feat(memory): add grounded observation synthesis layer

plugins/memory/holographic/__init__.py

@@ -26,6 +26,7 @@ from agent.memory_provider import MemoryProvider
 from tools.registry import tool_error
 from .store import MemoryStore
 from .retrieval import FactRetriever
+from .observations import ObservationSynthesizer
 
 logger = logging.getLogger(__name__)
 
@@ -37,28 +38,29 @@ logger = logging.getLogger(__name__)
 FACT_STORE_SCHEMA = {
     "name": "fact_store",
     "description": (
-        "Deep structured memory with algebraic reasoning. "
+        "Deep structured memory with algebraic reasoning and grounded observation synthesis. "
         "Use alongside the memory tool — memory for always-on context, "
-        "fact_store for deep recall and compositional queries.\n\n"
+        "fact_store for deep recall, compositional queries, and higher-order observations.\n\n"
         "ACTIONS (simple → powerful):\n"
         "• add — Store a fact the user would expect you to remember.\n"
         "• search — Keyword lookup ('editor config', 'deploy process').\n"
         "• probe — Entity recall: ALL facts about a person/thing.\n"
         "• related — What connects to an entity? Structural adjacency.\n"
         "• reason — Compositional: facts connected to MULTIPLE entities simultaneously.\n"
+        "• observe — Synthesized higher-order observations backed by supporting facts.\n"
         "• contradict — Memory hygiene: find facts making conflicting claims.\n"
         "• update/remove/list — CRUD operations.\n\n"
-        "IMPORTANT: Before answering questions about the user, ALWAYS probe or reason first."
+        "IMPORTANT: Before answering questions about the user, ALWAYS probe/reason/observe first."
     ),
     "parameters": {
         "type": "object",
         "properties": {
             "action": {
                 "type": "string",
-                "enum": ["add", "search", "probe", "related", "reason", "contradict", "update", "remove", "list"],
+                "enum": ["add", "search", "probe", "related", "reason", "observe", "contradict", "update", "remove", "list"],
             },
             "content": {"type": "string", "description": "Fact content (required for 'add')."},
-            "query": {"type": "string", "description": "Search query (required for 'search')."},
+            "query": {"type": "string", "description": "Search query (required for 'search'/'observe')."},
             "entity": {"type": "string", "description": "Entity name for 'probe'/'related'."},
             "entities": {"type": "array", "items": {"type": "string"}, "description": "Entity names for 'reason'."},
             "fact_id": {"type": "integer", "description": "Fact ID for 'update'/'remove'."},
@@ -66,6 +68,12 @@ FACT_STORE_SCHEMA = {
             "tags": {"type": "string", "description": "Comma-separated tags."},
             "trust_delta": {"type": "number", "description": "Trust adjustment for 'update'."},
             "min_trust": {"type": "number", "description": "Minimum trust filter (default: 0.3)."},
+            "min_confidence": {"type": "number", "description": "Minimum observation confidence (default: 0.6)."},
+            "observation_type": {
+                "type": "string",
+                "enum": ["recurring_preference", "stable_direction", "behavioral_pattern"],
+                "description": "Optional observation type filter for 'observe'.",
+            },
             "limit": {"type": "integer", "description": "Max results (default: 10)."},
         },
         "required": ["action"],
@@ -118,7 +126,9 @@ class HolographicMemoryProvider(MemoryProvider):
         self._config = config or _load_plugin_config()
         self._store = None
         self._retriever = None
+        self._observation_synth = None
         self._min_trust = float(self._config.get("min_trust_threshold", 0.3))
+        self._observation_min_confidence = float(self._config.get("observation_min_confidence", 0.6))
 
     @property
     def name(self) -> str:
@@ -177,6 +187,7 @@ class HolographicMemoryProvider(MemoryProvider):
             hrr_weight=hrr_weight,
             hrr_dim=hrr_dim,
         )
+        self._observation_synth = ObservationSynthesizer(self._store)
         self._session_id = session_id
 
     def system_prompt_block(self) -> str:
@@ -193,30 +204,76 @@ class HolographicMemoryProvider(MemoryProvider):
                 "# Holographic Memory\n"
                 "Active. Empty fact store — proactively add facts the user would expect you to remember.\n"
                 "Use fact_store(action='add') to store durable structured facts about people, projects, preferences, decisions.\n"
+                "Use fact_store(action='observe') to synthesize higher-order observations with evidence.\n"
                 "Use fact_feedback to rate facts after using them (trains trust scores)."
             )
         return (
             f"# Holographic Memory\n"
             f"Active. {total} facts stored with entity resolution and trust scoring.\n"
-            f"Use fact_store to search, probe entities, reason across entities, or add facts.\n"
+            f"Use fact_store to search, probe entities, reason across entities, or synthesize observations.\n"
             f"Use fact_feedback to rate facts after using them (trains trust scores)."
         )
 
     def prefetch(self, query: str, *, session_id: str = "") -> str:
-        if not self._retriever or not query:
+        if not query:
             return ""
+
+        parts = []
+        raw_results = []
         try:
-            results = self._retriever.search(query, min_trust=self._min_trust, limit=5)
-            if not results:
-                return ""
+            if self._retriever:
+                raw_results = self._retriever.search(query, min_trust=self._min_trust, limit=5)
+        except Exception as e:
+            logger.debug("Holographic prefetch fact search failed: %s", e)
+            raw_results = []
+
+        observations = []
+        try:
+            if self._observation_synth:
+                observations = self._observation_synth.observe(
+                    query,
+                    min_confidence=self._observation_min_confidence,
+                    limit=3,
+                    refresh=True,
+                )
+        except Exception as e:
+            logger.debug("Holographic prefetch observation search failed: %s", e)
+            observations = []
+
+        if not raw_results and observations:
+            seen_fact_ids = set()
+            evidence_backfill = []
+            for observation in observations:
+                for evidence in observation.get("evidence", []):
+                    fact_id = evidence.get("fact_id")
+                    if fact_id in seen_fact_ids:
+                        continue
+                    seen_fact_ids.add(fact_id)
+                    evidence_backfill.append(evidence)
+            raw_results = evidence_backfill[:5]
+
+        if raw_results:
             lines = []
-            for r in results:
+            for r in raw_results:
                 trust = r.get("trust_score", r.get("trust", 0))
                 lines.append(f"- [{trust:.1f}] {r.get('content', '')}")
-            return "## Holographic Memory\n" + "\n".join(lines)
-        except Exception as e:
-            logger.debug("Holographic prefetch failed: %s", e)
-            return ""
+            parts.append("## Holographic Memory\n" + "\n".join(lines))
+
+        if observations:
+            lines = []
+            for observation in observations:
+                evidence_ids = ", ".join(
+                    f"#{item['fact_id']}" for item in observation.get("evidence", [])[:3]
+                ) or "none"
+                lines.append(
+                    f"- [{observation.get('confidence', 0.0):.2f}] "
+                    f"{observation.get('observation_type', 'observation')}: "
+                    f"{observation.get('summary', '')} "
+                    f"(evidence: {evidence_ids})"
+                )
+            parts.append("## Holographic Observations\n" + "\n".join(lines))
+
+        return "\n\n".join(parts)
 
