/home/sebas/agents-database/src/shared_agent_memory/engine.py:76:        if job_type == "consolidation":
/home/sebas/agents-database/src/shared_agent_memory/engine.py:77:            return self.service.consolidate_candidates(config=job.get("metadata") or {})
/home/sebas/agents-database/src/shared_agent_memory/service.py:274:    def consolidate(self, inbox_items: list[str]) -> dict[str, Any]:
/home/sebas/agents-database/src/shared_agent_memory/service.py:275:        promoted: list[str] = []
/home/sebas/agents-database/src/shared_agent_memory/service.py:311:                    conn.execute("UPDATE memories SET status = ?, updated_at = ? WHERE id = ?", ("archived", utcnow(), memory_id))
/home/sebas/agents-database/src/shared_agent_memory/service.py:315:                promoted.append(memory_id)
/home/sebas/agents-database/src/shared_agent_memory/service.py:316:        return {"promoted": promoted, "linked": linked}
/home/sebas/agents-database/src/shared_agent_memory/service.py:318:    def consolidate_candidates(self, *, config: dict[str, Any]) -> dict[str, Any]:
/home/sebas/agents-database/src/shared_agent_memory/service.py:321:            "promoted": 0,
/home/sebas/agents-database/src/shared_agent_memory/service.py:322:            "archived": 0,
/home/sebas/agents-database/src/shared_agent_memory/service.py:324:            "conflicts": 0,
/home/sebas/agents-database/src/shared_agent_memory/service.py:332:        conflict_threshold = float(config.get("conflict_threshold", 0.55))
/home/sebas/agents-database/src/shared_agent_memory/service.py:388:                        ("archived", utcnow(), candidate["id"]),
/home/sebas/agents-database/src/shared_agent_memory/service.py:390:                    stats["archived"] += 1
/home/sebas/agents-database/src/shared_agent_memory/service.py:398:                        ("archived", utcnow(), candidate["id"]),
/home/sebas/agents-database/src/shared_agent_memory/service.py:400:                    stats["archived"] += 1
/home/sebas/agents-database/src/shared_agent_memory/service.py:433:                if best_match and best_score >= conflict_threshold and self._conflict_signal(candidate, best_match):
/home/sebas/agents-database/src/shared_agent_memory/service.py:434:                    self._record_conflict(conn, candidate["id"], best_match["id"], "potential_contradiction")
/home/sebas/agents-database/src/shared_agent_memory/service.py:436:                    stats["conflicts"] += 1
/home/sebas/agents-database/src/shared_agent_memory/service.py:443:                    stats["promoted"] += 1
/home/sebas/agents-database/src/shared_agent_memory/service.py:445:                    # Normalize qualifying active records as already-promoted durable memories.
/home/sebas/agents-database/src/shared_agent_memory/service.py:446:                    stats["promoted"] += 1
/home/sebas/agents-database/src/shared_agent_memory/service.py:492:            "memory_conflicts",
/home/sebas/agents-database/src/shared_agent_memory/service.py:516:            "memory_conflicts",
/home/sebas/agents-database/src/shared_agent_memory/service.py:1059:                            "archived" if approval["status"] != "pending" else "active",
/home/sebas/agents-database/src/shared_agent_memory/service.py:1246:                "id": "job_consolidation_daily_10",
/home/sebas/agents-database/src/shared_agent_memory/service.py:1247:                "job_type": "consolidation",
/home/sebas/agents-database/src/shared_agent_memory/service.py:1259:                    "conflict_threshold": 0.55,
/home/sebas/agents-database/src/shared_agent_memory/service.py:1266:                "id": "job_consolidation_daily_15",
/home/sebas/agents-database/src/shared_agent_memory/service.py:1267:                "job_type": "consolidation",
/home/sebas/agents-database/src/shared_agent_memory/service.py:1279:                    "conflict_threshold": 0.55,
/home/sebas/agents-database/src/shared_agent_memory/service.py:1286:                "id": "job_consolidation_weekly",
/home/sebas/agents-database/src/shared_agent_memory/service.py:1287:                "job_type": "consolidation",
/home/sebas/agents-database/src/shared_agent_memory/service.py:1299:                    "conflict_threshold": 0.55,
/home/sebas/agents-database/src/shared_agent_memory/service.py:1307:            legacy = conn.execute("SELECT id FROM maintenance_jobs WHERE id = ?", ("job_consolidation_daily",)).fetchone()
/home/sebas/agents-database/src/shared_agent_memory/service.py:1315:                    ("deprecated: split into 10:00 and 15:00 jobs", utcnow(), "job_consolidation_daily"),
/home/sebas/agents-database/src/shared_agent_memory/service.py:1659:    def _record_conflict(self, conn: Any, memory_id: str, conflicting_id: str, reason: str) -> None:
/home/sebas/agents-database/src/shared_agent_memory/service.py:1662:            INSERT INTO memory_conflicts (id, memory_id, conflicting_memory_id, reason, created_at, metadata_json)
/home/sebas/agents-database/src/shared_agent_memory/service.py:1665:            (make_id("mcf"), memory_id, conflicting_id, reason, utcnow(), self.store.dumps({})),
/home/sebas/agents-database/src/shared_agent_memory/service.py:1668:    def _conflict_signal(self, left: dict[str, Any], right: dict[str, Any]) -> bool:
/home/sebas/agents-database/src/shared_agent_memory/cli.py:99:    consolidate = subparsers.add_parser("consolidate")
/home/sebas/agents-database/src/shared_agent_memory/cli.py:100:    consolidate.add_argument("memory_ids", nargs="+")
/home/sebas/agents-database/src/shared_agent_memory/cli.py:238:    elif args.command == "consolidate":
/home/sebas/agents-database/src/shared_agent_memory/cli.py:239:        print(json.dumps(service.consolidate(args.memory_ids), indent=2))
/home/sebas/agents-database/src/shared_agent_memory/schema.py:129:CREATE TABLE IF NOT EXISTS memory_conflicts (
/home/sebas/agents-database/src/shared_agent_memory/schema.py:132:    conflicting_memory_id TEXT NOT NULL,
/home/sebas/agents-database/src/shared_agent_memory/schema.py:137:    FOREIGN KEY(conflicting_memory_id) REFERENCES memories(id)
/home/sebas/agents-database/src/shared_agent_memory/schema.py:224:CREATE INDEX IF NOT EXISTS idx_memory_conflicts_mem ON memory_conflicts(memory_id);
/home/sebas/pi-config/scripts/session-memory-sweeper.py:216:    promoted = 0
/home/sebas/pi-config/scripts/session-memory-sweeper.py:217:    archived = 0
/home/sebas/pi-config/scripts/session-memory-sweeper.py:219:    conflicts = 0
/home/sebas/pi-config/scripts/session-memory-sweeper.py:222:        promoted += int(stats.get("promoted") or 0)
/home/sebas/pi-config/scripts/session-memory-sweeper.py:223:        archived += int(stats.get("archived") or 0)
/home/sebas/pi-config/scripts/session-memory-sweeper.py:225:        conflicts += int(stats.get("conflicts") or 0)
/home/sebas/pi-config/scripts/session-memory-sweeper.py:226:    return {"promoted": promoted, "archived": archived, "linked": linked, "conflicts": conflicts}
/home/sebas/pi-config/scripts/session-memory-sweeper.py:276:            f"promoted {maintenance['promoted']}, "
/home/sebas/pi-config/scripts/session-memory-sweeper.py:277:            f"archived {maintenance['archived']}, "
/home/sebas/pi-config/scripts/session-memory-sweeper.py:279:            f"conflicts {maintenance['conflicts']}"
/home/sebas/pi-config/scripts/session-memory-analyzer.py:139:- Include strong durable memories and weaker candidate memories that may be worth later consolidation.
/home/sebas/pi-config/scripts/session-memory-analyzer.py:145:- It is OK to include lower-confidence candidates if they could become useful after consolidation.


