121-                """,
122-                record,
123-            )
124-            self._upsert_fts(conn, record)
125-        self._run_opportunistic_maintenance(limit=1)
126-        return self.get_memory(record["id"])
127-
128-    def ingest(self, memory_input: dict[str, Any]) -> dict[str, Any]:
129-        return self.create_memory(memory_input)
130-
131:    def capture_session(self, source_ref: str, scope: str, content: str, **metadata: Any) -> dict[str, Any]:
132-        return self._capture_source(
133-            trigger_type="session_close",
134-            source_kind="run",
135-            source_ref=source_ref,
136-            scope=scope,
137-            content=content,
138-            metadata=metadata,
139-        )
140-
141:    def capture_conversation(
142-        self, source_ref: str, scope: str, content: str, capture_mode: str = "manual_import", **metadata: Any
143-    ) -> dict[str, Any]:
144-        metadata = {"capture_mode": capture_mode, **metadata}
145-        return self._capture_source(
146-            trigger_type="conversation_capture",
147-            source_kind="conversation",
148-            source_ref=source_ref,
149-            scope=scope,
150-            content=content,
151-            metadata=metadata,
152-        )
153-
154-    def search(
155-        self,
156-        query: str,
157-        scopes: list[str] | None = None,
158-        filters: dict[str, Any] | None = None,
159-        limit: int = 10,
160-        include_inbox: bool = False,
161-    ) -> dict[str, Any]:
162-        filters = filters or {}
163-        query_terms = [term for term in normalize_text(query).split() if term]
164-        sql = ["SELECT * FROM memories WHERE 1=1"]
165-        params: list[Any] = []
166-
167-        if scopes:
168-            sql.append(f"AND scope IN ({','.join('?' for _ in scopes)})")
169-            params.extend(scopes)
170-        if not include_inbox:
171-            sql.append("AND status = ?")
172-            params.append("active")
173-        params.extend(self._memory_filter_sql(sql, filters))
174-
175-        with self.store.connection() as conn:
176-            rows = [self._row_to_memory(row) for row in conn.execute(" ".join(sql), params)]
177-
178-        results: list[SearchResult] = []
179-        query_embedding = self._text_embedding(query) if query_terms else []
180-        for row in rows:
181-            haystack = normalize_text(" ".join([row["title"], row["summary"], row["content"]]))
--
1099-                            utcnow(),
1100-                            utcnow(),
1101-                            self.store.dumps({}),
1102-                        ),
1103-                    )
1104-                    migrated["handoff_artifacts"] += 1
1105-                conn.execute("DROP TABLE handoffs")
1106-        after = self.audit_v2()
1107-        return {"status": "ok", "before": before, "after": after, "migrated": migrated}
1108-
1109:    def _capture_source(
1110-        self, trigger_type: str, source_kind: str, source_ref: str, scope: str, content: str, metadata: dict[str, Any]
1111-    ) -> dict[str, Any]:
1112-        now = utcnow()
1113-        source = {
1114-            "id": make_id("src"),
1115-            "source_kind": source_kind,
1116-            "title": metadata.get("title") or source_ref,
1117-            "content": content,
1118-            "source_ref": source_ref,
1119-            "metadata_json": self.store.dumps(metadata),
1120-            "created_at": now,
1121-        }
1122-        event = {
1123-            "id": make_id("ing"),
1124-            "trigger_type": trigger_type,
1125-            "source_id": source["id"],
1126-            "requested_scope": scope,
1127-            "job_state": "pending",
1128-            "processor_outcome": None,
1129-            "retry_count": 0,
1130-            "error_message": None,
1131-            "created_at": now,
1132-            "updated_at": now,
1133-            "processed_at": None,
1134-        }
1135-        with self.store.connection() as conn:
1136-            conn.execute(
1137-                """
1138-                INSERT INTO sources (id, source_kind, title, content, source_ref, metadata_json, created_at)
1139-                VALUES (:id, :source_kind, :title, :content, :source_ref, :metadata_json, :created_at)
1140-                """,
1141-                source,
1142-            )
1143-            conn.execute(
1144-                """
1145-                INSERT INTO ingestion_events (
1146-                    id, trigger_type, source_id, requested_scope, job_state, processor_outcome,
1147-                    retry_count, error_message, created_at, updated_at, processed_at
1148-                ) VALUES (
1149-                    :id, :trigger_type, :source_id, :requested_scope, :job_state, :processor_outcome,


    def _capture_source(
        self, trigger_type: str, source_kind: str, source_ref: str, scope: str, content: str, metadata: dict[str, Any]
    ) -> dict[str, Any]:
        now = utcnow()
        source = {
            "id": make_id("src"),
            "source_kind": source_kind,
            "title": metadata.get("title") or source_ref,
            "content": content,
            "source_ref": source_ref,
            "metadata_json": self.store.dumps(metadata),
            "created_at": now,
        }
        event = {
            "id": make_id("ing"),
            "trigger_type": trigger_type,
            "source_id": source["id"],
            "requested_scope": scope,
            "job_state": "pending",
            "processor_outcome": None,
            "retry_count": 0,
            "error_message": None,
            "created_at": now,
            "updated_at": now,
            "processed_at": None,
        }
        with self.store.connection() as conn:
            conn.execute(
                """
                INSERT INTO sources (id, source_kind, title, content, source_ref, metadata_json, created_at)
                VALUES (:id, :source_kind, :title, :content, :source_ref, :metadata_json, :created_at)
                """,
                source,
            )
            conn.execute(
                """
                INSERT INTO ingestion_events (
                    id, trigger_type, source_id, requested_scope, job_state, processor_outcome,
                    retry_count, error_message, created_at, updated_at, processed_at
                ) VALUES (
                    :id, :trigger_type, :source_id, :requested_scope, :job_state, :processor_outcome,
                    :retry_count, :error_message, :created_at, :updated_at, :processed_at
                )
                """,
                event,
            )
        self._run_opportunistic_maintenance(limit=1)
        return {"source": source, "event": event}

    def _run_opportunistic_maintenance(self, *, limit: int) -> None:
        if self._maintenance_check_running:
            return
        due_jobs = self.list_due_maintenance_jobs(limit=1)
        if not due_jobs:
            return
        self._maintenance_check_running = True
        try:
            from .engine import MemoryEngine

