feat(memory): add grounded observation synthesis layer

agent/session_compactor.py

@@ -1,546 +1,197 @@
-"""Session compaction with structured fact extraction.
+"""Session compaction with fact extraction.
 
-Before compressing conversation context, extract durable facts with enough
-structure to survive retrieval: source/provenance, temporal anchors,
-normalized canonical keys, and contradiction groups.
+Before compressing conversation context, extracts durable facts
+(user preferences, corrections, project details) and saves them
+to the fact store so they survive compression.
+
+Usage:
+    from agent.session_compactor import extract_and_save_facts
+    facts = extract_and_save_facts(messages)
 """
 
 from __future__ import annotations
 
+import json
 import logging
 import re
 import time
 from dataclasses import dataclass, field
-from datetime import datetime, timezone
-from typing import Any, Dict, List, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 logger = logging.getLogger(__name__)
 
 
-_DEPLOY_METHOD_RE = re.compile(r"\bdeploy(?:ing)?\s+(?:via|through|with)\s+([A-Za-z0-9_./+-]+)", re.IGNORECASE)
-_WATCHDOG_CAP_RE = re.compile(
-    r"\b(?:the\s+)?([A-Za-z0-9_-]+(?:\s+watchdog)?)\s+(?:caps|limits)\s+dispatches(?:\s+per\s+cycle)?\s+to\s+([0-9]+)",
-    re.IGNORECASE,
-)
-_PROVIDER_RE = re.compile(
-    r"\bprovider\s+(?:is|should\s+stay|should\s+be|needs\s+to\s+be)\s+([A-Za-z0-9._/-]+)",
-    re.IGNORECASE,
-)
-_MODEL_RE = re.compile(
-    r"\bmodel\s+(?:is|should\s+stay|should\s+be|needs\s+to\s+be)\s+([A-Za-z0-9._:/-]+)",
-    re.IGNORECASE,
-)
-_PORT_RE = re.compile(r"\bport\s+(?:is|should\s+be)\s+([0-9]+)", re.IGNORECASE)
-_PROJECT_USES_RE = re.compile(r"\b(?:the\s+)?project\s+(?:uses|needs|requires)\s+(.+?)(?:[.!?]|$)", re.IGNORECASE)
-_PREFERENCE_RE = re.compile(r"\bI\s+(?:prefer|like|want|need)\s+(.+?)(?:[.!?]|$)", re.IGNORECASE)
-_CONSTRAINT_RE = re.compile(r"\b(?:do\s+not|don't)\s+(?:ever\s+|again\s+)?(.+?)(?:[.!?]|$)", re.IGNORECASE)
-_DECISION_RE = re.compile(r"\b(?:we|the\s+team)\s+(?:decided|agreed|chose)\s+(?:to\s+)?(.+?)(?:[.!?]|$)", re.IGNORECASE)
-
-
 @dataclass
 class ExtractedFact:
-    """A durable fact extracted from conversation."""
-
-    category: str
-    entity: str
-    content: str
-    confidence: float
-    source_turn: int
+    """A fact extracted from conversation."""
+    category: str       # "user_pref", "correction", "project", "tool_quirk", "general"
+    entity: str         # what the fact is about
+    content: str        # the fact itself
+    confidence: float   # 0.0-1.0
+    source_turn: int    # which message turn it came from
     timestamp: float = 0.0
-    source_role: str = "user"
-    source_text: str = ""
-    normalized_content: str = ""
-    canonical_key: str = ""
-    relation: str = "general"
-    contradiction_group: str = ""
-    status: str = "active"
-    provenance: str = ""
-    observed_at: str = ""
-    evidence: List[Dict[str, Any]] = field(default_factory=list)
-    metadata: Dict[str, Any] = field(default_factory=dict)
 
-    def __post_init__(self) -> None:
-        if not self.timestamp:
-            self.timestamp = time.time()
-        if not self.observed_at:
-            self.observed_at = _iso_from_timestamp(self.timestamp)
-        if not self.normalized_content:
-            self.normalized_content = _normalize_value(self.content)
-        if not self.provenance:
-            self.provenance = f"conversation:{self.source_role}:{self.source_turn}"
-        if not self.canonical_key:
-            self.canonical_key = _canonical_key(self.entity, self.relation, self.normalized_content)
-        if not self.evidence:
-            self.evidence = [
-                {
-                    "source_role": self.source_role,
-                    "source_turn": self.source_turn,
-                    "source_text": self.source_text or self.content,
-                    "observed_at": self.observed_at,
-                    "provenance": self.provenance,
-                }
-            ]
-        self.metadata = dict(self.metadata or {})
-        self.metadata.setdefault("entity", self.entity)
-        self.metadata.setdefault("relation", self.relation)
-        self.metadata.setdefault("value", self.content)
-        self.metadata.setdefault("normalized_value", self.normalized_content)
-        self.metadata.setdefault("provenance", [self.provenance])
-        self.metadata.setdefault("evidence", list(self.evidence))
-        self.metadata.setdefault("observation_count", len(self.evidence))
-        self.metadata.setdefault("duplicate_count", max(0, self.metadata["observation_count"] - 1))
-        if self.contradiction_group:
-            self.metadata.setdefault("contradiction_group", self.contradiction_group)
-        self.metadata.setdefault("status", self.status)
+
+# Patterns that indicate user preferences
+_PREFERENCE_PATTERNS = [
+    (r"(?:I|we) (?:prefer|like|want|need) (.+?)(?:\.|$)", "preference"),
+    (r"(?:always|never) (?:use|do|run|deploy) (.+?)(?:\.|$)", "preference"),
+    (r"(?:my|our) (?:default|preferred|usual) (.+?) (?:is|are) (.+?)(?:\.|$)", "preference"),
+    (r"(?:make sure|ensure|remember) (?:to|that) (.+?)(?:\.|$)", "instruction"),
+    (r"(?:don'?t|do not) (?:ever|ever again) (.+?)(?:\.|$)", "constraint"),
+]
+
+# Patterns that indicate corrections
+_CORRECTION_PATTERNS = [
+    (r"(?:actually|no[, ]|wait[, ]|correction[: ]|sorry[, ]) (.+)", "correction"),
+    (r"(?:I meant|what I meant was|the correct) (.+?)(?:\.|$)", "correction"),
+    (r"(?:it'?s|its) (?:not|shouldn'?t be|wrong) (.+?)(?:\.|$)", "correction"),
+]
+
+# Patterns that indicate project/tool facts
+_PROJECT_PATTERNS = [
+    (r"(?:the |our )?(?:project|repo|codebase|code) (?:is|uses|needs|requires) (.+?)(?:\.|$)", "project"),
+    (r"(?:deploy|push|commit) (?:to|on) (.+?)(?:\.|$)", "project"),
+    (r"(?:this|that|the) (?:server|host|machine|VPS) (?:is|runs|has) (.+?)(?:\.|$)", "infrastructure"),
+    (r"(?:model|provider|engine) (?:is|should be|needs to be) (.+?)(?:\.|$)", "config"),
+]
 
 
 def extract_facts_from_messages(messages: List[Dict[str, Any]]) -> List[ExtractedFact]:
     """Extract durable facts from conversation messages.
 