bash

python3 - <<'PY'
import sqlite3, json
p='/home/sebas/agents-database/data/shared-agent-memory.sqlite3'
con=sqlite3.connect(p)
con.row_factory=sqlite3.Row
cur=con.cursor()
print('status counts')
for r in cur.execute("SELECT status, COUNT(*) c FROM memories GROUP BY status ORDER BY c DESC"):
    print(dict(r))
print('\nrecent active sample')
for r in cur.execute("SELECT id,type,scope,status,title,updated_at,confidence,freshness FROM memories WHERE status='active' ORDER BY updated_at DESC LIMIT 20"):
    print(dict(r))
PY


    def consolidate_candidates(self, *, config: dict[str, Any]) -> dict[str, Any]:
        stats = {
            "candidates": 0,
            "promoted": 0,
            "archived": 0,
            "linked": 0,
            "conflicts": 0,
            "llm_summaries": 0,
        }
        age_days = int(config.get("candidate_age_days", 7))
        max_candidates = int(config.get("max_candidates", 200))
        high_threshold = float(config.get("dedupe_high_threshold", 0.9))
        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),
                "source_ref": source_ref,
                "evidence_ref": evidence_ref,
                "metadata": {"kind": "profile_fact"},
            }
        )

    def explain(self, identifier: str) -> dict[str, Any]:
        with self.store.connection() as conn:
            retrieval = conn.execute("SELECT * FROM retrieval_logs WHERE id = ?", (identifier,)).fetchone()
            if retrieval:
                return {
                    "kind": "retrieval",
                    "id": retrieval["id"],
                    "query_text": retrieval["query_text"],
                    "filters": self.store.loads(retrieval["filters_json"]),
                    "results": self.store.loads(retrieval["results_json"]),
                }
            memory = conn.execute("SELECT * FROM memories WHERE id = ?", (identifier,)).fetchone()
            if memory:
                mem = self._row_to_memory(memory)
                sources = conn.execute("SELECT * FROM sources WHERE source_ref = ? OR id = ?", (mem["source_ref"], mem["source_ref"])).fetchall()
                return {"kind": "memory", "memory": mem, "sources": [dict(source) for source in sources]}
        raise KeyError(f"Unknown identifier: {identifier}")

    def export(self) -> dict[str, Any]:
        tables = [
            "projects",
            "repos",
            "sources",
            "ingestion_events",
            "memories",
            "memory_links",
            "memory_conflicts",
            "retrieval_logs",
            "maintenance_jobs",
            "maintenance_runs",
            "profiles",
            "tasks",
            "task_runs",
            "artifacts",
        ]
        exported: dict[str, list[dict[str, Any]]] = {}
        with self.store.connection() as conn:
            for table in tables:
                rows = conn.execute(f"SELECT * FROM {table}").fetchall()
                exported[table] = [dict(row) for row in rows]
        return exported

    def import_data(self, payload: dict[str, Any]) -> None:
        tables = [
            "projects",
            "repos",
            "sources",
            "ingestion_events",
            "memories",
            "memory_links",
            "memory_conflicts",
            "retrieval_logs",
            "maintenance_jobs",
            "maintenance_runs",
            "profiles",
            "tasks",
            "task_runs",
            "artifacts",
        ]
        with self.store.connection() as conn:
            for table in tables:
                rows = payload.get(table, [])
                if not rows:
                    continue
                columns = list(rows[0].keys())
                placeholder = ",".join("?" for _ in columns)
                sql = f"INSERT OR REPLACE INTO {table} ({','.join(columns)}) VALUES ({placeholder})"
                conn.executemany(sql, [tuple(row.get(col) for col in columns) for row in rows])
                if table == "memories":
                    for row in rows:
                        self._upsert_fts(conn, row)
        self.migrate_v2()

[1172 more lines in file. Use offset=538 to continue.]


    def _link_memories(self, conn: Any, from_id: str, to_id: str, relation: str, reason: str) -> None:
        conn.execute(
            """
            INSERT INTO memory_links (id, from_memory_id, to_memory_id, relation, created_at, metadata_json)
            VALUES (?, ?, ?, ?, ?, ?)
            """,
            (make_id("lnk"), from_id, to_id, relation, utcnow(), self.store.dumps({"reason": reason})),
        )

    def _record_conflict(self, conn: Any, memory_id: str, conflicting_id: str, reason: str) -> None:
        conn.execute(
            """
            INSERT INTO memory_conflicts (id, memory_id, conflicting_memory_id, reason, created_at, metadata_json)
            VALUES (?, ?, ?, ?, ?, ?)
            """,
            (make_id("mcf"), memory_id, conflicting_id, reason, utcnow(), self.store.dumps({})),
        )

    def _conflict_signal(self, left: dict[str, Any], right: dict[str, Any]) -> bool:
        allowed_types = {"profile", "decision", "project"}
        if left.get("type") not in allowed_types or right.get("type") not in allowed_types:
            return False
        negations = {"not", "no", "never", "nunca", "sin"}
        left_tokens = set(normalize_text(left["content"]).split())
        right_tokens = set(normalize_text(right["content"]).split())
        left_neg = bool(negations & left_tokens)
        right_neg = bool(negations & right_tokens)
        overlap = len(left_tokens & right_tokens)
        return overlap >= 3 and left_neg != right_neg

    def _llm_summarize_pair(
        self,
        left: dict[str, Any],
        right: dict[str, Any],
        *,
        provider: str,
        command: list[str],
    ) -> str | None:
        if provider != "opencode":
            return None
        prompt = (
            "Summarize and merge the two memory notes into one concise statement. Preserve key facts and avoid speculation.\n\n"
            f"Memory A:\n{left['content']}\n\nMemory B:\n{right['content']}\n"
        )
        try:
            result = subprocess.run(
                command,
                input=prompt,
                text=True,
                capture_output=True,
                timeout=60,
                check=False,
            )
            if result.returncode != 0:
                return None
            output = result.stdout.strip()
            return output or None
        except Exception:
            return None


status counts
{'status': 'active', 'c': 317}

recent active sample
{'id': 'mem_1bd596d2f0854d058b37e0a75d74587c', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] hace poco hicimos en un repo modificacion de zips para hacer…', 'updated_at': '2026-06-03T10:11:50.691236+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_c8667f8f64f747c7a7cc1407da9ff8bb', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] en el pdf diario de rss podriamos agregar el titulo del feed…', 'updated_at': '2026-06-03T10:11:50.684291+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_4870951158a94c72ac1dd324c720731e', 'type': 'decision', 'scope': 'project', 'status': 'active', 'title': 'rss-offline PDF uses reader-style print layout', 'updated_at': '2026-06-02T15:00:21.183318+00:00', 'confidence': 1.0, 'freshness': 1.0}
{'id': 'mem_31a3021ad8f349e19bc839199ae6697d', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] te animas a reproducir este logo a la perfeccion pero vector…', 'updated_at': '2026-06-02T10:11:58.227925+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_d356ae644c4f4f90982d6024a49085c0', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] limpia la carparta de dropzone que te voy a pasar nuevos', 'updated_at': '2026-06-02T10:11:58.222933+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_e80d2be0aecc4eed84d5aa179f83bfdd', 'type': 'episode', 'scope': 'project', 'status': 'active', 'title': 'pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…', 'updated_at': '2026-06-01T10:07:54.414849+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_89d7f9db9cb04d0da7539c33e3ccc024', 'type': 'episode', 'scope': 'project', 'status': 'active', 'title': 'pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…', 'updated_at': '2026-06-01T10:07:54.414093+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_a986376af1554bec8e3909c656585bb6', 'type': 'episode', 'scope': 'project', 'status': 'active', 'title': 'pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…', 'updated_at': '2026-06-01T10:07:54.413440+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_15c297bc9c334fed8e31075b26130378', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] loved this repo’s idea https://github.com/workos/case and it…', 'updated_at': '2026-06-01T10:07:54.412728+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_4814b92ec95a4376a9927668421d8503', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] te fijas que los links a pdfs en https://ballbox-first.emper…', 'updated_at': '2026-05-31T10:05:58.694147+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_f69a512f8b034dba821f31efc6b046db', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] busca una conversación de una sesión de pi de hoy, en donde…', 'updated_at': '2026-05-31T10:05:58.683372+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_0ffba9b736bf4c0aae1d3696fb36c65e', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] hay una sesion de tmux abierta?', 'updated_at': '2026-05-31T10:05:58.670977+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_a238b1334bbf4bba900d3d91b83f5174', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: hay una sesion de tmux abierta?', 'updated_at': '2026-05-31T10:05:58.664635+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_d45915de1d704362a10b78a4beb0a0b9', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] dale hacelo', 'updated_at': '2026-05-31T10:05:58.661030+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_1e0572197f9048ba9f99f84fbd0a0592', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] te llegaron?', 'updated_at': '2026-05-31T10:05:58.657005+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_165ae24535224d79b9e0370d64c1e7b4', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] el zip que paso son los archivos en una máquina expendedora.…', 'updated_at': '2026-05-31T10:05:58.649423+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_e9134aea6bfb4091b7051a97af2ae9e1', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'Ballbox machine rebrand releases published in dropbox repo', 'updated_at': '2026-05-31T08:07:39.615946+00:00', 'confidence': 1.0, 'freshness': 1.0}
{'id': 'mem_edf34d5e3b9a4dbe9276d288255bade7', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] resume ftp investigation for ballbox. Since we need a passwo…', 'updated_at': '2026-05-28T10:15:31.691079+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_3bca1201a684427e87bca9e7cc806afc', 'type': 'episode', 'scope': 'global', 'status': 'active', 'title': 'sebas: [telegram] how do we set up a deepseek api key to pi?', 'updated_at': '2026-05-28T10:15:31.681910+00:00', 'confidence': 0.72, 'freshness': 0.95}
{'id': 'mem_8517007aa63c464c9b84431f6562769a', 'type': 'artifact', 'scope': 'project', 'status': 'active', 'title': 'OurVend FTP host is cloud infra, not machine access', 'updated_at': '2026-05-27T21:59:48.292980+00:00', 'confidence': 1.0, 'freshness': 1.0}


**Running conflict checks**

I need to perform a dry run, possibly with a copy of the existing script. I'll inspect the default job configurations to adjust the thresholds. Using a Python script, I plan to simulate stricter thresholds and enhance conflict detection. The user has asked for a stricter conflict criterion. I’ll define a lower conflict threshold and look for near-duplicates. To run this, I’ll check memory schema fields and recent episodes for results without modifying the database.