            MemoryEngine(self).run_due_maintenance(limit=limit)
        except Exception:
            return
        finally:
            self._maintenance_check_running = False

    def _memory_filter_sql(self, sql: list[str], filters: dict[str, Any]) -> list[Any]:
        params: list[Any] = []
        allowed = {"project_id", "repo_id", "type", "subtype", "run_id", "task_id", "origin_agent", "url", "domain"}
        for key, value in filters.items():
            if key not in allowed or value is None:
                continue
            sql.append(f"AND {key} = ?")
            params.append(value)
        return params

    def _facet_score(self, memory: dict[str, Any], filters: dict[str, Any]) -> float:
        tracked = ["project_id", "repo_id", "type", "subtype", "run_id", "task_id", "origin_agent", "url", "domain"]
        applied = [key for key in tracked if filters.get(key) is not None]
        if not applied:
            return 0.0

[521 more lines in file. Use offset=1189 to continue.]


from __future__ import annotations

from typing import Any

from .service import MemoryService, make_id, utcnow

class MemoryEngine:
    def __init__(self, service: MemoryService) -> None:
        self.service = service

    def process_pending(self, limit: int | None = None) -> list[dict[str, Any]]:
        events = self.service.list_pending_events()
        if limit is not None:
            events = events[:limit]

        processed: list[dict[str, Any]] = []
        for event in events:
            try:
                processed.append(self._process_event(event))
                self.service.mark_event(event["id"], "completed", outcome="extracted")
            except Exception as exc:  # pragma: no cover - defensive
                self.service.mark_event(event["id"], "failed", error_message=str(exc))
        return processed

    def run_due_maintenance(self, *, limit: int | None = None, job_id: str | None = None) -> list[dict[str, Any]]:
        if job_id:
            jobs = [self.service.get_maintenance_job(job_id)]
        else:
            jobs = self.service.list_due_maintenance_jobs(limit=limit)
        results: list[dict[str, Any]] = []
        for job in jobs:
            run = self.service.create_maintenance_run(job["id"])
            try:
                stats = self._run_maintenance_job(job)
                self.service.finish_maintenance_run(
                    run["id"],
                    status="completed",
                    summary=f"{job['job_type']} completed",
                    stats=stats,
                )
                next_due = self.service._compute_next_due(
                    utcnow(),
                    job["cadence"],
                    job.get("interval_minutes"),
                    job.get("window_start"),
                    job.get("window_end"),
                )
                self.service.update_maintenance_job(
                    job["id"],
                    next_due_at=next_due,
                    last_run_at=utcnow(),
                    last_status="completed",
                    last_summary=f"{job['job_type']} completed",
                )
                results.append({"job_id": job["id"], "status": "completed", "stats": stats})
            except Exception as exc:
                self.service.finish_maintenance_run(
                    run["id"],
                    status="failed",
                    summary=f"{job['job_type']} failed",
                    stats={},
                    error_message=str(exc),
                )
                self.service.update_maintenance_job(
                    job["id"],
                    last_run_at=utcnow(),
                    last_status="failed",
                    last_summary=str(exc),
                )
                results.append({"job_id": job["id"], "status": "failed", "error": str(exc)})
        return results

    def _run_maintenance_job(self, job: dict[str, Any]) -> dict[str, Any]:
        job_type = job.get("job_type")
        if job_type == "consolidation":
            return self.service.consolidate_candidates(config=job.get("metadata") or {})
        return {"skipped": True, "reason": f"unknown job_type {job_type}"}

    def _process_event(self, event: dict[str, Any]) -> dict[str, Any]:
        with self.service.store.connection() as conn:
            source = conn.execute("SELECT * FROM sources WHERE id = ?", (event["source_id"],)).fetchone()
            if source is None:
                raise KeyError(event["source_id"])
            source_dict = dict(source)

        content = source_dict["content"]
        summary = self.service._make_summary(content)
        memory = self.service.ingest(
            {
                "id": make_id("mem"),
                "type": "episode",
                "scope": self._normalize_scope(event["requested_scope"]),
                "status": "inbox",
                "source_kind": source_dict["source_kind"],
                "title": source_dict["title"],
                "content": content,
                "summary": summary,
                "confidence": 0.6,
                "freshness": 1.0,
                "observed_at": source_dict["created_at"],
                "source_ref": source_dict["source_ref"],
                "evidence_ref": source_dict["id"],
                "embedding": self.service._text_embedding(content),
                "metadata": {
                    "ingested_by": "memory-engine",
                    "trigger_type": event["trigger_type"],
                    "captured_at": utcnow(),
                },
            }
        )
        return {"event_id": event["id"], "memory_id": memory["id"]}

    def _normalize_scope(self, requested_scope: str) -> str:
        if requested_scope in {"global", "project", "repo", "agent", "session"}:
            return requested_scope
        return "session"


bash

python3 - <<'PY'
import sqlite3, json
conn=sqlite3.connect('/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3')
conn.row_factory=sqlite3.Row
print('MEMORIES_BY_SOURCE_KIND')
for row in conn.execute("select source_kind, count(*) c from memories group by source_kind order by c desc"):
    print(dict(row))
print('\nRECENT_CONVERSATION_OR_RUN_MEMORIES')
for row in conn.execute("select id, type, status, source_kind, title, source_ref, created_at, updated_at, metadata_json from memories where source_kind in ('conversation','run') order by datetime(updated_at) desc limit 20"):
    print(json.dumps(dict(row), ensure_ascii=False))
print('\nRECENT_INGESTION_EVENTS')
for row in conn.execute("select id, trigger_type, requested_scope, job_state, processor_outcome, created_at, updated_at, processed_at from ingestion_events order by datetime(created_at) desc limit 20"):
    print(json.dumps(dict(row), ensure_ascii=False))
print('\nRECENT_SOURCES')
for row in conn.execute("select id, source_kind, title, source_ref, created_at, metadata_json from sources order by datetime(created_at) desc limit 20"):
    print(json.dumps(dict(row), ensure_ascii=False))
PY


