update

.gitignore

@@ -29,7 +29,7 @@ nltk_data/
 
 # Monitoring stack (Prometheus/Grafana binaries and data)
 .monitoring/
-.pgbouncer
+.pgbouncer/
 
 # Large benchmark datasets (will be downloaded automatically)
 **/longmemeval_s_cleaned.json

.pgbouncer/pgbouncer.ini

@@ -1,39 +0,0 @@
-[databases]
-; Connect to pg0 on port 5433
-; The actual pg0 database is called "hindsight"
-hindsight = host=127.0.0.1 port=5433 dbname=hindsight user=hindsight password=hindsight
-
-[pgbouncer]
-listen_addr = 127.0.0.1
-listen_port = 6432
-
-; Use md5 authentication (matches pg0's auth)
-auth_type = md5
-auth_file = /Users/nicoloboschi/dev/memory-poc/.pgbouncer/userlist.txt
-
-; Transaction pooling mode (recommended for hindsight)
-pool_mode = transaction
-
-; Reset connection state after each transaction
-server_reset_query = DISCARD ALL
-
-; Pool sizing
-default_pool_size = 20
-max_client_conn = 200
-min_pool_size = 5
-
-; Timeouts
-server_idle_timeout = 600
-server_lifetime = 3600
-query_timeout = 120
-
-; Logging
-log_connections = 1
-log_disconnections = 1
-log_pooler_errors = 1
-
-; Stats
-stats_period = 60
-
-; Admin console
-admin_users = admin

.pgbouncer/userlist.txt

@@ -1,2 +0,0 @@
-"hindsight" "md5d842ccb6249bcd3c53b2f648378092a6"
-"admin" ""

CLAUDE.md

@@ -199,6 +199,38 @@ When adding or modifying parameters in the dataplane API (hindsight-api), you mu
 - Pydantic models for request/response
 - Ruff for linting (line-length 120)
 - No Python files at project root - maintain clean directory structure
+- **Never use multi-item tuple return values** - prefer dataclass or Pydantic model for structured returns
+
+### Type Safety with Pydantic Models
+**NEVER use raw `dict` types for structured data.** Always use Pydantic models:
+- Use Pydantic `BaseModel` for all data structures passed between functions
+- Add `@field_validator` for type coercion (e.g., ensuring datetimes are timezone-aware)
+- Avoid `dict.get()` patterns - use typed model attributes instead
+- Parse external data (JSON, API responses) into Pydantic models at the boundary
+- This catches type errors at parse time, not deep in business logic
+
+```python
+# BAD - error-prone dict access
+def process(data: dict) -> str:
+    return data.get("name", "")  # No validation, silent failures
+
+# GOOD - typed and validated
+class UserData(BaseModel):
+    name: str
+    created_at: datetime
+
+    @field_validator("created_at", mode="before")
+    @classmethod
+    def ensure_tz_aware(cls, v):
+        if isinstance(v, str):
+            v = datetime.fromisoformat(v.replace("Z", "+00:00"))
+        if v.tzinfo is None:
+            return v.replace(tzinfo=timezone.utc)
+        return v
+
+def process(data: UserData) -> str:
+    return data.name  # Type-safe, validated at construction
+```
 
 ### TypeScript Style
 - Next.js App Router for control plane

hindsight-api/hindsight_api/alembic/versions/j5e6f7g8h9i0_mental_model_versions.py

@@ -0,0 +1,95 @@
+"""mental_model_versions
+
+Revision ID: j5e6f7g8h9i0
+Revises: i4d5e6f7g8h9
+Create Date: 2026-01-16 00:00:00.000000
+
+This migration adds versioning support for mental models:
+1. Creates mental_model_versions table to store observation snapshots
+2. Adds version column to mental_models for tracking current version
+
+This enables changelog/diff functionality for mental model observations.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+# revision identifiers, used by Alembic.
+revision: str = "j5e6f7g8h9i0"
+down_revision: str | Sequence[str] | None = "i4d5e6f7g8h9"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Create mental_model_versions table and add version tracking."""
+    schema = _get_schema_prefix()
+
+    # Create mental_model_versions table for storing observation snapshots
+    op.execute(f"""
+        CREATE TABLE {schema}mental_model_versions (
+            id SERIAL PRIMARY KEY,
+            mental_model_id VARCHAR(64) NOT NULL,
+            bank_id VARCHAR(64) NOT NULL,
+            version INT NOT NULL,
+            observations JSONB NOT NULL DEFAULT '{{"observations": []}}'::jsonb,
+            created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
+            FOREIGN KEY (mental_model_id, bank_id)
+                REFERENCES {schema}mental_models(id, bank_id) ON DELETE CASCADE,
+            UNIQUE (mental_model_id, bank_id, version)
+        )
+    """)
+
+    # Index for efficient version queries (get latest, list versions)
+    op.execute(f"""
+        CREATE INDEX idx_mental_model_versions_lookup
+        ON {schema}mental_model_versions(mental_model_id, bank_id, version DESC)
+    """)
+
+    # Add version column to mental_models to track current version
+    op.execute(f"""
+        ALTER TABLE {schema}mental_models
+        ADD COLUMN IF NOT EXISTS version INT NOT NULL DEFAULT 0
+    """)
+
+    # Migrate existing mental models: create version 1 for any that have observations
+    op.execute(f"""
+        INSERT INTO {schema}mental_model_versions (mental_model_id, bank_id, version, observations, created_at)
+        SELECT id, bank_id, 1, observations, COALESCE(last_updated, created_at)
+        FROM {schema}mental_models
+        WHERE observations IS NOT NULL
+          AND observations != '{{"observations": []}}'::jsonb
+          AND (observations->'observations') IS NOT NULL
+          AND jsonb_array_length(observations->'observations') > 0
+    """)
+
+    # Update version to 1 for migrated mental models
+    op.execute(f"""
+        UPDATE {schema}mental_models
+        SET version = 1
+        WHERE observations IS NOT NULL
+          AND observations != '{{"observations": []}}'::jsonb
+          AND (observations->'observations') IS NOT NULL
+          AND jsonb_array_length(observations->'observations') > 0
+    """)
+
+
+def downgrade() -> None:
+    """Remove mental_model_versions table and version column."""
+    schema = _get_schema_prefix()
+
+    # Drop index
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_mental_model_versions_lookup")
+
+    # Drop versions table
+    op.execute(f"DROP TABLE IF EXISTS {schema}mental_model_versions")
+
+    # Remove version column from mental_models
+    op.execute(f"ALTER TABLE {schema}mental_models DROP COLUMN IF EXISTS version")

hindsight-api/hindsight_api/alembic/versions/k6f7g8h9i0j1_add_directive_subtype.py

@@ -0,0 +1,58 @@
+"""add_directive_subtype
+
+Revision ID: k6f7g8h9i0j1
+Revises: j5e6f7g8h9i0
+Create Date: 2026-01-16 00:00:00.000000
+
+This migration adds 'directive' to the mental_models subtype constraint.
+Directives are hard rules with user-provided observations that the reflect agent must follow.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+# revision identifiers, used by Alembic.
+revision: str = "k6f7g8h9i0j1"
+down_revision: str | Sequence[str] | None = "j5e6f7g8h9i0"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Add 'directive' to mental_models subtype constraint."""
+    schema = _get_schema_prefix()
+
+    # Drop existing constraint
+    op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS ck_mental_models_subtype")
+
+    # Create new constraint with 'directive' added
+    op.execute(f"""
+        ALTER TABLE {schema}mental_models
+        ADD CONSTRAINT ck_mental_models_subtype
+        CHECK (subtype IN ('structural', 'emergent', 'pinned', 'learned', 'directive'))
+    """)
+
+
+def downgrade() -> None:
+    """Remove 'directive' from mental_models subtype constraint."""
+    schema = _get_schema_prefix()
+
+    # First delete any directives (cannot downgrade if they exist)
+    op.execute(f"DELETE FROM {schema}mental_models WHERE subtype = 'directive'")
+
+    # Drop constraint with directive
+    op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS ck_mental_models_subtype")
+
+    # Recreate original constraint without directive
+    op.execute(f"""
+        ALTER TABLE {schema}mental_models
+        ADD CONSTRAINT ck_mental_models_subtype
+        CHECK (subtype IN ('structural', 'emergent', 'pinned', 'learned'))
+    """)

hindsight-api/hindsight_api/api/http.py

@@ -36,6 +36,7 @@ from pydantic import BaseModel, ConfigDict, Field, field_validator
 from hindsight_api import MemoryEngine
 from hindsight_api.engine.db_utils import acquire_with_retry
 from hindsight_api.engine.memory_engine import Budget, fq_table
+from hindsight_api.engine.reflect.observations import Observation
 from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES, TokenUsage
 from hindsight_api.engine.search.tags import TagsMatch
 from hindsight_api.extensions import HttpExtension, OperationValidationError, load_extension
@@ -559,9 +560,10 @@ class ReflectMentalModel(BaseModel):
     id: str = Field(description="Mental model ID")
     name: str = Field(description="Mental model name")
     type: str = Field(description="Mental model type: entity, concept, event")
-    subtype: str = Field(description="Mental model subtype: structural, emergent, learned")
-    description: str = Field(description="Brief description")
-    summary: str | None = Field(default=None, description="Full summary (when looked up in detail)")
+    subtype: str = Field(description="Mental model subtype: structural, emergent, learned, directive")
+    observations: list[str] | None = Field(
+        default=None, description="Observations for directive mental models (subtype='directive')"
+    )
 
 
 class ReflectBasedOn(BaseModel):
@@ -578,6 +580,10 @@ class ReflectTrace(BaseModel):
 
     tool_calls: list[ReflectToolCall] = Field(default_factory=list, description="Tool calls made during reflection")
     llm_calls: list[ReflectLLMCall] = Field(default_factory=list, description="LLM calls made during reflection")
+    mental_models: list[ReflectMentalModel] = Field(
+        default_factory=list,
+        description="Mental models used during reflection (includes directives with subtype='directive')",
+    )
 
 
 class CreatedMentalModel(BaseModel):
@@ -1045,12 +1051,40 @@ class BankStatsResponse(BaseModel):
 # Mental Model models
 
 
-class MentalModelObservationResponse(BaseModel):
-    """An observation within a mental model with its supporting memories."""
+class ObservationEvidenceResponse(BaseModel):
+    """A single piece of evidence supporting an observation."""
 
-    title: str = Field(description="Observation header (empty for intro)")
-    text: str = Field(description="Observation content")
-    based_on: list[str] = Field(default_factory=list, description="Memory IDs supporting this observation")
+    memory_id: str = Field(description="ID of the memory unit this evidence comes from")
+    quote: str = Field(description="Exact quote from the memory supporting the observation")
+    relevance: str = Field(description="Brief explanation of how this quote supports the observation")
+    timestamp: str = Field(description="When the source memory was created (ISO format)")
+
+
+class MentalModelObservationResponse(BaseModel):
+    """An observation within a mental model with its supporting evidence."""
+
+    title: str = Field(description="Short summary title for the observation")
+    content: str = Field(description="The observation content - detailed explanation")
+    evidence: list[ObservationEvidenceResponse] = Field(
+        default_factory=list, description="Supporting evidence with quotes"
+    )
+    created_at: str = Field(description="When this observation was first created (ISO format)")
+    trend: str = Field(description="Computed trend: stable, strengthening, weakening, new, stale")
+    evidence_count: int = Field(description="Number of evidence items supporting this observation")
+    evidence_span: dict = Field(description="Time span of evidence: {from: iso_date, to: iso_date}")
+
+
+class MentalModelFreshnessResponse(BaseModel):
+    """Freshness information for a mental model."""
+
+    is_up_to_date: bool = Field(description="Whether the model has been refreshed since the last memory was added")
+    last_refresh_at: str | None = Field(description="When the model was last refreshed (ISO format)")
+    memories_since_refresh: int = Field(description="Number of memories added since last refresh")
+    reasons: list[str] = Field(
+        default_factory=list,
+        description="Reasons why the model needs refresh (empty if up to date). "
+        "Possible values: never_refreshed, new_memories, mission_changed, disposition_changed, directives_changed",
+    )
 
 
 class MentalModelResponse(BaseModel):
@@ -1064,11 +1098,36 @@ class MentalModelResponse(BaseModel):
                 "subtype": "structural",
                 "name": "Team Structure",
                 "description": "Who's on the team and their roles",
-                "observations": [{"title": "Overview", "text": "The team consists of...", "based_on": ["uuid1"]}],
+                "observations": [
+                    {
+                        "title": "Prefers async communication",
+                        "content": "The team prefers async communication over synchronous meetings",
+                        "evidence": [
+                            {
+                                "memory_id": "uuid1",
+                                "quote": "I prefer Slack over meetings",
+                                "relevance": "Shows async preference",
+                                "timestamp": "2024-01-10T08:00:00Z",
+                            }
+                        ],
+                        "created_at": "2024-01-15T10:30:00Z",
+                        "trend": "stable",
+                        "evidence_count": 1,
+                        "evidence_span": {"from": "2024-01-10T08:00:00Z", "to": "2024-01-10T08:00:00Z"},
+                    }
+                ],
+                "version": 1,
                 "entity_id": None,
                 "links": [],
                 "tags": ["project-x"],
                 "last_updated": "2024-01-15T10:30:00Z",
+                "last_refresh_at": "2024-01-15T10:30:00Z",
+                "freshness": {
+                    "is_up_to_date": True,
+                    "last_refresh_at": "2024-01-15T10:30:00Z",
+                    "memories_since_refresh": 0,
+                    "reasons": [],
+                },
                 "created_at": "2024-01-10T08:00:00Z",
             }
         }
@@ -1082,10 +1141,15 @@ class MentalModelResponse(BaseModel):
     observations: list[MentalModelObservationResponse] = Field(
         default_factory=list, description="Structured observations with per-observation fact attribution"
     )
+    version: int = Field(default=0, description="Version number of the mental model observations")
     entity_id: str | None = None
     links: list[str] = []
     tags: list[str] = []
     last_updated: str | None = None
+    last_refresh_at: str | None = Field(default=None, description="When observations were last refreshed (ISO format)")
+    freshness: MentalModelFreshnessResponse | None = Field(
+        default=None, description="Freshness info (null for directive subtypes which don't need refresh)"
+    )
     created_at: str
 
 
@@ -1095,6 +1159,39 @@ class MentalModelListResponse(BaseModel):
     items: list[MentalModelResponse]
 
 
+def _observation_to_response(obs: Observation) -> MentalModelObservationResponse:
+    """Convert internal Observation model to API response model."""
+    return MentalModelObservationResponse(
+        title=obs.title,
+        content=obs.content,
+        evidence=[
+            ObservationEvidenceResponse(
+                memory_id=ev.memory_id,
+                quote=ev.quote,
+                relevance=ev.relevance,
+                timestamp=ev.timestamp.isoformat(),
+            )
+            for ev in obs.evidence
+        ],
+        created_at=obs.created_at.isoformat(),
+        trend=obs.trend.value,
+        evidence_count=obs.evidence_count,
+        evidence_span=obs.evidence_span,
+    )
+
+
+def _prepare_mental_model_response(model: dict[str, Any]) -> MentalModelResponse:
+    """Convert internal mental model dict to API response model.
+
+    Handles conversion of Observation models to MentalModelObservationResponse.
+    """
+    observations = model.get("observations", [])
+    converted_observations = [
+        _observation_to_response(obs) if isinstance(obs, Observation) else obs for obs in observations
+    ]
+    return MentalModelResponse(**{**model, "observations": converted_observations})
+
+
 class RefreshMentalModelsRequest(BaseModel):
     """Request model for refresh mental models endpoint."""
 
@@ -1107,24 +1204,63 @@ class RefreshMentalModelsRequest(BaseModel):
     )
 
 
+class ObservationInput(BaseModel):
+    """Input model for a single observation."""
+
+    title: str = Field(description="Short title/header for the observation")
+    content: str = Field(description="Content of the observation")
+
+
 class CreateMentalModelRequest(BaseModel):
-    """Request model for creating a pinned mental model."""
+    """Request model for creating a mental model."""
 
     model_config = ConfigDict(
         json_schema_extra={
-            "example": {
-                "name": "Product Roadmap",
-                "description": "Key product priorities and upcoming features",
-                "tags": ["project-x"],
-            }
+            "examples": [
+                {
+                    "name": "Product Roadmap",
+                    "description": "Key product priorities and upcoming features",
+                    "tags": ["project-x"],
+                },
+                {
+                    "name": "Meeting Rules",
+                    "description": "Rules about scheduling meetings",
+                    "subtype": "directive",
+                    "observations": [{"title": "Morning meetings", "content": "Never schedule meetings before 10am"}],
+                },
+            ]
         }
     )
 
     name: str = Field(description="Human-readable name for the mental model")
     description: str = Field(description="One-liner description for quick scanning")
+    subtype: str = Field(
+        default="pinned",
+        description="Type of mental model: 'pinned' (observations LLM-generated) or 'directive' (observations user-provided)",
+    )
+    observations: list[ObservationInput] | None = Field(
+        default=None,
+        description="For directives only: list of user-provided observations. Required when subtype='directive'.",
+    )
     tags: list[str] = Field(default_factory=list, description="Tags for scoped visibility")
 
 
+class UpdateMentalModelRequest(BaseModel):
+    """Request model for updating a mental model."""
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "name": "Updated Name",
+                "description": "Updated description with new rules",
+            }
+        }
+    )
+
+    name: str | None = Field(default=None, description="New name for the mental model")
+    description: str | None = Field(default=None, description="New description/rule text")
+
+
 class OperationResponse(BaseModel):
     """Response model for a single async operation."""
 
@@ -1742,14 +1878,12 @@ def _register_routes(app: FastAPI):
                         name=mm.name,
                         type=mm.type,
                         subtype=mm.subtype,
-                        description=mm.description,
-                        summary=mm.summary,
                     )
                     for mm in core_result.mental_models
                 ]
                 based_on_result = ReflectBasedOn(memories=memories, mental_models=mental_models)
 
-            # Build trace (tool_calls + llm_calls) if tool_calls is requested
+            # Build trace (tool_calls + llm_calls + mental_models) if tool_calls is requested
             trace_result: ReflectTrace | None = None
             if request.include.tool_calls is not None:
                 include_output = request.include.tool_calls.output
@@ -1764,7 +1898,24 @@ def _register_routes(app: FastAPI):
                     for tc in core_result.tool_trace
                 ]
                 llm_calls = [ReflectLLMCall(scope=lc.scope, duration_ms=lc.duration_ms) for lc in core_result.llm_trace]
-                trace_result = ReflectTrace(tool_calls=tool_calls, llm_calls=llm_calls)
+                # Build map of directive observations by id
+                directive_observations = {d.id: d.rules for d in core_result.directives_applied}
+                # Include all mental models (including directives with subtype='directive')
+                trace_mental_models = [
+                    ReflectMentalModel(
+                        id=mm.id,
+                        name=mm.name,
+                        type=mm.type,
+                        subtype=mm.subtype,
+                        observations=directive_observations.get(mm.id) if mm.subtype == "directive" else None,
+                    )
+                    for mm in core_result.mental_models
+                ]
+                trace_result = ReflectTrace(
+                    tool_calls=tool_calls,
+                    llm_calls=llm_calls,
+                    mental_models=trace_mental_models,
+                )
 
             # Build mental_models_created from tool trace (learn tool outputs)
             created_models: list[CreatedMentalModel] = []
@@ -2076,7 +2227,46 @@ def _register_routes(app: FastAPI):
                 tags_match=tags_match,
                 request_context=request_context,
             )
-            return MentalModelListResponse(items=[MentalModelResponse(**m) for m in models])
+
+            # Add freshness to each model (skip for directives)
+            # Get data needed for freshness computation (once for all models)
+            from hindsight_api.engine.reflect.mental_model_reflect import (
+                BankProfile,
+                DirectiveMentalModel,
+                check_needs_refresh,
+            )
+
+            total_memories = await app.state.memory._count_memories_since(bank_id, None)
+            bank_profile_dict = await app.state.memory.get_bank_profile(bank_id, request_context=request_context)
+
+            # Convert to typed models at the boundary
+            bank_profile = BankProfile.model_validate(bank_profile_dict)
+            directives = [DirectiveMentalModel.model_validate(m) for m in models if m.get("subtype") == "directive"]
+
+            for model in models:
+                if model.get("subtype") != "directive":
+                    last_refresh_at = model.get("last_refresh_at")
+                    memories_since = await app.state.memory._count_memories_since(bank_id, last_refresh_at)
+
+                    # Use check_needs_refresh to get reasons
+                    stored_refresh_state = model.get("refresh_state")
+                    refresh_check = check_needs_refresh(
+                        stored_state=stored_refresh_state,
+                        current_memories_count=total_memories,
+                        bank_profile=bank_profile,
+                        directives=directives,
+                    )
+
+                    model["freshness"] = {
+                        "is_up_to_date": not refresh_check.needs_refresh,
+                        "last_refresh_at": last_refresh_at,
+                        "memories_since_refresh": memories_since,
+                        "reasons": refresh_check.reasons,
+                    }
+                else:
+                    model["freshness"] = None
+
+            return MentalModelListResponse(items=[_prepare_mental_model_response(m) for m in models])
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -2090,7 +2280,11 @@ def _register_routes(app: FastAPI):
         "/v1/default/banks/{bank_id}/mental-models",
         response_model=MentalModelResponse,
         summary="Create mental model",
-        description="Create a pinned mental model. Pinned models are user-defined and persist across refreshes.",
+        description=(
+            "Create a mental model. Supports two subtypes:\n"
+            "- 'pinned' (default): User-defined topic, observations are LLM-generated on refresh\n"
+            "- 'directive': User-defined hard rules, observations are provided at creation and never regenerated"
+        ),
         operation_id="create_mental_model",
         tags=["Mental Models"],
     )
@@ -2099,16 +2293,23 @@ def _register_routes(app: FastAPI):
         body: CreateMentalModelRequest,
         request_context: RequestContext = Depends(get_request_context),
     ):
-        """Create a pinned mental model."""
+        """Create a mental model (pinned or directive)."""
         try:
+            # Convert observations to list of dicts if provided
+            observations_list = None
+            if body.observations:
+                observations_list = [{"title": obs.title, "content": obs.content} for obs in body.observations]
+
             model = await app.state.memory.create_mental_model(
                 bank_id=bank_id,
                 name=body.name,
                 description=body.description,
+                subtype=body.subtype,
+                observations=observations_list,
                 tags=body.tags,
                 request_context=request_context,
             )
-            return MentalModelResponse(**model)
+            return _prepare_mental_model_response(model)
         except ValueError as e:
             raise HTTPException(status_code=400, detail=str(e))
         except (AuthenticationError, HTTPException):
@@ -2142,7 +2343,47 @@ def _register_routes(app: FastAPI):
             )
             if model is None:
                 raise HTTPException(status_code=404, detail=f"Mental model '{model_id}' not found")
-            return MentalModelResponse(**model)
+
+            # Compute freshness for non-directive models
+            if model.get("subtype") != "directive":
+                from hindsight_api.engine.reflect.mental_model_reflect import (
+                    BankProfile,
+                    DirectiveMentalModel,
+                    check_needs_refresh,
+                )
+
+                last_refresh_at = model.get("last_refresh_at")
+                total_memories = await app.state.memory._count_memories_since(bank_id, None)
+                memories_since = await app.state.memory._count_memories_since(bank_id, last_refresh_at)
+                bank_profile_dict = await app.state.memory.get_bank_profile(bank_id, request_context=request_context)
+                directives_dicts = await app.state.memory.list_mental_models(
+                    bank_id, subtype="directive", request_context=request_context
+                )
+
+                # Convert to typed models at the boundary
+                bank_profile = BankProfile.model_validate(bank_profile_dict)
+                directives = [DirectiveMentalModel.model_validate(d) for d in directives_dicts]
+
+                # Use check_needs_refresh to get reasons
+                stored_refresh_state = model.get("refresh_state")
+                refresh_check = check_needs_refresh(
+                    stored_state=stored_refresh_state,
+                    current_memories_count=total_memories,
+                    bank_profile=bank_profile,
+                    directives=directives,
+                )
+
+                model["freshness"] = {
+                    "is_up_to_date": not refresh_check.needs_refresh,
+                    "last_refresh_at": last_refresh_at,
+                    "memories_since_refresh": memories_since,
+                    "reasons": refresh_check.reasons,
+                }
+            else:
+                # Directives don't need freshness - they're static
+                model["freshness"] = None
+
+            return _prepare_mental_model_response(model)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -2227,23 +2468,61 @@ def _register_routes(app: FastAPI):
             logger.error(f"Error in DELETE /v1/default/banks/{bank_id}/mental-models/{model_id}: {error_detail}")
             raise HTTPException(status_code=500, detail=str(e))
 