     def sync_turn(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None:
         # Holographic memory stores explicit facts via tools, not auto-sync.
@@ -252,6 +309,7 @@ class HolographicMemoryProvider(MemoryProvider):
     def shutdown(self) -> None:
         self._store = None
         self._retriever = None
+        self._observation_synth = None
 
     # -- Tool handlers -------------------------------------------------------
 
@@ -305,6 +363,19 @@ class HolographicMemoryProvider(MemoryProvider):
                 )
                 return json.dumps({"results": results, "count": len(results)})
 
+            elif action == "observe":
+                synthesizer = self._observation_synth
+                if not synthesizer:
+                    return tool_error("Observation synthesizer is not initialized")
+                observations = synthesizer.observe(
+                    args.get("query", ""),
+                    observation_type=args.get("observation_type"),
+                    min_confidence=float(args.get("min_confidence", self._observation_min_confidence)),
+                    limit=int(args.get("limit", 10)),
+                    refresh=True,
+                )
+                return json.dumps({"observations": observations, "count": len(observations)})
+
             elif action == "contradict":
                 results = retriever.contradict(
                     category=args.get("category"),

plugins/memory/holographic/observations.py

@@ -0,0 +1,249 @@
+"""Higher-order observation synthesis for holographic memory.
+
+Builds grounded observations from accumulated facts and keeps them in a
+separate retrieval layer with explicit evidence links back to supporting facts.
+"""
+
+from __future__ import annotations
+
+import re
+from typing import Any
+
+from .store import MemoryStore
+
+_TOKEN_RE = re.compile(r"[a-z0-9_]+")
+_HIGHER_ORDER_CUES = {
+    "prefer",
+    "preference",
+    "preferences",
+    "style",
+    "pattern",
+    "patterns",
+    "behavior",
+    "behaviour",
+    "habit",
+    "habits",
+    "workflow",
+    "direction",
+    "trajectory",
+    "strategy",
+    "tend",
+    "usually",
+}
+
+_OBSERVATION_PATTERNS = [
+    {
+        "observation_type": "recurring_preference",
+        "subject": "communication_style",
+        "categories": {"user_pref", "general"},
+        "labels": {
+            "concise": ["concise", "terse", "brief", "short", "no fluff"],
+            "result_first": ["result-only", "result only", "outcome only", "quick", "quickly"],
+            "silent_ops": ["silent", "no status", "no repetitive status", "no questions"],
+        },
+        "summary_prefix": "Recurring preference",
+    },
+    {
+        "observation_type": "stable_direction",
+        "subject": "project_direction",
+        "categories": {"project", "general", "tool"},
+        "labels": {
+            "local_first": ["local-first", "local first", "local-only", "local only", "ollama", "own hardware"],
+            "gitea_first": ["gitea-first", "gitea first", "forge", "pull request", "pr flow", "issue flow"],
+            "ansible": ["ansible", "playbook", "role", "deploy via ansible"],
+        },
+        "summary_prefix": "Stable direction",
+    },
+    {
+        "observation_type": "behavioral_pattern",
+        "subject": "operator_workflow",
+        "categories": {"general", "project", "tool", "user_pref"},
+        "labels": {
+            "commit_early": ["commit early", "commits early", "commit after", "wip commit"],
+            "pr_first": ["open pr", "push a pr", "pull request", "pr immediately", "create pr"],
+            "dedup_guard": ["no dupes", "no duplicates", "avoid duplicate", "existing pr"],
+        },
+        "summary_prefix": "Behavioral pattern",
+    },
+]
+
+_TYPE_QUERY_HINTS = {
+    "recurring_preference": {"prefer", "preference", "style", "communication", "likes", "wants"},
+    "stable_direction": {"direction", "trajectory", "strategy", "project", "roadmap", "moving"},
+    "behavioral_pattern": {"pattern", "behavior", "workflow", "habit", "operator", "agent", "usually"},
+}
+
+
+class ObservationSynthesizer:
+    """Synthesizes grounded observations from facts and retrieves them by query."""
+
+    def __init__(self, store: MemoryStore):
+        self.store = store
+
+    def synthesize(
+        self,
+        *,
+        persist: bool = True,
+        min_confidence: float = 0.6,
+        limit: int = 10,
+    ) -> list[dict[str, Any]]:
+        facts = self.store.list_facts(min_trust=0.0, limit=1000)
+        observations: list[dict[str, Any]] = []
+
+        for pattern in _OBSERVATION_PATTERNS:
+            candidate = self._build_candidate(pattern, facts, min_confidence=min_confidence)
+            if not candidate:
+                continue
+
+            if persist:
+                candidate["observation_id"] = self.store.upsert_observation(
+                    candidate["observation_type"],
+                    candidate["subject"],
+                    candidate["summary"],
+                    candidate["confidence"],
+                    candidate["evidence_fact_ids"],
+                    metadata=candidate["metadata"],
+                )
+
+            candidate["evidence"] = self._expand_evidence(candidate["evidence_fact_ids"])
+            candidate["evidence_count"] = len(candidate["evidence"])
+            candidate.pop("evidence_fact_ids", None)
+            observations.append(candidate)
+
+        observations.sort(
+            key=lambda item: (item["confidence"], item.get("evidence_count", 0)),
+            reverse=True,
+        )
+        return observations[:limit]
+
+    def observe(
+        self,
+        query: str = "",
+        *,
+        observation_type: str | None = None,
+        min_confidence: float = 0.6,
+        limit: int = 10,
+        refresh: bool = True,
+    ) -> list[dict[str, Any]]:
+        if refresh:
+            self.synthesize(persist=True, min_confidence=min_confidence, limit=limit)
+
