feat(memory): add grounded observation synthesis layer

agent/anthropic_adapter.py

@@ -1396,8 +1396,6 @@ def normalize_anthropic_response(
         "tool_use": "tool_calls",
         "max_tokens": "length",
         "stop_sequence": "stop",
-        "refusal": "content_filter",
-        "model_context_window_exceeded": "length",
     }
     finish_reason = stop_reason_map.get(response.stop_reason, "stop")
 
@@ -1411,42 +1409,3 @@ def normalize_anthropic_response(
         ),
         finish_reason,
     )
-
-
-def normalize_anthropic_response_v2(
-    response,
-    strip_tool_prefix: bool = False,
-) -> "NormalizedResponse":
-    """Normalize Anthropic response to NormalizedResponse.
-
-    Wraps the existing normalize_anthropic_response() and maps its output
-    to the shared transport types. This allows incremental migration
-    without disturbing the legacy call sites.
-    """
-    from agent.transports.types import NormalizedResponse, build_tool_call
-
-    assistant_msg, finish_reason = normalize_anthropic_response(response, strip_tool_prefix)
-
-    tool_calls = None
-    if assistant_msg.tool_calls:
-        tool_calls = [
-            build_tool_call(
-                id=tc.id,
-                name=tc.function.name,
-                arguments=tc.function.arguments,
-            )
-            for tc in assistant_msg.tool_calls
-        ]
-
-    provider_data = {}
-    if getattr(assistant_msg, "reasoning_details", None):
-        provider_data["reasoning_details"] = assistant_msg.reasoning_details
-
-    return NormalizedResponse(
-        content=assistant_msg.content,
-        tool_calls=tool_calls,
-        finish_reason=finish_reason,
-        reasoning=getattr(assistant_msg, "reasoning", None),
-        usage=None,
-        provider_data=provider_data or None,
-    )

agent/transports/__init__.py

@@ -1,57 +0,0 @@
-"""Transport layer types and registry for provider response normalization.
-
-Usage:
-    from agent.transports import get_transport
-    transport = get_transport("anthropic_messages")
-    result = transport.normalize_response(raw_response)
-"""
-
-from agent.transports.types import (  # noqa: F401
-    NormalizedResponse,
-    ToolCall,
-    Usage,
-    build_tool_call,
-    map_finish_reason,
-)
-
-_REGISTRY: dict = {}
-
-
-def register_transport(api_mode: str, transport_cls: type) -> None:
-    """Register a transport class for an api_mode string."""
-    _REGISTRY[api_mode] = transport_cls
-
-
-def get_transport(api_mode: str):
-    """Get a transport instance for the given api_mode.
-
-    Returns None if no transport is registered for this api_mode.
-    This allows gradual migration — call sites can check for None
-    and fall back to the legacy code path.
-    """
-    if not _REGISTRY:
-        _discover_transports()
-    cls = _REGISTRY.get(api_mode)
-    if cls is None:
-        return None
-    return cls()
-
-
-def _discover_transports() -> None:
-    """Import all transport modules to trigger auto-registration."""
-    try:
-        import agent.transports.anthropic  # noqa: F401
-    except ImportError:
-        pass
-    try:
-        import agent.transports.codex  # noqa: F401
-    except ImportError:
-        pass
-    try:
-        import agent.transports.chat_completions  # noqa: F401
-    except ImportError:
-        pass
-    try:
-        import agent.transports.bedrock  # noqa: F401
-    except ImportError:
-        pass