-    @app.post(
-        "/v1/default/banks/{bank_id}/mental-models/{model_id}/generate",
-        response_model=AsyncOperationSubmitResponse,
-        summary="Generate mental model content (async)",
-        description="Submit a background job to generate/refresh content for a specific mental model. "
-        "This is useful for newly created learned models or to regenerate content for any model.",
-        operation_id="generate_mental_model",
+    @app.patch(
+        "/v1/default/banks/{bank_id}/mental-models/{model_id}",
+        response_model=MentalModelResponse,
+        summary="Update mental model",
+        description="Update a mental model's name and/or description. Useful for editing directives.",
+        operation_id="update_mental_model",
         tags=["Mental Models"],
     )
-    async def api_generate_mental_model(
+    async def api_update_mental_model(
+        bank_id: str,
+        model_id: str,
+        body: UpdateMentalModelRequest,
+        request_context: RequestContext = Depends(get_request_context),
+    ):
+        """Update a mental model's name and/or description."""
+        try:
+            if body.name is None and body.description is None:
+                raise HTTPException(status_code=400, detail="At least one of 'name' or 'description' must be provided")
+
+            updated = await app.state.memory.update_mental_model(
+                bank_id=bank_id,
+                model_id=model_id,
+                name=body.name,
+                description=body.description,
+                request_context=request_context,
+            )
+            if not updated:
+                raise HTTPException(status_code=404, detail=f"Mental model '{model_id}' not found")
+            return _prepare_mental_model_response(updated)
+        except (AuthenticationError, HTTPException):
+            raise
+        except Exception as e:
+            import traceback
+
+            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+            logger.error(f"Error in PATCH /v1/default/banks/{bank_id}/mental-models/{model_id}: {error_detail}")
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.post(
+        "/v1/default/banks/{bank_id}/mental-models/{model_id}/refresh",
+        response_model=AsyncOperationSubmitResponse,
+        summary="Refresh mental model content (async)",
+        description="Submit a background job to refresh content for a specific mental model. "
+        "This is useful for newly created learned models or to refresh content for any model.",
+        operation_id="refresh_mental_model",
+        tags=["Mental Models"],
+    )
+    async def api_refresh_mental_model(
         bank_id: str,
         model_id: str,
         request_context: RequestContext = Depends(get_request_context),
     ):
-        """Generate content for a specific mental model."""
+        """Refresh content for a specific mental model."""
         try:
-            result = await app.state.memory.generate_mental_model_async(
+            result = await app.state.memory.refresh_mental_model_async(
                 bank_id=bank_id,
                 model_id=model_id,
                 request_context=request_context,
@@ -2260,7 +2539,74 @@ def _register_routes(app: FastAPI):
             import traceback
 
             error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
-            logger.error(f"Error in POST /v1/default/banks/{bank_id}/mental-models/{model_id}/generate: {error_detail}")
+            logger.error(f"Error in POST /v1/default/banks/{bank_id}/mental-models/{model_id}/refresh: {error_detail}")
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.get(
+        "/v1/default/banks/{bank_id}/mental-models/{model_id}/versions",
+        summary="List mental model version history",
+        description="List all saved versions of a mental model's observations, ordered by version descending.",
+        operation_id="list_mental_model_versions",
+        tags=["Mental Models"],
+    )
+    async def api_list_mental_model_versions(
+        bank_id: str,
+        model_id: str,
+        request_context: RequestContext = Depends(get_request_context),
+    ):
+        """List version history for a mental model."""
+        try:
+            versions = await app.state.memory.get_mental_model_versions(
+                bank_id=bank_id,
+                model_id=model_id,
+                request_context=request_context,
+            )
+            return {"versions": versions}
+        except (AuthenticationError, HTTPException):
+            raise
+        except Exception as e:
+            import traceback
+
+            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+            logger.error(f"Error in GET /v1/default/banks/{bank_id}/mental-models/{model_id}/versions: {error_detail}")
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.get(
+        "/v1/default/banks/{bank_id}/mental-models/{model_id}/versions/{version}",
+        summary="Get specific mental model version",
+        description="Get observations from a specific version of a mental model.",
+        operation_id="get_mental_model_version",
+        tags=["Mental Models"],
+    )
+    async def api_get_mental_model_version(
+        bank_id: str,
+        model_id: str,
+        version: int,
+        request_context: RequestContext = Depends(get_request_context),
+    ):
+        """Get a specific version of a mental model."""
+        try:
+            version_data = await app.state.memory.get_mental_model_version(
+                bank_id=bank_id,
+                model_id=model_id,
+                version=version,
+                request_context=request_context,
+            )
+            if not version_data:
+                raise HTTPException(
+                    status_code=404,
+                    detail=f"Version {version} not found for mental model '{model_id}'",
+                )
+            return version_data
+        except (AuthenticationError, HTTPException):
+            raise
+        except Exception as e:
+            import traceback
+
+            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+            logger.error(
+                f"Error in GET /v1/default/banks/{bank_id}/mental-models/{model_id}/versions/{version}: {error_detail}"
+            )
             raise HTTPException(status_code=500, detail=str(e))
 
     @app.get(

hindsight-api/hindsight_api/engine/mental_models/models.py

@@ -14,7 +14,8 @@ class MentalModelSubtype(str, Enum):
     STRUCTURAL = "structural"  # Derived from mission, created upfront
     EMERGENT = "emergent"  # Discovered from data patterns
     LEARNED = "learned"  # Formed through reflection
-    PINNED = "pinned"  # User-defined, persists across refreshes
+    PINNED = "pinned"  # User-defined topic, observations LLM-generated
+    DIRECTIVE = "directive"  # User-defined hard rules, observations user-provided
 
 
 class MentalModel(BaseModel):

hindsight-api/hindsight_api/engine/reflect/agent.py

@@ -6,12 +6,37 @@ import asyncio
 import json
 import logging
 import time
-from typing import TYPE_CHECKING, Any, Awaitable, Callable, Literal
+from typing import TYPE_CHECKING, Any, Awaitable, Callable
 
-from .models import LLMCall, MentalModelInput, Observation, ReflectAgentResult, ToolCall
-from .prompts import FINAL_SYSTEM_PROMPT, build_final_prompt, build_system_prompt_for_tools
+from .models import DirectiveInfo, LLMCall, MentalModelInput, ReflectAgentResult, ToolCall
+from .prompts import FINAL_SYSTEM_PROMPT, _extract_directive_rules, build_final_prompt, build_system_prompt_for_tools
 from .tools_schema import get_reflect_tools
 
+
+def _build_directives_applied(directives: list[dict[str, Any]] | None) -> list[DirectiveInfo]:
+    """Build list of DirectiveInfo from directive mental models."""
+    if not directives:
+        return []
+
+    result = []
+    for directive in directives:
+        directive_id = directive.get("id", "")
+        directive_name = directive.get("name", "")
+        observations = directive.get("observations", [])
+
+        rules = []
+        for obs in observations:
+            # Support both Pydantic Observation objects and dicts
+            if hasattr(obs, "content"):
+                rules.append(obs.content)
+            elif isinstance(obs, dict) and obs.get("content"):
+                rules.append(obs["content"])
+
+        result.append(DirectiveInfo(id=directive_id, name=directive_name, rules=rules))
+
+    return result
+
+
 if TYPE_CHECKING:
     from ..llm_wrapper import LLMProvider
     from ..response_models import LLMToolCall
@@ -136,7 +161,7 @@ async def run_reflect_agent(
     max_iterations: int = DEFAULT_MAX_ITERATIONS,
     max_tokens: int | None = None,
     response_schema: dict | None = None,
-    output_mode: Literal["answer", "observations"] = "answer",
+    directives: list[dict[str, Any]] | None = None,
 ) -> ReflectAgentResult:
     """
     Execute the reflect agent loop using native tool calling.
@@ -158,7 +183,7 @@ async def run_reflect_agent(
         max_iterations: Maximum number of iterations before forcing response
         max_tokens: Maximum tokens for the final response
         response_schema: Optional JSON Schema for structured output in final response
-        output_mode: "answer" returns final text, "observations" returns structured observations
+        directives: Optional list of directive mental models to inject as hard rules
 
     Returns:
         ReflectAgentResult with final answer and metadata
@@ -167,11 +192,17 @@ async def run_reflect_agent(
     reflect_id = f"{bank_id[:8]}-{int(time.time() * 1000) % 100000}"
     start_time = time.time()
 
-    # Get tools for this agent
-    tools = get_reflect_tools(enable_learn=enable_learn, output_mode=output_mode)
+    # Build directives_applied for the trace
+    directives_applied = _build_directives_applied(directives)
 
-    # Build initial messages
-    system_prompt = build_system_prompt_for_tools(bank_profile, context, output_mode=output_mode)
+    # Extract directive rules for tool schema (if any)
+    directive_rules = _extract_directive_rules(directives) if directives else None
+
+    # Get tools for this agent (with directive compliance field if directives exist)
+    tools = get_reflect_tools(enable_learn=enable_learn, directive_rules=directive_rules)
+
+    # Build initial messages (directives are injected into system prompt at START and END)
+    system_prompt = build_system_prompt_for_tools(bank_profile, context, directives=directives)
     messages: list[dict[str, Any]] = [
         {"role": "system", "content": system_prompt},
         {"role": "user", "content": query},
@@ -189,44 +220,43 @@ async def run_reflect_agent(
     available_memory_ids: set[str] = set()
     available_model_ids: set[str] = set()
 
-    # In answer mode, pre-fetch mental models so the agent always starts with this knowledge
-    if output_mode == "answer":
-        prefetch_start = time.time()
-        models_result = await lookup_fn(None)  # List all mental models
-        prefetch_duration = int((time.time() - prefetch_start) * 1000)
+    # Pre-fetch mental models so the agent always starts with this knowledge
+    prefetch_start = time.time()
+    models_result = await lookup_fn(None)  # List all mental models
+    prefetch_duration = int((time.time() - prefetch_start) * 1000)
 
-        # Track available model IDs
-        if isinstance(models_result, dict) and "models" in models_result:
-            for model in models_result["models"]:
-                if "id" in model:
-                    available_model_ids.add(model["id"])
+    # Track available model IDs
+    if isinstance(models_result, dict) and "models" in models_result:
+        for model in models_result["models"]:
+            if "id" in model:
+                available_model_ids.add(model["id"])
 
-        # Add to context history for the agent
-        context_history.append({"tool": "list_mental_models", "output": models_result})
+    # Add to context history for the agent
+    context_history.append({"tool": "list_mental_models", "output": models_result})
 
-        # Add to tool trace
-        tool_trace.append(
-            ToolCall(
-                tool="list_mental_models",
-                input={"tool": "list_mental_models"},
-                output=models_result,
-                duration_ms=prefetch_duration,
-                iteration=0,
-            )
+    # Add to tool trace
+    tool_trace.append(
+        ToolCall(
+            tool="list_mental_models",
+            input={"tool": "list_mental_models"},
+            output=models_result,
+            duration_ms=prefetch_duration,
+            iteration=0,
         )
-        tool_trace_summary.append(
-            {
-                "tool": "list_mental_models",
-                "input_summary": "(prefetch)",
-                "duration_ms": prefetch_duration,
-                "output_chars": len(json.dumps(models_result, default=str)),
-            }
-        )
-        total_tools_called += 1
+    )
+    tool_trace_summary.append(
+        {
+            "tool": "list_mental_models",
+            "input_summary": "(prefetch)",
+            "duration_ms": prefetch_duration,
+            "output_chars": len(json.dumps(models_result, default=str)),
+        }
+    )
+    total_tools_called += 1
 
-        # Include in the user message so the agent sees it
-        models_info = json.dumps(models_result, indent=2, default=str)
-        messages[1]["content"] = f"{query}\n\n## Available Mental Models (pre-fetched)\n```json\n{models_info}\n```"
+    # Include in the user message so the agent sees it
+    models_info = json.dumps(models_result, indent=2, default=str)
+    messages[1]["content"] = f"{query}\n\n## Available Mental Models (pre-fetched)\n```json\n{models_info}\n```"
 
     def _get_llm_trace() -> list[LLMCall]:
         return [LLMCall(scope=c["scope"], duration_ms=c["duration_ms"]) for c in llm_trace]
@@ -288,6 +318,7 @@ async def run_reflect_agent(
                 mental_models_created=mental_models_created,
                 tool_trace=tool_trace,
                 llm_trace=_get_llm_trace(),
+                directives_applied=directives_applied,
             )
 
         # Call LLM with tools
@@ -338,6 +369,7 @@ async def run_reflect_agent(
                 mental_models_created=mental_models_created,
                 tool_trace=tool_trace,
                 llm_trace=_get_llm_trace(),
+                directives_applied=directives_applied,
             )
 
         # No tool calls - LLM wants to respond with text
@@ -361,6 +393,7 @@ async def run_reflect_agent(
                     mental_models_created=mental_models_created,
                     tool_trace=tool_trace,
                     llm_trace=_get_llm_trace(),
+                    directives_applied=directives_applied,
                 )
             # Empty response, force final
             prompt = build_final_prompt(query, context_history, bank_profile, context)
@@ -390,10 +423,11 @@ async def run_reflect_agent(
                 mental_models_created=mental_models_created,
                 tool_trace=tool_trace,
                 llm_trace=_get_llm_trace(),
+                directives_applied=directives_applied,
             )
 
-        # Check for done tool call
-        done_call = next((tc for tc in result.tool_calls if tc.name == "done"), None)
+        # Check for done tool call (handle both 'done' and 'functions.done')
+        done_call = next((tc for tc in result.tool_calls if tc.name == "done" or tc.name == "functions.done"), None)
         if done_call:
             # Guardrail: Require evidence before done
             has_gathered_evidence = bool(available_memory_ids) or bool(available_model_ids)
@@ -421,7 +455,6 @@ async def run_reflect_agent(
             # Process done tool
             return await _process_done_tool(
                 done_call,
-                output_mode,
                 available_memory_ids,
                 available_model_ids,
                 iteration + 1,
@@ -431,12 +464,13 @@ async def run_reflect_agent(
                 _get_llm_trace(),
                 _log_completion,
                 reflect_id,
+                directives_applied=directives_applied,
                 llm_config=llm_config,
                 response_schema=response_schema,
             )
 
-        # Execute other tools in parallel
-        other_tools = [tc for tc in result.tool_calls if tc.name != "done"]
+        # Execute other tools in parallel (exclude done and functions.done)
+        other_tools = [tc for tc in result.tool_calls if tc.name not in ("done", "functions.done")]
         if other_tools:
             # Add assistant message with tool calls
             messages.append(
@@ -534,6 +568,7 @@ async def run_reflect_agent(
         mental_models_created=mental_models_created,
         tool_trace=tool_trace,
         llm_trace=_get_llm_trace(),
+        directives_applied=directives_applied,
     )
 
 
@@ -551,7 +586,6 @@ def _tool_call_to_dict(tc: "LLMToolCall") -> dict[str, Any]:
 
 async def _process_done_tool(
     done_call: "LLMToolCall",
-    output_mode: str,
     available_memory_ids: set[str],
     available_model_ids: set[str],
     iterations: int,
@@ -561,60 +595,13 @@ async def _process_done_tool(
     llm_trace: list[LLMCall],
     log_completion: Callable,
     reflect_id: str,
+    directives_applied: list[DirectiveInfo],
     llm_config: "LLMProvider | None" = None,
     response_schema: dict | None = None,
 ) -> ReflectAgentResult:
     """Process the done tool call and return the result."""
     args = done_call.arguments
 
-    if output_mode == "observations" and "observations" in args:
-        # Process observations - handle both list and nested {"observations": [...]} format
-        observations: list[Observation] = []
-        used_memory_ids: list[str] = []
-
-        obs_list = args["observations"]
-        # Handle nested format where LLM outputs {"observations": [...]} instead of just [...]
-        if isinstance(obs_list, dict) and "observations" in obs_list:
-            obs_list = obs_list["observations"]
-
-        for obs_data in obs_list:
-            validated_mids = []
-            for mid in obs_data.get("memory_ids", []):
-                if mid in available_memory_ids:
-                    validated_mids.append(mid)
-                    if mid not in used_memory_ids:
-                        used_memory_ids.append(mid)
-
-            observations.append(
-                Observation(
-                    title=obs_data.get("title", ""),
-                    text=obs_data.get("text", ""),
-                    memory_ids=validated_mids,
-                )
-            )
-
-        # Build text from observations
-        text_parts = []
-        for obs in observations:
-            if obs.title:
-                text_parts.append(f"## {obs.title}\n{obs.text}")
-            else:
-                text_parts.append(obs.text)
-        answer = "\n\n".join(text_parts)
-
-        log_completion(answer, iterations)
-        return ReflectAgentResult(
-            text=answer,
-            observations=observations,
-            iterations=iterations,
-            tools_called=total_tools_called,
-            mental_models_created=mental_models_created,
-            tool_trace=tool_trace,
-            llm_trace=llm_trace,
-            used_memory_ids=used_memory_ids,
-        )
-
-    # Default: answer mode
     answer = args.get("answer", "").strip()
     if not answer:
         answer = "No answer provided."
@@ -639,6 +626,7 @@ async def _process_done_tool(
         llm_trace=llm_trace,
         used_memory_ids=used_memory_ids,
         used_model_ids=used_model_ids,
+        directives_applied=directives_applied,
     )
 
 
@@ -665,6 +653,10 @@ async def _execute_tool(
     learn_fn: Callable[[MentalModelInput], Awaitable[dict[str, Any]]] | None = None,
 ) -> dict[str, Any]:
     """Execute a single tool by name."""
+    # Normalize tool name - some LLMs return 'functions.done' instead of 'done'
+    if tool_name.startswith("functions."):
+        tool_name = tool_name[len("functions.") :]
+
     if tool_name == "list_mental_models":
         return await lookup_fn(None)
 

hindsight-api/hindsight_api/engine/reflect/models.py

@@ -87,21 +87,18 @@ class LLMCall(BaseModel):
     duration_ms: int = Field(description="Execution time in milliseconds")
 
 
-class Observation(BaseModel):
-    """A single observation with supporting memories."""
+class DirectiveInfo(BaseModel):
+    """Information about a directive that was applied during reflect."""
 
-    title: str = Field(description="Observation title/header")
-    text: str = Field(description="Observation content")
-    memory_ids: list[str] = Field(default_factory=list, description="Memory IDs supporting this observation")
+    id: str = Field(description="Directive mental model ID")
+    name: str = Field(description="Directive name")
+    rules: list[str] = Field(default_factory=list, description="Directive rules/observations that were applied")
 
 
 class ReflectAgentResult(BaseModel):
     """Result from the reflect agent."""
 
     text: str = Field(description="Final answer text")
-    observations: list[Observation] = Field(
-        default_factory=list, description="Structured observations (when output_mode=observations)"
-    )
     structured_output: dict[str, Any] | None = Field(
         default=None, description="Structured output parsed according to provided response_schema"
     )
@@ -112,3 +109,6 @@ class ReflectAgentResult(BaseModel):
     llm_trace: list[LLMCall] = Field(default_factory=list, description="Trace of all LLM calls made")
     used_memory_ids: list[str] = Field(default_factory=list, description="Validated memory IDs actually used in answer")
     used_model_ids: list[str] = Field(default_factory=list, description="Validated model IDs actually used in answer")
+    directives_applied: list[DirectiveInfo] = Field(
+        default_factory=list, description="Directive mental models that affected this reflection"
+    )

hindsight-api/hindsight_api/engine/reflect/observations.py

@@ -0,0 +1,248 @@
+"""
+Models and utilities for evidence-grounded observations with computed trends.
+
+Observations are part of mental models and represent patterns/beliefs derived
+from memories. Each observation must be grounded in specific evidence (quotes)
+from memories, and trends are computed algorithmically from evidence timestamps.
+"""
+
+from datetime import datetime, timedelta, timezone
+from enum import Enum
+
+from pydantic import BaseModel, Field, computed_field, field_validator
+
+
+class Trend(str, Enum):
+    """Computed trend for an observation based on evidence timestamps.
+
+    Trends indicate how an observation's evidence is distributed over time:
+    - STABLE: Evidence spread across time, continues to present
+    - STRENGTHENING: More/denser evidence recently than before
+    - WEAKENING: Evidence mostly old, sparse recently
+    - NEW: All evidence within recent window
+    - STALE: No evidence in recent window (may no longer apply)
+    """
+
+    STABLE = "stable"
+    STRENGTHENING = "strengthening"
+    WEAKENING = "weakening"
+    NEW = "new"
+    STALE = "stale"
+
+
+class ObservationEvidence(BaseModel):
+    """A single piece of evidence supporting an observation.
+
+    Each evidence item must include an exact quote from the source memory
+    to ensure observations are grounded and verifiable.
+    """
+
+    memory_id: str = Field(description="ID of the memory unit this evidence comes from")
+    quote: str = Field(description="Exact quote from the memory supporting the observation")
+    relevance: str = Field(default="", description="Brief explanation of how this quote supports the observation")
+    timestamp: datetime = Field(description="When the source memory was created")
+
+    @field_validator("timestamp", mode="before")
+    @classmethod
+    def ensure_timezone_aware(cls, v: datetime | str | None) -> datetime:
+        """Ensure timestamp is always timezone-aware UTC."""
+        if v is None:
+            return datetime.now(timezone.utc)
+        if isinstance(v, str):
+            # Parse ISO format string, handling 'Z' suffix
+            v = datetime.fromisoformat(v.replace("Z", "+00:00"))
+        if isinstance(v, datetime):
+            if v.tzinfo is None:
+                return v.replace(tzinfo=timezone.utc)
+            return v
+        raise ValueError(f"Invalid timestamp type: {type(v)}")
+
+
+class Observation(BaseModel):
+    """A single observation within a mental model.
+
+    Observations represent patterns, preferences, beliefs, or other insights
+    derived from memories. Each observation must be grounded in evidence
+    with exact quotes from source memories.
+    """
+
+    title: str = Field(description="Short summary title for the observation (5-10 words)")
+    content: str = Field(description="The observation content - detailed explanation of what we believe to be true")
+    evidence: list[ObservationEvidence] = Field(default_factory=list, description="Supporting evidence with quotes")
+    created_at: datetime = Field(
+        default_factory=lambda: datetime.now(timezone.utc), description="When this observation was first created"
+    )
+
+    @field_validator("created_at", mode="before")
+    @classmethod
+    def ensure_created_at_timezone_aware(cls, v: datetime | str | None) -> datetime:
+        """Ensure created_at is always timezone-aware UTC."""
+        if v is None:
+            return datetime.now(timezone.utc)
+        if isinstance(v, str):
+            v = datetime.fromisoformat(v.replace("Z", "+00:00"))
+        if isinstance(v, datetime):
+            if v.tzinfo is None:
+                return v.replace(tzinfo=timezone.utc)
+            return v
+        raise ValueError(f"Invalid created_at type: {type(v)}")
+
+    @computed_field
+    @property
+    def trend(self) -> Trend:
+        """Compute trend from evidence timestamps."""
+        return compute_trend(self.evidence)
+
+    @computed_field
+    @property
+    def evidence_span(self) -> dict[str, str | None]:
+        """Get the time span covered by evidence."""
+        if not self.evidence:
+            return {"from": None, "to": None}
+        timestamps = [e.timestamp for e in self.evidence]
+        return {
+            "from": min(timestamps).isoformat(),
+            "to": max(timestamps).isoformat(),
+        }
+
+    @computed_field
+    @property
+    def evidence_count(self) -> int:
+        """Number of evidence items supporting this observation."""
+        return len(self.evidence)
+
+
+def compute_trend(
+    evidence: list[ObservationEvidence],
+    now: datetime | None = None,
+    recent_days: int = 30,
+    old_days: int = 90,
+) -> Trend:
+    """Compute the trend for an observation based on evidence timestamps.
+
+    The trend indicates how the evidence is distributed over time:
+    - STABLE: Evidence spread across time, continues to present
+    - STRENGTHENING: More evidence recently than historically
+    - WEAKENING: Evidence mostly old, sparse recently
+    - NEW: All evidence is recent (within recent_days)
+    - STALE: No evidence in recent window
+
+    Args:
+        evidence: List of evidence items with timestamps
+        now: Reference time for calculations (defaults to current UTC time)
+        recent_days: Number of days to consider "recent" (default 30)
+        old_days: Number of days to consider "old" (default 90)
+
+    Returns:
+        Computed Trend enum value
+    """
+    if now is None:
+        now = datetime.now(timezone.utc)
+
+    # Ensure now is timezone-aware
+    if now.tzinfo is None:
+        now = now.replace(tzinfo=timezone.utc)
+
+    if not evidence:
+        return Trend.STALE
+
+    recent_cutoff = now - timedelta(days=recent_days)
+    old_cutoff = now - timedelta(days=old_days)
+
+    # Normalize timestamps to UTC for comparison
+    def normalize_ts(ts: datetime) -> datetime:
+        if ts.tzinfo is None:
+            return ts.replace(tzinfo=timezone.utc)
+        return ts
+
+    recent = [e for e in evidence if normalize_ts(e.timestamp) > recent_cutoff]
+    old = [e for e in evidence if normalize_ts(e.timestamp) < old_cutoff]
+    middle = [e for e in evidence if old_cutoff <= normalize_ts(e.timestamp) <= recent_cutoff]
+
+    # No recent evidence = stale
+    if not recent:
+        return Trend.STALE
+
+    # All evidence is recent = new
+    if not old and not middle:
+        return Trend.NEW
+
+    # Compare density (evidence per day)
+    recent_density = len(recent) / recent_days if recent_days > 0 else 0
+    older_period = old_days - recent_days
+    older_density = (len(old) + len(middle)) / older_period if older_period > 0 else 0
+
+    # Avoid division by zero
+    if older_density == 0:
+        return Trend.NEW
+
+    ratio = recent_density / older_density
+
+    if ratio > 1.5:
+        return Trend.STRENGTHENING
+    elif ratio < 0.5:
+        return Trend.WEAKENING
+    else:
+        return Trend.STABLE
+
+
+class CandidateObservation(BaseModel):
+    """A candidate observation generated during the seed phase.
+
+    Candidates are preliminary observations that need evidence validation
+    before becoming full observations.
+    """
+
+    content: str = Field(description="The proposed observation content")
+    seed_memory_ids: list[str] = Field(default_factory=list, description="Memory IDs that inspired this candidate")
+
+
+class CandidateWithEvidence(BaseModel):
+    """A candidate observation with gathered supporting and contradicting evidence."""
+
+    candidate: CandidateObservation
+    supporting_memories: list[dict] = Field(default_factory=list, description="Memories that support this observation")
+    contradicting_memories: list[dict] = Field(
+        default_factory=list, description="Memories that contradict this observation"
+    )
+
+
+class MentalModelSnapshot(BaseModel):
+    """A versioned snapshot of a mental model's observations.
+
+    Used for tracking changes over time and enabling diff views.
+    """
+
+    version: int = Field(description="Version number (1-indexed)")
+    observations: list[Observation] = Field(default_factory=list, description="Observations at this version")
+    created_at: datetime = Field(
+        default_factory=lambda: datetime.now(timezone.utc), description="When this version was created"
+    )
+    reflect_summary: str | None = Field(default=None, description="Summary of changes in this version")
+
+
+def verify_evidence_quotes(
+    observation: Observation,
+    memories: dict[str, str],
+) -> tuple[bool, list[str]]:
+    """Verify that all evidence quotes exist in the referenced memories.
+
+    Args:
+        observation: The observation to verify
+        memories: Dict mapping memory_id to memory content
+
+    Returns:
+        Tuple of (is_valid, list of error messages)
+    """
+    errors = []
+
+    for evidence in observation.evidence:
+        memory_content = memories.get(evidence.memory_id)
+        if memory_content is None:
+            errors.append(f"Memory {evidence.memory_id} not found")
+            continue
+
+        if evidence.quote not in memory_content:
+            errors.append(f"Quote not found in memory {evidence.memory_id}: '{evidence.quote[:50]}...'")
+
+    return len(errors) == 0, errors

