test: cover patch did-you-mean end-to-end (#960 )

- add focused QA tests for replace-mode rich hints without legacy generic hints - verify ambiguous replace failures do not show did-you-mean noise - verify V4A patch validation surfaces rich hints - verify skill patching surfaces rich hints on true no-match failures
wip: fix patch did-you-mean dependencies (#960 )
2026-04-22 10:43:34 -04:00 · 2026-04-22 10:41:29 -04:00 · 2026-04-22 10:30:00 -04:00 · 2026-04-22 10:28:40 -04:00
13 changed files with 680 additions and 1088 deletions
--- a/agent/session_compactor.py
+++ b/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
--- a/plugins/memory/holographic/init.py
+++ b/plugins/memory/holographic/init.py
@@ -356,57 +356,44 @@ 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)


 # ---------------------------------------------------------------------------
--- a/plugins/memory/holographic/store.py
+++ b/plugins/memory/holographic/store.py
@@ -3,7 +3,6 @@ SQLite-backed fact store with entity resolution and trust scoring.
 Single-user Hermes memory store plugin.
 """

-import json
 import re
 import sqlite3
 import threading
@@ -16,24 +15,16 @@ 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 +41,9 @@ 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);
@@ -140,23 +129,10 @@ class MemoryStore:
        """Create tables, indexes, and triggers if they do not exist. Enable WAL mode."""
        self._conn.execute("PRAGMA journal_mode=WAL")
        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 +144,41 @@ 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 +211,6 @@ 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 +336,7 @@ 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}
@@ -525,89 +387,6 @@ 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 +560,8 @@ 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."""
--- a/tests/fixtures/memory_extraction_fragments.json
+++ b/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"
-    }
-  ]
-}
--- a/tests/plugins/memory/test_holographic_auto_extract.py
+++ b/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",
-        }
--- a/tests/test_session_compactor.py
+++ b/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):
--- a/tests/tools/test_fuzzy_match.py
+++ b/tests/tools/test_fuzzy_match.py
@@ -148,3 +148,184 @@ class TestStrategyNameSurfaced:
        assert count == 0
        assert strategy is None
        assert err is not None
+
+
+class TestEscapeDriftGuard:
+    """Tests for the escape-drift guard that catches bash/JSON serialization
+    artifacts where an apostrophe gets prefixed with a spurious backslash
+    in tool-call transport.
+    """
+
+    def test_drift_blocked_apostrophe(self):
+        """File has ', old_string and new_string both have \\' — classic
+        tool-call drift. Guard must block with a helpful error instead of
+        writing \\' literals into source code."""
+        content = "x = \"hello there\"\n"
+        # Simulate transport-corrupted old_string and new_string where an
+        # apostrophe-like context got prefixed with a backslash. The content
+        # itself has no apostrophe, but both strings do — matching via
+        # whitespace/anchor strategies would otherwise succeed.
+        old_string = "x = \"hello there\" # don\\'t edit\n"
+        new_string = "x = \"hi there\" # don\\'t edit\n"
+        # This particular pair won't match anything, so it exits via
+        # no-match path. Build a case where a non-exact strategy DOES match.
+        content = "line\n    x = 1\nline"
+        old_string = "line\n  x = \\'a\\'\nline"
+        new_string = "line\n  x = \\'b\\'\nline"
+        new, count, strategy, err = fuzzy_find_and_replace(content, old_string, new_string)
+        assert count == 0
+        assert err is not None and "Escape-drift" in err
+        assert "backslash" in err.lower()
+        assert new == content  # file untouched
+
+    def test_drift_blocked_double_quote(self):
+        """Same idea but with \\" drift instead of \\'."""
+        content = 'line\n    x = 1\nline'
+        old_string = 'line\n  x = \\"a\\"\nline'
+        new_string = 'line\n  x = \\"b\\"\nline'
+        new, count, strategy, err = fuzzy_find_and_replace(content, old_string, new_string)
+        assert count == 0
+        assert err is not None and "Escape-drift" in err
+
+    def test_drift_allowed_when_file_genuinely_has_backslash_escapes(self):
+        """If the file already contains \\' (e.g. inside an existing escaped
+        string), the model is legitimately preserving it. Guard must NOT
+        fire."""
