En otra sesión vimos que el servicio de memoria prácticamente no usa inbox sino que promueve automáticamente todas las memorias que ingresan y esto de por sí no está mal pero parecería que estamos ingresando pocas memorias de buena calidad y perdiendo una mayor cantidad de memorias de inicialmente menor calidad que podrían ser promovidas y generar más insights. Este claim de que hay pocas memorias igual también puede estar relacionado al hecho de que uno estaba andando el hook de session end de la gente y recientemente se arregló así que deberían haber corrido unos cinco o seis eventos de estos. ¿Quieres extrapolar si de haber estado andando esto hubieran sido muchas más memorias o no tantas?
Empecemos por ahí el análisis y si es el caso de que igual no hubieran sido tantas memorias podemos repensar la manera en que se ingiren memorias para obtener más a pesar de que puedan ser de menor calidad.


**Analyzing memory service**

I need to analyze the memory service and possibly inspect the agents-database data. The user is asking if the session-end hook could have created more memories, so I should maybe query the service stats and check the current counts in the memory database. It might be helpful to look into recent events too. I’ll also consider using the skill memory-read, as that could be useful for this inspection. Let’s see what I find!


---
name: memory-read
description: Read persisted research memory, claims, and tasks
---

# personal-memory-search

## Workflow

1. Execute:
   - `curl -fsSL "${AGENTS_DB_API:-http://100.116.176.16:8091}/api/search?q=<query>&scope=global&scope=project&limit=20"`
2. Return matching runs, claims, and tasks from the JSON response.
3. Prefer reuse of prior work before starting a new run.


**Looking into service API**

I’m thinking I might need to use a service API here. I should also inspect the repo to get more context. Locating the agents-database could be the next step, maybe in /mnt/rpi? Using bash with the `rg` command could help me search for what I need! It's important to ensure I'm on the right track for this. Let's see what I can find!

bash

find /home/sebas -maxdepth 3 -type d \( -name 'agents-database' -o -name '*memory*' \) 2>/dev/null | sed 's#^#/##' | head -200

curl -fsSL 'http://100.116.176.16:8091/api/search?q=session%20end&scope=global&scope=project&limit=10' || true


//home/sebas/pi-config/sessions/session-memory-jobs
//home/sebas/pi-jobs/jobs/20260430T235040Z-validate-memory-promotion
//home/sebas/.agents/skills/memory-read
//home/sebas/.agents/skills/memory-write
//home/sebas/.agents/skills.bck/personal-memory-search
//home/sebas/agents-database
//home/sebas/agents-database/src/shared_agent_memory


Fix GitHub credential automation so repo pushes and PR creation use the intended account reliably

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.

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

User confirmed this credential automation task is post-V1 and not a blocker for V1 closeout.

PR creation required direct credential workarounds; automation should simplify this later

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: unexpected argument '--ask-for-approval' found

  tip: to pass '--ask-for-approval' as a value, use '-- --ask-for-approval'

Usage: codex exec --sandbox <SANDBOX_MODE> [PROMPT]

For more information, try '--help'.

Handoff personal-agent -> ai-dev-workflow

ai-dev-workflow accepted the handoff and persisted intake context.

Handoff personal-agent -> implementation-agent

Finish remaining V1 migration and GitHub cross-reference work

Finished legacy bridge and V1 multi-repo cross-reference work; shared DB now contains the resume anchors and completion artifacts.

Execution state snapshot

CLI Smoke Readiness Summary

## Findings
- 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`.
- 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.
- 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.
- 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.
- Confirmed from docs/scripts: the only declared repo gate is `./scripts/run-checks.sh`, which runs `python3 -m unittest discover -s tests`.
- 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`.
- 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.
- 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.

## Risks
- Main near-term risk is false confidence from static docs plus partial command success while writable-path and temp-dir assumptions remain unverified.
- Working tree is dirty, so the codex CLI fix appears in progress but not yet fully validated by a successful full gate.

## Recommended Next Action
- 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.

Execution Plan

# Plan for Fresh CLI smoke task: summarize current system readiness and suggest one safe internal coordinati...

- Route: personal
- Secondary route: code
- Planning source: codex
- 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.

## Subtasks
- Collect readiness signals
- Write smoke summary
- Propose one coordination step

Normalized Intent

Request: Fresh CLI smoke task: summarize current system readiness and suggest one safe internal coordination step only. No external actions.
Primary route: personal
Secondary route: code
Reason: Planning/synthesis task with light repo-readiness context; no external action.
Planning source: codex
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.
Relevant memory:
- Sebas idea: custom AI solutions as alternative income [mem_sebas_custom_ai_solutions_income_idea]
- Research run: Produce a production-readiness plan for ballbox-payment-integration [legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f]
- Sebas desired relationship with money [mem_sebas_money_relationship]

Execution Plan

# Plan for Smoke task: summarize current system readiness and suggest one safe internal coordination step on...

- Route: personal
- Planning source: fallback

## Subtasks
- Clarify objective and constraints
- Gather context and evidence
- Produce final recommendation

Normalized Intent

Request: Smoke task: summarize current system readiness and suggest one safe internal coordination step only. No external actions.
Primary route: personal
Secondary route: none
Reason: default personal route
Planning source: fallback
Relevant memory:
- Sebas idea: custom AI solutions as alternative income [mem_sebas_custom_ai_solutions_income_idea]
- Research run: Produce a production-readiness plan for ballbox-payment-integration [legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f]
- Sebas desired relationship with money [mem_sebas_money_relationship]

Handoff result from ai-dev-workflow

Execution Plan

# Plan for Smoke task: assess current V1 readiness and propose one safe internal next step, no repo work and...

- Route: code
- Planning source: fallback
- Delegation target: ai-dev-workflow

## Subtasks
- Inspect technical context
- Delegate code execution
- Summarize technical outcome

Normalized Intent

Request: Smoke task: assess current V1 readiness and propose one safe internal next step, no repo work and no external actions.
Primary route: code
Secondary route: none
Reason: code work (repo)
Planning source: fallback
Relevant memory:
- Research run: Produce a production-readiness plan for ballbox-payment-integration [legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f]
- Research run: Analyze the best next steps to make ballbox-payment-integration production-ready [legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7]
- Research run: Production-readiness plan for ballbox-payment-integration [legacy_run_35bd54a1-0135-414e-9c6b-245fca772f54]

Handoff result from ai-dev-workflow

Execution Plan

# Plan for Smoke test after codex CLI fix: summarize current runtime health and suggest one safe internal ne...

- Route: code
- Planning source: fallback
- Delegation target: ai-dev-workflow

## Subtasks
- Inspect technical context
- Delegate code execution
- Summarize technical outcome

Normalized Intent

Request: Smoke test after codex CLI fix: summarize current runtime health and suggest one safe internal next step only.
Primary route: code
Secondary route: none
Reason: code work (fix)
Planning source: fallback
Relevant memory:
- Refactor runtime toward Codex-agentic orchestration [mem_runtime_agentic_refactor_priority]
- Research run: Analyze the best next steps to make ballbox-payment-integration production-ready [legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7]
- Sebas idea: custom AI solutions as alternative income [mem_sebas_custom_ai_solutions_income_idea]

Execution Plan

# Plan for Smoke test: summarize current V1 readiness and recommend the next safe internal step without exte...

- Route: personal
- Planning source: fallback

## Subtasks
- Clarify objective and constraints
- Gather context and evidence
- Produce final recommendation

Normalized Intent

Request: Smoke test: summarize current V1 readiness and recommend the next safe internal step without external side effects.
Primary route: personal
Secondary route: none
Reason: default personal route
Planning source: fallback
Relevant memory:
- Research run: Produce a production-readiness plan for ballbox-payment-integration [legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f]
- Refactor runtime toward Codex-agentic orchestration [mem_runtime_agentic_refactor_priority]
- Research run: Analyze the best next steps to make ballbox-payment-integration production-ready [legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7]

