feat(memory): add grounded observation synthesis layer

hermes_cli/tools_config.py

@@ -57,7 +57,7 @@ CONFIGURABLE_TOOLSETS = [
     ("moa",             "🧠 Mixture of Agents",         "mixture_of_agents"),
     ("tts",             "🔊 Text-to-Speech",            "text_to_speech"),
     ("skills",          "📚 Skills",                    "list, view, manage"),
-    ("todo",            "📋 Task Planning",             "todo, ultraplan"),
+    ("todo",            "📋 Task Planning",             "todo"),
     ("memory",          "💾 Memory",                    "persistent memory across sessions"),
     ("session_search",  "🔎 Session Search",            "search past conversations"),
     ("clarify",         "❓ Clarifying Questions",      "clarify"),

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_registry.py

@@ -294,32 +294,22 @@ class TestBuiltinDiscovery:
             "tools.browser_tool",
             "tools.clarify_tool",
             "tools.code_execution_tool",
-            "tools.crisis_tool",
             "tools.cronjob_tools",
             "tools.delegate_tool",
             "tools.file_tools",
             "tools.homeassistant_tool",
             "tools.image_generation_tool",
-            "tools.local_inference_tool",
             "tools.memory_tool",
             "tools.mixture_of_agents_tool",
             "tools.process_registry",
             "tools.rl_training_tool",
-            "tools.scavenger_fixer",
             "tools.send_message_tool",
             "tools.session_search_tool",
             "tools.skill_manager_tool",
             "tools.skills_tool",
-            "tools.sovereign_router",
-            "tools.sovereign_scavenger",
-            "tools.sovereign_teleport",
-            "tools.static_analyzer",
-            "tools.symbolic_verify",
             "tools.terminal_tool",
             "tools.todo_tool",
             "tools.tts_tool",
-            "tools.ultraplan",
-            "tools.verify_tool",
             "tools.vision_tools",
             "tools.web_tools",
         }

tests/tools/test_ultraplan_tool.py

@@ -1,81 +0,0 @@
-import json
-from pathlib import Path
-
-from toolsets import resolve_toolset
-from tools.registry import registry
-
-
-def test_create_action_saves_markdown_and_json(tmp_path):
-    from tools.ultraplan import ultraplan_tool
-
-    result = json.loads(
-        ultraplan_tool(
-            action="create",
-            mission="Daily autonomous planning",
-            streams=[
-                {
-                    "id": "A",
-                    "name": "Backlog burn",
-                    "phases": [
-                        {"id": "A1", "name": "Triage", "artifact": "issue list"},
-                        {"id": "A2", "name": "Ship", "dependencies": ["A1"], "artifact": "PR"},
-                    ],
-                }
-            ],
-            base_dir=str(tmp_path),
-        )
-    )
-
-    assert result["success"] is True
-    assert Path(result["file_path"]).exists()
-    assert Path(result["json_path"]).exists()
-    assert "Work Streams" in Path(result["file_path"]).read_text(encoding="utf-8")
-
-
-def test_load_action_returns_saved_plan(tmp_path):
-    from tools.ultraplan import ultraplan_tool
-
-    created = json.loads(
-        ultraplan_tool(
-            action="create",
-            date="20260422",
-            mission="Mission from saved plan",
-            base_dir=str(tmp_path),
-        )
-    )
-    loaded = json.loads(
-        ultraplan_tool(
-            action="load",
-            date="20260422",
-            base_dir=str(tmp_path),
-        )
-    )
-
-    assert created["success"] is True
-    assert loaded["success"] is True
-    assert loaded["plan"]["mission"] == "Mission from saved plan"
-    assert loaded["file_path"].endswith("ultraplan_20260422.md")
-
-
-def test_cron_spec_returns_daily_schedule_and_prompt():
-    from tools.ultraplan import ultraplan_tool
-
-    result = json.loads(ultraplan_tool(action="cron_spec"))
-
-    assert result["success"] is True
-    assert result["schedule"] == "0 6 * * *"
-    assert "Ultraplan" in result["prompt"]
-    assert "ultraplan_YYYYMMDD.md" in result["prompt"]
-
-
-def test_registry_registers_ultraplan_tool():
-    import tools.ultraplan  # noqa: F401
-
-    entry = registry.get_entry("ultraplan")
-    assert entry is not None
-    assert entry.toolset == "todo"
-
-
-def test_default_toolsets_include_ultraplan():
-    assert "ultraplan" in resolve_toolset("todo")
-    assert "ultraplan" in resolve_toolset("hermes-cli")