+        observations = self.store.list_observations(
+            observation_type=observation_type,
+            min_confidence=min_confidence,
+            limit=max(limit * 4, 20),
+        )
+        if not observations:
+            return []
+
+        if not query:
+            return observations[:limit]
+
+        query_tokens = self._tokenize(query)
+        is_higher_order = bool(query_tokens & _HIGHER_ORDER_CUES)
+        ranked: list[dict[str, Any]] = []
+
+        for item in observations:
+            searchable = " ".join(
+                [
+                    item.get("summary", ""),
+                    item.get("subject", ""),
+                    item.get("observation_type", ""),
+                    " ".join(item.get("metadata", {}).get("labels", [])),
+                ]
+            )
+            overlap = self._overlap_score(query_tokens, self._tokenize(searchable))
+            type_bonus = self._type_bonus(query_tokens, item.get("observation_type", ""))
+            if overlap <= 0 and type_bonus <= 0 and not is_higher_order:
+                continue
+            ranked_item = dict(item)
+            ranked_item["score"] = round(item.get("confidence", 0.0) + overlap + type_bonus, 3)
+            ranked.append(ranked_item)
+
+        if not ranked and is_higher_order:
+            ranked = [
+                {**item, "score": round(float(item.get("confidence", 0.0)), 3)}
+                for item in observations
+            ]
+
+        ranked.sort(
+            key=lambda item: (item.get("score", 0.0), item.get("confidence", 0.0), item.get("evidence_count", 0)),
+            reverse=True,
+        )
+        return ranked[:limit]
+
+    def _build_candidate(
+        self,
+        pattern: dict[str, Any],
+        facts: list[dict[str, Any]],
+        *,
+        min_confidence: float,
+    ) -> dict[str, Any] | None:
+        matched_fact_ids: set[int] = set()
+        matched_labels: dict[str, set[int]] = {label: set() for label in pattern["labels"]}
+
+        for fact in facts:
+            if fact.get("category") not in pattern["categories"]:
+                continue
+            haystack = f"{fact.get('content', '')} {fact.get('tags', '')}".lower()
+            local_match = False
+            for label, keywords in pattern["labels"].items():
+                if any(keyword in haystack for keyword in keywords):
+                    matched_labels[label].add(int(fact["fact_id"]))
+                    local_match = True
+            if local_match:
+                matched_fact_ids.add(int(fact["fact_id"]))
+
+        if len(matched_fact_ids) < 2:
+            return None
+
+        active_labels = sorted(label for label, ids in matched_labels.items() if ids)
+        confidence = min(0.95, 0.35 + 0.12 * len(matched_fact_ids) + 0.08 * len(active_labels))
+        confidence = round(confidence, 3)
+        if confidence < min_confidence:
+            return None
+
+        label_summary = ", ".join(label.replace("_", "-") for label in active_labels)
+        subject_text = pattern["subject"].replace("_", " ")
+        summary = (
+            f"{pattern['summary_prefix']}: {subject_text} trends toward {label_summary} "
+            f"based on {len(matched_fact_ids)} supporting facts."
+        )
+        return {
+            "observation_type": pattern["observation_type"],
+            "subject": pattern["subject"],
+            "summary": summary,
+            "confidence": confidence,
+            "metadata": {
+                "labels": active_labels,
+                "evidence_count": len(matched_fact_ids),
+            },
+            "evidence_fact_ids": sorted(matched_fact_ids),
+        }
+
+    def _expand_evidence(self, fact_ids: list[int]) -> list[dict[str, Any]]:
+        facts_by_id = {
+            fact["fact_id"]: fact
+            for fact in self.store.list_facts(min_trust=0.0, limit=1000)
+        }
+        return [facts_by_id[fact_id] for fact_id in fact_ids if fact_id in facts_by_id]
+
+    @staticmethod
+    def _tokenize(text: str) -> set[str]:
+        return set(_TOKEN_RE.findall(text.lower()))
+
+    @staticmethod
+    def _overlap_score(query_tokens: set[str], text_tokens: set[str]) -> float:
+        if not query_tokens or not text_tokens:
+            return 0.0
+        overlap = query_tokens & text_tokens
+        if not overlap:
+            return 0.0
+        return round(len(overlap) / max(len(query_tokens), 1), 3)
+
+    @staticmethod
+    def _type_bonus(query_tokens: set[str], observation_type: str) -> float:
+        hints = _TYPE_QUERY_HINTS.get(observation_type, set())
+        if not hints:
+            return 0.0
+        return 0.25 if query_tokens & hints else 0.0

plugins/memory/holographic/store.py

@@ -3,6 +3,7 @@ SQLite-backed fact store with entity resolution and trust scoring.
 Single-user Hermes memory store plugin.
 """
 
+import json
 import re
 import sqlite3
 import threading
@@ -73,6 +74,28 @@ CREATE TABLE IF NOT EXISTS memory_banks (
     fact_count INTEGER DEFAULT 0,
     updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
 );
+
+CREATE TABLE IF NOT EXISTS observations (
+    observation_id   INTEGER PRIMARY KEY AUTOINCREMENT,
+    observation_type TEXT NOT NULL,
+    subject          TEXT NOT NULL,
+    summary          TEXT NOT NULL,
+    confidence       REAL DEFAULT 0.0,
+    metadata_json    TEXT DEFAULT '{}',
+    created_at       TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at       TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    UNIQUE(observation_type, subject)
+);
+
+CREATE TABLE IF NOT EXISTS observation_evidence (
+    observation_id INTEGER REFERENCES observations(observation_id) ON DELETE CASCADE,
+    fact_id        INTEGER REFERENCES facts(fact_id) ON DELETE CASCADE,
+    evidence_weight REAL DEFAULT 1.0,
+    PRIMARY KEY (observation_id, fact_id)
+);
+
+CREATE INDEX IF NOT EXISTS idx_observations_type ON observations(observation_type);
+CREATE INDEX IF NOT EXISTS idx_observations_confidence ON observations(confidence DESC);
 """
 