V1 final closeout

# V1 Closeout

## Outcome
V1 is complete.

`personal-agent` now operates as the shared-memory front door with:
- shared operational state in `agents-database`
- intake planning and runtime orchestration backed by Codex outputs
- daemon/dashboard surfaces for status, blockers, approvals, and next actions
- approval resolution with automatic resume
- stable subagent handoff contract for `ai-dev-workflow` and `ballbox-company-agent`
- legacy bridge for old personal-agent research/task inspection
- documented sandbox write convention for canonical shared DB access

## Verification
- local repo gate: `./scripts/run-checks.sh` passed with 26 tests
- direct suite: `python3 -m pytest tests/test_personal.py` passed with 26 tests
- PR CI: run `23112079488` passed on 2026-03-15 (`unit-tests`)

## Runtime use cases covered by tests
- intake creates operational task, subtasks, and specialist handoff
- Codex planning path and fallback path
- worker terminal outcomes: `complete`, `needs_approval`, `blocked`
- approval resolution paths: `approved` resumes and completes; `rejected` stays blocked
- structured runtime actions: follow-up task, handoff, artifact
- dashboard snapshot enrichment: summary, next action, latest run, pending approval, route summary
- end-to-end flow: intake -> approval -> resume -> complete
- Codex command wiring includes `--add-dir /Users/sebas/agents-database`
- handoff dispatch completion persists result artifact
- preference blocker can resolve from memory without human input
- daemon API surface covers status, intake, blocker response, and approval resolution
- CLI accepts `--json` before or after subcommands

## Scope boundary
- task `task_33d854e72d094660a2fb293a8723f5c5` is explicitly post-V1 follow-up, not a V1 blocker
- remaining work after V1 should continue as follow-up capabilities and operator hardening, not as part of this milestone

Phase 2 closeout brief

Phase 2 closeout:
- structured runtime actions implemented
- approval loop resolves and resumes
- execution_state snapshots persisted
- dashboard/status surfaces expose next actions and latest run state
- end-to-end runtime tests cover intake -> approval -> resume -> complete

Credential automation follow-up note

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.


ballbox-first no longer depends on /mnt/rpi for home symlinks and core services

On 2026-05-05, symlinked home paths and core service/runtime paths were repatriated from /mnt/rpi to internal storage under /home/sebas. Moved ballbox, caches, browser profiles, eiffel-monitor venv, agents-database, pi-config service paths, browser-harness, pi-jobs, and qBittorrent media dirs. Removed /mnt/rpi mount from /etc/fstab and unmounted it. Verified agents-database, session-memory-dispatcher, eiffel-monitor, and pi-job-status-writer run from internal paths. ttyd-browser remains broken because ttyd binary is missing, which was already failing before migration.

Summary: On 2026-05-05, symlinked home paths and core service/runtime paths were repatriated from /mnt/rpi to internal storage under /home/sebas. Moved ballbox, caches, browser profiles, e…

Explanation: facets=0.00 lexical=1.00 semantic=0.00 confidence=1.00 freshness=1.00

Prefer session-end memory writes

Prefer storing durable memories at session end via session analysis instead of mid-conversation. Write mid-conversation only when the user explicitly asks, handoff risk is high, or the information would likely be lost before session end.

Summary: Prefer storing durable memories at session end via session analysis instead of mid-conversation. Write mid-conversation only when the user explicitly asks, handoff risk is high, o…

Ballbox company overview

Ballbox is an early-stage company focused on selling advertising on screens and selling vending machines for ball tubes. The machines themselves also display ads. Initial go-to-market is padel centers.

Summary: Ballbox is an early-stage company focused on selling advertising on screens and selling vending machines for ball tubes. The machines themselves also display ads. Initial go-to-ma…

Explanation: facets=0.00 lexical=0.50 semantic=0.99 confidence=0.97 freshness=0.95

Research claim: A private repository named Trading Mastery backend is plausible given the org ha

A private repository named Trading Mastery backend is plausible given the org has zero public repos, but its existence cannot be confirmed from public sources alone.

Explanation: facets=0.00 lexical=0.50 semantic=0.99 confidence=0.45 freshness=0.70

Legacy task: Document all gaps between the current mock demo and a production vending-machine

Document all gaps between the current mock demo and a production vending-machine flow
kind=subtask
status=open
notes=

Explanation: facets=0.00 lexical=0.50 semantic=0.99 confidence=0.65 freshness=0.60

Legacy task: Capture exact Android machine launcher payload and constraints from the Chinese

Capture exact Android machine launcher payload and constraints from the Chinese vending screen environment
kind=task
status=open
notes=

Research claim: A first production rollout depends on external contract discovery because the re

A first production rollout depends on external contract discovery because the real TCN/Yunshu dispense adapter is not implemented and the repo itself marks that as pending phase-2 work.

Explanation: facets=0.00 lexical=0.50 semantic=0.99 confidence=0.96 freshness=0.70

Research claim: Current ballbox-payment-integration repo is directionally aligned with a vending

Current ballbox-payment-integration repo is directionally aligned with a vending-machine QR bridge, but still depends on mocked TCN/Yunshu callback and placeholder MercadoPago production details.

Explanation: facets=0.00 lexical=0.50 semantic=0.98 confidence=0.85 freshness=0.70

Research run: Drive ballbox-payment-integration to a demoable vending-machine QR payment flow

Goal: Drive ballbox-payment-integration to a demoable vending-machine QR payment flow
Scope: Current target repo is ballbox-payment-integration; confirm fit to machine Android launcher flow and iterate toward demo
Assumptions: volvox reference maps to this repo; final flow is machine launcher -> QR page -> payment approval -> callback -> success/demo output
Summary: Drive ballbox-payment-integration to a demoable vending-machine QR payment flow

Explanation: facets=0.00 lexical=0.50 semantic=0.98 confidence=0.85 freshness=0.80

Legacy task: User clarified: final target is the Android app flow integrated in the Chinese v

User clarified: final target is the Android app flow integrated in the Chinese vending machine screen
kind=clarification
status=noted
notes=

Explanation: facets=0.00 lexical=0.50 semantic=0.98 confidence=0.65 freshness=0.60


**Inspecting database schema**

I need to gather better data from the database. It seems like I should inspect the schema and look into the session end jobs. I think getting counts of memories by date or source reference would help, especially for session ends. I'll need to query SQLite to get the relevant information. Let's take a closer look at the tables that are connected to this query.

cd /home/sebas/agents-database && rg -n "session.*end|session-end|memory dispatch|inbox|promot" -S . | head -200