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

RECENT_CONVERSATION_OR_RUN_MEMORIES
{"id": "legacy_run_9fdccb34-cf1e-47bd-b133-a224f363a6d4", "type": "episode", "status": "active", "source_kind": "run", "title": "Research run: Investigate personal assistant patterns", "source_ref": "personal-agent:run:9fdccb34-cf1e-47bd-b133-a224f363a6d4", "created_at": "2026-03-13T14:01:39.630931+00:00", "updated_at": "2026-04-30T17:32:27.368015+00:00", "metadata_json": "{\"assumptions\": \"v1 defaults\", \"goal\": \"Investigate personal assistant patterns\", \"run_status\": \"completed\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"recent setup patterns\", \"subtype\": \"research_run\", \"summary\": \"Seeded baseline run for personal-agent verification.\"}"}
{"id": "legacy_run_a8512264-8cd5-4c3c-b62f-7528625227f1", "type": "episode", "status": "active", "source_kind": "run", "title": "Research run: Verify capture-url flow", "source_ref": "personal-agent:run:a8512264-8cd5-4c3c-b62f-7528625227f1", "created_at": "2026-03-13T14:01:39.632489+00:00", "updated_at": "2026-04-30T17:32:27.368368+00:00", "metadata_json": "{\"assumptions\": \"e2e verification\", \"goal\": \"Verify capture-url flow\", \"run_status\": \"active\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"local file capture\", \"subtype\": \"research_run\", \"summary\": \"Verify capture-url flow\"}"}
{"id": "legacy_run_55b6eaa0-9ff5-438c-a375-50f0452079cb", "type": "episode", "status": "active", "source_kind": "run", "title": "Research run: Drive Ballbox payment integration toward Volvox-style demo on vending machine Android flow", "source_ref": "personal-agent:run:55b6eaa0-9ff5-438c-a375-50f0452079cb", "created_at": "2026-03-13T14:01:39.633673+00:00", "updated_at": "2026-04-30T17:32:27.368445+00:00", "metadata_json": "{\"assumptions\": \"Current repo is ballbox-payment-integration unless another Volvox-specific repo appears later\", \"goal\": \"Drive Ballbox payment integration toward Volvox-style demo on vending machine Android flow\", \"run_status\": \"active\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"Confirm repo alignment, identify gaps, iterate to demoable QR payment flow\", \"subtype\": \"research_run\", \"summary\": \"Drive Ballbox payment integration toward Volvox-style demo on vending machine Android flow\"}"}
{"id": "legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "type": "episode", "status": "active", "source_kind": "run", "title": "Research run: Source 50+ inch China displays for Ballbox advertising in padel venues", "source_ref": "personal-agent:run:bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "created_at": "2026-03-13T14:01:39.634873+00:00", "updated_at": "2026-04-30T17:32:27.368506+00:00", "metadata_json": "{\"assumptions\": \"Need shortlist first, not final purchase decision\", \"goal\": \"Source 50+ inch China displays for Ballbox advertising in padel venues\", \"run_status\": \"active\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"Made-in-China, Alibaba, similar suppliers; indoor or outdoor; browser/fullscreen capable preferred\", \"subtype\": \"research_run\", \"summary\": \"Source 50+ inch China displays for Ballbox advertising in padel venues\"}"}
{"id": "legacy_run_8248124f-5145-475f-a3c7-a37b619abfa0", "type": "episode", "status": "active", "source_kind": "run", "title": "Research run: Test intake flow", "source_ref": "personal-agent:run:8248124f-5145-475f-a3c7-a37b619abfa0", "created_at": "2026-03-13T14:01:39.636174+00:00", "updated_at": "2026-04-30T17:32:27.368568+00:00", "metadata_json": "{\"assumptions\": \"none\", \"goal\": \"Test intake flow\", \"run_status\": \"active\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"demo\", \"subtype\": \"research_run\", \"summary\": \"Test intake flow\"}"}
{"id": "legacy_run_b07bc538-5cab-4243-a1d3-c7a7fa55a0a7", "type": "episode", "status": "active", "source_kind": "run", "title": "Research run: Drive ballbox-payment-integration to a demoable vending-machine QR payment flow", "source_ref": "personal-agent:run:b07bc538-5cab-4243-a1d3-c7a7fa55a0a7", "created_at": "2026-03-13T14:01:39.637766+00:00", "updated_at": "2026-04-30T17:32:27.368626+00:00", "metadata_json": "{\"assumptions\": \"volvox reference maps to this repo; final flow is machine launcher -> QR page -> payment approval -> callback -> success/demo output\", \"goal\": \"Drive ballbox-payment-integration to a demoable vending-machine QR payment flow\", \"run_status\": \"active\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"Current target repo is ballbox-payment-integration; confirm fit to machine Android launcher flow and iterate toward demo\", \"subtype\": \"research_run\", \"summary\": \"Drive ballbox-payment-integration to a demoable vending-machine QR payment flow\"}"}
{"id": "legacy_run_2c54c32b-3cf1-426b-98a6-9c43971573ff", "type": "episode", "status": "active", "source_kind": "run", "title": "Research run: Find China-sourced 50+ inch displays for Ballbox advertising in covered padel clubs", "source_ref": "personal-agent:run:2c54c32b-3cf1-426b-98a6-9c43971573ff", "created_at": "2026-03-13T14:01:39.638922+00:00", "updated_at": "2026-04-30T17:32:27.368687+00:00", "metadata_json": "{\"assumptions\": \"Fullscreen browser preferred; exact hardware/software stack can be validated later; no final purchase decision yet\", \"goal\": \"Find China-sourced 50+ inch displays for Ballbox advertising in covered padel clubs\", \"run_status\": \"active\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"Prioritize covered clubs first; shortlist suppliers/screens from Alibaba, Made-in-China, or similar\", \"subtype\": \"research_run\", \"summary\": \"Find China-sourced 50+ inch displays for Ballbox advertising in covered padel clubs\"}"}
{"id": "legacy_run_fbe3fc0d-b05c-455e-9afe-1b17b4640d97", "type": "episode", "status": "active", "source_kind": "run", "title": "Research run: Responder a Codex sin salir de una película en fullscreen, con UI mínima y poco intrusiva, y evaluar cómo implementarlo", "source_ref": "personal-agent:run:fbe3fc0d-b05c-455e-9afe-1b17b4640d97", "created_at": "2026-03-13T14:01:39.640125+00:00", "updated_at": "2026-04-30T17:32:27.368751+00:00", "metadata_json": "{\"assumptions\": \"La prioridad es uso personal en la Mac de Sebas; se acepta prototipo primero; no se decidi\\u00f3 todav\\u00eda si ser\\u00e1 app nativa, helper global, overlay del sistema o integraci\\u00f3n con una app existente\", \"goal\": \"Responder a Codex sin salir de una pel\\u00edcula en fullscreen, con UI m\\u00ednima y poco intrusiva, y evaluar c\\u00f3mo implementarlo\", \"run_status\": \"completed\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"Mac local; tarea doble: investigar enfoques posibles y luego construir/probar un overlay o input transparente que aparezca solo al responder mientras una pel\\u00edcula est\\u00e1 en pantalla completa\", \"subtype\": \"research_run\", \"summary\": \"Conclusi\\u00f3n: no conviene app nativa primero. Orden simple: 1) BetterTouchTool para overlay/input sin reinventar, 2) Raycast si basta una UI ya hecha, 3) Hammerspoon si hace falta overlay transparente y m\\u00e1s control. Gaps: falta probar cu\\u00e1l de las tres convive mejor con tu reproductor fullscreen concreto y con el env\\u00edo real a Codex.\"}"}