+        content = "line\n  x = \\'a\\'\nline"
+        old_string = "line\n  x = \\'a\\'\nline"
+        new_string = "line\n  x = \\'b\\'\nline"
+        new, count, strategy, err = fuzzy_find_and_replace(content, old_string, new_string)
+        assert err is None
+        assert count == 1
+        assert "\\'b\\'" in new
+
+    def test_drift_allowed_on_exact_match(self):
+        """Exact matches bypass the drift guard entirely — if the file
+        really contains the exact bytes old_string specified, it's not
+        drift."""
+        content = "hello \\'world\\'"
+        new, count, strategy, err = fuzzy_find_and_replace(
+            content, "hello \\'world\\'", "hello \\'there\\'"
+        )
+        assert err is None
+        assert count == 1
+        assert strategy == "exact"
+
+    def test_drift_allowed_when_adding_escaped_strings(self):
+        """Model is adding new content with \\' that wasn't in the original.
+        old_string has no \\', so guard doesn't fire."""
+        content = "line1\nline2\nline3"
+        old_string = "line1\nline2\nline3"
+        new_string = "line1\nprint(\\'added\\')\nline2\nline3"
+        new, count, strategy, err = fuzzy_find_and_replace(content, old_string, new_string)
+        assert err is None
+        assert count == 1
+        assert "\\'added\\'" in new
+
+    def test_no_drift_check_when_new_string_lacks_suspect_chars(self):
+        """Fast-path: if new_string has no \\' or \\", guard must not
+        fire even on fuzzy match."""
+        content = "def foo():\n    pass"  # extra space ignored by line_trimmed
+        old_string = "def foo():\n  pass"
+        new_string = "def bar():\n  return 1"
+        new, count, strategy, err = fuzzy_find_and_replace(content, old_string, new_string)
+        assert err is None
+        assert count == 1
+
+
+class TestFindClosestLines:
+    def setup_method(self):
+        from tools.fuzzy_match import find_closest_lines
+        self.find_closest_lines = find_closest_lines
+
+    def test_finds_similar_line(self):
+        content = "def foo():\n    pass\ndef bar():\n    return 1\n"
+        result = self.find_closest_lines("def baz():", content)
+        assert "def foo" in result or "def bar" in result
+
+    def test_returns_empty_for_no_match(self):
+        content = "completely different content here"
+        result = self.find_closest_lines("xyzzy_no_match_possible_!!!", content)
+        assert result == ""
+
+    def test_returns_empty_for_empty_inputs(self):
+        assert self.find_closest_lines("", "some content") == ""
+        assert self.find_closest_lines("old string", "") == ""
+
+    def test_includes_context_lines(self):
+        content = "line1\nline2\ndef target():\n    pass\nline5\n"
+        result = self.find_closest_lines("def target():", content)
+        assert "target" in result
+
+    def test_includes_line_numbers(self):
+        content = "line1\nline2\ndef foo():\n    pass\n"
+        result = self.find_closest_lines("def foo():", content)
+        # Should include line numbers in format "N| content"
+        assert "|" in result
+
+
+class TestFormatNoMatchHint:
+    """Gating tests for format_no_match_hint — the shared helper that decides
+    whether a 'Did you mean?' snippet should be appended to an error.
+    """
+
+    def setup_method(self):
+        from tools.fuzzy_match import format_no_match_hint
+        self.fmt = format_no_match_hint
+
+    def test_fires_on_could_not_find_with_match(self):
+        """Classic no-match: similar content exists → hint fires."""
+        content = "def foo():\n    pass\ndef bar():\n    pass\n"
+        result = self.fmt(
+            "Could not find a match for old_string in the file",
+            0, "def baz():", content,
+        )
+        assert "Did you mean" in result
+        assert "foo" in result or "bar" in result
+
+    def test_silent_on_ambiguous_match_error(self):
+        """'Found N matches' is not a missing-match failure — no hint."""
+        content = "aaa bbb aaa\n"
+        result = self.fmt(
+            "Found 2 matches for old_string. Provide more context to make it unique, or use replace_all=True.",
+            0, "aaa", content,
+        )
+        assert result == ""
+
+    def test_silent_on_escape_drift_error(self):
+        """Escape-drift errors are intentional blocks — hint would mislead."""
+        content = "x = 1\n"
+        result = self.fmt(
+            "Escape-drift detected: old_string and new_string contain the literal sequence '\\\\''...",
+            0, "x = \\'1\\'", content,
+        )
+        assert result == ""
+
+    def test_silent_on_identical_strings(self):
+        """old_string == new_string — hint irrelevant."""
+        result = self.fmt(
+            "old_string and new_string are identical",
+            0, "foo", "foo bar\n",
+        )
+        assert result == ""
+
+    def test_silent_when_match_count_nonzero(self):
+        """If match succeeded, we shouldn't be in the error path — defense in depth."""