python3 - <<'PY'
import sqlite3, json, re
from datetime import datetime, timezone, timedelta
from pathlib import Path

DB='/home/sebas/agents-database/data/shared-agent-memory.sqlite3'
con=sqlite3.connect(DB)
con.row_factory=sqlite3.Row
cur=con.cursor()

cutoff=(datetime.now(timezone.utc)-timedelta(days=7)).isoformat()
rows=cur.execute('''
SELECT * FROM memories
WHERE ((status='inbox') OR (type='episode' AND status='active'))
  AND created_at <= ?
ORDER BY created_at ASC
LIMIT 200
''',(cutoff,)).fetchall()
mems=[dict(r) for r in rows]
print('candidates', len(mems))

def norm(s):
    s=s.lower()
    s=re.sub(r'\[telegram\]', ' ', s)
    s=re.sub(r'https?://\S+', ' URL ', s)
    s=re.sub(r'[^\w\s]', ' ', s)
    s=' '.join(s.split())
    return s

def toks(s):
    return set(norm(s).split())

def jacc(a,b):
    if not a or not b: return 0.0
    return len(a&b)/len(a|b)

def overlap_ratio(a,b):
    if not a or not b: return 0.0
    return len(a&b)/min(len(a),len(b))
neg={'not','no','never','nunca','sin'}
conflict_pairs=[]
dupe_pairs=[]
link_pairs=[]
for i,m in enumerate(mems):
    mt=toks(m['content'])
    for j,n in enumerate(mems[:i]):
        if m['type']!=n['type'] or m['scope']!=n['scope']:
            continue
        nt=toks(n['content'])
        jac=jacc(mt,nt)
        ov=overlap_ratio(mt,nt)
        # stricter consolidation: normalize channel noise and catch heavy overlap
        if jac>=0.62 or ov>=0.78:
            dupe_pairs.append((jac,ov,m,n))
        elif jac>=0.42 or ov>=0.62:
            link_pairs.append((jac,ov,m,n))
        # stricter conflict: same type/scope, overlap, opposite negation, or explicit antonym-ish cues
        if m['type'] in {'profile','decision','project','artifact','episode'}:
            mn=bool(neg & mt); nn=bool(neg & nt)
            anti=False
            terms=[('always','never'),('with','without'),('enable','disable'),('enabled','disabled'),('public','private'),('local','remote')]
            ms=' '+norm(m['content'])+' '
            ns=' '+norm(n['content'])+' '
            for a,b in terms:
                if ((f' {a} ' in ms and f' {b} ' in ns) or (f' {b} ' in ms and f' {a} ' in ns)):
                    anti=True; break
            if (jac>=0.28 or ov>=0.5) and ((mn!=nn) or anti):
                conflict_pairs.append((jac,ov,m,n,'negation' if mn!=nn else 'antonym'))

dupe_pairs=sorted(dupe_pairs,key=lambda x:(x[0],x[1]), reverse=True)
link_pairs=sorted(link_pairs,key=lambda x:(x[0],x[1]), reverse=True)
conflict_pairs=sorted(conflict_pairs,key=lambda x:(x[0],x[1]), reverse=True)
print('dryrun_dupe_archive', len(dupe_pairs))
print('dryrun_related_link', len(link_pairs))
print('dryrun_conflicts', len(conflict_pairs))
print('\nTOP DUPES')
for jac,ov,m,n in dupe_pairs[:12]:
    print(json.dumps({
        'score': round(jac,3), 'overlap': round(ov,3),
        'a': m['id'], 'a_title': m['title'][:90],
        'b': n['id'], 'b_title': n['title'][:90]
    }, ensure_ascii=False))
print('\nTOP LINKS')
for jac,ov,m,n in link_pairs[:12]:
    print(json.dumps({
        'score': round(jac,3), 'overlap': round(ov,3),
        'a': m['id'], 'a_title': m['title'][:90],
        'b': n['id'], 'b_title': n['title'][:90]
    }, ensure_ascii=False))
print('\nTOP CONFLICTS')
for jac,ov,m,n,reason in conflict_pairs[:12]:
    print(json.dumps({
        'reason': reason, 'score': round(jac,3), 'overlap': round(ov,3),
        'a': m['id'], 'a_title': m['title'][:90],
        'b': n['id'], 'b_title': n['title'][:90]
    }, ensure_ascii=False))
PY


candidates 51
dryrun_dupe_archive 2
dryrun_related_link 4
dryrun_conflicts 3

TOP DUPES
{"score": 0.623, "overlap": 0.786, "a": "mem_17eea5c0658f4cd3beece90a93d35e36", "a_title": "Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726", "b": "mem_04dc02a90cad40cc826ed1e8f93aff36", "b_title": "Subagent intake task_ed6a38c1b49f4e829e067d49d206148b"}
{"score": 0.52, "overlap": 0.788, "a": "legacy_run_2c54c32b-3cf1-426b-98a6-9c43971573ff", "a_title": "Research run: Find China-sourced 50+ inch displays for Ballbox advertising in covered pade", "b": "legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "b_title": "Research run: Source 50+ inch China displays for Ballbox advertising in padel venues"}

TOP LINKS
{"score": 0.489, "overlap": 0.697, "a": "legacy_run_b07bc538-5cab-4243-a1d3-c7a7fa55a0a7", "a_title": "Research run: Drive ballbox-payment-integration to a demoable vending-machine QR payment f", "b": "legacy_run_55b6eaa0-9ff5-438c-a375-50f0452079cb", "b_title": "Research run: Drive Ballbox payment integration toward Volvox-style demo on vending machin"}
{"score": 0.167, "overlap": 0.667, "a": "legacy_run_8248124f-5145-475f-a3c7-a37b619abfa0", "a_title": "Research run: Test intake flow", "b": "legacy_run_55b6eaa0-9ff5-438c-a375-50f0452079cb", "b_title": "Research run: Drive Ballbox payment integration toward Volvox-style demo on vending machin"}
{"score": 0.15, "overlap": 0.667, "a": "legacy_run_b07bc538-5cab-4243-a1d3-c7a7fa55a0a7", "a_title": "Research run: Drive ballbox-payment-integration to a demoable vending-machine QR payment f", "b": "legacy_run_8248124f-5145-475f-a3c7-a37b619abfa0", "b_title": "Research run: Test intake flow"}
{"score": 0.15, "overlap": 0.667, "a": "legacy_run_a7fe5fbd-4892-4398-ab1a-71337f923ed7", "a_title": "Research run: Analyze the best next steps to make ballbox-payment-integration production-r", "b": "legacy_run_8248124f-5145-475f-a3c7-a37b619abfa0", "b_title": "Research run: Test intake flow"}

TOP CONFLICTS
{"reason": "negation", "score": 0.623, "overlap": 0.786, "a": "mem_17eea5c0658f4cd3beece90a93d35e36", "a_title": "Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726", "b": "mem_04dc02a90cad40cc826ed1e8f93aff36", "b_title": "Subagent intake task_ed6a38c1b49f4e829e067d49d206148b"}
{"reason": "negation", "score": 0.154, "overlap": 0.5, "a": "legacy_run_35bd54a1-0135-414e-9c6b-245fca772f54", "a_title": "Research run: Production-readiness plan for ballbox-payment-integration", "b": "legacy_run_a8512264-8cd5-4c3c-b62f-7528625227f1", "b_title": "Research run: Verify capture-url flow"}
{"reason": "negation", "score": 0.087, "overlap": 0.606, "a": "mem_c1cd76a28d434774ad10b1507577bab3", "a_title": "sebas: [telegram] necesito para ballbox un entregable en pdf profesional para…", "b": "mem_a1f632320762487c8864dd45f2cbf394", "b_title": "sebas: [telegram] hazme en inglés, un texto explicando con flechas, el flujo m…"}


**Investigating suspicious items**

I think I need to closely inspect the most suspicious items because some could be false positives. I want to ensure that I'm reading the contents carefully to verify their legitimacy. It's always tricky to identify what's really off versus what's just standard. I'll take my time to analyze everything thoroughly to avoid any mistakes. Let’s make sure I'm getting it right this time!

python3 - <<'PY'
import sqlite3, json
ids=[
'mem_17eea5c0658f4cd3beece90a93d35e36','mem_04dc02a90cad40cc826ed1e8f93aff36',
'legacy_run_2c54c32b-3cf1-426b-98a6-9c43971573ff','legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7',
'legacy_run_35bd54a1-0135-414e-9c6b-245fca772f54','legacy_run_a8512264-8cd5-4c3c-b62f-7528625227f1',
'mem_c1cd76a28d434774ad10b1507577bab3','mem_a1f632320762487c8864dd45f2cbf394'
]
con=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')
con.row_factory=sqlite3.Row
cur=con.cursor()
for mid in ids:
 r=cur.execute('SELECT id,type,scope,title,content,created_at FROM memories WHERE id=?',(mid,)).fetchone()
 print('\nID',mid)
 print(json.dumps(dict(r), ensure_ascii=False, indent=2))
PY


ID mem_17eea5c0658f4cd3beece90a93d35e36
{
  "id": "mem_17eea5c0658f4cd3beece90a93d35e36",
  "type": "episode",
  "scope": "agent",
  "title": "Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726",
  "content": "Task ID: task_2e1c07a93b9d40598bb4bfa504a5d726\nOrigin: personal-agent\nReason: code work (repo)\nPayload: {\"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\"}",
  "created_at": "2026-03-15T14:18:48.581429+00:00"
}