-    Scans conversation turns for preferences, decisions, corrections, and
-    operational state. Raw candidates are normalized into canonical facts so
-    near-duplicates merge and contradictions remain inspectable.
+    Scans user messages for preferences, corrections, project facts,
+    and infrastructure details that should survive compression.
     """
+    facts = []
+    seen_contents = set()
 
-    raw_candidates: list[ExtractedFact] = []
     for turn_idx, msg in enumerate(messages):
         role = msg.get("role", "")
         content = msg.get("content", "")
-        if role not in {"user", "assistant"}:
+
+        # Only scan user messages and assistant responses with corrections
+        if role not in ("user", "assistant"):
             continue
+        if not content or not isinstance(content, str):
+            continue
+        if len(content) < 10:
+            continue
+
+        # Skip tool results and system messages
         if role == "assistant" and msg.get("tool_calls"):
             continue
-        if not isinstance(content, str) or len(content.strip()) < 10:
-            continue
 
-        timestamp, observed_at = _message_time(msg)
-        raw_candidates.extend(
-            _extract_from_text(
-                content.strip(),
-                turn_idx=turn_idx,
-                role=role,
-                timestamp=timestamp,
-                observed_at=observed_at,
-            )
-        )
+        extracted = _extract_from_text(content, turn_idx, role)
 
-    return _normalize_candidates(raw_candidates)
-
-
-def evaluate_extraction_quality(messages: List[Dict[str, Any]]) -> Dict[str, Any]:
-    """Return before/after metrics for raw vs normalized extraction quality."""
-
-    raw_candidates: list[ExtractedFact] = []
-    for turn_idx, msg in enumerate(messages):
-        role = msg.get("role", "")
-        content = msg.get("content", "")
-        if role not in {"user", "assistant"}:
-            continue
-        if role == "assistant" and msg.get("tool_calls"):
-            continue
-        if not isinstance(content, str) or len(content.strip()) < 10:
-            continue
-        timestamp, observed_at = _message_time(msg)
-        raw_candidates.extend(
-            _extract_from_text(
-                content.strip(),
-                turn_idx=turn_idx,
-                role=role,
-                timestamp=timestamp,
-                observed_at=observed_at,
-            )
-        )
-
-    normalized = _normalize_candidates(raw_candidates)
-    raw_count = len(raw_candidates)
-    normalized_count = len(normalized)
-    contradiction_groups = {
-        fact.contradiction_group
-        for fact in normalized
-        if fact.status == "contradiction" and fact.contradiction_group
-    }
-    duplicate_count = max(0, raw_count - normalized_count)
-    noise_reduction = (duplicate_count / raw_count) if raw_count else 0.0
-
-    return {
-        "raw_candidates": raw_count,
-        "normalized_facts": normalized_count,
-        "duplicates_merged": duplicate_count,
-        "contradiction_groups": len(contradiction_groups),
-        "noise_reduction": round(noise_reduction, 3),
-    }
-
-
-def _extract_from_text(
-    text: str,
-    *,
-    turn_idx: int,
-    role: str,
-    timestamp: float,
-    observed_at: str,
-) -> List[ExtractedFact]:
-    """Extract raw fact candidates from a single text block."""
-
-    facts: list[ExtractedFact] = []
-    if role != "user":
-        return facts
-
-    deploy_match = _DEPLOY_METHOD_RE.search(text)
-    if deploy_match:
-        method = deploy_match.group(1).strip()
-        facts.append(
-            _build_fact(
-                category="project.decision",
-                entity="project",
-                relation="workflow.deploy_method",
-                value=method,
-                content=f"Deploy via {method}",
-                confidence=0.88,
-                source_turn=turn_idx,
-                source_role=role,
-                source_text=text,
-                timestamp=timestamp,
-                observed_at=observed_at,
-                unique_slot=True,
-            )
-        )
-
-    watchdog_match = _WATCHDOG_CAP_RE.search(text)
-    if watchdog_match:
-        watchdog = watchdog_match.group(1).strip()
-        cap = watchdog_match.group(2).strip()
-        facts.append(
-            _build_fact(
-                category="project.operational",
-                entity=_normalize_entity(watchdog),
-                relation="fleet.dispatch_cap",
-                value=cap,
-                content=f"{watchdog} caps dispatches per cycle to {cap}",
-                confidence=0.92,
-                source_turn=turn_idx,
-                source_role=role,
-                source_text=text,
-                timestamp=timestamp,
-                observed_at=observed_at,
-                unique_slot=True,
-            )
-        )
-
-    provider_match = _PROVIDER_RE.search(text)
-    if provider_match:
-        provider = provider_match.group(1).strip()
-        facts.append(
-            _build_fact(
-                category="project.config",
-                entity="project",
-                relation="config.provider",
-                value=provider,
-                content=f"Provider should stay {provider}",
-                confidence=0.91,
-                source_turn=turn_idx,
-                source_role=role,
-                source_text=text,
-                timestamp=timestamp,
-                observed_at=observed_at,
-                unique_slot=True,
-            )
-        )
-
-    model_match = _MODEL_RE.search(text)
-    if model_match:
-        model = model_match.group(1).strip()
-        facts.append(
-            _build_fact(
-                category="project.config",
-                entity="project",
-                relation="config.model",
-                value=model,
-                content=f"Model should stay {model}",
-                confidence=0.9,
-                source_turn=turn_idx,
-                source_role=role,
-                source_text=text,
-                timestamp=timestamp,
-                observed_at=observed_at,
-                unique_slot=True,
-            )
-        )
-
-    port_match = _PORT_RE.search(text)
-    if port_match:
-        port = port_match.group(1).strip()
-        facts.append(
-            _build_fact(
-                category="project.config",
-                entity="project",
-                relation="config.port",
-                value=port,
-                content=f"Port is {port}",
-                confidence=0.9,
-                source_turn=turn_idx,
-                source_role=role,
-                source_text=text,
-                timestamp=timestamp,
-                observed_at=observed_at,
-                unique_slot=True,
-            )
-        )
-
-    project_match = _PROJECT_USES_RE.search(text)
-    if project_match:
-        value = project_match.group(1).strip().rstrip(".")
-        facts.append(
-            _build_fact(
-                category="project.stack",
-                entity="project",
-                relation="project.stack",
-                value=value,
-                content=f"Project uses {value}",
-                confidence=0.74,
-                source_turn=turn_idx,
-                source_role=role,
-                source_text=text,
-                timestamp=timestamp,
-                observed_at=observed_at,
-                unique_slot=False,
-            )
-        )
-
-    preference_match = _PREFERENCE_RE.search(text)
-    if preference_match:
-        value = preference_match.group(1).strip().rstrip(".")
-        facts.append(
-            _build_fact(
-                category="user_pref.preference",
-                entity="user",
-                relation="user.preference",
-                value=value,
-                content=value,
-                confidence=0.72,
-                source_turn=turn_idx,
-                source_role=role,
-                source_text=text,
-                timestamp=timestamp,
-                observed_at=observed_at,
-                unique_slot=False,
-            )
-        )
-
-    constraint_match = _CONSTRAINT_RE.search(text)
-    if constraint_match:
-        value = constraint_match.group(1).strip().rstrip(".")
-        facts.append(
-            _build_fact(
-                category="user_pref.constraint",
-                entity="user",
-                relation="user.constraint",
-                value=value,
-                content=f"Do not {value}",
-                confidence=0.82,
-                source_turn=turn_idx,
-                source_role=role,
-                source_text=text,
-                timestamp=timestamp,
-                observed_at=observed_at,
-                unique_slot=False,
-            )
-        )
-
-    decision_match = _DECISION_RE.search(text)
-    if decision_match:
-        value = decision_match.group(1).strip().rstrip(".")
-        facts.append(
-            _build_fact(
-                category="project.decision",
-                entity="project",
-                relation="project.decision",
-                value=value,
-                content=f"Decision: {value}",
-                confidence=0.79,
-                source_turn=turn_idx,
-                source_role=role,
-                source_text=text,
-                timestamp=timestamp,
-                observed_at=observed_at,
-                unique_slot=False,
-            )
-        )
+        # Deduplicate by content
+        for fact in extracted:
+            key = f"{fact.category}:{fact.content[:100]}"
+            if key not in seen_contents:
+                seen_contents.add(key)
+                facts.append(fact)
 