+        result = self.fmt(
+            "Could not find a match for old_string in the file",
+            1, "foo", "foo bar\n",
+        )
+        assert result == ""
+
+    def test_silent_on_none_error(self):
+        """No error at all — no hint."""
+        result = self.fmt(None, 0, "foo", "bar\n")
+        assert result == ""
+
+    def test_silent_when_no_similar_content(self):
+        """Even for a valid no-match error, skip hint when nothing similar exists."""
+        result = self.fmt(
+            "Could not find a match for old_string in the file",
+            0, "totally_unique_xyzzy_qux", "abc\nxyz\n",
+        )
+        assert result == ""
--- a/tests/tools/test_patch_did_you_mean.py
+++ b/tests/tools/test_patch_did_you_mean.py
@@ -0,0 +1,114 @@
+import json
+import os
+import textwrap
+from pathlib import Path
+
+import tools.skill_manager_tool as skill_manager_tool
+from tools.file_tools import patch_tool
+from tools.skill_manager_tool import _create_skill, _patch_skill
+
+
+def _disable_patch_tool_guards(monkeypatch):
+    monkeypatch.setattr("tools.file_tools._check_sensitive_path", lambda _path: None)
+    monkeypatch.setattr("tools.file_tools._check_file_staleness", lambda _path, _task_id: None)
+    monkeypatch.setattr("tools.file_tools._log_and_check_conflict", lambda _path, _task_id, _action: None)
+
+
+def test_patch_tool_replace_no_match_shows_rich_hint_without_legacy_hint(tmp_path, monkeypatch):
+    _disable_patch_tool_guards(monkeypatch)
+    sample = tmp_path / "sample.py"
+    sample.write_text("def foo():\n    return 1\n\ndef bar():\n    return 2\n", encoding="utf-8")
+
+    raw = patch_tool(
+        mode="replace",
+        path=str(sample),
+        old_string="def barycentric():",
+        new_string="def barycentric_new():",
+        task_id="qa960-replace-rich-hint",
+    )
+
+    result = json.loads(raw)
+    assert result["success"] is False
+    assert "Could not find a match" in result["error"]
+    assert "Did you mean one of these sections?" in result["error"]
+    assert "def bar():" in result["error"] or "def foo():" in result["error"]
+    assert "[Hint:" not in raw
+
+
+def test_patch_tool_replace_ambiguous_error_does_not_show_did_you_mean(tmp_path, monkeypatch):
+    _disable_patch_tool_guards(monkeypatch)
+    sample = tmp_path / "sample.py"
+    sample.write_text("aaa\nbbb\naaa\n", encoding="utf-8")
+
+    raw = patch_tool(
+        mode="replace",
+        path=str(sample),
+        old_string="aaa",
+        new_string="ccc",
+        task_id="qa960-replace-ambiguous",
+    )
+
+    result = json.loads(raw)
+    assert result["success"] is False
+    assert "Found 2 matches" in result["error"]
+    assert "Did you mean one of these sections?" not in result["error"]
+    assert "[Hint:" not in raw
+
+
+def test_patch_tool_v4a_no_match_shows_rich_hint(tmp_path, monkeypatch):
+    _disable_patch_tool_guards(monkeypatch)
+    sample = tmp_path / "sample.py"
+    sample.write_text("def foo():\n    return 1\n", encoding="utf-8")
+
+    patch = textwrap.dedent(
+        f"""\
+        *** Begin Patch
+        *** Update File: {sample}
+        @@
+        -def barycentric():
+        +def barycentric_new():
+        *** End Patch
+        """
+    )
+
+    raw = patch_tool(mode="patch", patch=patch, task_id="qa960-v4a-rich-hint")
+    result = json.loads(raw)
+    assert result["success"] is False
+    assert "Patch validation failed" in result["error"]
+    assert "Did you mean one of these sections?" in result["error"]
+    assert "def foo():" in result["error"]
+
+
+def test_skill_patch_no_match_shows_rich_hint(tmp_path, monkeypatch):
+    monkeypatch.setenv("HERMES_HOME", str(tmp_path))
+    skills_dir = tmp_path / "skills"
+    skills_dir.mkdir(parents=True, exist_ok=True)
+    monkeypatch.setattr(skill_manager_tool, "SKILLS_DIR", skills_dir)
+    monkeypatch.setattr(skill_manager_tool, "_security_scan_skill", lambda _skill_dir: None)
+
+    _create_skill(
+        "qa-skill",
+        textwrap.dedent(
+            """\
+            ---
+            name: qa-skill
+            description: test
+            ---
+
+            Step 1: Do the thing.
