \"needs_mimo\": false,\n162: \"source\": \"deterministic\"\n163: },\n164: \"mimo_needed\": false\n165: },\n166: \"deterministic\": true,\n167: \"claims\": [\n168: {\n169: \"id\": \"I1\",\n170: \"claim\": \"Relevant file: src/rune/curator.py\",\n171: \"evidence\": [\n172: \"src/rune/curator.py\"\n173: ],\n174: \"status\": \"CURRENT\"\n175: },\n176: {\n177: \"id\": \"I2\",\n178: \"claim\": \"Relevant file: src/rune/rune_tui.py\",\n179: \"evidence\": [\n180: \"src/rune/rune_tui.py\"\n181: ],\n182: \"status\": \"CURRENT\"\n183: },\n184: {\n185: \"id\": \"I3\",\n186: \"claim\": \"Relevant file: src/rune/graph_memory.py\",\n187: \"evidence\": [\n188: \"src/rune/graph_memory.py\"\n189: ],\n190: \"status\": \"CURRENT\"\n191: },\n192: {\n193: \"id\": \"I4\",\n194: \"claim\": \"Relevant file: src/rune/sleep_time.py\",\n195: \"evidence\": [\n196: \"src/rune/sleep_time.py\"\n197: ],\n198: \"status\": \"CURRENT\"\n199: },\n200: {\n201: \"id\": \"I5\",\n202: \"claim\": \"Relevant file: src/rune/elastic_cognition.py\",\n203: \"evidence\": [\n204: \"src/rune/elastic_cognition.py\"\n205: ],\n206: \"status\": \"CURRENT\"\n207: }\n208: ]\n209: },\n210: \"OUTWARD\": {\n211: \"stage\": \"OUTWARD\",\n212: \"issue\": 163,\n213: \"round\": 1,\n214: \"attempt\": 2,\n215: \"discovery\": {\n216: \"queries\": [\n217: {\n218: \"id\": \"Q1\",\n219: \"tool\": \"webfetch (DuckDuckGo HTML)\",\n220: \"query\": \"neo4j vs falkordb graph database benchmark comparison\",\n221: \"receipt\": \"fetched OK; 10 results returned inline\"\n222: },\n223: {\n224: \"id\": \"Q2\",\n225: \"tool\": \"webfetch (DuckDuckGo HTML)\",\n226: \"query\": \"graphiti zep temporal knowledge graph agent memory neo4j falkordb\",\n227: \"receipt\": \"fetched OK; 10 results returned inline\"\n228: },\n229: {\n230: \"id\": \"Q3\",\n231: \"tool\": \"webfetch (DuckDuckGo HTML)\",\n232: \"query\": \"ladybugdb graph database kuzu fork\",\n233: \"receipt\": \"fetched OK; 10 results returned inline\"\n234: },\n235: {\n236: \"id\": \"Q4\",\n237: \"tool\": \"webfetch (DuckDuckGo HTML)\",\n238: \"query\": \"falkordb redisgraph successor cypher features github\",\n239: \"receipt\": \"fetched OK; 10 results returned inline\"\n240: },\n241: {\n242: \"id\": \"Q5\",\n243: \"tool\": \"webfetch (DuckDuckGo HTML)\",\n244: \"query\": \"neo4j temporal versioning bitemporal cypher valid time\",\n245: \"receipt\": \"fetched OK; 10 results returned inline\"\n246: }\n247: ],\n248: \"sources_discovered\": [\n249: {\n250: \"id\": \"O1\",\n251: \"url\": \"https://aimultiple.com/graph-databases\",\n252: \"class\": \"external (independent benchmark, updated 2026-04-15)\",\n253: \"note\": \"SELECTED and read\",\n254: \"read_receipt\": true\n255: },\n256: {\n257: \"id\": \"O2\",\n258: \"url\": \"https://www.falkordb.com/blog/graph-database-performance-benchmarks-falkordb-vs-neo4j/\",\n259: \"class\": \"external (vendor blog, pub 2024-12-02, updated 2026-08-23)\",\n260: \"note\": \"SELECTED and read\",\n261: \"read_receipt\": true\n262: },\n263: {\n264: \"id\": \"O3\",\n265: \"url\": \"https://www.puppygraph.com/blog/falkordb-vs-neo4j\",\n266: \"class\": \"external (competing-vendor comparison)\",\n267: \"note\": \"not selected \\u2014 competing vendor has stake in the comparison\",\n268: \"read_receipt\": false\n269: },\n270: {\n271: \"id\": \"O4\",\n272: \"url\": \"https://knodegraph.com/blog/falkordb-vs-neo4j/\",\n273: \"class\": \"external (third-party blog w/ 2026 benchmarks)\",\n274: \"note\": \"not selected \\u2014 O1 covers the independent-benchmark slot with fuller methodology\",\n275: \"read_receipt\": false\n276: },\n277: {\n278: \"id\": \"O5\",\n279: \"url\": \"https://github.com/FalkorDB/benchmark\",\n280: \"class\": \"repo (vendor benchmark harness)\",\n281: \"note\": \"SELECTED; README 404 on master and main; repo metadata read via GitHub API instead\",\n282: \"read_receipt\": true\n283: },\n284: {\n285: \"id\": \"O6\",\n286: \"url\": \"https://www.plushcap.com/content/falkordb/blog/falkordb-falkordb-vs-neo4j-graph-database-performance-benchmarks\",\n287: \"class\": \"external (mirror of O2)\",\n288: \"note\": \"not selected \\u2014 duplicate\",\n289: \"read_receipt\": false\n290: },\n291: {\n292: \"id\": \"O7\",\n293: \"url\": \"https://inferensys.com/differences/neuro-symbolic-ai-frameworks/knowledge-graph-construction-tools/neo4j-vs-falkordb\",\n294: \"class\": \"external (aggregator comparison)\",\n295: \"note\": \"not selected\",\n296: \"read_receipt\": false\n297: },\n298: {\n299: \"id\": \"O8\",\n300: \"url\": \"https://inferensys.com/differences/ai-software-factory-platforms/code-knowledge-graph-systems/neo4j-vs-falkordb-for-code-graphs\",\n301: \"class\": \"external (aggregator comparison)\",\n302: \"note\": \"not selected\",\n303: \"read_receipt\": false\n304: },\n305: {\n306: \"id\": \"O9\",\n307: \"url\": \"https://3nuggets.io/compare-neo4j-vs-falkordb\",\n308: \"class\": \"external (practitioner fit comparison)\",\n309: \"note\": \"not selected\",\n310: \"read_receipt\": false\n311: },\n312: {\n313: \"id\": \"O10\",\n314: \"url\": \"https://github.com/getzep/graphiti\",\n315: \"class\": \"repo (Graphiti primary)\",\n316: \"note\": \"SELECTED and read (README via raw.githubusercontent.com/main)\",\n317: \"read_receipt\": true\n318: },\n319: {\n320: \"id\": \"O11\",\n321: \"url\": \"https://neo4j.com/blog/developer/graphiti-knowledge-graph-memory/\",\n322: \"class\": \"external (Neo4j