ID mem_04dc02a90cad40cc826ed1e8f93aff36
{
  "id": "mem_04dc02a90cad40cc826ed1e8f93aff36",
  "type": "episode",
  "scope": "agent",
  "title": "Subagent intake task_ed6a38c1b49f4e829e067d49d206148b",
  "content": "Task ID: task_ed6a38c1b49f4e829e067d49d206148b\nOrigin: personal-agent\nReason: code work (fix)\nPayload: {\"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\"}",
  "created_at": "2026-03-15T14:18:33.490715+00:00"
}

ID legacy_run_2c54c32b-3cf1-426b-98a6-9c43971573ff
{
  "id": "legacy_run_2c54c32b-3cf1-426b-98a6-9c43971573ff",
  "type": "episode",
  "scope": "global",
  "title": "Research run: Find China-sourced 50+ inch displays for Ballbox advertising in covered padel clubs",
  "content": "Goal: Find China-sourced 50+ inch displays for Ballbox advertising in covered padel clubs\nScope: Prioritize covered clubs first; shortlist suppliers/screens from Alibaba, Made-in-China, or similar\nAssumptions: Fullscreen browser preferred; exact hardware/software stack can be validated later; no final purchase decision yet\nSummary: Find China-sourced 50+ inch displays for Ballbox advertising in covered padel clubs",
  "created_at": "2026-03-13T14:01:39.638922+00:00"
}

ID legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7
{
  "id": "legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7",
  "type": "episode",
  "scope": "global",
  "title": "Research run: Source 50+ inch China displays for Ballbox advertising in padel venues",
  "content": "Goal: Source 50+ inch China displays for Ballbox advertising in padel venues\nScope: Made-in-China, Alibaba, similar suppliers; indoor or outdoor; browser/fullscreen capable preferred\nAssumptions: Need shortlist first, not final purchase decision\nSummary: Source 50+ inch China displays for Ballbox advertising in padel venues",
  "created_at": "2026-03-13T14:01:39.634873+00:00"
}

ID legacy_run_35bd54a1-0135-414e-9c6b-245fca772f54
{
  "id": "legacy_run_35bd54a1-0135-414e-9c6b-245fca772f54",
  "type": "episode",
  "scope": "global",
  "title": "Research run: Production-readiness plan for ballbox-payment-integration",
  "content": "Goal: Production-readiness plan for ballbox-payment-integration\nScope: Capture repository-based evidence, claims, and follow-up tasks for first production deployment readiness.\nAssumptions: Local repo assessment only; external provider contracts and production infra not yet validated.\nSummary: Production-readiness plan for ballbox-payment-integration",
  "created_at": "2026-03-13T14:01:39.642436+00:00"
}

ID legacy_run_a8512264-8cd5-4c3c-b62f-7528625227f1
{
  "id": "legacy_run_a8512264-8cd5-4c3c-b62f-7528625227f1",
  "type": "episode",
  "scope": "global",
  "title": "Research run: Verify capture-url flow",
  "content": "Goal: Verify capture-url flow\nScope: local file capture\nAssumptions: e2e verification\nSummary: Verify capture-url flow",
  "created_at": "2026-03-13T14:01:39.632489+00:00"
}

ID mem_c1cd76a28d434774ad10b1507577bab3
{
  "id": "mem_c1cd76a28d434774ad10b1507577bab3",
  "type": "episode",
  "scope": "global",
  "title": "sebas: [telegram] necesito para ballbox un entregable en pdf profesional para…",
  "content": "User asked: [telegram] necesito para ballbox un entregable en pdf profesional para mandar a una imprenta a que hagan un ploteo de las maquinas. tiene que ser el logo de ballbox con un qr a su derecha que lleve a https://ballbox.app el logo sacalo de la webapp clonada local y corre todo esto en un ralph loop con 5 iteraciones para estar bien seguros de que el trabajo este hecho. mandame el link cuando termine…\nResult: Usá este: **Prompt** Diseñá un logo nuevo para una startup llamada **Ballbox**. Contexto de marca: - Ballbox combina **máquinas físicas inteligentes** con **software** - vende **tubos de pelotas** y también puede mostrar **publicidad en pantallas integradas** - foco inicial: **padel clubs** - la marca tiene que sentirse: - **tecnológica** - **moderna** - **simple** - **premium** - **fácil de reconocer a distancia** - **aplicable tanto en máquina física como en app/web** Objetivo del logo: - que funcione muy bien en: - frente de una **máquina expendedora** - **pantalla grande** - **app móvil** - **favicon** - **vinilo / ploteo** - **redes** - debe verse fuerte tanto en tamaños grandes como chicos - debe ser **minimalista**, no recargado - debe transmitir una mezcla de: - **deporte** - **automatización** - **tecnología** - **movimiento** - **confianza** Dirección visual: - evitar look infantil - evitar estilo genérico de startup SaaS - evitar parecer marca de energía, cripto o gaming - tomar inspiración sutil de: - **pelota / tubo de pelotas** - **padel / racket sports** - **módulo / caja / dispenser** - **pantalla / frame / interfaz** - **flujo físico + digital** - no usar demasiados detalles finos - formas limpias, geométricas, memorables - idealmente que tenga: - **isotipo** - **wordmark** - y versión combinada Color: - explorar una paleta principal con: - **verde lima / verde tenis sofisticado** - **negro / carbón** - **blanco** - opcional: variantes monocromo - el verde debe sentirse más **premium-tech** que deportivo barato Entrega esperada: - 3 a 5 conceptos claramente distintos - para cada concepto: - breve racional - qué simboliza - por qué sirve para Ballbox - mostrar variantes: - fondo blanco - fondo negro - solo isotipo - horizontal - vertical - priorizar logos que se puedan producir fácil en: - impresión - corte de vinilo - señalética - pantalla Estilo de salida: - alta calidad de branding - dirección creativa profesional - aspecto de marca real lista pa…",
  "created_at": "2026-05-25T03:14:20.619368+00:00"
}

ID mem_a1f632320762487c8864dd45f2cbf394
{
  "id": "mem_a1f632320762487c8864dd45f2cbf394",
  "type": "episode",
  "scope": "global",
  "title": "sebas: [telegram] hazme en inglés, un texto explicando con flechas, el flujo m…",
  "content": "User asked: [telegram] hazme en inglés, un texto explicando con flechas, el flujo muy simple de cómo se paga en máquina expendedora. Muy grandes rasgos para explicar el rol del código QR\nResult: Hecho. Roté la sesión.",
  "created_at": "2026-05-24T15:14:28.619357+00:00"
}


**Considering database checks**

I’m thinking it might be important to run a better dry run against the entire database or perhaps focus on the most recent two weeks. The user is interested in seeing what results come up, particularly concerning any false positives or key issues. I could also take a closer look at the default configurations to ensure everything is in order. There’s definitely a lot to consider here!

python3 - <<'PY'
import sqlite3, json
con=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')
con.row_factory=sqlite3.Row
cur=con.cursor()
for job in cur.execute("SELECT id, metadata_json FROM maintenance_jobs WHERE job_type='consolidation' ORDER BY id"):
    print(job['id'], job['metadata_json'])
PY


job_consolidation_daily_10 {"candidate_age_days": 0, "conflict_threshold": 0.55, "dedupe_high_threshold": 0.9, "dedupe_mid_threshold": 0.75, "llm_command": ["opencode", "-m", "MiniMax M2.5 Free"], "llm_enabled": false, "llm_provider": "opencode", "max_candidates": 200, "mode": "daily_10", "promote_confidence": 0.7}
job_consolidation_daily_15 {"candidate_age_days": 0, "conflict_threshold": 0.55, "dedupe_high_threshold": 0.9, "dedupe_mid_threshold": 0.75, "llm_command": ["opencode", "-m", "MiniMax M2.5 Free"], "llm_enabled": false, "llm_provider": "opencode", "max_candidates": 200, "mode": "daily_15", "promote_confidence": 0.7}
job_consolidation_weekly {"candidate_age_days": 0, "conflict_threshold": 0.55, "dedupe_high_threshold": 0.9, "dedupe_mid_threshold": 0.75, "llm_command": ["opencode", "-m", "MiniMax M2.5 Free"], "llm_enabled": true, "llm_provider": "opencode", "max_candidates": 800, "mode": "weekly", "promote_confidence": 0.7}


**Explaining candidate age logic**

I need to clarify that a candidate age of 0 means it includes all candidates older than the current date. Next, I'll consider running a more faithful dry run using the actual algorithm but with stricter thresholds and no writing involved. It's essential to count how everything archives, links, promotes, or conflicts among all 200 candidates. I think I can implement this based on the actual logic, only changing the thresholds and normalization. That might actually be sufficient.

python3 - <<'PY'
import sqlite3, json, math, re
from datetime import datetime, timezone, timedelta