/bin/bash: line 1: sqlite3: command not found

Command exited with code 127


EISDIR: illegal operation on a directory, read


./src/shared_agent_memory/http_api.py:100:                            include_inbox=_parse_bool(_first(query, "include_inbox")),
./docs/system-v1.md:12:- authoritative place to implement memory promotion, consolidation, and conflict handling
./docs/system-v1.md:38:- `consolidate_candidates(config)` handles dedup, conflict detection, promotion, and optional synthesis
./docs/system-v1.md:42:Promotion and conflict semantics:
./docs/system-v1.md:44:- `episode` and `inbox` records are candidates, not truth by default
./docs/system-v1.md:47:- inbox memories can be promoted to `active` once they pass the configured threshold
./docs/shared-agent-memory-rfc.md:105:- promotion of raw conversation snippets directly into durable truth
./docs/shared-agent-memory-rfc.md:127:   The engine derives candidate memories from the raw material. These land as `inbox` records.
./docs/shared-agent-memory-rfc.md:132:4. `promote`
./docs/shared-agent-memory-rfc.md:148:- Agents may auto-create `episode` records and candidate `inbox` facts.
./docs/shared-agent-memory-rfc.md:149:- Durable `profile` facts about Sebas should require explicit confirmation or a promotion rule.
./docs/shared-agent-memory-rfc.md:157:- Every promoted memory must retain source/evidence references.
./docs/shared-agent-memory-rfc.md:224:- `inbox`
./docs/shared-agent-memory-rfc.md:380:These writes first create `inbox` records. They are candidates, not durable truth.
./docs/shared-agent-memory-rfc.md:390:- promote only sufficiently grounded memories to `active`
./docs/shared-agent-memory-rfc.md:395:- candidate facts extracted from episodes stay `inbox` until consolidation.
./docs/shared-agent-memory-rfc.md:396:- profile/worldview memories should be promoted with explicit confirmation or policy.
./docs/shared-agent-memory-rfc.md:408:- `consolidate(inbox_items)`
./docs/shared-agent-memory-rfc.md:455:4. A conversation imported from outside the system stays raw until explicitly captured, then becomes `inbox`, then later may be promoted.
./docs/http-api.md:51:- `episode` and `inbox` records are candidates, not canonical truth, until consolidation or explicit promotion.
./src/shared_agent_memory/cli.py:91:    search.add_argument("--include-inbox", action="store_true")
./src/shared_agent_memory/cli.py:235:        print(json.dumps(service.search(args.query, args.scopes, filters, include_inbox=args.include_inbox), indent=2))
./docs/memory-consolidation-rfc.md:13:- Keep the existing capture→extract→consolidate→promote→index flow.
./docs/memory-consolidation-rfc.md:25:- Autonomous truth rewriting without explicit promotion rules.
./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:59:4. **Promotion**: only promote to `active` when above confidence threshold.
./docs/memory-consolidation-rfc.md:145:- Candidate age: >= 7 days for `episode` and `inbox`.
./docs/memory-consolidation-rfc.md:151:- Promotion threshold: confidence >= 0.70
./docs/memory-consolidation-rfc.md:169:- Daily light runs at 10:00 and 15:00: dedupe + promotion checks (no LLM by default).
./src/shared_agent_memory/engine.py:94:                "status": "inbox",
./tests/test_memory_service.py:50:def test_captured_conversation_stays_inbox_until_consolidated(tmp_path: Path) -> None:
./tests/test_memory_service.py:59:    assert memory["status"] == "inbox"
./tests/test_memory_service.py:61:    search_without_inbox = service.search("active machine", scopes=["project"])
./tests/test_memory_service.py:62:    assert search_without_inbox["results"] == []
./tests/test_memory_service.py:64:    promoted = service.consolidate([memory_id])
./tests/test_memory_service.py:65:    assert promoted["promoted"] == [memory_id]
./tests/test_memory_service.py:395:def test_high_confidence_inbox_memory_auto_promotes_on_write(tmp_path: Path) -> None:
./tests/test_memory_service.py:401:            "id": "mem_auto_promote",
./tests/test_memory_service.py:404:            "status": "inbox",
./tests/test_memory_service.py:405:            "title": "Auto promote me",
./tests/test_memory_service.py:429:            "id": "dec_inbox",
./tests/test_memory_service.py:432:            "status": "inbox",
./tests/test_memory_service.py:444:            "promote_confidence": 1.0,
./src/shared_agent_memory/service.py:160:        include_inbox: bool = False,
./src/shared_agent_memory/service.py:170:        if not include_inbox:
./src/shared_agent_memory/service.py:215:                    self.store.dumps({"scopes": scopes, "filters": filters, "include_inbox": include_inbox}),
./src/shared_agent_memory/service.py:274:    def consolidate(self, inbox_items: list[str]) -> dict[str, Any]:
./src/shared_agent_memory/service.py:275:        promoted: list[str] = []
./src/shared_agent_memory/service.py:278:            for memory_id in inbox_items:
./src/shared_agent_memory/service.py:315:                promoted.append(memory_id)
./src/shared_agent_memory/service.py:316:        return {"promoted": promoted, "linked": linked}
./src/shared_agent_memory/service.py:321:            "promoted": 0,
./src/shared_agent_memory/service.py:331:        promote_threshold = float(config.get("promote_confidence", 0.7))
./src/shared_agent_memory/service.py:343:                    status = 'inbox'
./src/shared_agent_memory/service.py:438:                if candidate["status"] == "inbox" and candidate.get("confidence", 0.0) >= promote_threshold:
./src/shared_agent_memory/service.py:443:                    stats["promoted"] += 1
./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:445:                    # Normalize qualifying active records as already-promoted durable memories.
./src/shared_agent_memory/service.py:446:                    stats["promoted"] += 1
./src/shared_agent_memory/service.py:1258:                    "promote_confidence": 0.7,
./src/shared_agent_memory/service.py:1278:                    "promote_confidence": 0.7,
./src/shared_agent_memory/service.py:1298:                    "promote_confidence": 0.7,


**Inspecting service behavior**

I'm thinking about inspecting the service's behavior, particularly focusing on the scripts related to session endings. It seems crucial to find the relevant files for this task. I'll dive into that to ensure I have a clear understanding of how it operates. This could help identify any issues or enhance performance. It's essential to keep everything running smoothly and efficiently, especially if there are potential improvements to make!

find /home/sebas/pi-config/sessions -maxdepth 2 -type f | sort


        mid_threshold = float(config.get("dedupe_mid_threshold", 0.75))
        promote_threshold = float(config.get("promote_confidence", 0.7))
        conflict_threshold = float(config.get("conflict_threshold", 0.55))
        llm_enabled = bool(config.get("llm_enabled", False))
        llm_provider = config.get("llm_provider") or "opencode"
        llm_command = config.get("llm_command") or ["opencode", "-m", "MiniMax M2.5 Free"]
        cutoff = (datetime.now(UTC) - timedelta(days=age_days)).isoformat()

        with self.store.connection() as conn:
            rows = conn.execute(
                """
                SELECT * FROM memories
                WHERE (
                    status = 'inbox'
                    OR (type = 'episode' AND status = 'active')
                )
                  AND created_at <= ?
                ORDER BY created_at ASC
                LIMIT ?
                """,
                (cutoff, max_candidates),
            ).fetchall()

            candidates = [self._row_to_memory(row) for row in rows]
            stats["candidates"] = len(candidates)

            for candidate in candidates:
                related = conn.execute(
                    """
                    SELECT * FROM memories
                    WHERE status = 'active'
                      AND id != ?
                      AND type = ?
                      AND scope = ?
                    ORDER BY updated_at DESC
                    LIMIT 50
                    """,
                    (candidate["id"], candidate["type"], candidate["scope"]),
                ).fetchall()
                related_memories = [self._row_to_memory(row) for row in related]

                best_match = None
                best_score = 0.0
                for other in related_memories:
                    lexical = self._lexical_score(
                        normalize_text(candidate["content"]).split(),
                        normalize_text(other["content"]),
                    )
                    semantic = self._semantic_score(candidate.get("embedding") or [], other.get("embedding"))
                    score = lexical if semantic == 0.0 else (lexical * 0.6 + semantic * 0.4)
                    if score > best_score:
                        best_score = score
                        best_match = other

                if best_match and candidate["content"] == best_match["content"]:
                    self._link_memories(conn, candidate["id"], best_match["id"], "related_to", "exact_duplicate")
                    conn.execute(
                        "UPDATE memories SET status = ?, updated_at = ? WHERE id = ?",
                        ("archived", utcnow(), candidate["id"]),
                    )
                    stats["archived"] += 1
                    stats["linked"] += 1
                    continue

                if best_match and best_score >= high_threshold:
                    self._link_memories(conn, candidate["id"], best_match["id"], "related_to", "high_similarity")
                    conn.execute(
                        "UPDATE memories SET status = ?, updated_at = ? WHERE id = ?",
                        ("archived", utcnow(), candidate["id"]),
                    )
                    stats["archived"] += 1
                    stats["linked"] += 1
                    continue

                if best_match and best_score >= mid_threshold:
                    if llm_enabled:
                        synthesized = self._llm_summarize_pair(
                            candidate,
                            best_match,
                            provider=llm_provider,
                            command=llm_command,
                        )
                        if synthesized:
                            conn.execute(
                                "UPDATE memories SET summary = ?, updated_at = ?, metadata_json = ? WHERE id = ?",
                                (
                                    synthesized,
                                    utcnow(),
                                    self.store.dumps(
                                        {
                                            **candidate.get("metadata", {}),
                                            "synthesis_sources": [candidate["id"], best_match["id"]],
                                            "synthesis_provider": llm_provider,
                                            "synthesis_command": llm_command,
                                        }
                                    ),
                                    candidate["id"],
                                ),
                            )
                            stats["llm_summaries"] += 1
                    self._link_memories(conn, candidate["id"], best_match["id"], "related_to", "mid_similarity")
                    stats["linked"] += 1