official blog on Graphiti)\",\n323: \"note\": \"not selected \\u2014 O10 is primary\",\n324: \"read_receipt\": false\n325: },\n326: {\n327: \"id\": \"O12\",\n328: \"url\": \"https://docs.falkordb.com/agentic-memory/graphiti\",\n329: \"class\": \"external (FalkorDB vendor docs)\",\n330: \"note\": \"not selected\",\n331: \"read_receipt\": false\n332: },\n333: {\n334: \"id\": \"O13\",\n335: \"url\": \"https://github.com/Memory-Agents/graphiti/\",\n336: \"class\": \"repo (mirror)\",\n337: \"note\": \"not selected \\u2014 duplicate of O10\",\n338: \"read_receipt\": false\n339: },\n340: {\n341: \"id\": \"O14\",\n342: \"url\": \"https://codex.danielvaughan.com/2026/03/30/graphiti-agent-memory-store/\",\n343: \"class\": \"external (practitioner review)\",\n344: \"note\": \"not selected\",\n345: \"read_receipt\": false\n346: },\n347: {\n348: \"id\": \"O15\",\n349: \"url\": \"https://memorypapers.org/papers/zep-a-temporal-knowledge-graph-architecture-for-agent-memory\",\n350: \"class\": \"external (paper summary site)\",\n351: \"note\": \"not selected \\u2014 primary paper is arXiv 2501.13956, referenced from O10\",\n352: \"read_receipt\": false\n353: },\n354: {\n355: \"id\": \"O16\",\n356: \"url\": \"https://www.falkordb.com/blog/graphiti-falkordb-multi-agent-performance/\",\n357: \"class\": \"external (vendor integration blog)\",\n358: \"note\": \"not selected\",\n359: \"read_receipt\": false\n360: },\n361: {\n362: \"id\": \"O17\",\n363: \"url\": \"https://www.falkordb.com/blog/building-temporal-knowledge-graphs-graphiti/\",\n364: \"class\": \"external (vendor blog)\",\n365: \"note\": \"not selected\",\n366: \"read_receipt\": false\n367: },\n368: {\n369: \"id\": \"O18\",\n370: \"url\": \"https://thegtmdirectory.com/tools/graphiti\",\n371: \"class\": \"external (directory)\",\n372: \"note\": \"not selected\",\n373: \"read_receipt\": false\n374: },\n375: {\n376: \"id\": \"O19\",\n377: \"url\": \"https://www.getzep.com/platform/graphiti/\",\n378: \"class\": \"external (Zep platform page)\",\n379: \"note\": \"not selected\",\n380: \"read_receipt\": false\n381: },\n382: {\n383: \"id\": \"O20\",\n384: \"url\": \"https://ladybugdb.com/\",\n385: \"class\": \"external (official site)\",\n386: \"note\": \"not read \\u2014 superseded by O21 README + O28 encyclopedia\",\n387: \"read_receipt\": false\n388: },\n389: {\n390: \"id\": \"O21\",\n391: \"url\": \"https://github.com/LadybugDB/ladybug\",\n392: \"class\": \"repo (LadybugDB primary)\",\n393: \"note\": \"SELECTED and read (README via raw.githubusercontent.com/main)\",\n394: \"read_receipt\": true\n395: },\n396: {\n397: \"id\": \"O22\",\n398: \"url\": \"https://github.com/lcretan/LadybugDB-KuzuDB.ladybug\",\n399: \"class\": \"repo (mirror)\",\n400: \"note\": \"not selected \\u2014 duplicate\",\n401: \"read_receipt\": false\n402: },\n403: {\n404: \"id\": \"O23\",\n405: \"url\": \"https://docs.ladybugdb.com/\",\n406: \"class\": \"external (official docs)\",\n407: \"note\": \"not selected\",\n408: \"read_receipt\": false\n409: },\n410: {\n411: \"id\": \"O24\",\n412: \"url\": \"https://thedataquarry.com/blog/from-kuzu-to-ladybug/\",\n413: \"class\": \"external (practitioner blog by ex-Kuzu engineer, 2026-04-11)\",\n414: \"note\": \"SELECTED and read\",\n415: \"read_receipt\": true\n416: },\n417: {\n418: \"id\": \"O25\",\n419: \"url\": \"https://mpr.crossjam.net/wp/mpr/2025/11/kuzudb-and-ladybugdb/\",\n420: \"class\": \"external (context blog on Kuzu archival)\",\n421: \"note\": \"not selected \\u2014 snippet-level evidence only\",\n422: \"read_receipt\": false\n423: },\n424: {\n425: \"id\": \"O26\",\n426: \"url\": \"https://gdb-engines.com/db/ladybugdb/\",\n427: \"class\": \"external (DB directory)\",\n428: \"note\": \"not selected\",\n429: \"read_receipt\": false\n430: },\n431: {\n432: \"id\": \"O27\",\n433: \"url\": \"https://cocoindex.io/docs-v0/targets/ladybug/\",\n434: \"class\": \"external (integration docs)\",\n435: \"note\": \"not selected\",\n436: \"read_receipt\": false\n437: },\n438: {\n439: \"id\": \"O28\",\n440: \"url\": \"https://dbdb.io/db/ladybugdb\",\n441: \"class\": \"external (CMU Database of Databases encyclopedia)\",\n442: \"note\": \"SELECTED and read\",\n443: \"read_receipt\": true\n444: },\n445: {\n446: \"id\": \"O29\",\n447: \"url\": \"https://github.com/FalkorDB/FalkorDB\",\n448: \"class\": \"repo (FalkorDB primary)\",\n449: \"note\": \"SELECTED and read (README via raw.githubusercontent.com/master)\",\n450: \"read_receipt\": true\n451: },\n452: {\n453: \"id\": \"O30\",\n454: \"url\": \"https://www.falkordb.com/blog/redisgraph-eol-migration-guide/\",\n455: \"class\": \"external (vendor migration guide)\",\n456: \"note\": \"not selected\",\n457: \"read_receipt\": false\n458: },\n459: {\n460: \"id\": \"O31\",\n461: \"url\": \"https://basekv.com/articles/redisgraph-deprecated-now-what\",\n462: \"class\": \"external (RedisGraph EOL article)\",\n463: \"note\": \"not selected \\u2014 snippet-level evidence only\",\n464: \"read_receipt\": false\n465: },\n466: {\n467: \"id\": \"O32\",\n468: \"url\": \"https://stackharbor.com/en/knowledge-base/redis-redisgraph-module/\",\n469: \"class\": \"external (knowledge base)\",\n470: \"note\": \"not selected\",\n471: \"read_receipt\": false\n472: },\n473: {\n474: \"id\": \"O33\",\n475: \"url\": \"https://orchestrator.dev/blog/2025-12-11-falkordb/\",\n476: \"class\": \"external (blog)\",\n477: \"note\": \"not selected\",\n478: \"read_receipt\": false\n479: },\n480: {\n481: \"id\": \"O34\",\n482: \"url\": \"https://falkordb.github.io/docs-staging/\",\n483: \"class\": \"external (staging docs)\",\n484: \"note\": \"not selected\",\n485: \"read_receipt\": false\n486: },\n487: {\n488: \"id\": \"O35\",\n489: \"url\": \"https://www.solosoft.dev/post/falkordb-graph-database-2026/\",\n490: \"class\": \"external (blog)\",\n491: \"note\": \"not selected\",\n492: \"read_receipt\": false\n493: },\n494: {\n495: \"id\": \"O36\",\n496: \"url\": \"https://hydradb.com/blog/falkordb-alternatives\",\n497: \"class\": \"external (competitor listicle)\",\n498: \"note\": \"not selected\",\n499: \"read_receipt\": false\n500: },\n501: {\n502: \"id\": \"O37\",\n503: \"url\": \"https://groups.google.com/g/redis-db/c/aoBMFIszBRg\",\n504: \"class\": \"external (fork announcement)\",\n505: \"note\": \"not selected\",\n506: \"read_receipt\": false\n507: },\n508: {\n509: \"id\": \"O38\",\n510: \"url\": \"https://neo4j.com/docs/cypher-manual/current/values-and-types/temporal/\",\n511: \"class\": \"external (Neo4j official docs)\",\n512: \"note\": \"SELECTED and read (page truncated at ~40KB; temporal type table captured)\",\n513: \"read_receipt\": true\n514: },\n515: {\n516: \"id\": \"O39\",\n517: \"url\": \"https://dev.to/satyam_shree_087caef77512/a-practical-guide-to-temporal-versioning-in-neo4j-nodes-relationships-and-historical-graph-1m5g\",\n518: \"class\": \"external (practitioner bitemporal guide)\",\n519: \"note\": \"not selected \\u2014 O41 evidences the same gap at greater authority\",\n520: \"read_receipt\": false\n521: },\n522: {\n523: \"id\": \"O40\",\n524: \"url\": \"https://kindatechnical.com/cypher/cypher-date-and-time-functions.html\",\n525: \"class\": \"external (tutorial)\",\n526: \"note\": \"not selected\",\n527: \"read_receipt\": false\n528: },\n529: {\n530: \"id\": \"O41\",\n531: \"url\": \"https://github.com/DIGITAL-FABRIC-AI/neo4j-temporal-graph\",\n532: \"class\": \"repo (NEP-001 reference implementation)\",\n533: \"note\": \"SELECTED and read (NEP-001.md via raw.githubusercontent.com/master)\",\n534: \"read_receipt\": true\n535: },\n536: {\n537: \"id\": \"O42\",\n538: \"url\": \"https://medium.com/neo4j/keeping-track-of-graph-changes-using-temporal-versioning-3b0f854536fa\",\n539: \"class\": \"external (Neo4j staff blog)\",\n540: \"note\": \"not selected\",\n541: \"read_receipt\": false\n542: },\n543: {\n544: \"id\": \"O43\",\n545: \"url\": \"https://www.cosmiclearn.com/neo4j/cypher-datetime-duration.php\",\n546: \"class\": \"external (tutorial)\",\n547: \"note\": \"not selected\",\n548: \"read_receipt\": false\n549: },\n550: {\n551: \"id\": \"O44\",\n552: \"url\": \"https://dev.graphacademy.neo4j.com/blog/til-replace-apoc-temporal-format\",\n553: \"class\": \"external (Neo4j academy TIL)\",\n554: \"note\": \"not selected\",\n555: \"read_receipt\": false\n556: },\n557: {\n558: \"id\": \"O45\",\n559: \"url\": \"https://stackoverflow.com/questions/15365387/neo4j-relationships-with-time-constraints\",\n560: \"class\": \"external (Stack Overflow)\",\n561: \"note\": \"not selected\",\n562: \"read_receipt\": false\n563: }\n564: ],\n565: \"source_count\": 45\n566: },\n567: \"reading\": {\n568: \"sources_selected\": [],\n569: \"sources_read\": [\n570: \"O1\",\n571: \"O2\",\n572: \"O5\",\n573: \"O10\",\n574: \"O21\",\n575: \"O24\",\n576: \"O28\",\n577: \"O29\",\n578: \"O38\",\n579: \"O41\"\n580: ],\n581: \"evidence_extracted\": 10\n582: },\n583: \"contradictory_evidence\": [\n584: {\n585: \"topic\": \"Magnitude of FalkorDB's performance advantage over Neo4j\",\n586: \"resolution\": \"Vendor benchmark (O2/E9) reports p99 136.2ms vs 46,923.8ms (~345x) and p50 55ms vs 577.5ms (~10x); the independent benchmark (O1/E2-E3) reports 6.7x concurrent throughput and 11/12 query wins, but Neo4j WINS the heaviest aggregation and FalkorDB's throughput ceiling is Redis's single-threaded core. Not strictly contradictory (different datasets, hardware, query mixes, and metrics), but the vendor number is an outlier that independent evidence does not approach; treat O2 magnitudes as marketing-adjacent until reproduced.\"\n587: },\n588: {\n589: \"topic\": \"Why Kuzu died / what LadybugDB is a revival of\",\n590: \"resolution\": \"O28 (dbdb.io): 'revival fork of Kuzu after its acquisition and closure by Apple'; O25 (snippet): 'abandoned by its creator and sponsor Kuzu Inc'; O24 (ex-Kuzu): 'Kuzu got archived' Sept 2025 without naming a cause. All agree the upstream archived in 2025 and LadybugDB is the maintained fork (active commits Sep 2026, E24); the acquirer identity is contested/unverified.\"\n591: },\n592: {\n593: \"topic\": \"FalkorDB production-readiness framing\",\n594: \"resolution\": \"O2 FAQ claims production-readiness as 'a continuation of the production-tested RedisGraph codebase' (E10), while the same evidence set records that RedisGraph itself reached EOL (support ended Jan 31, 2025 per O31 snippet; repo deprecated). Continuity of codebase is real (E25 fork lineage), but 'production-tested' inherits from a discontinued upstream; additionally the core engine is being rewritten in Rust (E11), which resets part of that maturity claim.