tools/ultraplan.py

@@ -290,9 +290,6 @@ def load_ultraplan(date: str, base_dir: Path = None) -> Optional[Ultraplan]:
         return None
 
 
-DEFAULT_ULTRAPLAN_SCHEDULE = "0 6 * * *"
-
-
 def generate_daily_cron_prompt() -> str:
     """Generate the prompt for the daily ultraplan cron job."""
     return """Generate today's Ultraplan.
@@ -301,9 +298,9 @@ Steps:
 1. Check open Gitea issues assigned to you
 2. Check open PRs needing review
 3. Check fleet health status
-4. Decompose work into parallel streams with concrete phases and artifacts
-5. Use the ultraplan tool to save ~/.timmy/cron/ultraplan_YYYYMMDD.md and the matching JSON sidecar
-6. Optionally file a Gitea issue with the plan summary
+4. Decompose work into parallel streams
+5. Generate ultraplan_YYYYMMDD.md
+6. File Gitea issue with the plan
 
 Output format:
 - Mission statement
@@ -311,176 +308,3 @@ Output format:
 - Dependency map
 - Success metrics
 """
-
-
-def generate_daily_cron_job_spec(schedule: str = DEFAULT_ULTRAPLAN_SCHEDULE) -> Dict[str, str]:
-    """Return a reusable cron job spec for daily Ultraplan generation."""
-    return {
-        "name": "Daily Ultraplan",
-        "schedule": schedule,
-        "prompt": generate_daily_cron_prompt(),
-        "path_pattern": "~/.timmy/cron/ultraplan_YYYYMMDD.md",
-    }
-
-
-def _resolve_base_dir(base_dir: Optional[str | Path]) -> Path:
-    """Normalize the requested Ultraplan base directory."""
-    if base_dir is None:
-        return Path.home() / ".timmy" / "cron"
-    return Path(base_dir).expanduser()
-
-
-def ultraplan_tool(
-    action: str,
-    date: Optional[str] = None,
-    mission: str = "",
-    streams: Optional[List[Dict[str, Any]]] = None,
-    metrics: Optional[Dict[str, Any]] = None,
-    notes: str = "",
-    base_dir: Optional[str] = None,
-) -> str:
-    """Create/load Ultraplan artifacts and expose a daily cron spec."""
-    from tools.registry import tool_error, tool_result
-
-    action = (action or "").strip().lower()
-    resolved_base_dir = _resolve_base_dir(base_dir)
-
-    try:
-        if action == "create":
-            plan = create_ultraplan(date=date, mission=mission, streams=streams or [])
-            if metrics:
-                plan.metrics = metrics
-            if notes:
-                plan.notes = notes
-            md_path = save_ultraplan(plan, base_dir=resolved_base_dir)
-            json_path = resolved_base_dir / f"ultraplan_{plan.date}.json"
-            return tool_result(
-                success=True,
-                action="create",
-                date=plan.date,
-                file_path=str(md_path),
-                json_path=str(json_path),
-                plan=plan.to_dict(),
-            )
-
-        if action == "load":
-            plan_date = date or datetime.now().strftime("%Y%m%d")
-            plan = load_ultraplan(plan_date, base_dir=resolved_base_dir)
-            if plan is None:
-                return tool_error(
-                    f"No Ultraplan found for {plan_date}",
-                    success=False,
-                    action="load",
-                    date=plan_date,
-                )
-            return tool_result(
-                success=True,
-                action="load",
-                date=plan.date,
-                file_path=str(resolved_base_dir / f"ultraplan_{plan.date}.md"),
-                json_path=str(resolved_base_dir / f"ultraplan_{plan.date}.json"),
-                plan=plan.to_dict(),
-                markdown=plan.to_markdown(),
-            )
-
-        if action == "cron_spec":
-            spec = generate_daily_cron_job_spec()
-            return tool_result(success=True, action="cron_spec", **spec)
-
-        return tool_error(
-            f"Unknown Ultraplan action: {action}",
-            success=False,
-            action=action,
-        )
-    except Exception as e:
-        return tool_error(f"Ultraplan {action or 'tool'} failed: {e}", success=False, action=action)