                if best_match and best_score >= conflict_threshold and self._conflict_signal(candidate, best_match):
                    self._record_conflict(conn, candidate["id"], best_match["id"], "potential_contradiction")
                    self._link_memories(conn, candidate["id"], best_match["id"], "contradicts", "heuristic")
                    stats["conflicts"] += 1

                if candidate["status"] == "inbox" and candidate.get("confidence", 0.0) >= promote_threshold:
                    conn.execute(
                        "UPDATE memories SET status = ?, updated_at = ? WHERE id = ?",
                        ("active", utcnow(), candidate["id"]),
                    )
                    stats["promoted"] += 1
                elif candidate["status"] == "active" and candidate["type"] in {"episode", "artifact", "decision", "project", "procedural", "semantic"} and candidate.get("confidence", 0.0) >= promote_threshold:
                    # Normalize qualifying active records as already-promoted durable memories.
                    stats["promoted"] += 1

        return stats

    def upsert_profile_fact(
        self, title: str, content: str, evidence_ref: str | None = None, source_ref: str | None = None
    ) -> dict[str, Any]:
        return self.create_memory(
            {
                "type": "profile",
                "scope": "global",
                "title": title,
                "content": content,
                "summary": self._make_summary(content),

[1250 more lines in file. Use offset=460 to continue.]


    job = service.get_maintenance_job("job_consolidation_daily_10")
    runs = service.list_maintenance_runs(job_id="job_consolidation_daily_10")

    assert job["last_status"] == "completed"
    assert runs

def test_high_confidence_inbox_memory_auto_promotes_on_write(tmp_path: Path) -> None:
    service = make_service(tmp_path)
    service.update_maintenance_job("job_consolidation_daily_10", next_due_at="2026-03-20T10:00:00+00:00")

    memory = service.create_memory(
        {
            "id": "mem_auto_promote",
            "type": "episode",
            "scope": "project",
            "status": "inbox",
            "title": "Auto promote me",
            "content": "A durable session note that should become active automatically.",
            "confidence": 0.95,
            "freshness": 0.9,
        }
    )

    assert memory["status"] == "active"

def test_conflicts_only_for_selected_types(tmp_path: Path) -> None:
    service = make_service(tmp_path)
    service.create_memory(
        {
            "id": "dec_active",
            "type": "decision",
            "scope": "project",
            "status": "active",
            "title": "Decision A",
            "content": "Feature X is enabled for launch.",
        }
    )
    service.create_memory(
        {
            "id": "dec_inbox",
            "type": "decision",
            "scope": "project",
            "status": "inbox",
            "title": "Decision B",
            "content": "Feature X is not enabled for launch.",
        }
    )
    service.consolidate_candidates(
        config={
            "candidate_age_days": 0,
            "max_candidates": 10,
            "conflict_threshold": 0.1,
            "dedupe_high_threshold": 0.99,
            "dedupe_mid_threshold": 0.99,
            "promote_confidence": 1.0,
            "llm_enabled": False,
        }
    )
    with service.store.connection() as conn:
        conflicts = conn.execute("SELECT COUNT(*) AS count FROM memory_conflicts").fetchone()["count"]

    assert conflicts == 1

def test_run_maintenance_daemon_wrapper_runs_from_repo_checkout(tmp_path: Path) -> None:
    db_path = tmp_path / "memory.sqlite3"
    service = MemoryService(str(db_path))
    service.update_maintenance_job("job_consolidation_daily_10", next_due_at="2026-03-20T10:00:00+00:00")

    script = Path(__file__).resolve().parents[1] / "scripts" / "run-maintenance-daemon.sh"
    env = os.environ.copy()
    env["DB_PATH"] = str(db_path)
    env["INTERVAL_SECONDS"] = "1"
    env["JITTER_SECONDS"] = "0"
    result = subprocess.run(
        [str(script), "--once", "--job-id", "job_consolidation_daily_10"],
        capture_output=True,
        text=True,

[120 more lines in file. Use offset=468 to continue.]


python3 - <<'PY'
import sqlite3, json
conn=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')
conn.row_factory=sqlite3.Row
print([r[0] for r in conn.execute("select name from sqlite_master where type='table' order by name")])
PY

cd /home/sebas/agents-database && rg -n "auto_promote|status = 'active'|status\": \"active\"|promote_confidence" src/shared_agent_memory/service.py | head -80


['artifacts', 'ingestion_events', 'maintenance_jobs', 'maintenance_runs', 'memories', 'memories_fts', 'memories_fts_config', 'memories_fts_content', 'memories_fts_data', 'memories_fts_docsize', 'memories_fts_idx', 'memory_conflicts', 'memory_links', 'profiles', 'projects', 'repos', 'retrieval_logs', 'sources', 'task_runs', 'tasks']


287:                      AND status = 'active'
331:        promote_threshold = float(config.get("promote_confidence", 0.7))
344:                    OR (type = 'episode' AND status = 'active')
360:                    WHERE status = 'active'
1258:                    "promote_confidence": 0.7,
1278:                    "promote_confidence": 0.7,
1298:                    "promote_confidence": 0.7,


**Inspecting job files**

I need to query counts by source kind or manually check something. It seems essential to look into session end ingestion events. I’ll inspect the job files and also review the ingestion_events table since that might include the source reference. I’m planning to use Python for this, but I want to ensure I'm covering all necessary aspects for effective data retrieval and analysis. Let's get into this!

python3 - <<'PY'
import sqlite3, json
conn=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')
conn.row_factory=sqlite3.Row
for table in ['ingestion_events','memories']:
    cols=conn.execute(f'PRAGMA table_info({table})').fetchall()
    print('\nTABLE',table)
    for c in cols:
        print(c['name'], c['type'])
PY


TABLE ingestion_events
id TEXT
trigger_type TEXT
source_id TEXT
requested_scope TEXT
job_state TEXT
processor_outcome TEXT
retry_count INTEGER
error_message TEXT
created_at TEXT
updated_at TEXT
processed_at TEXT

TABLE memories
id TEXT
type TEXT
scope TEXT
status TEXT
project_id TEXT
repo_id TEXT
agent_id TEXT
source_kind TEXT
title TEXT
content TEXT
summary TEXT
confidence REAL
freshness REAL
created_at TEXT
updated_at TEXT
observed_at TEXT
source_ref TEXT
evidence_ref TEXT
embedding_json TEXT
metadata_json TEXT
schema_version TEXT
subtype TEXT
run_id TEXT
task_id TEXT
url TEXT
domain TEXT
origin_agent TEXT