hindsight-api/hindsight_api/engine/reflect/prompts.py

@@ -6,10 +6,111 @@ import json
 from typing import Any
 
 
+def _extract_directive_rules(directives: list[dict[str, Any]]) -> list[str]:
+    """
+    Extract directive rules as a list of strings.
+
+    Args:
+        directives: List of directive mental models with observations
+
+    Returns:
+        List of directive rule strings
+    """
+    rules = []
+    for directive in directives:
+        directive_name = directive.get("name", "")
+        observations = directive.get("observations", [])
+        if observations:
+            for obs in observations:
+                # Support both Pydantic Observation objects and dicts
+                if hasattr(obs, "title"):
+                    title = obs.title
+                    content = obs.content
+                else:
+                    title = obs.get("title", "")
+                    content = obs.get("content", "")
+                if title and content:
+                    rules.append(f"**{title}**: {content}")
+                elif content:
+                    rules.append(content)
+        elif directive_name:
+            # Fallback to description if no observations
+            desc = directive.get("description", "")
+            if desc:
+                rules.append(f"**{directive_name}**: {desc}")
+    return rules
+
+
+def build_directives_section(directives: list[dict[str, Any]]) -> str:
+    """
+    Build the directives section for the system prompt.
+
+    Directives are hard rules that MUST be followed in all responses.
+
+    Args:
+        directives: List of directive mental models with observations
+    """
+    if not directives:
+        return ""
+
+    rules = _extract_directive_rules(directives)
+    if not rules:
+        return ""
+
+    parts = [
+        "## DIRECTIVES (MANDATORY)",
+        "These are hard rules you MUST follow in ALL responses:",
+        "",
+    ]
+
+    for rule in rules:
+        parts.append(f"- {rule}")
+
+    parts.extend(
+        [
+            "",
+            "NEVER violate these directives, even if other context suggests otherwise.",
+            "IMPORTANT: Do NOT explain or justify how you handled directives in your answer. Just follow them silently.",
+            "",
+        ]
+    )
+    return "\n".join(parts)
+
+
+def build_directives_reminder(directives: list[dict[str, Any]]) -> str:
+    """
+    Build a reminder section for directives to place at the end of the prompt.
+
+    Args:
+        directives: List of directive mental models with observations
+    """
+    if not directives:
+        return ""
+
+    rules = _extract_directive_rules(directives)
+    if not rules:
+        return ""
+
+    parts = [
+        "",
+        "## REMINDER: MANDATORY DIRECTIVES",
+        "Before responding, ensure your answer complies with ALL of these directives:",
+        "",
+    ]
+
+    for i, rule in enumerate(rules, 1):
+        parts.append(f"{i}. {rule}")
+
+    parts.append("")
+    parts.append("Your response will be REJECTED if it violates any directive above.")
+    parts.append("Do NOT include any commentary about how you handled directives - just follow them.")
+    return "\n".join(parts)
+
+
 def build_system_prompt_for_tools(
     bank_profile: dict[str, Any],
     context: str | None = None,
-    output_mode: str = "answer",
+    directives: list[dict[str, Any]] | None = None,
 ) -> str:
     """
     Build the system prompt for tool-calling reflect agent.
@@ -19,128 +120,90 @@ def build_system_prompt_for_tools(
     Args:
         bank_profile: Bank profile with name and mission
         context: Optional additional context
-        output_mode: "answer" for plain text response, "observations" for structured observations
+        directives: Optional list of directive mental models to inject as hard rules
     """
     name = bank_profile.get("name", "Assistant")
     mission = bank_profile.get("mission", "")
 
-    # Build critical rules based on mode
-    if output_mode == "observations":
-        no_info_rule = "- Only say 'I don't have information' AFTER trying recall with no relevant results"
-    else:
-        no_info_rule = (
-            "- Only say 'I don't have information' AFTER trying list_mental_models AND recall with no relevant results"
-        )
+    no_info_rule = (
+        "- Only say 'I don't have information' AFTER trying list_mental_models AND recall with no relevant results"
+    )
 
-    parts = [
-        "You are a reflection agent that answers questions by reasoning over retrieved memories.",
-        "",
-        "## CRITICAL RULES",
-        "- You must NEVER fabricate information that has no basis in retrieved data",
-        "- You SHOULD synthesize, infer, and reason from the retrieved memories",
-        "- You MUST call recall() before saying you don't have information",
-        no_info_rule,
-        "",
-        "## How to Reason",
-        "- If memories mention someone did an activity, you can infer they likely enjoyed it",
-        "- Synthesize a coherent narrative from related memories",
-        "- Be a thoughtful interpreter, not just a literal repeater",
-        "- When the exact answer isn't stated, use what IS stated to give the best answer",
-        "",
-        "## Query Strategy (IMPORTANT)",
-        "recall() uses semantic search. NEVER just echo the user's question - decompose it into targeted searches:",
-        "",
-        "BAD: User asks 'recurring lesson themes between students' → recall('recurring lesson themes between students')",
-        "GOOD: Break it down into component searches:",
-        "  1. recall('lessons') - find all lesson-related memories",
-        "  2. recall('teaching sessions') - alternative phrasing",
-        "  3. recall('student progress') - find student-related memories",
-        "  4. recall('topics taught') - find subject matter",
-        "",
-        "Think: What ENTITIES and CONCEPTS does this question involve? Search for each separately.",
-        "- Questions about patterns → search for the individual instances first",
-        "- Questions comparing things → search for each thing separately",
-        "- Questions about relationships → search for each party involved",
-        "",
-        "## Workflow",
-    ]
+    parts = []
 
-    # Mode-specific workflow and output format
-    if output_mode == "observations":
-        # Observations mode: for mental model generation - no mental model lookup tools
-        parts.extend(
-            [
-                "1. DECOMPOSE the topic into component searches (see Query Strategy above)",
-                "   - Don't search for the topic name itself - search for related concepts",
-                "   - Example for 'Coffee preferences': search 'coffee', 'drinks', 'morning routine', 'caffeine'",
-                "2. Run multiple recall() calls with varied, targeted queries",
-                "3. IMPORTANT: Use expand(memory_ids, 'chunk') to verify memories before using them",
-                "   - Always verify the source chunk to confirm the memory is actually relevant",
-                "   - Don't assume a memory is relevant based on the summary alone",
-                "   - Only include memories you've verified via expand()",
-                "4. When ready, call done() with MULTIPLE structured observations",
-                "",
-                "## Output Format: MULTIPLE Structured Observations",
-                "",
-                "CRITICAL: You MUST create MULTIPLE separate observations in the array - one for each theme.",
-                "Do NOT put all content in a single observation!",
-                "",
-                "- Create 3-8 separate observations, each as its OWN item in the observations array",
-                "- Each observation covers ONE specific theme (preferences, history, relationships, etc.)",
-                "- Each observation has: title (short header), text (content), memory_ids (full UUIDs)",
-                "",
-                "Text format for each observation:",
-                "- Main insight or finding (no markdown headers)",
-                "- End with 'Key evidence:' section containing DIRECT QUOTES from memories in *italics*",
-                "- Quote the actual memory text, don't summarize - use *italics* for citations",
-                "",
-                "Example done() call with MULTIPLE observations:",
-                "```json",
-                "{",
-                '  "observations": [',
-                "    {",
-                '      "title": "Work Preferences",',
-                '      "text": "Prefers async communication and flexible schedules.\\n\\nKey evidence:\\n- *I prefer Slack over calls for most communication*\\n- *Flexible hours help me do my best work*",',
-                '      "memory_ids": ["abc123-full-uuid", "def456-full-uuid"]',
-                "    },",
-                "    {",
-                '      "title": "Technical Background",',
-                '      "text": "Has extensive ML experience spanning a decade.\\n\\nKey evidence:\\n- *I have 10 years of experience in machine learning*\\n- *Led the ML team at my previous company*",',
-                '      "memory_ids": ["ghi789-full-uuid"]',
-                "    }",
-                "  ]",
-                "}",
-                "```",
-            ]
-        )
-    else:
-        # Answer mode: include mental model lookup in workflow
-        parts.extend(
-            [
-                "1. Review the pre-fetched mental models for relevant synthesized knowledge",
-                "2. If relevant, call get_mental_model(model_id) for full observations",
-                "3. DECOMPOSE the question into component searches (see Query Strategy above)",
-                "   - Identify entities and concepts in the question",
-                "   - Search for each separately with targeted queries",
-                "4. Run multiple recall() calls - don't just echo the user's question",
-                "5. Use expand() if you need more context on specific memories",
-                "6. If you discover an important recurring topic worth tracking, use learn() to create a mental model",
-                "7. When ready, call done() with your answer and supporting memory_ids",
-                "",
-                "## When to Use learn()",
-                "Use learn() to create a new mental model when you discover:",
-                "- A person, project, or concept that appears frequently in memories",
-                "- An important topic the user seems to care about but has no mental model for",
-                "- A pattern or relationship worth synthesizing for future reference",
-                "Example: learn(name='Project Alpha', description='Track goals, status, and key decisions for Project Alpha')",
-                "",
-                "## Output Format: Plain Text Answer",
-                "Call done() with a plain text 'answer' field.",
-                "- Do NOT use markdown formatting",
-                "- NEVER include memory IDs, UUIDs, or 'Memory references' in the answer text",
-                "- Put memory IDs ONLY in the memory_ids array parameter, not in the answer",
-            ]
-        )
+    # Inject directives at the VERY START for maximum prominence
+    if directives:
+        parts.append(build_directives_section(directives))
+
+    parts.extend(
+        [
+            "You are a reflection agent that answers questions by reasoning over retrieved memories.",
+            "",
+        ]
+    )
+
+    parts.extend(
+        [
+            "## CRITICAL RULES",
+            "- You must NEVER fabricate information that has no basis in retrieved data",
+            "- You SHOULD synthesize, infer, and reason from the retrieved memories",
+            "- You MUST call recall() before saying you don't have information",
+            no_info_rule,
+            "",
+            "## How to Reason",
+            "- If memories mention someone did an activity, you can infer they likely enjoyed it",
+            "- Synthesize a coherent narrative from related memories",
+            "- Be a thoughtful interpreter, not just a literal repeater",
+            "- When the exact answer isn't stated, use what IS stated to give the best answer",
+            "",
+            "## Query Strategy (IMPORTANT)",
+            "recall() uses semantic search. NEVER just echo the user's question - decompose it into targeted searches:",
+            "",
+            "BAD: User asks 'recurring lesson themes between students' → recall('recurring lesson themes between students')",
+            "GOOD: Break it down into component searches:",
+            "  1. recall('lessons') - find all lesson-related memories",
+            "  2. recall('teaching sessions') - alternative phrasing",
+            "  3. recall('student progress') - find student-related memories",
+            "  4. recall('topics taught') - find subject matter",
+            "",
+            "Think: What ENTITIES and CONCEPTS does this question involve? Search for each separately.",
+            "- Questions about patterns → search for the individual instances first",
+            "- Questions comparing things → search for each thing separately",
+            "- Questions about relationships → search for each party involved",
+            "",
+            "## Workflow",
+        ]
+    )
+
+    # Answer mode: include mental model lookup in workflow
+    parts.extend(
+        [
+            "1. Review the pre-fetched mental models for relevant synthesized knowledge",
+            "2. If relevant, call get_mental_model(model_id) for full observations",
+            "3. DECOMPOSE the question into component searches (see Query Strategy above)",
+            "   - Identify entities and concepts in the question",
+            "   - Search for each separately with targeted queries",
+            "4. Run multiple recall() calls - don't just echo the user's question",
+            "5. Use expand() if you need more context on specific memories",
+            "6. BEFORE answering: Check if any person/project/concept from the memories deserves a mental model - use learn() if so",
+            "7. When ready, call done() with your answer and supporting memory_ids",
+            "",
+            "## When to Use learn() - IMPORTANT",
+            "ACTIVELY look for opportunities to use learn() when you discover:",
+            "- A person mentioned in 2+ memories who has no mental model yet",
+            "- A project or concept the user asks about that has no mental model",
+            "- A pattern or topic worth tracking for future questions",
+            "",
+            "DO NOT wait to be asked - proactively create models when you see the need.",
+            "Example: learn(name='Project Alpha', description='Track goals, status, and key decisions for Project Alpha')",
+            "",
+            "## Output Format: Plain Text Answer",
+            "Call done() with a plain text 'answer' field.",
+            "- Do NOT use markdown formatting",
+            "- NEVER include memory IDs, UUIDs, or 'Memory references' in the answer text",
+            "- Put memory IDs ONLY in the memory_ids array parameter, not in the answer",
+        ]
+    )
 
     parts.append("")
     parts.append(f"## Memory Bank: {name}")
@@ -164,6 +227,10 @@ def build_system_prompt_for_tools(
     if context:
         parts.append(f"\n## Additional Context\n{context}")
 
+    # Add directive reminder at the END for recency effect
+    if directives:
+        parts.append(build_directives_reminder(directives))
+
     return "\n".join(parts)
 
 
@@ -310,3 +377,386 @@ Your approach:
 
 Only say "I don't have information" if the retrieved data is truly unrelated to the question.
 Do NOT fabricate information that has no basis in the retrieved data."""
+
+
+# =============================================================================
+# 4-Phase Mental Model Reflect Prompts
+# =============================================================================
+
+SEED_PHASE_SYSTEM_PROMPT = """You are analyzing memories to discover NEW patterns and generate candidate observations.
+
+Your task is to identify potential observations (beliefs, preferences, patterns, behaviors) that could be part of a mental model about this person/topic.
+
+## Important: Avoid Redundancy
+If existing observations are provided, DO NOT generate candidates that are essentially the same.
+Focus on discovering NEW patterns not already covered by existing observations.
+
+## Rules
+- Generate 5-15 candidate observations for NEW patterns only
+- Each candidate should be specific and testable (can be supported or contradicted by evidence)
+- Note which memory IDs inspired each candidate (these are seeds, not final evidence)
+- Focus on patterns that appear MULTIPLE TIMES across many memories - the more the better
+- The best candidates are ones you can find 10, 20, or even 50+ supporting memories for
+- Skip patterns that are already covered by existing observations
+
+## Output Format
+Return a JSON array of candidate observations:
+```json
+{
+  "candidates": [
+    {
+      "content": "The specific observation/belief/pattern - be detailed and specific",
+      "seed_memory_ids": ["memory_id_1", "memory_id_2", "memory_id_3"]
+    }
+  ]
+}
+```
+
+Focus on patterns that appear multiple times or have strong signals. Don't generate obvious or trivial observations.
+Prefer candidates with MORE seed memories - they're more likely to be real patterns.
+Return an empty candidates array if no genuinely new patterns are found."""
+
+
+def build_seed_phase_prompt(
+    memories: list[dict],
+    topic: str | None = None,
+    existing_observations: list[dict] | None = None,
+) -> str:
+    """Build the user prompt for the seed phase.
+
+    Args:
+        memories: List of memories to analyze
+        topic: Optional topic focus for the mental model
+        existing_observations: Optional list of existing observations to avoid rediscovering
+    """
+    parts = []
+
+    if topic:
+        parts.append(f"## Topic Focus\n{topic}\n")
+
+    # Include existing observations so we don't rediscover them
+    if existing_observations:
+        parts.append("## Existing Observations (DO NOT regenerate these)")
+        parts.append("These patterns are already tracked. Focus on discovering NEW patterns:\n")
+        for i, obs in enumerate(existing_observations, 1):
+            title = obs.get("title", "")
+            content = obs.get("content", "")
+            parts.append(f"{i}. **{title}**: {content}\n")
+        parts.append("")
+
+    parts.append("## Memories to Analyze")
+    parts.append("Review these memories and identify patterns, preferences, beliefs, and behaviors:\n")
+
+    for mem in memories:
+        mem_id = mem.get("id", "unknown")
+        content = mem.get("content", mem.get("text", ""))
+        timestamp = mem.get("timestamp", mem.get("created_at", ""))
+        parts.append(f"[{mem_id}] ({timestamp}): {content}\n")
+
+    parts.append("\n## Instructions")
+    if existing_observations:
+        parts.append("Generate candidate observations for NEW patterns not already covered above.")
+        parts.append("If all patterns are already covered by existing observations, return an empty candidates array.")
+    else:
+        parts.append("Generate candidate observations based on patterns you see in these memories.")
+    parts.append("Look for: recurring themes, stated preferences, behavioral patterns, beliefs, values, goals.")
+
+    return "\n".join(parts)
+
+
+VALIDATE_PHASE_SYSTEM_PROMPT = """You are validating candidate observations against evidence.
+
+For each candidate, you have:
+- Supporting memories (evidence FOR the observation)
+- Contradicting memories (evidence AGAINST the observation)
+
+## Your Task
+1. Evaluate each candidate based on the evidence
+2. For valid candidates, extract EXACT QUOTES from supporting memories
+3. Discard candidates with insufficient or contradicting evidence
+4. Merge similar candidates into single, refined observations
+
+## Rules for Quotes
+- Quotes must be EXACT text from the memory, not paraphrased
+- Each quote should directly support the observation
+- The MORE evidence quotes, the BETTER - don't limit yourself, include ALL relevant quotes (10, 20, 50+)
+- Observations with only 1-2 quotes are weak and should be discarded unless the evidence is exceptionally strong
+- Stronger observations have more supporting evidence - aim for comprehensive coverage
+
+## Output Format
+Return validated observations with evidence:
+```json
+{
+  "observations": [
+    {
+      "title": "Short descriptive title (3-8 words) - like a headline",
+      "content": "The full observation content - detailed explanation of the pattern/belief",
+      "evidence": [
+        {
+          "memory_id": "exact_memory_id",
+          "quote": "Exact quote from the memory text",
+          "relevance": "Brief explanation of how this supports the observation",
+          "timestamp": "2024-01-15T10:00:00Z"
+        }
+      ]
+    }
+  ],
+  "discarded": [
+    {
+      "content": "The discarded candidate",
+      "reason": "Why it was discarded (insufficient evidence, contradicted, etc.)"
+    }
+  ],
+  "merged": [
+    {
+      "from": ["candidate 1 content", "candidate 2 content"],
+      "into": "The merged observation content"
+    }
+  ]
+}
+```
+
+## Title Guidelines
+- Title should be a SHORT label (like "Prefers morning meetings" or "Coffee enthusiast")
+- NOT a truncated version of the content
+- Think of it as a category/tag for the observation
+
+Be rigorous: only keep observations with clear, verifiable evidence from multiple memories."""
+
+
+def build_validate_phase_prompt(candidates_with_evidence: list[dict]) -> str:
+    """Build the user prompt for the validate phase."""
+    parts = ["## Candidates to Validate\n"]
+
+    for i, item in enumerate(candidates_with_evidence, 1):
+        candidate = item.get("candidate", {})
+        supporting = item.get("supporting_memories", [])
+        contradicting = item.get("contradicting_memories", [])
+
+        parts.append(f"### Candidate {i}: {candidate.get('content', '')}")
+
+        if supporting:
+            parts.append("\n**Supporting Evidence:**")
+            for mem in supporting:
+                mem_id = mem.get("id", "unknown")
+                content = mem.get("content", mem.get("text", ""))
+                timestamp = mem.get("timestamp", mem.get("created_at", ""))
+                parts.append(f"- [{mem_id}] ({timestamp}): {content}")
+
+        if contradicting:
+            parts.append("\n**Contradicting Evidence:**")
+            for mem in contradicting:
+                mem_id = mem.get("id", "unknown")
+                content = mem.get("content", mem.get("text", ""))
+                timestamp = mem.get("timestamp", mem.get("created_at", ""))
+                parts.append(f"- [{mem_id}] ({timestamp}): {content}")
+
+        if not supporting and not contradicting:
+            parts.append("\n*No additional evidence found*")
+
+        parts.append("")
+
+    parts.append("## Instructions")
+    parts.append("1. Evaluate each candidate based on its evidence")
+    parts.append("2. Keep candidates with strong supporting evidence")
+    parts.append("3. Discard candidates with no evidence or strong contradictions")
+    parts.append("4. Merge similar candidates")
+    parts.append("5. Extract EXACT quotes (copy-paste from memory text) for evidence")
+
+    return "\n".join(parts)
+
+
+COMPARE_PHASE_SYSTEM_PROMPT = """You are merging new observations with an existing mental model.
+
+You have:
+- EXISTING observations (from the current mental model)
+- NEW observations (from this reflect cycle)
+
+## Your Task
+Produce the final, complete mental model by:
+1. Keeping existing observations that are still valid
+2. Updating existing observations with new evidence (ADD new evidence to existing)
+3. Adding new observations that don't overlap with existing
+4. Removing existing observations that are contradicted by new evidence
+5. Merging overlapping observations
+
+## Rules
+- The final model should have no contradictions
+- Each observation must have evidence with exact quotes
+- COMBINE evidence from both existing and new observations
+- If an existing observation has new supporting evidence, ADD ALL the new evidence to it
+- Include ALL relevant evidence - the more quotes the better (10, 20, 50+ is great)
+- Observations with more evidence are more reliable - don't limit the number of quotes
+
+## Output Format
+Return the complete, final mental model:
+```json
+{
+  "observations": [
+    {
+      "title": "Short descriptive title (3-8 words)",
+      "content": "Full observation content - detailed explanation",
+      "evidence": [
+        {
+          "memory_id": "id",
+          "quote": "exact quote",
+          "relevance": "explanation",
+          "timestamp": "ISO timestamp"
+        }
+      ],
+      "created_at": "ISO timestamp of when observation was first created"
+    }
+  ],
+  "changes": {
+    "kept": ["Observation that was kept unchanged"],
+    "updated": [{"from": "old content", "to": "new content", "reason": "why"}],
+    "added": ["New observation that was added"],
+    "removed": [{"content": "removed observation", "reason": "why removed"}],
+    "merged": [{"from": ["obs1", "obs2"], "into": "merged observation"}]
+  }
+}
+```"""
+
+
+def build_compare_phase_prompt(
+    existing_observations: list[dict],
+    new_observations: list[dict],
+) -> str:
+    """Build the user prompt for the compare phase."""
+    parts = []
+
+    parts.append("## Existing Mental Model Observations")
+    if existing_observations:
+        for i, obs in enumerate(existing_observations, 1):
+            title = obs.get("title", "")
+            content = obs.get("content", obs.get("text", ""))
+            evidence = obs.get("evidence", [])
+            parts.append(f"\n### Existing {i}: {title}")
+            parts.append(f"Content: {content}")
+            if evidence:
+                parts.append(f"Evidence ({len(evidence)} items):")
+                for ev in evidence[:5]:  # Show max 5 evidence items
+                    parts.append(f'  - [{ev.get("memory_id", "?")}]: "{ev.get("quote", "")}"')
+                if len(evidence) > 5:
+                    parts.append(f"  ... and {len(evidence) - 5} more")
+    else:
+        parts.append("*No existing observations*")
+
+    parts.append("\n## New Observations from This Reflect")
+    if new_observations:
+        for i, obs in enumerate(new_observations, 1):
+            title = obs.get("title", "")
+            content = obs.get("content", "")
+            evidence = obs.get("evidence", [])
+            parts.append(f"\n### New {i}: {title}")
+            parts.append(f"Content: {content}")
+            if evidence:
+                parts.append(f"Evidence ({len(evidence)} items):")
+                for ev in evidence:
+                    parts.append(f'  - [{ev.get("memory_id", "?")}]: "{ev.get("quote", "")}"')
+    else:
+        parts.append("*No new observations*")
+
+    parts.append("\n## Instructions")
+    parts.append("Merge these into a coherent, non-contradictory mental model.")
+    parts.append("Preserve all valid evidence. Remove stale or contradicted observations.")
+
+    return "\n".join(parts)
+
+
+# =============================================================================
+# UPDATE EXISTING Phase Prompts (for diff-based refresh)
+# =============================================================================
+
+UPDATE_EXISTING_SYSTEM_PROMPT = """You are updating existing observations with newly found evidence.
+
+For each existing observation, you have been given:
+- The original observation (title, content, existing evidence)
+- Newly found supporting memories
+- Newly found contradicting memories
+
+## Your Task
+1. Extract EXACT QUOTES from new supporting memories to add to the observation
+2. Flag observations with strong contradicting evidence for potential removal
+3. Keep existing evidence intact - only ADD new evidence
+
+## Rules for Quotes
+- Quotes must be EXACT text from the memory, not paraphrased
+- Each quote should directly support the observation
+- Include ALL relevant quotes from the new memories
+
+## Output Format
+Return updated observations with new evidence:
+```json
+{
+  "updated_observations": [
+    {
+      "title": "Original title",
+      "content": "Original content",
+      "existing_evidence_count": 5,
+      "new_evidence": [
+        {
+          "memory_id": "exact_memory_id",
+          "quote": "Exact quote from the memory text",
+          "relevance": "Brief explanation of how this supports the observation",
+          "timestamp": "2024-01-15T10:00:00Z"
+        }
+      ],
+      "has_contradiction": false,
+      "contradiction_note": null
+    }
+  ]
+}
+```
+
+If an observation has strong contradicting evidence, set has_contradiction=true and explain in contradiction_note."""
+
+
+def build_update_existing_prompt(observations_with_evidence: list[dict]) -> str:
+    """Build the user prompt for the update existing phase.
+
+    Args:
+        observations_with_evidence: List of existing observations with new evidence found
+    """
+    parts = ["## Existing Observations to Update\n"]
+
+    for i, item in enumerate(observations_with_evidence, 1):
+        obs = item.get("observation", {})
+        supporting = item.get("supporting_memories", [])
+        contradicting = item.get("contradicting_memories", [])
+
+        title = obs.get("title", "")
+        content = obs.get("content", "")
+        existing_evidence = obs.get("evidence", [])
+
+        parts.append(f"### Observation {i}: {title}")
+        parts.append(f"Content: {content}")
+        parts.append(f"Existing evidence count: {len(existing_evidence)}")
+
+        if supporting:
+            parts.append("\n**New Supporting Memories:**")
+            for mem in supporting:
+                mem_id = mem.get("id", "unknown")
+                mem_content = mem.get("content", mem.get("text", ""))
+                timestamp = mem.get("timestamp", mem.get("created_at", ""))
+                parts.append(f"- [{mem_id}] ({timestamp}): {mem_content}")
+
+        if contradicting:
+            parts.append("\n**New Contradicting Memories:**")
+            for mem in contradicting:
+                mem_id = mem.get("id", "unknown")
+                mem_content = mem.get("content", mem.get("text", ""))
+                timestamp = mem.get("timestamp", mem.get("created_at", ""))
+                parts.append(f"- [{mem_id}] ({timestamp}): {mem_content}")
+
+        if not supporting and not contradicting:
+            parts.append("\n*No new evidence found*")
+
+        parts.append("")
+
+    parts.append("## Instructions")
+    parts.append("1. Extract EXACT quotes from new supporting memories")
+    parts.append("2. Flag observations with strong contradictions")
+    parts.append("3. Return the updated observations with new evidence added")
+
+    return "\n".join(parts)