 # Trust adjustment constants
@@ -128,6 +151,7 @@ class MemoryStore:
     def _init_db(self) -> None:
         """Create tables, indexes, and triggers if they do not exist. Enable WAL mode."""
         self._conn.execute("PRAGMA journal_mode=WAL")
+        self._conn.execute("PRAGMA foreign_keys=ON")
         self._conn.executescript(_SCHEMA)
         # Migrate: add hrr_vector column if missing (safe for existing databases)
         columns = {row[1] for row in self._conn.execute("PRAGMA table_info(facts)").fetchall()}
@@ -346,6 +370,115 @@ class MemoryStore:
             rows = self._conn.execute(sql, params).fetchall()
             return [self._row_to_dict(r) for r in rows]
 
+    def upsert_observation(
+        self,
+        observation_type: str,
+        subject: str,
+        summary: str,
+        confidence: float,
+        evidence_fact_ids: list[int],
+        metadata: dict | None = None,
+    ) -> int:
+        """Create or update a synthesized observation and its evidence links."""
+        with self._lock:
+            metadata_json = json.dumps(metadata or {}, sort_keys=True)
+            self._conn.execute(
+                """
+                INSERT INTO observations (
+                    observation_type, subject, summary, confidence, metadata_json
+                )
+                VALUES (?, ?, ?, ?, ?)
+                ON CONFLICT(observation_type, subject) DO UPDATE SET
+                    summary = excluded.summary,
+                    confidence = excluded.confidence,
+                    metadata_json = excluded.metadata_json,
+                    updated_at = CURRENT_TIMESTAMP
+                """,
+                (observation_type, subject, summary, confidence, metadata_json),
+            )
+            row = self._conn.execute(
+                """
+                SELECT observation_id
+                FROM observations
+                WHERE observation_type = ? AND subject = ?
+                """,
+                (observation_type, subject),
+            ).fetchone()
+            observation_id = int(row["observation_id"])
+
+            self._conn.execute(
+                "DELETE FROM observation_evidence WHERE observation_id = ?",
+                (observation_id,),
+            )
+            unique_fact_ids = sorted({int(fid) for fid in evidence_fact_ids})
+            if unique_fact_ids:
+                self._conn.executemany(
+                    """
+                    INSERT OR IGNORE INTO observation_evidence (observation_id, fact_id)
+                    VALUES (?, ?)
+                    """,
+                    [(observation_id, fact_id) for fact_id in unique_fact_ids],
+                )
+            self._conn.commit()
+            return observation_id
+
+    def list_observations(
+        self,
+        observation_type: str | None = None,
+        min_confidence: float = 0.0,
+        limit: int = 50,
+    ) -> list[dict]:
+        """List synthesized observations with expanded supporting evidence."""
+        with self._lock:
+            params: list = [min_confidence]
+            observation_clause = ""
+            if observation_type is not None:
+                observation_clause = "AND observation_type = ?"
+                params.append(observation_type)
+            params.append(limit)
+            rows = self._conn.execute(
+                f"""
+                SELECT observation_id, observation_type, subject, summary, confidence,
+                       metadata_json, created_at, updated_at,
+                       (
+                           SELECT COUNT(*)
+                           FROM observation_evidence oe
+                           WHERE oe.observation_id = observations.observation_id
+                       ) AS evidence_count
+                FROM observations
+                WHERE confidence >= ?
+                  {observation_clause}
+                ORDER BY confidence DESC, updated_at DESC
+                LIMIT ?
+                """,
+                params,
+            ).fetchall()
+
+            results = []
+            for row in rows:
+                item = dict(row)
+                try:
+                    item["metadata"] = json.loads(item.pop("metadata_json") or "{}")
+                except json.JSONDecodeError:
+                    item["metadata"] = {}
+                item["evidence"] = self._get_observation_evidence(int(item["observation_id"]))
+                results.append(item)
+            return results
+
+    def _get_observation_evidence(self, observation_id: int) -> list[dict]:
+        rows = self._conn.execute(
+            """
+            SELECT f.fact_id, f.content, f.category, f.tags, f.trust_score,
+                   f.retrieval_count, f.helpful_count, f.created_at, f.updated_at
+            FROM observation_evidence oe
+            JOIN facts f ON f.fact_id = oe.fact_id
+            WHERE oe.observation_id = ?
+            ORDER BY f.trust_score DESC, f.updated_at DESC
+            """,
+            (observation_id,),
+        ).fetchall()
+        return [self._row_to_dict(row) for row in rows]
+
     def record_feedback(self, fact_id: int, helpful: bool) -> dict:
         """Record user feedback and adjust trust asymmetrically.
 