{
  "job_id": "20260505T172551604683Z",
  "queued_at": "2026-05-05T17:25:51.604736+00:00",
  "status": "queued",
  "job_type": "session_memory_analysis",
  "payload": {
    "session_id": "46c88030",
    "session_file": "/home/sebas/pi-config/sessions/--home-sebas--/2026-05-05T13-44-38-590Z_019df862-33be-7759-b796-3d6b48b23493.jsonl",
    "reason": "before_switch:resume",
    "cwd": "/home/sebas",
    "scope": "global",
    "user_messages": 11,
    "assistant_messages": 56,
    "last_user_text": "and yeah harden like you suggested",
    "last_assistant_text": "Done. Manual trigger, last 10 sessions: - `6` saved memories - `4` analyzed and skipped - `0` failed New saved memory ids: - `mem_812ea22ea06d4368bdb66fe531e64fdc` - `mem_f7f168513be944169a0a7790b25293f0` - `mem_02a8d3bf9ae04a85aa700eb4d5c045e9` - `mem_c636b2dc79794c57aded818ba6fdae2f` - `mem_5be93517d726499fbdf0f614da57ef6e` - `mem_68d91c0d9b6244f384fac0abfff678e7` Hardening done: - analyzer now resolves `pi` via absolute/fallback paths - dispatcher now logs analyzer output to per-job `.log` files - service env includes: - `PI_SESSION_MEMORY_PI_BIN=/home/sebas/.npm-global/bin/pi` - user service restarted successfully - reboot persistence: yes Current service: - `install-session-memory-dispatcher.service` - active and enabled under user systemd Note: - rerunning already-completed jobs created some duplicate-style memories for previously saved sessions - future runs should be fine; this was from manual replay If you want, next I can: 1. dedupe the duplicates created today 2. do one fresh close/switch test and verify auto path end-to-end without manual replay",
    "branch": [
      {
        "type": "model_change",
        "id": "0ac359e9",
        "parentId": null,
        "timestamp": "2026-05-05T13:44:39.932Z",
        "provider": "openai-codex",
        "modelId": "gpt-5.4"
      },
      {
        "type": "thinking_level_change",
        "id": "ce055adc",
        "parentId": "0ac359e9",
        "timestamp": "2026-05-05T13:44:39.932Z",
        "thinkingLevel": "low"
      },
      {
        "type": "message",
        "id": "78bd71eb",
        "parentId": "ce055adc",
        "timestamp": "2026-05-05T13:44:54.745Z",
        "message": {
          "role": "user",
          "content": [
            {
              "type": "text",
              "text": "how is the memory service going? latest runs?"
            }
          ],
          "timestamp": 1777988694740
        }
      },
      {
        "type": "message",
        "id": "ce4f751d",
        "parentId": "78bd71eb",
        "timestamp": "2026-05-05T13:45:02.104Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "**Checking memory service status**\n\nI 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!",
              "thinkingSignature": "{\"id\":\"rs_0d852cbc8d6115830169f9f45809dc8194ac8c50eae10cd6fb\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-fRdZfsQMmKiQr7IuqZaGQJxPyF7qwM13ZMJvYbIm637odUWw6iZaKyQmWshCgal7G9dXSxX-EUn6SgN1ckPNEnDzSzXurlLRqjx6QQWWY2vcjiPWVWgKCVfgazlANV8wD71DLSfjTH75Coj2z3X2iHpiO8x-7tW4r4w4Q6vav7bSjQq5MC7b2wVxUHKzO4lMiMmbbxg0ZK-gSJZmYworvKCr3N2RMuon02zOYHtiPzIaAW62s8wAzYhXOqN8jLSCsd-HhJqEXracIhnWrmsVviCps5y1aa_gznU227vx2EbypcdjpSq07uhDxtGDA2IUb4BBrhEu9m-9cDVdWCpXUjoXvH3KwBd2xXKHlOJgxDDn3EFml-3Y09O8-pljlk2gE9164AX_W3wMznPH0914npLUNR2ekIhyjYHJAr7IEejkU21jNIg4ZS0T_gnn7zJV0gJ8xTKc0ZiAoLq6jpJvIFV55oeXwIz8oC_NDeRg3fD51ctwWeTZd8Qf8biqBNvYM-3IDSPMXu2xt0uVAiev0Dkey_k-HWWZb25VAmd4rmZk68aDcNnbERc72gKaZsVH-NCQtMnxpf_3p2IGsfqZnXjoAWaPC2_VruPEKvgli20sJFZLCGP8_9wiFbjtbDjnQBQdMMV7t5aswPfDSs-pm_Zn4G2gH0M2oQiGyuH5_GBY14j4RQ32UWV8vEqkx_E3Vwz2kb8gbs5RdD0N4Yr9Z4G6empmWMTHJSIWVcQa1dWKIgkOaw8XG5vtrO8KA3AOwuK0JvcS8wnLYSnaL2ngQzzxIZRGXy7QnuWjOlTci8xKuqv9eNxiogSfxNfuX-34KnfRTnuZweuw3kuIypomc0axNe9h2UjRaPlYkmZcuuavP1fvN_EAKdBD9eIYgGTpAjdNd4vfAqYmtcZGKvwqD1FEtzibED9DQqKkNJtDC0rAVIKjdj_vajq5oWCTz_gMzKOoUxtJ-B89bcZtkSyIvwwFdVn0J9WUa3HPzBpy0OPOcMTHAeO8qjnBBBIoVRmGIeLr37MzWzNVmp5xc3d_HOOPH42SgdrKtNyILa3jwai_IKS9JWPmh2pAktQMCPyPkN83yfPdobIaNBbE_idbJZ_Zbod31KYTPenbgsvraEeTgS4RPjhmhL4Awk27VFBNdtgSoKFeK9e98KqU1EzdysH9-njjgqUx0QbUn7AN0JAdNdwieWpC_MJ0waMwZD-SxNA-okpPhDmdBKlv4Vwl9tB_KcFNLp7Ri_HQ8D9Q0uybVrAE61y8RQh5baN9M1ajH5Wo4SYj2oL9E6aGjLtqi8sIBVrfLJQyEe7dZQbze26xOQZvYMAlV4EWGchQqVqdZgWhiUKsKN6dsfjPM5bFAUCV-xCGvRGyG9P9KxkeTQeZZUWV4lQYHx54e-EA59y0QY6mjRNN8JD4tK50V5VbocTfRlJZPZbbHX3YYL64ydTtWmfrNLdUDg7SYwqhOgA4TMFnP5yytaYxyIJ-RMFczTsqKZXYQR9RNtCZq6_i5OKxomTt7K4lQIvKNeDFMwk1q6g\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Checking memory service status**\\n\\nI 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!\"}]}"
            },
            {
              "type": "toolCall",
              "id": "call_Rhjt6X3LrnHl6ttHliPK1uFU|fc_0d852cbc8d6115830169f9f45de4488194ad0be0d63b9f9ea7",
              "name": "bash",
              "arguments": {
                "command": "set -e\nprintf 'STATUS\\n'\ncurl -sf http://100.116.176.16:8091/api/status || true\nprintf '\\n\\nROOT\\n'\ncurl -sf http://100.116.176.16:8091/ || true\nprintf '\\n\\nOPENAPI?\\n'\ncurl -sf http://100.116.176.16:8091/openapi.json || true",
                "timeout": 15
              }
            },
            {
              "type": "toolCall",
              "id": "call_bTQg7QCVQOl7qteQl50hO0yL|fc_0d852cbc8d6115830169f9f45de4588194ab579f888979c45d",
              "name": "bash",
              "arguments": {
                "command": "set -e\nps -ef | grep -i '[a]gents-database' || true\nsystemctl --no-pager --full status agents-database 2>/dev/null || true\njournalctl -u agents-database -n 40 --no-pager 2>/dev/null || true",
                "timeout": 15
              }
            }
          ],
          "api": "openai-codex-responses",
          "provider": "openai-codex",
          "model": "gpt-5.4",
          "usage": {
            "input": 3968,
            "output": 299,
            "cacheRead": 0,
            "cacheWrite": 0,
            "totalTokens": 4267,
            "cost": {
              "input": 0.00992,
              "output": 0.004485,
              "cacheRead": 0,
              "cacheWrite": 0,
              "total": 0.014405000000000001
            }
          },