hindsight-api/hindsight_api/engine/reflect/tools.py

@@ -5,9 +5,11 @@ Tool implementations for the reflect agent.
 import logging
 import re
 import uuid
+from datetime import datetime, timezone
 from typing import TYPE_CHECKING, Any
 
 from .models import MentalModelInput
+from .observations import Observation, ObservationEvidence, Trend
 
 if TYPE_CHECKING:
     from asyncpg import Connection
@@ -26,6 +28,37 @@ def generate_model_id(name: str) -> str:
     return normalized[:50]
 
 
+def _parse_observations(observations_raw: list) -> list[Observation]:
+    """Parse raw observation dicts into typed Observation models."""
+    observations: list[Observation] = []
+    for obs in observations_raw:
+        if not isinstance(obs, dict):
+            continue
+
+        try:
+            parsed = Observation(
+                title=obs.get("title", ""),
+                content=obs.get("content", ""),
+                evidence=[
+                    ObservationEvidence(
+                        memory_id=ev.get("memory_id", ""),
+                        quote=ev.get("quote", ""),
+                        relevance=ev.get("relevance", ""),
+                        timestamp=ev.get("timestamp"),
+                    )
+                    for ev in obs.get("evidence", [])
+                    if isinstance(ev, dict)
+                ],
+                created_at=obs.get("created_at"),
+            )
+            observations.append(parsed)
+        except Exception as e:
+            logger.warning(f"Failed to parse observation: {e}")
+            continue
+
+    return observations
+
+
 async def tool_lookup(
     conn: "Connection",
     bank_id: str,
@@ -66,18 +99,8 @@ async def tool_lookup(
                 obs_data = json.loads(obs_data)
             observations_raw = obs_data.get("observations", []) if isinstance(obs_data, dict) else obs_data
 
-            # Normalize observation format: map memory_ids/fact_ids to based_on
-            observations = []
-            for obs in observations_raw:
-                if isinstance(obs, dict):
-                    based_on = obs.get("memory_ids") or obs.get("fact_ids") or []
-                    observations.append(
-                        {
-                            "title": obs.get("title", ""),
-                            "text": obs.get("text", ""),
-                            "based_on": based_on,
-                        }
-                    )
+            # Parse observations into typed models
+            observations = _parse_observations(observations_raw)
 
             return {
                 "found": True,
@@ -86,7 +109,7 @@ async def tool_lookup(
                     "subtype": row["subtype"],
                     "name": row["name"],
                     "description": row["description"],
-                    "observations": observations,  # [{title, text, based_on}, ...]
+                    "observations": observations,
                     "entity_id": str(row["entity_id"]) if row["entity_id"] else None,
                     "last_updated": row["last_updated"].isoformat() if row["last_updated"] else None,
                 },
@@ -95,6 +118,8 @@ async def tool_lookup(
     else:
         # List mental models (compact: id, name, description only)
         # Full observations are retrieved via get_mental_model(model_id)
+        # NOTE: Directives (subtype='directive') are excluded from listing -
+        # they are injected into the system prompt, not discoverable via tools
         # Filter by tags if provided
         if tags:
             if tags_match == "all":
@@ -103,7 +128,7 @@ async def tool_lookup(
                     """
                     SELECT id, subtype, name, description
                     FROM mental_models
-                    WHERE bank_id = $1 AND tags @> $2::varchar[]
+                    WHERE bank_id = $1 AND tags @> $2::varchar[] AND subtype != 'directive'
                     ORDER BY last_updated DESC NULLS LAST, created_at DESC
                     """,
                     bank_id,
@@ -115,7 +140,7 @@ async def tool_lookup(
                     """
                     SELECT id, subtype, name, description
                     FROM mental_models
-                    WHERE bank_id = $1 AND tags && $2::varchar[]
+                    WHERE bank_id = $1 AND tags && $2::varchar[] AND subtype != 'directive'
                     ORDER BY last_updated DESC NULLS LAST, created_at DESC
                     """,
                     bank_id,
@@ -126,7 +151,7 @@ async def tool_lookup(
                 """
                 SELECT id, subtype, name, description
                 FROM mental_models
-                WHERE bank_id = $1
+                WHERE bank_id = $1 AND subtype != 'directive'
                 ORDER BY last_updated DESC NULLS LAST, created_at DESC
                 """,
                 bank_id,

hindsight-api/hindsight_api/engine/reflect/tools_schema.py

@@ -4,8 +4,6 @@ Tool schema definitions for the reflect agent.
 These are OpenAI-format tool definitions used with native tool calling.
 """
 
-from typing import Literal
-
 # Tool definitions in OpenAI format
 TOOL_LIST_MENTAL_MODELS = {
     "type": "function",
@@ -134,68 +132,76 @@ TOOL_DONE_ANSWER = {
     },
 }
 
-TOOL_DONE_OBSERVATIONS = {
-    "type": "function",
-    "function": {
-        "name": "done",
-        "description": "Signal completion with MULTIPLE structured observations. Each observation must be a SEPARATE item in the array covering ONE theme. Do NOT combine all content into a single observation.",
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "observations": {
-                    "type": "array",
-                    "minItems": 3,
-                    "items": {
-                        "type": "object",
-                        "properties": {
-                            "title": {
-                                "type": "string",
-                                "description": "Short header for this observation's theme (e.g., 'Work Style', 'Technical Skills')",
-                            },
-                            "text": {
-                                "type": "string",
-                                "description": "Observation content about ONE theme. End with 'Key evidence:' containing text citations (summaries of what memories say), NOT memory IDs.",
-                            },
-                            "memory_ids": {
-                                "type": "array",
-                                "items": {"type": "string"},
-                                "description": "Full UUIDs of memories supporting this observation (put IDs here, not in text)",
-                            },
-                        },
-                        "required": ["title", "text", "memory_ids"],
+
+def _build_done_tool_with_directives(directive_rules: list[str]) -> dict:
+    """
+    Build the done tool schema with directive compliance field.
+
+    When directives are present, adds a required field that forces the agent
+    to confirm compliance with each directive before submitting.
+
+    Args:
+        directive_rules: List of directive rule strings
+    """
+    from typing import Any, cast
+
+    # Build rules list for description
+    rules_list = "\n".join(f"  {i + 1}. {rule}" for i, rule in enumerate(directive_rules))
+
+    # Build the tool with directive compliance field
+    return {
+        "type": "function",
+        "function": {
+            "name": "done",
+            "description": (
+                "Signal completion with your final answer. IMPORTANT: You must confirm directive compliance before submitting. "
+                "Your answer will be REJECTED if it violates any directive."
+            ),
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "answer": {
+                        "type": "string",
+                        "description": "Your response as plain text. Do NOT use markdown formatting. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array.",
+                    },
+                    "memory_ids": {
+                        "type": "array",
+                        "items": {"type": "string"},
+                        "description": "Array of memory IDs that support your answer (put IDs here, NOT in answer text)",
+                    },
+                    "model_ids": {
+                        "type": "array",
+                        "items": {"type": "string"},
+                        "description": "Array of mental model IDs that support your answer",
+                    },
+                    "directive_compliance": {
+                        "type": "string",
+                        "description": f"REQUIRED: Confirm your answer complies with ALL directives. List each directive and how your answer follows it:\n{rules_list}\n\nFormat: 'Directive 1: [how answer complies]. Directive 2: [how answer complies]...'",
                     },
-                    "description": "Array of 3-8 observations, each covering a DIFFERENT aspect/theme. Do NOT put everything in one observation.",
                 },
+                "required": ["answer", "directive_compliance"],
             },
-            "required": ["observations"],
         },
-    },
-}
+    }
 
 
-def get_reflect_tools(
-    enable_learn: bool = True, output_mode: Literal["answer", "observations"] = "answer"
-) -> list[dict]:
+def get_reflect_tools(enable_learn: bool = True, directive_rules: list[str] | None = None) -> list[dict]:
     """
     Get the list of tools for the reflect agent.
 
     Args:
         enable_learn: Whether to include the learn tool
-        output_mode: "answer" or "observations" - determines done tool format
-                     In observations mode, mental model tools are excluded to avoid
-                     using potentially outdated models during regeneration.
+        directive_rules: Optional list of directive rule strings. If provided,
+                        the done() tool will require directive compliance confirmation.
 
     Returns:
         List of tool definitions in OpenAI format
     """
     tools = []
 
-    # In answer mode, include mental model tools for lookup
-    # In observations mode (mental model generation), exclude them to avoid circular references
-    if output_mode == "answer":
-        tools.append(TOOL_LIST_MENTAL_MODELS)
-        tools.append(TOOL_GET_MENTAL_MODEL)
-
+    # Include mental model tools for lookup
+    tools.append(TOOL_LIST_MENTAL_MODELS)
+    tools.append(TOOL_GET_MENTAL_MODEL)
     tools.append(TOOL_RECALL)
 
     if enable_learn:
@@ -203,9 +209,9 @@ def get_reflect_tools(
 
     tools.append(TOOL_EXPAND)
 
-    # Add appropriate done tool based on output mode
-    if output_mode == "observations":
-        tools.append(TOOL_DONE_OBSERVATIONS)
+    # Use directive-aware done tool if directives are present
+    if directive_rules:
+        tools.append(_build_done_tool_with_directives(directive_rules))
     else:
         tools.append(TOOL_DONE_ANSWER)
 

hindsight-api/hindsight_api/engine/response_models.py

@@ -58,6 +58,14 @@ class MentalModelRef(BaseModel):
     summary: str | None = Field(default=None, description="Full summary (when looked up in detail)")
 
 
+class DirectiveRef(BaseModel):
+    """Reference to a directive that was applied during reflect."""
+
+    id: str = Field(description="Directive mental model ID")
+    name: str = Field(description="Directive name")
+    rules: list[str] = Field(default_factory=list, description="Directive rules/observations that were applied")
+
+
 class TokenUsage(BaseModel):
     """
     Token usage metrics for LLM calls.
@@ -252,7 +260,11 @@ class ReflectResult(BaseModel):
     )
     mental_models: list[MentalModelRef] = Field(
         default_factory=list,
-        description="Mental models accessed during reflection. Only present when include.facts is enabled.",
+        description="Mental models accessed during reflection, including directives (subtype='directive').",
+    )
+    directives_applied: list[DirectiveRef] = Field(
+        default_factory=list,
+        description="Directive mental models that were applied during this reflection.",
     )
 
 

hindsight-api/hindsight_api/extensions/__init__.py

@@ -27,6 +27,8 @@ from hindsight_api.extensions.operation_validator import (
     RecallResult,
     ReflectContext,
     ReflectResultContext,
+    RefreshMentalModelContext,
+    RefreshMentalModelResult,
     RetainContext,
     RetainResult,
     ValidationResult,
@@ -54,6 +56,8 @@ __all__ = [
     "RecallResult",
     "ReflectContext",
     "ReflectResultContext",
+    "RefreshMentalModelContext",
+    "RefreshMentalModelResult",
     "RetainContext",
     "RetainResult",
     "ValidationResult",

hindsight-api/hindsight_api/extensions/operation_validator.py

@@ -97,6 +97,18 @@ class ReflectContext:
     context: str | None = None
 
 
+@dataclass
+class RefreshMentalModelContext:
+    """Context for a refresh mental model operation validation (pre-operation).
+
+    Contains ALL user-provided parameters for the refresh mental model operation.
+    """
+
+    bank_id: str
+    model_id: str
+    request_context: "RequestContext"
+
+
 # =============================================================================
 # Post-operation Contexts (includes results)
 # =============================================================================
@@ -164,6 +176,27 @@ class ReflectResultContext:
     error: str | None = None
 
 
+@dataclass
+class RefreshMentalModelResult:
+    """Result context for post-refresh-mental-model hook.
+
+    Contains the operation parameters and the result including token usage.
+    """
+
+    bank_id: str
+    model_id: str
+    request_context: "RequestContext"
+    # Result
+    model_name: str | None = None
+    observations_count: int = 0
+    input_tokens: int = 0
+    output_tokens: int = 0
+    total_tokens: int = 0
+    duration_ms: int = 0
+    success: bool = True
+    error: str | None = None
+
+
 class OperationValidatorExtension(Extension, ABC):
     """
     Validates and hooks into retain/recall/reflect operations.
@@ -265,6 +298,25 @@ class OperationValidatorExtension(Extension, ABC):
         """
         ...
 
+    @abstractmethod
+    async def validate_refresh_mental_model(self, ctx: RefreshMentalModelContext) -> ValidationResult:
+        """
+        Validate a refresh mental model operation before execution.
+
+        Called before the refresh mental model operation is processed.
+        Return ValidationResult.reject() to prevent the operation from executing.
+
+        Args:
+            ctx: Context containing all user-provided parameters:
+                - bank_id: Bank identifier
+                - model_id: Mental model ID to refresh
+                - request_context: Request context with auth info
+
+        Returns:
+            ValidationResult indicating whether the operation is allowed.
+        """
+        ...
+
     # =========================================================================
     # Post-operation hooks (optional - override to implement)
     # =========================================================================
@@ -325,3 +377,28 @@ class OperationValidatorExtension(Extension, ABC):
                 - error: Error message (if failed)
         """
         pass
+
+    async def on_refresh_mental_model_complete(self, result: RefreshMentalModelResult) -> None:
+        """
+        Called after a refresh mental model operation completes (success or failure).
+
+        Override this method to implement post-operation logic such as:
+        - Token usage tracking and billing
+        - Audit logging
+        - Metrics collection
+
+        Args:
+            result: Result context containing:
+                - bank_id: Bank identifier
+                - model_id: Mental model ID
+                - request_context: Request context with auth info
+                - model_name: Name of the mental model (if success)
+                - observations_count: Number of observations generated
+                - input_tokens: Number of input tokens used
+                - output_tokens: Number of output tokens used
+                - total_tokens: Total tokens used (input + output)
+                - duration_ms: Total operation duration in milliseconds
+                - success: Whether the operation succeeded
+                - error: Error message (if failed)
+        """
+        pass

hindsight-api/tests/test_extensions.py

@@ -17,6 +17,8 @@ from hindsight_api.extensions import (
     RecallResult,
     ReflectContext,
     ReflectResultContext,
+    RefreshMentalModelContext,
+    RefreshMentalModelResult,
     RequestContext,
     RetainContext,
     RetainResult,
@@ -93,6 +95,7 @@ class RateLimitingValidator(OperationValidatorExtension):
         self.retain_counts: dict[str, int] = defaultdict(int)
         self.recall_counts: dict[str, int] = defaultdict(int)
         self.reflect_counts: dict[str, int] = defaultdict(int)
+        self.refresh_mental_model_counts: dict[str, int] = defaultdict(int)
 
     async def validate_retain(self, ctx: RetainContext) -> ValidationResult:
         self.retain_counts[ctx.bank_id] += 1
@@ -118,6 +121,16 @@ class RateLimitingValidator(OperationValidatorExtension):
             )
         return ValidationResult.accept()
 
+    async def validate_refresh_mental_model(
+        self, ctx: RefreshMentalModelContext
+    ) -> ValidationResult:
+        self.refresh_mental_model_counts[ctx.bank_id] += 1
+        if self.refresh_mental_model_counts[ctx.bank_id] > self.max_attempts:
+            return ValidationResult.reject(
+                f"Refresh mental model limit exceeded for bank {ctx.bank_id}"
+            )
+        return ValidationResult.accept()
+
 
 class TrackingValidator(OperationValidatorExtension):
     """
@@ -132,10 +145,12 @@ class TrackingValidator(OperationValidatorExtension):
         self.pre_retain_calls: list[RetainContext] = []
         self.pre_recall_calls: list[RecallContext] = []
         self.pre_reflect_calls: list[ReflectContext] = []
+        self.pre_refresh_mental_model_calls: list[RefreshMentalModelContext] = []
         # Post-hook tracking
         self.post_retain_calls: list[RetainResult] = []
         self.post_recall_calls: list[RecallResult] = []
         self.post_reflect_calls: list[ReflectResultContext] = []
+        self.post_refresh_mental_model_calls: list[RefreshMentalModelResult] = []
 
     async def validate_retain(self, ctx: RetainContext) -> ValidationResult:
         self.pre_retain_calls.append(ctx)
@@ -149,6 +164,12 @@ class TrackingValidator(OperationValidatorExtension):
         self.pre_reflect_calls.append(ctx)
         return ValidationResult.accept()
 
+    async def validate_refresh_mental_model(
+        self, ctx: RefreshMentalModelContext
+    ) -> ValidationResult:
+        self.pre_refresh_mental_model_calls.append(ctx)
+        return ValidationResult.accept()
+
     async def on_retain_complete(self, result: RetainResult) -> None:
         self.post_retain_calls.append(result)
 
@@ -158,6 +179,11 @@ class TrackingValidator(OperationValidatorExtension):
     async def on_reflect_complete(self, result: ReflectResultContext) -> None:
         self.post_reflect_calls.append(result)
 
+    async def on_refresh_mental_model_complete(
+        self, result: RefreshMentalModelResult
+    ) -> None:
+        self.post_refresh_mental_model_calls.append(result)
+
 
 class TestMemoryEngineValidation:
     """Tests for validation integration with MemoryEngine.
@@ -515,6 +541,105 @@ class TestOperationHooksParameters:
         assert len(validator.pre_recall_calls) == 1
         assert len(validator.post_recall_calls) == 1
 
+    @pytest.mark.asyncio
+    async def test_refresh_mental_model_pre_hook_receives_all_parameters(
+        self, memory_with_tracking_validator
+    ):
+        """Pre-refresh-mental-model hook receives all user-provided parameters."""
+        import uuid
+
+        memory, validator = memory_with_tracking_validator
+        bank_id = f"test-refresh-mm-params-{uuid.uuid4().hex[:8]}"
+        ctx = RequestContext(api_key="test-key")
+
+        # Create bank first (get_bank_profile auto-creates if needed)
+        await memory.get_bank_profile(bank_id, request_context=ctx)
+
+        # Create a pinned mental model
+        model = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Test Model",
+            description="Test description",
+            subtype="pinned",
+            request_context=ctx,
+        )
+
+        assert model is not None
+        model_id = model["id"]
+
+        # Attempt to refresh (may not actually refresh if no data, but hook should be called)
+        try:
+            await memory.refresh_mental_model(
+                bank_id=bank_id,
+                model_id=model_id,
+                request_context=ctx,
+            )
+        except Exception:
+            pass  # May fail if no data
+
+        # Check pre-hook was called
+        assert len(validator.pre_refresh_mental_model_calls) == 1
+        pre_ctx = validator.pre_refresh_mental_model_calls[0]
+        assert pre_ctx.bank_id == bank_id
+        assert pre_ctx.model_id == model_id
+        assert pre_ctx.request_context == ctx
+
+    @pytest.mark.asyncio
+    async def test_refresh_mental_model_post_hook_receives_token_usage(
+        self, memory_with_tracking_validator
+    ):
+        """Post-refresh-mental-model hook receives token usage information."""
+        import uuid
+
+        memory, validator = memory_with_tracking_validator
+        bank_id = f"test-refresh-mm-tokens-{uuid.uuid4().hex[:8]}"
+        ctx = RequestContext(api_key="test-key")
+
+        # Store some content first
+        await memory.retain_batch_async(
+            bank_id=bank_id,
+            contents=[
+                {"content": "Alice is a software engineer who works on machine learning."},
+                {"content": "Alice enjoys hiking and outdoor activities on weekends."},
+                {"content": "Alice has been working at the company for 5 years."},
+            ],
+            request_context=ctx,
+        )
+
+        # Create a pinned mental model
+        model = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Alice Profile",
+            description="Profile of Alice including work and hobbies",
+            subtype="pinned",
+            request_context=ctx,
+        )
+
+        if model:
+            model_id = model["id"]
+
+            # Refresh the mental model
+            result = await memory.refresh_mental_model(
+                bank_id=bank_id,
+                model_id=model_id,
+                request_context=ctx,
+            )
+
+            # Check post-hook was called with token usage
+            if validator.post_refresh_mental_model_calls:
+                post_result = validator.post_refresh_mental_model_calls[0]
+                assert post_result.bank_id == bank_id
+                assert post_result.model_id == model_id
+                assert post_result.request_context == ctx
+                assert post_result.success is True
+                assert post_result.error is None
+
+                # Token usage should be populated (may be 0 if refresh was skipped)
+                assert post_result.total_tokens >= 0
+                assert post_result.input_tokens >= 0
+                assert post_result.output_tokens >= 0
+                assert post_result.duration_ms >= 0
+
 
 class TestTenantExtension:
     """Tests for TenantExtension and ApiKeyTenantExtension."""

hindsight-api/tests/test_llm_tools.py

@@ -259,23 +259,11 @@ class TestReflectToolSchemas:
         assert "recall" in tool_names
         assert "done" in tool_names
 
-    def test_get_reflect_tools_observations_mode(self):
-        """Test getting reflect tools with observations output mode."""
-        from hindsight_api.engine.reflect.tools_schema import get_reflect_tools
-
-        tools = get_reflect_tools(output_mode="observations")
-
-        done_tool = next(t for t in tools if t["function"]["name"] == "done")
-        params = done_tool["function"]["parameters"]["properties"]
-
-        assert "observations" in params
-        assert "answer" not in params
-
     def test_get_reflect_tools_answer_mode(self):
         """Test getting reflect tools with answer output mode."""
         from hindsight_api.engine.reflect.tools_schema import get_reflect_tools
 
-        tools = get_reflect_tools(output_mode="answer")
+        tools = get_reflect_tools()
 
         done_tool = next(t for t in tools if t["function"]["name"] == "done")
         params = done_tool["function"]["parameters"]["properties"]

hindsight-api/tests/test_main_module.py

@@ -363,6 +363,7 @@ from hindsight_api.extensions import (
     RetainContext,
     RecallContext,
     ReflectContext,
+    RefreshMentalModelContext,
 )
 
