docs: add grounded tensorzero evaluation packet (#860 )

- add a script that inventories Hermes routing/evaluation surfaces relevant to a TensorZero cutover - generate a markdown and JSON evaluation packet for issue #860 - score gateway replacement, config migration, canary rollout, session feedback, and eval-suite readiness - add focused regression tests for touchpoint scanning, requirement scoring, and report rendering Refs #860
2026-04-22 11:33:31 -04:00
10 changed files with 2028 additions and 1078 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/docs/evaluations/tensorzero-860-evaluation.json
+++ b/docs/evaluations/tensorzero-860-evaluation.json
--- a/docs/evaluations/tensorzero-860-evaluation.md
+++ b/docs/evaluations/tensorzero-860-evaluation.md
@@ -0,0 +1,217 @@
+# TensorZero Evaluation Packet
+
+Issue #860: [tensorzero LLMOps platform evaluation](https://forge.alexanderwhitestone.com/Timmy_Foundation/hermes-agent/issues/860)
+
+## Scope
+
+This packet evaluates TensorZero as a possible replacement for Hermes' custom provider-routing stack.
+It is intentionally grounded in the current repo state rather than a speculative cutover plan.
+
+## Issue requirements being evaluated
+
+- Deploy tensorzero gateway (Rust binary)
+- Migrate provider routing config
+- Test with canary (10% traffic) before full cutover
+- Feed session data for prompt optimization
+- Evaluation suite for A/B testing models
+
+## Recommendation
+
+Not ready for direct replacement. Recommend a shadow-evaluation phase first: keep Hermes routing live, inventory the migration seams, export SessionDB/trajectory data into an offline TensorZero experiment loop, and only design a canary gateway once percentage-based rollout controls exist.
+
+## Requirement matrix
+
+| Requirement | Status | Evidence labels | Summary |
+| --- | --- | --- | --- |
+| Gateway replacement scope | partial | fallback_chain, runtime_provider, gateway_provider_routing, cron_runtime_provider, auxiliary_fallback_chain, delegate_runtime_provider | Hermes already spreads provider routing across core agent, runtime provider, gateway, cron, auxiliary, and delegation seams; TensorZero would need parity across all of them before it can replace the gateway layer. |
+| Config migration | partial | provider_routing_config, runtime_provider, smart_model_routing, fallback_chain | Hermes has multiple config concepts to migrate (`provider_routing`, `fallback_providers`, `smart_model_routing`, runtime provider resolution), so TensorZero is not a drop-in config swap. |
+| 10% traffic canary | gap | — | The repo shows semantic routing and fallback, but no grounded 10% traffic-split canary mechanism. A TensorZero cutover would need new percentage-based rollout controls and observability hooks. |
+| Session data for prompt optimization | partial | session_db, trajectory_export | Hermes already has SessionDB and trajectory export surfaces that can feed offline optimization data, but not a TensorZero-native ingestion path yet. |
+| Evaluation suite / A/B testing | partial | benchmark_suite, trajectory_export | Hermes already has benchmark/trajectory machinery that can seed TensorZero A/B evaluation, but no integrated TensorZero experiment runner or live evaluation gateway. |
+
+## Grounded Hermes touchpoints
+
+- `run_agent.py:601` — [fallback_chain] fallback_model: Dict[str, Any] = None,
+- `run_agent.py:995` — [fallback_chain] # failure).  Supports both legacy single-dict ``fallback_model`` and
+- `run_agent.py:996` — [fallback_chain] # new list ``fallback_providers`` format.
+- `run_agent.py:997` — [fallback_chain] if isinstance(fallback_model, list):
+- `run_agent.py:998` — [fallback_chain] self._fallback_chain = [
+- `run_agent.py:999` — [fallback_chain] f for f in fallback_model
+- `run_agent.py:1002` — [fallback_chain] elif isinstance(fallback_model, dict) and fallback_model.get("provider") and fallback_model.get("model"):
+- `run_agent.py:1003` — [fallback_chain] self._fallback_chain = [fallback_model]
+- `run_agent.py:1005` — [fallback_chain] self._fallback_chain = []
+- `run_agent.py:1009` — [fallback_chain] self._fallback_model = self._fallback_chain[0] if self._fallback_chain else None
+- `run_agent.py:1010` — [fallback_chain] if self._fallback_chain and not self.quiet_mode:
+- `run_agent.py:1011` — [fallback_chain] if len(self._fallback_chain) == 1:
+- `run_agent.py:1012` — [fallback_chain] fb = self._fallback_chain[0]
+- `run_agent.py:1015` — [fallback_chain] print(f"🔄 Fallback chain ({len(self._fallback_chain)} providers): " +
+- `run_agent.py:1016` — [fallback_chain] " → ".join(f"{f['model']} ({f['provider']})" for f in self._fallback_chain))
+- `run_agent.py:5624` — [fallback_chain] if self._fallback_index >= len(self._fallback_chain):
+- `run_agent.py:5627` — [fallback_chain] fb = self._fallback_chain[self._fallback_index]
+- `run_agent.py:8559` — [fallback_chain] if self._fallback_index < len(self._fallback_chain):
+- `run_agent.py:9355` — [fallback_chain] if is_rate_limited and self._fallback_index < len(self._fallback_chain):
+- `run_agent.py:10460` — [fallback_chain] if _truly_empty and self._fallback_chain:
+- `run_agent.py:10514` — [fallback_chain] + (" and fallback attempts." if self._fallback_chain else
+- `cli.py:241` — [provider_routing_config] "smart_model_routing": {
+- `cli.py:370` — [provider_routing_config] # (e.g. platform_toolsets, provider_routing, memory, honcho, etc.)
+- `cli.py:1753` — [provider_routing_config] pr = CLI_CONFIG.get("provider_routing", {}) or {}
+- `cli.py:1762` — [provider_routing_config] # Supports new list format (fallback_providers) and legacy single-dict (fallback_model).
