how is the memory service going? latest runs?


**Checking memory service status**

I need to find out about the memory service's current status and its latest runs. I think checking the agents database and possibly the service logs could help. It might make sense to use a memory-read skill since the user is looking for current information. I could inspect local services or search memories; the term "memory service" likely refers to the agents database. So, gathering status from the API or checking service status endpoints seems essential!

bash

set -e
printf 'STATUS\n'
curl -sf http://100.116.176.16:8091/api/status || true
printf '\n\nROOT\n'
curl -sf http://100.116.176.16:8091/ || true
printf '\n\nOPENAPI?\n'
curl -sf http://100.116.176.16:8091/openapi.json || true

set -e
ps -ef | grep -i '[a]gents-database' || true
systemctl --no-pager --full status agents-database 2>/dev/null || true
journalctl -u agents-database -n 40 --no-pager 2>/dev/null || true


STATUS
{"ok": true, "db_path": "/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3", "dashboard": {"active_tasks": [{"id": "task_33d854e72d094660a2fb293a8723f5c5", "title": "Fix GitHub credential automation so repo pushes and PR creation use the intended account reliably", "intent": "The current credential helper flow can select the wrong GitHub identity during multi-repo push and PR automation. Investigate the root cause across gh auth git credential helper and repo remote handling, then design a safer simpler script or convention.", "kind": "subtask", "status": "open", "priority": 9, "project_id": "proj_personal_agent", "repo_id": "repo_personal_agent", "parent_task_id": "task_afed9eaf504f42b99348a815d56148cc", "origin": "implementation", "owner_agent": "personal-agent", "blocked_reason": null, "requires_human_input": false, "due_at": null, "created_at": "2026-03-14T22:07:02.464013+00:00", "updated_at": "2026-03-15T14:13:45.052882+00:00", "schema_version": "shared-agent-memory-v2", "run_id": null, "metadata": {"acceptance": ["push uses intended GitHub account without token workarounds", "PR creation works consistently across public and private repos", "documented operator flow is simpler than the current state"], "area": "git_credentials", "classification_note": "User confirmed this credential automation task is post-V1 and not a blocker for V1 closeout.", "kind": "low_priority_followup", "milestone": "post-V1", "relevant_repos": ["/Users/sebas/personal-agent", "/Users/sebas/agents-database", "/Users/sebas/ai-dev-workflow", "/Users/sebas/ballbox-company-agent"], "requested_on": "2026-03-14", "v1_closeout_epic_id": "task_afed9eaf504f42b99348a815d56148cc", "v1_excluded": true, "why_now": "PR creation required direct credential workarounds; automation should simplify this later"}}], "blocked_tasks": [], "recent_runs": [{"id": "run_d37c8577c9674333b7157a945274385a", "task_id": "task_3d7271f7d19e456791b5796695fbe2fa", "agent_id": "personal-agent", "status": "completed", "result_summary": "Local readiness is partial: repo docs and runtime surface exist, current status API works, but full verification is not yet proven in this sandbox.", "error_message": null, "started_at": "2026-03-15T14:21:34.691945+00:00", "completed_at": "2026-03-15T14:22:58.675711+00:00", "created_at": "2026-03-15T14:21:34.691945+00:00", "updated_at": "2026-03-15T14:22:58.675711+00:00", "input_payload": {"mode": "codex-agentic"}, "metadata": {}}, {"id": "run_ce032d1c6fbf4e3aa23a26df10d8d91f", "task_id": "task_46bb6d81471e47e39340198a889252a1", "agent_id": "personal-agent", "status": "failed", "result_summary": null, "error_message": "error: unexpected argument '--ask-for-approval' found\n\n  tip: to pass '--ask-for-approval' as a value, use '-- --ask-for-approval'\n\nUsage: codex exec --sandbox <SANDBOX_MODE> [PROMPT]\n\nFor more information, try '--help'.", "started_at": "2026-03-15T14:19:23.634939+00:00", "completed_at": "2026-03-15T14:19:23.690219+00:00", "created_at": "2026-03-15T14:19:23.634939+00:00", "updated_at": "2026-03-15T14:19:23.690219+00:00", "input_payload": {"mode": "codex-agentic"}, "metadata": {}}, {"id": "run_d7ea2a60bbd74e9b8fdaa628115d7cbd", "task_id": "task_4a80a42f5d424f7191b151c3640db934", "agent_id": "personal-agent", "status": "failed", "result_summary": null, "error_message": "error: unexpected argument '--ask-for-approval' found\n\n  tip: to pass '--ask-for-approval' as a value, use '-- --ask-for-approval'\n\nUsage: codex exec --sandbox <SANDBOX_MODE> [PROMPT]\n\nFor more information, try '--help'.", "started_at": "2026-03-15T14:16:58.205690+00:00", "completed_at": "2026-03-15T14:16:58.257806+00:00", "created_at": "2026-03-15T14:16:58.205690+00:00", "updated_at": "2026-03-15T14:16:58.257806+00:00", "input_payload": {"mode": "codex-agentic"}, "metadata": {}}], "recent_artifacts": [{"id": "art_d9ed019809c24381919c280f28481652", "task_id": "task_2e1c07a93b9d40598bb4bfa504a5d726", "artifact_type": "delegation_note", "title": "Handoff personal-agent -> ai-dev-workflow", "content": "{\"completed_at\": \"2026-03-15T14:18:48.588043+00:00\", \"error_message\": null, \"from_agent\": \"personal-agent\", \"payload\": {\"expected_artifacts\": [\"report\"], \"idempotency_key\": \"task_2e1c07a93b9d40598bb4bfa504a5d726\", \"intent\": \"Smoke task: assess current V1 readiness and propose one safe internal next step, no repo work and no external actions.\", \"route\": {\"codex_instruction\": \"\", \"delegation_target\": \"ai-dev-workflow\", \"planning_source\": \"fallback\", \"primary_agent\": \"code\", \"reason\": \"code work (repo)\", \"secondary_agent\": null}, \"task_id\": \"task_2e1c07a93b9d40598bb4bfa504a5d726\"}, \"reason\": \"code work (repo)\", \"result_summary\": \"ai-dev-workflow accepted the handoff and persisted intake context.\", \"status\": \"accepted\", \"to_agent\": \"ai-dev-workflow\"}", "format": "json", "status": "active", "source_ref": "migration:v2", "created_at": "2026-04-30T17:32:27.377428+00:00", "updated_at": "2026-04-30T17:32:27.377432+00:00", "metadata": {}}, {"id": "art_4e83c1c6fdf94b3ca70f6e0dbb9be2d7", "task_id": "task_ed6a38c1b49f4e829e067d49d206148b", "artifact_type": "delegation_note", "title": "Handoff personal-agent -> ai-dev-workflow", "content": "{\"completed_at\": \"2026-03-15T14:18:33.506326+00:00\", \"error_message\": null, \"from_agent\": \"personal-agent\", \"payload\": {\"expected_artifacts\": [\"report\"], \"idempotency_key\": \"task_ed6a38c1b49f4e829e067d49d206148b\", \"intent\": \"Smoke test after codex CLI fix: summarize current runtime health and suggest one safe internal next step only.\", \"route\": {\"codex_instruction\": \"\", \"delegation_target\": \"ai-dev-workflow\", \"planning_source\": \"fallback\", \"primary_agent\": \"code\", \"reason\": \"code work (fix)\", \"secondary_agent\": null}, \"task_id\": \"task_ed6a38c1b49f4e829e067d49d206148b\"}, \"reason\": \"code work (fix)\", \"result_summary\": \"ai-dev-workflow accepted the handoff and persisted intake context.\", \"status\": \"accepted\", \"to_agent\": \"ai-dev-workflow\"}", "format": "json", "status": "active", "source_ref": "migration:v2", "created_at": "2026-04-30T17:32:27.377374+00:00", "updated_at": "2026-04-30T17:32:27.377379+00:00", "metadata": {}}, {"id": "art_7ed9c6867a814f7fb9ad4f45fc9a578e", "task_id": "task_afed9eaf504f42b99348a815d56148cc", "artifact_type": "delegation_note", "title": "Handoff personal-agent -> implementation-agent", "content": "{\"completed_at\": \"2026-03-14T21:29:18.517341+00:00\", \"error_message\": null, \"from_agent\": \"personal-agent\", \"payload\": {\"artifact_id\": \"art_598b5da707dd47709fc0fe9bb3e79e28\", \"epic_id\": \"task_afed9eaf504f42b99348a815d56148cc\", \"expected_artifacts\": [\"migration_report\", \"v1_cross_reference_report\"], \"idempotency_key\": \"task_afed9eaf504f42b99348a815d56148cc\", \"remaining_task_ids\": [\"task_a240986d7c8a4474ba5246536769deed\", \"task_c0b65186af7d4013b2c1d2906ea6a28e\"]}, \"reason\": \"Finish remaining V1 migration and GitHub cross-reference work\", \"result_summary\": \"Finished legacy bridge and V1 multi-repo cross-reference work; shared DB now contains the resume anchors and completion artifacts.\", \"status\": \"completed\", \"to_agent\": \"implementation-agent\"}", "format": "json", "status": "active", "source_ref": "migration:v2", "created_at": "2026-04-30T17:32:27.377239+00:00", "updated_at": "2026-04-30T17:32:27.377247+00:00", "metadata": {}}, {"id": "art_eb70bf77f5524240a4782e1e6e5eddb3", "task_id": "task_3d7271f7d19e456791b5796695fbe2fa", "artifact_type": "execution_state", "title": "Execution state snapshot", "content": "{\n  \"action_results\": [\n    {\n      \"task_id\": \"task_561a1444260c4c23b716ab65ad2ebe3d\",\n      \"type\": \"create_followup_task\"\n    }\n  ],\n  \"outcome\": \"complete\",\n  \"run_id\": \"run_d37c8577c9674333b7157a945274385a\",\n  \"summary\": \"Local readiness is partial: repo docs and runtime surface exist, current status API works, but full verification is not yet proven in this sandbox.\"\n}", "format": "json", "status": "active", "source_ref": "personal-agent", "created_at": "2026-03-15T14:22:58.674979+00:00", "updated_at": "2026-03-15T14:22:58.674979+00:00", "metadata": {"outcome": "complete", "task_run_id": "run_d37c8577c9674333b7157a945274385a"}}, {"id": "art_f2e3c950a9b8427e89e083457683077e", "task_id": "task_3d7271f7d19e456791b5796695fbe2fa", "artifact_type": "report", "title": "CLI Smoke Readiness Summary", "content": "## Findings\n- Confirmed locally on 2026-03-15: `python3 scripts/personal.py --json status` works and returns live task/run state. Current snapshot shows `active_task_count=19`, `blocked_task_count=2`, `pending_approval_count=0`.\n- Confirmed locally on 2026-03-15: `codex exec --help` matches the newer CLI shape and supports `-C/--cd`, `--add-dir`, `--output-last-message`; it does not advertise the older `--ask-for-approval` flow.\n- Confirmed from repo state: local edits in `personal_agent/planner.py`, `personal_agent/runtime.py`, and `tests/test_personal.py` replace the obsolete `--ask-for-approval` and `--search` flags with `-C`, and add test coverage for that command shape.\n- Confirmed from runtime evidence: recent stored runs failed with exact error `unexpected argument '--ask-for-approval' found`, so the earlier smoke failures were real and explainable.\n- Confirmed from docs/scripts: the only declared repo gate is `./scripts/run-checks.sh`, which runs `python3 -m unittest discover -s tests`.\n- Not confirmed: full repo test pass. In this read-only sandbox, the test suite fails before exercising app behavior because Python cannot create temp dirs: `FileNotFoundError: No usable temporary directory found`.\n- Not confirmed: shared-memory operational readiness under this sandbox. Targeted memory search failed with `sqlite3.OperationalError: attempt to write a readonly database`, so shared DB interaction is not proven here.\n- Not confirmed: production readiness. Docs still describe dashboard, daemon, worker, approvals, and multi-repo handoff topology, but this run did not validate dashboard serving, worker completion, or end-to-end shared-memory writes.\n\n## Risks\n- Main near-term risk is false confidence from static docs plus partial command success while writable-path and temp-dir assumptions remain unverified.\n- Working tree is dirty, so the codex CLI fix appears in progress but not yet fully validated by a successful full gate.\n\n## Recommended Next Action\n- Queue one internal follow-up task: rerun the full local smoke gate in a writable environment after the current codex CLI flag fix lands, with focus on two proofs only: tests pass and shared-memory-backed worker flow completes without the old CLI error.", "format": "md", "status": "active", "source_ref": "personal-agent", "created_at": "2026-03-15T14:22:58.671693+00:00", "updated_at": "2026-03-15T14:22:58.671693+00:00", "metadata": {"outcome": "complete", "task_run_id": "run_d37c8577c9674333b7157a945274385a"}}, {"id": "art_504acf83d6fe481f82082c3fa518b6cb", "task_id": "task_3d7271f7d19e456791b5796695fbe2fa", "artifact_type": "plan", "title": "Execution Plan", "content": "# Plan for Fresh CLI smoke task: summarize current system readiness and suggest one safe internal coordinati...\n\n- Route: personal\n- Secondary route: code\n- Planning source: codex\n- Codex instruction: Use Python as the shell entrypoint; inspect current local readiness signals, summarize gaps conservatively, and record one internal-only coordination next step in shared DB/audit state.\n\n## Subtasks\n- Collect readiness signals\n- Write smoke summary\n- Propose one coordination step", "format": "md", "status": "active", "source_ref": "personal-agent", "created_at": "2026-03-15T14:21:32.622366+00:00", "updated_at": "2026-03-15T14:21:32.622366+00:00", "metadata": {"route": {"codex_instruction": "Use Python as the shell entrypoint; inspect current local readiness signals, summarize gaps conservatively, and record one internal-only coordination next step in shared DB/audit state.", "delegation_target": null, "planning_source": "codex", "primary_agent": "personal", "reason": "Planning/synthesis task with light repo-readiness context; no external action.", "secondary_agent": "code"}}}, {"id": "art_52e95ab045ea45df8cd59f44707194ef", "task_id": "task_3d7271f7d19e456791b5796695fbe2fa", "artifact_type": "normalized_intent", "title": "Normalized Intent", "content": "Request: Fresh CLI smoke task: summarize current system readiness and suggest one safe internal coordination step only. No external actions.\nPrimary route: personal\nSecondary route: code\nReason: Planning/synthesis task with light repo-readiness context; no external action.\nPlanning source: codex\nCodex instruction: Use Python as the shell entrypoint; inspect current local readiness signals, summarize gaps conservatively, and record one internal-only coordination next step in shared DB/audit state.