 
@@ -394,3 +395,6 @@ class MockOperationValidator(OperationValidatorExtension):
 
     async def validate_reflect(self, ctx: ReflectContext) -> ValidationResult:
         return ValidationResult.accept()
+
+    async def validate_refresh_mental_model(self, ctx: RefreshMentalModelContext) -> ValidationResult:
+        return ValidationResult.accept()

hindsight-api/tests/test_mental_models.py

@@ -793,3 +793,642 @@ class TestMentalModelTags:
             )
             assert "tags" in model
             assert isinstance(model["tags"], list)
+
+
+class TestDirectives:
+    """Test directive mental model functionality."""
+
+    async def test_create_directive(self, memory: MemoryEngine, request_context):
+        """Test creating a directive mental model with user-provided observations."""
+        bank_id = f"test-directive-{uuid.uuid4().hex[:8]}"
+
+        # Ensure bank exists
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        # Create a directive with observations
+        model = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Competitor Policy",
+            description="Rules about mentioning competitors",
+            subtype="directive",
+            observations=[
+                {"title": "Never mention", "content": "Never mention competitor product names directly"},
+                {"title": "Redirect", "content": "If asked about competitors, redirect to our features"},
+            ],
+            request_context=request_context,
+        )
+
+        assert model["name"] == "Competitor Policy"
+        assert model["description"] == "Rules about mentioning competitors"
+        assert model["subtype"] == "directive"
+        assert len(model["observations"]) == 2
+        assert model["observations"][0].title == "Never mention"
+        assert model["observations"][0].content == "Never mention competitor product names directly"
+
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    async def test_directive_included_in_list(self, memory: MemoryEngine, request_context):
+        """Test that directives are included in list_mental_models for admin visibility."""
+        bank_id = f"test-directive-list-{uuid.uuid4().hex[:8]}"
+
+        # Set up bank with mission
+        await memory.set_bank_mission(
+            bank_id=bank_id,
+            mission="Test mission",
+            request_context=request_context,
+        )
+
+        # Create a directive
+        directive = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Test Directive",
+            description="A test directive",
+            subtype="directive",
+            observations=[{"title": "Rule", "content": "Follow this rule"}],
+            request_context=request_context,
+        )
+
+        # Create a pinned model
+        pinned = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Test Pinned",
+            description="A test pinned model",
+            request_context=request_context,
+        )
+
+        # List without subtype filter - both should appear
+        models = await memory.list_mental_models(
+            bank_id=bank_id,
+            request_context=request_context,
+        )
+
+        # Both should appear (directives included in API listing for admin visibility)
+        model_ids = [m["id"] for m in models]
+        assert pinned["id"] in model_ids
+        assert directive["id"] in model_ids
+
+        # List with directive subtype filter - should find only directive
+        directives = await memory.list_mental_models(
+            bank_id=bank_id,
+            subtype="directive",
+            request_context=request_context,
+        )
+        assert len(directives) == 1
+        assert directives[0]["id"] == directive["id"]
+
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    async def test_directive_get_includes_observations(self, memory: MemoryEngine, request_context):
+        """Test that getting a directive returns its user-provided observations."""
+        bank_id = f"test-directive-get-{uuid.uuid4().hex[:8]}"
+
+        # Ensure bank exists
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        # Create a directive with observations
+        created = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Meeting Rules",
+            description="Rules for scheduling meetings",
+            subtype="directive",
+            observations=[
+                {"title": "No mornings", "content": "Never schedule meetings before noon"},
+                {"title": "Max duration", "content": "Meetings should be 30 minutes max"},
+            ],
+            request_context=request_context,
+        )
+
+        # Get the directive
+        retrieved = await memory.get_mental_model(
+            bank_id=bank_id,
+            model_id=created["id"],
+            request_context=request_context,
+        )
+
+        assert retrieved is not None
+        assert retrieved["subtype"] == "directive"
+        assert len(retrieved["observations"]) == 2
+        assert retrieved["observations"][0].title == "No mornings"
+        assert retrieved["observations"][1].title == "Max duration"
+
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    async def test_directive_survives_refresh(self, memory: MemoryEngine, request_context):
+        """Test that directives are not modified during refresh_mental_models."""
+        bank_id = f"test-directive-refresh-{uuid.uuid4().hex[:8]}"
+
+        # Set up bank with mission
+        await memory.set_bank_mission(
+            bank_id=bank_id,
+            mission="Test mission",
+            request_context=request_context,
+        )
+
+        # Add some test data
+        await memory.retain_batch_async(
+            bank_id=bank_id,
+            contents=[{"content": "Alice is the engineer."}],
+            request_context=request_context,
+        )
+        await memory.wait_for_background_tasks()
+
+        # Create a directive
+        directive = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Important Rule",
+            description="A critical rule",
+            subtype="directive",
+            observations=[{"title": "Rule 1", "content": "Always follow this rule"}],
+            request_context=request_context,
+        )
+
+        # Refresh mental models
+        await memory.refresh_mental_models(
+            bank_id=bank_id,
+            request_context=request_context,
+        )
+        await memory.wait_for_background_tasks()
+
+        # Directive should still exist with same observations
+        retrieved = await memory.get_mental_model(
+            bank_id=bank_id,
+            model_id=directive["id"],
+            request_context=request_context,
+        )
+
+        assert retrieved is not None
+        assert retrieved["subtype"] == "directive"
+        assert len(retrieved["observations"]) == 1
+        assert retrieved["observations"][0].title == "Rule 1"
+        assert retrieved["observations"][0].content == "Always follow this rule"
+
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    async def test_directive_requires_observations(self, memory: MemoryEngine, request_context):
+        """Test that creating a directive without observations fails."""
+        bank_id = f"test-directive-no-obs-{uuid.uuid4().hex[:8]}"
+
+        # Ensure bank exists
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        # Try to create directive without observations
+        with pytest.raises(ValueError) as exc_info:
+            await memory.create_mental_model(
+                bank_id=bank_id,
+                name="Bad Directive",
+                description="A directive without observations",
+                subtype="directive",
+                # No observations provided
+                request_context=request_context,
+            )
+
+        assert "observations" in str(exc_info.value).lower()
+
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+class TestDirectivesInReflect:
+    """Test that directives are followed during reflect operations."""
+
+    async def test_reflect_follows_language_directive(self, memory: MemoryEngine, request_context):
+        """Test that reflect follows a directive to respond in a specific language."""
+        bank_id = f"test-directive-reflect-{uuid.uuid4().hex[:8]}"
+
+        # Ensure bank exists
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        # Add some content in English
+        await memory.retain_batch_async(
+            bank_id=bank_id,
+            contents=[
+                {"content": "Alice is a software engineer who works at Google."},
+                {"content": "Alice enjoys hiking on weekends and has been to Yosemite."},
+                {"content": "Alice is currently working on a machine learning project."},
+            ],
+            request_context=request_context,
+        )
+        await memory.wait_for_background_tasks()
+
+        # Create a directive to always respond in French
+        await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Language Policy",
+            description="Rules about language usage",
+            subtype="directive",
+            observations=[
+                {
+                    "title": "French Only",
+                    "content": "ALWAYS respond in French language. Never respond in English.",
+                },
+            ],
+            request_context=request_context,
+        )
+
+        # Run reflect query
+        result = await memory.reflect_async(
+            bank_id=bank_id,
+            query="What does Alice do for work?",
+            request_context=request_context,
+        )
+
+        assert result.text is not None
+        assert len(result.text) > 0
+
+        # Check that the response contains French words/patterns
+        # Common French words that would appear when talking about someone's job
+        french_indicators = [
+            "elle",
+            "travaille",
+            "est",
+            "une",
+            "le",
+            "la",
+            "qui",
+            "chez",
+            "logiciel",
+            "ingénieur",
+            "ingénieure",
+            "développeur",
+            "développeuse",
+        ]
+        response_lower = result.text.lower()
+
+        # At least some French words should appear in the response
+        french_word_count = sum(1 for word in french_indicators if word in response_lower)
+        assert (
+            french_word_count >= 2
+        ), f"Expected French response, but got: {result.text[:200]}"
+
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+class TestMentalModelTagsFiltering:
+    """Test tags filtering for mental models (all types)."""
+
+    async def test_tags_match_any_includes_untagged(self, memory: MemoryEngine, request_context):
+        """Test that 'any' tags_match mode includes untagged mental models."""
+        bank_id = f"test-mm-tags-any-{uuid.uuid4().hex[:8]}"
+
+        # Ensure bank exists
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        # Create an UNTAGGED pinned model
+        await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Global Model",
+            description="A global mental model",
+            subtype="pinned",
+            tags=[],  # No tags - should be included with "any" mode
+            request_context=request_context,
+        )
+
+        # Test 1: list_mental_models with tags and tags_match="any" should include untagged
+        models_any = await memory.list_mental_models(
+            bank_id=bank_id,
+            tags=["some-tag"],
+            tags_match="any",  # Should include untagged
+            request_context=request_context,
+        )
+        assert len(models_any) == 1, f"Expected untagged model with 'any' mode, got {len(models_any)}"
+
+        # Test 2: list_mental_models with tags and tags_match="any_strict" should exclude untagged
+        models_strict = await memory.list_mental_models(
+            bank_id=bank_id,
+            tags=["some-tag"],
+            tags_match="any_strict",  # Should exclude untagged
+            request_context=request_context,
+        )
+        assert len(models_strict) == 0, f"Expected no models with 'any_strict' mode, got {len(models_strict)}"
+
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    async def test_tags_match_strict_modes(self, memory: MemoryEngine, request_context):
+        """Test that strict modes only include mental models with matching tags."""
+        bank_id = f"test-mm-tags-strict-{uuid.uuid4().hex[:8]}"
+
+        # Ensure bank exists
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        # Create a TAGGED pinned model
+        await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Tagged Model",
+            description="A tagged mental model",
+            subtype="pinned",
+            tags=["project-a"],
+            request_context=request_context,
+        )
+
+        # Create an UNTAGGED pinned model
+        await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Untagged Model",
+            description="An untagged mental model",
+            subtype="pinned",
+            tags=[],  # No tags
+            request_context=request_context,
+        )
+
+        # Test 1: any_strict with matching tag - should get ONLY the tagged model
+        models_match = await memory.list_mental_models(
+            bank_id=bank_id,
+            tags=["project-a"],
+            tags_match="any_strict",
+            request_context=request_context,
+        )
+        assert len(models_match) == 1, f"Expected 1 model with matching tag, got {len(models_match)}"
+        assert models_match[0]["name"] == "Tagged Model"
+
+        # Test 2: any_strict with different tag - should get NO models
+        models_no_match = await memory.list_mental_models(
+            bank_id=bank_id,
+            tags=["project-b"],
+            tags_match="any_strict",
+            request_context=request_context,
+        )
+        assert len(models_no_match) == 0, f"Expected no models with non-matching tag, got {len(models_no_match)}"
+
+        # Test 3: any (non-strict) with any tag - should get BOTH models
+        models_any = await memory.list_mental_models(
+            bank_id=bank_id,
+            tags=["project-a"],
+            tags_match="any",
+            request_context=request_context,
+        )
+        assert len(models_any) == 2, f"Expected 2 models with 'any' mode, got {len(models_any)}"
+
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    async def test_tags_match_all_strict(self, memory: MemoryEngine, request_context):
+        """Test that 'all_strict' requires ALL tags to be present."""
+        bank_id = f"test-mm-tags-all-{uuid.uuid4().hex[:8]}"
+
+        # Ensure bank exists
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        # Create a model with multiple tags
+        await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Multi-Tag Model",
+            description="Has project-a and project-b tags",
+            subtype="pinned",
+            tags=["project-a", "project-b"],
+            request_context=request_context,
+        )
+
+        # Create a model with only one tag
+        await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Single-Tag Model",
+            description="Has only project-a tag",
+            subtype="pinned",
+            tags=["project-a"],
+            request_context=request_context,
+        )
+
+        # Test 1: all_strict with both tags - should get ONLY the multi-tag model
+        models_all = await memory.list_mental_models(
+            bank_id=bank_id,
+            tags=["project-a", "project-b"],
+            tags_match="all_strict",
+            request_context=request_context,
+        )
+        assert len(models_all) == 1, f"Expected 1 model with all tags, got {len(models_all)}"
+        assert models_all[0]["name"] == "Multi-Tag Model"
+
+        # Test 2: all (non-strict) with both tags - should include untagged too
+        # Add an untagged model
+        await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Untagged Model",
+            description="No tags",
+            subtype="pinned",
+            tags=[],
+            request_context=request_context,
+        )
+
+        models_all_non_strict = await memory.list_mental_models(
+            bank_id=bank_id,
+            tags=["project-a", "project-b"],
+            tags_match="all",
+            request_context=request_context,
+        )
+        # Should get Multi-Tag Model + Untagged Model
+        assert len(models_all_non_strict) == 2, f"Expected 2 models with 'all' mode, got {len(models_all_non_strict)}"
+
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+class TestDirectivesPromptInjection:
+    """Test that directives are properly injected into the system prompt."""
+
+    def test_build_directives_section_empty(self):
+        """Test that empty directives returns empty string."""
+        from hindsight_api.engine.reflect.prompts import build_directives_section
+
+        result = build_directives_section([])
+        assert result == ""
+
+    def test_build_directives_section_with_observations(self):
+        """Test that directives with observations are formatted correctly."""
+        from hindsight_api.engine.reflect.prompts import build_directives_section
+
+        directives = [
+            {
+                "name": "Competitor Policy",
+                "observations": [
+                    {"title": "Never mention", "content": "Never mention competitor names"},
+                    {"title": "Redirect", "content": "Redirect to our features"},
+                ],
+            }
+        ]
+
+        result = build_directives_section(directives)
+
+        assert "## DIRECTIVES (MANDATORY)" in result
+        assert "**Never mention**: Never mention competitor names" in result
+        assert "**Redirect**: Redirect to our features" in result
+        assert "NEVER violate these directives" in result
+
+    def test_build_directives_section_fallback_to_description(self):
+        """Test that directives without observations fall back to description."""
+        from hindsight_api.engine.reflect.prompts import build_directives_section
+
+        directives = [
+            {
+                "name": "Simple Rule",
+                "description": "Just a simple rule description",
+                "observations": [],
+            }
+        ]
+
+        result = build_directives_section(directives)
+
+        assert "**Simple Rule**: Just a simple rule description" in result
+
+    def test_system_prompt_includes_directives(self):
+        """Test that build_system_prompt_for_tools includes directives."""
+        from hindsight_api.engine.reflect.prompts import build_system_prompt_for_tools
+
+        bank_profile = {"name": "Test Bank", "mission": "Test mission"}
+        directives = [
+            {
+                "name": "Test Directive",
+                "observations": [{"title": "Rule", "content": "Follow this rule"}],
+            }
+        ]
+
+        prompt = build_system_prompt_for_tools(
+            bank_profile=bank_profile,
+            directives=directives,
+        )
+
+        assert "## DIRECTIVES (MANDATORY)" in prompt
+        assert "**Rule**: Follow this rule" in prompt
+        # Directives should appear before CRITICAL RULES
+        directives_pos = prompt.find("## DIRECTIVES")
+        critical_rules_pos = prompt.find("## CRITICAL RULES")
+        assert directives_pos < critical_rules_pos
+
+
+class TestMentalModelVersioning:
+    """Test mental model versioning functionality."""
+
+    async def test_refresh_creates_version(self, memory_with_mission, request_context):
+        """Test that refreshing a mental model creates a version entry."""
+        memory, bank_id = memory_with_mission
+
+        # First create a mental model via refresh_mental_models
+        await memory.refresh_mental_models(
+            bank_id=bank_id,
+            request_context=request_context,
+        )
+        await memory.wait_for_background_tasks()
+
+        # Get the created models
+        models = await memory.list_mental_models(
+            bank_id=bank_id,
+            request_context=request_context,
+        )
+        assert len(models) > 0
+
+        model_id = models[0]["id"]
+
+        # Refresh the specific model to trigger versioning
+        result = await memory.refresh_mental_model(
+            bank_id=bank_id,
+            model_id=model_id,
+            request_context=request_context,
+        )
+
+        assert result is not None
+        # Version should be incremented
+        assert result.get("version", 0) >= 1
+
+        # Check version history
+        versions = await memory.get_mental_model_versions(
+            bank_id=bank_id,
+            model_id=model_id,
+            request_context=request_context,
+        )
+
+        assert len(versions) >= 1
+        assert versions[0]["version"] >= 1
+        assert "created_at" in versions[0]
+        assert "observation_count" in versions[0]
+
+    async def test_get_specific_version(self, memory_with_mission, request_context):
+        """Test retrieving a specific version of a mental model."""
+        memory, bank_id = memory_with_mission
+
+        # Create and refresh a mental model
+        await memory.refresh_mental_models(
+            bank_id=bank_id,
+            request_context=request_context,
+        )
+        await memory.wait_for_background_tasks()
+
+        models = await memory.list_mental_models(
+            bank_id=bank_id,
+            request_context=request_context,
+        )
+        assert len(models) > 0
+
+        model_id = models[0]["id"]
+
+        # Refresh to create version
+        await memory.refresh_mental_model(
+            bank_id=bank_id,
+            model_id=model_id,
+            request_context=request_context,
+        )
+
+        # Get versions
+        versions = await memory.get_mental_model_versions(
+            bank_id=bank_id,
+            model_id=model_id,
+            request_context=request_context,
+        )
+        assert len(versions) >= 1
+
+        # Get specific version
+        version_num = versions[0]["version"]
+        version_data = await memory.get_mental_model_version(
+            bank_id=bank_id,
+            model_id=model_id,
+            version=version_num,
+            request_context=request_context,
+        )
+
+        assert version_data is not None
+        assert version_data["version"] == version_num
+        assert "observations" in version_data
+
+    async def test_version_cleanup_keeps_max_versions(self, memory_with_mission, request_context):
+        """Test that old versions are cleaned up when max is exceeded."""
+        memory, bank_id = memory_with_mission
+
+        # Create a mental model
+        await memory.refresh_mental_models(
+            bank_id=bank_id,
+            request_context=request_context,
+        )
+        await memory.wait_for_background_tasks()
+
+        models = await memory.list_mental_models(
+            bank_id=bank_id,
+            request_context=request_context,
+        )
+        assert len(models) > 0
+
+        model_id = models[0]["id"]
+
+        # Refresh multiple times to create versions
+        for _ in range(3):
+            await memory.refresh_mental_model(
+                bank_id=bank_id,
+                model_id=model_id,
+                request_context=request_context,
+            )
+
+        # Get versions - should have multiple but within max limit
+        versions = await memory.get_mental_model_versions(
+            bank_id=bank_id,
+            model_id=model_id,
+            request_context=request_context,
+        )
+
+        # Should have versions (exact count depends on config, but at least some)
+        assert len(versions) >= 1
+        # Versions should be in descending order
+        if len(versions) > 1:
+            assert versions[0]["version"] > versions[1]["version"]
+

hindsight-api/tests/test_observation_trends.py

@@ -0,0 +1,405 @@
+"""Tests for observation trend computation and evidence-grounded models."""
+
+from datetime import datetime, timedelta, timezone
+
+import pytest
+
+from hindsight_api.engine.reflect.observations import (
+    CandidateObservation,
+    Observation,
+    ObservationEvidence,
+    Trend,
+    compute_trend,
+    verify_evidence_quotes,
+)
+
+
+class TestComputeTrend:
+    """Tests for the compute_trend function."""
+
+    def test_empty_evidence_returns_stale(self):
+        """No evidence should return STALE trend."""
+        trend = compute_trend([])
+        assert trend == Trend.STALE
+
+    def test_all_recent_evidence_returns_new(self):
+        """All evidence within recent window (30 days) should return NEW trend.
+
+        Scenario: User just started using the app and mentioned they like coffee twice.
+        Both mentions are within the last 2 weeks, so this is a NEW observation.
+        """
+        now = datetime.now(timezone.utc)
+        evidence = [
+            ObservationEvidence(
+                memory_id="mem-coffee-morning",
+                quote="I always start my day with a large black coffee",
+                relevance="Shows preference for coffee and morning routine",
+                timestamp=now - timedelta(days=5),
+            ),
+            ObservationEvidence(
+                memory_id="mem-coffee-meeting",
+                quote="grabbed coffee before the standup meeting",
+                relevance="Confirms regular coffee consumption",
+                timestamp=now - timedelta(days=10),
+            ),
+        ]
+
+        trend = compute_trend(evidence, now=now)
+        assert trend == Trend.NEW
+
+    def test_no_recent_evidence_returns_stale(self):
+        """No evidence in recent window should return STALE trend.
+
+        Scenario: User mentioned running 3 months ago but hasn't mentioned it since.
+        The observation about running as a hobby may no longer be accurate.
+        """
+        now = datetime.now(timezone.utc)
+        evidence = [
+            ObservationEvidence(
+                memory_id="mem-running-march",
+                quote="training for a half marathon in the spring",
+                relevance="Shows interest in running",
+                timestamp=now - timedelta(days=60),
+            ),
+            ObservationEvidence(
+                memory_id="mem-running-feb",
+                quote="went for a 10k run this morning",
+                relevance="Active runner",
+                timestamp=now - timedelta(days=100),
+            ),
+        ]
+
+        trend = compute_trend(evidence, now=now)
+        assert trend == Trend.STALE
+
+    def test_stable_evidence_distribution(self):
+        """Evidence spread evenly across time should return STABLE trend.
+
+        Scenario: User has consistently mentioned working remotely over 4 months.
+        Evidence is well-distributed, indicating a stable, ongoing preference.
+        """
+        now = datetime.now(timezone.utc)
+        evidence = [
+            # Recent (within 30 days)
+            ObservationEvidence(
+                memory_id="mem-remote-jan",
+                quote="working from my home office today",
+                relevance="Current remote work",
+                timestamp=now - timedelta(days=5),
+            ),
+            ObservationEvidence(
+                memory_id="mem-remote-dec",
+                quote="the flexibility of remote work is great",
+                relevance="Values remote work",
+                timestamp=now - timedelta(days=15),
+            ),
+            # Middle period (30-90 days)
+            ObservationEvidence(
+                memory_id="mem-remote-nov",
+                quote="set up a standing desk at home",
+                relevance="Invested in home office",
+                timestamp=now - timedelta(days=45),
+            ),
+            ObservationEvidence(
+                memory_id="mem-remote-oct",
+                quote="prefer async communication over meetings",
+                relevance="Remote work style preference",
+                timestamp=now - timedelta(days=60),
+            ),
+            # Older (90+ days)
+            ObservationEvidence(
+                memory_id="mem-remote-sep",
+                quote="switched to fully remote last quarter",
+                relevance="Original transition to remote",
+                timestamp=now - timedelta(days=100),
+            ),
+            ObservationEvidence(
+                memory_id="mem-remote-aug",
+                quote="negotiated remote work in my new contract",
+                relevance="Intentional choice for remote",
+                timestamp=now - timedelta(days=120),
+            ),
+        ]
+
+        trend = compute_trend(evidence, now=now)
+        assert trend == Trend.STABLE
+
+    def test_strengthening_trend(self):
+        """Much more recent evidence than older should return STRENGTHENING trend.
+
+        Scenario: User has been increasingly talking about learning Python recently
+        after mentioning it once months ago. Interest appears to be growing.
+        """
+        now = datetime.now(timezone.utc)
+        evidence = [
+            # Lots of recent evidence - actively learning
+            ObservationEvidence(
+                memory_id="mem-python-project",
+                quote="finished my first Python project - a web scraper",
+                relevance="Completed Python project",
+                timestamp=now - timedelta(days=2),
+            ),
+            ObservationEvidence(
+                memory_id="mem-python-course",
+                quote="halfway through the Python bootcamp",
+                relevance="Active learning",
+                timestamp=now - timedelta(days=5),
+            ),
+            ObservationEvidence(
+                memory_id="mem-python-book",
+                quote="reading Fluent Python, it's excellent",
+                relevance="Deepening knowledge",
+                timestamp=now - timedelta(days=10),
+            ),
+            ObservationEvidence(
+                memory_id="mem-python-practice",
+                quote="solved 50 LeetCode problems in Python",
+                relevance="Practicing skills",
+                timestamp=now - timedelta(days=15),
+            ),
+            ObservationEvidence(
+                memory_id="mem-python-ide",
+                quote="set up VS Code with all the Python extensions",
+                relevance="Setting up environment",
+                timestamp=now - timedelta(days=20),
+            ),
+            # Only one old mention - initial interest
+            ObservationEvidence(
+                memory_id="mem-python-start",
+                quote="thinking about learning Python someday",
+                relevance="Initial interest",
+                timestamp=now - timedelta(days=100),
+            ),
+        ]
+
+        trend = compute_trend(evidence, now=now)
+        assert trend == Trend.STRENGTHENING
+
+    def test_weakening_trend(self):
+        """Much less recent evidence than older should return WEAKENING trend.
+
+        Scenario: User was very active in a book club last year but mentions
+        have tapered off. The observation about being a book club member
+        may be becoming less relevant.
+        """
+        now = datetime.now(timezone.utc)
+        evidence = [
+            # Only one recent mention
+            ObservationEvidence(
+                memory_id="mem-book-recent",
+                quote="haven't had time for book club lately",
+                relevance="Reduced participation",
+                timestamp=now - timedelta(days=10),
+            ),
+            # Lots of older evidence - was very active
+            ObservationEvidence(
+                memory_id="mem-book-aug",
+                quote="hosting book club at my place next week",
+                relevance="Active organizer",
+                timestamp=now - timedelta(days=40),
+            ),
+            ObservationEvidence(
+                memory_id="mem-book-july",
+                quote="leading the discussion on 1984",
+                relevance="Active participant",
+                timestamp=now - timedelta(days=50),
+            ),
+            ObservationEvidence(
+                memory_id="mem-book-june",
+                quote="we picked The Midnight Library for June",
+                relevance="Regular member",
+                timestamp=now - timedelta(days=60),
+            ),
+            ObservationEvidence(
+                memory_id="mem-book-may",
+                quote="book club was amazing tonight",
+                relevance="Enthusiastic member",
+                timestamp=now - timedelta(days=100),
+            ),
+            ObservationEvidence(
+                memory_id="mem-book-april",
+                quote="joined a new book club in my neighborhood",
+                relevance="Started participation",
+                timestamp=now - timedelta(days=110),
+            ),
+            ObservationEvidence(
+                memory_id="mem-book-march",
+                quote="excited to finally join a book club",
+                relevance="Initial enthusiasm",
+                timestamp=now - timedelta(days=120),
+            ),
+        ]
+
+        trend = compute_trend(evidence, now=now)
+        assert trend == Trend.WEAKENING
+
+
+class TestObservationModel:
+    """Tests for the Observation model."""
+
+    def test_observation_computed_trend(self):
+        """Observation should have computed trend property based on evidence."""
+        now = datetime.now(timezone.utc)
+        obs = Observation(
+            title="Morning meeting preference",
+            content="Prefers morning meetings over afternoon ones",
+            evidence=[
+                ObservationEvidence(
+                    memory_id="mem-morning-standup",
+                    quote="I'm most productive in morning meetings",
+                    relevance="Direct preference statement",
+                    timestamp=now - timedelta(days=5),
+                ),
+            ],
+            created_at=now,
+        )
+
+        assert obs.trend == Trend.NEW
+        assert obs.evidence_count == 1
+
+    def test_observation_evidence_span(self):
+        """Observation should compute evidence span correctly.
+
+        The span shows the date range of supporting evidence, helping
+        understand how long this pattern has been observed.
+        """
+        now = datetime.now(timezone.utc)
+        old_time = now - timedelta(days=100)
+        recent_time = now - timedelta(days=5)
+
+        obs = Observation(
+            title="Values work-life balance",
+            content="Values work-life balance highly",
+            evidence=[
+                ObservationEvidence(
+                    memory_id="mem-balance-old",
+                    quote="turned down a promotion because of the hours",
+                    relevance="Prioritized balance over advancement",
+                    timestamp=old_time,
+                ),
+                ObservationEvidence(
+                    memory_id="mem-balance-recent",
+                    quote="always log off by 6pm no matter what",
+                    relevance="Maintains boundaries",
+                    timestamp=recent_time,
+                ),
+            ],
+            created_at=now,
+        )
+
+        evidence_span = obs.evidence_span
+        assert evidence_span["from"] == old_time.isoformat()
+        assert evidence_span["to"] == recent_time.isoformat()
+
+    def test_observation_empty_evidence_span(self):
+        """Observation with no evidence should have null span."""
+        obs = Observation(
+            title="Test observation",
+            content="Test observation without evidence",
+            evidence=[],
+        )
+
+        evidence_span = obs.evidence_span
+        assert evidence_span["from"] is None
+        assert evidence_span["to"] is None
+
+
+class TestVerifyEvidenceQuotes:
+    """Tests for evidence quote verification.
+
+    This ensures the LLM isn't hallucinating quotes - every quote
+    must actually appear in the source memory.
+    """
+
+    def test_valid_quotes(self):
+        """Should return True when quotes exist in their source memories."""
+        obs = Observation(
+            title="Enjoys hiking",
+            content="Enjoys hiking on weekends",
+            evidence=[
+                ObservationEvidence(
+                    memory_id="mem-hiking-trip",
+                    quote="went hiking at Mount Tam",
+                    relevance="Shows hiking activity",
+                    timestamp=datetime.now(timezone.utc),
+                ),
+            ],
+        )
+
+        memories = {
+            "mem-hiking-trip": "Had a great Saturday - went hiking at Mount Tam with friends and saw amazing views."
+        }
+        is_valid, errors = verify_evidence_quotes(obs, memories)
+
+        assert is_valid is True
+        assert len(errors) == 0
+
+    def test_invalid_quote(self):
+        """Should return False when quote doesn't exist in memory.
+
+        This catches LLM hallucinations where it fabricates quotes.
+        """
+        obs = Observation(
+            title="Loves spicy food",
+            content="Loves spicy food",
+            evidence=[
+                ObservationEvidence(
+                    memory_id="mem-dinner",
+                    quote="I love extra hot salsa",
+                    relevance="Shows spicy food preference",
+                    timestamp=datetime.now(timezone.utc),
+                ),
+            ],
+        )
+
+        memories = {"mem-dinner": "Had tacos for dinner. The guacamole was really fresh."}
+        is_valid, errors = verify_evidence_quotes(obs, memories)
+
+        assert is_valid is False
+        assert len(errors) == 1
+        assert "Quote not found" in errors[0]
+
+    def test_missing_memory(self):
+        """Should return False when referenced memory doesn't exist.
+
+        This catches cases where the LLM references a memory ID that
+        was never actually retrieved.
+        """
+        obs = Observation(
+            title="Has a dog named Max",
+            content="Has a dog named Max",
+            evidence=[
+                ObservationEvidence(
+                    memory_id="mem-pet-story",
+                    quote="took Max to the vet",
+                    relevance="Shows pet ownership",
+                    timestamp=datetime.now(timezone.utc),
+                ),
+            ],
+        )
+
+        memories = {"mem-different-id": "Some unrelated memory content"}
+        is_valid, errors = verify_evidence_quotes(obs, memories)
+
+        assert is_valid is False
+        assert len(errors) == 1
+        assert "not found" in errors[0]
+
+
+class TestCandidateObservation:
+    """Tests for candidate observation model.
+
+    Candidates are generated in the SEED phase and validated
+    before becoming full observations.
+    """
+
+    def test_create_candidate(self):
+        """Should create candidate with content and seed memories."""
+        candidate = CandidateObservation(
+            content="User prefers async communication over meetings",
+            seed_memory_ids=["mem-slack-pref", "mem-meeting-decline"],
+        )
+
+        assert candidate.content == "User prefers async communication over meetings"
+        assert len(candidate.seed_memory_ids) == 2
+        assert "mem-slack-pref" in candidate.seed_memory_ids