agent/transports/anthropic.py

@@ -1,95 +0,0 @@
-"""Anthropic Messages API transport.
-
-Delegates to the existing adapter functions in agent/anthropic_adapter.py.
-This transport owns format conversion and normalization — NOT client lifecycle.
-"""
-
-from typing import Any, Dict, List, Optional
-
-from agent.transports.base import ProviderTransport
-from agent.transports.types import NormalizedResponse
-
-
-class AnthropicTransport(ProviderTransport):
-    """Transport for api_mode='anthropic_messages'."""
-
-    @property
-    def api_mode(self) -> str:
-        return "anthropic_messages"
-
-    def convert_messages(self, messages: List[Dict[str, Any]], **kwargs) -> Any:
-        from agent.anthropic_adapter import convert_messages_to_anthropic
-
-        base_url = kwargs.get("base_url")
-        return convert_messages_to_anthropic(messages, base_url=base_url)
-
-    def convert_tools(self, tools: List[Dict[str, Any]]) -> Any:
-        from agent.anthropic_adapter import convert_tools_to_anthropic
-
-        return convert_tools_to_anthropic(tools)
-
-    def build_kwargs(
-        self,
-        model: str,
-        messages: List[Dict[str, Any]],
-        tools: Optional[List[Dict[str, Any]]] = None,
-        **params,
-    ) -> Dict[str, Any]:
-        from agent.anthropic_adapter import build_anthropic_kwargs
-
-        return build_anthropic_kwargs(
-            model=model,
-            messages=messages,
-            tools=tools,
-            max_tokens=params.get("max_tokens", 16384),
-            reasoning_config=params.get("reasoning_config"),
-            tool_choice=params.get("tool_choice"),
-            is_oauth=params.get("is_oauth", False),
-            preserve_dots=params.get("preserve_dots", False),
-            context_length=params.get("context_length"),
-            base_url=params.get("base_url"),
-            fast_mode=params.get("fast_mode", False),
-        )
-
-    def normalize_response(self, response: Any, **kwargs) -> NormalizedResponse:
-        from agent.anthropic_adapter import normalize_anthropic_response_v2
-
-        strip_tool_prefix = kwargs.get("strip_tool_prefix", False)
-        return normalize_anthropic_response_v2(response, strip_tool_prefix=strip_tool_prefix)
-
-    def validate_response(self, response: Any) -> bool:
-        if response is None:
-            return False
-        content_blocks = getattr(response, "content", None)
-        if not isinstance(content_blocks, list):
-            return False
-        if not content_blocks:
-            return False
-        return True
-
-    def extract_cache_stats(self, response: Any):
-        usage = getattr(response, "usage", None)
-        if usage is None:
-            return None
-        cached = getattr(usage, "cache_read_input_tokens", 0) or 0
-        written = getattr(usage, "cache_creation_input_tokens", 0) or 0
-        if cached or written:
-            return {"cached_tokens": cached, "creation_tokens": written}
-        return None
-
-    _STOP_REASON_MAP = {
-        "end_turn": "stop",
-        "tool_use": "tool_calls",
-        "max_tokens": "length",
-        "stop_sequence": "stop",
-        "refusal": "content_filter",
-        "model_context_window_exceeded": "length",
-    }
-
-    def map_finish_reason(self, raw_reason: str) -> str:
-        return self._STOP_REASON_MAP.get(raw_reason, "stop")
-
-
-from agent.transports import register_transport  # noqa: E402
-
-register_transport("anthropic_messages", AnthropicTransport)