\"\n595: },\n596: {\n597: \"topic\": \"Embedded-backend path via Graphiti\",\n598: \"resolution\": \"Graphiti supports an embedded option (falkordblite, E16) and a Kuzu driver, but the Kuzu driver is deprecated with upstream declared unmaintained (E15) \\u2014 and Graphiti lists no LadybugDB driver, even though LadybugDB IS the maintained Kuzu fork (E20, E21, E24). So 'Graphiti as reference' currently endorses Neo4j or FalkorDB only; using it against LadybugDB would require a new driver (the framework documents how, E16).\"\n599: }\n600: ],\n601: \"limitations\": [\n602: \"FalkorDB/benchmark (O5) README could not be retrieved (404 on raw master and main despite default_branch='master' per GitHub API) \\u2014 the vendor benchmark's methodology and raw results were not independently verified; its headline numbers enter this evidence set only via the vendor blog (O2).\",\n603: \"Both performance evidence sources are workload-specific and non-overlapping: O1 uses a 381K-node synthetic review graph on 8 vCPU; O2 uses the SNAP Pokec social network on a GitHub Actions runner. No source benchmarks Rune's regime (single-node, sub-1M entities, embedded/local-first).\",\n604: \"Neo4j temporal docs (O38) were fetched but truncated (~40KB); the temporal functions/operators pages were not read. The captured claim (E27) covers value types only.\",\n605: \"No FalkorDB-native temporal feature documentation was read (docs.falkordb.com TTL/time-function pages were not fetched) \\u2014 FalkorDB's own temporal capabilities beyond Cypher property storage are unevidenced in this set.\",\n606: \"No Neo4j licensing/pricing source was read: Community-vs-Enterprise constraints (clustering, RBAC) appear only as asides inside O1/O2 and are not independently evidenced.\",\n607: \"The Kuzu-closure cause is single-sourced: dbdb.io (O28) says 'acquisition and closure by Apple'; mpr.crossjam (O25, snippet only) says 'abandoned by its creator and sponsor Kuzu Inc'; thedataquarry (O24, ex-Kuzu) says only 'archived'. The acquirer claim is unverified.\",\n608: \"Graphiti's own benchmark claims (sub-second retrieval, LongMemEval/DRIFT-style SOTA via the Zep paper) were not read from the primary paper (arXiv 2501.13956) \\u2014 only the README's characterization (E18, E19).\",\n609: \"All numbers are third-party; nothing was reproduced locally, and the stage contract forbids running spikes.\"\n610: ],\n611: \"supporting_evidence\": [],\n612: \"alternatives\": [],\n613: \"adjacent_field_leads\": [],\n614: \"unresolved_questions\": [],\n615: \"tool_receipts\": [\n616: {\n617: \"kind\": \"TOOL\",\n618: \"tool\": \"skill_search\",\n619: \"input_keys\": [\n620: \"query\"\n621: ],\n622: \"ts\": 1788951375.3570032\n623: },\n624: {\n625: \"kind\": \"TOOL\",\n626: \"tool\": \"memory\",\n627: \"input_keys\": [\n628: \"operation\",\n629: \"query\"\n630: ],\n631: \"ts\": 1788951375.3570263\n632: },\n633: {\n634: \"kind\": \"TOOL\",\n635: \"tool\": \"bash\",\n636: \"input_keys\": [\n637: \"command\",\n638: 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{\n773: \"kind\": \"QUERY\",\n774: \"tool\": \"webfetch\",\n775: \"url\": \"https://duckduckgo.com/html/?q=falkordb+redisgraph+successor+cypher+features+github\",\n776: \"ts\": 1788951375.3575594\n777: },\n778: {\n779: \"kind\": \"SOURCE_READ\",\n780: \"tool\": \"webfetch\",\n781: \"url\": \"https://thedataquarry.com/blog/from-kuzu-to-ladybug/\",\n782: \"ts\": 1788951375.3576458\n783: },\n784: {\n785: \"kind\": \"SOURCE_READ\",\n786: \"tool\": \"webfetch\",\n787: \"url\": \"https://raw.githubusercontent.com/LadybugDB/ladybug/main/README.md\",\n788: \"ts\": 1788951375.3576615\n789: },\n790: {\n791: \"kind\": \"TOOL_FAILED\",\n792: \"tool\": \"webfetch\",\n793: \"input_keys\": [\n794: \"format\",\n795: \"url\"\n796: ],\n797: \"ts\": 1788951375.3576686\n798: },\n799: {\n800: \"kind\": \"SOURCE_READ\",\n801: \"tool\": \"webfetch\",\n802: \"url\": \"https://raw.githubusercontent.com/getzep/graphiti/main/README.md\",\n803: \"ts\": 1788951375.3577497\n804: },\n805: {\n806: \"kind\": 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},\n835: {\n836: \"kind\": \"TOOL_FAILED\",\n837: \"tool\": \"webfetch\",\n838: \"input_keys\": [\n839: \"format\",\n840: \"url\"\n841: ],\n842: \"ts\": 1788951375.3581324\n843: },\n844: {\n845: \"kind\": \"SOURCE_READ\",\n846: \"tool\": \"webfetch\",\n847: \"url\": \"https://dbdb.io/db/ladybugdb\",\n848: \"ts\": 1788951375.358159\n849: },\n850: {\n851: \"kind\": \"SOURCE_READ\",\n852: \"tool\": \"webfetch\",\n853: \"url\": \"https://api.github.com/repos/FalkorDB/benchmark\",\n854: \"ts\": 1788951375.3581996\n855: },\n856: {\n857: \"kind\": \"TOOL\",\n858: \"tool\": \"bash\",\n859: \"input_keys\": [\n860: \"command\",\n861: \"description\"\n862: ],\n863: \"ts\": 1788951375.358221\n864: },\n865: {\n866: \"kind\": \"TOOL\",\n867: \"tool\": \"bash\",\n868: \"input_keys\": [\n869: \"command\",\n870: \"description\"\n871: ],\n872: \"ts\": 1788951375.3582418\n873: },\n874: {\n875: \"kind\": \"TOOL\",\n876: \"tool\": \"write\",\n877: \"input_keys\": [\n878: \"content\",\n879: \"file_path\"\n880: ],\n881: \"ts\": 1788951375.358437\n882: },\n883: {\n884: \"kind\": \"TOOL\",\n885: \"tool\": \"bash\",\n886: \"input_keys\": [\n887: \"command\",\n888: \"description\"\n889: ],\n890: \"ts\": 1788951375.358455\n891: }\n892: ],\n893: \"worker_artifact_file\": true,\n894: \"event_counts\": {\n895: \"queries\": 5,\n896: \"sources_discovered\": 0,\n897: \"sources_read\": 10,\n898: \"evidence_accepted\": 0,\n899: \"tool_failures\": 2,\n900: \"tool_calls\": 34\n901: },\n902: \"elapsed\": 0.0\n903: },\n904: \"APPLICABILITY\": {\n905: \"stage\": \"APPLICABILITY\",\n906: \"issue\": 163,\n907: \"round\": 1,\n908: \"attempt\": 1,\n909: \"route\": \"INNOVATION_WARRANTED\",\n910: \"candidates\": [\n911: {\n912: \"id\": \"C1\",\n913: \"candidate\": \"Neo4j as replacement graph backend\",\n914: \"source_ids\": [\n915: \"O1\",\n916: \"O2\",\n917: \"O38\",\n918: \"O41\"\n919: ],\n920: \"classification\": \"CONFLICTS_WITH_RUNE\",\n921: \"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.