{"id": "legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7", "type": "episode", "status": "active", "source_kind": "run", "title": "Research run: Analyze the best next steps to make ballbox-payment-integration production-ready", "source_ref": "personal-agent:run:a7fe5fbd-4892-4398-ab1a-71337f923ed7", "created_at": "2026-03-13T14:01:39.641266+00:00", "updated_at": "2026-04-30T17:32:27.368808+00:00", "metadata_json": "{\"assumptions\": \"Current repo now has a working demo flow in mock mode; production requires real launcher, payment, and dispense contracts\", \"goal\": \"Analyze the best next steps to make ballbox-payment-integration production-ready\", \"run_status\": \"active\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"From validated mock demo flow to real machine/payment production path\", \"subtype\": \"research_run\", \"summary\": \"Analyze the best next steps to make ballbox-payment-integration production-ready\"}"}
{"id": "legacy_run_35bd54a1-0135-414e-9c6b-245fca772f54", "type": "episode", "status": "active", "source_kind": "run", "title": "Research run: Production-readiness plan for ballbox-payment-integration", "source_ref": "personal-agent:run:35bd54a1-0135-414e-9c6b-245fca772f54", "created_at": "2026-03-13T14:01:39.642436+00:00", "updated_at": "2026-04-30T17:32:27.368861+00:00", "metadata_json": "{\"assumptions\": \"Local repo assessment only; external provider contracts and production infra not yet validated.\", \"goal\": \"Production-readiness plan for ballbox-payment-integration\", \"run_status\": \"active\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"Capture repository-based evidence, claims, and follow-up tasks for first production deployment readiness.\", \"subtype\": \"research_run\", \"summary\": \"Production-readiness plan for ballbox-payment-integration\"}"}
{"id": "legacy_run_1f1a3fd4-eb28-4e99-891f-07c97243a06f", "type": "episode", "status": "active", "source_kind": "run", "title": "Research run: Produce a production-readiness plan for ballbox-payment-integration", "source_ref": "personal-agent:run:1f1a3fd4-eb28-4e99-891f-07c97243a06f", "created_at": "2026-03-13T14:01:39.643717+00:00", "updated_at": "2026-04-30T17:32:27.368925+00:00", "metadata_json": "{\"assumptions\": \"Plan based on the local repository state on 2026-03-11; no current vendor docs or live infrastructure state were validated; target is first production deployment, not long-term platform redesign.\", \"goal\": \"Produce a production-readiness plan for ballbox-payment-integration\", \"run_status\": \"active\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"Assess the current repository state, identify the minimum work required for a safe production deployment, and sequence the work into an executable plan without changing the application code yet.\", \"subtype\": \"research_run\", \"summary\": \"Produce a production-readiness plan for ballbox-payment-integration\"}"}
{"id": "legacy_run_7e6517b3-759f-4063-8d98-121d9cba45c5", "type": "episode", "status": "active", "source_kind": "run", "title": "Research run: Find online references for the GitHub organization Trading-Mastery", "source_ref": "personal-agent:run:7e6517b3-759f-4063-8d98-121d9cba45c5", "created_at": "2026-03-13T14:01:39.644949+00:00", "updated_at": "2026-04-30T17:32:27.368981+00:00", "metadata_json": "{\"assumptions\": \"Use public web sources only; private repo access unavailable; scripts/run.sh referenced by the skill is absent so personal.py is used as fallback.\", \"goal\": \"Find online references for the GitHub organization Trading-Mastery\", \"run_status\": \"completed\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"Identify public references tied to the Trading-Mastery GitHub org and distinguish confirmed facts from likely-but-unverified associations.\", \"subtype\": \"research_run\", \"summary\": \"Confirmed: Trading-Mastery is a public GitHub org with zero public repos, and Trading Mastery has an active public web presence at tradingmastery.com.au plus members/go subdomains. Unconfirmed: no public source found in this pass that directly links the website business to the GitHub org or proves the private repo named Trading Mastery backend.\"}"}
{"id": "legacy_run_b12b10fb-c56e-4774-aaa6-c2088b2668ab", "type": "episode", "status": "active", "source_kind": "run", "title": "Research run: Map named individuals to Humand engineering via public references", "source_ref": "personal-agent:run:b12b10fb-c56e-4774-aaa6-c2088b2668ab", "created_at": "2026-03-13T14:01:39.646260+00:00", "updated_at": "2026-04-30T17:32:27.369041+00:00", "metadata_json": "{\"assumptions\": \"Private repo access unavailable; names may be ambiguous; no sensitive behavioral labels will be stored.\", \"goal\": \"Map named individuals to Humand engineering via public references\", \"run_status\": \"active\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"Check whether the named people can be publicly linked to Humand, especially engineering roles, and store only verified/tentative public affiliation data.\", \"subtype\": \"research_run\", \"summary\": \"Map named individuals to Humand engineering via public references\"}"}
{"id": "legacy_run_73698c1a-612d-456d-b178-6286ae9ab9cd", "type": "episode", "status": "active", "source_kind": "run", "title": "Research run: Persist user/project facts from chat", "source_ref": "personal-agent:run:73698c1a-612d-456d-b178-6286ae9ab9cd", "created_at": "2026-03-13T14:01:39.647471+00:00", "updated_at": "2026-04-30T17:32:27.369103+00:00", "metadata_json": "{\"assumptions\": \"User-provided facts are acceptable as first-party memory without external source\", \"goal\": \"Persist user/project facts from chat\", \"run_status\": \"completed\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"Sebas preferences and project wiring\", \"subtype\": \"research_run\", \"summary\": \"Stored Sebas project wiring facts for street-cast-pwa and street-cast-server main branches, plus instruction to persist future notable user/project facts.\"}"}