agent/transports/base.py

@@ -1,61 +0,0 @@
-"""Abstract base for provider transports.
-
-A transport owns the data path for one api_mode:
-  convert_messages → convert_tools → build_kwargs → normalize_response
-
-It does NOT own: client construction, streaming, credential refresh,
-prompt caching, interrupt handling, or retry logic. Those stay on AIAgent.
-"""
-
-from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional
-
-from agent.transports.types import NormalizedResponse
-
-
-class ProviderTransport(ABC):
-    """Base class for provider-specific format conversion and normalization."""
-
-    @property
-    @abstractmethod
-    def api_mode(self) -> str:
-        """The api_mode string this transport handles."""
-        ...
-
-    @abstractmethod
-    def convert_messages(self, messages: List[Dict[str, Any]], **kwargs) -> Any:
-        """Convert OpenAI-format messages to provider-native format."""
-        ...
-
-    @abstractmethod
-    def convert_tools(self, tools: List[Dict[str, Any]]) -> Any:
-        """Convert OpenAI-format tool definitions to provider-native format."""
-        ...
-
-    @abstractmethod
-    def build_kwargs(
-        self,
-        model: str,
-        messages: List[Dict[str, Any]],
-        tools: Optional[List[Dict[str, Any]]] = None,
-        **params,
-    ) -> Dict[str, Any]:
-        """Build the complete provider kwargs dict."""
-        ...
-
-    @abstractmethod
-    def normalize_response(self, response: Any, **kwargs) -> NormalizedResponse:
-        """Normalize a raw provider response to the shared NormalizedResponse type."""
-        ...
-
-    def validate_response(self, response: Any) -> bool:
-        """Optional structural validation for raw responses."""
-        return True
-
-    def extract_cache_stats(self, response: Any) -> Optional[Dict[str, int]]:
-        """Optional cache stats extraction."""
-        return None
-
-    def map_finish_reason(self, raw_reason: str) -> str:
-        """Optional stop-reason mapping. Defaults to passthrough."""
-        return raw_reason

agent/transports/types.py

@@ -1,58 +0,0 @@
-"""Shared types for normalized provider responses."""
-
-from __future__ import annotations
-
-import json
-from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional
-
-
-@dataclass
-class ToolCall:
-    """A normalized tool call from any provider."""
-
-    id: Optional[str]
-    name: str
-    arguments: str
-    provider_data: Optional[Dict[str, Any]] = field(default=None, repr=False)
-
-
-@dataclass
-class Usage:
-    """Token usage from an API response."""
-
-    prompt_tokens: int = 0
-    completion_tokens: int = 0
-    total_tokens: int = 0
-    cached_tokens: int = 0
-
-
-@dataclass
-class NormalizedResponse:
-    """Normalized API response from any provider."""
-
-    content: Optional[str]
-    tool_calls: Optional[List[ToolCall]]
-    finish_reason: str
-    reasoning: Optional[str] = None
-    usage: Optional[Usage] = None
-    provider_data: Optional[Dict[str, Any]] = field(default=None, repr=False)
-
-
-def build_tool_call(
-    id: Optional[str],
-    name: str,
-    arguments: Any,
-    **provider_fields: Any,
-) -> ToolCall:
-    """Build a ToolCall, auto-serialising dict arguments."""
-    args_str = json.dumps(arguments) if isinstance(arguments, dict) else str(arguments)
-    provider_data = dict(provider_fields) if provider_fields else None
-    return ToolCall(id=id, name=name, arguments=args_str, provider_data=provider_data)
-
-
-def map_finish_reason(reason: Optional[str], mapping: Dict[str, str]) -> str:
-    """Translate a provider-specific stop reason to the normalized set."""
-    if reason is None:
-        return "stop"
-    return mapping.get(reason, "stop")