\nRelevant memory:\n- Sebas idea: custom AI solutions as alternative income [mem_sebas_custom_ai_solutions_income_idea]\n- Research run: Produce a production-readiness plan for ballbox-payment-integration [legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f]\n- Sebas desired relationship with money [mem_sebas_money_relationship]", "format": "md", "status": "active", "source_ref": "personal-agent", "created_at": "2026-03-15T14:21:32.621453+00:00", "updated_at": "2026-03-15T14:21:32.621453+00:00", "metadata": {"route": {"codex_instruction": "Use Python as the shell entrypoint; inspect current local readiness signals, summarize gaps conservatively, and record one internal-only coordination next step in shared DB/audit state.", "delegation_target": null, "planning_source": "codex", "primary_agent": "personal", "reason": "Planning/synthesis task with light repo-readiness context; no external action.", "secondary_agent": "code"}}}, {"id": "art_64eb875c9be041c2bf56ef7825e7b49d", "task_id": "task_46bb6d81471e47e39340198a889252a1", "artifact_type": "plan", "title": "Execution Plan", "content": "# Plan for Smoke task: summarize current system readiness and suggest one safe internal coordination step on...\n\n- Route: personal\n- Planning source: fallback\n\n## Subtasks\n- Clarify objective and constraints\n- Gather context and evidence\n- Produce final recommendation", "format": "md", "status": "active", "source_ref": "personal-agent", "created_at": "2026-03-15T14:19:21.818641+00:00", "updated_at": "2026-03-15T14:19:21.818641+00:00", "metadata": {"route": {"codex_instruction": "", "delegation_target": null, "planning_source": "fallback", "primary_agent": "personal", "reason": "default personal route", "secondary_agent": null}}}, {"id": "art_0f68333747c744728f5291e4e64d563a", "task_id": "task_46bb6d81471e47e39340198a889252a1", "artifact_type": "normalized_intent", "title": "Normalized Intent", "content": "Request: Smoke task: summarize current system readiness and suggest one safe internal coordination step only. No external actions.\nPrimary route: personal\nSecondary route: none\nReason: default personal route\nPlanning source: fallback\nRelevant memory:\n- Sebas idea: custom AI solutions as alternative income [mem_sebas_custom_ai_solutions_income_idea]\n- Research run: Produce a production-readiness plan for ballbox-payment-integration [legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f]\n- Sebas desired relationship with money [mem_sebas_money_relationship]", "format": "md", "status": "active", "source_ref": "personal-agent", "created_at": "2026-03-15T14:19:21.818049+00:00", "updated_at": "2026-03-15T14:19:21.818049+00:00", "metadata": {"route": {"codex_instruction": "", "delegation_target": null, "planning_source": "fallback", "primary_agent": "personal", "reason": "default personal route", "secondary_agent": null}}}, {"id": "art_a60c0d4bffb14d67be795c5f45124318", "task_id": "task_2e1c07a93b9d40598bb4bfa504a5d726", "artifact_type": "handoff_result", "title": "Handoff result from ai-dev-workflow", "content": "{\n  \"artifacts\": [],\n  \"memory_id\": \"mem_17eea5c0658f4cd3beece90a93d35e36\",\n  \"status\": \"accepted\",\n  \"summary\": \"ai-dev-workflow accepted the handoff and persisted intake context.\",\n  \"task_id\": \"task_2e1c07a93b9d40598bb4bfa504a5d726\"\n}", "format": "json", "status": "active", "source_ref": "ai-dev-workflow", "created_at": "2026-03-15T14:18:48.589136+00:00", "updated_at": "2026-03-15T14:18:48.589136+00:00", "metadata": {"handoff_id": "hof_bdc2e7b08a7c4cdb803c986fc6ec84fc"}}, {"id": "art_ecadce16c6ce44e9b8b534630a2aea26", "task_id": "task_2e1c07a93b9d40598bb4bfa504a5d726", "artifact_type": "plan", "title": "Execution Plan", "content": "# Plan for Smoke task: assess current V1 readiness and propose one safe internal next step, no repo work and...\n\n- Route: code\n- Planning source: fallback\n- Delegation target: ai-dev-workflow\n\n## Subtasks\n- Inspect technical context\n- Delegate code execution\n- Summarize technical outcome", "format": "md", "status": "active", "source_ref": "personal-agent", "created_at": "2026-03-15T14:18:47.342014+00:00", "updated_at": "2026-03-15T14:18:47.342014+00:00", "metadata": {"route": {"codex_instruction": "", "delegation_target": "ai-dev-workflow", "planning_source": "fallback", "primary_agent": "code", "reason": "code work (repo)", "secondary_agent": null}}}, {"id": "art_73f79012e7f0471085d2749541f5748b", "task_id": "task_2e1c07a93b9d40598bb4bfa504a5d726", "artifact_type": "normalized_intent", "title": "Normalized Intent", "content": "Request: Smoke task: assess current V1 readiness and propose one safe internal next step, no repo work and no external actions.\nPrimary route: code\nSecondary route: none\nReason: code work (repo)\nPlanning source: fallback\nRelevant memory:\n- Research run: Produce a production-readiness plan for ballbox-payment-integration [legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f]\n- Research run: Analyze the best next steps to make ballbox-payment-integration production-ready [legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7]\n- Research run: Production-readiness plan for ballbox-payment-integration [legacy_run_35bd54a1-0135-414e-9c6b-245fca772f54]", "format": "md", "status": "active", "source_ref": "personal-agent", "created_at": "2026-03-15T14:18:47.341454+00:00", "updated_at": "2026-03-15T14:18:47.341454+00:00", "metadata": {"route": {"codex_instruction": "", "delegation_target": "ai-dev-workflow", "planning_source": "fallback", "primary_agent": "code", "reason": "code work (repo)", "secondary_agent": null}}}, {"id": "art_1accabbcec71437d8592292c9e412153", "task_id": "task_ed6a38c1b49f4e829e067d49d206148b", "artifact_type": "handoff_result", "title": "Handoff result from ai-dev-workflow", "content": "{\n  \"artifacts\": [],\n  \"memory_id\": \"mem_04dc02a90cad40cc826ed1e8f93aff36\",\n  \"status\": \"accepted\",\n  \"summary\": \"ai-dev-workflow accepted the handoff and persisted intake context.\",\n  \"task_id\": \"task_ed6a38c1b49f4e829e067d49d206148b\"\n}", "format": "json", "status": "active", "source_ref": "ai-dev-workflow", "created_at": "2026-03-15T14:18:33.507460+00:00", "updated_at": "2026-03-15T14:18:33.507460+00:00", "metadata": {"handoff_id": "hof_d7a1028b907341fa93a5cb8cb460e2c7"}}, {"id": "art_39c706c5d09f421a9198fc938020c79f", "task_id": "task_ed6a38c1b49f4e829e067d49d206148b", "artifact_type": "plan", "title": "Execution Plan", "content": "# Plan for Smoke test after codex CLI fix: summarize current runtime health and suggest one safe internal ne...\n\n- Route: code\n- Planning source: fallback\n- Delegation target: ai-dev-workflow\n\n## Subtasks\n- Inspect technical context\n- Delegate code execution\n- Summarize technical outcome", "format": "md", "status": "active", "source_ref": "personal-agent", "created_at": "2026-03-15T14:18:28.454377+00:00", "updated_at": "2026-03-15T14:18:28.454377+00:00", "metadata": {"route": {"codex_instruction": "", "delegation_target": "ai-dev-workflow", "planning_source": "fallback", "primary_agent": "code", "reason": "code work (fix)", "secondary_agent": null}}}, {"id": "art_8225d4c69b224564ba55a14f20519717", "task_id": "task_ed6a38c1b49f4e829e067d49d206148b", "artifact_type": "normalized_intent", "title": "Normalized Intent", "content": "Request: Smoke test after codex CLI fix: summarize current runtime health and suggest one safe internal next step only.\nPrimary route: code\nSecondary route: none\nReason: code work (fix)\nPlanning source: fallback\nRelevant memory:\n- Refactor runtime toward Codex-agentic orchestration [mem_runtime_agentic_refactor_priority]\n- Research run: Analyze the best next steps to make ballbox-payment-integration production-ready [legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7]\n- Sebas idea: custom AI solutions as alternative income [mem_sebas_custom_ai_solutions_income_idea]", "format": "md", "status": "active", "source_ref": "personal-agent", "created_at": "2026-03-15T14:18:28.453697+00:00", "updated_at": "2026-03-15T14:18:28.453697+00:00", "metadata": {"route": {"codex_instruction": "", "delegation_target": "ai-dev-workflow", "planning_source": "fallback", "primary_agent": "code", "reason": "code work (fix)", "secondary_agent": null}}}, {"id": "art_4b872101defe41188e12e81c2c5e0ffc", "task_id": "task_4a80a42f5d424f7191b151c3640db934", "artifact_type": "plan", "title": "Execution Plan", "content": "# Plan for Smoke test: summarize current V1 readiness and recommend the next safe internal step without exte...\n\n- Route: personal\n- Planning source: fallback\n\n## Subtasks\n- Clarify objective and constraints\n- Gather context and evidence\n- Produce final recommendation", "format": "md", "status": "active", "source_ref": "personal-agent", "created_at": "2026-03-15T14:16:55.509883+00:00", "updated_at": "2026-03-15T14:16:55.509883+00:00", "metadata": {"route": {"codex_instruction": "", "delegation_target": null, "planning_source": "fallback", "primary_agent": "personal", "reason": "default personal route", "secondary_agent": null}}}, {"id": "art_0f65fad23c6e4f698fa606feead854e1", "task_id": "task_4a80a42f5d424f7191b151c3640db934", "artifact_type": "normalized_intent", "title": "Normalized Intent", "content": "Request: Smoke test: summarize current V1 readiness and recommend the next safe internal step without external side effects.\nPrimary route: personal\nSecondary route: none\nReason: default personal route\nPlanning source: fallback\nRelevant memory:\n- Research run: Produce a production-readiness plan for ballbox-payment-integration [legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f]\n- Refactor runtime toward Codex-agentic orchestration [mem_runtime_agentic_refactor_priority]\n- Research run: Analyze the best next steps to make ballbox-payment-integration production-ready [legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7]", "format": "md", "status": "active", "source_ref": "personal-agent", "created_at": "2026-03-15T14:16:55.509067+00:00", "updated_at": "2026-03-15T14:16:55.509067+00:00", "metadata": {"route": {"codex_instruction": "", "delegation_target": null, "planning_source": "fallback", "primary_agent": "personal", "reason": "default personal route", "secondary_agent": null}}}, {"id": "art_f7fad73c89674ef1b9f9248c7b829ca1", "task_id": "task_afed9eaf504f42b99348a815d56148cc", "artifact_type": "closeout", "title": "V1 final closeout", "content": "# V1 Closeout\n\n## Outcome\nV1 is complete.\n\n`personal-agent` now operates as the shared-memory front door with:\n- shared operational state in `agents-database`\n- intake planning and runtime orchestration backed by Codex outputs\n- daemon/dashboard surfaces for status, blockers, approvals, and next actions\n- approval resolution with automatic resume\n- stable subagent handoff contract for `ai-dev-workflow` and `ballbox-company-agent`\n- legacy bridge for old personal-agent research/task inspection\n- documented sandbox write convention for canonical shared DB access\n\n## Verification\n- local repo gate: `./scripts/run-checks.sh` passed with 26 tests\n- direct suite: `python3 -m pytest tests/test_personal.py` passed with 26 tests\n- PR CI: run `23112079488` passed on 2026-03-15 (`unit-tests`)\n\n## Runtime use cases covered by tests\n- intake creates operational task, subtasks, and specialist handoff\n- Codex planning path and fallback path\n- worker terminal outcomes: `complete`, `needs_approval`, `blocked`\n- approval resolution paths: `approved` resumes and completes; `rejected` stays blocked\n- structured runtime actions: follow-up task, handoff, artifact\n- dashboard snapshot enrichment: summary, next action, latest run, pending approval, route summary\n- end-to-end flow: intake -> approval -> resume -> complete\n- Codex command wiring includes `--add-dir /Users/sebas/agents-database`\n- handoff dispatch completion persists result artifact\n- preference blocker can resolve from memory without human input\n- daemon API surface covers status, intake, blocker response, and approval resolution\n- CLI accepts `--json` before or after subcommands\n\n## Scope boundary\n- task `task_33d854e72d094660a2fb293a8723f5c5` is explicitly post-V1 follow-up, not a V1 blocker\n- remaining work after V1 should continue as follow-up capabilities and operator hardening, not as part of this milestone\n", "format": "md", "status": "active", "source_ref": "personal-agent", "created_at": "2026-03-15T14:13:45.050563+00:00", "updated_at": "2026-03-15T14:13:45.050563+00:00", "metadata": {"completed_on": "2026-03-15", "kind": "v1_closeout", "post_v1_followup_task_id": "task_33d854e72d094660a2fb293a8723f5c5", "verification": {"pr_ci_run_id": "23112079488", "pr_ci_status": "success", "pytest": "26 passed", "run_checks": "passed"}}}, {"id": "art_4151fdb5c75c48449199130022f7a909", "task_id": "task_eaf005df03fb441ab2b84df7ea05178d", "artifact_type": "continuation_brief", "title": "Phase 2 closeout brief", "content": "Phase 2 closeout:\n- structured runtime actions implemented\n- approval loop resolves and resumes\n- execution_state snapshots persisted\n- dashboard/status surfaces expose next actions and latest run state\n- end-to-end runtime tests cover intake -> approval -> resume -> complete\n", "format": "md", "status": "active", "source_ref": "personal-agent", "created_at": "2026-03-14T22:18:49.489775+00:00", "updated_at": "2026-03-14T22:18:49.489775+00:00", "metadata": {"kind": "phase2_closeout"}}, {"id": "art_b4b8c8b4bcc344ae81c206fa3ca57724", "task_id": "task_33d854e72d094660a2fb293a8723f5c5", "artifact_type": "task_note", "title": "Credential automation follow-up note", "content": "Low-priority follow-up. During multi-repo PR prep, git push and gh PR creation hit mixed-account behavior. Temporary workaround used direct credential injection. Future work should simplify and harden this flow instead of relying on manual recovery steps.", "format": "md", "status": "active", "source_ref": "personal-agent", "created_at": "2026-03-14T22:07:02.464680+00:00", "updated_at": "2026-03-14T22:07:02.464680+00:00", "metadata": {"kind": "followup_context"}}]}, "audit": {"schema_version": "shared-agent-memory-v2", "memories": 223, "tasks": 39, "approvals": 0, "handoffs": 0, "maintenance_jobs": 3, "legacy_memory_rows": 0, "legacy_task_rows": 0}}