\",\n922: \"benefit\": \"Deepest ecosystem, best planner on heavy aggregations (E3), mature tooling (E8).\",\n923: \"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).\",\n924: \"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).\",\n925: \"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.\",\n926: \"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.\",\n927: \"required_adaptation\": \"None - rejection is the correct disposition.\",\n928: \"evidence\": [\n929: \"E2\",\n930: \"E3\",\n931: \"E4\",\n932: \"E6\",\n933: \"E8\",\n934: \"E27\",\n935: \"E28\",\n936: \"E29\"\n937: ]\n938: },\n939: {\n940: \"id\": \"C2\",\n941: \"candidate\": \"FalkorDB as replacement graph backend\",\n942: \"source_ids\": [\n943: \"O1\",\n944: \"O2\",\n945: \"O5\",\n946: \"O29\",\n947: \"O10\"\n948: ],\n949: \"classification\": \"CONFLICTS_WITH_RUNE\",\n950: \"integration_point\": \"Would replace the SQLite tier; only embedded path is Graphiti's falkordblite extra (redislite-based, Python 3.12+) per E16.\",\n951: \"benefit\": \"Strong concurrent throughput and low latency at scale (E2, E3, E9), implicit indexing (E5), GraphRAG positioning (E10).\",\n952: \"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).\",\n953: \"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).\",\n954: \"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.\",\n955: \"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.\",\n956: \"required_adaptation\": \"None - rejection is the correct disposition.\",\n957: \"evidence\": [\n958: \"E1\",\n959: \"E2\",\n960: \"E3\",\n961: \"E5\",\n962: \"E9\",\n963: \"E10\",\n964: \"E11\",\n965: \"E12\",\n966: \"E13\",\n967: \"E16\",\n968: \"E25\",\n969: \"E26\"\n970: ]\n971: },\n972: {\n973: \"id\": \"C3\",\n974: \"candidate\": \"LadybugDB as replacement graph backend\",\n975: \"source_ids\": [\n976: \"O21\",\n977: \"O24\",\n978: \"O28\"\n979: ],\n980: \"classification\": \"INSUFFICIENT_EVIDENCE\",\n981: \"integration_point\": \"Hypothetically the same slot as the SQLite tier (genuinely embedded, MIT, native FTS + vector index, ACID, E20).\",\n982: \"benefit\": \"Best architectural fit of the three spiked backends: embedded, MIT, Cypher, FTS+vector built in (E20, E24).\",\n983: \"cost\": \"Migration cost from a working SQLite schema for no evidenced capability gain at Rune's scale.\",\n984: \"architectural_fit\": \"Good in shape, unproven in substance: young revival fork (start year 2025, tagged 'Revival Project'/'AI-Assisted', E24) whose upstream died once already (dossier contradiction #2); no Graphiti driver exists for it (dossier contradiction #4); no benchmark anywhere near Rune's regime (dossier limitation #2).\",\n985: \"authority_implications\": \"None new (embedded, in-process) - but supply-chain risk of betting the memory tier on a single-maintainer fork.\",\n986: \"risks\": \"Upstream mortality repeated; API instability during revival; the dossier contains zero evidence LadybugDB outperforms SQLite for Rune's workload - 'experimental' per the issue title itself.\",\n987: \"required_adaptation\": \"Re-evaluate if Rune's graph ever exceeds SQLite's comfortable range or if LadybugDB matures (maintained release cadence, Graphiti/driver ecosystem support).\",\n988: \"evidence\": [\n989: \"E20\",\n990: \"E21\",\n991: \"E24\"\n992: ]\n993: },\n994: {\n995: \"id\": \"C4\",\n996: \"candidate\": \"Graphiti bi-temporal semantic model (validity windows, invalidate-not-delete, episode provenance)\",\n997: \"source_ids\": [\n998: \"O10\"\n999: ],\n1000: \"classification\": \"DIRECTLY_APPLICABLE\",\n1001: \"integration_point\": \"Already integrated: src/rune/graph_memory.py edges table (valid_from/valid_until/learned_at, status/superseded_by, evidence/source) implements E14's model 1:1, including supersede-on-contradiction (add_edge) and confidence-gated writes.\",\n1002: \"benefit\": \"Confirms the current design against the reference architecture; zero further work for the core semantic model.\",\n1003: \"cost\": \"Already sunk; none incremental.\",\n1004: \"architectural_fit\": \"Exact. The load-bearing fact (E17: Zep's engine decouples temporal semantics from storage) validates Rune's framework-over-SQLite layering (repo_grounding_note).\",\n1005: \"authority_implications\": \"None - write authority already vested in the curator via graph_memory's gated add path.\",\n1006: \"risks\": \"Drift: Rune's schema must keep tracking Graphiti's semantic evolution (e.g., transaction-time versioning if NEP-001-style immutability lands).\",\n1007: \"required_adaptation\": \"None for the model itself. Residual gap is query-side, not model-side (see C5).\",\n1008: \"evidence\": [\n1009: \"E14\",\n1010: \"E17\",\n1011: \"E19\"\n1012: ]\n1013: },\n1014: {\n1015: \"id\": \"C5\",\n1016: \"candidate\": \"NEP-001 'AS OF' temporal query semantics (Phase 1 syntactic sugar over valid_from/valid_to convention)\",\n1017: \"source_ids\": [\n1018: \"O41\",\n1019: \"O38\",\n1020: \"O10\"\n1021: ],\n1022: \"classification\": \"APPLICABLE_WITH_ADAPTATION\",\n1023: \"integration_point\": \"src/rune/graph_memory.py query() - add an --as-of parameter filtering edges by valid_from <= t < (valid_until || NULL-open) and learned_at <= t, instead of the current status='active'-only filter; BFS frontier unchanged.