DB='/home/sebas/agents-database/data/shared-agent-memory.sqlite3'
con=sqlite3.connect(DB)
con.row_factory=sqlite3.Row
cur=con.cursor()
cutoff=datetime.now(timezone.utc).isoformat()  # mirrors candidate_age_days=0
rows=cur.execute('''
SELECT * FROM memories
WHERE ((status='inbox') OR (type='episode' AND status='active'))
  AND created_at <= ?
ORDER BY created_at ASC
LIMIT 200
''',(cutoff,)).fetchall()
mems=[dict(r) for r in rows]
active=[dict(r) for r in cur.execute("SELECT * FROM memories WHERE status='active'")]
by_id={m['id']:m for m in active}

# stricter-than-live dry run
HIGH=0.80
MID=0.55
CONFLICT=0.35
PROMOTE=0.75

def normalize_text(v:str)->str:
    return ' '.join(v.lower().split())

def lexical_score(query_terms, haystack:str)->float:
    if not query_terms:
        return 0.0
    hay_terms=set(normalize_text(haystack).split())
    if not hay_terms:
        return 0.0
    q_terms=set(query_terms)
    inter=len(q_terms & hay_terms)
    union=len(q_terms | hay_terms)
    return inter/union if union else 0.0

def semantic_score(left,right):
    return 0.0

def clean(v:str)->str:
    v=v.lower()
    v=re.sub(r'\[telegram\]',' ',v)
    v=re.sub(r'https?://\S+',' URL ',v)
    v=re.sub(r'[^\w\s]',' ',v)
    return ' '.join(v.split())

def strict_conflict(left,right):
    if left.get('type') not in {'profile','decision','project','episode'} or right.get('type') not in {'profile','decision','project','episode'}:
        return False,None
    lt=set(clean(left['content']).split())
    rt=set(clean(right['content']).split())
    overlap=len(lt & rt)
    if overlap < 4:
        return False,None
    neg={'not','no','never','nunca','sin'}
    ln=bool(neg & lt); rn=bool(neg & rt)
    if ln != rn:
        return True,'negation'
    antonyms=[('always','never'),('with','without'),('enabled','disabled'),('public','private'),('local','remote'),('first','later')]
    ls=' '+clean(left['content'])+' '; rs=' '+clean(right['content'])+' '
    for a,b in antonyms:
        if ((f' {a} ' in ls and f' {b} ' in rs) or (f' {b} ' in ls and f' {a} ' in rs)):
            return True,f'antonym:{a}/{b}'
    return False,None

stats={'candidates':len(mems),'promoted':0,'archived':0,'linked':0,'conflicts':0}
dupes=[]; links=[]; conflicts=[]
for candidate in mems:
    related=[m for m in active if m['id']!=candidate['id'] and m['type']==candidate['type'] and m['scope']==candidate['scope']][:50]
    best_match=None; best_score=0.0
    cand_terms=clean(candidate['content']).split()
    for other in related:
        lexical=lexical_score(cand_terms, clean(other['content']))
        semantic=semantic_score([],[])
        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 clean(candidate['content'])==clean(best_match['content']):
        stats['archived']+=1; stats['linked']+=1
        dupes.append((best_score,candidate,best_match,'exact_normalized'))
        continue
    if best_match and best_score>=HIGH:
        stats['archived']+=1; stats['linked']+=1
        dupes.append((best_score,candidate,best_match,'high_similarity'))
        continue
    if best_match and best_score>=MID:
        stats['linked']+=1
        links.append((best_score,candidate,best_match,'mid_similarity'))
    if best_match and best_score>=CONFLICT:
        ok,reason=strict_conflict(candidate,best_match)
        if ok:
            stats['conflicts']+=1
            conflicts.append((best_score,candidate,best_match,reason))
    if candidate['status']=='inbox' and float(candidate.get('confidence') or 0)>=PROMOTE:
        stats['promoted']+=1
    elif candidate['status']=='active' and candidate['type'] in {'episode','artifact','decision','project','procedural','semantic'} and float(candidate.get('confidence') or 0)>=PROMOTE:
        stats['promoted']+=1

print(json.dumps(stats, indent=2))
print('\nTOP_ARCHIVE')
for score,a,b,reason in sorted(dupes,key=lambda x:x[0], reverse=True)[:10]:
    print(json.dumps({'score':round(score,3),'reason':reason,'a':a['id'],'a_title':a['title'][:85],'b':b['id'],'b_title':b['title'][:85]}, ensure_ascii=False))
print('\nTOP_LINK')
for score,a,b,reason in sorted(links,key=lambda x:x[0], reverse=True)[:10]:
    print(json.dumps({'score':round(score,3),'reason':reason,'a':a['id'],'a_title':a['title'][:85],'b':b['id'],'b_title':b['title'][:85]}, ensure_ascii=False))
print('\nTOP_CONFLICT')
for score,a,b,reason in sorted(conflicts,key=lambda x:x[0], reverse=True)[:10]:
    print(json.dumps({'score':round(score,3),'reason':reason,'a':a['id'],'a_title':a['title'][:85],'b':b['id'],'b_title':b['title'][:85]}, ensure_ascii=False))
PY


{
  "candidates": 67,
  "promoted": 31,
  "archived": 0,
  "linked": 5,
  "conflicts": 7
}

TOP_ARCHIVE

TOP_LINK
{"score": 0.722, "reason": "mid_similarity", "a": "mem_a986376af1554bec8e3909c656585bb6", "a_title": "pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…", "b": "mem_89d7f9db9cb04d0da7539c33e3ccc024", "b_title": "pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…"}
{"score": 0.722, "reason": "mid_similarity", "a": "mem_89d7f9db9cb04d0da7539c33e3ccc024", "a_title": "pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…", "b": "mem_a986376af1554bec8e3909c656585bb6", "b_title": "pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…"}
{"score": 0.712, "reason": "mid_similarity", "a": "mem_e80d2be0aecc4eed84d5aa179f83bfdd", "a_title": "pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…", "b": "mem_89d7f9db9cb04d0da7539c33e3ccc024", "b_title": "pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…"}
{"score": 0.623, "reason": "mid_similarity", "a": "mem_04dc02a90cad40cc826ed1e8f93aff36", "a_title": "Subagent intake task_ed6a38c1b49f4e829e067d49d206148b", "b": "mem_17eea5c0658f4cd3beece90a93d35e36", "b_title": "Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726"}
{"score": 0.623, "reason": "mid_similarity", "a": "mem_17eea5c0658f4cd3beece90a93d35e36", "a_title": "Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726", "b": "mem_04dc02a90cad40cc826ed1e8f93aff36", "b_title": "Subagent intake task_ed6a38c1b49f4e829e067d49d206148b"}

TOP_CONFLICT
{"score": 0.722, "reason": "negation", "a": "mem_a986376af1554bec8e3909c656585bb6", "a_title": "pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…", "b": "mem_89d7f9db9cb04d0da7539c33e3ccc024", "b_title": "pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…"}
{"score": 0.722, "reason": "negation", "a": "mem_89d7f9db9cb04d0da7539c33e3ccc024", "a_title": "pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…", "b": "mem_a986376af1554bec8e3909c656585bb6", "b_title": "pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…"}
{"score": 0.712, "reason": "negation", "a": "mem_e80d2be0aecc4eed84d5aa179f83bfdd", "a_title": "pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…", "b": "mem_89d7f9db9cb04d0da7539c33e3ccc024", "b_title": "pi-config: You are running one deterministic phase-machine pass. Task folder: /hom…"}
{"score": 0.623, "reason": "negation", "a": "mem_04dc02a90cad40cc826ed1e8f93aff36", "a_title": "Subagent intake task_ed6a38c1b49f4e829e067d49d206148b", "b": "mem_17eea5c0658f4cd3beece90a93d35e36", "b_title": "Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726"}
{"score": 0.623, "reason": "negation", "a": "mem_17eea5c0658f4cd3beece90a93d35e36", "a_title": "Subagent intake task_2e1c07a93b9d40598bb4bfa504a5d726", "b": "mem_04dc02a90cad40cc826ed1e8f93aff36", "b_title": "Subagent intake task_ed6a38c1b49f4e829e067d49d206148b"}
{"score": 0.52, "reason": "antonym:first/later", "a": "legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "a_title": "Research run: Source 50+ inch China displays for Ballbox advertising in padel venues", "b": "legacy_run_2c54c32b-3cf1-426b-98a6-9c43971573ff", "b_title": "Research run: Find China-sourced 50+ inch displays for Ballbox advertising in covered"}
{"score": 0.52, "reason": "antonym:first/later", "a": "legacy_run_2c54c32b-3cf1-426b-98a6-9c43971573ff", "a_title": "Research run: Find China-sourced 50+ inch displays for Ballbox advertising in covered", "b": "legacy_run_bfe3ad5d-ed7c-4727-bd72-7b9f5b7b0cc7", "b_title": "Research run: Source 50+ inch China displays for Ballbox advertising in padel venues"}


**Inspecting conflicts and results**

I'm noticing that most of these conflicts are actually false positives due to the negation catching phrases like "no repo work." I need to look into why that's happening. However, it's helpful that the stricter heuristic is revealing junk duplicates. I should explain this further, perhaps by inspecting the types of candidates and their confidence levels. That might give me more clarity on these issues!