ROOT

OPENAPI?


sebas       1366    1289  0 10:07 ?        00:00:00 python3 -m shared_agent_memory.cli --db /mnt/rpi/agents-database/data/shared-agent-memory.sqlite3 serve-http --host 0.0.0.0 --port 8091
sebas       1369    1289  0 10:07 ?        00:00:00 /mnt/rpi/agents-database/.venv/bin/python -m shared_agent_memory.cli --db /mnt/rpi/agents-database/data/shared-agent-memory.sqlite3 maintenance-daemon --interval-seconds 900 --jitter-seconds 60
root        1505    1273  0 10:07 ?        00:00:00 sudo -n -u sebas /usr/sbin/tailscaled serve-taildrive memory /home/sebas/agents-database/data
sebas       1511    1505  0 10:07 ?        00:00:00 /usr/sbin/tailscaled serve-taildrive memory /home/sebas/agents-database/data
sebas      12842   10916  0 10:45 ?        00:00:00 /bin/bash -c set -e ps -ef | grep -i '[a]gents-database' || true systemctl --no-pager --full status agents-database 2>/dev/null || true journalctl -u agents-database -n 40 --no-pager 2>/dev/null || true
-- No entries --


**Evaluating recent runs**

I need to check if I can summarize recent runs, maybe focusing on more than three of them. I could mention there aren't any systemd unit logs, but the processes are running well. Additionally, I'm considering looking into maintenance jobs or their last execution times. It might be ideal to utilize an API's recent runs endpoint or even inspect the repo. I think querying the SQLite database directly for the top runs and maintenance jobs could be simpler.

python3 - <<'PY'
import sqlite3, json
path='/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3'
conn=sqlite3.connect(path)
conn.row_factory=sqlite3.Row
print('RUNS')
for row in conn.execute("select id, task_id, agent_id, status, started_at, completed_at, substr(coalesce(result_summary,error_message,''),1,160) as summary from task_runs order by datetime(created_at) desc limit 8"):
    print(json.dumps(dict(row), ensure_ascii=False))
print('JOBS')
for row in conn.execute("select job_name, status, started_at, completed_at, items_processed, error_message from maintenance_jobs order by datetime(created_at) desc limit 5"):
    print(json.dumps(dict(row), ensure_ascii=False))
PY


RUNS
{"id": "run_d37c8577c9674333b7157a945274385a", "task_id": "task_3d7271f7d19e456791b5796695fbe2fa", "agent_id": "personal-agent", "status": "completed", "started_at": "2026-03-15T14:21:34.691945+00:00", "completed_at": "2026-03-15T14:22:58.675711+00:00", "summary": "Local readiness is partial: repo docs and runtime surface exist, current status API works, but full verification is not yet proven in this sandbox."}
{"id": "run_ce032d1c6fbf4e3aa23a26df10d8d91f", "task_id": "task_46bb6d81471e47e39340198a889252a1", "agent_id": "personal-agent", "status": "failed", "started_at": "2026-03-15T14:19:23.634939+00:00", "completed_at": "2026-03-15T14:19:23.690219+00:00", "summary": "error: unexpected argument '--ask-for-approval' found\n\n  tip: to pass '--ask-for-approval' as a value, use '-- --ask-for-approval'\n\nUsage: codex exec --sandbox "}
{"id": "run_d7ea2a60bbd74e9b8fdaa628115d7cbd", "task_id": "task_4a80a42f5d424f7191b151c3640db934", "agent_id": "personal-agent", "status": "failed", "started_at": "2026-03-15T14:16:58.205690+00:00", "completed_at": "2026-03-15T14:16:58.257806+00:00", "summary": "error: unexpected argument '--ask-for-approval' found\n\n  tip: to pass '--ask-for-approval' as a value, use '-- --ask-for-approval'\n\nUsage: codex exec --sandbox "}
JOBS
Traceback (most recent call last):
  File "<stdin>", line 9, in <module>
sqlite3.OperationalError: no such column: job_name

Command exited with code 1


**Inspecting schema for maintenance jobs**

I see that I need to inspect the schema for maintenance jobs. It’s important to ensure everything’s running smoothly. I’ll take a closer look at the structure, checking for any inconsistencies or areas that might need attention. After all, a good schema inspection can prevent potential problems down the line! So, let's get to it and figure out what needs to be done.

python3 - <<'PY'
import sqlite3
conn=sqlite3.connect('/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3')
for row in conn.execute("pragma table_info(maintenance_jobs)"):
    print(row)
PY


(0, 'id', 'TEXT', 0, None, 1)
(1, 'job_type', 'TEXT', 1, None, 0)
(2, 'cadence', 'TEXT', 1, None, 0)
(3, 'interval_minutes', 'INTEGER', 0, None, 0)
(4, 'window_start', 'TEXT', 0, None, 0)
(5, 'window_end', 'TEXT', 0, None, 0)
(6, 'next_due_at', 'TEXT', 1, None, 0)
(7, 'last_run_at', 'TEXT', 0, None, 0)
(8, 'last_status', 'TEXT', 0, None, 0)
(9, 'last_summary', 'TEXT', 0, None, 0)
(10, 'enabled', 'INTEGER', 1, '1', 0)
(11, 'metadata_json', 'TEXT', 1, None, 0)
(12, 'created_at', 'TEXT', 1, None, 0)
(13, 'updated_at', 'TEXT', 1, None, 0)


python3 - <<'PY'
import sqlite3, json
conn=sqlite3.connect('/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3')
conn.row_factory=sqlite3.Row
print('MAINTENANCE')
for row in conn.execute("select id, job_type, cadence, interval_minutes, next_due_at, last_run_at, last_status, substr(coalesce(last_summary,''),1,160) as last_summary, enabled from maintenance_jobs order by datetime(updated_at) desc limit 5"):
    print(json.dumps(dict(row)))
PY


MAINTENANCE
{"id": "job_consolidation_daily_10", "job_type": "consolidation", "cadence": "daily", "interval_minutes": null, "next_due_at": "2026-05-06T10:00:00+00:00", "last_run_at": "2026-05-05T10:00:21.617623+00:00", "last_status": "completed", "last_summary": "consolidation completed", "enabled": 1}
{"id": "job_consolidation_daily_15", "job_type": "consolidation", "cadence": "daily", "interval_minutes": null, "next_due_at": "2026-05-05T15:00:00+00:00", "last_run_at": "2026-05-04T15:01:30.515284+00:00", "last_status": "completed", "last_summary": "consolidation completed", "enabled": 1}
{"id": "job_consolidation_weekly", "job_type": "consolidation", "cadence": "weekly", "interval_minutes": null, "next_due_at": "2026-05-07T17:32:27.534973+00:00", "last_run_at": null, "last_status": null, "last_summary": "", "enabled": 1}


Memory service up.

- HTTP status: `ok=true`
- DB: `/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3`
- Process up:
  - API server on `:8091`
  - maintenance daemon running
  - taildrive export for data running

Current counts:
- memories: 223
- tasks: 39
- approvals: 0
- maintenance jobs: 3

Latest runs:
1. `run_d37c8577c9674333b7157a945274385a` — completed
   - time: `2026-03-15 14:21 UTC`
   - summary: local readiness partial; status API works; full verification not proven in that sandbox

2. `run_ce032d1c6fbf4e3aa23a26df10d8d91f` — failed
   - time: `2026-03-15 14:19 UTC`
   - cause: old `codex exec --ask-for-approval` flag no longer valid

3. `run_d7ea2a60bbd74e9b8fdaa628115d7cbd` — failed
   - time: `2026-03-15 14:16 UTC`
   - same cause: invalid `--ask-for-approval` flag

Maintenance:
- `job_consolidation_daily_10` ran today `2026-05-05 10:00 UTC` — completed
- `job_consolidation_daily_15` last ran `2026-05-04 15:01 UTC` — completed
- weekly consolidation exists, not run yet

Notable:
- no active approvals
- one active open task in dashboard: GitHub credential automation follow-up
- no systemd unit logs; service appears to be running as direct processes, not via `agents-database.service`

If you want, I can also pull:
- latest memory ingests
- latest task creations/updates
- API health deeper than `/api/status`


what about mid agent session memory updates or the ones after session end?