hindsight-api/tests/test_reflect_agent.py

@@ -88,7 +88,19 @@ class TestToolLookup:
             "subtype": "learned",
             "name": "Model 1",
             "description": "First model",
-            "observations": {"observations": [{"title": "Overview", "text": "Full summary of model 1", "memory_ids": ["mem-1", "mem-2"]}]},
+            "observations": {
+                "observations": [
+                    {
+                        "title": "Overview",
+                        "content": "Full summary of model 1",
+                        "evidence": [
+                            {"memory_id": "mem-1", "quote": "quote 1", "relevance": "relevant", "timestamp": "2024-01-01T00:00:00Z"},
+                            {"memory_id": "mem-2", "quote": "quote 2", "relevance": "relevant", "timestamp": "2024-01-01T00:00:00Z"},
+                        ],
+                        "created_at": "2024-01-01T00:00:00Z",
+                    }
+                ]
+            },
             "entity_id": None,
             "last_updated": MagicMock(isoformat=lambda: "2024-01-01T00:00:00"),
         }
@@ -98,9 +110,12 @@ class TestToolLookup:
         assert result["found"] is True
         assert result["model"]["id"] == "model-1"
         assert len(result["model"]["observations"]) == 1
-        assert result["model"]["observations"][0]["text"] == "Full summary of model 1"
-        # Verify memory_ids are mapped to based_on
-        assert result["model"]["observations"][0]["based_on"] == ["mem-1", "mem-2"]
+        # Observations are now Observation objects
+        obs = result["model"]["observations"][0]
+        assert obs.content == "Full summary of model 1"
+        assert obs.title == "Overview"
+        assert len(obs.evidence) == 2
+        assert obs.evidence[0].memory_id == "mem-1"
 
     async def test_model_not_found(self, mock_conn):
         """Test looking up non-existent model."""
@@ -840,6 +855,158 @@ class TestReflectAgent:
 
         assert result.text == "The answer is simple and direct."
 
+    async def test_agent_includes_directives_in_system_prompt(self, mock_llm, bank_profile, mock_tools):
+        """Test that directives are included in the system prompt."""
+        from hindsight_api.engine.reflect.observations import Observation
+
+        # Create directive with Observation objects (new format)
+        directives = [
+            {
+                "id": "response-rules",
+                "name": "Response Rules",
+                "description": "Rules for responses",
+                "subtype": "directive",
+                "observations": [
+                    Observation(
+                        title="No Speculation",
+                        content="Never speculate about information not in the memories.",
+                        evidence=[],
+                    ),
+                    Observation(
+                        title="Be Concise",
+                        content="Always keep responses under 100 words.",
+                        evidence=[],
+                    ),
+                ],
+            },
+        ]
+
+        # Capture the system prompt
+        captured_messages = []
+
+        async def capture_call(*args, **kwargs):
+            if "messages" in kwargs:
+                captured_messages.extend(kwargs["messages"])
+            return self._make_tool_result([{"name": "done", "arguments": {"answer": "Done."}}])
+
+        mock_llm.call_with_tools.side_effect = [
+            # First: gather evidence (guardrail requirement)
+            self._make_tool_result([{"name": "recall", "arguments": {"query": "test"}}]),
+            # Then: done
+            self._make_tool_result([{"name": "done", "arguments": {"answer": "Done."}}]),
+        ]
+
+        # Store original to check messages
+        original_call = mock_llm.call_with_tools
+
+        async def wrapped_call(*args, **kwargs):
+            if "messages" in kwargs:
+                captured_messages.extend(kwargs["messages"])
+            return await original_call(*args, **kwargs)
+
+        mock_llm.call_with_tools = wrapped_call
+
+        result = await run_reflect_agent(
+            llm_config=mock_llm,
+            bank_id="test-bank",
+            query="What do we know?",
+            bank_profile=bank_profile,
+            directives=directives,
+            **mock_tools,
+        )
+
+        # Find the system message
+        system_messages = [m for m in captured_messages if m.get("role") == "system"]
+        assert len(system_messages) > 0, "No system message found"
+
+        system_content = system_messages[0]["content"]
+
+        # Verify directives are in the system prompt
+        assert "DIRECTIVES" in system_content, "Directives section not found in system prompt"
+        assert "No Speculation" in system_content, "Directive title not found"
+        assert "Never speculate" in system_content, "Directive content not found"
+        assert "Be Concise" in system_content, "Second directive title not found"
+        assert "100 words" in system_content, "Second directive content not found"
+        assert "NEVER violate these directives" in system_content, "Directive warning not found"
+
+
+class TestDirectivesSection:
+    """Test the directives section builder."""
+
+    def test_build_directives_section_with_observation_objects(self):
+        """Test building directives section with Observation objects."""
+        from hindsight_api.engine.reflect.observations import Observation
+        from hindsight_api.engine.reflect.prompts import build_directives_section
+
+        directives = [
+            {
+                "name": "Safety Rules",
+                "observations": [
+                    Observation(
+                        title="No Harmful Content",
+                        content="Never generate harmful or dangerous content.",
+                        evidence=[],
+                    ),
+                ],
+            },
+        ]
+
+        result = build_directives_section(directives)
+
+        assert "DIRECTIVES" in result
+        assert "No Harmful Content" in result
+        assert "Never generate harmful" in result
+        assert "NEVER violate" in result
+
+    def test_build_directives_section_with_dicts(self):
+        """Test building directives section with dict observations."""
+        from hindsight_api.engine.reflect.prompts import build_directives_section
+
+        directives = [
+            {
+                "name": "Safety Rules",
+                "observations": [
+                    {
+                        "title": "No Harmful Content",
+                        "content": "Never generate harmful or dangerous content.",
+                    },
+                ],
+            },
+        ]
+
+        result = build_directives_section(directives)
+
+        assert "DIRECTIVES" in result
+        assert "No Harmful Content" in result
+        assert "Never generate harmful" in result
+
+    def test_build_directives_section_fallback_to_description(self):
+        """Test that directives without observations use description."""
+        from hindsight_api.engine.reflect.prompts import build_directives_section
+
+        directives = [
+            {
+                "name": "Simple Rule",
+                "description": "This is a simple rule to follow.",
+                "observations": [],
+            },
+        ]
+
+        result = build_directives_section(directives)
+
+        assert "Simple Rule" in result
+        assert "simple rule to follow" in result
+
+    def test_build_directives_section_empty(self):
+        """Test that empty directives returns empty string."""
+        from hindsight_api.engine.reflect.prompts import build_directives_section
+
+        result = build_directives_section([])
+        assert result == ""
+
+        result = build_directives_section(None)
+        assert result == ""
+
 
 @pytest.mark.integration
 class TestReflectIntegration:

hindsight-api/tests/test_retain.py

@@ -2058,3 +2058,26 @@ async def test_user_provided_entities(memory, request_context):
 
     finally:
         await memory.delete_bank(bank_id, request_context=request_context)
+
+
+def test_recall_result_model_empty_construction():
+    """
+    Test that RecallResultModel can be constructed with empty results.
+
+    This is a regression test for the bug where constructing an empty RecallResultModel
+    would cause an UnboundLocalError because RecallResult was imported as RecallResultModel
+    but the code mistakenly used the wrong name.
+
+    The fix ensures RecallResultModel is used consistently throughout memory_engine.py.
+    """
+    from hindsight_api.engine.response_models import RecallResult
+
+    # This should not raise any errors
+    result = RecallResult(results=[], entities={}, chunks={})
+
+    assert result is not None, "Should create a result object"
+    assert result.results == [], "Should have empty results"
+    assert result.entities == {}, "Should have empty entities"
+    assert result.chunks == {}, "Should have empty chunks"
+
+    logger.info("✓ RecallResult empty construction works correctly")

hindsight-api/tests/test_server_module.py

@@ -257,6 +257,7 @@ from hindsight_api.extensions import (
     RetainContext,
     RecallContext,
     ReflectContext,
+    RefreshMentalModelContext,
 )
 
 
@@ -288,3 +289,6 @@ class MockOperationValidator(OperationValidatorExtension):
 
     async def validate_reflect(self, ctx: ReflectContext) -> ValidationResult:
         return ValidationResult.accept()
+
+    async def validate_refresh_mental_model(self, ctx: RefreshMentalModelContext) -> ValidationResult:
+        return ValidationResult.accept()

hindsight-clients/python/.openapi-generator/FILES

@@ -45,9 +45,12 @@ hindsight_client_api/models/list_documents_response.py
 hindsight_client_api/models/list_memory_units_response.py
 hindsight_client_api/models/list_tags_response.py
 hindsight_client_api/models/memory_item.py
+hindsight_client_api/models/mental_model_freshness_response.py
 hindsight_client_api/models/mental_model_list_response.py
 hindsight_client_api/models/mental_model_observation_response.py
 hindsight_client_api/models/mental_model_response.py
+hindsight_client_api/models/observation_evidence_response.py
+hindsight_client_api/models/observation_input.py
 hindsight_client_api/models/operation_response.py
 hindsight_client_api/models/operation_status_response.py
 hindsight_client_api/models/operations_list_response.py
@@ -70,6 +73,7 @@ hindsight_client_api/models/tag_item.py
 hindsight_client_api/models/token_usage.py
 hindsight_client_api/models/tool_calls_include_options.py
 hindsight_client_api/models/update_disposition_request.py
+hindsight_client_api/models/update_mental_model_request.py
 hindsight_client_api/models/validation_error.py
 hindsight_client_api/models/validation_error_loc_inner.py
 hindsight_client_api/rest.py

hindsight-clients/python/hindsight_client_api/__init__.py

@@ -70,9 +70,12 @@ from hindsight_client_api.models.list_documents_response import ListDocumentsRes
 from hindsight_client_api.models.list_memory_units_response import ListMemoryUnitsResponse
 from hindsight_client_api.models.list_tags_response import ListTagsResponse
 from hindsight_client_api.models.memory_item import MemoryItem
+from hindsight_client_api.models.mental_model_freshness_response import MentalModelFreshnessResponse
 from hindsight_client_api.models.mental_model_list_response import MentalModelListResponse
 from hindsight_client_api.models.mental_model_observation_response import MentalModelObservationResponse
 from hindsight_client_api.models.mental_model_response import MentalModelResponse
+from hindsight_client_api.models.observation_evidence_response import ObservationEvidenceResponse
+from hindsight_client_api.models.observation_input import ObservationInput
 from hindsight_client_api.models.operation_response import OperationResponse
 from hindsight_client_api.models.operation_status_response import OperationStatusResponse
 from hindsight_client_api.models.operations_list_response import OperationsListResponse
@@ -95,5 +98,6 @@ from hindsight_client_api.models.tag_item import TagItem
 from hindsight_client_api.models.token_usage import TokenUsage
 from hindsight_client_api.models.tool_calls_include_options import ToolCallsIncludeOptions
 from hindsight_client_api.models.update_disposition_request import UpdateDispositionRequest
+from hindsight_client_api.models.update_mental_model_request import UpdateMentalModelRequest
 from hindsight_client_api.models.validation_error import ValidationError
 from hindsight_client_api.models.validation_error_loc_inner import ValidationErrorLocInner

hindsight-clients/python/hindsight_client_api/models/__init__.py

@@ -47,9 +47,12 @@ from hindsight_client_api.models.list_documents_response import ListDocumentsRes
 from hindsight_client_api.models.list_memory_units_response import ListMemoryUnitsResponse
 from hindsight_client_api.models.list_tags_response import ListTagsResponse
 from hindsight_client_api.models.memory_item import MemoryItem
+from hindsight_client_api.models.mental_model_freshness_response import MentalModelFreshnessResponse
 from hindsight_client_api.models.mental_model_list_response import MentalModelListResponse
 from hindsight_client_api.models.mental_model_observation_response import MentalModelObservationResponse
 from hindsight_client_api.models.mental_model_response import MentalModelResponse
+from hindsight_client_api.models.observation_evidence_response import ObservationEvidenceResponse
+from hindsight_client_api.models.observation_input import ObservationInput
 from hindsight_client_api.models.operation_response import OperationResponse
 from hindsight_client_api.models.operation_status_response import OperationStatusResponse
 from hindsight_client_api.models.operations_list_response import OperationsListResponse
@@ -72,5 +75,6 @@ from hindsight_client_api.models.tag_item import TagItem
 from hindsight_client_api.models.token_usage import TokenUsage
 from hindsight_client_api.models.tool_calls_include_options import ToolCallsIncludeOptions
 from hindsight_client_api.models.update_disposition_request import UpdateDispositionRequest
+from hindsight_client_api.models.update_mental_model_request import UpdateMentalModelRequest
 from hindsight_client_api.models.validation_error import ValidationError
 from hindsight_client_api.models.validation_error_loc_inner import ValidationErrorLocInner

hindsight-clients/python/hindsight_client_api/models/create_mental_model_request.py

@@ -19,17 +19,20 @@ import json
 
 from pydantic import BaseModel, ConfigDict, Field, StrictStr
 from typing import Any, ClassVar, Dict, List, Optional
+from hindsight_client_api.models.observation_input import ObservationInput
 from typing import Optional, Set
 from typing_extensions import Self
 
 class CreateMentalModelRequest(BaseModel):
     """
-    Request model for creating a pinned mental model.
+    Request model for creating a mental model.
     """ # noqa: E501
     name: StrictStr = Field(description="Human-readable name for the mental model")
     description: StrictStr = Field(description="One-liner description for quick scanning")
+    subtype: Optional[StrictStr] = Field(default='pinned', description="Type of mental model: 'pinned' (observations LLM-generated) or 'directive' (observations user-provided)")
+    observations: Optional[List[ObservationInput]] = None
     tags: Optional[List[StrictStr]] = Field(default=None, description="Tags for scoped visibility")
-    __properties: ClassVar[List[str]] = ["name", "description", "tags"]
+    __properties: ClassVar[List[str]] = ["name", "description", "subtype", "observations", "tags"]
 
     model_config = ConfigDict(
         populate_by_name=True,
@@ -70,6 +73,18 @@ class CreateMentalModelRequest(BaseModel):
             exclude=excluded_fields,
             exclude_none=True,
         )
+        # override the default output from pydantic by calling `to_dict()` of each item in observations (list)
+        _items = []
+        if self.observations:
+            for _item_observations in self.observations:
+                if _item_observations:
+                    _items.append(_item_observations.to_dict())
+            _dict['observations'] = _items
+        # set to None if observations (nullable) is None
+        # and model_fields_set contains the field
+        if self.observations is None and "observations" in self.model_fields_set:
+            _dict['observations'] = None
+
         return _dict
 
     @classmethod
@@ -84,6 +99,8 @@ class CreateMentalModelRequest(BaseModel):
         _obj = cls.model_validate({
             "name": obj.get("name"),
             "description": obj.get("description"),
+            "subtype": obj.get("subtype") if obj.get("subtype") is not None else 'pinned',
+            "observations": [ObservationInput.from_dict(_item) for _item in obj["observations"]] if obj.get("observations") is not None else None,
             "tags": obj.get("tags")
         })
         return _obj

hindsight-clients/python/hindsight_client_api/models/mental_model_freshness_response.py

@@ -0,0 +1,98 @@
+# coding: utf-8
+
+"""
+    Hindsight HTTP API
+
+    HTTP API for Hindsight
+
+    The version of the OpenAPI document: 0.1.0
+    Generated by OpenAPI Generator (https://openapi-generator.tech)
+
+    Do not edit the class manually.
+"""  # noqa: E501
+
+
+from __future__ import annotations
+import pprint
+import re  # noqa: F401
+import json
+
+from pydantic import BaseModel, ConfigDict, Field, StrictBool, StrictInt, StrictStr
+from typing import Any, ClassVar, Dict, List, Optional
+from typing import Optional, Set
+from typing_extensions import Self
+
+class MentalModelFreshnessResponse(BaseModel):
+    """
+    Freshness information for a mental model.
+    """ # noqa: E501
+    is_up_to_date: StrictBool = Field(description="Whether the model has been refreshed since the last memory was added")
+    last_refresh_at: Optional[StrictStr]
+    memories_since_refresh: StrictInt = Field(description="Number of memories added since last refresh")
+    reasons: Optional[List[StrictStr]] = Field(default=None, description="Reasons why the model needs refresh (empty if up to date). Possible values: never_refreshed, new_memories, mission_changed, disposition_changed, directives_changed")
+    __properties: ClassVar[List[str]] = ["is_up_to_date", "last_refresh_at", "memories_since_refresh", "reasons"]
+
+    model_config = ConfigDict(
+        populate_by_name=True,
+        validate_assignment=True,
+        protected_namespaces=(),
+    )
+
+
+    def to_str(self) -> str:
+        """Returns the string representation of the model using alias"""
+        return pprint.pformat(self.model_dump(by_alias=True))
+
+    def to_json(self) -> str:
+        """Returns the JSON representation of the model using alias"""
+        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
+        return json.dumps(self.to_dict())
+
+    @classmethod
+    def from_json(cls, json_str: str) -> Optional[Self]:
+        """Create an instance of MentalModelFreshnessResponse from a JSON string"""
+        return cls.from_dict(json.loads(json_str))
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Return the dictionary representation of the model using alias.
+
+        This has the following differences from calling pydantic's
+        `self.model_dump(by_alias=True)`:
+
+        * `None` is only added to the output dict for nullable fields that
+          were set at model initialization. Other fields with value `None`
+          are ignored.
+        """
+        excluded_fields: Set[str] = set([
+        ])
+
+        _dict = self.model_dump(
+            by_alias=True,
+            exclude=excluded_fields,
+            exclude_none=True,
+        )
+        # set to None if last_refresh_at (nullable) is None
+        # and model_fields_set contains the field
+        if self.last_refresh_at is None and "last_refresh_at" in self.model_fields_set:
+            _dict['last_refresh_at'] = None
+
+        return _dict
+
+    @classmethod
+    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
+        """Create an instance of MentalModelFreshnessResponse from a dict"""
+        if obj is None:
+            return None
+
+        if not isinstance(obj, dict):
+            return cls.model_validate(obj)
+
+        _obj = cls.model_validate({
+            "is_up_to_date": obj.get("is_up_to_date"),
+            "last_refresh_at": obj.get("last_refresh_at"),
+            "memories_since_refresh": obj.get("memories_since_refresh"),
+            "reasons": obj.get("reasons")
+        })
+        return _obj
+
+

hindsight-clients/python/hindsight_client_api/models/mental_model_observation_response.py