+- `cli.py:1763` — [provider_routing_config] fb = CLI_CONFIG.get("fallback_providers") or CLI_CONFIG.get("fallback_model") or []
+- `cli.py:1770` — [provider_routing_config] self._smart_model_routing = CLI_CONFIG.get("smart_model_routing", {}) or {}
+- `cli.py:2771` — [provider_routing_config] from agent.smart_model_routing import resolve_turn_route
+- `cli.py:2776` — [provider_routing_config] self._smart_model_routing,
+- `hermes_cli/runtime_provider.py:209` — [runtime_provider] def resolve_requested_provider(requested: Optional[str] = None) -> str:
+- `hermes_cli/runtime_provider.py:649` — [runtime_provider] def resolve_runtime_provider(
+- `agent/smart_model_routing.py:62` — [smart_model_routing] def choose_cheap_model_route(user_message: str, routing_config: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
+- `agent/smart_model_routing.py:110` — [smart_model_routing] def resolve_turn_route(user_message: str, routing_config: Optional[Dict[str, Any]], primary: Dict[str, Any]) -> Dict[str, Any]:
+- `gateway/run.py:1271` — [gateway_provider_routing] def _load_provider_routing() -> dict:
+- `gateway/run.py:1285` — [gateway_provider_routing] def _load_fallback_model() -> list | dict | None:
+- `gateway/run.py:1306` — [gateway_provider_routing] def _load_smart_model_routing() -> dict:
+- `cron/scheduler.py:684` — [cron_runtime_provider] pr = _cfg.get("provider_routing", {})
+- `cron/scheduler.py:688` — [cron_runtime_provider] resolve_runtime_provider,
+- `cron/scheduler.py:697` — [cron_runtime_provider] runtime = resolve_runtime_provider(**runtime_kwargs)
+- `cron/scheduler.py:702` — [cron_runtime_provider] from agent.smart_model_routing import resolve_turn_route
+- `cron/scheduler.py:703` — [cron_runtime_provider] turn_route = resolve_turn_route(
+- `cron/scheduler.py:717` — [cron_runtime_provider] fallback_model = _cfg.get("fallback_providers") or _cfg.get("fallback_model") or None
+- `cron/scheduler.py:746` — [cron_runtime_provider] fallback_model=fallback_model,
+- `agent/auxiliary_client.py:1018` — [auxiliary_fallback_chain] def _get_provider_chain() -> List[tuple]:
+- `agent/auxiliary_client.py:1107` — [auxiliary_fallback_chain] for label, try_fn in _get_provider_chain():
+- `agent/auxiliary_client.py:1189` — [auxiliary_fallback_chain] # ── Step 2: aggregator / fallback chain ──────────────────────────────
+- `agent/auxiliary_client.py:1191` — [auxiliary_fallback_chain] for label, try_fn in _get_provider_chain():
+- `agent/auxiliary_client.py:2397` — [auxiliary_fallback_chain] # error, fall through to the fallback chain below.
+- `agent/auxiliary_client.py:2417` — [auxiliary_fallback_chain] # auto (the default) = best-effort fallback chain.  (#7559)
+- `agent/auxiliary_client.py:2589` — [auxiliary_fallback_chain] # error, fall through to the fallback chain below.
+- `tools/delegate_tool.py:662` — [delegate_runtime_provider] # bundle (base_url, api_key, api_mode) via the same runtime provider system
+- `tools/delegate_tool.py:854` — [delegate_runtime_provider] provider) is resolved via the runtime provider system — the same path used
+- `tools/delegate_tool.py:909` — [delegate_runtime_provider] from hermes_cli.runtime_provider import resolve_runtime_provider
+- `tools/delegate_tool.py:910` — [delegate_runtime_provider] runtime = resolve_runtime_provider(requested=configured_provider)
+- `hermes_state.py:115` — [session_db] class SessionDB:
+- `batch_runner.py:320` — [trajectory_export] save_trajectories=False,  # We handle saving ourselves
+- `batch_runner.py:346` — [trajectory_export] trajectory = agent._convert_to_trajectory_format(
+- `batch_runner.py:460` — [trajectory_export] trajectory_entry = {
+- `batch_runner.py:474` — [trajectory_export] f.write(json.dumps(trajectory_entry, ensure_ascii=False) + "\n")
+- `benchmarks/tool_call_benchmark.py:3` — [benchmark_suite] Tool-Calling Benchmark — Gemma 4 vs mimo-v2-pro regression test.
+- `benchmarks/tool_call_benchmark.py:9` — [benchmark_suite] python3 benchmarks/tool_call_benchmark.py                  # full 100-call suite
+- `benchmarks/tool_call_benchmark.py:10` — [benchmark_suite] python3 benchmarks/tool_call_benchmark.py --limit 10       # quick smoke test
+- `benchmarks/tool_call_benchmark.py:11` — [benchmark_suite] python3 benchmarks/tool_call_benchmark.py --models nous     # single model
+- `benchmarks/tool_call_benchmark.py:12` — [benchmark_suite] python3 benchmarks/tool_call_benchmark.py --category file   # single category
+- `benchmarks/tool_call_benchmark.py:37` — [benchmark_suite] class ToolCall:
+- `benchmarks/tool_call_benchmark.py:51` — [benchmark_suite] ToolCall("file-01", "file", "Read the file /tmp/test_bench.txt and show me its contents.",
+- `benchmarks/tool_call_benchmark.py:53` — [benchmark_suite] ToolCall("file-02", "file", "Write 'hello benchmark' to /tmp/test_bench_out.txt",
+- `benchmarks/tool_call_benchmark.py:55` — [benchmark_suite] ToolCall("file-03", "file", "Search for the word 'import' in all Python files in the current directory.",
+- `benchmarks/tool_call_benchmark.py:57` — [benchmark_suite] ToolCall("file-04", "file", "Read lines 1-20 of /etc/hosts",
+- `benchmarks/tool_call_benchmark.py:59` — [benchmark_suite] ToolCall("file-05", "file", "Patch /tmp/test_bench_out.txt: replace 'hello' with 'goodbye'",