plugins/memory/holographic/__init__.py

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

plugins/memory/holographic/observations.py

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

plugins/memory/holographic/store.py

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

tests/agent/test_anthropic_normalize_v2.py

@@ -1,213 +0,0 @@
-"""Regression tests: normalize_anthropic_response_v2 vs v1.
-
-Constructs mock Anthropic responses and asserts that the v2 function
-(returning NormalizedResponse) produces identical field values to the
-original v1 function (returning SimpleNamespace + finish_reason).
-"""
-
-from types import SimpleNamespace
-
-import pytest
-
-from agent.anthropic_adapter import (
-    normalize_anthropic_response,
-    normalize_anthropic_response_v2,
-)
-from agent.transports.types import NormalizedResponse
-
-
-def _text_block(text: str):
-    return SimpleNamespace(type="text", text=text)
-
-
-def _thinking_block(thinking: str, signature: str = "sig_abc"):
-    return SimpleNamespace(type="thinking", thinking=thinking, signature=signature)
-
-
-def _tool_use_block(id: str, name: str, input: dict):
-    return SimpleNamespace(type="tool_use", id=id, name=name, input=input)
-
-
-def _response(content_blocks, stop_reason="end_turn"):
-    return SimpleNamespace(
-        content=content_blocks,
-        stop_reason=stop_reason,
-        usage=SimpleNamespace(input_tokens=10, output_tokens=5),
-    )
-
-
-class TestTextOnly:
-    def setup_method(self):
-        self.resp = _response([_text_block("Hello world")])
-        self.v1_msg, self.v1_finish = normalize_anthropic_response(self.resp)
-        self.v2 = normalize_anthropic_response_v2(self.resp)
-
-    def test_type(self):
-        assert isinstance(self.v2, NormalizedResponse)
-
-    def test_content_matches(self):
-        assert self.v2.content == self.v1_msg.content
-
-    def test_finish_reason_matches(self):
-        assert self.v2.finish_reason == self.v1_finish
-
-    def test_no_tool_calls(self):
-        assert self.v2.tool_calls is None
-        assert self.v1_msg.tool_calls is None
-
-    def test_no_reasoning(self):
-        assert self.v2.reasoning is None
-        assert self.v1_msg.reasoning is None
-
-
-class TestWithToolCalls:
-    def setup_method(self):
-        self.resp = _response(
-            [
-                _text_block("I'll check that"),
-                _tool_use_block("toolu_abc", "terminal", {"command": "ls"}),
-                _tool_use_block("toolu_def", "read_file", {"path": "/tmp"}),
-            ],
-            stop_reason="tool_use",
-        )
-        self.v1_msg, self.v1_finish = normalize_anthropic_response(self.resp)
-        self.v2 = normalize_anthropic_response_v2(self.resp)
-
-    def test_finish_reason(self):
-        assert self.v2.finish_reason == "tool_calls"
-        assert self.v1_finish == "tool_calls"
-
-    def test_tool_call_count(self):
-        assert len(self.v2.tool_calls) == 2
-        assert len(self.v1_msg.tool_calls) == 2
-
-    def test_tool_call_ids_match(self):
-        for i in range(2):
-            assert self.v2.tool_calls[i].id == self.v1_msg.tool_calls[i].id
-
-    def test_tool_call_names_match(self):
-        assert self.v2.tool_calls[0].name == "terminal"
-        assert self.v2.tool_calls[1].name == "read_file"
-        for i in range(2):
-            assert self.v2.tool_calls[i].name == self.v1_msg.tool_calls[i].function.name
-
-    def test_tool_call_arguments_match(self):
-        for i in range(2):
-            assert self.v2.tool_calls[i].arguments == self.v1_msg.tool_calls[i].function.arguments
-
-    def test_content_preserved(self):
-        assert self.v2.content == self.v1_msg.content
-        assert "check that" in self.v2.content
-
-
-class TestWithThinking:
-    def setup_method(self):
-        self.resp = _response([
-            _thinking_block("Let me think about this carefully..."),
-            _text_block("The answer is 42."),
-        ])
-        self.v1_msg, self.v1_finish = normalize_anthropic_response(self.resp)
-        self.v2 = normalize_anthropic_response_v2(self.resp)