\",\n1024: \"benefit\": \"Time-travel queries over the history Rune already preserves (superseded edges) but cannot currently read - closing the exact gap NEP-001 documents (E29: convention is error-prone and does not compose; E30: Phase 1 is low complexity and delivers most of the value).\",\n1025: \"cost\": \"Low: one filter clause + CLI flag in a single 416-line module; SQLite already indexes subj_id/obj_id; half-open-interval logic is the only subtle part (NEP-001's documented pitfalls become a test checklist).\",\n1026: \"architectural_fit\": \"High. Rune already stores both temporal axes (valid time + learned_at = transaction-time proxy, E14), so AS-OF is pure read-layer work on an existing schema - no backend change, consistent with the framework-layer pattern Rune already follows.\",\n1027: \"authority_implications\": \"None. Read-only query-layer change; write authority untouched.\",\n1028: \"risks\": \"learned_at is a transaction-time proxy, not a real bitemporal system (NEP-001 defers true bitemporality as 'Very high' complexity, E30) - document as as-of-knowledge approximation; NULL-as-open-ended and <=/< pitfalls need explicit tests (E29).\",\n1029: \"required_adaptation\": \"Translate NEP-001's Cypher WHERE-convention into Python/SQLite parameterized filters over the existing edges table; keep active-status fast path as default.\",\n1030: \"evidence\": [\n1031: \"E28\",\n1032: \"E29\",\n1033: \"E30\",\n1034: \"E14\",\n1035: \"E27\"\n1036: ]\n1037: },\n1038: {\n1039: \"id\": \"C6\",\n1040: \"candidate\": \"Hybrid retrieval entry point (semantic embeddings + BM25 keyword + graph traversal)\",\n1041: \"source_ids\": [\n1042: \"O10\",\n1043: \"O1\"\n1044: ],\n1045: \"classification\": \"APPLICABLE_WITH_ADAPTATION\",\n1046: \"integration_point\": \"src/rune/graph_memory.py query() entity resolution (lines 225-237: exact/alias/substring only) - add SQLite FTS5 index over entities(name, summary) + aliases(alias) as a lexical fallback tier before the LIKE last resort.\",\n1047: \"benefit\": \"Graphiti attributes its sub-second retrieval to hybrid entry (E18); Rune's entry point currently fails on paraphrase/typo unless substring matches, which caps the graph's practical recall regardless of BFS depth.\",\n1048: \"cost\": \"Moderate: FTS5 is in-process and dependency-free (fits embedded posture); requires index maintenance on entity/alias writes and a tokenizer decision. Full embedding tier deferred - would introduce model dependencies disproportionate to Rune's graph size.\",\n1049: \"architectural_fit\": \"Good with adaptation: keep the lexical tier only (BM25-equivalent via FTS5), skip the embedding tier. SQLite already hosts the graph, so the index lives in the same file - no new service, consistent with 'reachable by the curator directly'.\",\n1050: \"authority_implications\": \"Index writes stay inside graph_memory's own DB and its existing write path; no authority change.\",\n1051: \"risks\": \"FTS5 index/write amplification on add-bulk; BM25 ranking quality unevidenced for Rune's vocabulary (evidence is Graphiti's claim, not a Rune-regime measurement - dossier limitation #7).\",\n1052: \"required_adaptation\": \"Adapt from Graphiti's three-tier hybrid to a one-tier lexical hybrid over SQLite FTS5; entity lookup order becomes exact -> alias -> FTS5 BM25 -> substring.\",\n1053: \"evidence\": [\n1054: \"E18\",\n1055: \"E14\"\n1056: ]\n1057: },\n1058: {\n1059: \"id\": \"C7\",\n1060: \"candidate\": \"Graphiti pluggable driver architecture (Neo4jDriver/FalkorDriver/KuzuDriver/NeptuneDriver, custom-driver recipe)\",\n1061: \"source_ids\": [\n1062: \"O10\"\n1063: ],\n1064: \"classification\": \"TOO_COMPLEX_FOR_VALUE\",\n1065: \"integration_point\": \"Would sit between graph_memory.py and sqlite3.\",\n1066: \"benefit\": \"Backend optionality (E16).\",\n1067: \"cost\": \"An abstraction layer for a single-backend system with a decided answer (C1-C3 all reject or defer backend swap).\",\n1068: \"architectural_fit\": \"Premature. Rune's framework-layer semantics (C4) already achieve the decoupling that matters (E17); a driver interface adds indirection with no second backend to plug.\",\n1069: \"authority_implications\": \"None material.\",\n1070: \"risks\": \"Interface churn; abstraction tax on a 416-line module that is currently trivially auditable.\",\n1071: \"required_adaptation\": \"Revisit only if a second backend becomes live (e.g., LadybugDB maturing, C3).\",\n1072: \"evidence\": [\n1073: \"E16\",\n1074: \"E17\"\n1075: ]\n1076: },\n1077: {\n1078: \"id\": \"C8\",\n1079: \"candidate\": \"Cypher temporal VALUE types (DATE/DATETIME/DURATION as property types)\",\n1080: \"source_ids\": [\n1081: \"O38\"\n1082: ],\n1083: \"classification\": \"TOO_COMPLEX_FOR_VALUE\",\n1084: \"integration_point\": \"Edges' valid_from/valid_until/learned_at columns.\",\n1085: \"benefit\": \"Typed temporal arithmetic, timezone-aware comparisons (E27).\",\n1086: \"cost\": \"Rune already stores ISO-8601 UTC strings with a fixed now_iso() producer; string comparison is correct and lexicographically ordered for UTC ISO-8601. Typing buys nothing at this scale and Cypher types are not portable to SQLite anyway.