          "stopReason": "toolUse",
          "timestamp": 1777988694742,
          "responseId": "resp_0d852cbc8d6115830169f9f45716588194a436c10ff146e102"
        }
      },
      {
        "type": "message",
        "id": "1a272a01",
        "parentId": "ce4f751d",
        "timestamp": "2026-05-05T13:45:02.253Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_Rhjt6X3LrnHl6ttHliPK1uFU|fc_0d852cbc8d6115830169f9f45de4488194ad0be0d63b9f9ea7",
          "toolName": "bash",
          "content": [
            {
              "type": "text",
              "text": "STATUS\n{\"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}}\n\nROOT\n\n\nOPENAPI?\n"
            }
          ],
          "isError": false,
          "timestamp": 1777988702250
        }
      },
      {
        "type": "message",
        "id": "a7b555b1",
        "parentId": "1a272a01",
        "timestamp": "2026-05-05T13:45:02.253Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_bTQg7QCVQOl7qteQl50hO0yL|fc_0d852cbc8d6115830169f9f45de4588194ab579f888979c45d",
          "toolName": "bash",
          "content": [
            {
              "type": "text",
              "text": "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\nsebas       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\nroot        1505    1273  0 10:07 ?        00:00:00 sudo -n -u sebas /usr/sbin/tailscaled serve-taildrive memory /home/sebas/agents-database/data\nsebas       1511    1505  0 10:07 ?        00:00:00 /usr/sbin/tailscaled serve-taildrive memory /home/sebas/agents-database/data\nsebas      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\n-- No entries --\n"
            }
          ],
          "isError": false,
          "timestamp": 1777988702253
        }
      },
      {
        "type": "message",
        "id": "7b364d5d",
        "parentId": "a7b555b1",
        "timestamp": "2026-05-05T13:45:08.427Z",
        "message": {
          "role": "assistant",
          "content": [
            {
              "type": "thinking",
              "thinking": "**Evaluating recent runs**\n\nI 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.",
              "thinkingSignature": "{\"id\":\"rs_0d852cbc8d6115830169f9f45f29388194ac8dd67c1f99348e\",\"type\":\"reasoning\",\"encrypted_content\":\"gAAAAABp-fRi5h77qPAbnvrkXgWjtcQcRk7xSHO7G6i0BiCWoe7ZHvgECfjuf2sAYN-QtJll41I_kVkCojI1YZc_bPpO8Tnb_NVqHKO32Cc_Zjz4jhzXl77demnf0osULwnuGLzxFJiY7pgaSFBxxKGDGBWgDKtFV9U1EsdkkAF6jSX-5lo6ZOwyie5jx5LsVFYjjZAjyUYlaSk8y6_oya2Cyh3dYoISECNDZbYBxrZisAfQ4kdsjCuIGShYOYn1DOkSTtqr8BopdXRmbEfBoeA9U6CVPM82aGpfDUUfWVctahCW4urILELLcbTvVVAHyLpZIC0qHAHBZ1H9VapxO1dQ5ao8JIhN1kOrgcxyKyXdqPQpE24REpEpWxwTAwpNA5Y2ztviObf8bslgKQg8xnuNii09CH1CpIG3ek2-RfMHumS-XqA4viN9RvwamdE4f5g5FuGA25cCyaPyHrcz0C4NNuMZNcoJ3cS2r-gUOv2ZZI5wK32PpS8VGe5xzSnTb3gpBeqOWQPZp_dbbs_vrme2NQcgW3C57R-MeYr83sdRxZBmLORJM0ZH7ob5gxQjDYG-jKyIwyx0jc8NqSYd5JJpPfES4FiQ0HeqljW-hGqC6ZFb2TqhbWLkqNjy-GSSPhLc_VMVWT7kd5E_sXRlaxWVV-KXxBzHmcXP1zX9wAjBM9s02GyQlr40Qm-tQ4t3qo2x8n34Dpflcff9-ePMDTdxtMn52za8moS3boKXunD20Zoiap1tvvod9n5DWBcRq_4LTEsPcyHqMgedUNTytnYWmcaVbgAmud0KiipLdokj30zppI2LsbDzuWAJ4jO8q6JAg2xlHE5v1QHFDmjSydkA6el7rFzK9Yc4SRX-GdSuOfgkzoOw1Z0JmKcJ04AX5KgqZFDEb6i-FVwcdrlCaGdd3L8MTf3tJfouEusgYNTUfmWhq-Vl5dKN2MNBKFnFVGPJHEf46PvgW66_7_ZbbqiuxsOW1bzMHqnrH6RNHtTu766GX4SF1-DfhP_Ullwl47OO7yhJDT-ouEBZEZGRVHm6Z9WEVVCyqr5hq2plJihel4w8s8KZscV9FK2kGQK9zEtlLMqPPS9z4QSetGAVdEpjVvcn9RgJk9erC6a_Wm33D0KNPpeSdJ5JyRk5yVNoMwdbMhXCbYK3JZQToQjGGtsTy5k57x8zT0DGyL8CcVpBOERYhtFruJkhLQtXSo4syr9h8h1vHT37ui-PfAIZ7vIGtafG77028r3MJsetIlt5esueziRBmmrL9Q8_q35dQEWyvZm0Pso7Hxqa4c_aVMOGkn9CHNnLAsTDUqsRfvkM3zjE1zeNpVpSS332YCTo5nkOwEYwFPfcs9GTsMEGDzDz1wpcLbheDqNvXlwYqrgRl4yLVC6nHbY3BMo1Zl-RXd5-X--PfVyGwA9aiHuTPnf9Mt1xHw_vkg==\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Evaluating recent runs**\\n\\nI 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.\"}]}"
            },
            {
              "type": "toolCall",
              "id": "call_zXnKI5qXft0536IAhNudgvdB|fc_0d852cbc8d6115830169f9f462e620819491276b2c7a575213",
              "name": "bash",
              "arguments": {
                "command": "python3 - <<'PY'\nimport sqlite3, json\npath='/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3'\nconn=sqlite3.connect(path)\nconn.row_factory=sqlite3.Row\nprint('RUNS')\nfor 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\"):\n    print(json.dumps(dict(row), ensure_ascii=False))\nprint('JOBS')\nfor 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\"):\n    print(json.dumps(dict(row), ensure_ascii=False))\nPY",
                "timeout": 15
              }
            }
          ],
          "api": "openai-codex-responses",
          "provider": "openai-codex",
          "model": "gpt-5.4",
          "usage": {
            "input": 9368,
            "output": 283,
            "cacheRead": 3584,
            "cacheWrite": 0,
            "totalTokens": 13235,
            "cost": {
              "input": 0.023420000000000003,
              "output": 0.0042450000000000005,
              "cacheRead": 0.000896,
              "cacheWrite": 0,
              "total": 0.028561000000000003
            }
          },
          "stopReason": "toolUse",
          "timestamp": 1777988702254,
          "responseId": "resp_0d852cbc8d6115830169f9f45e864c81948533736ec94fe8bc"
        }
      },
      {
        "type": "message",
        "id": "b54400f6",
        "parentId": "7b364d5d",
        "timestamp": "2026-05-05T13:45:08.483Z",
        "message": {
          "role": "toolResult",
          "toolCallId": "call_zXnKI5qXft0536IAhNudgvdB|fc_0d852cbc8d6115830169f9f462e620819491276b2c7a575213",
          "toolName": "bash",
          "content": [
            {
              "type": "text",
              "text": "RUNS\n{\"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.\"}\n{\"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 \"}\n{\"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 \"}\nJOBS\nTraceback (most recent call last):\n  File \"<stdin>\", line 9, in <module>\nsqlite3.OperationalError: no such column: job_name\n\n\nCommand exited with code 1"
            }
          ],
          "details": {},