     return facts
 
 
-def _build_fact(
-    *,
-    category: str,
-    entity: str,
-    relation: str,
-    value: str,
-    content: str,
-    confidence: float,
-    source_turn: int,
-    source_role: str,
-    source_text: str,
-    timestamp: float,
-    observed_at: str,
-    unique_slot: bool,
-) -> ExtractedFact:
-    normalized_value = _normalize_value(value.rstrip(".!?"))
-    value = value.rstrip(".!?")
-    content = content.rstrip(".!?")
-    provenance = f"conversation:{source_role}:{source_turn}"
-    contradiction_group = relation if unique_slot else ""
-    evidence = [
-        {
-            "source_role": source_role,
-            "source_turn": source_turn,
-            "source_text": source_text,
-            "observed_at": observed_at,
-            "provenance": provenance,
-        }
-    ]
-    metadata = {
-        "entity": entity,
-        "relation": relation,
-        "value": value,
-        "normalized_value": normalized_value,
-        "provenance": [provenance],
-        "evidence": list(evidence),
-        "observation_count": 1,
-        "duplicate_count": 0,
-        "status": "active",
-    }
-    if contradiction_group:
-        metadata["contradiction_group"] = contradiction_group
-    return ExtractedFact(
-        category=category,
-        entity=entity,
-        content=content,
-        confidence=confidence,
-        source_turn=source_turn,
-        timestamp=timestamp,
-        source_role=source_role,
-        source_text=source_text,
-        normalized_content=normalized_value,
-        canonical_key=_canonical_key(entity, relation, normalized_value),
-        relation=relation,
-        contradiction_group=contradiction_group,
-        status="active",
-        provenance=provenance,
-        observed_at=observed_at,
-        evidence=evidence,
-        metadata=metadata,
-    )
+def _extract_from_text(text: str, turn_idx: int, role: str) -> List[ExtractedFact]:
+    """Extract facts from a single text block."""
+    facts = []
+    timestamp = time.time()
 
+    # Clean text for pattern matching
+    clean = text.strip()
 
-def _normalize_candidates(candidates: List[ExtractedFact]) -> List[ExtractedFact]:
-    """Merge duplicates and mark contradictions while preserving evidence."""
+    # User preference patterns (from user messages)
+    if role == "user":
+        for pattern, subcategory in _PREFERENCE_PATTERNS:
+            for match in re.finditer(pattern, clean, re.IGNORECASE):
+                content = match.group(1).strip() if match.lastindex else match.group(0).strip()
+                if len(content) > 5:
+                    facts.append(ExtractedFact(
+                        category=f"user_pref.{subcategory}",
+                        entity="user",
+                        content=content[:200],
+                        confidence=0.7,
+                        source_turn=turn_idx,
+                        timestamp=timestamp,
+                    ))
 
-    by_key: dict[str, ExtractedFact] = {}
-    contradiction_groups: dict[str, list[ExtractedFact]] = {}
+    # Correction patterns (from user messages)
+    if role == "user":
+        for pattern, subcategory in _CORRECTION_PATTERNS:
+            for match in re.finditer(pattern, clean, re.IGNORECASE):
+                content = match.group(1).strip() if match.lastindex else match.group(0).strip()
+                if len(content) > 5:
+                    facts.append(ExtractedFact(
+                        category=f"correction.{subcategory}",
+                        entity="user",
+                        content=content[:200],
+                        confidence=0.8,
+                        source_turn=turn_idx,
+                        timestamp=timestamp,
+                    ))
 
-    for candidate in candidates:
-        existing = by_key.get(candidate.canonical_key)
-        if existing is not None:
-            by_key[candidate.canonical_key] = _merge_fact(existing, candidate)
-            continue
+    # Project/infrastructure patterns (from both user and assistant)
+    for pattern, subcategory in _PROJECT_PATTERNS:
+        for match in re.finditer(pattern, clean, re.IGNORECASE):
+            content = match.group(1).strip() if match.lastindex else match.group(0).strip()
+            if len(content) > 5:
+                facts.append(ExtractedFact(
+                    category=f"project.{subcategory}",
+                    entity=subcategory,
+                    content=content[:200],
+                    confidence=0.6,
+                    source_turn=turn_idx,
+                    timestamp=timestamp,
+                ))
 
-        by_key[candidate.canonical_key] = candidate
-        if candidate.contradiction_group:
-            contradiction_groups.setdefault(candidate.contradiction_group, []).append(candidate)
-
-    for group, facts in contradiction_groups.items():
-        canonical_keys = {fact.canonical_key for fact in facts}
-        if len(canonical_keys) <= 1:
-            continue
-        for fact in facts:
-            fact.status = "contradiction"
-            fact.metadata["status"] = "contradiction"
-            fact.metadata["contradiction_group"] = group
-            fact.metadata["contradiction_keys"] = sorted(canonical_keys - {fact.canonical_key})
-
-    return sorted(by_key.values(), key=lambda fact: (fact.source_turn, fact.timestamp, fact.canonical_key))
-
-
-def _merge_fact(existing: ExtractedFact, incoming: ExtractedFact) -> ExtractedFact:
-    existing.confidence = max(existing.confidence, incoming.confidence)
-    existing.timestamp = min(existing.timestamp, incoming.timestamp)
-    existing.source_turn = min(existing.source_turn, incoming.source_turn)
-    if not existing.observed_at or (incoming.observed_at and incoming.observed_at < existing.observed_at):
-        existing.observed_at = incoming.observed_at
-    existing.provenance = min(existing.provenance, incoming.provenance)
-
-    provenance = _ordered_unique(existing.metadata.get("provenance", []), incoming.metadata.get("provenance", []))
-    evidence = _merge_evidence(existing.metadata.get("evidence", []), incoming.metadata.get("evidence", []))
-    observation_count = int(existing.metadata.get("observation_count", len(existing.evidence) or 1))
-    observation_count += int(incoming.metadata.get("observation_count", len(incoming.evidence) or 1))
-
-    existing.evidence = evidence
-    existing.metadata["provenance"] = provenance
-    existing.metadata["evidence"] = evidence
-    existing.metadata["observation_count"] = observation_count
-    existing.metadata["duplicate_count"] = max(0, observation_count - 1)
-    existing.metadata["status"] = existing.status
-    return existing
+    return facts
 
 
 def save_facts_to_store(facts: List[ExtractedFact], fact_store_fn=None) -> int:
     """Save extracted facts to the fact store.
 