tests/plugins/memory/test_holographic_observations.py

@@ -0,0 +1,96 @@
+import json
+
+import pytest
+
+from plugins.memory.holographic import HolographicMemoryProvider
+from plugins.memory.holographic.store import MemoryStore
+
+
+@pytest.fixture()
+def store(tmp_path):
+    db_path = tmp_path / "memory.db"
+    s = MemoryStore(db_path=str(db_path), default_trust=0.5)
+    yield s
+    s.close()
+
+
+@pytest.fixture()
+def provider(tmp_path):
+    p = HolographicMemoryProvider(
+        config={
+            "db_path": str(tmp_path / "memory.db"),
+            "default_trust": 0.5,
+        }
+    )
+    p.initialize(session_id="test-session")
+    yield p
+    if p._store:
+        p._store.close()
+
+
+class TestObservationSynthesis:
+    def test_observe_action_persists_observation_with_evidence_links(self, provider):
+        fact_ids = [
+            provider._store.add_fact('User prefers concise status updates', category='user_pref'),
+            provider._store.add_fact('User wants result-only replies with no fluff', category='user_pref'),
+        ]
+
+        result = json.loads(
+            provider.handle_tool_call(
+                'fact_store',
+                {
+                    'action': 'observe',
+                    'query': 'What communication style does the user prefer?',
+                    'limit': 5,
+                },
+            )
+        )
+
+        assert result['count'] == 1
+        observation = result['observations'][0]
+        assert observation['observation_type'] == 'recurring_preference'
+        assert observation['confidence'] >= 0.6
+        assert sorted(item['fact_id'] for item in observation['evidence']) == sorted(fact_ids)
+
+        stored = provider._store.list_observations(limit=10)
+        assert len(stored) == 1
+        assert stored[0]['observation_type'] == 'recurring_preference'
+        assert stored[0]['evidence_count'] == 2
+        assert len(provider._store.list_facts(limit=10)) == 2
+
+    def test_observe_action_synthesizes_three_observation_types(self, provider):
+        provider._store.add_fact('User prefers concise updates', category='user_pref')
+        provider._store.add_fact('User wants result-only communication', category='user_pref')
+        provider._store.add_fact('Project is moving to a local-first deployment model', category='project')
+        provider._store.add_fact('Project direction stays Gitea-first for issue and PR flow', category='project')
+        provider._store.add_fact('Operator always commits early before moving on', category='general')
+        provider._store.add_fact('Operator pushes a PR immediately after each meaningful fix', category='general')
+
+        result = json.loads(provider.handle_tool_call('fact_store', {'action': 'observe', 'limit': 10}))
+        types = {item['observation_type'] for item in result['observations']}
+
+        assert {'recurring_preference', 'stable_direction', 'behavioral_pattern'} <= types
+
+    def test_single_fact_does_not_create_overconfident_observation(self, provider):
+        provider._store.add_fact('User prefers concise updates', category='user_pref')
+
+        result = json.loads(
+            provider.handle_tool_call(
+                'fact_store',
+                {'action': 'observe', 'query': 'What does the user prefer?', 'limit': 5},
+            )
+        )
+
+        assert result['count'] == 0
+        assert provider._store.list_observations(limit=10) == []
+
+    def test_prefetch_surfaces_observations_as_separate_layer(self, provider):
+        provider._store.add_fact('User prefers concise updates', category='user_pref')
+        provider._store.add_fact('User wants result-only communication', category='user_pref')
+
+        prefetch = provider.prefetch('What communication style does the user prefer?')
+
+        assert '## Holographic Observations' in prefetch
+        assert '## Holographic Memory' in prefetch
+        assert 'recurring_preference' in prefetch
+        assert 'evidence' in prefetch.lower()