[3977 more lines in file. Use offset=201 to continue.]


Warning: No models match pattern "gemini-3-flash-preview:minimal"
Warning: No models match pattern "gemini-3-flash-preview:medium"
Warning: No models match pattern "claude-3-7-sonnet:low"
Warning: No models match pattern "claude-3-7-sonnet:medium"
Warning: No models match pattern "claude-3-7-sonnet:high"
Warning: No models match pattern "o3-mini:low"
Warning: No models match pattern "o3-mini:medium"
Warning: No models match pattern "o3-mini:high"
Warning: No models match pattern "minimax-m2.5-free:minimal"
Warning: No models match pattern "qwen/qwen3-coder:free:minimal"
Warning: No models match pattern "google/gemma-3-27b-it:free:minimal"
[openai-codex] Token refresh failed: 401 {
  "error": {
    "message": "Your refresh token has already been used to generate a new access token. Please try signing in again.",
    "type": "invalid_request_error",
    "param": null,
    "code": "refresh_token_reused"
  }
}
No API key for provider: openai-codex


**Analyzing database jobs**

I need to conduct our own database analysis using Python. It looks like we need job logs since we've had recent failures due to a missing API key. I wonder if 5 or 6 hooks would have yielded many memories, especially from recent jobs. I need to count session memory analysis jobs, their outcomes, and saved memories by source reference and creation date. Let's inspect the logs for any successes and build a script to query the database.

cd /home/sebas/agents-database && rg -n "create_memory\(|source_ref.*conversation|session_memory|last_user_text|memory_id|save": src tests scripts /home/sebas/pi-config -S | head -200

python3 - <<'PY'
import sqlite3, json
conn=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')
conn.row_factory=sqlite3.Row
print('maintenance_jobs cols:')
for r in conn.execute('pragma table_info(maintenance_jobs)'): print(r['name'])
print('maintenance_runs cols:')
for r in conn.execute('pragma table_info(maintenance_runs)'): print(r['name'])
PY


src/shared_agent_memory/schema.py:83:    from_memory_id TEXT NOT NULL,
src/shared_agent_memory/schema.py:84:    to_memory_id TEXT NOT NULL,
src/shared_agent_memory/schema.py:88:    FOREIGN KEY(from_memory_id) REFERENCES memories(id),
src/shared_agent_memory/schema.py:89:    FOREIGN KEY(to_memory_id) REFERENCES memories(id)
src/shared_agent_memory/schema.py:131:    memory_id TEXT NOT NULL,
src/shared_agent_memory/schema.py:132:    conflicting_memory_id TEXT NOT NULL,
src/shared_agent_memory/schema.py:136:    FOREIGN KEY(memory_id) REFERENCES memories(id),
src/shared_agent_memory/schema.py:137:    FOREIGN KEY(conflicting_memory_id) REFERENCES memories(id)
src/shared_agent_memory/schema.py:224:CREATE INDEX IF NOT EXISTS idx_memory_conflicts_mem ON memory_conflicts(memory_id);
src/shared_agent_memory/schema.py:227:    memory_id UNINDEXED,
src/shared_agent_memory/engine.py:112:        return {"event_id": event["id"], "memory_id": memory["id"]}
src/shared_agent_memory/cli.py:100:    consolidate.add_argument("memory_ids", nargs="+")
src/shared_agent_memory/cli.py:239:        print(json.dumps(service.consolidate(args.memory_ids), indent=2))
tests/test_memory_service.py:57:    memory_id = processed[0]["memory_id"]
tests/test_memory_service.py:58:    memory = service.get_memory(memory_id)
tests/test_memory_service.py:64:    promoted = service.consolidate([memory_id])
tests/test_memory_service.py:65:    assert promoted["promoted"] == [memory_id]
tests/test_memory_service.py:68:    assert search_with_active["results"][0]["memory"]["id"] == memory_id
tests/test_memory_service.py:76:    service.create_memory(
tests/test_memory_service.py:86:    service.create_memory(
tests/test_memory_service.py:96:    service.create_memory(
tests/test_memory_service.py:154:    service.create_memory(
tests/test_memory_service.py:166:    service.create_memory(
tests/test_memory_service.py:188:    service.create_memory(
tests/test_memory_service.py:283:    service.create_memory(
tests/test_memory_service.py:319:    service.create_memory(
tests/test_memory_service.py:399:    memory = service.create_memory(
tests/test_memory_service.py:417:    service.create_memory(
tests/test_memory_service.py:427:    service.create_memory(
src/shared_agent_memory/service.py:71:    def create_memory(self, memory_input: dict[str, Any]) -> dict[str, Any]:
src/shared_agent_memory/service.py:129:        return self.create_memory(memory_input)
src/shared_agent_memory/service.py:278:            for memory_id in inbox_items:
src/shared_agent_memory/service.py:279:                row = conn.execute("SELECT * FROM memories WHERE id = ?", (memory_id,)).fetchone()
src/shared_agent_memory/service.py:293:                    (memory_id, memory["type"], memory["scope"], memory["content"]),
src/shared_agent_memory/service.py:299:                        INSERT INTO memory_links (id, from_memory_id, to_memory_id, relation, created_at, metadata_json)
src/shared_agent_memory/service.py:304:                            memory_id,
src/shared_agent_memory/service.py:311:                    conn.execute("UPDATE memories SET status = ?, updated_at = ? WHERE id = ?", ("archived", utcnow(), memory_id))
src/shared_agent_memory/service.py:312:                    linked.append({"from": memory_id, "to": duplicate["id"], "relation": "related_to"})
src/shared_agent_memory/service.py:314:                conn.execute("UPDATE memories SET status = ?, updated_at = ? WHERE id = ?", ("active", utcnow(), memory_id))
src/shared_agent_memory/service.py:315:                promoted.append(memory_id)
src/shared_agent_memory/service.py:453:        return self.create_memory(
src/shared_agent_memory/service.py:540:    def get_memory(self, memory_id: str) -> dict[str, Any]:
src/shared_agent_memory/service.py:542:            row = conn.execute("SELECT * FROM memories WHERE id = ?", (memory_id,)).fetchone()
src/shared_agent_memory/service.py:544:                raise KeyError(memory_id)
src/shared_agent_memory/service.py:1534:        conn.execute("DELETE FROM memories_fts WHERE memory_id = ?", (record["id"],))
src/shared_agent_memory/service.py:1536:            "INSERT INTO memories_fts (memory_id, title, summary, content) VALUES (?, ?, ?, ?)",
src/shared_agent_memory/service.py:1653:            INSERT INTO memory_links (id, from_memory_id, to_memory_id, relation, created_at, metadata_json)
src/shared_agent_memory/service.py:1659:    def _record_conflict(self, conn: Any, memory_id: str, conflicting_id: str, reason: str) -> None:
src/shared_agent_memory/service.py:1662:            INSERT INTO memory_conflicts (id, memory_id, conflicting_memory_id, reason, created_at, metadata_json)
src/shared_agent_memory/service.py:1665:            (make_id("mcf"), memory_id, conflicting_id, reason, utcnow(), self.store.dumps({})),
/home/sebas/pi-config/extensions/session-memory-dispatcher.ts:4:const DISPATCH_URL = process.env.PI_SESSION_MEMORY_DISPATCH_URL || "http://127.0.0.1:17882/api/session-memory";
/home/sebas/pi-config/extensions/session-memory-dispatcher.ts:72:		last_user_text: truncate(stats.lastUserText, 2000),
/home/sebas/pi-config/SYSTEM.md:33:Especially save: stable user preferences, recurring constraints, important project facts, decisions, reusable sources/artifacts, and notable blocked state or handoff context.
/home/sebas/pi-config/scripts/session-memory-dispatcher.py:13:JOB_DIR = Path(os.environ.get("PI_SESSION_MEMORY_JOB_DIR", BASE_DIR / "sessions" / "session-memory-jobs"))
/home/sebas/pi-config/scripts/session-memory-dispatcher.py:14:ANALYZER = Path(os.environ.get("PI_SESSION_MEMORY_ANALYZER", BASE_DIR / "scripts" / "session-memory-analyzer.py"))
/home/sebas/pi-config/scripts/session-memory-dispatcher.py:25:        "job_type": "session_memory_analysis",
/home/sebas/pi-config/scripts/session-memory-dispatcher.py:30:            "memory_id": None,
/home/sebas/pi-config/scripts/session-memory-analyzer.py:15:PI_MODEL = os.environ.get("PI_SESSION_MEMORY_MODEL", "gpt-5.4-mini")
/home/sebas/pi-config/scripts/session-memory-analyzer.py:16:PI_THINKING = os.environ.get("PI_SESSION_MEMORY_THINKING", "off")
/home/sebas/pi-config/scripts/session-memory-analyzer.py:17:PI_BIN = os.environ.get("PI_SESSION_MEMORY_PI_BIN")
/home/sebas/pi-config/scripts/session-memory-analyzer.py:137:- last_user_text: {str(meta.get('last_user_text') or '')[:2000]}
/home/sebas/pi-config/scripts/session-memory-analyzer.py:157:        "memory_id": None,
/home/sebas/pi-config/scripts/session-memory-analyzer.py:199:            job["analysis"]["memory_id"] = saved.get("id")
/home/sebas/pi-config/scripts/session-memory-analyzer.py:203:            job["analysis"]["memory_id"] = None
/home/sebas/pi-config/scripts/install-session-memory-dispatcher.service:12:Environment=PI_SESSION_MEMORY_PI_BIN=/home/sebas/.npm-global/bin/pi


maintenance_jobs cols:
id
job_type
cadence
interval_minutes
window_start
window_end
next_due_at
last_run_at
last_status
last_summary
enabled
metadata_json
created_at
updated_at
maintenance_runs cols:
id
job_id
status
started_at
completed_at
summary
stats_json
error_message