python3 - <<'PY'
import sqlite3
con=sqlite3.connect('/home/sebas/agents-database/data/shared-agent-memory.sqlite3')
con.row_factory=sqlite3.Row
cur=con.cursor()
for r in cur.execute("SELECT type, scope, ROUND(confidence,2) conf, COUNT(*) c FROM memories WHERE ((status='inbox') OR (type='episode' AND status='active')) GROUP BY type,scope,conf ORDER BY c DESC LIMIT 20"):
    print(dict(r))
PY


{'type': 'episode', 'scope': 'global', 'conf': 0.72, 'c': 32}
{'type': 'episode', 'scope': 'global', 'conf': 0.85, 'c': 14}
{'type': 'episode', 'scope': 'project', 'conf': 0.72, 'c': 4}
{'type': 'episode', 'scope': 'global', 'conf': 1.0, 'c': 3}
{'type': 'episode', 'scope': 'agent', 'conf': 0.91, 'c': 2}
{'type': 'episode', 'scope': 'global', 'conf': 0.95, 'c': 2}
{'type': 'episode', 'scope': 'project', 'conf': 0.99, 'c': 2}
{'type': 'episode', 'scope': 'agent', 'conf': 1.0, 'c': 1}
{'type': 'episode', 'scope': 'global', 'conf': 0.78, 'c': 1}
{'type': 'episode', 'scope': 'global', 'conf': 0.81, 'c': 1}
{'type': 'episode', 'scope': 'global', 'conf': 0.94, 'c': 1}
{'type': 'episode', 'scope': 'global', 'conf': 0.96, 'c': 1}
{'type': 'episode', 'scope': 'project', 'conf': 0.9, 'c': 1}
{'type': 'episode', 'scope': 'project', 'conf': 0.94, 'c': 1}
{'type': 'episode', 'scope': 'project', 'conf': 1.0, 'c': 1}


Updated #1 (in_progress → completed)

Dry run stricter memory consolidation/conflict detection

Inspect current consolidation logic, run a stricter dry-run against recent memories, report what conflicts/consolidations would surface without changing live data.


Sí. Hice dry run, sin tocar nada.

Base real hoy:
- job live:
  - `dedupe_high`: `0.90`
  - `dedupe_mid`: `0.75`
  - `conflict`: `0.55`
  - `promote_conf`: `0.70`

Dry run más estricto/agresivo:
- `dedupe_high`: `0.80`
- `dedupe_mid`: `0.55`
- `conflict`: `0.35`
- `promote_conf`: `0.75`
- además normalicé ruido tipo `[telegram]`, urls, puntuación

Qué salió sobre `67` candidatos:
- promotions: `31`
- archive por dedupe fuerte: `0`
- links por similitud: `5`
- conflicts: `7`

Pero lo importante:
- casi todo lo que “saltó” fue basura estructural
- no aparecieron contradicciones de negocio fuertes
- sí aparecieron memorias repetitivas de sistema

Lo que saltó de verdad:
1. episodios repetidos de `pi-config`
   - “You are running one deterministic phase-machine pass…”
2. episodios repetidos de `Subagent intake task_...`
3. un par de `legacy_run` muy parecidos
   - displays China / Ballbox
   - investigación parecida

Lectura:
- el problema no es que haya conflictos semánticos ocultos grandes
- el problema es que se están guardando muchos episodios operativos parecidos
- el motor actual promueve mucho y limpia poco
- si lo hacés más agresivo, lo primero que emerge es ruido de sistema, no verdad contradictoria

Conclusión corta:
- sí, con criterio más duro aparecen cosas
- pero mayormente son duplicados/near-duplicates operativos
- no vi una mina de conflictos útiles esperando salir

Si querés, siguiente paso útil:
1. excluir memorias operativas de sistema
2. consolidar episodios repetidos por plantilla
3. recién después endurecer conflicto semántico

Eso sí movería la aguja.


[telegram] si coincido entonces no enfoquemos en mejorar conflict detection sino en sacar ruido del sistema, consolidar memorias similares


**Considering memory creation**

I think I need to respond concisely and consider creating a memory for the user’s decision, which seems durable. It might be helpful to save it for later, possibly even mid-conversation. I could acknowledge their statement and outline the next steps, but since they didn’t explicitly ask me to execute anything, I should frame my response appropriately. Let's ensure I’m clear without assuming too much!

Sí. Mejor foco.

Prioridad buena:
1. filtrar ruido de sistema
2. consolidar memorias repetidas
3. dejar conflict detection para después

Porque hoy:
- el falso volumen viene más por duplicación
  que por contradicción real
- mejorar conflicts ahora te da poco valor

Cambio de criterio recomendado:
- no guardar episodios de prompts internos
- no guardar intake/subagent boilerplate
- colapsar runs casi iguales
- endurecer dedupe de episodios operativos

Resultado esperado:
- menos inflación
- métricas más honestas
- memoria más útil para retrieval

Si querés, próximo paso hago plan concreto de limpieza y reglas.


[telegram] dale


**Creating a concise plan**

I want to focus on providing a clear and concise answer with a concrete plan. It seems like there's no need for tools in this case, so I'll keep it straightforward. My aim is to present the information directly and in an easily digestible format. This way, the user can understand what they need without any unnecessary complexity. Let's get started on that concise plan!

Plan concreto:

1. cortar ingreso de ruido
- excluir episodios que sean:
  - prompts de sistema
  - phase-machine / loop boilerplate
  - subagent intake boilerplate
  - job envelopes / routing payloads
- regla: si el valor durable es casi cero,
  no entra a memoria

2. bajar promoción automática
- no tratar `episode active` como
  “ya promovido” por default
- exigir más señal para episodios
  operativos antes de contarlos

3. dedupe por normalización
- comparar contenido normalizado:
  - sin `[telegram]`
  - sin urls
  - sin ids/task ids
  - sin timestamps
  - sin payload json crudo
- eso va a unir muchas memorias
  que hoy parecen distintas

4. consolidación por plantilla
- detectar familias repetidas:
  - `Subagent intake task_*`
  - `You are running one deterministic...`
  - runs/research con mismo goal
- convertir varias en una sola memoria
  resumida o archivar duplicadas

5. separar “operativo” de “durable”
- episodios operativos:
  - default inbox o archivables
- memorias durables:
  - decisiones
  - preferencias
  - facts de proyecto
  - artifacts útiles
- así retrieval deja de mezclar ruido
  con cosas importantes

6. métricas más honestas
- reportar separado:
  - promoted durable
  - linked duplicates
  - archived noise
  - skipped system noise
- no inflar “promoted” con episodios
  activos casi automáticos

Orden:
- primero filtro de ingreso
- después dedupe/consolidación
- después métricas

Impacto esperado:
- menos volumen basura
- mejor search/context
- promotions más creíbles

Si querés, lo próximo hago ya:
- reglas exactas
- archivos a tocar
- cambio mínimo seguro primero


[telegram] si


Cambio mínimo seguro primero:

Archivos a tocar:
- `/home/sebas/pi-config/scripts/session-memory-analyzer.py`
- `/home/sebas/agents-database/src/shared_agent_memory/service.py`

Reglas exactas:

1. filtro de ingreso
No guardar memoria si contenido/título matchea:
- `You are running one deterministic`
- `Subagent intake task_`
- `Task ID: task_`
- `Payload: {`
- `route\": {`
- `phase-machine pass`
- prompts internos de loop/iteration

2. filtro por forma
No guardar si:
- casi todo el contenido es boilerplate
- domina JSON/routing/payload
- no hay user fact, decision, artifact,
  o resultado reusable claro

3. dedupe normalizado
Antes de consolidar, comparar versión
normalizada sin:
- `[telegram]`
- urls
- task ids / run ids
- timestamps
- blobs JSON
- espacios/puntuación extra

4. promotions honestas
No contar `episode active` como
promotion automática.
Solo promotion real si:
- venía `inbox` y sube a `active`
- o pasa filtro durable explícito

5. consolidación más dura para episodios
Para `episode`:
- lower dedupe thresholds
- si es near-duplicate operativo:
  link + archive

Implementación mínima:
- en analyzer:
  - skip de memorias candidatas ruidosas
- en service:
  - helper `normalize_for_dedupe()`
  - usar eso en duplicate/high-similarity
  - sacar conteo inflado de promoted

Orden de trabajo:
1. meter filtros de ruido
2. meter dedupe normalizado
3. corregir métricas de promotion
4. correr dry run de nuevo
5. recién después decidir si activarlo full

Esperado:
- baja inmediata de ruido nuevo
- menos “promotions” falsas
- más links/archives útiles

Si querés, siguiente mensaje:
hago el cambio.


[telegram] dale hacelo


**Modifying files**

I need to modify some files, which means I'll focus on inspecting the relevant sections with the analyzer. I'm thinking it’s crucial to ensure I'm checking the right parts and making needed changes effectively. I need to take a closer look at what needs to be adjusted. I'll ensure I'm thorough in this inspection, as I want to get everything just right without missing anything important. So, let's dive into those sections!

todo

Implement memory noise filtering and normalized consolidation

Apply minimum safe changes to reduce system-noise memories, normalize dedupe, and make promotion metrics honest; validate with dry run.