-    If a callback is supplied, prefer the structured signature but fall back to
-    the legacy four-argument callback for compatibility.
+    Args:
+        facts: List of extracted facts.
+        fact_store_fn: Optional callable(category, entity, content, trust).
+            If None, uses the holographic fact store if available.
+
+    Returns:
+        Number of facts saved.
     """
-
     saved = 0
-    for fact in facts:
-        payload = {
-            "category": _store_category(fact.category),
-            "entity": fact.entity,
-            "content": fact.content,
-            "trust": fact.confidence,
-            "metadata": dict(fact.metadata),
-            "canonical_key": fact.canonical_key,
-            "observed_at": fact.observed_at,
-            "source_role": fact.source_role,
-            "source_turn": fact.source_turn,
-            "contradiction_group": fact.contradiction_group,
-            "status": fact.status,
-            "relation": fact.relation,
-        }
 
-        if fact_store_fn:
+    if fact_store_fn:
+        for fact in facts:
             try:
-                fact_store_fn(**payload)
+                fact_store_fn(
+                    category=fact.category,
+                    entity=fact.entity,
+                    content=fact.content,
+                    trust=fact.confidence,
+                )
                 saved += 1
-                continue
-            except TypeError:
-                try:
-                    fact_store_fn(payload["category"], payload["entity"], payload["content"], payload["trust"])
-                    saved += 1
-                    continue
-                except Exception as exc:
-                    logger.debug("Failed to save fact via callback: %s", exc)
-                    continue
-            except Exception as exc:
-                logger.debug("Failed to save fact via callback: %s", exc)
-                continue
-
+            except Exception as e:
+                logger.debug("Failed to save fact: %s", e)
+    else:
+        # Try holographic fact store
         try:
             from fact_store import fact_store as _fs
-
-            tags = ",".join(filter(None, [fact.entity, fact.relation, fact.status]))
-            _fs(
-                action="add",
-                content=fact.content,
-                category=_store_category(fact.category),
-                tags=tags,
-                trust_delta=fact.confidence - 0.5,
-            )
-            saved += 1
+            for fact in facts:
+                try:
+                    _fs(
+                        action="add",
+                        content=fact.content,
+                        category=fact.category,
+                        tags=fact.entity,
+                        trust_delta=fact.confidence - 0.5,
+                    )
+                    saved += 1
+                except Exception as e:
+                    logger.debug("Failed to save fact via fact_store: %s", e)
         except ImportError:
             logger.debug("fact_store not available — facts not persisted")
-            break
-        except Exception as exc:
-            logger.debug("Failed to save fact via fact_store: %s", exc)
 
     return saved
 
@@ -553,10 +204,9 @@ def extract_and_save_facts(
 
     Returns (extracted_facts, saved_count).
     """
-
     facts = extract_facts_from_messages(messages)
     if facts:
-        logger.info("Extracted %d normalized facts from conversation", len(facts))
+        logger.info("Extracted %d facts from conversation", len(facts))
         saved = save_facts_to_store(facts, fact_store_fn)
         logger.info("Saved %d/%d facts to store", saved, len(facts))
     else:
@@ -566,105 +216,16 @@ def extract_and_save_facts(
 
 def format_facts_summary(facts: List[ExtractedFact]) -> str:
     """Format extracted facts as a readable summary."""
-
     if not facts:
         return "No facts extracted."
 
-    by_category: dict[str, list[ExtractedFact]] = {}
-    for fact in facts:
-        by_category.setdefault(fact.category, []).append(fact)
+    by_category = {}
+    for f in facts:
+        by_category.setdefault(f.category, []).append(f)
 
     lines = [f"Extracted {len(facts)} facts:", ""]
-    for category, category_facts in sorted(by_category.items()):
-        lines.append(f"  {category}:")
-        for fact in category_facts:
-            suffix = f" [{fact.status}]" if fact.status != "active" else ""
-            lines.append(f"    - {fact.content[:80]}{suffix}")
+    for cat, cat_facts in sorted(by_category.items()):
+        lines.append(f"  {cat}:")
+        for f in cat_facts:
+            lines.append(f"    - {f.content[:80]}")
     return "\n".join(lines)
-
-
-def _store_category(category: str) -> str:
-    if category.startswith("user_pref"):
-        return "user_pref"
-    if category.startswith("project"):
-        return "project"
-    if category.startswith("tool"):
-        return "tool"
-    return "general"
-
-
-def _message_time(msg: Dict[str, Any]) -> Tuple[float, str]:
-    for key in ("created_at", "timestamp", "time"):
-        value = msg.get(key)
-        if value is None:
-            continue
-        if isinstance(value, (int, float)):
-            ts = float(value)
-            return ts, _iso_from_timestamp(ts)
-        if isinstance(value, str):
-            parsed = _parse_time_string(value)
-            if parsed is not None:
-                return parsed, _iso_from_timestamp(parsed) if "T" not in value else value.replace("+00:00", "Z")
-            return time.time(), value
-    now = time.time()
-    return now, _iso_from_timestamp(now)
-
-
-def _parse_time_string(value: str) -> float | None:
-    text = value.strip()
-    if not text:
-        return None
-    try:
-        return float(text)
-    except ValueError:
-        pass
-    try:
-        normalized = text[:-1] + "+00:00" if text.endswith("Z") else text
-        return datetime.fromisoformat(normalized).timestamp()
-    except ValueError:
-        return None
-
-
-def _iso_from_timestamp(value: float) -> str:
-    return datetime.fromtimestamp(value, tz=timezone.utc).isoformat().replace("+00:00", "Z")
-
-
-def _normalize_value(value: str) -> str:
-    normalized = re.sub(r"[^a-z0-9]+", " ", value.lower())
-    normalized = re.sub(r"\s+", " ", normalized).strip()
-    return normalized
-
-
-def _normalize_entity(value: str) -> str:
-    return _normalize_value(value).replace(" ", "_") or "entity"
-
-
-def _canonical_key(entity: str, relation: str, normalized_value: str) -> str:
-    return f"{entity}|{relation}|{normalized_value}"
-
-
-def _ordered_unique(*groups: List[str]) -> List[str]:
-    seen: set[str] = set()
-    ordered: list[str] = []
-    for group in groups:
-        for item in group:
-            if item and item not in seen:
-                seen.add(item)
-                ordered.append(item)
-    return ordered
-
-
-def _merge_evidence(existing: List[Dict[str, Any]], incoming: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
-    seen: set[tuple[str, str, str]] = set()
-    merged: list[dict[str, Any]] = []
-    for item in list(existing) + list(incoming):
-        key = (
-            str(item.get("provenance", "")),
-            str(item.get("observed_at", "")),
-            str(item.get("source_text", "")),
-        )
-        if key in seen:
-            continue
-        seen.add(key)
-        merged.append(dict(item))
-    return merged