\",\n1087: \"architectural_fit\": \"None - it is a Neo4j-engine feature, not a portable pattern.\",\n1088: \"authority_implications\": \"None.\",\n1089: \"risks\": \"None beyond wasted effort.\",\n1090: \"required_adaptation\": \"Not applicable.\",\n1091: \"evidence\": [\n1092: \"E27\"\n1093: ]\n1094: },\n1095: {\n1096: \"id\": \"C9\",\n1097: \"candidate\": \"Ladybug Memory lakehouse stack (Pgembed + DuckDB columnar aging + Icebug analytics + LadybugDB store) as local-first memory substrate\",\n1098: \"source_ids\": [\n1099: \"O24\",\n1100: \"O28\"\n1101: ],\n1102: \"classification\": \"PROMISING_FOR_INNOVATION\",\n1103: \"integration_point\": \"Future: an analytics/aging tier beside memory/graph/graph.sqlite (DuckDB/Arrow reads of the SQLite graph) rather than a replacement of it.\",\n1104: \"benefit\": \"E22's vision - transactional multi-agent writes, automatic columnar migration of aging data, zero-copy graph analytics (Icebug PageRank: 8x networkit, E23, affiliated benchmark) - is a credible long-range direction for Rune's memory umbrellas (curator/sleep-time consolidation, I1/I4).\",\n1105: \"cost\": \"High and undated: roadmap-stage components (SparseTensors, GPU), affiliated-only benchmarks, and it chains to C3's immature store.\",\n1106: \"architectural_fit\": \"Directionally strong (local-first unified memory substrate for AI agents, E22) but architecturally premature for Rune's current sub-1M regime.\",\n1107: \"authority_implications\": \"A multi-writer embedded store (Pgembed) would change the memory tier's single-curator write authority - flag for any future design.\",\n1108: \"risks\": \"Vision-stage dependency; affiliated benchmarks (E23); inherits C3's fork-mortality risk.\",\n1109: \"required_adaptation\": \"Track as an adjacent-field lead; do not couple to the current spike.\",\n1110: \"evidence\": [\n1111: \"E22\",\n1112: \"E23\",\n1113: \"E24\"\n1114: ]\n1115: },\n1116: {\n1117: \"id\": \"C10\",\n1118: \"candidate\": \"Graphiti LLM-dependent ingestion pipeline (structured-output extraction, SEMAPHORE_LIMIT throttling)\",\n1119: \"source_ids\": [\n1120: \"O10\"\n1121: ],\n1122: \"classification\": \"TOO_COMPLEX_FOR_VALUE\",\n1123: \"integration_point\": \"Would replace the curator's extraction feeding graph_memory add/add-bulk.\",\n1124: \"benefit\": \"Automated entity/edge extraction with dedup (E18).\",\n1125: \"cost\": \"Rune already has the write-side discipline the research converges on: write-time gating (strength<3, mirroring E18's own cited 2603.15994), fixed predicate vocabulary against schema drift, and alias-first resolution (C4). Importing Graphiti's pipeline duplicates this with new LLM-provider dependencies and 429-throttling complexity.\",\n1126: \"architectural_fit\": \"Redundant - the extraction boundary already exists at curator.py/sleep_time.py.\",\n1127: \"authority_implications\": \"Would route memory writes through LLM structured-output calls, weakening the deterministic gating Rune relies on.\",\n1128: \"risks\": \"Provider coupling in the memory write path.\",\n1129: \"required_adaptation\": \"Not applicable.\",\n1130: \"evidence\": [\n1131: \"E18\",\n1132: \"E14\"\n1133: ]\n1134: }\n1135: ],\n1136: \"notes\": {\n1137: \"route_rationale\": \"The spike's literal question (which backend) resolves to STANDARD: no backend swap is warranted - Neo4j and FalkorDB conflict with Rune's embedded posture (C1, C2), LadybugDB lacks sufficient evidence (C3), and the reference architecture's load-bearing pattern (temporal semantics at the framework layer, E17) is already Rune's design (C4). However, INNOVATION is warranted on a strictly narrower front: combining researched mechanisms with Rune's existing SQLite tier yields capabilities the status quo genuinely lacks - (a) AS-OF time-travel queries over the full edge history Rune already persists but cannot query temporally (C5, NEP-001 E28-E30, low complexity per E30), and (b) a lexical FTS5 hybrid entry point fixing Rune's capped recall (C6, E18). Both are materially better than 'keep SQLite as-is', both stay inside the embedded/no-new-dependency posture, and neither exists as a standard drop-in Rune could adopt instead - which is exactly the combine-research-with-Rune's-architecture clause, not novelty for its own sake.\",\n1138: \"evidence_constraints\": \"Dossier limitations carried forward: no source benchmarks Rune's regime (single-node, sub-1M entities); O2 vendor magnitudes are marketing-adjacent until reproduced (contradiction #1); FalkorDB-native temporal features unevidenced (limitation #4); Graphiti performance claims not read from the primary paper arXiv 2501.13956 (limitation #7). C5/C6 benefits should be validated with a Rune-local benchmark before synthesis hardens them into recommendations.\",\n1139: \"non_goals\": \"No code modified, no research re-run, no stage advanced; backend migration explicitly NOT part of the warranted innovation scope.\",\n1140: \"next_stage_hint\": \"ADVERSARIAL should attack C5's NULL/half-open-interval semantics and C6's FTS5 write amplification; INNOVATION stage may merge C5+C6 into a single 'temporal+lexical query layer over graph.sqlite' proposal.