@@ -17,19 +17,24 @@ import pprint
 import re  # noqa: F401
 import json
 
-from pydantic import BaseModel, ConfigDict, Field, StrictStr
+from pydantic import BaseModel, ConfigDict, Field, StrictInt, StrictStr
 from typing import Any, ClassVar, Dict, List, Optional
+from hindsight_client_api.models.observation_evidence_response import ObservationEvidenceResponse
 from typing import Optional, Set
 from typing_extensions import Self
 
 class MentalModelObservationResponse(BaseModel):
     """
-    An observation within a mental model with its supporting memories.
+    An observation within a mental model with its supporting evidence.
     """ # noqa: E501
-    title: StrictStr = Field(description="Observation header (empty for intro)")
-    text: StrictStr = Field(description="Observation content")
-    based_on: Optional[List[StrictStr]] = Field(default=None, description="Memory IDs supporting this observation")
-    __properties: ClassVar[List[str]] = ["title", "text", "based_on"]
+    title: StrictStr = Field(description="Short summary title for the observation")
+    content: StrictStr = Field(description="The observation content - detailed explanation")
+    evidence: Optional[List[ObservationEvidenceResponse]] = Field(default=None, description="Supporting evidence with quotes")
+    created_at: StrictStr = Field(description="When this observation was first created (ISO format)")
+    trend: StrictStr = Field(description="Computed trend: stable, strengthening, weakening, new, stale")
+    evidence_count: StrictInt = Field(description="Number of evidence items supporting this observation")
+    evidence_span: Dict[str, Any] = Field(description="Time span of evidence: {from: iso_date, to: iso_date}")
+    __properties: ClassVar[List[str]] = ["title", "content", "evidence", "created_at", "trend", "evidence_count", "evidence_span"]
 
     model_config = ConfigDict(
         populate_by_name=True,
@@ -70,6 +75,13 @@ class MentalModelObservationResponse(BaseModel):
             exclude=excluded_fields,
             exclude_none=True,
         )
+        # override the default output from pydantic by calling `to_dict()` of each item in evidence (list)
+        _items = []
+        if self.evidence:
+            for _item_evidence in self.evidence:
+                if _item_evidence:
+                    _items.append(_item_evidence.to_dict())
+            _dict['evidence'] = _items
         return _dict
 
     @classmethod
@@ -83,8 +95,12 @@ class MentalModelObservationResponse(BaseModel):
 
         _obj = cls.model_validate({
             "title": obj.get("title"),
-            "text": obj.get("text"),
-            "based_on": obj.get("based_on")
+            "content": obj.get("content"),
+            "evidence": [ObservationEvidenceResponse.from_dict(_item) for _item in obj["evidence"]] if obj.get("evidence") is not None else None,
+            "created_at": obj.get("created_at"),
+            "trend": obj.get("trend"),
+            "evidence_count": obj.get("evidence_count"),
+            "evidence_span": obj.get("evidence_span")
         })
         return _obj
 

hindsight-clients/python/hindsight_client_api/models/mental_model_response.py

@@ -17,8 +17,9 @@ import pprint
 import re  # noqa: F401
 import json
 
-from pydantic import BaseModel, ConfigDict, Field, StrictStr
+from pydantic import BaseModel, ConfigDict, Field, StrictInt, StrictStr
 from typing import Any, ClassVar, Dict, List, Optional
+from hindsight_client_api.models.mental_model_freshness_response import MentalModelFreshnessResponse
 from hindsight_client_api.models.mental_model_observation_response import MentalModelObservationResponse
 from typing import Optional, Set
 from typing_extensions import Self
@@ -33,12 +34,15 @@ class MentalModelResponse(BaseModel):
     name: StrictStr
     description: StrictStr
     observations: Optional[List[MentalModelObservationResponse]] = Field(default=None, description="Structured observations with per-observation fact attribution")
+    version: Optional[StrictInt] = Field(default=0, description="Version number of the mental model observations")
     entity_id: Optional[StrictStr] = None
     links: Optional[List[StrictStr]] = None
     tags: Optional[List[StrictStr]] = None
     last_updated: Optional[StrictStr] = None
+    last_refresh_at: Optional[StrictStr] = None
+    freshness: Optional[MentalModelFreshnessResponse] = None
     created_at: StrictStr
-    __properties: ClassVar[List[str]] = ["id", "bank_id", "subtype", "name", "description", "observations", "entity_id", "links", "tags", "last_updated", "created_at"]
+    __properties: ClassVar[List[str]] = ["id", "bank_id", "subtype", "name", "description", "observations", "version", "entity_id", "links", "tags", "last_updated", "last_refresh_at", "freshness", "created_at"]
 
     model_config = ConfigDict(
         populate_by_name=True,
@@ -86,6 +90,9 @@ class MentalModelResponse(BaseModel):
                 if _item_observations:
                     _items.append(_item_observations.to_dict())
             _dict['observations'] = _items
+        # override the default output from pydantic by calling `to_dict()` of freshness
+        if self.freshness:
+            _dict['freshness'] = self.freshness.to_dict()
         # set to None if entity_id (nullable) is None
         # and model_fields_set contains the field
         if self.entity_id is None and "entity_id" in self.model_fields_set:
@@ -96,6 +103,16 @@ class MentalModelResponse(BaseModel):
         if self.last_updated is None and "last_updated" in self.model_fields_set:
             _dict['last_updated'] = None
 
+        # set to None if last_refresh_at (nullable) is None
+        # and model_fields_set contains the field
+        if self.last_refresh_at is None and "last_refresh_at" in self.model_fields_set:
+            _dict['last_refresh_at'] = None
+
+        # set to None if freshness (nullable) is None
+        # and model_fields_set contains the field
+        if self.freshness is None and "freshness" in self.model_fields_set:
+            _dict['freshness'] = None
+
         return _dict
 
     @classmethod
@@ -114,10 +131,13 @@ class MentalModelResponse(BaseModel):
             "name": obj.get("name"),
             "description": obj.get("description"),
             "observations": [MentalModelObservationResponse.from_dict(_item) for _item in obj["observations"]] if obj.get("observations") is not None else None,
+            "version": obj.get("version") if obj.get("version") is not None else 0,
             "entity_id": obj.get("entity_id"),
             "links": obj.get("links"),
             "tags": obj.get("tags"),
             "last_updated": obj.get("last_updated"),
+            "last_refresh_at": obj.get("last_refresh_at"),
+            "freshness": MentalModelFreshnessResponse.from_dict(obj["freshness"]) if obj.get("freshness") is not None else None,
             "created_at": obj.get("created_at")
         })
         return _obj

hindsight-clients/python/hindsight_client_api/models/observation_evidence_response.py

@@ -0,0 +1,93 @@
+# coding: utf-8
+
+"""
+    Hindsight HTTP API
+
+    HTTP API for Hindsight
+
+    The version of the OpenAPI document: 0.1.0
+    Generated by OpenAPI Generator (https://openapi-generator.tech)
+
+    Do not edit the class manually.
+"""  # noqa: E501
+
+
+from __future__ import annotations
+import pprint
+import re  # noqa: F401
+import json
+
+from pydantic import BaseModel, ConfigDict, Field, StrictStr
+from typing import Any, ClassVar, Dict, List
+from typing import Optional, Set
+from typing_extensions import Self
+
+class ObservationEvidenceResponse(BaseModel):
+    """
+    A single piece of evidence supporting an observation.
+    """ # noqa: E501
+    memory_id: StrictStr = Field(description="ID of the memory unit this evidence comes from")
+    quote: StrictStr = Field(description="Exact quote from the memory supporting the observation")
+    relevance: StrictStr = Field(description="Brief explanation of how this quote supports the observation")
+    timestamp: StrictStr = Field(description="When the source memory was created (ISO format)")
+    __properties: ClassVar[List[str]] = ["memory_id", "quote", "relevance", "timestamp"]
+
+    model_config = ConfigDict(
+        populate_by_name=True,
+        validate_assignment=True,
+        protected_namespaces=(),
+    )
+
+
+    def to_str(self) -> str:
+        """Returns the string representation of the model using alias"""
+        return pprint.pformat(self.model_dump(by_alias=True))
+
+    def to_json(self) -> str:
+        """Returns the JSON representation of the model using alias"""
+        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
+        return json.dumps(self.to_dict())
+
+    @classmethod
+    def from_json(cls, json_str: str) -> Optional[Self]:
+        """Create an instance of ObservationEvidenceResponse from a JSON string"""
+        return cls.from_dict(json.loads(json_str))
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Return the dictionary representation of the model using alias.
+
+        This has the following differences from calling pydantic's
+        `self.model_dump(by_alias=True)`:
+
+        * `None` is only added to the output dict for nullable fields that
+          were set at model initialization. Other fields with value `None`
+          are ignored.
+        """
+        excluded_fields: Set[str] = set([
+        ])
+
+        _dict = self.model_dump(
+            by_alias=True,
+            exclude=excluded_fields,
+            exclude_none=True,
+        )
+        return _dict
+
+    @classmethod
+    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
+        """Create an instance of ObservationEvidenceResponse from a dict"""
+        if obj is None:
+            return None
+
+        if not isinstance(obj, dict):
+            return cls.model_validate(obj)
+
+        _obj = cls.model_validate({
+            "memory_id": obj.get("memory_id"),
+            "quote": obj.get("quote"),
+            "relevance": obj.get("relevance"),
+            "timestamp": obj.get("timestamp")
+        })
+        return _obj
+
+

hindsight-clients/python/hindsight_client_api/models/observation_input.py

@@ -0,0 +1,89 @@
+# coding: utf-8
+
+"""
+    Hindsight HTTP API
+
+    HTTP API for Hindsight
+
+    The version of the OpenAPI document: 0.1.0
+    Generated by OpenAPI Generator (https://openapi-generator.tech)
+
+    Do not edit the class manually.
+"""  # noqa: E501
+
+
+from __future__ import annotations
+import pprint
+import re  # noqa: F401
+import json
+
+from pydantic import BaseModel, ConfigDict, Field, StrictStr
+from typing import Any, ClassVar, Dict, List
+from typing import Optional, Set
+from typing_extensions import Self
+
+class ObservationInput(BaseModel):
+    """
+    Input model for a single observation.
+    """ # noqa: E501
+    title: StrictStr = Field(description="Short title/header for the observation")
+    content: StrictStr = Field(description="Content of the observation")
+    __properties: ClassVar[List[str]] = ["title", "content"]
+
+    model_config = ConfigDict(
+        populate_by_name=True,
+        validate_assignment=True,
+        protected_namespaces=(),
+    )
+
+
+    def to_str(self) -> str:
+        """Returns the string representation of the model using alias"""
+        return pprint.pformat(self.model_dump(by_alias=True))
+
+    def to_json(self) -> str:
+        """Returns the JSON representation of the model using alias"""
+        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
+        return json.dumps(self.to_dict())
+
+    @classmethod
+    def from_json(cls, json_str: str) -> Optional[Self]:
+        """Create an instance of ObservationInput from a JSON string"""
+        return cls.from_dict(json.loads(json_str))
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Return the dictionary representation of the model using alias.
+
+        This has the following differences from calling pydantic's
+        `self.model_dump(by_alias=True)`:
+
+        * `None` is only added to the output dict for nullable fields that
+          were set at model initialization. Other fields with value `None`
+          are ignored.
+        """
+        excluded_fields: Set[str] = set([
+        ])
+
+        _dict = self.model_dump(
+            by_alias=True,
+            exclude=excluded_fields,
+            exclude_none=True,
+        )
+        return _dict
+
+    @classmethod
+    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
+        """Create an instance of ObservationInput from a dict"""
+        if obj is None:
+            return None
+
+        if not isinstance(obj, dict):
+            return cls.model_validate(obj)
+
+        _obj = cls.model_validate({
+            "title": obj.get("title"),
+            "content": obj.get("content")
+        })
+        return _obj
+
+

hindsight-clients/python/hindsight_client_api/models/reflect_mental_model.py

@@ -29,10 +29,9 @@ class ReflectMentalModel(BaseModel):
     id: StrictStr = Field(description="Mental model ID")
     name: StrictStr = Field(description="Mental model name")
     type: StrictStr = Field(description="Mental model type: entity, concept, event")
-    subtype: StrictStr = Field(description="Mental model subtype: structural, emergent, learned")
-    description: StrictStr = Field(description="Brief description")
-    summary: Optional[StrictStr] = None
-    __properties: ClassVar[List[str]] = ["id", "name", "type", "subtype", "description", "summary"]
+    subtype: StrictStr = Field(description="Mental model subtype: structural, emergent, learned, directive")
+    observations: Optional[List[StrictStr]] = None
+    __properties: ClassVar[List[str]] = ["id", "name", "type", "subtype", "observations"]
 
     model_config = ConfigDict(
         populate_by_name=True,
@@ -73,10 +72,10 @@ class ReflectMentalModel(BaseModel):
             exclude=excluded_fields,
             exclude_none=True,
         )
-        # set to None if summary (nullable) is None
+        # set to None if observations (nullable) is None
         # and model_fields_set contains the field
-        if self.summary is None and "summary" in self.model_fields_set:
-            _dict['summary'] = None
+        if self.observations is None and "observations" in self.model_fields_set:
+            _dict['observations'] = None
 
         return _dict
 
@@ -94,8 +93,7 @@ class ReflectMentalModel(BaseModel):
             "name": obj.get("name"),
             "type": obj.get("type"),
             "subtype": obj.get("subtype"),
-            "description": obj.get("description"),
-            "summary": obj.get("summary")
+            "observations": obj.get("observations")
         })
         return _obj
 

hindsight-clients/python/hindsight_client_api/models/reflect_trace.py

@@ -20,6 +20,7 @@ import json
 from pydantic import BaseModel, ConfigDict, Field
 from typing import Any, ClassVar, Dict, List, Optional
 from hindsight_client_api.models.reflect_llm_call import ReflectLLMCall
+from hindsight_client_api.models.reflect_mental_model import ReflectMentalModel
 from hindsight_client_api.models.reflect_tool_call import ReflectToolCall
 from typing import Optional, Set
 from typing_extensions import Self
@@ -30,7 +31,8 @@ class ReflectTrace(BaseModel):
     """ # noqa: E501
     tool_calls: Optional[List[ReflectToolCall]] = Field(default=None, description="Tool calls made during reflection")
     llm_calls: Optional[List[ReflectLLMCall]] = Field(default=None, description="LLM calls made during reflection")
-    __properties: ClassVar[List[str]] = ["tool_calls", "llm_calls"]
+    mental_models: Optional[List[ReflectMentalModel]] = Field(default=None, description="Mental models used during reflection (includes directives with subtype='directive')")
+    __properties: ClassVar[List[str]] = ["tool_calls", "llm_calls", "mental_models"]
 
     model_config = ConfigDict(
         populate_by_name=True,
@@ -85,6 +87,13 @@ class ReflectTrace(BaseModel):
                 if _item_llm_calls:
                     _items.append(_item_llm_calls.to_dict())
             _dict['llm_calls'] = _items
+        # override the default output from pydantic by calling `to_dict()` of each item in mental_models (list)
+        _items = []
+        if self.mental_models:
+            for _item_mental_models in self.mental_models:
+                if _item_mental_models:
+                    _items.append(_item_mental_models.to_dict())
+            _dict['mental_models'] = _items
         return _dict
 
     @classmethod
@@ -98,7 +107,8 @@ class ReflectTrace(BaseModel):
 
         _obj = cls.model_validate({
             "tool_calls": [ReflectToolCall.from_dict(_item) for _item in obj["tool_calls"]] if obj.get("tool_calls") is not None else None,
-            "llm_calls": [ReflectLLMCall.from_dict(_item) for _item in obj["llm_calls"]] if obj.get("llm_calls") is not None else None
+            "llm_calls": [ReflectLLMCall.from_dict(_item) for _item in obj["llm_calls"]] if obj.get("llm_calls") is not None else None,
+            "mental_models": [ReflectMentalModel.from_dict(_item) for _item in obj["mental_models"]] if obj.get("mental_models") is not None else None
         })
         return _obj
 

hindsight-clients/python/hindsight_client_api/models/update_mental_model_request.py

@@ -0,0 +1,99 @@
+# coding: utf-8
+
+"""
+    Hindsight HTTP API
+
+    HTTP API for Hindsight
+
+    The version of the OpenAPI document: 0.1.0
+    Generated by OpenAPI Generator (https://openapi-generator.tech)
+
+    Do not edit the class manually.
+"""  # noqa: E501
+
+
+from __future__ import annotations
+import pprint
+import re  # noqa: F401
+import json
+
+from pydantic import BaseModel, ConfigDict, StrictStr
+from typing import Any, ClassVar, Dict, List, Optional
+from typing import Optional, Set
+from typing_extensions import Self
+
+class UpdateMentalModelRequest(BaseModel):
+    """
+    Request model for updating a mental model.
+    """ # noqa: E501
+    name: Optional[StrictStr] = None
+    description: Optional[StrictStr] = None
+    __properties: ClassVar[List[str]] = ["name", "description"]
+
+    model_config = ConfigDict(
+        populate_by_name=True,
+        validate_assignment=True,
+        protected_namespaces=(),
+    )
+
+
+    def to_str(self) -> str:
+        """Returns the string representation of the model using alias"""
+        return pprint.pformat(self.model_dump(by_alias=True))
+
+    def to_json(self) -> str:
+        """Returns the JSON representation of the model using alias"""
+        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
+        return json.dumps(self.to_dict())
+
+    @classmethod
+    def from_json(cls, json_str: str) -> Optional[Self]:
+        """Create an instance of UpdateMentalModelRequest from a JSON string"""
+        return cls.from_dict(json.loads(json_str))
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Return the dictionary representation of the model using alias.
+
+        This has the following differences from calling pydantic's
+        `self.model_dump(by_alias=True)`:
+
+        * `None` is only added to the output dict for nullable fields that
+          were set at model initialization. Other fields with value `None`
+          are ignored.
+        """
+        excluded_fields: Set[str] = set([
+        ])
+
+        _dict = self.model_dump(
+            by_alias=True,
+            exclude=excluded_fields,
+            exclude_none=True,
+        )
+        # set to None if name (nullable) is None
+        # and model_fields_set contains the field
+        if self.name is None and "name" in self.model_fields_set:
+            _dict['name'] = None
+
+        # set to None if description (nullable) is None
+        # and model_fields_set contains the field
+        if self.description is None and "description" in self.model_fields_set:
+            _dict['description'] = None
+
+        return _dict
+
+    @classmethod
+    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
+        """Create an instance of UpdateMentalModelRequest from a dict"""
+        if obj is None:
+            return None
+
+        if not isinstance(obj, dict):
+            return cls.model_validate(obj)
+
+        _obj = cls.model_validate({
+            "name": obj.get("name"),
+            "description": obj.get("description")
+        })
+        return _obj
+
+

hindsight-clients/typescript/generated/sdk.gen.ts

@@ -27,9 +27,6 @@ import type {
   DeleteMentalModelData,
   DeleteMentalModelErrors,
   DeleteMentalModelResponses,
-  GenerateMentalModelData,
-  GenerateMentalModelErrors,
-  GenerateMentalModelResponses,
   GetAgentStatsData,
   GetAgentStatsErrors,
   GetAgentStatsResponses,
@@ -54,6 +51,9 @@ import type {
   GetMentalModelData,
   GetMentalModelErrors,
   GetMentalModelResponses,
+  GetMentalModelVersionData,
+  GetMentalModelVersionErrors,
+  GetMentalModelVersionResponses,
   GetOperationStatusData,
   GetOperationStatusErrors,
   GetOperationStatusResponses,
@@ -74,6 +74,9 @@ import type {
   ListMentalModelsData,
   ListMentalModelsErrors,
   ListMentalModelsResponses,
+  ListMentalModelVersionsData,
+  ListMentalModelVersionsErrors,
+  ListMentalModelVersionsResponses,
   ListOperationsData,
   ListOperationsErrors,
   ListOperationsResponses,
@@ -88,6 +91,9 @@ import type {
   ReflectData,
   ReflectErrors,
   ReflectResponses,
+  RefreshMentalModelData,
+  RefreshMentalModelErrors,
+  RefreshMentalModelResponses,
   RefreshMentalModelsData,
   RefreshMentalModelsErrors,
   RefreshMentalModelsResponses,
@@ -103,6 +109,9 @@ import type {
   UpdateBankDispositionResponses,
   UpdateBankErrors,
   UpdateBankResponses,
+  UpdateMentalModelData,
+  UpdateMentalModelErrors,
+  UpdateMentalModelResponses,
 } from "./types.gen";
 
 export type Options<
@@ -343,7 +352,9 @@ export const listMentalModels = <ThrowOnError extends boolean = false>(
 /**
  * Create mental model
  *
- * Create a pinned mental model. Pinned models are user-defined and persist across refreshes.
+ * Create a mental model. Supports two subtypes:
+ * - 'pinned' (default): User-defined topic, observations are LLM-generated on refresh
+ * - 'directive': User-defined hard rules, observations are provided at creation and never regenerated
  */
 export const createMentalModel = <ThrowOnError extends boolean = false>(
   options: Options<CreateMentalModelData, ThrowOnError>,
@@ -395,6 +406,27 @@ export const getMentalModel = <ThrowOnError extends boolean = false>(
     ...options,
   });
 
+/**
+ * Update mental model
+ *
+ * Update a mental model's name and/or description. Useful for editing directives.
+ */
+export const updateMentalModel = <ThrowOnError extends boolean = false>(
+  options: Options<UpdateMentalModelData, ThrowOnError>,
+) =>
+  (options.client ?? client).patch<
+    UpdateMentalModelResponses,
+    UpdateMentalModelErrors,
+    ThrowOnError
+  >({
+    url: "/v1/default/banks/{bank_id}/mental-models/{model_id}",
+    ...options,
+    headers: {
+      "Content-Type": "application/json",
+      ...options.headers,
+    },
+  });
+
 /**
  * Refresh mental models (async)
  *
@@ -417,19 +449,53 @@ export const refreshMentalModels = <ThrowOnError extends boolean = false>(
   });
 
 /**
- * Generate mental model content (async)
+ * Refresh mental model content (async)
  *
- * Submit a background job to generate/refresh content for a specific mental model. This is useful for newly created learned models or to regenerate content for any model.
+ * Submit a background job to refresh content for a specific mental model. This is useful for newly created learned models or to refresh content for any model.
  */
-export const generateMentalModel = <ThrowOnError extends boolean = false>(
-  options: Options<GenerateMentalModelData, ThrowOnError>,
+export const refreshMentalModel = <ThrowOnError extends boolean = false>(
+  options: Options<RefreshMentalModelData, ThrowOnError>,
 ) =>
   (options.client ?? client).post<
-    GenerateMentalModelResponses,
-    GenerateMentalModelErrors,
+    RefreshMentalModelResponses,
+    RefreshMentalModelErrors,
     ThrowOnError
   >({
-    url: "/v1/default/banks/{bank_id}/mental-models/{model_id}/generate",
+    url: "/v1/default/banks/{bank_id}/mental-models/{model_id}/refresh",
+    ...options,
+  });
+
+/**
+ * List mental model version history
+ *
+ * List all saved versions of a mental model's observations, ordered by version descending.
+ */
+export const listMentalModelVersions = <ThrowOnError extends boolean = false>(
+  options: Options<ListMentalModelVersionsData, ThrowOnError>,
+) =>
+  (options.client ?? client).get<
+    ListMentalModelVersionsResponses,
+    ListMentalModelVersionsErrors,
+    ThrowOnError
+  >({
+    url: "/v1/default/banks/{bank_id}/mental-models/{model_id}/versions",
+    ...options,
+  });
+
+/**
+ * Get specific mental model version
+ *
+ * Get observations from a specific version of a mental model.
+ */
+export const getMentalModelVersion = <ThrowOnError extends boolean = false>(
+  options: Options<GetMentalModelVersionData, ThrowOnError>,
+) =>
+  (options.client ?? client).get<
+    GetMentalModelVersionResponses,
+    GetMentalModelVersionErrors,
+    ThrowOnError
+  >({
+    url: "/v1/default/banks/{bank_id}/mental-models/{model_id}/versions/{version}",
     ...options,
   });
 

hindsight-clients/typescript/generated/types.gen.ts

@@ -314,7 +314,7 @@ export type CreateBankRequest = {
 /**
  * CreateMentalModelRequest
  *
- * Request model for creating a pinned mental model.
+ * Request model for creating a mental model.
  */
 export type CreateMentalModelRequest = {
   /**
@@ -329,6 +329,18 @@ export type CreateMentalModelRequest = {
    * One-liner description for quick scanning
    */
   description: string;
+  /**
+   * Subtype
+   *
+   * Type of mental model: 'pinned' (observations LLM-generated) or 'directive' (observations user-provided)
+   */
+  subtype?: string;
+  /**
+   * Observations
+   *
+   * For directives only: list of user-provided observations. Required when subtype='directive'.
+   */
+  observations?: Array<ObservationInput> | null;
   /**
    * Tags
    *
@@ -830,6 +842,38 @@ export type MemoryItem = {
   tags?: Array<string> | null;
 };
 
+/**
+ * MentalModelFreshnessResponse
+ *
+ * Freshness information for a mental model.
+ */
+export type MentalModelFreshnessResponse = {
+  /**
+   * Is Up To Date
+   *
+   * Whether the model has been refreshed since the last memory was added
+   */
+  is_up_to_date: boolean;
+  /**
+   * Last Refresh At
+   *
+   * When the model was last refreshed (ISO format)
+   */
+  last_refresh_at: string | null;
+  /**
+   * Memories Since Refresh
+   *
+   * Number of memories added since last refresh
+   */
+  memories_since_refresh: number;
+  /**
+   * Reasons
+   *
+   * Reasons why the model needs refresh (empty if up to date). Possible values: never_refreshed, new_memories, mission_changed, disposition_changed, directives_changed
+   */
+  reasons?: Array<string>;
+};
+
 /**
  * MentalModelListResponse
  *
@@ -845,27 +889,53 @@ export type MentalModelListResponse = {
 /**
  * MentalModelObservationResponse
  *
- * An observation within a mental model with its supporting memories.
+ * An observation within a mental model with its supporting evidence.
  */
 export type MentalModelObservationResponse = {
   /**
    * Title
    *
-   * Observation header (empty for intro)
+   * Short summary title for the observation
    */
   title: string;
   /**
-   * Text
+   * Content
    *
-   * Observation content
+   * The observation content - detailed explanation
    */
-  text: string;
+  content: string;
   /**
-   * Based On
+   * Evidence
    *
-   * Memory IDs supporting this observation
+   * Supporting evidence with quotes
    */
-  based_on?: Array<string>;
+  evidence?: Array<ObservationEvidenceResponse>;
+  /**
+   * Created At
+   *
+   * When this observation was first created (ISO format)
+   */
+  created_at: string;
+  /**
+   * Trend
+   *
+   * Computed trend: stable, strengthening, weakening, new, stale
+   */
+  trend: string;
+  /**
+   * Evidence Count
+   *
+   * Number of evidence items supporting this observation
+   */
+  evidence_count: number;
+  /**
+   * Evidence Span
+   *
+   * Time span of evidence: {from: iso_date, to: iso_date}
+   */
+  evidence_span: {
+    [key: string]: unknown;
+  };
 };
 