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"),
@@ -356,57 +427,44 @@ class HolographicMemoryProvider(MemoryProvider):
     # -- Auto-extraction (on_session_end) ------------------------------------
 
     def _auto_extract_facts(self, messages: list) -> None:
-        from agent.session_compactor import evaluate_extraction_quality, extract_facts_from_messages
-
-        def _store_category(category: str) -> str:
-            if category.startswith("user_pref"):
-                return "user_pref"
-            if category.startswith("project"):
-                return "project"
-            if category.startswith("tool"):
-                return "tool"
-            return "general"
-
-        facts = extract_facts_from_messages(messages)
-        if not facts:
-            return
+        _PREF_PATTERNS = [
+            re.compile(r'\bI\s+(?:prefer|like|love|use|want|need)\s+(.+)', re.IGNORECASE),
+            re.compile(r'\bmy\s+(?:favorite|preferred|default)\s+\w+\s+is\s+(.+)', re.IGNORECASE),
+            re.compile(r'\bI\s+(?:always|never|usually)\s+(.+)', re.IGNORECASE),
+        ]
+        _DECISION_PATTERNS = [
+            re.compile(r'\bwe\s+(?:decided|agreed|chose)\s+(?:to\s+)?(.+)', re.IGNORECASE),
+            re.compile(r'\bthe\s+project\s+(?:uses|needs|requires)\s+(.+)', re.IGNORECASE),
+        ]
 
         extracted = 0
-        for fact in facts:
-            try:
-                metadata = dict(fact.metadata)
-                metadata.setdefault("relation", fact.relation)
-                metadata.setdefault("value", fact.content)
-                metadata.setdefault("provenance", [fact.provenance])
-                metadata.setdefault("evidence", list(fact.evidence))
-                metadata.setdefault("observation_count", len(fact.evidence))
-                metadata.setdefault("duplicate_count", max(0, len(fact.evidence) - 1))
-                self._store.add_fact(
-                    fact.content,
-                    category=_store_category(fact.category),
-                    tags=",".join(filter(None, [fact.entity, fact.relation, fact.status])),
-                    canonical_key=fact.canonical_key,
-                    metadata=metadata,
-                    confidence=fact.confidence,
-                    source_role=fact.source_role,
-                    source_turn=fact.source_turn,
-                    observed_at=fact.observed_at,
-                    contradiction_group=fact.contradiction_group,
-                    status=fact.status,
-                )
-                extracted += 1
-            except Exception as exc:
-                logger.debug("Structured auto-extract failed for %s: %s", fact.canonical_key, exc)
+        for msg in messages:
+            if msg.get("role") != "user":
+                continue
+            content = msg.get("content", "")
+            if not isinstance(content, str) or len(content) < 10:
+                continue
+
+            for pattern in _PREF_PATTERNS:
+                if pattern.search(content):
+                    try:
+                        self._store.add_fact(content[:400], category="user_pref")
+                        extracted += 1
+                    except Exception:
+                        pass
+                    break
+
+            for pattern in _DECISION_PATTERNS:
+                if pattern.search(content):
+                    try:
+                        self._store.add_fact(content[:400], category="project")
+                        extracted += 1
+                    except Exception:
+                        pass
+                    break
 
         if extracted:
-            metrics = evaluate_extraction_quality(messages)
-            logger.info(
-                "Auto-extracted %d structured facts from conversation (raw=%d normalized=%d contradictions=%d)",
-                extracted,
-                metrics["raw_candidates"],
-                metrics["normalized_facts"],
-                metrics["contradiction_groups"],
-            )
+            logger.info("Auto-extracted %d facts from conversation", extracted)
 
 
 # ---------------------------------------------------------------------------

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

@@ -16,24 +16,16 @@ except ImportError:
 
 _SCHEMA = """
 CREATE TABLE IF NOT EXISTS facts (
-    fact_id             INTEGER PRIMARY KEY AUTOINCREMENT,
-    content             TEXT NOT NULL UNIQUE,
-    category            TEXT DEFAULT 'general',
-    tags                TEXT DEFAULT '',
-    trust_score         REAL DEFAULT 0.5,
-    retrieval_count     INTEGER DEFAULT 0,
-    helpful_count       INTEGER DEFAULT 0,
-    canonical_key       TEXT DEFAULT '',
-    metadata_json       TEXT DEFAULT '{}',
-    confidence          REAL DEFAULT 0.5,
-    source_role         TEXT DEFAULT '',
-    source_turn         INTEGER DEFAULT -1,
-    observed_at         TEXT DEFAULT '',
-    contradiction_group TEXT DEFAULT '',
-    status              TEXT DEFAULT 'active',
-    created_at          TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
-    updated_at          TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
-    hrr_vector          BLOB
+    fact_id         INTEGER PRIMARY KEY AUTOINCREMENT,
+    content         TEXT NOT NULL UNIQUE,
+    category        TEXT DEFAULT 'general',
+    tags            TEXT DEFAULT '',
+    trust_score     REAL DEFAULT 0.5,
+    retrieval_count INTEGER DEFAULT 0,
+    helpful_count   INTEGER DEFAULT 0,
+    created_at      TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at      TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    hrr_vector      BLOB
 );
 
 CREATE TABLE IF NOT EXISTS entities (
@@ -50,11 +42,9 @@ CREATE TABLE IF NOT EXISTS fact_entities (
     PRIMARY KEY (fact_id, entity_id)
 );
 
-CREATE INDEX IF NOT EXISTS idx_facts_trust               ON facts(trust_score DESC);
-CREATE INDEX IF NOT EXISTS idx_facts_category            ON facts(category);
-CREATE INDEX IF NOT EXISTS idx_facts_canonical_key       ON facts(canonical_key);
-CREATE INDEX IF NOT EXISTS idx_facts_contradiction_group ON facts(contradiction_group);
-CREATE INDEX IF NOT EXISTS idx_entities_name             ON entities(name);
+CREATE INDEX IF NOT EXISTS idx_facts_trust    ON facts(trust_score DESC);
+CREATE INDEX IF NOT EXISTS idx_facts_category ON facts(category);
+CREATE INDEX IF NOT EXISTS idx_entities_name  ON entities(name);
 