+- `benchmarks/tool_call_benchmark.py:61` — [benchmark_suite] ToolCall("file-06", "file", "Search for files matching *.py in the current directory.",
+- `benchmarks/tool_call_benchmark.py:63` — [benchmark_suite] ToolCall("file-07", "file", "Read the first 10 lines of /etc/passwd",
+- `benchmarks/tool_call_benchmark.py:65` — [benchmark_suite] ToolCall("file-08", "file", "Write a JSON config to /tmp/bench_config.json with key 'debug': true",
+- `benchmarks/tool_call_benchmark.py:67` — [benchmark_suite] ToolCall("file-09", "file", "Search for 'def test_' in Python test files.",
+- `benchmarks/tool_call_benchmark.py:69` — [benchmark_suite] ToolCall("file-10", "file", "Read /tmp/bench_config.json and tell me what's in it.",
+- `benchmarks/tool_call_benchmark.py:71` — [benchmark_suite] ToolCall("file-11", "file", "Create a file /tmp/bench_readme.md with one line: '# Benchmark'",
+- `benchmarks/tool_call_benchmark.py:73` — [benchmark_suite] ToolCall("file-12", "file", "Search for 'TODO' comments in all .py files.",
+- `benchmarks/tool_call_benchmark.py:75` — [benchmark_suite] ToolCall("file-13", "file", "Read /tmp/bench_readme.md",
+- `benchmarks/tool_call_benchmark.py:77` — [benchmark_suite] ToolCall("file-14", "file", "Patch /tmp/bench_readme.md: replace '# Benchmark' with '# Tool Benchmark'",
+- `benchmarks/tool_call_benchmark.py:78` — [benchmark_suite] "patch", "Tool Benchmark"),
+- `benchmarks/tool_call_benchmark.py:79` — [benchmark_suite] ToolCall("file-15", "file", "Write a Python one-liner to /tmp/bench_hello.py that prints hello.",
+- `benchmarks/tool_call_benchmark.py:81` — [benchmark_suite] ToolCall("file-16", "file", "Search for all .json files in /tmp/.",
+- `benchmarks/tool_call_benchmark.py:83` — [benchmark_suite] ToolCall("file-17", "file", "Read /tmp/bench_hello.py and verify it has print('hello').",
+- `benchmarks/tool_call_benchmark.py:85` — [benchmark_suite] ToolCall("file-18", "file", "Patch /tmp/bench_hello.py to print 'hello world' instead of 'hello'.",
+- `benchmarks/tool_call_benchmark.py:87` — [benchmark_suite] ToolCall("file-19", "file", "List files matching 'bench*' in /tmp/.",
+- `benchmarks/tool_call_benchmark.py:89` — [benchmark_suite] ToolCall("file-20", "file", "Read /tmp/test_bench.txt again and summarize its contents.",
+- `benchmarks/tool_call_benchmark.py:93` — [benchmark_suite] ToolCall("term-01", "terminal", "Run `echo hello world` in the terminal.",
+- `benchmarks/tool_call_benchmark.py:95` — [benchmark_suite] ToolCall("term-02", "terminal", "Run `date` to get the current date and time.",
+- `benchmarks/tool_call_benchmark.py:97` — [benchmark_suite] ToolCall("term-03", "terminal", "Run `uname -a` to get system information.",
+- `benchmarks/tool_call_benchmark.py:99` — [benchmark_suite] ToolCall("term-04", "terminal", "Run `pwd` to show the current directory.",
+- `benchmarks/tool_call_benchmark.py:101` — [benchmark_suite] ToolCall("term-05", "terminal", "Run `ls -la /tmp/ | head -20` to list temp files.",
+- `benchmarks/tool_call_benchmark.py:103` — [benchmark_suite] ToolCall("term-06", "terminal", "Run `whoami` to show the current user.",
+- `benchmarks/tool_call_benchmark.py:105` — [benchmark_suite] ToolCall("term-07", "terminal", "Run `df -h` to show disk usage.",
+- `benchmarks/tool_call_benchmark.py:107` — [benchmark_suite] ToolCall("term-08", "terminal", "Run `python3 --version` to check Python version.",
+- `benchmarks/tool_call_benchmark.py:109` — [benchmark_suite] ToolCall("term-09", "terminal", "Run `cat /etc/hostname` to get the hostname.",
+- `benchmarks/tool_call_benchmark.py:111` — [benchmark_suite] ToolCall("term-10", "terminal", "Run `uptime` to see system uptime.",
+- `benchmarks/tool_call_benchmark.py:113` — [benchmark_suite] ToolCall("term-11", "terminal", "Run `env | grep PATH` to show the PATH variable.",
+- `benchmarks/tool_call_benchmark.py:115` — [benchmark_suite] ToolCall("term-12", "terminal", "Run `wc -l /etc/passwd` to count lines.",
+- `benchmarks/tool_call_benchmark.py:117` — [benchmark_suite] ToolCall("term-13", "terminal", "Run `echo $SHELL` to show the current shell.",
+- `benchmarks/tool_call_benchmark.py:119` — [benchmark_suite] ToolCall("term-14", "terminal", "Run `free -h || vm_stat` to check memory usage.",
+- `benchmarks/tool_call_benchmark.py:121` — [benchmark_suite] ToolCall("term-15", "terminal", "Run `id` to show user and group IDs.",
+- `benchmarks/tool_call_benchmark.py:123` — [benchmark_suite] ToolCall("term-16", "terminal", "Run `hostname` to get the machine hostname.",
+- `benchmarks/tool_call_benchmark.py:125` — [benchmark_suite] ToolCall("term-17", "terminal", "Run `echo {1..5}` to test brace expansion.",
+- `benchmarks/tool_call_benchmark.py:127` — [benchmark_suite] ToolCall("term-18", "terminal", "Run `seq 1 5` to generate a number sequence.",
+- `benchmarks/tool_call_benchmark.py:129` — [benchmark_suite] ToolCall("term-19", "terminal", "Run `python3 -c 'print(2+2)'` to compute 2+2.",
+- `benchmarks/tool_call_benchmark.py:131` — [benchmark_suite] ToolCall("term-20", "terminal", "Run `ls -d /tmp/bench* 2>/dev/null | wc -l` to count bench files.",
+- `benchmarks/tool_call_benchmark.py:135` — [benchmark_suite] ToolCall("code-01", "code", "Execute a Python script that computes factorial of 10.",