Created #2: Implement memory noise filtering and normalized consolidation (pending)


Updated #2 (pending → in_progress)


#!/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

FALLBACK_TITLE_MAX = 72

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")
MAX_CANDIDATES = int(os.environ.get("PI_SESSION_MEMORY_MAX_CANDIDATES", "6"))
ACTIVE_IMPORTANCE_THRESHOLD = float(os.environ.get("PI_SESSION_MEMORY_ACTIVE_IMPORTANCE_THRESHOLD", "0.92"))
ACTIVE_CONFIDENCE_THRESHOLD = float(os.environ.get("PI_SESSION_MEMORY_ACTIVE_CONFIDENCE_THRESHOLD", "0.9"))
DURABLE_TYPES = {"profile", "decision", "project", "artifact", "procedural", "semantic"}

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 fallback_analysis(job: dict[str, Any], error: Exception) -> dict[str, Any]:
    meta = session_meta(job)
    last_user = (meta.get("last_user_text") or "").strip()
    last_assistant = (meta.get("last_assistant_text") or "").strip()
    first_user = (meta.get("first_user_text") or last_user or "").strip()
    if not first_user or not last_assistant:
        return {"memories": [], "reason": f"fallback-no-summary:{error}"}
    title_base = first_user.splitlines()[0].strip()
    if len(title_base) > FALLBACK_TITLE_MAX:
        title_base = title_base[: FALLBACK_TITLE_MAX - 1].rstrip() + "…"
    cwd = meta.get("cwd") or ""
    cwd_name = Path(cwd).name or "session"
    return {
        "memories": [
            {
                "type": "episode",
                "subtype": "session_summary",
                "scope": meta.get("scope") or "project",
                "title": f"{cwd_name}: {title_base}",
                "content": f"User asked: {first_user}\nResult: {last_assistant}",
                "source_ref": f"session:{meta.get('session_id') or Path(str(meta.get('session_file') or '')).stem or 'unknown'}",
                "evidence_ref": meta.get("session_file"),
                "confidence": 0.72,
                "freshness": 0.95,
                "importance": 0.56,
                "reason": f"Deterministic fallback because model analysis was unavailable: {error}",
            }
        ],
        "reason": f"fallback-analysis:{error}",
    }

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 extracting reusable memory candidates from a Pi session.

Goal:
- Prefer broad candidate capture over premature rejection.
- Return 0 to {MAX_CANDIDATES} memories from this session.
- Include strong durable memories and weaker candidate memories that may be worth later consolidation.

Rules:
- 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.
- Favor stable user preferences, enduring project facts, reusable external artifacts, decisions, procedures, blockers, and compact episode summaries with future reuse value.
- It is OK to include lower-confidence candidates if they could become useful after consolidation.
- Avoid trivial chatter, one-off execution noise, and duplicates of the same point unless they capture materially different memories.
- Prefer at most one memory per distinct insight.
- Output JSON only. No markdown. No extra text.

Return this schema:
{{
  "memories": [
    {{
      "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
    }}
  ],
  "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.
- Use lower confidence/importance for tentative candidates rather than omitting them.

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 normalize_memories(analysis: dict[str, Any]) -> list[dict[str, Any]]:
    memories = analysis.get("memories")
    if isinstance(memories, list):
        return [item for item in memories if isinstance(item, dict)]
    if bool(analysis.get("save")):
        return [analysis]
    return []

def choose_status(memory_type: str, confidence: float, importance: float) -> str:
    if memory_type in DURABLE_TYPES and confidence >= ACTIVE_CONFIDENCE_THRESHOLD and importance >= ACTIVE_IMPORTANCE_THRESHOLD:
        return "active"
    return "inbox"

def build_memory_payload(candidate: dict[str, Any], meta: dict[str, Any], job_file: Path, analysis_reason: str | None, index: int) -> dict[str, Any]:
    memory_type = candidate.get("type") or "episode"
    confidence = float(candidate.get("confidence") or 0.6)
    importance = float(candidate.get("importance") or 0.5)
    status = choose_status(memory_type, confidence, importance)
    return {
        "type": memory_type,
        "subtype": candidate.get("subtype"),
        "scope": candidate.get("scope") or meta.get("scope") or "global",
        "status": status,
        "source_kind": "manual",
        "title": candidate.get("title") or f"Session insight {index}",
        "content": candidate.get("content") or "",
        "confidence": confidence,
        "freshness": float(candidate.get("freshness") or 0.9),
        "importance": importance,
        "source_ref": candidate.get("source_ref") or f"pi-session:{meta.get('session_file') or meta.get('session_id') or 'unknown'}",
        "evidence_ref": candidate.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_reason,
            "candidate_reason": candidate.get("reason"),
            "trigger": meta.get("trigger"),
            "promotion_candidate": status == "inbox",
            "session_candidate_index": index,
        },
    }

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["status"] = "running"
    job["analysis"] = {
        "status": "running",
        "save": None,
        "saved": None,
        "memory_id": None,
        "memory_ids": [],
        "saved_count": 0,
    }
    save_job(job_file, job)

    try:
        fallback_error = None
        try:
            raw = call_pi(build_prompt(job))
            analysis = extract_json(raw)
        except Exception as exc:
            fallback_error = exc
            analysis = fallback_analysis(job, exc)
            raw = json.dumps(analysis, ensure_ascii=False)
        candidates = normalize_memories(analysis)[:MAX_CANDIDATES]
        saved_items: list[dict[str, Any]] = []

        for index, candidate in enumerate(candidates, start=1):
            memory = build_memory_payload(candidate, meta, job_file, analysis.get("reason"), index)
            if not memory["content"].strip() or not memory["title"].strip():
                continue
            saved_items.append(ingest(memory))

        job["analysis"]["raw"] = raw
        job["analysis"]["result"] = analysis
        job["analysis"]["status"] = "completed"
        job["status"] = "completed"
        if fallback_error is not None:
            job["analysis"]["fallback_error"] = str(fallback_error)
        job["analysis"]["save"] = bool(saved_items)
        job["analysis"]["saved"] = bool(saved_items)
        job["analysis"]["saved_count"] = len(saved_items)
        job["analysis"]["memory_ids"] = [item.get("id") for item in saved_items if item.get("id")]
        job["analysis"]["memory_id"] = job["analysis"]["memory_ids"][0] if job["analysis"]["memory_ids"] else None

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


    def consolidate_candidates(self, *, config: dict[str, Any]) -> dict[str, Any]:
        stats = {
            "candidates": 0,
            "promoted": 0,
            "archived": 0,
            "linked": 0,
            "conflicts": 0,
            "llm_summaries": 0,
        }
        age_days = int(config.get("candidate_age_days", 7))
        max_candidates = int(config.get("max_candidates", 200))
        high_threshold = float(config.get("dedupe_high_threshold", 0.9))
        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),
                "source_ref": source_ref,
                "evidence_ref": evidence_ref,
                "metadata": {"kind": "profile_fact"},
            }
        )

    def explain(self, identifier: str) -> dict[str, Any]:
        with self.store.connection() as conn:
            retrieval = conn.execute("SELECT * FROM retrieval_logs WHERE id = ?", (identifier,)).fetchone()
            if retrieval:
                return {
                    "kind": "retrieval",
                    "id": retrieval["id"],
                    "query_text": retrieval["query_text"],
                    "filters": self.store.loads(retrieval["filters_json"]),
                    "results": self.store.loads(retrieval["results_json"]),
                }
            memory = conn.execute("SELECT * FROM memories WHERE id = ?", (identifier,)).fetchone()
            if memory:
                mem = self._row_to_memory(memory)
                sources = conn.execute("SELECT * FROM sources WHERE source_ref = ? OR id = ?", (mem["source_ref"], mem["source_ref"])).fetchall()
                return {"kind": "memory", "memory": mem, "sources": [dict(source) for source in sources]}
        raise KeyError(f"Unknown identifier: {identifier}")

    def export(self) -> dict[str, Any]:
        tables = [
            "projects",
            "repos",
            "sources",
            "ingestion_events",
            "memories",
            "memory_links",
            "memory_conflicts",
            "retrieval_logs",
            "maintenance_jobs",
            "maintenance_runs",
            "profiles",
            "tasks",
            "task_runs",
            "artifacts",
        ]
        exported: dict[str, list[dict[str, Any]]] = {}
        with self.store.connection() as conn:
            for table in tables:
                rows = conn.execute(f"SELECT * FROM {table}").fetchall()
                exported[table] = [dict(row) for row in rows]
        return exported

[1202 more lines in file. Use offset=508 to continue.]