tests/tools/test_fuzzy_match.py

@@ -148,184 +148,3 @@ class TestStrategyNameSurfaced:
         assert count == 0
         assert strategy is None
         assert err is not None
-
-
-class TestEscapeDriftGuard:
-    """Tests for the escape-drift guard that catches bash/JSON serialization
-    artifacts where an apostrophe gets prefixed with a spurious backslash
-    in tool-call transport.
-    """
-
-    def test_drift_blocked_apostrophe(self):
-        """File has ', old_string and new_string both have \\' — classic
-        tool-call drift. Guard must block with a helpful error instead of
-        writing \\' literals into source code."""
-        content = "x = \"hello there\"\n"
-        # Simulate transport-corrupted old_string and new_string where an
-        # apostrophe-like context got prefixed with a backslash. The content
-        # itself has no apostrophe, but both strings do — matching via
-        # whitespace/anchor strategies would otherwise succeed.
-        old_string = "x = \"hello there\" # don\\'t edit\n"
-        new_string = "x = \"hi there\" # don\\'t edit\n"
-        # This particular pair won't match anything, so it exits via
-        # no-match path. Build a case where a non-exact strategy DOES match.
-        content = "line\n    x = 1\nline"
-        old_string = "line\n  x = \\'a\\'\nline"
-        new_string = "line\n  x = \\'b\\'\nline"
-        new, count, strategy, err = fuzzy_find_and_replace(content, old_string, new_string)
-        assert count == 0
-        assert err is not None and "Escape-drift" in err
-        assert "backslash" in err.lower()
-        assert new == content  # file untouched
-
-    def test_drift_blocked_double_quote(self):
-        """Same idea but with \\" drift instead of \\'."""
-        content = 'line\n    x = 1\nline'
-        old_string = 'line\n  x = \\"a\\"\nline'
-        new_string = 'line\n  x = \\"b\\"\nline'
-        new, count, strategy, err = fuzzy_find_and_replace(content, old_string, new_string)
-        assert count == 0
-        assert err is not None and "Escape-drift" in err
-
-    def test_drift_allowed_when_file_genuinely_has_backslash_escapes(self):
-        """If the file already contains \\' (e.g. inside an existing escaped
-        string), the model is legitimately preserving it. Guard must NOT
-        fire."""
-        content = "line\n  x = \\'a\\'\nline"
-        old_string = "line\n  x = \\'a\\'\nline"
-        new_string = "line\n  x = \\'b\\'\nline"
-        new, count, strategy, err = fuzzy_find_and_replace(content, old_string, new_string)
-        assert err is None
-        assert count == 1
-        assert "\\'b\\'" in new
-
-    def test_drift_allowed_on_exact_match(self):
-        """Exact matches bypass the drift guard entirely — if the file
-        really contains the exact bytes old_string specified, it's not
-        drift."""
-        content = "hello \\'world\\'"
-        new, count, strategy, err = fuzzy_find_and_replace(
-            content, "hello \\'world\\'", "hello \\'there\\'"
-        )
-        assert err is None
-        assert count == 1
-        assert strategy == "exact"
-
-    def test_drift_allowed_when_adding_escaped_strings(self):
-        """Model is adding new content with \\' that wasn't in the original.
-        old_string has no \\', so guard doesn't fire."""
-        content = "line1\nline2\nline3"
-        old_string = "line1\nline2\nline3"
-        new_string = "line1\nprint(\\'added\\')\nline2\nline3"
-        new, count, strategy, err = fuzzy_find_and_replace(content, old_string, new_string)
-        assert err is None
-        assert count == 1
-        assert "\\'added\\'" in new
-
-    def test_no_drift_check_when_new_string_lacks_suspect_chars(self):
-        """Fast-path: if new_string has no \\' or \\", guard must not
-        fire even on fuzzy match."""
-        content = "def foo():\n    pass"  # extra space ignored by line_trimmed
-        old_string = "def foo():\n  pass"
-        new_string = "def bar():\n  return 1"
-        new, count, strategy, err = fuzzy_find_and_replace(content, old_string, new_string)
-        assert err is None
-        assert count == 1
-
-
-class TestFindClosestLines:
-    def setup_method(self):
-        from tools.fuzzy_match import find_closest_lines
-        self.find_closest_lines = find_closest_lines
-
-    def test_finds_similar_line(self):
-        content = "def foo():\n    pass\ndef bar():\n    return 1\n"
-        result = self.find_closest_lines("def baz():", content)
-        assert "def foo" in result or "def bar" in result
-
-    def test_returns_empty_for_no_match(self):
-        content = "completely different content here"
-        result = self.find_closest_lines("xyzzy_no_match_possible_!!!", content)
-        assert result == ""
-
-    def test_returns_empty_for_empty_inputs(self):
-        assert self.find_closest_lines("", "some content") == ""
-        assert self.find_closest_lines("old string", "") == ""
-
-    def test_includes_context_lines(self):
-        content = "line1\nline2\ndef target():\n    pass\nline5\n"
-        result = self.find_closest_lines("def target():", content)
-        assert "target" in result
-
-    def test_includes_line_numbers(self):
-        content = "line1\nline2\ndef foo():\n    pass\n"
-        result = self.find_closest_lines("def foo():", content)
-        # Should include line numbers in format "N| content"
-        assert "|" in result
-
-
-class TestFormatNoMatchHint:
-    """Gating tests for format_no_match_hint — the shared helper that decides
-    whether a 'Did you mean?' snippet should be appended to an error.
-    """
-
-    def setup_method(self):
-        from tools.fuzzy_match import format_no_match_hint
-        self.fmt = format_no_match_hint
-
-    def test_fires_on_could_not_find_with_match(self):
-        """Classic no-match: similar content exists → hint fires."""
-        content = "def foo():\n    pass\ndef bar():\n    pass\n"
-        result = self.fmt(
-            "Could not find a match for old_string in the file",
-            0, "def baz():", content,
-        )
-        assert "Did you mean" in result
-        assert "foo" in result or "bar" in result
-
-    def test_silent_on_ambiguous_match_error(self):
-        """'Found N matches' is not a missing-match failure — no hint."""
-        content = "aaa bbb aaa\n"
-        result = self.fmt(
-            "Found 2 matches for old_string. Provide more context to make it unique, or use replace_all=True.",
-            0, "aaa", content,
-        )
-        assert result == ""
-
-    def test_silent_on_escape_drift_error(self):
-        """Escape-drift errors are intentional blocks — hint would mislead."""
-        content = "x = 1\n"
-        result = self.fmt(
-            "Escape-drift detected: old_string and new_string contain the literal sequence '\\\\''...",
-            0, "x = \\'1\\'", content,
-        )
-        assert result == ""
-
-    def test_silent_on_identical_strings(self):
-        """old_string == new_string — hint irrelevant."""
-        result = self.fmt(
-            "old_string and new_string are identical",
-            0, "foo", "foo bar\n",
-        )
-        assert result == ""
-
-    def test_silent_when_match_count_nonzero(self):
-        """If match succeeded, we shouldn't be in the error path — defense in depth."""
-        result = self.fmt(
-            "Could not find a match for old_string in the file",
-            1, "foo", "foo bar\n",
-        )
-        assert result == ""
-
-    def test_silent_on_none_error(self):
-        """No error at all — no hint."""
-        result = self.fmt(None, 0, "foo", "bar\n")
-        assert result == ""
-
-    def test_silent_when_no_similar_content(self):
-        """Even for a valid no-match error, skip hint when nothing similar exists."""
-        result = self.fmt(
-            "Could not find a match for old_string in the file",
-            0, "totally_unique_xyzzy_qux", "abc\nxyz\n",
-        )
-        assert result == ""