**Investigating memory updates**

I need to figure out if I should gather the latest information on mid-agent session memory updates or those that happen after a session ends. It’s essential to understand whether the service is actually receiving these updates. I should probably check recent memory records and look for timestamps and types. Let’s think about querying the database for this metadata and see what I can find out!

python3 - <<'PY'
import sqlite3, json
conn=sqlite3.connect('/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3')
conn.row_factory=sqlite3.Row
print('RECENT_MEMORIES')
for row in conn.execute("select id, memory_type, title, source_ref, created_at, updated_at, substr(content,1,140) as preview from memories order by datetime(updated_at) desc limit 12"):
    print(json.dumps(dict(row), ensure_ascii=False))
PY


RECENT_MEMORIES
Traceback (most recent call last):
  File "<stdin>", line 5, in <module>
sqlite3.OperationalError: no such column: memory_type

Command exited with code 1


python3 - <<'PY'
import sqlite3
conn=sqlite3.connect('/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3')
for row in conn.execute("pragma table_info(memories)"):
    print(row)
PY


(0, 'id', 'TEXT', 0, None, 1)
(1, 'type', 'TEXT', 1, None, 0)
(2, 'scope', 'TEXT', 1, None, 0)
(3, 'status', 'TEXT', 1, None, 0)
(4, 'project_id', 'TEXT', 0, None, 0)
(5, 'repo_id', 'TEXT', 0, None, 0)
(6, 'agent_id', 'TEXT', 0, None, 0)
(7, 'source_kind', 'TEXT', 1, None, 0)
(8, 'title', 'TEXT', 1, None, 0)
(9, 'content', 'TEXT', 1, None, 0)
(10, 'summary', 'TEXT', 1, None, 0)
(11, 'confidence', 'REAL', 1, None, 0)
(12, 'freshness', 'REAL', 1, None, 0)
(13, 'created_at', 'TEXT', 1, None, 0)
(14, 'updated_at', 'TEXT', 1, None, 0)
(15, 'observed_at', 'TEXT', 1, None, 0)
(16, 'source_ref', 'TEXT', 0, None, 0)
(17, 'evidence_ref', 'TEXT', 0, None, 0)
(18, 'embedding_json', 'TEXT', 0, None, 0)
(19, 'metadata_json', 'TEXT', 1, None, 0)
(20, 'schema_version', 'TEXT', 1, "'shared-agent-memory-v2'", 0)
(21, 'subtype', 'TEXT', 0, None, 0)
(22, 'run_id', 'TEXT', 0, None, 0)
(23, 'task_id', 'TEXT', 0, None, 0)
(24, 'url', 'TEXT', 0, None, 0)
(25, 'domain', 'TEXT', 0, None, 0)
(26, 'origin_agent', 'TEXT', 0, None, 0)


python3 - <<'PY'
import sqlite3, json
conn=sqlite3.connect('/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3')
conn.row_factory=sqlite3.Row
for row in conn.execute("select id, type, scope, title, origin_agent, source_kind, run_id, task_id, source_ref, created_at, updated_at, substr(summary,1,120) as summary from memories order by datetime(updated_at) desc limit 15"):
    print(json.dumps(dict(row), ensure_ascii=False))
PY


{"id": "mem_3c62253993584122a81f9a21de4acc33", "type": "artifact", "scope": "global", "title": "ballbox-first microSD migration handoff", "origin_agent": "personal-agent", "source_kind": "manual", "run_id": null, "task_id": null, "source_ref": "file:/home/sebas/microsd-migration-handoff.md", "created_at": "2026-05-04T21:29:59.519213+00:00", "updated_at": "2026-05-04T21:29:59.519213+00:00", "summary": "Created full migration handoff for moving ballbox-first from current 16GB microSD to a new 64GB microSD. Recommended pat"}
{"id": "mem_90f4bcce6d284ed0b6f6edb9a6ec6cc2", "type": "decision", "scope": "global", "title": "Prefer session-end memory writes", "origin_agent": "personal-agent", "source_kind": "manual", "run_id": null, "task_id": null, "source_ref": "conversation:2026-05-01", "created_at": "2026-05-01T19:46:36.793893+00:00", "updated_at": "2026-05-01T19:46:36.793893+00:00", "summary": "Prefer storing durable memories at session end via session analysis instead of mid-conversation. Write mid-conversation "}
{"id": "mem_69278f6716654a3c8fc5db37f97d2a6c", "type": "profile", "scope": "global", "title": "Follow-up instructions go to todo first", "origin_agent": "personal-agent", "source_kind": "manual", "run_id": null, "task_id": null, "source_ref": "conversation:2026-05-01", "created_at": "2026-05-01T19:45:29.105695+00:00", "updated_at": "2026-05-01T19:45:29.105695+00:00", "summary": "Sebas prefers that follow-up instructions during an active task be added to the todo list first instead of derailing int"}
{"id": "mem_b9dfde5e35a84944b86841b537be2b4c", "type": "project", "scope": "global", "title": "Vibes service needs provider env", "origin_agent": "personal-agent", "source_kind": "manual", "run_id": null, "task_id": null, "source_ref": null, "created_at": "2026-05-01T16:08:52.128088+00:00", "updated_at": "2026-05-01T16:08:52.128088+00:00", "summary": "On ballbox-first, Vibes runs as /etc/systemd/system/vibes.service and needs OPENROUTER_API_KEY via EnvironmentFile /etc/"}
{"id": "mem_4b3428254ffb442ca1762d1a5895100d", "type": "episode", "scope": "global", "title": "Installed Vibes on ballbox-first", "origin_agent": "personal-agent", "source_kind": "manual", "run_id": null, "task_id": null, "source_ref": "conversation:2026-05-01:vibes-install", "created_at": "2026-05-01T04:56:36.842289+00:00", "updated_at": "2026-05-01T04:56:36.842289+00:00", "summary": "Installed rcarmo/vibes at /mnt/rpi/apps/vibes. Built local binary with subpath support for /vibes/, created systemd serv"}
{"id": "mem_ebbca254ff484623b6d63073ee758d8d", "type": "decision", "scope": "global", "title": "pi-tau-mux stale ctx fix in tau-client", "origin_agent": "personal-agent", "source_kind": "manual", "run_id": null, "task_id": null, "source_ref": "conversation:2026-05-01:/new-stale-ctx", "created_at": "2026-05-01T04:46:27.346689+00:00", "updated_at": "2026-05-01T04:46:27.346689+00:00", "summary": "Local hotfix on ballbox-first: /home/sebas/.npm-global/lib/node_modules/pi-tau-mux/extensions/tau-client.ts now avoids c"}
{"id": "mem_6485e21fbc354ebf98cea3fa5a1cc6e6", "type": "episode", "scope": "global", "title": "agents-database maintenance wrapper fix", "origin_agent": null, "source_kind": "manual", "run_id": null, "task_id": null, "source_ref": "conversation:2026-04-30:maintenance-wrapper-fix", "created_at": "2026-04-30T23:53:17.928610+00:00", "updated_at": "2026-04-30T23:53:17.928610+00:00", "summary": "On branch feat/memory-promotion-automation-clean, automatic maintenance was broken because scripts/run-maintenance-daemo"}
{"id": "mem_94ec049a0b6345ad9e54746c62a51908", "type": "decision", "scope": "global", "title": "Native tmux pi batch jobs for one-prompt subagents", "origin_agent": "personal-agent", "source_kind": "manual", "run_id": null, "task_id": null, "source_ref": "conversation:2026-04-30", "created_at": "2026-04-30T23:34:35.028872+00:00", "updated_at": "2026-04-30T23:34:35.028872+00:00", "summary": "Use native tmux-backed jobs instead of agent-start for one-prompt subagent runs when exact cwd matters. Run pi --no-exte"}
{"id": "mem_be158f743dcc43c2a38b3bb6ca348352", "type": "artifact", "scope": "repo", "title": "Vending setup day manual added", "origin_agent": "personal-agent", "source_kind": "manual", "run_id": null, "task_id": null, "source_ref": "docs/vending-machine-setup-day-manual.md", "created_at": "2026-04-30T19:20:49.492199+00:00", "updated_at": "2026-04-30T19:20:49.492199+00:00", "summary": "Repo now has docs/vending-machine-setup-day-manual.md: a setup-day runbook for first TCN machine arrival covering receiv"}
{"id": "legacy_run_9fdccb34-cf1e-47bd-b133-a224f363a6d4", "type": "episode", "scope": "global", "title": "Research run: Investigate personal assistant patterns", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "9fdccb34-cf1e-47bd-b133-a224f363a6d4", "task_id": null, "source_ref": "personal-agent:run:9fdccb34-cf1e-47bd-b133-a224f363a6d4", "created_at": "2026-03-13T14:01:39.630931+00:00", "updated_at": "2026-04-30T17:32:27.368015+00:00", "summary": "Seeded baseline run for personal-agent verification."}
{"id": "legacy_run_a8512264-8cd5-4c3c-b62f-7528625227f1", "type": "episode", "scope": "global", "title": "Research run: Verify capture-url flow", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "a8512264-8cd5-4c3c-b62f-7528625227f1", "task_id": null, "source_ref": "personal-agent:run:a8512264-8cd5-4c3c-b62f-7528625227f1", "created_at": "2026-03-13T14:01:39.632489+00:00", "updated_at": "2026-04-30T17:32:27.368368+00:00", "summary": "Verify capture-url flow"}
{"id": "legacy_run_55b6eaa0-9ff5-438c-a375-50f0452079cb", "type": "episode", "scope": "global", "title": "Research run: Drive Ballbox payment integration toward Volvox-style demo on vending machine Android flow", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "55b6eaa0-9ff5-438c-a375-50f0452079cb", "task_id": null, "source_ref": "personal-agent:run:55b6eaa0-9ff5-438c-a375-50f0452079cb", "created_at": "2026-03-13T14:01:39.633673+00:00", "updated_at": "2026-04-30T17:32:27.368445+00:00", "summary": "Drive Ballbox payment integration toward Volvox-style demo on vending machine Android flow"}
{"id": "legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "type": "episode", "scope": "global", "title": "Research run: Source 50+ inch China displays for Ballbox advertising in padel venues", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "task_id": null, "source_ref": "personal-agent:run:bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "created_at": "2026-03-13T14:01:39.634873+00:00", "updated_at": "2026-04-30T17:32:27.368506+00:00", "summary": "Source 50+ inch China displays for Ballbox advertising in padel venues"}
{"id": "legacy_run_8248124f-5145-475f-a3c7-a37b619abfa0", "type": "episode", "scope": "global", "title": "Research run: Test intake flow", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "8248124f-5145-475f-a3c7-a37b619abfa0", "task_id": null, "source_ref": "personal-agent:run:8248124f-5145-475f-a3c7-a37b619abfa0", "created_at": "2026-03-13T14:01:39.636174+00:00", "updated_at": "2026-04-30T17:32:27.368568+00:00", "summary": "Test intake flow"}
{"id": "legacy_run_b07bc538-5cab-4243-a1d3-c7a7fa55a0a7", "type": "episode", "scope": "global", "title": "Research run: Drive ballbox-payment-integration to a demoable vending-machine QR payment flow", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "b07bc538-5cab-4243-a1d3-c7a7fa55a0a7", "task_id": null, "source_ref": "personal-agent:run:b07bc538-5cab-4243-a1d3-c7a7fa55a0a7", "created_at": "2026-03-13T14:01:39.637766+00:00", "updated_at": "2026-04-30T17:32:27.368626+00:00", "summary": "Drive ballbox-payment-integration to a demoable vending-machine QR payment flow"}


**Exploring session memory updates**

I need to address the question about mid-agent session memory updates and those after a session ends. The user likely wants to know the status of these updates. I can infer from recent memories and possibly inspect code or documentation to see if any automation exists for session-end updates. It seems that recent writes are mostly manual rather than automated during or after a session. I'll check if the source kind or run ID indicates any automation and query recent memories for more clarity.

python3 - <<'PY'
import sqlite3, json
conn=sqlite3.connect('/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3')
conn.row_factory=sqlite3.Row
print('COUNTS_BY_SOURCE_KIND')
for row in conn.execute("select source_kind, count(*) c from memories group by source_kind order by c desc"):
    print(dict(row))
print('\nRECENT_WITH_RUN_OR_TASK')
for row in conn.execute("select id, type, title, origin_agent, source_kind, run_id, task_id, created_at from memories where run_id is not null or task_id is not null order by datetime(updated_at) desc limit 12"):
    print(json.dumps(dict(row), ensure_ascii=False))
PY


COUNTS_BY_SOURCE_KIND
{'source_kind': 'manual', 'c': 187}
{'source_kind': 'document', 'c': 22}
{'source_kind': 'run', 'c': 14}