+            Step 2: Verify the thing.
+            """
+        ),
+    )
+
+    result = _patch_skill(
+        "qa-skill",
+        "Step 1: Do the production rollout.",
+        "Step 1: Updated.",
+    )
+
+    assert result["success"] is False
+    assert "Could not find a match" in result["error"]
+    assert "Did you mean one of these sections?" in result["error"]
+    assert "Step 1: Do the thing." in result["error"]
+    assert "file_preview" in result
--- a/tools/file_operations.py
+++ b/tools/file_operations.py
@@ -757,12 +757,14 @@ class ShellFileOperations(FileOperations):
            content, old_string, new_string, replace_all
        )
        
-        if error:
-            return PatchResult(error=error)
-        
-        if match_count == 0:
-            return PatchResult(error=f"Could not find match for old_string in {path}")
-        
+        if error or match_count == 0:
+            err_msg = error or f"Could not find match for old_string in {path}"
+            try:
+                from tools.fuzzy_match import format_no_match_hint
+                err_msg += format_no_match_hint(err_msg, match_count, old_string, content)
+            except Exception:
+                pass
+            return PatchResult(error=err_msg)
        # Write back
        write_result = self.write_file(path, new_content)
        if write_result.error:
--- a/tools/file_tools.py
+++ b/tools/file_tools.py
@@ -8,6 +8,7 @@ import os
 import threading
 import time
 from pathlib import Path
+from typing import Any, Dict, Optional
 from tools.binary_extensions import has_binary_extension
 from tools.file_operations import ShellFileOperations
 from agent.redact import redact_sensitive_text
@@ -690,8 +691,11 @@ def patch_tool(mode: str = "replace", path: str = None, old_string: str = None,
        result_json = json.dumps(result_dict, ensure_ascii=False)
        # Hint when old_string not found — saves iterations where the agent
        # retries with stale content instead of re-reading the file.
+        # Suppressed when patch_replace already attached a rich "Did you mean?"
+        # snippet (which is strictly more useful than the generic hint).
        if result_dict.get("error") and "Could not find" in str(result_dict["error"]):
-            result_json += "\n\n[Hint: old_string not found. Use read_file to verify the current content, or search_files to locate the text.]"
+            if "Did you mean one of these sections?" not in str(result_dict["error"]):
+                result_json += "\n\n[Hint: old_string not found. Use read_file to verify the current content, or search_files to locate the text.]"
        return result_json
    except Exception as e:
        return tool_error(str(e))
--- a/tools/fuzzy_match.py
+++ b/tools/fuzzy_match.py
@@ -93,6 +93,21 @@ def fuzzy_find_and_replace(content: str, old_string: str, new_string: str,
                    f"Provide more context to make it unique, or use replace_all=True."
                )

+            # Escape-drift guard: when the matched strategy is NOT `exact`,
+            # we matched via some form of normalization. If new_string
+            # contains shell/JSON-style escape sequences (\\' or \\") that
+            # would be written literally into the file but the matched
+            # region of the file has no such sequences, this is almost
+            # certainly tool-call serialization drift — the model typed
+            # an apostrophe/quote and the transport added a stray
+            # backslash. Writing new_string as-is would corrupt the file.
+            # Block with a helpful error so the model re-reads and retries
+            # instead of the caller silently persisting garbage (or not).
+            if strategy_name != "exact":
+                drift_err = _detect_escape_drift(content, matches, old_string, new_string)
+                if drift_err:
+                    return content, 0, None, drift_err
+
            # Perform replacement
            new_content = _apply_replacements(content, matches, new_string)
            return new_content, len(matches), strategy_name, None
@@ -101,6 +116,46 @@ def fuzzy_find_and_replace(content: str, old_string: str, new_string: str,
    return content, 0, None, "Could not find a match for old_string in the file"


+def _detect_escape_drift(content: str, matches: List[Tuple[int, int]],
+                         old_string: str, new_string: str) -> Optional[str]:
+    """Detect tool-call escape-drift artifacts in new_string.
+
+    Looks for ``\\'`` or ``\\"`` sequences that are present in both
+    old_string and new_string (i.e. the model copy-pasted them as "context"
+    it intended to preserve) but don't exist in the matched region of the
+    file. That pattern indicates the transport layer inserted spurious
+    shell-style escapes around apostrophes or quotes — writing new_string
+    verbatim would literally insert ``\\'`` into source code.