+- `benchmarks/tool_call_benchmark.py:137` — [benchmark_suite] ToolCall("code-02", "code", "Run Python to read /tmp/test_bench.txt and count its words.",
+- `benchmarks/tool_call_benchmark.py:139` — [benchmark_suite] ToolCall("code-03", "code", "Execute Python to generate the first 20 Fibonacci numbers.",
+- `benchmarks/tool_call_benchmark.py:141` — [benchmark_suite] ToolCall("code-04", "code", "Run Python to parse JSON from a string and print keys.",
+- `benchmarks/tool_call_benchmark.py:143` — [benchmark_suite] ToolCall("code-05", "code", "Execute Python to list all files in /tmp/ matching 'bench*'.",
+- `benchmarks/tool_call_benchmark.py:145` — [benchmark_suite] ToolCall("code-06", "code", "Run Python to compute the sum of squares from 1 to 100.",
+- `benchmarks/tool_call_benchmark.py:147` — [benchmark_suite] ToolCall("code-07", "code", "Execute Python to check if 'racecar' is a palindrome.",
+- `benchmarks/tool_call_benchmark.py:149` — [benchmark_suite] ToolCall("code-08", "code", "Run Python to create a CSV string with 5 rows of sample data.",
+- `benchmarks/tool_call_benchmark.py:151` — [benchmark_suite] ToolCall("code-09", "code", "Execute Python to sort a list [5,2,8,1,9] and print the result.",
+- `benchmarks/tool_call_benchmark.py:153` — [benchmark_suite] ToolCall("code-10", "code", "Run Python to count lines in /etc/passwd.",
+- `benchmarks/tool_call_benchmark.py:155` — [benchmark_suite] ToolCall("code-11", "code", "Execute Python to hash the string 'benchmark' with SHA256.",
+- `benchmarks/tool_call_benchmark.py:157` — [benchmark_suite] ToolCall("code-12", "code", "Run Python to get the current UTC timestamp.",
+- `benchmarks/tool_call_benchmark.py:159` — [benchmark_suite] ToolCall("code-13", "code", "Execute Python to convert 'hello world' to uppercase and reverse it.",
+- `benchmarks/tool_call_benchmark.py:161` — [benchmark_suite] ToolCall("code-14", "code", "Run Python to create a dictionary of system info (platform, python version).",
+- `benchmarks/tool_call_benchmark.py:163` — [benchmark_suite] ToolCall("code-15", "code", "Execute Python to check internet connectivity by resolving google.com.",
+- `benchmarks/tool_call_benchmark.py:167` — [benchmark_suite] ToolCall("deleg-01", "delegate", "Use a subagent to find all .log files in /tmp/.",
+- `benchmarks/tool_call_benchmark.py:169` — [benchmark_suite] ToolCall("deleg-02", "delegate", "Delegate to a subagent: what is 15 * 37?",
+- `benchmarks/tool_call_benchmark.py:171` — [benchmark_suite] ToolCall("deleg-03", "delegate", "Use a subagent to check if Python 3 is installed and its version.",
+- `benchmarks/tool_call_benchmark.py:173` — [benchmark_suite] ToolCall("deleg-04", "delegate", "Delegate: read /tmp/test_bench.txt and summarize it in one sentence.",
+- `benchmarks/tool_call_benchmark.py:175` — [benchmark_suite] ToolCall("deleg-05", "delegate", "Use a subagent to list the contents of /tmp/ directory.",
+- `benchmarks/tool_call_benchmark.py:177` — [benchmark_suite] ToolCall("deleg-06", "delegate", "Delegate: count the number of .py files in the current directory.",
+- `benchmarks/tool_call_benchmark.py:179` — [benchmark_suite] ToolCall("deleg-07", "delegate", "Use a subagent to check disk space with df -h.",
+- `benchmarks/tool_call_benchmark.py:181` — [benchmark_suite] ToolCall("deleg-08", "delegate", "Delegate: what OS are we running on?",
+- `benchmarks/tool_call_benchmark.py:183` — [benchmark_suite] ToolCall("deleg-09", "delegate", "Use a subagent to find the hostname of this machine.",
+- `benchmarks/tool_call_benchmark.py:185` — [benchmark_suite] ToolCall("deleg-10", "delegate", "Delegate: create a temp file /tmp/bench_deleg.txt with 'done'.",
+- `benchmarks/tool_call_benchmark.py:189` — [benchmark_suite] ToolCall("todo-01", "todo", "Add a todo item: 'Run benchmark suite'",
+- `benchmarks/tool_call_benchmark.py:190` — [benchmark_suite] "todo", "benchmark"),
+- `benchmarks/tool_call_benchmark.py:191` — [benchmark_suite] ToolCall("todo-02", "todo", "Show me the current todo list.",
+- `benchmarks/tool_call_benchmark.py:193` — [benchmark_suite] ToolCall("todo-03", "todo", "Mark the first todo item as completed.",
+- `benchmarks/tool_call_benchmark.py:195` — [benchmark_suite] ToolCall("todo-04", "todo", "Add a todo: 'Review benchmark results' with status pending.",
+- `benchmarks/tool_call_benchmark.py:197` — [benchmark_suite] ToolCall("todo-05", "todo", "Clear all completed todos.",
+- `benchmarks/tool_call_benchmark.py:199` — [benchmark_suite] ToolCall("todo-06", "memory", "Save this to memory: 'benchmark ran on {date}'".format(
+- `benchmarks/tool_call_benchmark.py:201` — [benchmark_suite] "memory", "benchmark"),
+- `benchmarks/tool_call_benchmark.py:202` — [benchmark_suite] ToolCall("todo-07", "memory", "Search memory for 'benchmark'.",
+- `benchmarks/tool_call_benchmark.py:203` — [benchmark_suite] "memory", "benchmark"),
+- `benchmarks/tool_call_benchmark.py:204` — [benchmark_suite] ToolCall("todo-08", "memory", "Add a memory note: 'test models are gemma-4 and mimo-v2-pro'.",
+- `benchmarks/tool_call_benchmark.py:206` — [benchmark_suite] ToolCall("todo-09", "todo", "Add three todo items: 'analyze', 'report', 'cleanup'.",
+- `benchmarks/tool_call_benchmark.py:208` — [benchmark_suite] ToolCall("todo-10", "memory", "Search memory for any notes about models.",