 CREATE VIRTUAL TABLE IF NOT EXISTS facts_fts
     USING fts5(content, tags, content=facts, content_rowid=fact_id);
@@ -84,6 +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
@@ -139,24 +151,12 @@ 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()}
-        migrations = {
-            "hrr_vector": "ALTER TABLE facts ADD COLUMN hrr_vector BLOB",
-            "canonical_key": "ALTER TABLE facts ADD COLUMN canonical_key TEXT DEFAULT ''",
-            "metadata_json": "ALTER TABLE facts ADD COLUMN metadata_json TEXT DEFAULT '{}'",
-            "confidence": "ALTER TABLE facts ADD COLUMN confidence REAL DEFAULT 0.5",
-            "source_role": "ALTER TABLE facts ADD COLUMN source_role TEXT DEFAULT ''",
-            "source_turn": "ALTER TABLE facts ADD COLUMN source_turn INTEGER DEFAULT -1",
-            "observed_at": "ALTER TABLE facts ADD COLUMN observed_at TEXT DEFAULT ''",
-            "contradiction_group": "ALTER TABLE facts ADD COLUMN contradiction_group TEXT DEFAULT ''",
-            "status": "ALTER TABLE facts ADD COLUMN status TEXT DEFAULT 'active'",
-        }
-        for column, ddl in migrations.items():
-            if column not in columns:
-                self._conn.execute(ddl)
-        self._conn.execute("CREATE INDEX IF NOT EXISTS idx_facts_canonical_key ON facts(canonical_key)")
-        self._conn.execute("CREATE INDEX IF NOT EXISTS idx_facts_contradiction_group ON facts(contradiction_group)")
+        if "hrr_vector" not in columns:
+            self._conn.execute("ALTER TABLE facts ADD COLUMN hrr_vector BLOB")
         self._conn.commit()
 
     # ------------------------------------------------------------------
@@ -168,148 +168,41 @@ class MemoryStore:
         content: str,
         category: str = "general",
         tags: str = "",
-        *,
-        canonical_key: str = "",
-        metadata: dict | None = None,
-        confidence: float | None = None,
-        source_role: str = "",
-        source_turn: int = -1,
-        observed_at: str = "",
-        contradiction_group: str = "",
-        status: str = "active",
     ) -> int:
         """Insert a fact and return its fact_id.
 
-        Exact duplicates are deduplicated by content. Near-duplicates are
-        normalized by canonical_key, with provenance/evidence merged into the
-        existing row. Contradictions sharing the same contradiction_group remain
-        stored as separate rows and are marked inspectably.
+        Deduplicates by content (UNIQUE constraint). On duplicate, returns
+        the existing fact_id without modifying the row. Extracts entities from
+        the content and links them to the fact.
         """
         with self._lock:
             content = content.strip()
             if not content:
                 raise ValueError("content must not be empty")
 
-            metadata = dict(metadata or {})
-            canonical_key = canonical_key.strip()
-            contradiction_group = contradiction_group.strip()
-            observed_at = observed_at.strip()
-            status = status or "active"
-            trust_score = self.default_trust if confidence is None else _clamp_trust(confidence)
-            metadata_json = json.dumps(metadata, sort_keys=True)
-
-            if canonical_key:
-                existing = self._conn.execute(
-                    "SELECT fact_id, metadata_json, trust_score, confidence, observed_at FROM facts WHERE canonical_key = ?",
-                    (canonical_key,),
-                ).fetchone()
-                if existing is not None:
-                    merged_metadata = self._merge_metadata(existing["metadata_json"], metadata)
-                    merged_trust = max(float(existing["trust_score"]), trust_score)
-                    merged_observed_at = existing["observed_at"] or observed_at
-                    if observed_at and merged_observed_at:
-                        merged_observed_at = min(merged_observed_at, observed_at)
-                    elif observed_at:
-                        merged_observed_at = observed_at
-                    self._conn.execute(
-                        """
-                        UPDATE facts
-                        SET metadata_json = ?,
-                            trust_score = ?,
-                            confidence = ?,
-                            observed_at = ?,
-                            updated_at = CURRENT_TIMESTAMP
-                        WHERE fact_id = ?
-                        """,
-                        (
-                            json.dumps(merged_metadata, sort_keys=True),
-                            merged_trust,
-                            max(float(existing["confidence"] or 0.0), confidence or trust_score),
-                            merged_observed_at,
-                            existing["fact_id"],
-                        ),
-                    )
-                    self._conn.commit()
-                    return int(existing["fact_id"])
-
-            contradiction_rows = []
-            if contradiction_group:
-                contradiction_rows = self._conn.execute(
-                    """
-                    SELECT fact_id, canonical_key, metadata_json
-                    FROM facts
-                    WHERE contradiction_group = ?
-                      AND canonical_key != ?
-                    """,
-                    (contradiction_group, canonical_key),
-                ).fetchall()
-                if contradiction_rows:
-                    status = "contradiction"
-                    metadata = dict(metadata)
-                    metadata["status"] = "contradiction"
-                    metadata["contradiction_group"] = contradiction_group
-                    metadata["contradiction_keys"] = sorted(
-                        {
-                            canonical_key,
-                            *[str(row["canonical_key"]) for row in contradiction_rows if row["canonical_key"]],
-                        }
-                        - {""}
-                    )
-                    metadata_json = json.dumps(metadata, sort_keys=True)
-
             try:
                 cur = self._conn.execute(
                     """
-                    INSERT INTO facts (
-                        content,
-                        category,
-                        tags,
-                        trust_score,
-                        canonical_key,
-                        metadata_json,
-                        confidence,
-                        source_role,
-                        source_turn,
-                        observed_at,
-                        contradiction_group,
-                        status
-                    )
-                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                    INSERT INTO facts (content, category, tags, trust_score)
+                    VALUES (?, ?, ?, ?)
                     """,
-                    (
-                        content,
-                        category,
-                        tags,
-                        trust_score,
-                        canonical_key,
-                        metadata_json,
-                        confidence if confidence is not None else trust_score,
-                        source_role,
-                        source_turn,
-                        observed_at,
-                        contradiction_group,
-                        status,
-                    ),
+                    (content, category, tags, self.default_trust),
                 )
                 self._conn.commit()
                 fact_id: int = cur.lastrowid  # type: ignore[assignment]
             except sqlite3.IntegrityError:
+                # Duplicate content — return existing id
                 row = self._conn.execute(
                     "SELECT fact_id FROM facts WHERE content = ?", (content,)
                 ).fetchone()
                 return int(row["fact_id"])
 
-            if contradiction_rows:
-                self._mark_contradictions(
-                    contradiction_group=contradiction_group,
-                    new_canonical_key=canonical_key,
-                    existing_rows=contradiction_rows,
-                )
-
+            # Entity extraction and linking
             for name in self._extract_entities(content):
                 entity_id = self._resolve_entity(name)
                 self._link_fact_entity(fact_id, entity_id)
 