-
-    def test_reasoning_matches(self):
-        assert self.v2.reasoning == self.v1_msg.reasoning
-        assert "think about this" in self.v2.reasoning
-
-    def test_reasoning_details_in_provider_data(self):
-        v1_details = self.v1_msg.reasoning_details
-        v2_details = self.v2.provider_data.get("reasoning_details") if self.v2.provider_data else None
-        assert v1_details is not None
-        assert v2_details is not None
-        assert len(v2_details) == len(v1_details)
-
-    def test_content_excludes_thinking(self):
-        assert self.v2.content == "The answer is 42."
-
-
-class TestMixed:
-    def setup_method(self):
-        self.resp = _response(
-            [
-                _thinking_block("Planning my approach..."),
-                _text_block("I'll run the command"),
-                _tool_use_block("toolu_xyz", "terminal", {"command": "pwd"}),
-            ],
-            stop_reason="tool_use",
-        )
-        self.v1_msg, self.v1_finish = normalize_anthropic_response(self.resp)
-        self.v2 = normalize_anthropic_response_v2(self.resp)
-
-    def test_all_fields_present(self):
-        assert self.v2.content is not None
-        assert self.v2.tool_calls is not None
-        assert self.v2.reasoning is not None
-        assert self.v2.finish_reason == "tool_calls"
-
-    def test_content_matches(self):
-        assert self.v2.content == self.v1_msg.content
-
-    def test_reasoning_matches(self):
-        assert self.v2.reasoning == self.v1_msg.reasoning
-
-    def test_tool_call_matches(self):
-        assert self.v2.tool_calls[0].id == self.v1_msg.tool_calls[0].id
-        assert self.v2.tool_calls[0].name == self.v1_msg.tool_calls[0].function.name
-
-
-class TestStopReasons:
-    @pytest.mark.parametrize("stop_reason,expected", [
-        ("end_turn", "stop"),
-        ("tool_use", "tool_calls"),
-        ("max_tokens", "length"),
-        ("stop_sequence", "stop"),
-        ("refusal", "content_filter"),
-        ("model_context_window_exceeded", "length"),
-        ("unknown_future_reason", "stop"),
-    ])
-    def test_stop_reason_mapping(self, stop_reason, expected):
-        resp = _response([_text_block("x")], stop_reason=stop_reason)
-        _v1_msg, v1_finish = normalize_anthropic_response(resp)
-        v2 = normalize_anthropic_response_v2(resp)
-        assert v2.finish_reason == v1_finish == expected
-
-
-class TestStripToolPrefix:
-    def test_prefix_stripped(self):
-        resp = _response(
-            [_tool_use_block("toolu_1", "mcp_terminal", {"cmd": "ls"})],
-            stop_reason="tool_use",
-        )
-        v1_msg, _ = normalize_anthropic_response(resp, strip_tool_prefix=True)
-        v2 = normalize_anthropic_response_v2(resp, strip_tool_prefix=True)
-        assert v1_msg.tool_calls[0].function.name == "terminal"
-        assert v2.tool_calls[0].name == "terminal"
-
-    def test_prefix_kept(self):
-        resp = _response(
-            [_tool_use_block("toolu_1", "mcp_terminal", {"cmd": "ls"})],
-            stop_reason="tool_use",
-        )
-        v1_msg, _ = normalize_anthropic_response(resp, strip_tool_prefix=False)
-        v2 = normalize_anthropic_response_v2(resp, strip_tool_prefix=False)
-        assert v1_msg.tool_calls[0].function.name == "mcp_terminal"
-        assert v2.tool_calls[0].name == "mcp_terminal"
-
-
-class TestEdgeCases:
-    def test_empty_content_blocks(self):
-        resp = _response([])
-        v1_msg, _v1_finish = normalize_anthropic_response(resp)
-        v2 = normalize_anthropic_response_v2(resp)
-        assert v2.content == v1_msg.content
-        assert v2.content is None
-
-    def test_no_reasoning_details_means_none_provider_data(self):
-        resp = _response([_text_block("hi")])
-        v2 = normalize_anthropic_response_v2(resp)
-        assert v2.provider_data is None
-
-    def test_v2_returns_dataclass_not_namespace(self):
-        resp = _response([_text_block("hi")])
-        v2 = normalize_anthropic_response_v2(resp)
-        assert isinstance(v2, NormalizedResponse)
-        assert not isinstance(v2, SimpleNamespace)