+- `benchmarks/tool_call_benchmark.py:212` — [benchmark_suite] ToolCall("skill-01", "skills", "List all available skills.",
+- `benchmarks/tool_call_benchmark.py:214` — [benchmark_suite] ToolCall("skill-02", "skills", "View the skill called 'test-driven-development'.",
+- `benchmarks/tool_call_benchmark.py:216` — [benchmark_suite] ToolCall("skill-03", "skills", "Search for skills related to 'git'.",
+- `benchmarks/tool_call_benchmark.py:218` — [benchmark_suite] ToolCall("skill-04", "skills", "View the 'code-review' skill.",
+- `benchmarks/tool_call_benchmark.py:220` — [benchmark_suite] ToolCall("skill-05", "skills", "List all skills in the 'devops' category.",
+- `benchmarks/tool_call_benchmark.py:222` — [benchmark_suite] ToolCall("skill-06", "skills", "View the 'systematic-debugging' skill.",
+- `benchmarks/tool_call_benchmark.py:224` — [benchmark_suite] ToolCall("skill-07", "skills", "Search for skills about 'testing'.",
+- `benchmarks/tool_call_benchmark.py:226` — [benchmark_suite] ToolCall("skill-08", "skills", "View the 'writing-plans' skill.",
+- `benchmarks/tool_call_benchmark.py:228` — [benchmark_suite] ToolCall("skill-09", "skills", "List skills in 'software-development' category.",
+- `benchmarks/tool_call_benchmark.py:230` — [benchmark_suite] ToolCall("skill-10", "skills", "View the 'pr-review-discipline' skill.",
+- `benchmarks/tool_call_benchmark.py:234` — [benchmark_suite] ToolCall("file-21", "file", "Write a Python snippet to /tmp/bench_sort.py that sorts [3,1,2].",
+- `benchmarks/tool_call_benchmark.py:236` — [benchmark_suite] ToolCall("file-22", "file", "Read /tmp/bench_sort.py back and confirm it exists.",
+- `benchmarks/tool_call_benchmark.py:238` — [benchmark_suite] ToolCall("file-23", "file", "Search for 'class' in all .py files in the benchmarks directory.",
+- `benchmarks/tool_call_benchmark.py:240` — [benchmark_suite] ToolCall("term-21", "terminal", "Run `cat /etc/os-release 2>/dev/null || sw_vers 2>/dev/null` for OS info.",
+- `benchmarks/tool_call_benchmark.py:242` — [benchmark_suite] ToolCall("term-22", "terminal", "Run `nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null` for CPU count.",
+- `benchmarks/tool_call_benchmark.py:244` — [benchmark_suite] ToolCall("code-16", "code", "Execute Python to flatten a nested list [[1,2],[3,4],[5]].",
+- `benchmarks/tool_call_benchmark.py:246` — [benchmark_suite] ToolCall("code-17", "code", "Run Python to check if a number 17 is prime.",
+- `benchmarks/tool_call_benchmark.py:248` — [benchmark_suite] ToolCall("deleg-11", "delegate", "Delegate: what is the current working directory?",
+- `benchmarks/tool_call_benchmark.py:250` — [benchmark_suite] ToolCall("todo-11", "todo", "Add a todo: 'Finalize benchmark report' status pending.",
+- `benchmarks/tool_call_benchmark.py:252` — [benchmark_suite] ToolCall("todo-12", "memory", "Store fact: 'benchmark categories: file, terminal, code, delegate, todo, memory, skills'.",
+- `benchmarks/tool_call_benchmark.py:254` — [benchmark_suite] ToolCall("skill-11", "skills", "Search for skills about 'deployment'.",
+- `benchmarks/tool_call_benchmark.py:256` — [benchmark_suite] ToolCall("skill-12", "skills", "View the 'gitea-burn-cycle' skill.",
+- `benchmarks/tool_call_benchmark.py:258` — [benchmark_suite] ToolCall("skill-13", "skills", "List all available skill categories.",
+- `benchmarks/tool_call_benchmark.py:260` — [benchmark_suite] ToolCall("skill-14", "skills", "Search for skills related to 'memory'.",
+- `benchmarks/tool_call_benchmark.py:262` — [benchmark_suite] ToolCall("skill-15", "skills", "View the 'mimo-swarm' skill.",
+- `benchmarks/tool_call_benchmark.py:311` — [benchmark_suite] """Create prerequisite files for the benchmark."""
+- `benchmarks/tool_call_benchmark.py:313` — [benchmark_suite] "This is a benchmark test file.\n"
+- `benchmarks/tool_call_benchmark.py:349` — [benchmark_suite] "You are a benchmark test runner. Execute the user's request by calling "
+- `benchmarks/tool_call_benchmark.py:406` — [benchmark_suite] """Generate markdown benchmark report."""
+- `benchmarks/tool_call_benchmark.py:428` — [benchmark_suite] f"# Tool-Calling Benchmark Report",
+- `benchmarks/tool_call_benchmark.py:535` — [benchmark_suite] parser = argparse.ArgumentParser(description="Tool-calling benchmark")
+- `benchmarks/tool_call_benchmark.py:544` — [benchmark_suite] help="Output report path (default: benchmarks/gemma4-tool-calling-YYYY-MM-DD.md)")
+- `benchmarks/tool_call_benchmark.py:565` — [benchmark_suite] output_path = Path(args.output) if args.output else REPO_ROOT / "benchmarks" / f"gemma4-tool-calling-{date_str}.md"
+- `benchmarks/tool_call_benchmark.py:575` — [benchmark_suite] print(f"Benchmark: {len(suite)} tests × {len(model_specs)} models = {len(suite) * len(model_specs)} calls")
+
+## Suggested next slice
+
+1. Build an exporter that emits SessionDB + trajectory data into a TensorZero-friendly offline dataset.
+2. Define percentage-based canary controls before attempting any gateway replacement.
+3. Keep Hermes routing authoritative until TensorZero proves parity across CLI, gateway, cron, auxiliary, and delegation surfaces.
--- 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/scripts/tensorzero_eval_packet.py
+++ b/scripts/tensorzero_eval_packet.py
@@ -0,0 +1,318 @@
+#!/usr/bin/env python3
+"""Generate a grounded TensorZero evaluation packet for Hermes.