+            # Compute HRR vector after entity linking
             self._compute_hrr_vector(fact_id, content)
             self._rebuild_bank(category)
 
@@ -342,9 +235,6 @@ class MemoryStore:
             sql = f"""
                 SELECT f.fact_id, f.content, f.category, f.tags,
                        f.trust_score, f.retrieval_count, f.helpful_count,
-                       f.canonical_key, f.metadata_json, f.confidence,
-                       f.source_role, f.source_turn, f.observed_at,
-                       f.contradiction_group, f.status,
                        f.created_at, f.updated_at
                 FROM facts f
                 JOIN facts_fts fts ON fts.rowid = f.fact_id
@@ -470,11 +360,7 @@ class MemoryStore:
 
             sql = f"""
                 SELECT fact_id, content, category, tags, trust_score,
-                       retrieval_count, helpful_count,
-                       canonical_key, metadata_json, confidence,
-                       source_role, source_turn, observed_at,
-                       contradiction_group, status,
-                       created_at, updated_at
+                       retrieval_count, helpful_count, created_at, updated_at
                 FROM facts
                 WHERE trust_score >= ?
                   {category_clause}
@@ -484,6 +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.
 
@@ -525,89 +520,6 @@ class MemoryStore:
                 "helpful_count": row["helpful_count"] + helpful_increment,
             }
 
-    # ------------------------------------------------------------------
-    # Metadata helpers
-    # ------------------------------------------------------------------
-
-    def _load_metadata(self, metadata_json: str | None) -> dict:
-        if not metadata_json:
-            return {}
-        try:
-            data = json.loads(metadata_json)
-            return data if isinstance(data, dict) else {}
-        except Exception:
-            return {}
-
-    def _merge_metadata(self, existing_json: str | None, incoming: dict | None) -> dict:
-        existing = self._load_metadata(existing_json)
-        incoming = dict(incoming or {})
-        merged = dict(existing)
-        merged.update({k: v for k, v in incoming.items() if k not in {"provenance", "evidence", "observation_count", "duplicate_count", "contradiction_keys"}})
-
-        provenance = []
-        seen_provenance: set[str] = set()
-        for item in list(existing.get("provenance", [])) + list(incoming.get("provenance", [])):
-            if item and item not in seen_provenance:
-                seen_provenance.add(item)
-                provenance.append(item)
-
-        evidence = []
-        seen_evidence: set[tuple[str, str, str]] = set()
-        for item in list(existing.get("evidence", [])) + list(incoming.get("evidence", [])):
-            if not isinstance(item, dict):
-                continue
-            key = (
-                str(item.get("provenance", "")),
-                str(item.get("observed_at", "")),
-                str(item.get("source_text", "")),
-            )
-            if key in seen_evidence:
-                continue
-            seen_evidence.add(key)
-            evidence.append(dict(item))
-
-        observation_count = int(existing.get("observation_count", max(1, len(existing.get("evidence", [])) or 1)))
-        observation_count += int(incoming.get("observation_count", max(1, len(incoming.get("evidence", [])) or 1)))
-
-        contradiction_keys = []
-        seen_keys: set[str] = set()
-        for item in list(existing.get("contradiction_keys", [])) + list(incoming.get("contradiction_keys", [])):
-            if item and item not in seen_keys:
-                seen_keys.add(item)
-                contradiction_keys.append(item)
-
-        merged["provenance"] = provenance
-        merged["evidence"] = evidence
-        merged["observation_count"] = observation_count
-        merged["duplicate_count"] = max(0, observation_count - 1)
-        if contradiction_keys:
-            merged["contradiction_keys"] = contradiction_keys
-        return merged
-
-    def _mark_contradictions(self, contradiction_group: str, new_canonical_key: str, existing_rows: list[sqlite3.Row]) -> None:
-        for row in existing_rows:
-            metadata = self._load_metadata(row["metadata_json"])
-            keys = []
-            seen: set[str] = set()
-            for item in list(metadata.get("contradiction_keys", [])) + [new_canonical_key]:
-                if item and item not in seen:
-                    seen.add(item)
-                    keys.append(item)
-            metadata["status"] = "contradiction"
-            metadata["contradiction_group"] = contradiction_group
-            metadata["contradiction_keys"] = keys
-            self._conn.execute(
-                """
-                UPDATE facts
-                SET status = 'contradiction',
-                    metadata_json = ?,
-                    updated_at = CURRENT_TIMESTAMP
-                WHERE fact_id = ?
-                """,
-                (json.dumps(metadata, sort_keys=True), row["fact_id"]),
-            )
-        self._conn.commit()
-
     # ------------------------------------------------------------------
     # Entity helpers
     # ------------------------------------------------------------------
@@ -781,14 +693,8 @@ class MemoryStore:
     # ------------------------------------------------------------------
 
     def _row_to_dict(self, row: sqlite3.Row) -> dict:
-        """Convert a sqlite3.Row to a plain dict with decoded metadata."""
-        data = dict(row)
-        metadata = self._load_metadata(data.get("metadata_json"))
-        if metadata:
-            data["metadata"] = metadata
-            data.setdefault("relation", metadata.get("relation"))
-        data.pop("metadata_json", None)
-        return data
+        """Convert a sqlite3.Row to a plain dict."""
+        return dict(row)
 
     def close(self) -> None:
         """Close the database connection."""

tests/fixtures/memory_extraction_fragments.json

@@ -1,63 +0,0 @@
-{
-  "preferences_and_duplicates": [
-    {
-      "role": "user",
-      "content": "Deploy via Ansible for production changes.",
-      "created_at": "2026-04-22T10:00:00Z"
-    },
-    {
-      "role": "user",
-      "content": "We deploy through Ansible on this repo.",
-      "created_at": "2026-04-22T10:01:00Z"
-    },
-    {
-      "role": "user",
-      "content": "Gitea-first for repository work.",
-      "created_at": "2026-04-22T10:02:00Z"
-    }
-  ],
-  "operational_and_contradictions": [
-    {
-      "role": "user",
-      "content": "The BURN watchdog caps dispatches per cycle to 6.",
-      "created_at": "2026-04-22T11:00:00Z"
-    },
-    {
-      "role": "user",
-      "content": "The provider should stay openai-codex/gpt-5.4.",
-      "created_at": "2026-04-22T11:01:00Z"
-    },
-    {
-      "role": "user",
-      "content": "Correction: the provider should stay mimo-v2-pro.",
-      "created_at": "2026-04-22T11:02:00Z"
-    }
-  ],
-  "mixed_transcript": [
-    {
-      "role": "user",
-      "content": "Deploy via Ansible for production changes.",
-      "created_at": "2026-04-22T10:00:00Z"
-    },
-    {
-      "role": "user",
-      "content": "We deploy through Ansible on this repo.",
-      "created_at": "2026-04-22T10:01:00Z"
-    },
-    {
-      "role": "user",
-      "content": "The BURN watchdog caps dispatches per cycle to 6.",
-      "created_at": "2026-04-22T11:00:00Z"
-    },
-    {
-      "role": "user",
-      "content": "The provider should stay openai-codex/gpt-5.4.",
-      "created_at": "2026-04-22T11:01:00Z"
-    },
-    {
-      "role": "user",
-      "content": "Correction: the provider should stay mimo-v2-pro.",
-      "created_at": "2026-04-22T11:02:00Z"
-    }
-  ]
-}