tests/agent/transports/test_transport.py

@@ -1,208 +0,0 @@
-"""Tests for the transport ABC, registry, and AnthropicTransport."""
-
-from types import SimpleNamespace
-
-import pytest
-
-from agent.transports import _REGISTRY, get_transport, register_transport
-from agent.transports.base import ProviderTransport
-from agent.transports.types import NormalizedResponse
-
-
-class TestProviderTransportABC:
-    def test_cannot_instantiate_abc(self):
-        with pytest.raises(TypeError):
-            ProviderTransport()
-
-    def test_concrete_must_implement_all_abstract(self):
-        class Incomplete(ProviderTransport):
-            @property
-            def api_mode(self):
-                return "test"
-
-        with pytest.raises(TypeError):
-            Incomplete()
-
-    def test_minimal_concrete(self):
-        class Minimal(ProviderTransport):
-            @property
-            def api_mode(self):
-                return "test_minimal"
-
-            def convert_messages(self, messages, **kw):
-                return messages
-
-            def convert_tools(self, tools):
-                return tools
-
-            def build_kwargs(self, model, messages, tools=None, **params):
-                return {"model": model, "messages": messages}
-
-            def normalize_response(self, response, **kw):
-                return NormalizedResponse(content="ok", tool_calls=None, finish_reason="stop")
-
-        t = Minimal()
-        assert t.api_mode == "test_minimal"
-        assert t.validate_response(None) is True
-        assert t.extract_cache_stats(None) is None
-        assert t.map_finish_reason("end_turn") == "end_turn"
-
-
-class TestTransportRegistry:
-    def test_get_unregistered_returns_none(self):
-        assert get_transport("nonexistent_mode") is None
-
-    def test_anthropic_registered_on_import(self):
-        import agent.transports.anthropic  # noqa: F401
-
-        t = get_transport("anthropic_messages")
-        assert t is not None
-        assert t.api_mode == "anthropic_messages"
-
-    def test_register_and_get(self):
-        class DummyTransport(ProviderTransport):
-            @property
-            def api_mode(self):
-                return "dummy_test"
-
-            def convert_messages(self, messages, **kw):
-                return messages
-
-            def convert_tools(self, tools):
-                return tools
-
-            def build_kwargs(self, model, messages, tools=None, **params):
-                return {}
-
-            def normalize_response(self, response, **kw):
-                return NormalizedResponse(content=None, tool_calls=None, finish_reason="stop")
-
-        register_transport("dummy_test", DummyTransport)
-        t = get_transport("dummy_test")
-        assert t.api_mode == "dummy_test"
-        _REGISTRY.pop("dummy_test", None)
-
-
-class TestAnthropicTransport:
-    @pytest.fixture
-    def transport(self):
-        import agent.transports.anthropic  # noqa: F401
-
-        return get_transport("anthropic_messages")
-
-    def test_api_mode(self, transport):
-        assert transport.api_mode == "anthropic_messages"
-
-    def test_convert_tools_simple(self, transport):
-        tools = [{
-            "type": "function",
-            "function": {
-                "name": "test_tool",
-                "description": "A test",
-                "parameters": {"type": "object", "properties": {}},
-            },
-        }]
-        result = transport.convert_tools(tools)
-        assert len(result) == 1
-        assert result[0]["name"] == "test_tool"
-        assert "input_schema" in result[0]
-
-    def test_validate_response_none(self, transport):
-        assert transport.validate_response(None) is False
-
-    def test_validate_response_empty_content(self, transport):
-        r = SimpleNamespace(content=[])
-        assert transport.validate_response(r) is False
-
-    def test_validate_response_valid(self, transport):