RECENT_WITH_RUN_OR_TASK
{"id": "legacy_run_9fdccb34-cf1e-47bd-b133-a224f363a6d4", "type": "episode", "title": "Research run: Investigate personal assistant patterns", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "9fdccb34-cf1e-47bd-b133-a224f363a6d4", "task_id": null, "created_at": "2026-03-13T14:01:39.630931+00:00"}
{"id": "legacy_run_a8512264-8cd5-4c3c-b62f-7528625227f1", "type": "episode", "title": "Research run: Verify capture-url flow", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "a8512264-8cd5-4c3c-b62f-7528625227f1", "task_id": null, "created_at": "2026-03-13T14:01:39.632489+00:00"}
{"id": "legacy_run_55b6eaa0-9ff5-438c-a375-50f0452079cb", "type": "episode", "title": "Research run: Drive Ballbox payment integration toward Volvox-style demo on vending machine Android flow", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "55b6eaa0-9ff5-438c-a375-50f0452079cb", "task_id": null, "created_at": "2026-03-13T14:01:39.633673+00:00"}
{"id": "legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "type": "episode", "title": "Research run: Source 50+ inch China displays for Ballbox advertising in padel venues", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "task_id": null, "created_at": "2026-03-13T14:01:39.634873+00:00"}
{"id": "legacy_run_8248124f-5145-475f-a3c7-a37b619abfa0", "type": "episode", "title": "Research run: Test intake flow", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "8248124f-5145-475f-a3c7-a37b619abfa0", "task_id": null, "created_at": "2026-03-13T14:01:39.636174+00:00"}
{"id": "legacy_run_b07bc538-5cab-4243-a1d3-c7a7fa55a0a7", "type": "episode", "title": "Research run: Drive ballbox-payment-integration to a demoable vending-machine QR payment flow", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "b07bc538-5cab-4243-a1d3-c7a7fa55a0a7", "task_id": null, "created_at": "2026-03-13T14:01:39.637766+00:00"}
{"id": "legacy_run_2c54c32b-3cf1-426b-98a6-9c43971573ff", "type": "episode", "title": "Research run: Find China-sourced 50+ inch displays for Ballbox advertising in covered padel clubs", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "2c54c32b-3cf1-426b-98a6-9c43971573ff", "task_id": null, "created_at": "2026-03-13T14:01:39.638922+00:00"}
{"id": "legacy_run_fbe3fc0d-b05c-455e-9afe-1b17b4640d97", "type": "episode", "title": "Research run: Responder a Codex sin salir de una película en fullscreen, con UI mínima y poco intrusiva, y evaluar cómo implementarlo", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "fbe3fc0d-b05c-455e-9afe-1b17b4640d97", "task_id": null, "created_at": "2026-03-13T14:01:39.640125+00:00"}
{"id": "legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7", "type": "episode", "title": "Research run: Analyze the best next steps to make ballbox-payment-integration production-ready", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "a7fe5fbd-4892-4398-ab1a-71337f923ed7", "task_id": null, "created_at": "2026-03-13T14:01:39.641266+00:00"}
{"id": "legacy_run_35bd54a1-0135-414e-9c6b-245fca772f54", "type": "episode", "title": "Research run: Production-readiness plan for ballbox-payment-integration", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "35bd54a1-0135-414e-9c6b-245fca772f54", "task_id": null, "created_at": "2026-03-13T14:01:39.642436+00:00"}
{"id": "legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f", "type": "episode", "title": "Research run: Produce a production-readiness plan for ballbox-payment-integration", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "1f1a3fd4-eb28-4e99-891f-07c97243a06f", "task_id": null, "created_at": "2026-03-13T14:01:39.643717+00:00"}
{"id": "legacy_run_7e6517b3-759f-4063-8d98-121d9cba45c5", "type": "episode", "title": "Research run: Find online references for the GitHub organization Trading-Mastery", "origin_agent": "personal-agent", "source_kind": "run", "run_id": "7e6517b3-759f-4063-8d98-121d9cba45c5", "task_id": null, "created_at": "2026-03-13T14:01:39.644949+00:00"}


**Investigating memory service updates**

I need to check if the maintenance service is automatically promoting session-end memories. I could search the repository since the user might ask about the memory service, specifically regarding mid-session or after session updates. I should confirm that recent writes in the database are manual and not tied to any automatic processes. No recent task-bound writes since March mean that mid-session and post-session automated summaries don't seem to be happening right now. However, we haven't ruled out the possibility of temporary logic entirely.

Those look mostly not happening.

What DB shows:
- `223` total memories
- by `source_kind`:
  - `manual`: `187`
  - `document`: `22`
  - `run`: `14`

Key point:
- recent memory writes are almost all manual
- I do **not** see fresh run-linked/session-linked memories in recent data
- the only `source_kind=run` memories are old legacy run episodes from `2026-03-13`
- recent memories from `2026-04-30` to `2026-05-04` are manual writes like decisions, episodes, artifacts

So:

- **Mid-agent-session updates:** not showing up as an active pattern
- **Post-session/end-of-session summaries:** also not showing up automatically in recent writes
- **What is active now:** manual memory saves, plus maintenance/consolidation jobs

Likely state:
- service itself is healthy
- automatic memory capture flow is weak/inactive/not wired into current agent runs
- maintenance is running, but maintenance is not the same as new session-memory ingestion

Strong evidence:
- recent task runs exist, but recent memories do not carry fresh `run_id` / `task_id`
- recent memories have `source_kind=manual`, not run-derived

If you want, I can next check one of these:
1. code path for session-end memory ingestion
2. whether the current agent is supposed to call it but isn’t
3. logs for failed ingest attempts
4. exact repo status of the automation work


manual writes are ai agent writes, i do not manually run it
so they are mid-session writes and maybe also end session ones. any way we can check?


**Investigating for clarity**

I need to find an answer through some investigation. This means I should probably look into memory metadata or source references and check patterns in conversations, potentially examining code or logs to distinguish any specifics. I'll use the appropriate tools for this task. I'm curious how this will unfold, but it feels like a necessary step to ensure I get accurate results! Let's see what I can discover!

python3 - <<'PY'
import sqlite3, json
conn=sqlite3.connect('/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3')
conn.row_factory=sqlite3.Row
print('RECENT_MANUAL_WITH_METADATA')
for row in conn.execute("select id, type, title, origin_agent, source_kind, source_ref, run_id, task_id, created_at, updated_at, metadata_json from memories where source_kind='manual' order by datetime(updated_at) desc limit 15"):
    d=dict(row)
    try:
        d['metadata_json']=json.loads(d['metadata_json'])
    except Exception:
        pass
    print(json.dumps(d, ensure_ascii=False))
PY

cd /mnt/rpi/agents-database && rg -n "session-end|mid-conversation|mid-session|POST /api/ingest|source_kind.*manual|run_id|origin_agent|memory-write|session analysis|episode" -S .


RECENT_MANUAL_WITH_METADATA
{"id": "mem_3c62253993584122a81f9a21de4acc33", "type": "artifact", "title": "ballbox-first microSD migration handoff", "origin_agent": "personal-agent", "source_kind": "manual", "source_ref": "file:/home/sebas/microsd-migration-handoff.md", "run_id": null, "task_id": null, "created_at": "2026-05-04T21:29:59.519213+00:00", "updated_at": "2026-05-04T21:29:59.519213+00:00", "metadata_json": {}}
{"id": "mem_90f4bcce6d284ed0b6f6edb9a6ec6cc2", "type": "decision", "title": "Prefer session-end memory writes", "origin_agent": "personal-agent", "source_kind": "manual", "source_ref": "conversation:2026-05-01", "run_id": null, "task_id": null, "created_at": "2026-05-01T19:46:36.793893+00:00", "updated_at": "2026-05-01T19:46:36.793893+00:00", "metadata_json": {}}
{"id": "mem_69278f6716654a3c8fc5db37f97d2a6c", "type": "profile", "title": "Follow-up instructions go to todo first", "origin_agent": "personal-agent", "source_kind": "manual", "source_ref": "conversation:2026-05-01", "run_id": null, "task_id": null, "created_at": "2026-05-01T19:45:29.105695+00:00", "updated_at": "2026-05-01T19:45:29.105695+00:00", "metadata_json": {}}
{"id": "mem_b9dfde5e35a84944b86841b537be2b4c", "type": "project", "title": "Vibes service needs provider env", "origin_agent": "personal-agent", "source_kind": "manual", "source_ref": null, "run_id": null, "task_id": null, "created_at": "2026-05-01T16:08:52.128088+00:00", "updated_at": "2026-05-01T16:08:52.128088+00:00", "metadata_json": {}}
{"id": "mem_4b3428254ffb442ca1762d1a5895100d", "type": "episode", "title": "Installed Vibes on ballbox-first", "origin_agent": "personal-agent", "source_kind": "manual", "source_ref": "conversation:2026-05-01:vibes-install", "run_id": null, "task_id": null, "created_at": "2026-05-01T04:56:36.842289+00:00", "updated_at": "2026-05-01T04:56:36.842289+00:00", "metadata_json": {}}
{"id": "mem_ebbca254ff484623b6d63073ee758d8d", "type": "decision", "title": "pi-tau-mux stale ctx fix in tau-client", "origin_agent": "personal-agent", "source_kind": "manual", "source_ref": "conversation:2026-05-01:/new-stale-ctx", "run_id": null, "task_id": null, "created_at": "2026-05-01T04:46:27.346689+00:00", "updated_at": "2026-05-01T04:46:27.346689+00:00", "metadata_json": {}}
{"id": "mem_6485e21fbc354ebf98cea3fa5a1cc6e6", "type": "episode", "title": "agents-database maintenance wrapper fix", "origin_agent": null, "source_kind": "manual", "source_ref": "conversation:2026-04-30:maintenance-wrapper-fix", "run_id": null, "task_id": null, "created_at": "2026-04-30T23:53:17.928610+00:00", "updated_at": "2026-04-30T23:53:17.928610+00:00", "metadata_json": {}}
{"id": "mem_94ec049a0b6345ad9e54746c62a51908", "type": "decision", "title": "Native tmux pi batch jobs for one-prompt subagents", "origin_agent": "personal-agent", "source_kind": "manual", "source_ref": "conversation:2026-04-30", "run_id": null, "task_id": null, "created_at": "2026-04-30T23:34:35.028872+00:00", "updated_at": "2026-04-30T23:34:35.028872+00:00", "metadata_json": {}}
{"id": "mem_be158f743dcc43c2a38b3bb6ca348352", "type": "artifact", "title": "Vending setup day manual added", "origin_agent": "personal-agent", "source_kind": "manual", "source_ref": "docs/vending-machine-setup-day-manual.md", "run_id": null, "task_id": null, "created_at": "2026-04-30T19:20:49.492199+00:00", "updated_at": "2026-04-30T19:20:49.492199+00:00", "metadata_json": {"branch": "docs-vending-manual", "commit": "3239857", "repo": "ballbox"}}
{"id": "legacy_claim_9fdccb34-cf1e-47bd-b133-a224f363a6d4_782d8169bfd49348", "type": "artifact", "title": "Research claim: Personal assistants are trending toward local memory plus explicit approval gate", "origin_agent": "personal-agent", "source_kind": "manual", "source_ref": "personal-agent:run:9fdccb34-cf1e-47bd-b133-a224f363a6d4", "run_id": "9fdccb34-cf1e-47bd-b133-a224f363a6d4", "task_id": null, "created_at": "2026-03-13T14:01:39.670632+00:00", "updated_at": "2026-04-30T17:32:27.370306+00:00", "metadata_json": {"claim_status": "tentative", "schema": "shared-agent-memory-v2", "subtype": "research_claim"}}
{"id": "legacy_claim_55b6eaa0-9ff5-438c-a375-50f0452079cb_94c9a4ce114180c3", "type": "artifact", "title": "Research claim: Current ballbox-payment-integration repo is directionally aligned with a vending", "origin_agent": "personal-agent", "source_kind": "manual", "source_ref": "personal-agent:run:55b6eaa0-9ff5-438c-a375-50f0452079cb", "run_id": "55b6eaa0-9ff5-438c-a375-50f0452079cb", "task_id": null, "created_at": "2026-03-13T14:01:39.671440+00:00", "updated_at": "2026-04-30T17:32:27.370355+00:00", "metadata_json": {"claim_status": "tentative", "schema": "shared-agent-memory-v2", "subtype": "research_claim"}}
{"id": "legacy_claim_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7_debf10ea226a73ff", "type": "artifact", "title": "Research claim: There are multiple 55-inch China-sourced digital signage options already markete", "origin_agent": "personal-agent", "source_kind": "manual", "source_ref": "personal-agent:run:bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "run_id": "bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "task_id": null, "created_at": "2026-03-13T14:01:39.672284+00:00", "updated_at": "2026-04-30T17:32:27.370418+00:00", "metadata_json": {"claim_status": "tentative", "schema": "shared-agent-memory-v2", "subtype": "research_claim"}}
{"id": "legacy_claim_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7_c8f1da6eb425ce87", "type": "artifact", "title": "Research claim: Made-in-China currently lists multiple outdoor Android/high-brightness signage v", "origin_agent": "personal-agent", "source_kind": "manual", "source_ref": "personal-agent:run:bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "run_id": "bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "task_id": null, "created_at": "2026-03-13T14:01:39.673099+00:00", "updated_at": "2026-04-30T17:32:27.370471+00:00", "metadata_json": {"claim_status": "tentative", "schema": "shared-agent-memory-v2", "subtype": "research_claim"}}
{"id": "legacy_claim_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7_7e34ebfab9d62f01", "type": "artifact", "title": "Research claim: For padel venues with direct sunlight or semi-outdoor placement, 1000-2500 nit o", "origin_agent": "personal-agent", "source_kind": "manual", "source_ref": "personal-agent:run:bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "run_id": "bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "task_id": null, "created_at": "2026-03-13T14:01:39.673894+00:00", "updated_at": "2026-04-30T17:32:27.370522+00:00", "metadata_json": {"claim_status": "tentative", "schema": "shared-agent-memory-v2", "subtype": "research_claim"}}
{"id": "legacy_claim_fbe3fc0d-b05c-455e-9afe-1b17b4640d97_3b87a932d99841c5", "type": "artifact", "title": "Research claim: macOS fullscreen mueve la ventana principal a un Space separado; para convivir c", "origin_agent": "personal-agent", "source_kind": "manual", "source_ref": "personal-agent:run:fbe3fc0d-b05c-455e-9afe-1b17b4640d97", "run_id": "fbe3fc0d-b05c-455e-9afe-1b17b4640d97", "task_id": null, "created_at": "2026-03-13T14:01:39.675315+00:00", "updated_at": "2026-04-30T17:32:27.370586+00:00", "metadata_json": {"claim_status": "verified", "schema": "shared-agent-memory-v2", "subtype": "research_claim"}}