\"\n1141: },\n1142: \"tool_receipts\": [\n1143: {\n1144: \"kind\": \"TOOL\",\n1145: \"tool\": \"skill_search\",\n1146: \"input_keys\": [\n1147: \"query\"\n1148: ],\n1149: \"ts\": 1788951410.0520692\n1150: },\n1151: {\n1152: \"kind\": \"TOOL\",\n1153: \"tool\": \"bash\",\n1154: \"input_keys\": [\n1155: \"command\",\n1156: \"description\"\n1157: ],\n1158: \"ts\": 1788951410.0521476\n1159: },\n1160: {\n1161: \"kind\": \"TOOL\",\n1162: \"tool\": \"read\",\n1163: \"input_keys\": [\n1164: \"file_path\"\n1165: ],\n1166: \"ts\": 1788951419.0539339\n1167: },\n1168: {\n1169: \"kind\": \"TOOL\",\n1170: \"tool\": \"read\",\n1171: \"input_keys\": [\n1172: \"file_path\"\n1173: ],\n1174: \"ts\": 1788951419.0540595\n1175: },\n1176: {\n1177: \"kind\": \"TOOL\",\n1178: \"tool\": \"bash\",\n1179: \"input_keys\": [\n1180: \"command\",\n1181: \"description\"\n1182: ],\n1183: \"ts\": 1788951431.056008\n1184: },\n1185: {\n1186: \"kind\": \"TOOL\",\n1187: \"tool\": \"read\",\n1188: \"input_keys\": [\n1189: \"file_path\"\n1190: ],\n1191: \"ts\": 1788951441.057961\n1192: },\n1193: {\n1194: \"kind\": \"TOOL\",\n1195: \"tool\": \"write\",\n1196: \"input_keys\": [\n1197: \"content\",\n1198: \"file_path\"\n1199: ],\n1200: \"ts\": 1788951661.0954847\n1201: },\n1202: {\n1203: \"kind\": \"TOOL\",\n1204: \"tool\": \"bash\",\n\n(Output capped at 50 KB. Showing lines 1-1204. Use offset=1205 to continue.)\n","metadata":{"preview":"{\n \"issue\": 163,\n \"round\": 1,\n \"current_stage\": \"ADVERSARIAL\",\n \"completed_stages\": [\n \"ISSUE\",\n \"INWARD\",\n \"OUTWARD\",\n \"APPLICABILITY\",\n \"INNOVATION\"\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 ],","truncated":true,"loaded":[]},"title":"research/issue-163/task_tree.json","time":{"start":1788953649717,"end":1788953649729}},"id":"prt_g001a085f26202001etnFkqsqu","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085efdf2c001f54mSaXmz"}} +{"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":"/mnt/workdrive/issue161-harness/research/issue-163/innovation.json\nfile\n\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 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 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, or add-bulk latency regresses more than ~10% with no compensating recall gain, the lexical tier is dead weight. Either half failing independently kills only that half; both failing reduces the conclusion to NO_INNOVATION_WARRANTED. This operationalizes the applicability evidence_constraint ('validate with a Rune-local benchmark before synthesis hardens C5/C6 into recommendations').\"\n25: }\n26: ],\n27: \"not_proposed\": {\n28: \"C4\": \"DIRECTLY_APPLICABLE but already integrated — the edges schema, supersede-on-contradiction (insert-before-mark ordering verified at graph_memory.py:200-208), and confidence-gated writes (strength<3 gate verified at :126-135) implement E14's model 1:1. Listing it as an ADOPT candidate would manufacture novelty for completed work. Residual risk is drift-tracking only.\",\n29: \"C9\": \"PROMISING_FOR_INNOVATION (Ladybug Memory lakehouse, E22/E23) but explicitly future: roadmap-stage components, affiliated-only benchmarks, chains to C3's fork-mortality risk, and the applicability stage ruled 'track as an adjacent-field lead; do not couple to the current spike'. Proposed as a BUILD candidate it would fail the no-evidence-derivation bar at Rune's current regime. Revisit if the graph ever approaches SQLite's limits or LadybugDB matures.\"\n30: },\n31: \"verification\": {\n32: \"code_claims_checked\": [\n33: \"edges table implements valid_from/valid_until/learned_at, status/superseded_by, evidence/source (graph_memory.py:101-116) — confirms C4 1:1 claim\",\n34: \"query() filters status='active' only, no as-of path (graph_memory.py:254, :275) — confirms C5 gap is real\",\n35: \"entity resolution is exact -> lower(alias) -> substring LIKE only (graph_memory.py:224-237) — confirms C6 gap is real\",\n36: \"superseded history is persisted but unreachable via query() — confirms the temporal read gap\"\n37: ],\n38: \"evidence_claims_checked\": \"E14, E17, E18, E22, E23, E28, E29, E30 verified verbatim against research/issue-163/outward.json\",\n39: \"constraints_respected\": \"No code modified; no research re-run; no stage advanced; backend migration excluded from scope per applicability non_goals.\"\n40: },\n41: \"next_stage_hint\": \"ADVERSARIAL should attack INN-1's two independent halves separately: the learned_at-as-transaction-time-proxy approximation (what audit questions become unanswerable?), the half-open-interval/NULL semantics (E29's documented pitfalls), and FTS5 write amplification on add-bulk. The kill test above is designed to be executable at that stage.\"\n42: }\n\n(End of file - total 42 lines)\n","metadata":{"preview":"{\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 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 ],","truncated":false,"loaded":[]},"title":"research/issue-163/innovation.json","time":{"start":1788953649716,"end":1788953649732}},"id":"prt_g001a085f26046001XE43DOGMj","sessionID":"ses_-ffe5f7a12c34effeSzvjfsvWr","messageID":"msg_g001a085efdf2c001f54mSaXmz"}} +{"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":"/mnt/workdrive/issue161-harness/src/rune/memory_env.py\nfile\n\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 /memory)\n9: RUNE_SCRIPTS_DIR where the scripts live (default /src/rune)\n10: RUNE_STORE_DB explicit store path (default /store/memory.db)\n11: \n12: Rules:\n13: - Never hardcode a user path; always go through these helpers.\n14: - Scripts invoked as `python