+
+This script inventories the current Hermes routing/evaluation surfaces, then
+builds a markdown packet assessing how much of issue #860 can be satisfied by
+TensorZero and where the migration risk still lives.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+from dataclasses import asdict, dataclass
+from pathlib import Path
+from typing import Iterable
+
+ISSUE_NUMBER = 860
+ISSUE_TITLE = "tensorzero LLMOps platform evaluation"
+ISSUE_URL = "https://forge.alexanderwhitestone.com/Timmy_Foundation/hermes-agent/issues/860"
+DEFAULT_OUTPUT = Path("docs/evaluations/tensorzero-860-evaluation.md")
+DEFAULT_JSON_OUTPUT = Path("docs/evaluations/tensorzero-860-evaluation.json")
+
+
+@dataclass(frozen=True)
+class TouchpointPattern:
+    label: str
+    file_path: str
+    regex: str
+    description: str
+
+
+@dataclass(frozen=True)
+class Touchpoint:
+    label: str
+    file_path: str
+    line_number: int
+    matched_text: str
+
+
+@dataclass(frozen=True)
+class RequirementStatus:
+    key: str
+    name: str
+    status: str
+    evidence_labels: tuple[str, ...]
+    summary: str
+
+
+@dataclass(frozen=True)
+class EvaluationReport:
+    issue_number: int
+    issue_title: str
+    issue_url: str
+    recommendation: str
+    touchpoints: tuple[Touchpoint, ...]
+    requirements: tuple[RequirementStatus, ...]
+
+
+PATTERNS: tuple[TouchpointPattern, ...] = (
+    TouchpointPattern(
+        label="fallback_chain",
+        file_path="run_agent.py",
+        regex=r"_fallback_chain|fallback_providers|fallback_model",
+        description="Primary agent fallback-provider chain in the core conversation loop.",
+    ),
+    TouchpointPattern(
+        label="provider_routing_config",
+        file_path="cli.py",
+        regex=r"provider_routing|fallback_providers|smart_model_routing",
+        description="CLI-owned provider routing and fallback configuration surfaces.",
+    ),
+    TouchpointPattern(
+        label="runtime_provider",
+        file_path="hermes_cli/runtime_provider.py",
+        regex=r"def resolve_runtime_provider|def resolve_requested_provider",
+        description="Central runtime provider resolution for CLI, gateway, cron, and helpers.",
+    ),
+    TouchpointPattern(
+        label="smart_model_routing",
+        file_path="agent/smart_model_routing.py",
+        regex=r"def resolve_turn_route|def choose_cheap_model_route",
+        description="Cheap-vs-strong turn routing that TensorZero would need to absorb or replace.",
+    ),
+    TouchpointPattern(
+        label="gateway_provider_routing",
+        file_path="gateway/run.py",
+        regex=r"def _load_provider_routing|def _load_fallback_model|def _load_smart_model_routing",
+        description="Gateway-specific loading of routing, fallback, and smart-model policies.",
+    ),
+    TouchpointPattern(
+        label="cron_runtime_provider",
+        file_path="cron/scheduler.py",
+        regex=r"resolve_runtime_provider|resolve_turn_route|provider_routing|fallback_model",
+        description="Cron execution path that re-resolves providers and routing on every run.",
+    ),
+    TouchpointPattern(
+        label="auxiliary_fallback_chain",
+        file_path="agent/auxiliary_client.py",
+        regex=r"fallback chain|_get_provider_chain|provider chain",
+        description="Auxiliary task routing/fallback chain outside the main inference path.",
+    ),
+    TouchpointPattern(
+        label="delegate_runtime_provider",
+        file_path="tools/delegate_tool.py",
+        regex=r"runtime provider system|resolve the full credential bundle|resolve_runtime_provider",
+        description="Subagent/delegation routing path that would also need TensorZero parity.",
+    ),
+    TouchpointPattern(
+        label="session_db",
+        file_path="hermes_state.py",
+        regex=r"class SessionDB",
+        description="Session persistence surface that could feed TensorZero optimization/eval data.",
+    ),
+    TouchpointPattern(
+        label="trajectory_export",
+        file_path="batch_runner.py",
+        regex=r"trajectory_entry|save_trajectories|_convert_to_trajectory_format",
+        description="Trajectory export surface for offline optimization and replay data.",
+    ),
+    TouchpointPattern(
+        label="benchmark_suite",
+        file_path="benchmarks/tool_call_benchmark.py",
+        regex=r"ToolCall\(|class ToolCall|benchmark",
+        description="Existing benchmark/evaluation harness that could map to TensorZero experiments.",
+    ),
+)
+
+
+def _iter_matches(pattern: TouchpointPattern, text: str) -> Iterable[Touchpoint]:
+    regex = re.compile(pattern.regex, re.IGNORECASE)
+    for line_number, line in enumerate(text.splitlines(), start=1):
+        if regex.search(line):
+            yield Touchpoint(
+                label=pattern.label,
+                file_path=pattern.file_path,
+                line_number=line_number,
+                matched_text=line.strip(),
+            )
+
+
+def scan_touchpoints(repo_root: Path) -> list[Touchpoint]:
+    touchpoints: list[Touchpoint] = []
+    for pattern in PATTERNS:
+        path = repo_root / pattern.file_path
+        if not path.exists():
+            continue
+        text = path.read_text(encoding="utf-8")
+        touchpoints.extend(_iter_matches(pattern, text))
+    return touchpoints
+
+
+def build_requirement_matrix(touchpoints: list[Touchpoint]) -> list[RequirementStatus]:
+    labels = {tp.label for tp in touchpoints}
+
+    matrix: list[RequirementStatus] = []
+    gateway_labels = (
+        "fallback_chain",
+        "runtime_provider",
+        "gateway_provider_routing",
+        "cron_runtime_provider",
+        "auxiliary_fallback_chain",
+        "delegate_runtime_provider",
+    )
+    gateway_hits = tuple(label for label in gateway_labels if label in labels)
+    gateway_status = "partial" if len(gateway_hits) >= 4 else "gap"
+    gateway_summary = (
+        "Hermes already spreads provider routing across core agent, runtime provider, gateway, cron, auxiliary, and delegation seams; "
+        "TensorZero would need parity across all of them before it can replace the gateway layer."
+        if gateway_hits else
+        "No grounded routing surfaces were found for a gateway replacement assessment."