#!/usr/bin/env python3
from __future__ import annotations

import argparse
import json
import os
import shutil
import subprocess
import sys
from pathlib import Path
from typing import Any
from urllib import request

AGENTS_DB_API = os.environ.get("AGENTS_DB_API", "http://100.116.176.16:8091")
PI_MODEL = os.environ.get("PI_SESSION_MEMORY_MODEL", "gpt-5.4-mini")
PI_THINKING = os.environ.get("PI_SESSION_MEMORY_THINKING", "off")
PI_BIN = os.environ.get("PI_SESSION_MEMORY_PI_BIN")

def resolve_pi_bin() -> str:
    candidates = [
        PI_BIN,
        shutil.which("pi"),
        str(Path.home() / ".npm-global" / "bin" / "pi"),
        "/usr/local/bin/pi",
        "/usr/bin/pi",
    ]
    for candidate in candidates:
        if not candidate:
            continue
        if Path(candidate).exists():
            return candidate
    raise FileNotFoundError("pi")

def load_job(job_file: Path) -> dict[str, Any]:
    return json.loads(job_file.read_text(encoding="utf-8"))

def save_job(job_file: Path, payload: dict[str, Any]) -> None:
    job_file.write_text(json.dumps(payload, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")

def call_pi(prompt: str) -> str:
    cmd = [
        resolve_pi_bin(),
        "-p",
        "--provider",
        "openai-codex",
        "--model",
        PI_MODEL,
        "--thinking",
        PI_THINKING,
        "--no-tools",
        "--no-session",
        prompt,
    ]
    proc = subprocess.run(cmd, capture_output=True, text=True)
    if proc.returncode != 0:
        raise RuntimeError(proc.stderr.strip() or proc.stdout.strip() or f"pi failed ({proc.returncode})")
    return proc.stdout.strip()

def ingest(memory: dict[str, Any]) -> dict[str, Any]:
    req = request.Request(
        f"{AGENTS_DB_API}/api/ingest",
        method="POST",
        data=json.dumps(memory).encode(),
        headers={"Content-Type": "application/json"},
    )
    with request.urlopen(req, timeout=30) as resp:
        return json.loads(resp.read().decode("utf-8"))

def extract_json(text: str) -> dict[str, Any]:
    raw = text.strip()
    if raw.startswith("```"):
        raw = raw.strip("`")
        raw = raw.split("\n", 1)[-1] if "\n" in raw else raw
    start = raw.find("{")
    end = raw.rfind("}")
    if start >= 0 and end > start:
        raw = raw[start : end + 1]
    return json.loads(raw)

def session_meta(job: dict[str, Any]) -> dict[str, Any]:
    payload = job.get("payload")
    if isinstance(payload, dict):
        return payload
    return job

def build_prompt(job: dict[str, Any]) -> str:
    meta = session_meta(job)
    return f"""You are deciding whether a Pi session has durable value.

Rules:
- Prefer NOT saving anything unless it is durable and reusable.
- Session metadata alone is not enough.
- Do NOT save operational notes about this session-memory system, dispatch flow, or local implementation work unless they affect broader workflows beyond this repo.
- Save only if the session contains a stable user preference, enduring project fact, reusable external artifact, decision, procedure, or blocker that will matter in future unrelated sessions.
- If nothing durable exists, return save=false.
- If the memory is likely durable, assign the best type and include evidence.
- Output JSON only. No markdown. No extra text.

Return this schema:
{{
  "save": boolean,
  "type": "profile" | "decision" | "project" | "artifact" | "episode" | "procedural" | "semantic",
  "subtype": string | null,
  "scope": "global" | "project",
  "title": string,
  "content": string,
  "source_ref": string,
  "evidence_ref": string | null,
  "confidence": number,
  "freshness": number,
  "importance": number,
  "reason": string
}}

Scoring guidance:
- confidence: 0 to 1, how well supported the memory is.
- freshness: 0 to 1, how current it is.
- importance: 0 to 1, how much future value it has.
- evidence_ref: cite the strongest source in the session or branch excerpt when possible.

Session metadata:
- session_id: {meta.get('session_id')}
- session_file: {meta.get('session_file')}
- cwd: {meta.get('cwd')}
- scope: {meta.get('scope')}
- reason: {meta.get('reason')}
- user_messages: {meta.get('user_messages')}
- assistant_messages: {meta.get('assistant_messages')}
- last_user_text: {str(meta.get('last_user_text') or '')[:2000]}
- last_assistant_text: {str(meta.get('last_assistant_text') or '')[:2000]}
- trigger: {meta.get('trigger')}

Branch excerpt JSON:
{json.dumps(meta.get('branch') or [], ensure_ascii=False)[:30000]}
"""

def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("--job-file", required=True)
    args = parser.parse_args()

    job_file = Path(args.job_file)
    job = load_job(job_file)
    meta = session_meta(job)
    job["analysis"] = {
        "status": "running",
        "save": None,
        "memory_id": None,
    }
    save_job(job_file, job)

    try:
        raw = call_pi(build_prompt(job))
        analysis = extract_json(raw)
        job["analysis"]["raw"] = raw
        job["analysis"]["result"] = analysis
        job["analysis"]["status"] = "completed"

        if bool(analysis.get("save")):
            memory_type = analysis.get("type") or "episode"
            is_candidate = memory_type == "episode" or bool(float(analysis.get("importance") or 0.5) < 0.75)
            memory = {
                "type": memory_type,
                "subtype": analysis.get("subtype"),
                "scope": analysis.get("scope") or meta.get("scope") or "global",
                "status": "inbox" if is_candidate else "active",
                "source_kind": "manual",
                "title": analysis.get("title") or "Session insight",
                "content": analysis.get("content") or "",
                "confidence": float(analysis.get("confidence") or 0.75),
                "freshness": float(analysis.get("freshness") or 0.9),
                "importance": float(analysis.get("importance") or 0.5),
                "source_ref": analysis.get("source_ref") or f"pi-session:{meta.get('session_file') or meta.get('session_id') or 'unknown'}",
                "evidence_ref": analysis.get("evidence_ref"),
                "origin_agent": "pi-session-memory-analyzer",
                "metadata": {
                    "job_file": str(job_file),
                    "session_id": meta.get("session_id"),
                    "session_file": meta.get("session_file"),
                    "cwd": meta.get("cwd"),
                    "reason": meta.get("reason"),
                    "user_messages": meta.get("user_messages"),
                    "assistant_messages": meta.get("assistant_messages"),
                    "analysis_reason": analysis.get("reason"),
                    "trigger": meta.get("trigger"),
                    "promotion_candidate": not is_candidate,
                },
            }
            saved = ingest(memory)
            job["analysis"]["memory_id"] = saved.get("id")
            job["analysis"]["saved"] = True
        else:
            job["analysis"]["saved"] = False
            job["analysis"]["memory_id"] = None

        save_job(job_file, job)
        print(json.dumps(job["analysis"], ensure_ascii=False))
        return 0
    except Exception as exc:
        job["analysis"]["status"] = "failed"
        job["analysis"]["error"] = str(exc)
        save_job(job_file, job)
        print(json.dumps(job["analysis"], ensure_ascii=False), file=sys.stderr)
        return 1

if __name__ == "__main__":
    raise SystemExit(main())