+
+    Returns an error string if drift is detected, None otherwise.
+    """
+    # Cheap pre-check: bail out unless new_string actually contains a
+    # suspect escape sequence. This keeps the guard free for all the
+    # common, correct cases.
+    if "\\'" not in new_string and '\\"' not in new_string:
+        return None
+
+    # Aggregate matched regions of the file — that's what new_string will
+    # replace. If the suspect escapes are present there already, the
+    # model is genuinely preserving them (valid for some languages /
+    # escaped strings); accept the patch.
+    matched_regions = "".join(content[start:end] for start, end in matches)
+
+    for suspect in ("\\'", '\\"'):
+        if suspect in new_string and suspect in old_string and suspect not in matched_regions:
+            plain = suspect[1]  # "'" or '"'
+            return (
+                f"Escape-drift detected: old_string and new_string contain "
+                f"the literal sequence {suspect!r} but the matched region of "
+                f"the file does not. This is almost always a tool-call "
+                f"serialization artifact where an apostrophe or quote got "
+                f"prefixed with a spurious backslash. Re-read the file with "
+                f"read_file and pass old_string/new_string without "
+                f"backslash-escaping {plain!r} characters."
+            )
+    return None
+
+
 def _apply_replacements(content: str, matches: List[Tuple[int, int]], new_string: str) -> str:
    """
    Apply replacements at the given positions.
@@ -564,3 +619,86 @@ def _map_normalized_positions(original: str, normalized: str,
        original_matches.append((orig_start, min(orig_end, len(original))))
    
    return original_matches
+
+
+def find_closest_lines(old_string: str, content: str, context_lines: int = 2, max_results: int = 3) -> str:
+    """Find lines in content most similar to old_string for "did you mean?" feedback.
+
+    Returns a formatted string showing the closest matching lines with context,
+    or empty string if no useful match is found.
+    """
+    if not old_string or not content:
+        return ""
+
+    old_lines = old_string.splitlines()
+    content_lines = content.splitlines()
+
+    if not old_lines or not content_lines:
+        return ""
+
+    # Use first line of old_string as anchor for search
+    anchor = old_lines[0].strip()
+    if not anchor:
+        # Try second line if first is blank
+        candidates = [l.strip() for l in old_lines if l.strip()]
+        if not candidates:
+            return ""
+        anchor = candidates[0]
+
+    # Score each line in content by similarity to anchor
+    scored = []
+    for i, line in enumerate(content_lines):
+        stripped = line.strip()
+        if not stripped:
+            continue
+        ratio = SequenceMatcher(None, anchor, stripped).ratio()
+        if ratio > 0.3:
+            scored.append((ratio, i))
+
+    if not scored:
+        return ""
+
+    # Take top matches
+    scored.sort(key=lambda x: -x[0])
+    top = scored[:max_results]
+
+    parts = []
+    seen_ranges = set()
+    for _, line_idx in top:
+        start = max(0, line_idx - context_lines)
+        end = min(len(content_lines), line_idx + len(old_lines) + context_lines)
+        key = (start, end)
+        if key in seen_ranges:
+            continue
+        seen_ranges.add(key)
+        snippet = "\n".join(
+            f"{start + j + 1:4d}| {content_lines[start + j]}"
+            for j in range(end - start)
+        )
+        parts.append(snippet)
+
+    if not parts:
+        return ""
+
+    return "\n---\n".join(parts)
+
+
+def format_no_match_hint(error: Optional[str], match_count: int,
+                         old_string: str, content: str) -> str:
+    """Return a '\\n\\nDid you mean...' snippet for plain no-match errors.
+
+    Gated so the hint only fires for actual "old_string not found" failures.
+    Ambiguous-match ("Found N matches"), escape-drift, and identical-strings
+    errors all have ``match_count == 0`` but a "did you mean?" snippet would
+    be misleading — those failed for unrelated reasons.
+
+    Returns an empty string when there's nothing useful to append.