RECENT_INGESTION_EVENTS

RECENT_SOURCES


python3 - <<'PY'
import sqlite3, json
conn=sqlite3.connect('/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3')
conn.row_factory=sqlite3.Row
print('LONGEST_CONVERSATION_SPANS')
for row in conn.execute("select source_ref, count(*) c, min(created_at) first_at, max(created_at) last_at, round((julianday(max(created_at)) - julianday(min(created_at))) * 24 * 60, 2) as span_min from memories where source_kind='manual' and source_ref like 'conversation:%' group by source_ref having c > 1 order by span_min desc limit 10"):
    print(json.dumps(dict(row), ensure_ascii=False))
PY


LONGEST_CONVERSATION_SPANS
{"source_ref": "conversation:2026-04-29", "c": 3, "first_at": "2026-04-29T20:35:31.158198+00:00", "last_at": "2026-04-29T20:44:21.678062+00:00", "span_min": 8.84}
{"source_ref": "conversation:2026-05-01", "c": 2, "first_at": "2026-05-01T19:45:29.105695+00:00", "last_at": "2026-05-01T19:46:36.793893+00:00", "span_min": 1.13}
{"source_ref": "conversation:2026-03-13:user-questionnaire-followup-5", "c": 7, "first_at": "2026-03-13T21:40:53.176171+00:00", "last_at": "2026-03-13T21:40:53.187487+00:00", "span_min": 0.0}
{"source_ref": "conversation:2026-03-13:user-questionnaire-followup-3", "c": 4, "first_at": "2026-03-13T21:36:20.594252+00:00", "last_at": "2026-03-13T21:36:20.598406+00:00", "span_min": 0.0}
{"source_ref": "conversation:2026-03-13:user-questionnaire-followup-2", "c": 6, "first_at": "2026-03-13T21:26:33.694262+00:00", "last_at": "2026-03-13T21:26:33.708687+00:00", "span_min": 0.0}
{"source_ref": "conversation:2026-03-13:user-questionnaire-followup", "c": 12, "first_at": "2026-03-13T21:19:16.023880+00:00", "last_at": "2026-03-13T21:19:16.037409+00:00", "span_min": 0.0}
{"source_ref": "conversation:2026-03-13:ballbox-context", "c": 8, "first_at": "2026-03-13T14:42:32.059069+00:00", "last_at": "2026-03-13T14:42:32.071593+00:00", "span_min": 0.0}


Yes. Checked.

What “manual” means here:
- not human-manual
- it means **direct `POST /api/ingest` writes**
- that is what the `memory-write` helper does
- helper default is `--source-kind manual`

So agent-written memories can still be under `source_kind=manual`.

How to tell mid-session vs end-session:

### 1) Direct-ingest path vs capture pipeline
There are two different paths in the service:

- **Direct write**: `ingest()`
  -> creates memory row right away
  -> usually `source_kind=manual`

- **Session/conversation capture**: `capture_session()` / `capture_conversation()`
  -> writes to `sources` + `ingestion_events` first
  -> later `process_pending()` turns those into `episode` memories
  -> would show `source_kind=run` or `source_kind=conversation`

Current DB says:
- memories by source kind:
  - `manual`: 187
  - `document`: 22
  - `run`: 14
- `sources` table: no recent rows
- `ingestion_events` table: no recent rows
- no recent `conversation` source_kind memories at all
- recent `run` memories are only old legacy imported runs

So right now:
- **the active pattern is direct ingest by the agent**
- **the auto capture/session-close pipeline is basically not being used**

### 2) Heuristic from timestamps/source_ref
Recent agent-written memories have refs like:
- `conversation:2026-05-01`
- `conversation:2026-05-01:vibes-install`
- `conversation:2026-04-30:maintenance-wrapper-fix`

That means the agent is tagging them as conversation-derived, but still saving them through direct ingest.

Timing clues:
- `conversation:2026-05-01` → 2 memories over `1.13 min`
- `conversation:2026-04-29` → 3 memories over `8.84 min`

That strongly suggests at least some are **mid-session writes**, not pure “only after session end”.

If it were true end-of-session batching only, I’d expect:
- one compact episode per conversation, or
- a tight burst only at close time

Instead, some conversation refs span minutes.

### Bottom line
- **Yes, those “manual” rows are AI-agent writes.**
- **Most recent memory writes are direct agent ingests.**
- **Some are clearly mid-session.**
- **I do not see evidence that the formal post-session capture pipeline is active right now.**

Best current test:
- if we see rows in `sources` / `ingestion_events`, that’s session/conversation capture
- if we only see `memories` rows with `source_kind=manual`, that’s direct agent write
- current system is almost entirely the second case

If you want, I can next:
1. trace where the agent is calling `memory-write`
2. find whether session-end capture exists but is not wired
3. add a clean marker so future writes say `mid_session` vs `session_end` explicitly


lets test if "auto capture/session-close pipeline is not working"
ill open and close another session to trigger it and you check recent memories


Do that.

After you close it, send:
- approximate close time
- agent/session name if known

Then I’ll check:
- new `sources`
- new `ingestion_events`
- new `episode` memories from capture pipeline
- whether anything got promoted by maintenance

If you want cleaner detection, wait 1–2 min after closing before telling me.


just did


**Inspecting recent sources**

I think I need to examine recent sources or memories to gather relevant information. It's important to understand what has been noted lately and see if there’s anything significant to highlight. I want to make sure I'm up-to-date and accurate in whatever I share. So, this step will help me ensure I'm accessing the most recent insights or updates that might be needed for the user. Let’s get started!

python3 - <<'PY'
import sqlite3, json
conn=sqlite3.connect('/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3')
conn.row_factory=sqlite3.Row
print('RECENT_SOURCES')
for row in conn.execute("select id, source_kind, title, source_ref, created_at, metadata_json from sources order by datetime(created_at) desc limit 15"):
    print(json.dumps(dict(row), ensure_ascii=False))
print('\nRECENT_INGESTION_EVENTS')
for row in conn.execute("select id, trigger_type, source_id, requested_scope, job_state, processor_outcome, error_message, created_at, updated_at, processed_at from ingestion_events order by datetime(created_at) desc limit 15"):
    print(json.dumps(dict(row), ensure_ascii=False))
print('\nRECENT_EPISODE_MEMORIES')
for row in conn.execute("select id, status, source_kind, title, source_ref, created_at, updated_at, metadata_json from memories where type='episode' order by datetime(created_at) desc limit 15"):
    print(json.dumps(dict(row), ensure_ascii=False))
PY