+    )
+    matrix.append(RequirementStatus("gateway_replacement", "Gateway replacement scope", gateway_status, gateway_hits, gateway_summary))
+
+    config_labels = (
+        "provider_routing_config",
+        "runtime_provider",
+        "smart_model_routing",
+        "fallback_chain",
+    )
+    config_hits = tuple(label for label in config_labels if label in labels)
+    config_status = "partial" if len(config_hits) >= 3 else "gap"
+    config_summary = (
+        "Hermes has multiple config concepts to migrate (`provider_routing`, `fallback_providers`, `smart_model_routing`, runtime provider resolution), "
+        "so TensorZero is not a drop-in config swap."
+        if config_hits else
+        "No current config migration surface was found."
+    )
+    matrix.append(RequirementStatus("config_migration", "Config migration", config_status, config_hits, config_summary))
+
+    canary_hits: tuple[str, ...] = tuple()
+    canary_summary = (
+        "The repo shows semantic routing and fallback, but no grounded 10% traffic-split canary mechanism. "
+        "A TensorZero cutover would need new percentage-based rollout controls and observability hooks."
+    )
+    matrix.append(RequirementStatus("canary_rollout", "10% traffic canary", "gap", canary_hits, canary_summary))
+
+    session_labels = ("session_db", "trajectory_export")
+    session_hits = tuple(label for label in session_labels if label in labels)
+    session_status = "partial" if len(session_hits) == len(session_labels) else "gap"
+    session_summary = (
+        "Hermes already has SessionDB and trajectory export surfaces that can feed offline optimization data, "
+        "but not a TensorZero-native ingestion path yet."
+        if session_hits else
+        "No session-data surface was found for prompt optimization."
+    )
+    matrix.append(RequirementStatus("session_feedback", "Session data for prompt optimization", session_status, session_hits, session_summary))
+
+    eval_labels = ("benchmark_suite", "trajectory_export")
+    eval_hits = tuple(label for label in eval_labels if label in labels)
+    eval_status = "partial" if "benchmark_suite" in eval_hits else "gap"
+    eval_summary = (
+        "Hermes already has benchmark/trajectory machinery that can seed TensorZero A/B evaluation, "
+        "but no integrated TensorZero experiment runner or live evaluation gateway."
+        if eval_hits else
+        "No evaluation harness was found to support TensorZero A/B testing."
+    )
+    matrix.append(RequirementStatus("evaluation_suite", "Evaluation suite / A/B testing", eval_status, eval_hits, eval_summary))
+
+    return matrix
+
+
+def build_report(touchpoints: list[Touchpoint], requirement_matrix: list[RequirementStatus]) -> EvaluationReport:
+    recommendation = (
+        "Not ready for direct replacement. Recommend a shadow-evaluation phase first: keep Hermes routing live, "
+        "inventory the migration seams, export SessionDB/trajectory data into an offline TensorZero experiment loop, "
+        "and only design a canary gateway once percentage-based rollout controls exist."
+    )
+    return EvaluationReport(
+        issue_number=ISSUE_NUMBER,
+        issue_title=ISSUE_TITLE,
+        issue_url=ISSUE_URL,
+        recommendation=recommendation,
+        touchpoints=tuple(touchpoints),
+        requirements=tuple(requirement_matrix),
+    )
+
+
+def build_markdown(report: EvaluationReport) -> str:
+    lines: list[str] = []
+    lines.append("# TensorZero Evaluation Packet")
+    lines.append("")
+    lines.append(f"Issue #{report.issue_number}: [{report.issue_title}]({report.issue_url})")
+    lines.append("")
+    lines.append("## Scope")
+    lines.append("")
+    lines.append("This packet evaluates TensorZero as a possible replacement for Hermes' custom provider-routing stack.")
+    lines.append("It is intentionally grounded in the current repo state rather than a speculative cutover plan.")
+    lines.append("")
+    lines.append("## Issue requirements being evaluated")
+    lines.append("")
+    lines.append("- Deploy tensorzero gateway (Rust binary)")
+    lines.append("- Migrate provider routing config")
+    lines.append("- Test with canary (10% traffic) before full cutover")
+    lines.append("- Feed session data for prompt optimization")
+    lines.append("- Evaluation suite for A/B testing models")
+    lines.append("")
+    lines.append("## Recommendation")
+    lines.append("")
+    lines.append(report.recommendation)
+    lines.append("")
+    lines.append("## Requirement matrix")
+    lines.append("")
+    lines.append("| Requirement | Status | Evidence labels | Summary |")
+    lines.append("| --- | --- | --- | --- |")
+    for row in report.requirements:
+        evidence = ", ".join(row.evidence_labels) if row.evidence_labels else "—"
+        lines.append(f"| {row.name} | {row.status} | {evidence} | {row.summary} |")
+    lines.append("")
+    lines.append("## Grounded Hermes touchpoints")
+    lines.append("")
+    if report.touchpoints:
+        for tp in report.touchpoints:
+            lines.append(f"- `{tp.file_path}:{tp.line_number}` — [{tp.label}] {tp.matched_text}")
+    else:
+        lines.append("- No routing/evaluation touchpoints were found.")
+    lines.append("")
+    lines.append("## Suggested next slice")
+    lines.append("")
+    lines.append("1. Build an exporter that emits SessionDB + trajectory data into a TensorZero-friendly offline dataset.")
+    lines.append("2. Define percentage-based canary controls before attempting any gateway replacement.")
+    lines.append("3. Keep Hermes routing authoritative until TensorZero proves parity across CLI, gateway, cron, auxiliary, and delegation surfaces.")