tests/plugins/memory/test_holographic_auto_extract.py

@@ -1,50 +0,0 @@
-"""Integration tests for holographic auto-extraction with structured fact persistence."""
-
-import json
-import sys
-from pathlib import Path
-
-sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
-
-from plugins.memory.holographic import HolographicMemoryProvider
-
-_FIXTURE_PATH = Path(__file__).resolve().parents[2] / "fixtures" / "memory_extraction_fragments.json"
-
-
-def _load_fixture(name: str):
-    return json.loads(_FIXTURE_PATH.read_text())[name]
-
-
-class TestHolographicAutoExtract:
-    def test_auto_extract_persists_structured_metadata_and_normalizes_duplicates(self, tmp_path):
-        provider = HolographicMemoryProvider(
-            config={
-                "db_path": str(tmp_path / "memory_store.db"),
-                "auto_extract": True,
-                "default_trust": 0.5,
-            }
-        )
-        provider.initialize("test-session")
-
-        messages = _load_fixture("mixed_transcript")
-        provider.on_session_end(messages)
-        provider.on_session_end(messages)
-
-        facts = provider._store.list_facts(min_trust=0.0, limit=20)
-        deploy_facts = [f for f in facts if f.get("relation") == "workflow.deploy_method"]
-        provider_facts = [f for f in facts if f.get("contradiction_group") == "config.provider"]
-
-        assert len(deploy_facts) == 1
-        assert deploy_facts[0]["metadata"]["duplicate_count"] >= 3
-        assert deploy_facts[0]["observed_at"] == "2026-04-22T10:00:00Z"
-        assert deploy_facts[0]["metadata"]["provenance"] == [
-            "conversation:user:0",
-            "conversation:user:1",
-        ]
-
-        assert len(provider_facts) == 2
-        assert {f["status"] for f in provider_facts} == {"contradiction"}
-        assert {f["metadata"]["value"] for f in provider_facts} == {
-            "openai-codex/gpt-5.4",
-            "mimo-v2-pro",
-        }

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/test_session_compactor.py

@@ -1,6 +1,6 @@
 """Tests for session compaction with fact extraction."""
 
-import json
+import pytest
 import sys
 from pathlib import Path
 
@@ -8,19 +8,12 @@ sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
 
 from agent.session_compactor import (
     ExtractedFact,
-    evaluate_extraction_quality,
-    extract_and_save_facts,
     extract_facts_from_messages,
-    format_facts_summary,
     save_facts_to_store,
+    extract_and_save_facts,
+    format_facts_summary,
 )
 
-_FIXTURE_PATH = Path(__file__).resolve().parent / "fixtures" / "memory_extraction_fragments.json"
-
-
-def _load_fixture(name: str):
-    return json.loads(_FIXTURE_PATH.read_text())[name]
-
 
 class TestFactExtraction:
     def test_extract_preference(self):
@@ -67,48 +60,14 @@ class TestFactExtraction:
             {"role": "user", "content": "I prefer Python."},
         ]
         facts = extract_facts_from_messages(messages)
+        # Should deduplicate
         python_facts = [f for f in facts if "Python" in f.content]
         assert len(python_facts) == 1
 
-    def test_structured_fact_preserves_provenance_and_temporal_metadata(self):
-        facts = extract_facts_from_messages(_load_fixture("preferences_and_duplicates"))
-        deploy_fact = next(f for f in facts if f.relation == "workflow.deploy_method")
-        assert deploy_fact.source_role == "user"
-        assert deploy_fact.source_turn == 0
-        assert deploy_fact.observed_at == "2026-04-22T10:00:00Z"
-        assert deploy_fact.provenance == "conversation:user:0"
-        assert deploy_fact.canonical_key
-        assert deploy_fact.evidence
-        assert deploy_fact.evidence[0]["source_text"].startswith("Deploy via Ansible")
-
-    def test_near_duplicate_facts_are_normalized_into_one_canonical_fact(self):
-        facts = extract_facts_from_messages(_load_fixture("preferences_and_duplicates"))
-        deploy_facts = [f for f in facts if f.relation == "workflow.deploy_method"]
-        assert len(deploy_facts) == 1
-        assert len(deploy_facts[0].evidence) == 2
-        assert deploy_facts[0].metadata["duplicate_count"] == 1
-
-    def test_contradictory_facts_are_preserved_for_unique_slots(self):
-        facts = extract_facts_from_messages(_load_fixture("operational_and_contradictions"))
-        provider_facts = [f for f in facts if f.contradiction_group == "config.provider"]
-        assert len(provider_facts) == 2
-        assert {f.status for f in provider_facts} == {"contradiction"}
-        assert {f.normalized_content for f in provider_facts} == {
-            "openai codex gpt 5 4",
-            "mimo v2 pro",
-        }
-
-    def test_quality_evaluation_reports_noise_reduction(self):
-        metrics = evaluate_extraction_quality(_load_fixture("mixed_transcript"))
-        assert metrics["raw_candidates"] > metrics["normalized_facts"]
-        assert metrics["noise_reduction"] > 0
-        assert metrics["contradiction_groups"] == 1
-
 
 class TestSaveFacts:
     def test_save_with_callback(self):
         saved = []
-
         def mock_save(category, entity, content, trust):
             saved.append({"category": category, "content": content})
 
@@ -117,38 +76,6 @@ class TestSaveFacts:
         assert count == 1
         assert len(saved) == 1
 
-    def test_save_with_extended_callback_metadata(self):
-        saved = []
-
-        def mock_save(category, entity, content, trust, **kwargs):
-            saved.append({
-                "category": category,
-                "entity": entity,
-                "content": content,
-                "trust": trust,
-                **kwargs,
-            })
-
-        fact = ExtractedFact(
-            "project.operational",
-            "watchdog",
-            "BURN watchdog caps dispatches per cycle to 6",
-            0.9,
-            2,
-            source_role="user",
-            observed_at="2026-04-22T11:00:00Z",
-            provenance="conversation:user:2",
-            canonical_key="project.operational|watchdog|dispatch_cap|6",
-            relation="fleet.dispatch_cap",
-            contradiction_group="fleet.dispatch_cap",
-            metadata={"duplicate_count": 0},
-        )
-        count = save_facts_to_store([fact], fact_store_fn=mock_save)
-        assert count == 1
-        assert saved[0]["canonical_key"] == fact.canonical_key
-        assert saved[0]["observed_at"] == "2026-04-22T11:00:00Z"
-        assert saved[0]["metadata"]["duplicate_count"] == 0
-
 
 class TestFormatSummary:
     def test_empty(self):