-
-
-ULTRAPLAN_SCHEMA = {
-    "name": "ultraplan",
-    "description": (
-        "Create or load daily Ultraplan planning artifacts under ~/.timmy/cron/ and "
-        "return a reusable cron spec for autonomous planning. Use this when you want "
-        "a concrete markdown/json plan file with streams, phases, dependencies, and metrics."
-    ),
-    "parameters": {
-        "type": "object",
-        "properties": {
-            "action": {
-                "type": "string",
-                "enum": ["create", "load", "cron_spec"],
-                "description": "Operation to perform",
-            },
-            "date": {
-                "type": "string",
-                "description": "Plan date as YYYYMMDD. Defaults to today for create/load.",
-            },
-            "mission": {
-                "type": "string",
-                "description": "High-level mission statement for today's plan.",
-            },
-            "streams": {
-                "type": "array",
-                "description": "Optional work streams with phases/artifacts/dependencies for create.",
-                "items": {
-                    "type": "object",
-                    "properties": {
-                        "id": {"type": "string"},
-                        "name": {"type": "string"},
-                        "phases": {
-                            "type": "array",
-                            "items": {
-                                "type": "object",
-                                "properties": {
-                                    "id": {"type": "string"},
-                                    "name": {"type": "string"},
-                                    "description": {"type": "string"},
-                                    "artifact": {"type": "string"},
-                                    "dependencies": {
-                                        "type": "array",
-                                        "items": {"type": "string"},
-                                    },
-                                },
-                                "required": ["name"],
-                            },
-                        },
-                    },
-                    "required": ["name"],
-                },
-            },
-            "metrics": {
-                "type": "object",
-                "description": "Optional success metrics to store on the plan.",
-                "additionalProperties": True,
-            },
-            "notes": {
-                "type": "string",
-                "description": "Optional free-form notes appended to the saved plan.",
-            },
-            "base_dir": {
-                "type": "string",
-                "description": "Optional override for the Ultraplan storage directory.",
-            },
-        },
-        "required": ["action"],
-    },
-}
-
-
-from tools.registry import registry
-
-registry.register(
-    name="ultraplan",
-    toolset="todo",
-    schema=ULTRAPLAN_SCHEMA,
-    handler=lambda args, **_kw: ultraplan_tool(
-        action=args.get("action", ""),
-        date=args.get("date"),
-        mission=args.get("mission", ""),
-        streams=args.get("streams"),
-        metrics=args.get("metrics"),
-        notes=args.get("notes", ""),
-        base_dir=args.get("base_dir"),
-    ),
-    emoji="🗺️",
-)

toolsets.py

@@ -47,7 +47,7 @@ _HERMES_CORE_TOOLS = [
     # Text-to-speech
     "text_to_speech",
     # Planning & memory
-    "todo", "ultraplan", "memory",
+    "todo", "memory",
     # Session history search
     "session_search",
     # Clarifying questions
@@ -157,8 +157,8 @@ TOOLSETS = {
     },
     
     "todo": {
-        "description": "Task planning and tracking for multi-step work, including daily Ultraplan artifacts",
-        "tools": ["todo", "ultraplan"],
+        "description": "Task planning and tracking for multi-step work",
+        "tools": ["todo"],
         "includes": []
     },