+    lines.append("")
+    return "\n".join(lines).rstrip() + "\n"
+
+
+def write_outputs(report: EvaluationReport, markdown_path: Path, json_path: Path | None = None) -> None:
+    markdown_path.parent.mkdir(parents=True, exist_ok=True)
+    markdown_path.write_text(build_markdown(report), encoding="utf-8")
+    if json_path is not None:
+        json_path.parent.mkdir(parents=True, exist_ok=True)
+        json_path.write_text(json.dumps(asdict(report), indent=2), encoding="utf-8")
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Generate a grounded TensorZero evaluation packet for Hermes")
+    parser.add_argument("--repo-root", default=".", help="Hermes repo root to scan")
+    parser.add_argument("--output", default=str(DEFAULT_OUTPUT), help="Markdown output path")
+    parser.add_argument("--json-output", default=str(DEFAULT_JSON_OUTPUT), help="Optional JSON output path")
+    return parser.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    repo_root = Path(args.repo_root).resolve()
+    touchpoints = scan_touchpoints(repo_root)
+    matrix = build_requirement_matrix(touchpoints)
+    report = build_report(touchpoints, matrix)
+    json_output = Path(args.json_output) if args.json_output else None
+    write_outputs(report, Path(args.output), json_output)
+    print(f"Wrote {args.output}")
+    if json_output is not None:
+        print(f"Wrote {json_output}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
--- 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/test_tensorzero_eval_packet.py
+++ b/tests/test_tensorzero_eval_packet.py
@@ -0,0 +1,149 @@
+from pathlib import Path
+import sys
+
+SCRIPT_DIR = Path(__file__).resolve().parents[1] / "scripts"
+sys.path.insert(0, str(SCRIPT_DIR))
+
+import tensorzero_eval_packet as tz
+
+
+def test_scan_touchpoints_finds_expected_matches(tmp_path):
+    (tmp_path / "run_agent.py").write_text(
+        "self._fallback_chain = []\n# Provider fallback chain\n"
+    )
+    (tmp_path / "hermes_cli").mkdir()
+    (tmp_path / "hermes_cli" / "runtime_provider.py").write_text(
+        "def resolve_runtime_provider():\n    return {}\n"
+    )
+    (tmp_path / "agent").mkdir()
+    (tmp_path / "agent" / "smart_model_routing.py").write_text(
+        "def resolve_turn_route(user_message, routing_config, primary):\n    return primary\n"
+    )
+    (tmp_path / "gateway").mkdir()
+    (tmp_path / "gateway" / "run.py").write_text(
+        "def _load_provider_routing():\n    return {}\n"
+    )
+    (tmp_path / "cron").mkdir()
+    (tmp_path / "cron" / "scheduler.py").write_text(
+        "runtime = resolve_runtime_provider()\nturn_route = resolve_turn_route('x', {}, {})\n"
+    )
+    (tmp_path / "hermes_state.py").write_text("class SessionDB:\n    pass\n")
+    (tmp_path / "benchmarks").mkdir()
+    (tmp_path / "benchmarks" / "tool_call_benchmark.py").write_text(
+        "class ToolCall: ...\n"
+    )
+
+    touchpoints = tz.scan_touchpoints(tmp_path)
+
+    labels = {tp.label for tp in touchpoints}
+    assert "fallback_chain" in labels
+    assert "runtime_provider" in labels
+    assert "smart_model_routing" in labels
+    assert "gateway_provider_routing" in labels
+    assert "cron_runtime_provider" in labels
+    assert "session_db" in labels
+    assert "benchmark_suite" in labels
+
+
+def test_build_requirement_matrix_marks_canary_as_gap_without_split_support():
+    touchpoints = [
+        tz.Touchpoint(
+            label="runtime_provider",
+            file_path="hermes_cli/runtime_provider.py",
+            line_number=10,
+            matched_text="def resolve_runtime_provider",
+        ),
+        tz.Touchpoint(
+            label="provider_routing_config",
+            file_path="cli.py",
+            line_number=20,
+            matched_text='provider_routing',
+        ),
+        tz.Touchpoint(
+            label="fallback_chain",
+            file_path="run_agent.py",
+            line_number=21,
+            matched_text='_fallback_chain = []',
+        ),
+        tz.Touchpoint(
+            label="smart_model_routing",
+            file_path="agent/smart_model_routing.py",
+            line_number=30,
+            matched_text='resolve_turn_route',
+        ),
+        tz.Touchpoint(
+            label="gateway_provider_routing",
+            file_path="gateway/run.py",
+            line_number=35,
+            matched_text='def _load_provider_routing',
+        ),
+        tz.Touchpoint(
+            label="cron_runtime_provider",
+            file_path="cron/scheduler.py",
+            line_number=36,
+            matched_text='runtime = resolve_runtime_provider()',
+        ),
+        tz.Touchpoint(
+            label="session_db",
+            file_path="hermes_state.py",
+            line_number=40,
+            matched_text='class SessionDB',
+        ),
+        tz.Touchpoint(
+            label="trajectory_export",
+            file_path="batch_runner.py",
+            line_number=50,
+            matched_text='trajectory_entry',
+        ),
+        tz.Touchpoint(
+            label="benchmark_suite",
+            file_path="benchmarks/tool_call_benchmark.py",
+            line_number=60,
+            matched_text='ToolCall',
+        ),
+    ]
+
+    matrix = tz.build_requirement_matrix(touchpoints)
+    by_key = {row.key: row for row in matrix}
+
+    assert by_key["gateway_replacement"].status == "partial"
+    assert by_key["config_migration"].status == "partial"
+    assert by_key["canary_rollout"].status == "gap"
+    assert by_key["session_feedback"].status == "partial"
+    assert by_key["evaluation_suite"].status == "partial"
+
+
+def test_build_markdown_renders_recommendation_and_touchpoints():
+    touchpoints = [
+        tz.Touchpoint(
+            label="runtime_provider",
+            file_path="hermes_cli/runtime_provider.py",
+            line_number=10,
+            matched_text="def resolve_runtime_provider",
+        ),
+        tz.Touchpoint(
+            label="session_db",
+            file_path="hermes_state.py",
+            line_number=40,
+            matched_text='class SessionDB',
+        ),
+    ]
+    matrix = tz.build_requirement_matrix(touchpoints)
+    report = tz.build_report(touchpoints, matrix)
+    markdown = tz.build_markdown(report)
+
+    assert "# TensorZero Evaluation Packet" in markdown
+    assert "gateway_replacement" not in markdown  # human labels, not raw keys
+    assert "Gateway replacement scope" in markdown
+    assert "Not ready for direct replacement" in markdown
+    assert "hermes_cli/runtime_provider.py:10" in markdown
+    assert "hermes_state.py:40" in markdown
+
+
+def test_issue_context_is_embedded_in_report():
+    report = tz.build_report([], [])
+    markdown = tz.build_markdown(report)
+
+    assert "Issue #860" in markdown
+    assert "tensorzero" in markdown.lower()
+    assert "10% traffic" in markdown