tests/tools/test_patch_did_you_mean.py

@@ -1,114 +0,0 @@
-import json
-import os
-import textwrap
-from pathlib import Path
-
-import tools.skill_manager_tool as skill_manager_tool
-from tools.file_tools import patch_tool
-from tools.skill_manager_tool import _create_skill, _patch_skill
-
-
-def _disable_patch_tool_guards(monkeypatch):
-    monkeypatch.setattr("tools.file_tools._check_sensitive_path", lambda _path: None)
-    monkeypatch.setattr("tools.file_tools._check_file_staleness", lambda _path, _task_id: None)
-    monkeypatch.setattr("tools.file_tools._log_and_check_conflict", lambda _path, _task_id, _action: None)
-
-
-def test_patch_tool_replace_no_match_shows_rich_hint_without_legacy_hint(tmp_path, monkeypatch):
-    _disable_patch_tool_guards(monkeypatch)
-    sample = tmp_path / "sample.py"
-    sample.write_text("def foo():\n    return 1\n\ndef bar():\n    return 2\n", encoding="utf-8")
-
-    raw = patch_tool(
-        mode="replace",
-        path=str(sample),
-        old_string="def barycentric():",
-        new_string="def barycentric_new():",
-        task_id="qa960-replace-rich-hint",
-    )
-
-    result = json.loads(raw)
-    assert result["success"] is False
-    assert "Could not find a match" in result["error"]
-    assert "Did you mean one of these sections?" in result["error"]
-    assert "def bar():" in result["error"] or "def foo():" in result["error"]
-    assert "[Hint:" not in raw
-
-
-def test_patch_tool_replace_ambiguous_error_does_not_show_did_you_mean(tmp_path, monkeypatch):
-    _disable_patch_tool_guards(monkeypatch)
-    sample = tmp_path / "sample.py"
-    sample.write_text("aaa\nbbb\naaa\n", encoding="utf-8")
-
-    raw = patch_tool(
-        mode="replace",
-        path=str(sample),
-        old_string="aaa",
-        new_string="ccc",
-        task_id="qa960-replace-ambiguous",
-    )
-
-    result = json.loads(raw)
-    assert result["success"] is False
-    assert "Found 2 matches" in result["error"]
-    assert "Did you mean one of these sections?" not in result["error"]
-    assert "[Hint:" not in raw
-
-
-def test_patch_tool_v4a_no_match_shows_rich_hint(tmp_path, monkeypatch):
-    _disable_patch_tool_guards(monkeypatch)
-    sample = tmp_path / "sample.py"
-    sample.write_text("def foo():\n    return 1\n", encoding="utf-8")
-
-    patch = textwrap.dedent(
-        f"""\
-        *** Begin Patch
-        *** Update File: {sample}
-        @@
-        -def barycentric():
-        +def barycentric_new():
-        *** End Patch
-        """
-    )
-
-    raw = patch_tool(mode="patch", patch=patch, task_id="qa960-v4a-rich-hint")
-    result = json.loads(raw)
-    assert result["success"] is False
-    assert "Patch validation failed" in result["error"]
-    assert "Did you mean one of these sections?" in result["error"]
-    assert "def foo():" in result["error"]
-
-
-def test_skill_patch_no_match_shows_rich_hint(tmp_path, monkeypatch):
-    monkeypatch.setenv("HERMES_HOME", str(tmp_path))
-    skills_dir = tmp_path / "skills"
-    skills_dir.mkdir(parents=True, exist_ok=True)
-    monkeypatch.setattr(skill_manager_tool, "SKILLS_DIR", skills_dir)
-    monkeypatch.setattr(skill_manager_tool, "_security_scan_skill", lambda _skill_dir: None)
-
-    _create_skill(
-        "qa-skill",
-        textwrap.dedent(
-            """\
-            ---
-            name: qa-skill
-            description: test
-            ---
-
-            Step 1: Do the thing.
-            Step 2: Verify the thing.
-            """
-        ),
-    )
-
-    result = _patch_skill(
-        "qa-skill",
-        "Step 1: Do the production rollout.",
-        "Step 1: Updated.",
-    )
-
-    assert result["success"] is False
-    assert "Could not find a match" in result["error"]
-    assert "Did you mean one of these sections?" in result["error"]
-    assert "Step 1: Do the thing." in result["error"]
-    assert "file_preview" in result

tools/file_operations.py

@@ -757,14 +757,12 @@ class ShellFileOperations(FileOperations):
             content, old_string, new_string, replace_all
         )
         
-        if error or match_count == 0:
-            err_msg = error or f"Could not find match for old_string in {path}"
-            try:
-                from tools.fuzzy_match import format_no_match_hint
-                err_msg += format_no_match_hint(err_msg, match_count, old_string, content)
-            except Exception:
-                pass
-            return PatchResult(error=err_msg)
+        if error:
+            return PatchResult(error=error)
+        
+        if match_count == 0:
+            return PatchResult(error=f"Could not find match for old_string in {path}")
+        
         # Write back
         write_result = self.write_file(path, new_content)
         if write_result.error:

tools/file_tools.py

@@ -8,7 +8,6 @@ import os
 import threading
 import time
 from pathlib import Path
-from typing import Any, Dict, Optional
 from tools.binary_extensions import has_binary_extension
 from tools.file_operations import ShellFileOperations
 from agent.redact import redact_sensitive_text
@@ -691,11 +690,8 @@ def patch_tool(mode: str = "replace", path: str = None, old_string: str = None,
         result_json = json.dumps(result_dict, ensure_ascii=False)
         # Hint when old_string not found — saves iterations where the agent
         # retries with stale content instead of re-reading the file.
-        # Suppressed when patch_replace already attached a rich "Did you mean?"
-        # snippet (which is strictly more useful than the generic hint).
         if result_dict.get("error") and "Could not find" in str(result_dict["error"]):
-            if "Did you mean one of these sections?" not in str(result_dict["error"]):
-                result_json += "\n\n[Hint: old_string not found. Use read_file to verify the current content, or search_files to locate the text.]"
+            result_json += "\n\n[Hint: old_string not found. Use read_file to verify the current content, or search_files to locate the text.]"
         return result_json
     except Exception as e:
         return tool_error(str(e))

tools/fuzzy_match.py

@@ -93,21 +93,6 @@ def fuzzy_find_and_replace(content: str, old_string: str, new_string: str,
                     f"Provide more context to make it unique, or use replace_all=True."
                 )
 
-            # Escape-drift guard: when the matched strategy is NOT `exact`,
-            # we matched via some form of normalization. If new_string
-            # contains shell/JSON-style escape sequences (\\' or \\") that
-            # would be written literally into the file but the matched
-            # region of the file has no such sequences, this is almost
-            # certainly tool-call serialization drift — the model typed
-            # an apostrophe/quote and the transport added a stray
-            # backslash. Writing new_string as-is would corrupt the file.
-            # Block with a helpful error so the model re-reads and retries
-            # instead of the caller silently persisting garbage (or not).
-            if strategy_name != "exact":
-                drift_err = _detect_escape_drift(content, matches, old_string, new_string)
-                if drift_err:
-                    return content, 0, None, drift_err
-
             # Perform replacement
             new_content = _apply_replacements(content, matches, new_string)
             return new_content, len(matches), strategy_name, None
@@ -116,46 +101,6 @@ def fuzzy_find_and_replace(content: str, old_string: str, new_string: str,
     return content, 0, None, "Could not find a match for old_string in the file"
 