./src/shared_agent_memory/http_api.py:86:                            "run_id": _first(query, "run_id"),
./src/shared_agent_memory/http_api.py:88:                            "origin_agent": _first(query, "origin_agent"),
./src/shared_agent_memory/http_api.py:126:                                run_id=_first(query, "run_id"),
./src/shared_agent_memory/http_api.py:128:                                origin_agent=_first(query, "origin_agent"),
./src/shared_agent_memory/http_api.py:150:                                run_id=_first(query, "run_id"),
./src/shared_agent_memory/http_api.py:231:                            run_id=payload.get("run_id"),
./src/shared_agent_memory/http_api.py:256:                    run_id = parsed.path.split("/")[3]
./src/shared_agent_memory/http_api.py:263:                            run_id,
./src/shared_agent_memory/http_api.py:321:                        run_id=payload.get("run_id"),
./tests/test_memory_service.py:128:        run_id="run_alpha",
./tests/test_memory_service.py:147:    assert bundle["task"]["run_id"] == "run_alpha"
./tests/test_memory_service.py:157:            "type": "episode",
./tests/test_memory_service.py:162:            "run_id": "run-123",
./tests/test_memory_service.py:163:            "origin_agent": "personal-agent",
./tests/test_memory_service.py:173:            "run_id": "run-123",
./tests/test_memory_service.py:176:            "origin_agent": "personal-agent",
./tests/test_memory_service.py:180:    matches = service.list_memories(subtype="research_source", run_id="run-123", domain="example.com", origin_agent="personal-agent")
./tests/test_memory_service.py:266:            payload={"status": "in_progress", "run_id": run["id"]},
./tests/test_memory_service.py:270:        assert patched["run_id"] == run["id"]
./tests/test_memory_service.py:402:            "type": "episode",
./tests/test_memory_service.py:501:                "episode",
./tests/test_memory_service.py:516:                service.store.dumps({"legacy_system": "personal-agent", "legacy_kind": "research_run", "legacy_run_id": "run-123"}),
./tests/test_memory_service.py:534:                service.store.dumps({"legacy_kind": "task", "legacy_run_id": "run-123"}),
./src/shared_agent_memory/store.py:21:            self._ensure_column(conn, "memories", "run_id", "TEXT")
./src/shared_agent_memory/store.py:25:            self._ensure_column(conn, "memories", "origin_agent", "TEXT")
./src/shared_agent_memory/store.py:27:            self._ensure_column(conn, "tasks", "run_id", "TEXT")
./docs/http-api.md:17:- `POST /api/ingest`
./docs/http-api.md:51:- `episode` and `inbox` records are candidates, not canonical truth, until consolidation or explicit promotion.
./src/shared_agent_memory/engine.py:92:                "type": "episode",
./docs/shared-agent-memory-rfc.md:148:- Agents may auto-create `episode` records and candidate `inbox` facts.
./docs/shared-agent-memory-rfc.md:178:- `episode`
./docs/shared-agent-memory-rfc.md:272:- recent episodes when useful
./docs/shared-agent-memory-rfc.md:394:- `episode` records may be stored automatically.
./docs/shared-agent-memory-rfc.md:395:- candidate facts extracted from episodes stay `inbox` until consolidation.
./src/shared_agent_memory/service.py:75:        run_id = memory_input.get("run_id")
./src/shared_agent_memory/service.py:79:        origin_agent = memory_input.get("origin_agent") or metadata.get("origin_agent") or memory_input.get("agent_id")
./src/shared_agent_memory/service.py:90:            "origin_agent": origin_agent,
./src/shared_agent_memory/service.py:91:            "run_id": run_id,
./src/shared_agent_memory/service.py:95:            "source_kind": memory_input.get("source_kind", "manual"),
./src/shared_agent_memory/service.py:113:                    id, schema_version, type, subtype, scope, status, project_id, repo_id, agent_id, origin_agent,
./src/shared_agent_memory/service.py:114:                    run_id, task_id, url, domain, source_kind, title, content, summary, confidence, freshness,
./src/shared_agent_memory/service.py:117:                    :id, :schema_version, :type, :subtype, :scope, :status, :project_id, :repo_id, :agent_id, :origin_agent,
./src/shared_agent_memory/service.py:118:                    :run_id, :task_id, :url, :domain, :source_kind, :title, :content, :summary, :confidence, :freshness,
./src/shared_agent_memory/service.py:241:        project_memories = [item for item in project_memories if item["type"] in {"project", "decision", "artifact", "episode"}][:12]
./src/shared_agent_memory/service.py:242:        recent_episodes = self.list_memories(
./src/shared_agent_memory/service.py:244:            memory_type="episode",
./src/shared_agent_memory/service.py:261:            for item in [*profile_facts, *project_memories, *active_decisions, *recent_episodes]
./src/shared_agent_memory/service.py:268:            "recent_episodes": recent_episodes,
./src/shared_agent_memory/service.py:344:                    OR (type = 'episode' AND status = 'active')
./src/shared_agent_memory/service.py:444:                elif candidate["status"] == "active" and candidate["type"] in {"episode", "artifact", "decision", "project", "procedural", "semantic"} and candidate.get("confidence", 0.0) >= promote_threshold:
./src/shared_agent_memory/service.py:558:        run_id: str | None = None,
./src/shared_agent_memory/service.py:560:        origin_agent: str | None = None,
./src/shared_agent_memory/service.py:581:            "run_id": run_id,
./src/shared_agent_memory/service.py:583:            "origin_agent": origin_agent,
./src/shared_agent_memory/service.py:649:        run_id: str | None = None,
./src/shared_agent_memory/service.py:655:            "run_id": run_id,
./src/shared_agent_memory/service.py:677:                    id, schema_version, run_id, title, intent, kind, status, priority, project_id, repo_id,
./src/shared_agent_memory/service.py:681:                    :id, :schema_version, :run_id, :title, :intent, :kind, :status, :priority, :project_id, :repo_id,
./src/shared_agent_memory/service.py:704:        run_id: str | None = None,
./src/shared_agent_memory/service.py:718:        if run_id is not None:
./src/shared_agent_memory/service.py:719:            sql.append("AND run_id = ?")
./src/shared_agent_memory/service.py:720:            params.append(run_id)
./src/shared_agent_memory/service.py:736:        run_id: str | None = None,
./src/shared_agent_memory/service.py:742:        task["run_id"] = run_id if run_id is not None else task.get("run_id")
./src/shared_agent_memory/service.py:751:                SET status = ?, owner_agent = ?, blocked_reason = ?, requires_human_input = ?, run_id = ?, updated_at = ?, metadata_json = ?
./src/shared_agent_memory/service.py:759:                    task.get("run_id"),
./src/shared_agent_memory/service.py:807:    def get_task_run(self, run_id: str) -> dict[str, Any]:
./src/shared_agent_memory/service.py:809:            row = conn.execute("SELECT * FROM task_runs WHERE id = ?", (run_id,)).fetchone()
./src/shared_agent_memory/service.py:811:                raise KeyError(run_id)
./src/shared_agent_memory/service.py:816:        run_id: str,
./src/shared_agent_memory/service.py:830:                (status, result_summary, error_message, now, now, run_id),
./src/shared_agent_memory/service.py:833:        return self.get_task_run(run_id)
./src/shared_agent_memory/service.py:987:                    SET schema_version = ?, type = ?, subtype = ?, origin_agent = ?, run_id = ?, task_id = ?,
./src/shared_agent_memory/service.py:995:                        record["origin_agent"],
./src/shared_agent_memory/service.py:996:                        record["run_id"],
./src/shared_agent_memory/service.py:1015:                    SET schema_version = ?, run_id = ?, metadata_json = ?, updated_at = ?
./src/shared_agent_memory/service.py:1018:                    (record["schema_version"], record["run_id"], record["metadata_json"], utcnow(), record["id"]),
./src/shared_agent_memory/service.py:1176:        allowed = {"project_id", "repo_id", "type", "subtype", "run_id", "task_id", "origin_agent", "url", "domain"}
./src/shared_agent_memory/service.py:1185:        tracked = ["project_id", "repo_id", "type", "subtype", "run_id", "task_id", "origin_agent", "url", "domain"]
./src/shared_agent_memory/service.py:1197:        origin_agent = row.get("origin_agent") or metadata.get("origin_agent") or metadata.get("legacy_system") or row.get("agent_id")
./src/shared_agent_memory/service.py:1198:        run_id = row.get("run_id") or metadata.get("run_id") or metadata.get("legacy_run_id") or self._infer_run_id(row, subtype)
./src/shared_agent_memory/service.py:1202:        clean = {key: value for key, value in metadata.items() if key not in {"legacy_kind", "legacy_system", "legacy_run_id", "run_id", "task_id", "url", "domain", "source_url", "record_kind", "schema"}}
./src/shared_agent_memory/service.py:1212:            record_type = "episode"
./src/shared_agent_memory/service.py:1220:            "origin_agent": origin_agent,
./src/shared_agent_memory/service.py:1221:            "run_id": run_id,
./src/shared_agent_memory/service.py:1233:        run_id = row.get("run_id") or metadata.get("run_id") or metadata.get("legacy_run_id")
./src/shared_agent_memory/service.py:1234:        clean = {key: value for key, value in metadata.items() if key not in {"legacy_kind", "legacy_system", "legacy_run_id"}}
./src/shared_agent_memory/service.py:1239:            "run_id": run_id,
./src/shared_agent_memory/service.py:1450:        run_id = make_id("mrun")
./src/shared_agent_memory/service.py:1458:                (run_id, job_id, "running", now, None, None, self.store.dumps({}), None),
./src/shared_agent_memory/service.py:1460:        return self.get_maintenance_run(run_id)
./src/shared_agent_memory/service.py:1464:        run_id: str,
./src/shared_agent_memory/service.py:1479:                (status, now, summary, self.store.dumps(stats or {}), error_message, run_id),
./src/shared_agent_memory/service.py:1481:        return self.get_maintenance_run(run_id)
./src/shared_agent_memory/service.py:1483:    def get_maintenance_run(self, run_id: str) -> dict[str, Any]:
./src/shared_agent_memory/service.py:1485:            row = conn.execute("SELECT * FROM maintenance_runs WHERE id = ?", (run_id,)).fetchone()
./src/shared_agent_memory/service.py:1487:                raise KeyError(run_id)
./src/shared_agent_memory/service.py:1518:    def _infer_run_id(self, row: dict[str, Any], subtype: str | None) -> str | None:
./src/shared_agent_memory/schema.py:59:    origin_agent TEXT,
./src/shared_agent_memory/schema.py:60:    run_id TEXT,
./src/shared_agent_memory/schema.py:151:    run_id TEXT,
./src/shared_agent_memory/schema.py:210:CREATE INDEX IF NOT EXISTS idx_memories_run ON memories(run_id);
./src/shared_agent_memory/schema.py:214:CREATE INDEX IF NOT EXISTS idx_memories_origin_agent ON memories(origin_agent);
./src/shared_agent_memory/schema.py:218:CREATE INDEX IF NOT EXISTS idx_tasks_run ON tasks(run_id);
./src/shared_agent_memory/cli.py:258:                    run_id=args.run_id,
./src/shared_agent_memory/cli.py:260:                    origin_agent=args.origin_agent,
./docs/memory-consolidation-rfc.md:52:1. **Select candidates** from `memories` with status `inbox` or `episode` older than a minimum age.
./docs/memory-consolidation-rfc.md:145:- Candidate age: >= 7 days for `episode` and `inbox`.
./docs/system-v1.md:37:- `process_pending()` turns pending ingestion events into episode memories
./docs/system-v1.md:44:- `episode` and `inbox` records are candidates, not truth by default


**Exploring maintenance insights**

I need to check if there are any maintenance or consolidation promoted memories after the session ends. It could help to look at created_at versus updated_at timestamps. Some ways to check include examining the source_ref conversation date, timestamps relative to the session, the origin_agent, and whether run_id is null. It might be beneficial to inspect the service design documents too. I should read the relevant docs or RFCs, especially about how "process_pending" works for turning events into memories.