+    """
+    if match_count != 0:
+        return ""
+    if not error or not error.startswith("Could not find"):
+        return ""
+    hint = find_closest_lines(old_string, content)
+    if not hint:
+        return ""
+    return "\n\nDid you mean one of these sections?\n" + hint
--- a/tools/patch_parser.py
+++ b/tools/patch_parser.py
@@ -290,10 +290,16 @@ def _validate_operations(
                )
                if count == 0:
                    label = f"'{hunk.context_hint}'" if hunk.context_hint else "(no hint)"
-                    errors.append(
+                    msg = (
                        f"{op.file_path}: hunk {label} not found"
                        + (f" — {match_error}" if match_error else "")
                    )
+                    try:
+                        from tools.fuzzy_match import format_no_match_hint
+                        msg += format_no_match_hint(match_error, count, search_pattern, simulated)
+                    except Exception:
+                        pass
+                    errors.append(msg)
                else:
                    # Advance simulation so subsequent hunks validate correctly.
                    # Reuse the result from the call above — no second fuzzy run.
@@ -537,7 +543,13 @@ def _apply_update(op: PatchOperation, file_ops: Any) -> Tuple[bool, str]:
                            error = None
                
                if error:
-                    return False, f"Could not apply hunk: {error}"
+                    err_msg = f"Could not apply hunk: {error}"
+                    try:
+                        from tools.fuzzy_match import format_no_match_hint
+                        err_msg += format_no_match_hint(error, 0, search_pattern, new_content)
+                    except Exception:
+                        pass
+                    return False, err_msg
        else:
            # Addition-only hunk (no context or removed lines).
            # Insert at the location indicated by the context hint, or at end of file.
--- a/tools/skill_manager_tool.py
+++ b/tools/skill_manager_tool.py
@@ -575,9 +575,15 @@ def _patch_skill(
    if match_error:
        # Show a short preview of the file so the model can self-correct
        preview = content[:500] + ("..." if len(content) > 500 else "")
+        err_msg = match_error
+        try:
+            from tools.fuzzy_match import format_no_match_hint
+            err_msg += format_no_match_hint(match_error, match_count, old_string, content)
+        except Exception:
+            pass
        return {
            "success": False,
-            "error": match_error,
+            "error": err_msg,
            "file_preview": preview,
        }
Author	SHA1	Message	Date
Alexander Whitestone	e28d16b324	test: cover patch did-you-mean end-to-end (#960 ) All checks were successful Lint / lint (pull_request) Successful in 18s Details - add focused QA tests for replace-mode rich hints without legacy generic hints - verify ambiguous replace failures do not show did-you-mean noise - verify V4A patch validation surfaces rich hints - verify skill patching surfaces rich hints on true no-match failures	2026-04-22 10:43:34 -04:00
Alexander Whitestone	bc32047610	wip: fix patch did-you-mean dependencies (#960 ) - restore escape-drift guard needed by current fuzzy-match tests - import missing typing symbols in file_tools after porting patch hint logic	2026-04-22 10:41:29 -04:00
Teknium	3a24420d7d	fix(patch): gate 'did you mean?' to no-match + extend to v4a/skill_manage Follow-ups on top of @teyrebaz33's cherry-picked commit: 1. New shared helper format_no_match_hint() in fuzzy_match.py with a startswith('Could not find') gate so the snippet only appends to genuine no-match errors — not to 'Found N matches' (ambiguous), 'Escape-drift detected', or 'identical strings' errors, which would all mislead the model. 2. file_tools.patch_tool suppresses the legacy generic '[Hint: old_string not found...]' string when the rich 'Did you mean?' snippet is already attached — no more double-hint. 3. Wire the same helper into patch_parser.py (V4A patch mode, both _validate_operations and _apply_update) and skill_manager_tool.py so all three fuzzy callers surface the hint consistently. Tests: 7 new gating tests in TestFormatNoMatchHint cover every error class (ambiguous, drift, identical, non-zero match count, None error, no similar content, happy path). 34/34 test_fuzzy_match, 96/96 test_file_tools + test_patch_parser + test_skill_manager_tool pass. E2E verified across all four scenarios: no-match-with-similar, no-match-no-similar, ambiguous, success. V4A mode confirmed end-to-end with a non-matching hunk. (cherry picked from commit `5e6427a42c`)	2026-04-22 10:30:00 -04:00
teyrebaz33	d14c1c5a56	feat(patch): add 'did you mean?' feedback when patch fails to match When patch_replace() cannot find old_string in a file, the error message now includes the closest matching lines from the file with line numbers and context. This helps the LLM self-correct without a separate read_file call. Implements Phase 1 of #536: enhanced patch error feedback with no architectural changes. - tools/fuzzy_match.py: new find_closest_lines() using SequenceMatcher - tools/file_operations.py: attach closest-lines hint to patch errors - tests/tools/test_fuzzy_match.py: 5 new tests for find_closest_lines (cherry picked from commit `15abf4ed8f`)	2026-04-22 10:28:40 -04:00