-        r = SimpleNamespace(content=[SimpleNamespace(type="text", text="hello")])
-        assert transport.validate_response(r) is True
-
-    def test_map_finish_reason(self, transport):
-        assert transport.map_finish_reason("end_turn") == "stop"
-        assert transport.map_finish_reason("tool_use") == "tool_calls"
-        assert transport.map_finish_reason("max_tokens") == "length"
-        assert transport.map_finish_reason("stop_sequence") == "stop"
-        assert transport.map_finish_reason("refusal") == "content_filter"
-        assert transport.map_finish_reason("model_context_window_exceeded") == "length"
-        assert transport.map_finish_reason("unknown") == "stop"
-
-    def test_extract_cache_stats_none_usage(self, transport):
-        r = SimpleNamespace(usage=None)
-        assert transport.extract_cache_stats(r) is None
-
-    def test_extract_cache_stats_with_cache(self, transport):
-        usage = SimpleNamespace(cache_read_input_tokens=100, cache_creation_input_tokens=50)
-        r = SimpleNamespace(usage=usage)
-        result = transport.extract_cache_stats(r)
-        assert result == {"cached_tokens": 100, "creation_tokens": 50}
-
-    def test_extract_cache_stats_zero(self, transport):
-        usage = SimpleNamespace(cache_read_input_tokens=0, cache_creation_input_tokens=0)
-        r = SimpleNamespace(usage=usage)
-        assert transport.extract_cache_stats(r) is None
-
-    def test_normalize_response_text(self, transport):
-        r = SimpleNamespace(
-            content=[SimpleNamespace(type="text", text="Hello world")],
-            stop_reason="end_turn",
-            usage=SimpleNamespace(input_tokens=10, output_tokens=5),
-            model="claude-sonnet-4-6",
-        )
-        nr = transport.normalize_response(r)
-        assert isinstance(nr, NormalizedResponse)
-        assert nr.content == "Hello world"
-        assert nr.tool_calls is None or nr.tool_calls == []
-        assert nr.finish_reason == "stop"
-
-    def test_normalize_response_tool_calls(self, transport):
-        r = SimpleNamespace(
-            content=[
-                SimpleNamespace(type="tool_use", id="toolu_123", name="terminal", input={"command": "ls"}),
-            ],
-            stop_reason="tool_use",
-            usage=SimpleNamespace(input_tokens=10, output_tokens=20),
-            model="claude-sonnet-4-6",
-        )
-        nr = transport.normalize_response(r)
-        assert nr.finish_reason == "tool_calls"
-        assert len(nr.tool_calls) == 1
-        tc = nr.tool_calls[0]
-        assert tc.name == "terminal"
-        assert tc.id == "toolu_123"
-        assert '"command"' in tc.arguments
-
-    def test_normalize_response_thinking(self, transport):
-        r = SimpleNamespace(
-            content=[
-                SimpleNamespace(type="thinking", thinking="Let me think..."),
-                SimpleNamespace(type="text", text="The answer is 42"),
-            ],
-            stop_reason="end_turn",
-            usage=SimpleNamespace(input_tokens=10, output_tokens=15),
-            model="claude-sonnet-4-6",
-        )
-        nr = transport.normalize_response(r)
-        assert nr.content == "The answer is 42"
-        assert nr.reasoning == "Let me think..."
-
-    def test_build_kwargs_returns_dict(self, transport):
-        messages = [{"role": "user", "content": "Hello"}]
-        kw = transport.build_kwargs(
-            model="claude-sonnet-4-6",
-            messages=messages,
-            max_tokens=1024,
-        )
-        assert isinstance(kw, dict)
-        assert "model" in kw
-        assert "max_tokens" in kw
-        assert "messages" in kw
-
-    def test_convert_messages_extracts_system(self, transport):
-        messages = [
-            {"role": "system", "content": "You are helpful."},
-            {"role": "user", "content": "Hi"},
-        ]
-        system, msgs = transport.convert_messages(messages)
-        assert system is not None
-        assert len(msgs) >= 1