# Agents Database HTTP API

Remote-first topology:

- server `personal-agent`: task runner and dashboard API on `:8082`
- server `agents-database`: shared-memory and operational-data API on `:8091`
- local laptop: HTTP client only; no local shared DB required for normal operation

## Endpoints

- `GET /api/status`
- `GET /api/search?q=<query>&scope=global&scope=project&limit=10`
- `GET /api/search?id=<memory_id>`
- `GET /api/context?project=<id>&repo=<id>&task=<id>`
- `GET /api/memories?...filters...`
- `GET /api/memories/<id>`
- `POST /api/ingest`
- `GET /api/tasks?...filters...`
- `POST /api/tasks`
- `PATCH /api/tasks/<id>`
- `GET /api/tasks/<id>`
- `GET /api/tasks/<id>/bundle`
- `GET /api/task-runs?...filters...`
- `GET /api/task-runs/<id>`
- `POST /api/task-runs`
- `POST /api/task-runs/<id>/finish`
- `GET /api/artifacts?task_id=<id>`
- `GET /api/artifacts/<id>`
- `POST /api/artifacts`

## Run locally

```bash
python3 -m shared_agent_memory.cli \
  --db data/shared-agent-memory.sqlite3 \
  serve-http \
  --host 0.0.0.0 \
  --port 8091
```

## Server service

- script: `scripts/run-http-api.sh`
- systemd user unit: `systemd/user/agents-database-http.service`

## Memory semantics

- This HTTP API is the correct remote boundary for shared memory. Avoid routing memory CRUD/search through `personal-agent` unless the runtime actually owns that behavior.
- Use `GET /api/memories/<id>` for direct memory lookup by id.
- `consolidate_candidates()` and maintenance jobs live behind `MemoryService`; the HTTP API exposes the store, not a separate memory subsystem.
- `episode` and `inbox` records are candidates, not canonical truth, until consolidation or explicit promotion.

## Gotchas

- The existing `SERVICES-ARCHITECTURE.md` on the server may be stale relative to live routing. Verify real HTTP reachability with `curl`.


# Agents Database in System V1

`agents-database` is the durable store for System V1.

Canonical topology and machine bootstrap live in `~/personal-agent/docs/system-v1.md`.

## V1 Role

- canonical shared DB for memories, operational tasks, task runs, approvals, artifacts, and handoffs
- resumability anchor for multi-repo work
- migration target for legacy `personal-agent` state
- authoritative place to implement memory promotion, consolidation, and conflict handling

## Resume From Shared DB

Use the local CLI against `data/shared-agent-memory.sqlite3`:

```bash
python3 -m shared_agent_memory.cli --db data/shared-agent-memory.sqlite3 task-bundle task_<id>
python3 -m shared_agent_memory.cli --db data/shared-agent-memory.sqlite3 list-memories \
  --source-ref personal-agent:run:<run-id>
```

## Runtime Dependencies

- the configured CLI runner must be available for LLM consolidation steps (MiniMax M2.5 Free).
- local verification tools live in `./.venv` for this repo

## Memory pipeline

The memory pipeline lives in `MemoryService` / `MemoryEngine`, not in repo-local side scripts.

Current behavior:

- `ingest()` accepts durable memory writes
- `capture_session()` and `capture_conversation()` create raw source material
- `process_pending()` turns pending ingestion events into episode memories
- `consolidate_candidates(config)` handles dedup, conflict detection, promotion, and optional synthesis
- `maintenance_jobs` + `maintenance_runs` drive scheduled consolidation
- opportunistic checks run on startup and after writes

Promotion and conflict semantics:

- `episode` and `inbox` records are candidates, not truth by default
- exact duplicates are linked and archived
- conflicting claims produce explicit `memory_conflicts` rows and `contradicts` links
- inbox memories can be promoted to `active` once they pass the configured threshold
- the canonical output is always the shared DB record, not an external report

## Maintenance

```bash
.venv/bin/python -m shared_agent_memory.cli --db data/shared-agent-memory.sqlite3 maintenance-status
.venv/bin/python -m shared_agent_memory.cli --db data/shared-agent-memory.sqlite3 maintenance-tick --limit 10
.venv/bin/python -m shared_agent_memory.cli --db data/shared-agent-memory.sqlite3 maintenance-daemon --interval-seconds 900
```

Versioned service definitions:

- `systemd/user/agents-database-maintenance.service`
- `scripts/run-maintenance-daemon.sh`

Installed Linux user service:

- `~/.config/systemd/user/agents-database-maintenance.service`
- enabled with `systemctl --user enable --now agents-database-maintenance.service`
- `Restart=always` with `RestartSec=10`
- `loginctl show-user sebas --property=Linger` is `yes`, so the user service is eligible to come back after reboot without an interactive login

Operational checks:

```bash
systemctl --user status agents-database-maintenance.service --no-pager
systemctl --user show agents-database-maintenance.service --property=NRestarts,ExecMainPID,SubState
journalctl --user -u agents-database-maintenance.service -n 50 --no-pager
```

macOS launchd template:

- `docs/launchd/agents-database-maintenance.plist`

Current Linux runtime notes:

- the daemon now starts through `scripts/run-maintenance-daemon.sh`
- the wrapper exports `PYTHONPATH` for the repo checkout and prefers `./.venv/bin/python` when present
- the service was verified to auto-restart after a manual `systemctl --user kill agents-database-maintenance.service`
- `MemoryService` also performs opportunistic catch-up checks on startup and after normal write operations, so due jobs do not depend exclusively on the long-lived daemon

## Boundary

- new durable work: shared DB only
- legacy `~/personal-agent/data/personal-agent.sqlite3`: transition-only inspection and migration source

## RFCs

- `docs/memory-consolidation-rfc.md`


---
name: memory-write
description: Save a durable high-signal memory into agents-database
---

# memory-write

## Use When

- new information is likely useful beyond current chat or task
- saving a decision, preference, project fact, reusable source/artifact, or handoff summary
- user asks to remember something durable

## Do Not Use When

- content is trivial, redundant, ephemeral, or likely useless later
- content is secret unless user explicitly asked to store it

## Required Workflow

1. If useful context may already exist, optionally check first:
   - `curl -fsSL "${AGENTS_DB_API:-http://100.116.176.16:8091}/api/search?q=<query>&scope=global&scope=project&limit=10"`
2. Choose best memory type:
   - `profile`: stable user preference, priority, recurring constraint
   - `decision`: chosen approach or operating rule
   - `project`: durable project fact or invariant
   - `artifact`: reusable source, claim, reference, output
   - `episode`: concise session summary, blocked state, handoff context
3. Build a concise payload with:
   - required: `type`, `scope`, `title`, `content`
   - recommended when known: `subtype`, `project_id`, `repo_id`, `task_id`, `run_id`, `source_ref`, `origin_agent`, `url`, `domain`, `metadata`
4. Save it with the helper:
   - `~/.agents/skills/memory-write/memory-write --type <type> --scope <scope> --title "<title>" --content "<content>"`
5. Confirm the created memory id and what was stored.

## Quick Examples

```bash
~/.agents/skills/memory-write/memory-write \
  --type profile \
  --subtype working_preference \
  --scope global \
  --title "User prefers concise status updates" \
  --content "Sebas prefers short, direct updates with business impact first." \
  --source-ref "conversation:2026-04-17" \
  --origin-agent personal-agent

~/.agents/skills/memory-write/memory-write \
  --type decision \
  --subtype operating_rule \
  --scope global \
  --title "Agents should save durable high-signal memory opportunistically" \
  --content "Treat agents-database as shared durable memory, not only search." \
  --metadata '{"source":"conversation"}'
```

## Rules

- prefer one strong memory over many weak ones
- keep `title` short and searchable
- keep `content` factual and compact
- use `episode` for summaries, not for every chat turn
- include `url` and `domain` for web-derived artifacts when known
- include `project_id`, `repo_id`, `task_id`, and `run_id` when context exists


python3 - <<'PY'
import sqlite3, json
conn=sqlite3.connect('/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3')
conn.row_factory=sqlite3.Row
print('CONVERSATION_SOURCE_REFS')
for row in conn.execute("select source_ref, count(*) c, min(created_at) first_at, max(created_at) last_at from memories where source_kind='manual' and source_ref like 'conversation:%' group by source_ref order by datetime(last_at) desc limit 20"):
    print(json.dumps(dict(row), ensure_ascii=False))
PY

cd /mnt/rpi/agents-database && rg -n "capture_session|capture_conversation|process_pending|consolidate_candidates|ingest\(" src tests docs -S


#!/usr/bin/env python3
import argparse
import json
import sys
import urllib.request

def read_content(args):
    if args.content is not None:
        return args.content
    if args.content_file:
        with open(args.content_file, 'r', encoding='utf-8') as f:
            return f.read()
    if not sys.stdin.isatty():
        return sys.stdin.read()
    raise SystemExit('need --content, --content-file, or stdin')

def main():
    p = argparse.ArgumentParser(description='Write memory to agents-database')
    p.add_argument('--api', default=None)
    p.add_argument('--id')
    p.add_argument('--type', required=True)
    p.add_argument('--subtype')
    p.add_argument('--scope', required=True)
    p.add_argument('--status', default='active')
    p.add_argument('--source-kind', default='manual')
    p.add_argument('--title', required=True)
    p.add_argument('--content')
    p.add_argument('--content-file')
    p.add_argument('--summary')
    p.add_argument('--project-id')
    p.add_argument('--repo-id')
    p.add_argument('--task-id')
    p.add_argument('--run-id')
    p.add_argument('--source-ref')
    p.add_argument('--evidence-ref')
    p.add_argument('--origin-agent')
    p.add_argument('--url')
    p.add_argument('--domain')
    p.add_argument('--metadata', help='JSON object string')
    args = p.parse_args()

    content = read_content(args)
    metadata = json.loads(args.metadata) if args.metadata else None
    api = args.api or 'http://100.116.176.16:8091'
    payload = {
        'type': args.type,
        'scope': args.scope,
        'status': args.status,
        'source_kind': args.source_kind,
        'title': args.title,
        'content': content,
    }
    optional = {
        'id': args.id,
        'subtype': args.subtype,
        'summary': args.summary,
        'project_id': args.project_id,
        'repo_id': args.repo_id,
        'task_id': args.task_id,
        'run_id': args.run_id,
        'source_ref': args.source_ref,
        'evidence_ref': args.evidence_ref,
        'origin_agent': args.origin_agent,
        'url': args.url,
        'domain': args.domain,
        'metadata': metadata,
    }
    for k, v in optional.items():
        if v is not None:
            payload[k] = v

    req = urllib.request.Request(
        api.rstrip('/') + '/api/ingest',
        data=json.dumps(payload).encode('utf-8'),
        headers={'Content-Type': 'application/json'},
        method='POST',
    )
    with urllib.request.urlopen(req, timeout=30) as r:
        body = r.read().decode('utf-8')
        print(body)

if __name__ == '__main__':
    main()