RECENT_SOURCES

RECENT_INGESTION_EVENTS

RECENT_EPISODE_MEMORIES
{"id": "mem_4b3428254ffb442ca1762d1a5895100d", "status": "active", "source_kind": "manual", "title": "Installed Vibes on ballbox-first", "source_ref": "conversation:2026-05-01:vibes-install", "created_at": "2026-05-01T04:56:36.842289+00:00", "updated_at": "2026-05-01T04:56:36.842289+00:00", "metadata_json": "{}"}
{"id": "mem_6485e21fbc354ebf98cea3fa5a1cc6e6", "status": "active", "source_kind": "manual", "title": "agents-database maintenance wrapper fix", "source_ref": "conversation:2026-04-30:maintenance-wrapper-fix", "created_at": "2026-04-30T23:53:17.928610+00:00", "updated_at": "2026-04-30T23:53:17.928610+00:00", "metadata_json": "{}"}
{"id": "mem_807edebd292b4f86a344f5674f7685c8", "status": "active", "source_kind": "manual", "title": "Task completed: Fresh CLI smoke task: summarize current system readiness and suggest one safe internal coordinati...", "source_ref": "personal-agent", "created_at": "2026-03-15T14:22:58.677648+00:00", "updated_at": "2026-03-15T14:22:58.677648+00:00", "metadata_json": "{\"task_id\": \"task_3d7271f7d19e456791b5796695fbe2fa\", \"task_run_id\": \"run_d37c8577c9674333b7157a945274385a\"}"}
{"id": "mem_17eea5c0658f4cd3beece90a93d35e36", "status": "active", "source_kind": "manual", "title": "Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726", "source_ref": "ai-dev-workflow", "created_at": "2026-03-15T14:18:48.581429+00:00", "updated_at": "2026-03-15T14:18:48.581429+00:00", "metadata_json": "{\"idempotency_key\": \"task_2e1c07a93b9d40598bb4bfa504a5d726\", \"kind\": \"subagent_handoff\", \"reason\": \"code work (repo)\", \"task_id\": \"task_2e1c07a93b9d40598bb4bfa504a5d726\"}"}
{"id": "mem_04dc02a90cad40cc826ed1e8f93aff36", "status": "active", "source_kind": "manual", "title": "Subagent intake task_ed6a38c1b49f4e829e067d49d206148b", "source_ref": "ai-dev-workflow", "created_at": "2026-03-15T14:18:33.490715+00:00", "updated_at": "2026-03-15T14:18:33.490715+00:00", "metadata_json": "{\"idempotency_key\": \"task_ed6a38c1b49f4e829e067d49d206148b\", \"kind\": \"subagent_handoff\", \"reason\": \"code work (fix)\", \"task_id\": \"task_ed6a38c1b49f4e829e067d49d206148b\"}"}
{"id": "mem_v1_multi_repo_cross_reference", "status": "active", "source_kind": "manual", "title": "V1 multi-repo cross-reference", "source_ref": "personal-agent", "created_at": "2026-03-14T21:29:18.513932+00:00", "updated_at": "2026-03-14T21:29:18.513932+00:00", "metadata_json": "{\"kind\": \"cross_reference\", \"milestone\": \"V1\"}"}
{"id": "mem_ballbox_summary_snapshot", "status": "active", "source_kind": "manual", "title": "Ballbox snapshot March 2026", "source_ref": "conversation:2026-03-13:ballbox-context", "created_at": "2026-03-13T14:42:32.071593+00:00", "updated_at": "2026-03-13T14:42:32.071593+00:00", "metadata_json": "{\"date\": \"2026-03-13\", \"kind\": \"snapshot\", \"project\": \"ballbox\"}"}
{"id": "legacy_run_9fdccb34-cf1e-47bd-b133-a224f363a6d4", "status": "active", "source_kind": "run", "title": "Research run: Investigate personal assistant patterns", "source_ref": "personal-agent:run:9fdccb34-cf1e-47bd-b133-a224f363a6d4", "created_at": "2026-03-13T14:01:39.630931+00:00", "updated_at": "2026-04-30T17:32:27.368015+00:00", "metadata_json": "{\"assumptions\": \"v1 defaults\", \"goal\": \"Investigate personal assistant patterns\", \"run_status\": \"completed\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"recent setup patterns\", \"subtype\": \"research_run\", \"summary\": \"Seeded baseline run for personal-agent verification.\"}"}
{"id": "legacy_run_a8512264-8cd5-4c3c-b62f-7528625227f1", "status": "active", "source_kind": "run", "title": "Research run: Verify capture-url flow", "source_ref": "personal-agent:run:a8512264-8cd5-4c3c-b62f-7528625227f1", "created_at": "2026-03-13T14:01:39.632489+00:00", "updated_at": "2026-04-30T17:32:27.368368+00:00", "metadata_json": "{\"assumptions\": \"e2e verification\", \"goal\": \"Verify capture-url flow\", \"run_status\": \"active\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"local file capture\", \"subtype\": \"research_run\", \"summary\": \"Verify capture-url flow\"}"}
{"id": "legacy_run_55b6eaa0-9ff5-438c-a375-50f0452079cb", "status": "active", "source_kind": "run", "title": "Research run: Drive Ballbox payment integration toward Volvox-style demo on vending machine Android flow", "source_ref": "personal-agent:run:55b6eaa0-9ff5-438c-a375-50f0452079cb", "created_at": "2026-03-13T14:01:39.633673+00:00", "updated_at": "2026-04-30T17:32:27.368445+00:00", "metadata_json": "{\"assumptions\": \"Current repo is ballbox-payment-integration unless another Volvox-specific repo appears later\", \"goal\": \"Drive Ballbox payment integration toward Volvox-style demo on vending machine Android flow\", \"run_status\": \"active\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"Confirm repo alignment, identify gaps, iterate to demoable QR payment flow\", \"subtype\": \"research_run\", \"summary\": \"Drive Ballbox payment integration toward Volvox-style demo on vending machine Android flow\"}"}
{"id": "legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "status": "active", "source_kind": "run", "title": "Research run: Source 50+ inch China displays for Ballbox advertising in padel venues", "source_ref": "personal-agent:run:bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "created_at": "2026-03-13T14:01:39.634873+00:00", "updated_at": "2026-04-30T17:32:27.368506+00:00", "metadata_json": "{\"assumptions\": \"Need shortlist first, not final purchase decision\", \"goal\": \"Source 50+ inch China displays for Ballbox advertising in padel venues\", \"run_status\": \"active\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"Made-in-China, Alibaba, similar suppliers; indoor or outdoor; browser/fullscreen capable preferred\", \"subtype\": \"research_run\", \"summary\": \"Source 50+ inch China displays for Ballbox advertising in padel venues\"}"}
{"id": "legacy_run_8248124f-5145-475f-a3c7-a37b619abfa0", "status": "active", "source_kind": "run", "title": "Research run: Test intake flow", "source_ref": "personal-agent:run:8248124f-5145-475f-a3c7-a37b619abfa0", "created_at": "2026-03-13T14:01:39.636174+00:00", "updated_at": "2026-04-30T17:32:27.368568+00:00", "metadata_json": "{\"assumptions\": \"none\", \"goal\": \"Test intake flow\", \"run_status\": \"active\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"demo\", \"subtype\": \"research_run\", \"summary\": \"Test intake flow\"}"}
{"id": "legacy_run_b07bc538-5cab-4243-a1d3-c7a7fa55a0a7", "status": "active", "source_kind": "run", "title": "Research run: Drive ballbox-payment-integration to a demoable vending-machine QR payment flow", "source_ref": "personal-agent:run:b07bc538-5cab-4243-a1d3-c7a7fa55a0a7", "created_at": "2026-03-13T14:01:39.637766+00:00", "updated_at": "2026-04-30T17:32:27.368626+00:00", "metadata_json": "{\"assumptions\": \"volvox reference maps to this repo; final flow is machine launcher -> QR page -> payment approval -> callback -> success/demo output\", \"goal\": \"Drive ballbox-payment-integration to a demoable vending-machine QR payment flow\", \"run_status\": \"active\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"Current target repo is ballbox-payment-integration; confirm fit to machine Android launcher flow and iterate toward demo\", \"subtype\": \"research_run\", \"summary\": \"Drive ballbox-payment-integration to a demoable vending-machine QR payment flow\"}"}
{"id": "legacy_run_2c54c32b-3cf1-426b-98a6-9c43971573ff", "status": "active", "source_kind": "run", "title": "Research run: Find China-sourced 50+ inch displays for Ballbox advertising in covered padel clubs", "source_ref": "personal-agent:run:2c54c32b-3cf1-426b-98a6-9c43971573ff", "created_at": "2026-03-13T14:01:39.638922+00:00", "updated_at": "2026-04-30T17:32:27.368687+00:00", "metadata_json": "{\"assumptions\": \"Fullscreen browser preferred; exact hardware/software stack can be validated later; no final purchase decision yet\", \"goal\": \"Find China-sourced 50+ inch displays for Ballbox advertising in covered padel clubs\", \"run_status\": \"active\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"Prioritize covered clubs first; shortlist suppliers/screens from Alibaba, Made-in-China, or similar\", \"subtype\": \"research_run\", \"summary\": \"Find China-sourced 50+ inch displays for Ballbox advertising in covered padel clubs\"}"}
{"id": "legacy_run_fbe3fc0d-b05c-455e-9afe-1b17b4640d97", "status": "active", "source_kind": "run", "title": "Research run: Responder a Codex sin salir de una película en fullscreen, con UI mínima y poco intrusiva, y evaluar cómo implementarlo", "source_ref": "personal-agent:run:fbe3fc0d-b05c-455e-9afe-1b17b4640d97", "created_at": "2026-03-13T14:01:39.640125+00:00", "updated_at": "2026-04-30T17:32:27.368751+00:00", "metadata_json": "{\"assumptions\": \"La prioridad es uso personal en la Mac de Sebas; se acepta prototipo primero; no se decidi\\u00f3 todav\\u00eda si ser\\u00e1 app nativa, helper global, overlay del sistema o integraci\\u00f3n con una app existente\", \"goal\": \"Responder a Codex sin salir de una pel\\u00edcula en fullscreen, con UI m\\u00ednima y poco intrusiva, y evaluar c\\u00f3mo implementarlo\", \"run_status\": \"completed\", \"schema\": \"shared-agent-memory-v2\", \"scope\": \"Mac local; tarea doble: investigar enfoques posibles y luego construir/probar un overlay o input transparente que aparezca solo al responder mientras una pel\\u00edcula est\\u00e1 en pantalla completa\", \"subtype\": \"research_run\", \"summary\": \"Conclusi\\u00f3n: no conviene app nativa primero. Orden simple: 1) BetterTouchTool para overlay/input sin reinventar, 2) Raycast si basta una UI ya hecha, 3) Hammerspoon si hace falta overlay transparente y m\\u00e1s control. Gaps: falta probar cu\\u00e1l de las tres convive mejor con tu reproductor fullscreen concreto y con el env\\u00edo real a Codex.\"}"}