tests/agent/transports/test_types.py

@@ -1,130 +0,0 @@
-"""Tests for agent/transports/types.py — dataclass construction + helpers."""
-
-import json
-
-from agent.transports.types import (
-    NormalizedResponse,
-    ToolCall,
-    Usage,
-    build_tool_call,
-    map_finish_reason,
-)
-
-
-class TestToolCall:
-    def test_basic_construction(self):
-        tc = ToolCall(id="call_abc", name="terminal", arguments='{"cmd": "ls"}')
-        assert tc.id == "call_abc"
-        assert tc.name == "terminal"
-        assert tc.arguments == '{"cmd": "ls"}'
-        assert tc.provider_data is None
-
-    def test_none_id(self):
-        tc = ToolCall(id=None, name="read_file", arguments="{}")
-        assert tc.id is None
-
-    def test_provider_data(self):
-        tc = ToolCall(
-            id="call_x",
-            name="t",
-            arguments="{}",
-            provider_data={"call_id": "call_x", "response_item_id": "fc_x"},
-        )
-        assert tc.provider_data["call_id"] == "call_x"
-        assert tc.provider_data["response_item_id"] == "fc_x"
-
-
-class TestUsage:
-    def test_defaults(self):
-        u = Usage()
-        assert u.prompt_tokens == 0
-        assert u.completion_tokens == 0
-        assert u.total_tokens == 0
-        assert u.cached_tokens == 0
-
-    def test_explicit(self):
-        u = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150, cached_tokens=80)
-        assert u.total_tokens == 150
-
-
-class TestNormalizedResponse:
-    def test_text_only(self):
-        r = NormalizedResponse(content="hello", tool_calls=None, finish_reason="stop")
-        assert r.content == "hello"
-        assert r.tool_calls is None
-        assert r.finish_reason == "stop"
-        assert r.reasoning is None
-        assert r.usage is None
-        assert r.provider_data is None
-
-    def test_with_tool_calls(self):
-        tcs = [ToolCall(id="call_1", name="terminal", arguments='{"cmd":"pwd"}')]
-        r = NormalizedResponse(content=None, tool_calls=tcs, finish_reason="tool_calls")
-        assert r.finish_reason == "tool_calls"
-        assert len(r.tool_calls) == 1
-        assert r.tool_calls[0].name == "terminal"
-
-    def test_with_reasoning(self):
-        r = NormalizedResponse(
-            content="answer",
-            tool_calls=None,
-            finish_reason="stop",
-            reasoning="I thought about it",
-        )
-        assert r.reasoning == "I thought about it"
-
-    def test_with_provider_data(self):
-        r = NormalizedResponse(
-            content=None,
-            tool_calls=None,
-            finish_reason="stop",
-            provider_data={"reasoning_details": [{"type": "thinking", "thinking": "hmm"}]},
-        )
-        assert r.provider_data["reasoning_details"][0]["type"] == "thinking"
-
-
-class TestBuildToolCall:
-    def test_dict_arguments_serialized(self):
-        tc = build_tool_call(id="call_1", name="terminal", arguments={"cmd": "ls"})
-        assert tc.arguments == json.dumps({"cmd": "ls"})
-        assert tc.provider_data is None
-
-    def test_string_arguments_passthrough(self):
-        tc = build_tool_call(id="call_2", name="read_file", arguments='{"path": "/tmp"}')
-        assert tc.arguments == '{"path": "/tmp"}'
-
-    def test_provider_fields(self):
-        tc = build_tool_call(
-            id="call_3",
-            name="terminal",
-            arguments="{}",
-            call_id="call_3",
-            response_item_id="fc_3",
-        )
-        assert tc.provider_data == {"call_id": "call_3", "response_item_id": "fc_3"}
-
-    def test_none_id(self):
-        tc = build_tool_call(id=None, name="t", arguments="{}")
-        assert tc.id is None
-
-
-class TestMapFinishReason:
-    ANTHROPIC_MAP = {
-        "end_turn": "stop",
-        "tool_use": "tool_calls",
-        "max_tokens": "length",
-        "stop_sequence": "stop",
-        "refusal": "content_filter",
-    }
-
-    def test_known_reason(self):
-        assert map_finish_reason("end_turn", self.ANTHROPIC_MAP) == "stop"
-        assert map_finish_reason("tool_use", self.ANTHROPIC_MAP) == "tool_calls"
-        assert map_finish_reason("max_tokens", self.ANTHROPIC_MAP) == "length"
-        assert map_finish_reason("refusal", self.ANTHROPIC_MAP) == "content_filter"
-
-    def test_unknown_reason_defaults_to_stop(self):
-        assert map_finish_reason("something_new", self.ANTHROPIC_MAP) == "stop"
-
-    def test_none_reason(self):
-        assert map_finish_reason(None, self.ANTHROPIC_MAP) == "stop"

tests/plugins/memory/test_holographic_observations.py

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