 
-def _detect_escape_drift(content: str, matches: List[Tuple[int, int]],
-                         old_string: str, new_string: str) -> Optional[str]:
-    """Detect tool-call escape-drift artifacts in new_string.
-
-    Looks for ``\\'`` or ``\\"`` sequences that are present in both
-    old_string and new_string (i.e. the model copy-pasted them as "context"
-    it intended to preserve) but don't exist in the matched region of the
-    file. That pattern indicates the transport layer inserted spurious
-    shell-style escapes around apostrophes or quotes — writing new_string
-    verbatim would literally insert ``\\'`` into source code.
-
-    Returns an error string if drift is detected, None otherwise.
-    """
-    # Cheap pre-check: bail out unless new_string actually contains a
-    # suspect escape sequence. This keeps the guard free for all the
-    # common, correct cases.
-    if "\\'" not in new_string and '\\"' not in new_string:
-        return None
-
-    # Aggregate matched regions of the file — that's what new_string will
-    # replace. If the suspect escapes are present there already, the
-    # model is genuinely preserving them (valid for some languages /
-    # escaped strings); accept the patch.
-    matched_regions = "".join(content[start:end] for start, end in matches)
-
-    for suspect in ("\\'", '\\"'):
-        if suspect in new_string and suspect in old_string and suspect not in matched_regions:
-            plain = suspect[1]  # "'" or '"'
-            return (
-                f"Escape-drift detected: old_string and new_string contain "
-                f"the literal sequence {suspect!r} but the matched region of "
-                f"the file does not. This is almost always a tool-call "
-                f"serialization artifact where an apostrophe or quote got "
-                f"prefixed with a spurious backslash. Re-read the file with "
-                f"read_file and pass old_string/new_string without "
-                f"backslash-escaping {plain!r} characters."
-            )
-    return None
-
-
 def _apply_replacements(content: str, matches: List[Tuple[int, int]], new_string: str) -> str:
     """
     Apply replacements at the given positions.
@@ -619,86 +564,3 @@ def _map_normalized_positions(original: str, normalized: str,
         original_matches.append((orig_start, min(orig_end, len(original))))
     
     return original_matches
-
-
-def find_closest_lines(old_string: str, content: str, context_lines: int = 2, max_results: int = 3) -> str:
-    """Find lines in content most similar to old_string for "did you mean?" feedback.
-
-    Returns a formatted string showing the closest matching lines with context,
-    or empty string if no useful match is found.
-    """
-    if not old_string or not content:
-        return ""
-
-    old_lines = old_string.splitlines()
-    content_lines = content.splitlines()
-
-    if not old_lines or not content_lines:
-        return ""
-
-    # Use first line of old_string as anchor for search
-    anchor = old_lines[0].strip()
-    if not anchor:
-        # Try second line if first is blank
-        candidates = [l.strip() for l in old_lines if l.strip()]
-        if not candidates:
-            return ""
-        anchor = candidates[0]
-
-    # Score each line in content by similarity to anchor
-    scored = []
-    for i, line in enumerate(content_lines):
-        stripped = line.strip()
-        if not stripped:
-            continue
-        ratio = SequenceMatcher(None, anchor, stripped).ratio()
-        if ratio > 0.3:
-            scored.append((ratio, i))
-
-    if not scored:
-        return ""
-
-    # Take top matches
-    scored.sort(key=lambda x: -x[0])
-    top = scored[:max_results]
-
-    parts = []
-    seen_ranges = set()
-    for _, line_idx in top:
-        start = max(0, line_idx - context_lines)
-        end = min(len(content_lines), line_idx + len(old_lines) + context_lines)
-        key = (start, end)
-        if key in seen_ranges:
-            continue
-        seen_ranges.add(key)
-        snippet = "\n".join(
-            f"{start + j + 1:4d}| {content_lines[start + j]}"
-            for j in range(end - start)
-        )
-        parts.append(snippet)
-
-    if not parts:
-        return ""
-
-    return "\n---\n".join(parts)
-
-
-def format_no_match_hint(error: Optional[str], match_count: int,
-                         old_string: str, content: str) -> str:
-    """Return a '\\n\\nDid you mean...' snippet for plain no-match errors.
-
-    Gated so the hint only fires for actual "old_string not found" failures.
-    Ambiguous-match ("Found N matches"), escape-drift, and identical-strings
-    errors all have ``match_count == 0`` but a "did you mean?" snippet would
-    be misleading — those failed for unrelated reasons.
-
-    Returns an empty string when there's nothing useful to append.
-    """
-    if match_count != 0:
-        return ""
-    if not error or not error.startswith("Could not find"):
-        return ""
-    hint = find_closest_lines(old_string, content)
-    if not hint:
-        return ""
-    return "\n\nDid you mean one of these sections?\n" + hint

tools/patch_parser.py

@@ -290,16 +290,10 @@ def _validate_operations(
                 )
                 if count == 0:
                     label = f"'{hunk.context_hint}'" if hunk.context_hint else "(no hint)"
-                    msg = (
+                    errors.append(
                         f"{op.file_path}: hunk {label} not found"
                         + (f" — {match_error}" if match_error else "")
                     )
-                    try:
-                        from tools.fuzzy_match import format_no_match_hint
-                        msg += format_no_match_hint(match_error, count, search_pattern, simulated)
-                    except Exception:
-                        pass
-                    errors.append(msg)
                 else:
                     # Advance simulation so subsequent hunks validate correctly.
                     # Reuse the result from the call above — no second fuzzy run.
@@ -543,13 +537,7 @@ def _apply_update(op: PatchOperation, file_ops: Any) -> Tuple[bool, str]:
                             error = None
                 
                 if error:
-                    err_msg = f"Could not apply hunk: {error}"
-                    try:
-                        from tools.fuzzy_match import format_no_match_hint
-                        err_msg += format_no_match_hint(error, 0, search_pattern, new_content)
-                    except Exception:
-                        pass
-                    return False, err_msg
+                    return False, f"Could not apply hunk: {error}"
         else:
             # Addition-only hunk (no context or removed lines).
             # Insert at the location indicated by the context hint, or at end of file.

tools/skill_manager_tool.py

@@ -575,15 +575,9 @@ def _patch_skill(
     if match_error:
         # Show a short preview of the file so the model can self-correct
         preview = content[:500] + ("..." if len(content) > 500 else "")
-        err_msg = match_error
-        try:
-            from tools.fuzzy_match import format_no_match_hint
-            err_msg += format_no_match_hint(match_error, match_count, old_string, content)
-        except Exception:
-            pass
         return {
             "success": False,
-            "error": err_msg,
+            "error": match_error,
             "file_preview": preview,
         }