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.


Test result: looks not working.

What I checked right after your close:
- `sources`: empty
- `ingestion_events`: empty
- new `episode` memories: none
- total memories unchanged: `223`

What that means:
- no `capture_session()` write happened
- no pending ingest event got created
- no `process_pending()` output happened
- no maintenance promotion from a fresh captured session happened

So the formal auto capture/session-close pipeline did **not** trigger from that session close.

Strong conclusion:
- current memory activity is coming from direct agent `ingest` writes
- not from the session-close capture pipeline

If you want, next step should be:
1. find where session close is supposed to call `capture_session`
2. see whether that hook is missing vs failing silently
3. patch it and re-test end to end


wait try again, maybe it needed time


python3 - <<'PY'
import sqlite3, json
conn=sqlite3.connect('/mnt/rpi/agents-database/data/shared-agent-memory.sqlite3')
conn.row_factory=sqlite3.Row
print('COUNTS')
for name in ['sources','ingestion_events','memories']:
    try:
        c=conn.execute(f'select count(*) from {name}').fetchone()[0]
        print(name, c)
    except Exception as e:
        print(name, 'ERR', e)
print('\nRECENT_SOURCES')
for row in conn.execute("select id, source_kind, title, source_ref, created_at, metadata_json from sources order by datetime(created_at) desc limit 10"):
    print(json.dumps(dict(row), ensure_ascii=False))
print('\nRECENT_INGESTION_EVENTS')
for row in conn.execute("select id, trigger_type, source_id, requested_scope, job_state, processor_outcome, error_message, created_at, updated_at, processed_at from ingestion_events order by datetime(created_at) desc limit 10"):
    print(json.dumps(dict(row), ensure_ascii=False))
print('\nRECENT_MEMORIES')
for row in conn.execute("select id, type, status, source_kind, title, source_ref, created_at, updated_at from memories order by datetime(created_at) desc limit 12"):
    print(json.dumps(dict(row), ensure_ascii=False))
PY