**Editing for clarity**

I need to make some edits. First, I should add filter helpers in the analyzer and consider importing regex. It seems like it makes sense to import the `re` package in the service section. I'll want to find a suitable spot for that, likely near where I normalize the text. It's time to get these updates implemented, so let's dive in and make everything fit together nicely!

edit

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

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

DURABLE_TYPES = {"profile", "decision", "project", "artifact", "procedural", "semantic"}
NOISE_PATTERNS = [
    re.compile(pattern, re.IGNORECASE)
    for pattern in (
        r"you are running one deterministic",
        r"phase-machine pass",
        r"subagent intake task_",
        r"^task id:\s*task_",
        r"^payload:\s*\{",
        r'"route"\s*:\s*\{',
        r"you are iteration ",
        r"continue the bounded ralph-loop",
    )
]

def is_noise_memory(candidate: dict[str, Any]) -> bool:
    title = str(candidate.get("title") or "")
    content = str(candidate.get("content") or "")
    blob = f"{title}\n{content}".strip()
    if not blob:
        return True
    lowered = blob.lower()
    if any(pattern.search(blob) for pattern in NOISE_PATTERNS):
        return True
    if lowered.count("task_") >= 2:
        return True
    if lowered.count("payload:") >= 1 and lowered.count("route") >= 1:
        return True
    lines = [line.strip() for line in content.splitlines() if line.strip()]
    if lines:
        jsonish = sum(1 for line in lines if line.startswith(("Payload:", "{", "}", '"')) or '"' in line)
        if jsonish / len(lines) >= 0.45:
            return True
    return False

def build_memory_payload(candidate: dict[str, Any], meta: dict[str, Any], job_file: Path, analysis_reason: str | None, index: int) -> dict[str, Any]:

        for index, candidate in enumerate(candidates, start=1):
            memory = build_memory_payload(candidate, meta, job_file, analysis.get("reason"), index)
            if not memory["content"].strip() or not memory["title"].strip():
                continue
            saved_items.append(ingest(memory))

        for index, candidate in enumerate(candidates, start=1):
            if is_noise_memory(candidate):
                continue
            memory = build_memory_payload(candidate, meta, job_file, analysis.get("reason"), index)
            if not memory["content"].strip() or not memory["title"].strip():
                continue
            saved_items.append(ingest(memory))


Successfully replaced 4 block(s) in /home/sebas/pi-config/scripts/session-memory-analyzer.py.

     ...
   3
   4 import argparse
   5 import json
   6 import os
+  7 import re
   7 import shutil
   8 import subprocess
   9 import sys
  10 from pathlib import Path
     ...
  20 MAX_CANDIDATES = int(os.environ.get("PI_SESSION_MEMORY_MAX_CANDIDATES", "6"))
  21 ACTIVE_IMPORTANCE_THRESHOLD = float(os.environ.get("PI_SESSION_MEMORY_ACTIVE_IMPORTANCE_THRESHOLD", "0.92"))
  22 ACTIVE_CONFIDENCE_THRESHOLD = float(os.environ.get("PI_SESSION_MEMORY_ACTIVE_CONFIDENCE_THRESHOLD", "0.9"))
  23 DURABLE_TYPES = {"profile", "decision", "project", "artifact", "procedural", "semantic"}
+ 25 NOISE_PATTERNS = [
+ 26     re.compile(pattern, re.IGNORECASE)
+ 27     for pattern in (
+ 28         r"you are running one deterministic",
+ 29         r"phase-machine pass",
+ 30         r"subagent intake task_",
+ 31         r"^task id:\s*task_",
+ 32         r"^payload:\s*\{",
+ 33         r'"route"\s*:\s*\{',
+ 34         r"you are iteration ",
+ 35         r"continue the bounded ralph-loop",
+ 36     )
+ 37 ]
  24
  25
  26 def resolve_pi_bin() -> str:
  27     candidates = [
     ...
 205         return "active"
 206     return "inbox"
 207
 208
+223 def is_noise_memory(candidate: dict[str, Any]) -> bool:
+224     title = str(candidate.get("title") or "")
+225     content = str(candidate.get("content") or "")
+226     blob = f"{title}\n{content}".strip()
+227     if not blob:
+228         return True
+229     lowered = blob.lower()
+230     if any(pattern.search(blob) for pattern in NOISE_PATTERNS):
+231         return True
+232     if lowered.count("task_") >= 2:
+233         return True
+234     if lowered.count("payload:") >= 1 and lowered.count("route") >= 1:
+235         return True
+236     lines = [line.strip() for line in content.splitlines() if line.strip()]
+237     if lines:
+238         jsonish = sum(1 for line in lines if line.startswith(("Payload:", "{", "}", '"')) or '"' in line)
+239         if jsonish / len(lines) >= 0.45:
+240             return True
+241     return False
+242
+243
 209 def build_memory_payload(candidate: dict[str, Any], meta: dict[str, Any], job_file: Path, analysis_reason: str | None, index: int) -> dict[str, Any]:
 210     memory_type = candidate.get("type") or "episode"
 211     confidence = float(candidate.get("confidence") or 0.6)
 212     importance = float(candidate.get("importance") or 0.5)
     ...
 273         candidates = normalize_memories(analysis)[:MAX_CANDIDATES]
 274         saved_items: list[dict[str, Any]] = []
 275
 276         for index, candidate in enumerate(candidates, start=1):
+312             if is_noise_memory(candidate):
+313                 continue
 277             memory = build_memory_payload(candidate, meta, job_file, analysis.get("reason"), index)
 278             if not memory["content"].strip() or not memory["title"].strip():
 279                 continue
 280             saved_items.append(ingest(memory))
     ...

--- /home/sebas/pi-config/scripts/session-memory-analyzer.py
+++ /home/sebas/pi-config/scripts/session-memory-analyzer.py
@@ -3,8 +3,9 @@

 import argparse
 import json
 import os
+import re
 import shutil
 import subprocess
 import sys
 from pathlib import Path
@@ -20,8 +21,21 @@
 MAX_CANDIDATES = int(os.environ.get("PI_SESSION_MEMORY_MAX_CANDIDATES", "6"))
 ACTIVE_IMPORTANCE_THRESHOLD = float(os.environ.get("PI_SESSION_MEMORY_ACTIVE_IMPORTANCE_THRESHOLD", "0.92"))
 ACTIVE_CONFIDENCE_THRESHOLD = float(os.environ.get("PI_SESSION_MEMORY_ACTIVE_CONFIDENCE_THRESHOLD", "0.9"))
 DURABLE_TYPES = {"profile", "decision", "project", "artifact", "procedural", "semantic"}
+NOISE_PATTERNS = [
+    re.compile(pattern, re.IGNORECASE)
+    for pattern in (
+        r"you are running one deterministic",
+        r"phase-machine pass",
+        r"subagent intake task_",
+        r"^task id:\s*task_",
+        r"^payload:\s*\{",
+        r'"route"\s*:\s*\{',
+        r"you are iteration ",
+        r"continue the bounded ralph-loop",
+    )
+]

 def resolve_pi_bin() -> str:
     candidates = [
@@ -205,8 +219,29 @@
         return "active"
     return "inbox"

+def is_noise_memory(candidate: dict[str, Any]) -> bool:
+    title = str(candidate.get("title") or "")
+    content = str(candidate.get("content") or "")
+    blob = f"{title}\n{content}".strip()
+    if not blob:
+        return True
+    lowered = blob.lower()
+    if any(pattern.search(blob) for pattern in NOISE_PATTERNS):
+        return True
+    if lowered.count("task_") >= 2:
+        return True
+    if lowered.count("payload:") >= 1 and lowered.count("route") >= 1:
+        return True
+    lines = [line.strip() for line in content.splitlines() if line.strip()]
+    if lines:
+        jsonish = sum(1 for line in lines if line.startswith(("Payload:", "{", "}", '"')) or '"' in line)
+        if jsonish / len(lines) >= 0.45:
+            return True
+    return False
+
+
 def build_memory_payload(candidate: dict[str, Any], meta: dict[str, Any], job_file: Path, analysis_reason: str | None, index: int) -> dict[str, Any]:
     memory_type = candidate.get("type") or "episode"
     confidence = float(candidate.get("confidence") or 0.6)
     importance = float(candidate.get("importance") or 0.5)
@@ -273,8 +308,10 @@
         candidates = normalize_memories(analysis)[:MAX_CANDIDATES]
         saved_items: list[dict[str, Any]] = []

         for index, candidate in enumerate(candidates, start=1):
+            if is_noise_memory(candidate):
+                continue
             memory = build_memory_payload(candidate, meta, job_file, analysis.get("reason"), index)
             if not memory["content"].strip() or not memory["title"].strip():
                 continue
             saved_items.append(ingest(memory))


import json
import math
import subprocess
import uuid

import json
import math
import re
import subprocess
import uuid

def normalize_text(value: str) -> str:
    return " ".join(value.lower().split())

def normalize_for_dedupe(value: str) -> str:
    text = value.lower()
    text = re.sub(r"\[telegram\]", " ", text)
    text = re.sub(r"https?://\S+", " URL ", text)
    text = re.sub(r"\b(task|run|job|session)_[a-z0-9_-]+\b", " ID ", text)
    text = re.sub(r"\b\d{4}-\d{2}-\d{2}[t ][^\s]+\b", " TS ", text)
    text = re.sub(r"payload:\s*\{.*", " payload_json ", text, flags=re.IGNORECASE | re.DOTALL)
    text = re.sub(r'"route"\s*:\s*\{.*', " route_json ", text, flags=re.IGNORECASE | re.DOTALL)
    text = re.sub(r"[^\w\s]", " ", text)
    return " ".join(text.split())

                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"]:

                candidate_norm = normalize_for_dedupe(candidate["content"])
                best_match = None
                best_score = 0.0
                for other in related_memories:
                    other_norm = normalize_for_dedupe(other["content"])
                    lexical = self._lexical_score(candidate_norm.split(), other_norm)
                    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_norm == normalize_for_dedupe(best_match["content"]):

                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