CONVERSATION_SOURCE_REFS
{"source_ref": "conversation:2026-05-01", "c": 2, "first_at": "2026-05-01T19:45:29.105695+00:00", "last_at": "2026-05-01T19:46:36.793893+00:00"}
{"source_ref": "conversation:2026-05-01:vibes-install", "c": 1, "first_at": "2026-05-01T04:56:36.842289+00:00", "last_at": "2026-05-01T04:56:36.842289+00:00"}
{"source_ref": "conversation:2026-05-01:/new-stale-ctx", "c": 1, "first_at": "2026-05-01T04:46:27.346689+00:00", "last_at": "2026-05-01T04:46:27.346689+00:00"}
{"source_ref": "conversation:2026-04-30:maintenance-wrapper-fix", "c": 1, "first_at": "2026-04-30T23:53:17.928610+00:00", "last_at": "2026-04-30T23:53:17.928610+00:00"}
{"source_ref": "conversation:2026-04-30", "c": 1, "first_at": "2026-04-30T23:34:35.028872+00:00", "last_at": "2026-04-30T23:34:35.028872+00:00"}
{"source_ref": "conversation:2026-04-30:run-update-pi", "c": 1, "first_at": "2026-04-30T17:30:44.055250+00:00", "last_at": "2026-04-30T17:30:44.055250+00:00"}
{"source_ref": "conversation:2026-04-29", "c": 3, "first_at": "2026-04-29T20:35:31.158198+00:00", "last_at": "2026-04-29T20:44:21.678062+00:00"}
{"source_ref": "conversation:2026-04-23", "c": 1, "first_at": "2026-04-23T19:16:55.767526+00:00", "last_at": "2026-04-23T19:16:55.767526+00:00"}
{"source_ref": "conversation:2026-03-13:custom-ai-solutions-income-idea", "c": 1, "first_at": "2026-03-13T22:14:34.981125+00:00", "last_at": "2026-03-13T22:14:34.981125+00:00"}
{"source_ref": "conversation:2026-03-13:apartment-interior-design-goal", "c": 1, "first_at": "2026-03-13T22:11:51.271332+00:00", "last_at": "2026-03-13T22:11:51.271332+00:00"}
{"source_ref": "conversation:2026-03-13:user-questionnaire-followup-6", "c": 1, "first_at": "2026-03-13T21:41:53.187742+00:00", "last_at": "2026-03-13T21:41:53.187742+00:00"}
{"source_ref": "conversation:2026-03-13:user-questionnaire-followup-5", "c": 7, "first_at": "2026-03-13T21:40:53.176171+00:00", "last_at": "2026-03-13T21:40:53.187487+00:00"}
{"source_ref": "conversation:2026-03-13:user-questionnaire-followup-4", "c": 1, "first_at": "2026-03-13T21:37:34.115536+00:00", "last_at": "2026-03-13T21:37:34.115536+00:00"}
{"source_ref": "conversation:2026-03-13:user-questionnaire-followup-3", "c": 4, "first_at": "2026-03-13T21:36:20.594252+00:00", "last_at": "2026-03-13T21:36:20.598406+00:00"}
{"source_ref": "conversation:2026-03-13:user-questionnaire-followup-2", "c": 6, "first_at": "2026-03-13T21:26:33.694262+00:00", "last_at": "2026-03-13T21:26:33.708687+00:00"}
{"source_ref": "conversation:2026-03-13:user-questionnaire-followup", "c": 12, "first_at": "2026-03-13T21:19:16.023880+00:00", "last_at": "2026-03-13T21:19:16.037409+00:00"}
{"source_ref": "conversation:2026-03-13:ballbox-volvox-resolution-rule", "c": 1, "first_at": "2026-03-13T20:18:43.250199+00:00", "last_at": "2026-03-13T20:18:43.250199+00:00"}
{"source_ref": "conversation:2026-03-13:ballbox-volvox-alias", "c": 1, "first_at": "2026-03-13T20:16:10.623556+00:00", "last_at": "2026-03-13T20:16:10.623556+00:00"}
{"source_ref": "conversation:2026-03-13:ballbox-context", "c": 8, "first_at": "2026-03-13T14:42:32.059069+00:00", "last_at": "2026-03-13T14:42:32.071593+00:00"}


src/shared_agent_memory/http_api.py:211:                    self._send_json(service.ingest(payload), status=HTTPStatus.CREATED)
tests/test_memory_service.py:54:    service.capture_conversation("chat-1", "project", "We agreed to keep one active machine at a time.")
tests/test_memory_service.py:55:    processed = engine.process_pending()
tests/test_memory_service.py:437:    service.consolidate_candidates(
src/shared_agent_memory/engine.py:12:    def process_pending(self, limit: int | None = None) -> list[dict[str, Any]]:
src/shared_agent_memory/engine.py:77:            return self.service.consolidate_candidates(config=job.get("metadata") or {})
src/shared_agent_memory/engine.py:89:        memory = self.service.ingest(
src/shared_agent_memory/cli.py:157:        print(json.dumps(service.ingest(json.loads(args.payload_json)), indent=2))
src/shared_agent_memory/cli.py:161:                service.capture_conversation(args.source_ref, args.scope, args.content, capture_mode=args.capture_mode),
src/shared_agent_memory/cli.py:166:        print(json.dumps(service.capture_session(args.source_ref, args.scope, args.content), indent=2))
src/shared_agent_memory/cli.py:168:        print(json.dumps(engine.process_pending(args.limit), indent=2))
docs/http-api.md:50:- `consolidate_candidates()` and maintenance jobs live behind `MemoryService`; the HTTP API exposes the store, not a separate memory subsystem.
src/shared_agent_memory/service.py:128:    def ingest(self, memory_input: dict[str, Any]) -> dict[str, Any]:
src/shared_agent_memory/service.py:131:    def capture_session(self, source_ref: str, scope: str, content: str, **metadata: Any) -> dict[str, Any]:
src/shared_agent_memory/service.py:141:    def capture_conversation(
src/shared_agent_memory/service.py:318:    def consolidate_candidates(self, *, config: dict[str, Any]) -> dict[str, Any]:
docs/system-v1.md:35:- `ingest()` accepts durable memory writes
docs/system-v1.md:36:- `capture_session()` and `capture_conversation()` create raw source material
docs/system-v1.md:37:- `process_pending()` turns pending ingestion events into episode memories
docs/system-v1.md:38:- `consolidate_candidates(config)` handles dedup, conflict detection, promotion, and optional synthesis
docs/shared-agent-memory-rfc.md:403:- `ingest(memory_input)`
docs/shared-agent-memory-rfc.md:404:- `capture_session(source_ref, scope)`
docs/shared-agent-memory-rfc.md:405:- `capture_conversation(source_ref, scope, capture_mode)`


                )
                """,
                record,
            )
            self._upsert_fts(conn, record)
        self._run_opportunistic_maintenance(limit=1)
        return self.get_memory(record["id"])

    def ingest(self, memory_input: dict[str, Any]) -> dict[str, Any]:
        return self.create_memory(memory_input)

    def capture_session(self, source_ref: str, scope: str, content: str, **metadata: Any) -> dict[str, Any]:
        return self._capture_source(
            trigger_type="session_close",
            source_kind="run",
            source_ref=source_ref,
            scope=scope,
            content=content,
            metadata=metadata,
        )

    def capture_conversation(
        self, source_ref: str, scope: str, content: str, capture_mode: str = "manual_import", **metadata: Any
    ) -> dict[str, Any]:
        metadata = {"capture_mode": capture_mode, **metadata}
        return self._capture_source(
            trigger_type="conversation_capture",
            source_kind="conversation",
            source_ref=source_ref,
            scope=scope,
            content=content,
            metadata=metadata,
        )

    def search(
        self,
        query: str,
        scopes: list[str] | None = None,
        filters: dict[str, Any] | None = None,
        limit: int = 10,
        include_inbox: bool = False,
    ) -> dict[str, Any]:
        filters = filters or {}
        query_terms = [term for term in normalize_text(query).split() if term]
        sql = ["SELECT * FROM memories WHERE 1=1"]
        params: list[Any] = []

        if scopes:
            sql.append(f"AND scope IN ({','.join('?' for _ in scopes)})")
            params.extend(scopes)
        if not include_inbox:
            sql.append("AND status = ?")
            params.append("active")
        params.extend(self._memory_filter_sql(sql, filters))

        with self.store.connection() as conn:
            rows = [self._row_to_memory(row) for row in conn.execute(" ".join(sql), params)]

        results: list[SearchResult] = []
        query_embedding = self._text_embedding(query) if query_terms else []
        for row in rows:
            haystack = normalize_text(" ".join([row["title"], row["summary"], row["content"]]))
            lexical = self._lexical_score(query_terms, haystack)
            semantic = self._semantic_score(query_embedding, row.get("embedding")) if query_terms else 0.0
            facet = self._facet_score(row, filters)
            if query_terms and lexical == 0 and semantic == 0 and facet == 0:
                continue
            score = facet * 0.45 + lexical * 0.3 + semantic * 0.15 + row["confidence"] * 0.06 + row["freshness"] * 0.04
            explanation = (
                f"facets={facet:.2f} lexical={lexical:.2f} semantic={semantic:.2f} "
                f"confidence={row['confidence']:.2f} freshness={row['freshness']:.2f}"
            )
            results.append(SearchResult(row, score, explanation, facet, lexical, semantic))

        ordered = sorted(
            results,
            key=lambda item: (
                item.facet_score,
                item.lexical_score,
                item.semantic_score,

[1510 more lines in file. Use offset=200 to continue.]


        if source_ref.startswith("personal-agent:run:"):
            return source_ref.rsplit(":", 1)[-1]
        if subtype == "research_run":
            return row["id"]
        return None

    def _infer_url(self, row: dict[str, Any], subtype: str | None) -> str | None:
        if subtype in {"research_source", "research_claim", "research_artifact"}:
            evidence_ref = row.get("evidence_ref")
            if isinstance(evidence_ref, str) and evidence_ref.startswith(("http://", "https://")):
                return evidence_ref
        return None

    def _upsert_fts(self, conn: Any, record: dict[str, Any]) -> None:
        conn.execute("DELETE FROM memories_fts WHERE memory_id = ?", (record["id"],))
        conn.execute(
            "INSERT INTO memories_fts (memory_id, title, summary, content) VALUES (?, ?, ?, ?)",
            (record["id"], record["title"], record["summary"], record["content"]),
        )

    def _row_to_memory(self, row: Any) -> dict[str, Any]:
        memory = dict(row)
        memory["metadata"] = self.store.loads(memory.pop("metadata_json")) or {}
        memory["embedding"] = self._decode_embedding(memory.pop("embedding_json"))
        return memory

    def _row_to_task(self, row: Any) -> dict[str, Any]:
        task = dict(row)
        task["metadata"] = self.store.loads(task.pop("metadata_json")) or {}
        task["requires_human_input"] = bool(task["requires_human_input"])
        return task

    def _row_to_task_run(self, row: Any) -> dict[str, Any]:
        run = dict(row)
        run["input_payload"] = self.store.loads(run.pop("input_payload_json")) or {}
        run["metadata"] = self.store.loads(run.pop("metadata_json")) or {}
        return run

    def _row_to_artifact(self, row: Any) -> dict[str, Any]:
        artifact = dict(row)
        artifact["metadata"] = self.store.loads(artifact.pop("metadata_json")) or {}
        return artifact

    def _row_to_maintenance_job(self, row: Any) -> dict[str, Any]:
        job = dict(row)
        job["metadata"] = self.store.loads(job.pop("metadata_json")) or {}
        job["enabled"] = bool(job["enabled"])
        return job

    def _row_to_maintenance_run(self, row: Any) -> dict[str, Any]:
        run = dict(row)
        run["stats"] = self.store.loads(run.pop("stats_json")) or {}
        return run

    def _make_summary(self, content: str, max_len: int = 180) -> str:
        compact = " ".join(content.split())
        return compact[: max_len - 1] + "…" if len(compact) > max_len else compact

    def _encode_embedding(self, embedding: list[float] | None) -> str | None:
        if embedding is None:
            return None
        return json.dumps(embedding)

    def _decode_embedding(self, embedding: str | None) -> list[float] | None:
        if not embedding:
            return None
        return list(json.loads(embedding))

    def _lexical_score(self, query_terms: list[str], haystack: str) -> float:
        if not query_terms:
            return 0.0
        matches = sum(1 for term in query_terms if term in haystack)
        return matches / len(query_terms)

    def _text_embedding(self, text: str, dimensions: int = 8) -> list[float]:
        vector = [0.0] * dimensions
        for token in normalize_text(text).split():
            for idx, char in enumerate(token.encode("utf-8")):
                vector[idx % dimensions] += float(char)
        norm = math.sqrt(sum(value * value for value in vector))
        if norm == 0:
            return vector
        return [value / norm for value in vector]

    def _semantic_score(self, query_embedding: list[float], memory_embedding: list[float] | None) -> float:
        if not query_embedding or memory_embedding is None:
            return 0.0
        numerator = sum(a * b for a, b in zip(query_embedding, memory_embedding))
        return max(0.0, min(1.0, numerator))

    def _table_exists(self, conn: Any, table: str) -> bool:
        row = conn.execute("SELECT name FROM sqlite_master WHERE type = 'table' AND name = ?", (table,)).fetchone()
        return row is not None

    def _count_if_table_exists(self, conn: Any, table: str) -> int:
        if not self._table_exists(conn, table):
            return 0
        return conn.execute(f"SELECT COUNT(*) AS count FROM {table}").fetchone()["count"]

    def _compute_next_due(

[90 more lines in file. Use offset=1620 to continue.]


cd /mnt/rpi/agents-database && rg -n "def _capture_source|capture_session\(|capture_conversation\(" src/shared_agent_memory/service.py -n -A40 -B10