COUNTS
sources 0
ingestion_events 0
memories 223

RECENT_SOURCES

RECENT_INGESTION_EVENTS

RECENT_MEMORIES
{"id": "mem_3c62253993584122a81f9a21de4acc33", "type": "artifact", "status": "active", "source_kind": "manual", "title": "ballbox-first microSD migration handoff", "source_ref": "file:/home/sebas/microsd-migration-handoff.md", "created_at": "2026-05-04T21:29:59.519213+00:00", "updated_at": "2026-05-04T21:29:59.519213+00:00"}
{"id": "mem_90f4bcce6d284ed0b6f6edb9a6ec6cc2", "type": "decision", "status": "active", "source_kind": "manual", "title": "Prefer session-end memory writes", "source_ref": "conversation:2026-05-01", "created_at": "2026-05-01T19:46:36.793893+00:00", "updated_at": "2026-05-01T19:46:36.793893+00:00"}
{"id": "mem_69278f6716654a3c8fc5db37f97d2a6c", "type": "profile", "status": "active", "source_kind": "manual", "title": "Follow-up instructions go to todo first", "source_ref": "conversation:2026-05-01", "created_at": "2026-05-01T19:45:29.105695+00:00", "updated_at": "2026-05-01T19:45:29.105695+00:00"}
{"id": "mem_b9dfde5e35a84944b86841b537be2b4c", "type": "project", "status": "active", "source_kind": "manual", "title": "Vibes service needs provider env", "source_ref": null, "created_at": "2026-05-01T16:08:52.128088+00:00", "updated_at": "2026-05-01T16:08:52.128088+00:00"}
{"id": "mem_4b3428254ffb442ca1762d1a5895100d", "type": "episode", "status": "active", "source_kind": "manual", "title": "Installed Vibes on ballbox-first", "source_ref": "conversation:2026-05-01:vibes-install", "created_at": "2026-05-01T04:56:36.842289+00:00", "updated_at": "2026-05-01T04:56:36.842289+00:00"}
{"id": "mem_ebbca254ff484623b6d63073ee758d8d", "type": "decision", "status": "active", "source_kind": "manual", "title": "pi-tau-mux stale ctx fix in tau-client", "source_ref": "conversation:2026-05-01:/new-stale-ctx", "created_at": "2026-05-01T04:46:27.346689+00:00", "updated_at": "2026-05-01T04:46:27.346689+00:00"}
{"id": "mem_6485e21fbc354ebf98cea3fa5a1cc6e6", "type": "episode", "status": "active", "source_kind": "manual", "title": "agents-database maintenance wrapper fix", "source_ref": "conversation:2026-04-30:maintenance-wrapper-fix", "created_at": "2026-04-30T23:53:17.928610+00:00", "updated_at": "2026-04-30T23:53:17.928610+00:00"}
{"id": "mem_94ec049a0b6345ad9e54746c62a51908", "type": "decision", "status": "active", "source_kind": "manual", "title": "Native tmux pi batch jobs for one-prompt subagents", "source_ref": "conversation:2026-04-30", "created_at": "2026-04-30T23:34:35.028872+00:00", "updated_at": "2026-04-30T23:34:35.028872+00:00"}
{"id": "mem_be158f743dcc43c2a38b3bb6ca348352", "type": "artifact", "status": "active", "source_kind": "manual", "title": "Vending setup day manual added", "source_ref": "docs/vending-machine-setup-day-manual.md", "created_at": "2026-04-30T19:20:49.492199+00:00", "updated_at": "2026-04-30T19:20:49.492199+00:00"}
{"id": "mem_6d77b1a0123f491489990043b55e9962", "type": "project", "status": "active", "source_kind": "manual", "title": "pi CLI lives in ~/.npm-global but npm global prefix points to zed pi-acp", "source_ref": "conversation:2026-04-30:run-update-pi", "created_at": "2026-04-30T17:30:44.055250+00:00", "updated_at": "2026-04-30T17:30:44.055250+00:00"}
{"id": "mem_cda20c374a614e6ca531f9e9e121c5bd", "type": "project", "status": "active", "source_kind": "manual", "title": "Laptop helpers use dedicated SSH key on port 2222", "source_ref": "conversation:2026-04-29", "created_at": "2026-04-29T20:44:21.678062+00:00", "updated_at": "2026-04-29T20:44:21.678062+00:00"}
{"id": "mem_57f96c24431c4d38b5123f37e66bdae8", "type": "profile", "status": "active", "source_kind": "manual", "title": "Laptop access should avoid web auth", "source_ref": "conversation:2026-04-29", "created_at": "2026-04-29T20:37:30.323144+00:00", "updated_at": "2026-04-29T20:37:30.323144+00:00"}


Retried. Same result.

Still:
- `sources = 0`
- `ingestion_events = 0`
- `memories = 223`
- no new rows at all

So not “slow”.
Looks like **not wired / not firing**, not just delayed.

Stronger conclusion now:
- session-close auto capture pipeline is currently inactive
- recent memory behavior is direct agent ingest only

Next useful move:
- inspect where session shutdown should call `capture_session()`
- verify missing hook vs wrong DB / wrong service / swallowed error

If you want, I’ll trace that now.


lets debug, that call is wwhere most findings would occur (seeing the whole conversation) so its really valuable


todo

Debug missing session-close memory capture pipeline

Trace why session-close auto capture is not creating sources/ingestion_events or episode memories. Find the hook, verify whether it is missing, miswired, or failing silently, then patch and validate end to end.


Created #1: Debug missing session-close memory capture pipeline (pending)


Updated #1 (pending → in_progress)


bash

cd /home/sebas && rg -n "capture_session\(|capture_conversation\(|session_close|conversation_capture|/api/ingest|memory-write|source_kind.*manual|session analysis|session-end|session end|close session|on close|conversation:" -S .

cd /home/sebas && rg -n "memory-write|capture_session|capture_conversation|api/ingest|agents-database" ~/.agents /mnt/rpi/pi-config -S