 /**
@@ -900,6 +970,12 @@ export type MentalModelResponse = {
    * Structured observations with per-observation fact attribution
    */
   observations?: Array<MentalModelObservationResponse>;
+  /**
+   * Version
+   *
+   * Version number of the mental model observations
+   */
+  version?: number;
   /**
    * Entity Id
    */
@@ -916,12 +992,74 @@ export type MentalModelResponse = {
    * Last Updated
    */
   last_updated?: string | null;
+  /**
+   * Last Refresh At
+   *
+   * When observations were last refreshed (ISO format)
+   */
+  last_refresh_at?: string | null;
+  /**
+   * Freshness info (null for directive subtypes which don't need refresh)
+   */
+  freshness?: MentalModelFreshnessResponse | null;
   /**
    * Created At
    */
   created_at: string;
 };
 
+/**
+ * ObservationEvidenceResponse
+ *
+ * A single piece of evidence supporting an observation.
+ */
+export type ObservationEvidenceResponse = {
+  /**
+   * Memory Id
+   *
+   * ID of the memory unit this evidence comes from
+   */
+  memory_id: string;
+  /**
+   * Quote
+   *
+   * Exact quote from the memory supporting the observation
+   */
+  quote: string;
+  /**
+   * Relevance
+   *
+   * Brief explanation of how this quote supports the observation
+   */
+  relevance: string;
+  /**
+   * Timestamp
+   *
+   * When the source memory was created (ISO format)
+   */
+  timestamp: string;
+};
+
+/**
+ * ObservationInput
+ *
+ * Input model for a single observation.
+ */
+export type ObservationInput = {
+  /**
+   * Title
+   *
+   * Short title/header for the observation
+   */
+  title: string;
+  /**
+   * Content
+   *
+   * Content of the observation
+   */
+  content: string;
+};
+
 /**
  * OperationResponse
  *
@@ -1270,21 +1408,15 @@ export type ReflectMentalModel = {
   /**
    * Subtype
    *
-   * Mental model subtype: structural, emergent, learned
+   * Mental model subtype: structural, emergent, learned, directive
    */
   subtype: string;
   /**
-   * Description
+   * Observations
    *
-   * Brief description
+   * Observations for directive mental models (subtype='directive')
    */
-  description: string;
-  /**
-   * Summary
-   *
-   * Full summary (when looked up in detail)
-   */
-  summary?: string | null;
+  observations?: Array<string> | null;
 };
 
 /**
@@ -1436,6 +1568,12 @@ export type ReflectTrace = {
    * LLM calls made during reflection
    */
   llm_calls?: Array<ReflectLlmCall>;
+  /**
+   * Mental Models
+   *
+   * Mental models used during reflection (includes directives with subtype='directive')
+   */
+  mental_models?: Array<ReflectMentalModel>;
 };
 
 /**
@@ -1590,6 +1728,26 @@ export type UpdateDispositionRequest = {
   disposition: DispositionTraits;
 };
 
+/**
+ * UpdateMentalModelRequest
+ *
+ * Request model for updating a mental model.
+ */
+export type UpdateMentalModelRequest = {
+  /**
+   * Name
+   *
+   * New name for the mental model
+   */
+  name?: string | null;
+  /**
+   * Description
+   *
+   * New description/rule text
+   */
+  description?: string | null;
+};
+
 /**
  * ValidationError
  */
@@ -2226,6 +2384,48 @@ export type GetMentalModelResponses = {
 export type GetMentalModelResponse =
   GetMentalModelResponses[keyof GetMentalModelResponses];
 
+export type UpdateMentalModelData = {
+  body: UpdateMentalModelRequest;
+  headers?: {
+    /**
+     * Authorization
+     */
+    authorization?: string | null;
+  };
+  path: {
+    /**
+     * Bank Id
+     */
+    bank_id: string;
+    /**
+     * Model Id
+     */
+    model_id: string;
+  };
+  query?: never;
+  url: "/v1/default/banks/{bank_id}/mental-models/{model_id}";
+};
+
+export type UpdateMentalModelErrors = {
+  /**
+   * Validation Error
+   */
+  422: HttpValidationError;
+};
+
+export type UpdateMentalModelError =
+  UpdateMentalModelErrors[keyof UpdateMentalModelErrors];
+
+export type UpdateMentalModelResponses = {
+  /**
+   * Successful Response
+   */
+  200: MentalModelResponse;
+};
+
+export type UpdateMentalModelResponse =
+  UpdateMentalModelResponses[keyof UpdateMentalModelResponses];
+
 export type RefreshMentalModelsData = {
   /**
    * Body
@@ -2267,7 +2467,7 @@ export type RefreshMentalModelsResponses = {
 export type RefreshMentalModelsResponse =
   RefreshMentalModelsResponses[keyof RefreshMentalModelsResponses];
 
-export type GenerateMentalModelData = {
+export type RefreshMentalModelData = {
   body?: never;
   headers?: {
     /**
@@ -2286,28 +2486,110 @@ export type GenerateMentalModelData = {
     model_id: string;
   };
   query?: never;
-  url: "/v1/default/banks/{bank_id}/mental-models/{model_id}/generate";
+  url: "/v1/default/banks/{bank_id}/mental-models/{model_id}/refresh";
 };
 
-export type GenerateMentalModelErrors = {
+export type RefreshMentalModelErrors = {
   /**
    * Validation Error
    */
   422: HttpValidationError;
 };
 
-export type GenerateMentalModelError =
-  GenerateMentalModelErrors[keyof GenerateMentalModelErrors];
+export type RefreshMentalModelError =
+  RefreshMentalModelErrors[keyof RefreshMentalModelErrors];
 
-export type GenerateMentalModelResponses = {
+export type RefreshMentalModelResponses = {
   /**
    * Successful Response
    */
   200: AsyncOperationSubmitResponse;
 };
 
-export type GenerateMentalModelResponse =
-  GenerateMentalModelResponses[keyof GenerateMentalModelResponses];
+export type RefreshMentalModelResponse =
+  RefreshMentalModelResponses[keyof RefreshMentalModelResponses];
+
+export type ListMentalModelVersionsData = {
+  body?: never;
+  headers?: {
+    /**
+     * Authorization
+     */
+    authorization?: string | null;
+  };
+  path: {
+    /**
+     * Bank Id
+     */
+    bank_id: string;
+    /**
+     * Model Id
+     */
+    model_id: string;
+  };
+  query?: never;
+  url: "/v1/default/banks/{bank_id}/mental-models/{model_id}/versions";
+};
+
+export type ListMentalModelVersionsErrors = {
+  /**
+   * Validation Error
+   */
+  422: HttpValidationError;
+};
+
+export type ListMentalModelVersionsError =
+  ListMentalModelVersionsErrors[keyof ListMentalModelVersionsErrors];
+
+export type ListMentalModelVersionsResponses = {
+  /**
+   * Successful Response
+   */
+  200: unknown;
+};
+
+export type GetMentalModelVersionData = {
+  body?: never;
+  headers?: {
+    /**
+     * Authorization
+     */
+    authorization?: string | null;
+  };
+  path: {
+    /**
+     * Bank Id
+     */
+    bank_id: string;
+    /**
+     * Model Id
+     */
+    model_id: string;
+    /**
+     * Version
+     */
+    version: number;
+  };
+  query?: never;
+  url: "/v1/default/banks/{bank_id}/mental-models/{model_id}/versions/{version}";
+};
+
+export type GetMentalModelVersionErrors = {
+  /**
+   * Validation Error
+   */
+  422: HttpValidationError;
+};
+
+export type GetMentalModelVersionError =
+  GetMentalModelVersionErrors[keyof GetMentalModelVersionErrors];
+
+export type GetMentalModelVersionResponses = {
+  /**
+   * Successful Response
+   */
+  200: unknown;
+};
 
 export type ListDocumentsData = {
   body?: never;

hindsight-control-plane/package.json

@@ -38,6 +38,8 @@
     "@radix-ui/react-slider": "^1.3.6",
     "@radix-ui/react-slot": "^1.2.4",
     "@radix-ui/react-switch": "^1.2.6",
+    "@radix-ui/react-tabs": "^1.1.13",
+    "@radix-ui/react-tooltip": "^1.2.8",
     "@tailwindcss/postcss": "^4.1.17",
     "@tailwindcss/typography": "^0.5.19",
     "@types/cytoscape": "^3.21.9",

hindsight-control-plane/src/app/api/banks/[bankId]/mental-models/[modelId]/refresh/route.ts

@@ -16,19 +16,19 @@ export async function POST(
       return NextResponse.json({ error: "model_id is required" }, { status: 400 });
     }
 
-    const response = await sdk.generateMentalModel({
+    const response = await sdk.refreshMentalModel({
       client: lowLevelClient,
       path: { bank_id: bankId, model_id: modelId },
     });
 
     if (response.error) {
-      console.error("API error generating mental model:", response.error);
-      return NextResponse.json({ error: "Failed to generate mental model" }, { status: 500 });
+      console.error("API error refreshing mental model:", response.error);
+      return NextResponse.json({ error: "Failed to refresh mental model" }, { status: 500 });
     }
 
     return NextResponse.json(response.data, { status: 200 });
   } catch (error) {
-    console.error("Error generating mental model:", error);
-    return NextResponse.json({ error: "Failed to generate mental model" }, { status: 500 });
+    console.error("Error refreshing mental model:", error);
+    return NextResponse.json({ error: "Failed to refresh mental model" }, { status: 500 });
   }
 }

hindsight-control-plane/src/app/api/banks/[bankId]/mental-models/[modelId]/route.ts

@@ -1,6 +1,52 @@
 import { NextResponse } from "next/server";
 import { sdk, lowLevelClient } from "@/lib/hindsight-client";
 
+const DATAPLANE_URL = process.env.HINDSIGHT_CP_DATAPLANE_API_URL || "http://localhost:8888";
+
+export async function PATCH(
+  request: Request,
+  { params }: { params: Promise<{ bankId: string; modelId: string }> }
+) {
+  try {
+    const { bankId, modelId } = await params;
+
+    if (!bankId) {
+      return NextResponse.json({ error: "bank_id is required" }, { status: 400 });
+    }
+
+    if (!modelId) {
+      return NextResponse.json({ error: "model_id is required" }, { status: 400 });
+    }
+
+    const body = await request.json();
+
+    // Call the dataplane API directly since SDK may not have the update method yet
+    const response = await fetch(
+      `${DATAPLANE_URL}/v1/default/banks/${bankId}/mental-models/${modelId}`,
+      {
+        method: "PATCH",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify(body),
+      }
+    );
+
+    if (!response.ok) {
+      const errorText = await response.text();
+      console.error("API error updating mental model:", errorText);
+      return NextResponse.json(
+        { error: errorText || "Failed to update mental model" },
+        { status: response.status }
+      );
+    }
+
+    const data = await response.json();
+    return NextResponse.json(data, { status: 200 });
+  } catch (error) {
+    console.error("Error updating mental model:", error);
+    return NextResponse.json({ error: "Failed to update mental model" }, { status: 500 });
+  }
+}
+
 export async function DELETE(
   request: Request,
   { params }: { params: Promise<{ bankId: string; modelId: string }> }

hindsight-control-plane/src/app/api/banks/[bankId]/mental-models/[modelId]/versions/[version]/route.ts

@@ -0,0 +1,47 @@
+import { NextResponse } from "next/server";
+
+const DATAPLANE_URL = process.env.HINDSIGHT_CP_DATAPLANE_API_URL || "http://localhost:8888";
+
+export async function GET(
+  request: Request,
+  { params }: { params: Promise<{ bankId: string; modelId: string; version: string }> }
+) {
+  try {
+    const { bankId, modelId, version } = await params;
+
+    if (!bankId) {
+      return NextResponse.json({ error: "bank_id is required" }, { status: 400 });
+    }
+
+    if (!modelId) {
+      return NextResponse.json({ error: "model_id is required" }, { status: 400 });
+    }
+
+    if (!version) {
+      return NextResponse.json({ error: "version is required" }, { status: 400 });
+    }
+
+    const response = await fetch(
+      `${DATAPLANE_URL}/v1/default/banks/${bankId}/mental-models/${modelId}/versions/${version}`,
+      {
+        method: "GET",
+        headers: { "Content-Type": "application/json" },
+      }
+    );
+
+    if (!response.ok) {
+      const errorText = await response.text();
+      console.error("API error getting mental model version:", errorText);
+      return NextResponse.json(
+        { error: errorText || "Failed to get mental model version" },
+        { status: response.status }
+      );
+    }
+
+    const data = await response.json();
+    return NextResponse.json(data, { status: 200 });
+  } catch (error) {
+    console.error("Error getting mental model version:", error);
+    return NextResponse.json({ error: "Failed to get mental model version" }, { status: 500 });
+  }
+}

hindsight-control-plane/src/app/api/banks/[bankId]/mental-models/[modelId]/versions/route.ts

@@ -0,0 +1,43 @@
+import { NextResponse } from "next/server";
+
+const DATAPLANE_URL = process.env.HINDSIGHT_CP_DATAPLANE_API_URL || "http://localhost:8888";
+
+export async function GET(
+  request: Request,
+  { params }: { params: Promise<{ bankId: string; modelId: string }> }
+) {
+  try {
+    const { bankId, modelId } = await params;
+
+    if (!bankId) {
+      return NextResponse.json({ error: "bank_id is required" }, { status: 400 });
+    }
+
+    if (!modelId) {
+      return NextResponse.json({ error: "model_id is required" }, { status: 400 });
+    }
+
+    const response = await fetch(
+      `${DATAPLANE_URL}/v1/default/banks/${bankId}/mental-models/${modelId}/versions`,
+      {
+        method: "GET",
+        headers: { "Content-Type": "application/json" },
+      }
+    );
+
+    if (!response.ok) {
+      const errorText = await response.text();
+      console.error("API error listing mental model versions:", errorText);
+      return NextResponse.json(
+        { error: errorText || "Failed to list mental model versions" },
+        { status: response.status }
+      );
+    }
+
+    const data = await response.json();
+    return NextResponse.json(data, { status: 200 });
+  } catch (error) {
+    console.error("Error listing mental model versions:", error);
+    return NextResponse.json({ error: "Failed to list mental model versions" }, { status: 500 });
+  }
+}

hindsight-control-plane/src/app/api/banks/[bankId]/mental-models/route.ts

@@ -6,11 +6,34 @@ const DATAPLANE_URL = process.env.HINDSIGHT_CP_DATAPLANE_API_URL || "http://loca
 export async function GET(request: Request, { params }: { params: Promise<{ bankId: string }> }) {
   try {
     const { bankId } = await params;
+    const { searchParams } = new URL(request.url);
+    const subtype = searchParams.get("subtype");
 
     if (!bankId) {
       return NextResponse.json({ error: "bank_id is required" }, { status: 400 });
     }
 
+    // If subtype is specified, call the dataplane API directly with the query param
+    if (subtype) {
+      const response = await fetch(
+        `${DATAPLANE_URL}/v1/default/banks/${bankId}/mental-models?subtype=${subtype}`,
+        { method: "GET" }
+      );
+
+      if (!response.ok) {
+        const errorText = await response.text();
+        console.error("API error listing mental models:", errorText);
+        return NextResponse.json(
+          { error: "Failed to list mental models" },
+          { status: response.status }
+        );
+      }
+
+      const data = await response.json();
+      return NextResponse.json(data, { status: 200 });
+    }
+
+    // Default: use SDK which excludes directives
     const response = await sdk.listMentalModels({
       client: lowLevelClient,
       path: { bank_id: bankId },

hindsight-control-plane/src/components/data-view.tsx

@@ -18,6 +18,7 @@ import {
   Settings2,
   Eye,
   EyeOff,
+  RefreshCw,
 } from "lucide-react";
 import {
   Table,
@@ -248,11 +249,9 @@ export function DataView({ factType }: DataViewProps) {
   return (
     <div>
       {loading ? (
-        <div className="flex items-center justify-center py-20">
-          <div className="text-center">
-            <div className="text-4xl mb-2">⏳</div>
-            <div className="text-sm text-muted-foreground">Loading memories...</div>
-          </div>
+        <div className="text-center py-12">
+          <RefreshCw className="w-8 h-8 mx-auto mb-3 text-muted-foreground animate-spin" />
+          <p className="text-muted-foreground">Loading memories...</p>
         </div>
       ) : data ? (
         <>

hindsight-control-plane/src/components/memory-detail-modal.tsx

@@ -0,0 +1,391 @@
+"use client";
+
+import { useState, useEffect } from "react";
+import { client } from "@/lib/api";
+import { useBank } from "@/lib/bank-context";
+import { Dialog, DialogContent, DialogHeader, DialogTitle } from "@/components/ui/dialog";
+import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs";
+import { Loader2, Calendar, Tag, Users, FileText, Layers } from "lucide-react";
+
+interface MemoryDetail {
+  id: string;
+  text: string;
+  context: string;
+  date: string;
+  type: string;
+  mentioned_at: string | null;
+  occurred_start: string | null;
+  occurred_end: string | null;
+  entities: string[];
+  document_id: string | null;
+  chunk_id: string | null;
+  tags: string[];
+}
+
+interface MemoryDetailModalProps {
+  memoryId: string | null;
+  onClose: () => void;
+}
+
+export function MemoryDetailModal({ memoryId, onClose }: MemoryDetailModalProps) {
+  const { currentBank } = useBank();
+  const [memory, setMemory] = useState<MemoryDetail | null>(null);
+  const [loading, setLoading] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+  const [activeTab, setActiveTab] = useState("memory");
+
+  // Document and chunk data
+  const [document, setDocument] = useState<any>(null);
+  const [chunk, setChunk] = useState<any>(null);
+  const [loadingDocument, setLoadingDocument] = useState(false);
+  const [loadingChunk, setLoadingChunk] = useState(false);
+
+  // Load memory details
+  useEffect(() => {
+    if (!memoryId || !currentBank) return;
+
+    const loadMemory = async () => {
+      setLoading(true);
+      setError(null);
+      setMemory(null);
+      setDocument(null);
+      setChunk(null);
+      setActiveTab("memory");
+
+      try {
+        const data = await client.getMemory(memoryId, currentBank);
+        setMemory(data);
+      } catch (err) {
+        console.error("Error loading memory:", err);
+        setError((err as Error).message);
+      } finally {
+        setLoading(false);
+      }
+    };
+
+    loadMemory();
+  }, [memoryId, currentBank]);
+
+  // Load document when tab is selected
+  useEffect(() => {
+    if (activeTab !== "document" || !memory?.document_id || !currentBank || document) return;
+
+    const loadDocument = async () => {
+      setLoadingDocument(true);
+      try {
+        const data = await client.getDocument(memory.document_id!, currentBank);
+        setDocument(data);
+      } catch (err) {
+        console.error("Error loading document:", err);
+      } finally {
+        setLoadingDocument(false);
+      }
+    };
+
+    loadDocument();
+  }, [activeTab, memory?.document_id, currentBank, document]);
+
+  // Load chunk when tab is selected
+  useEffect(() => {
+    if (activeTab !== "chunk" || !memory?.chunk_id || chunk) return;
+
+    const loadChunk = async () => {
+      setLoadingChunk(true);
+      try {
+        const data = await client.getChunk(memory.chunk_id!);
+        setChunk(data);
+      } catch (err) {
+        console.error("Error loading chunk:", err);
+      } finally {
+        setLoadingChunk(false);
+      }
+    };
+
+    loadChunk();
+  }, [activeTab, memory?.chunk_id, chunk]);
+
+  const isOpen = memoryId !== null;
+
+  return (
+    <Dialog open={isOpen} onOpenChange={(open) => !open && onClose()}>
+      <DialogContent className="max-w-2xl max-h-[80vh] overflow-hidden flex flex-col">
+        <DialogHeader>
+          <DialogTitle>Memory Details</DialogTitle>
+        </DialogHeader>
+
+        {loading ? (
+          <div className="flex items-center justify-center py-20">
+            <Loader2 className="w-8 h-8 animate-spin text-muted-foreground" />
+          </div>
+        ) : error ? (
+          <div className="flex items-center justify-center py-20">
+            <div className="text-center text-destructive">
+              <div className="text-sm">Error: {error}</div>
+            </div>
+          </div>
+        ) : memory ? (
+          <Tabs
+            value={activeTab}
+            onValueChange={setActiveTab}
+            className="flex-1 flex flex-col overflow-hidden"
+          >
+            <TabsList className="grid w-full grid-cols-3">
+              <TabsTrigger value="memory" className="flex items-center gap-1.5">
+                <FileText className="w-3.5 h-3.5" />
+                Memory
+              </TabsTrigger>
+              <TabsTrigger
+                value="chunk"
+                disabled={!memory.chunk_id}
+                className="flex items-center gap-1.5"
+              >
+                <Layers className="w-3.5 h-3.5" />
+                Chunk
+              </TabsTrigger>
+              <TabsTrigger
+                value="document"
+                disabled={!memory.document_id}
+                className="flex items-center gap-1.5"
+              >
+                <FileText className="w-3.5 h-3.5" />
+                Document
+              </TabsTrigger>
+            </TabsList>
+
+            <div className="flex-1 overflow-y-auto mt-4">
+              <TabsContent value="memory" className="mt-0 space-y-4">
+                {/* Memory text */}
+                <div className="p-4 bg-muted rounded-lg">
+                  <div className="text-xs font-bold text-muted-foreground uppercase mb-2">
+                    Memory Text
+                  </div>
+                  <p className="text-sm text-foreground leading-relaxed">{memory.text}</p>
+                </div>
+
+                {/* Metadata grid */}
+                <div className="grid grid-cols-2 gap-3">
+                  <div className="p-3 bg-muted rounded-lg">
+                    <div className="text-xs font-bold text-muted-foreground uppercase mb-1">
+                      Type
+                    </div>
+                    <div className="text-sm text-foreground capitalize">{memory.type}</div>
+                  </div>
+                  {memory.context && (
+                    <div className="p-3 bg-muted rounded-lg">
+                      <div className="text-xs font-bold text-muted-foreground uppercase mb-1">
+                        Context
+                      </div>
+                      <div className="text-sm text-foreground">{memory.context}</div>
+                    </div>
+                  )}
+                </div>
+
+                {/* Dates */}
+                {(memory.mentioned_at || memory.occurred_start) && (
+                  <div className="grid grid-cols-2 gap-3">
+                    {memory.mentioned_at && (
+                      <div className="p-3 bg-muted rounded-lg">
+                        <div className="text-xs font-bold text-muted-foreground uppercase mb-1 flex items-center gap-1">
+                          <Calendar className="w-3 h-3" />
+                          Mentioned At
+                        </div>
+                        <div className="text-sm text-foreground">
+                          {new Date(memory.mentioned_at).toLocaleString()}
+                        </div>
+                      </div>
+                    )}
+                    {memory.occurred_start && (
+                      <div className="p-3 bg-muted rounded-lg">
+                        <div className="text-xs font-bold text-muted-foreground uppercase mb-1 flex items-center gap-1">
+                          <Calendar className="w-3 h-3" />
+                          Occurred
+                        </div>
+                        <div className="text-sm text-foreground">
+                          {new Date(memory.occurred_start).toLocaleDateString()}
+                          {memory.occurred_end && memory.occurred_end !== memory.occurred_start && (
+                            <> - {new Date(memory.occurred_end).toLocaleDateString()}</>
+                          )}
+                        </div>
+                      </div>
+                    )}
+                  </div>
+                )}
+
+                {/* Entities */}
+                {memory.entities && memory.entities.length > 0 && (
+                  <div className="p-3 bg-muted rounded-lg">
+                    <div className="text-xs font-bold text-muted-foreground uppercase mb-2 flex items-center gap-1">
+                      <Users className="w-3 h-3" />
+                      Entities
+                    </div>
+                    <div className="flex flex-wrap gap-1.5">
+                      {memory.entities.map((entity, idx) => (
+                        <span
+                          key={idx}
+                          className="px-2 py-0.5 bg-background rounded text-xs text-foreground"
+                        >
+                          {entity}
+                        </span>
+                      ))}
+                    </div>
+                  </div>
+                )}
+
+                {/* Tags */}
+                {memory.tags && memory.tags.length > 0 && (
+                  <div className="p-3 bg-muted rounded-lg">
+                    <div className="text-xs font-bold text-muted-foreground uppercase mb-2 flex items-center gap-1">
+                      <Tag className="w-3 h-3" />
+                      Tags
+                    </div>
+                    <div className="flex flex-wrap gap-1.5">
+                      {memory.tags.map((tag, idx) => (
+                        <span
+                          key={idx}
+                          className="px-2 py-0.5 bg-primary/10 text-primary rounded text-xs"
+                        >
+                          {tag}
+                        </span>
+                      ))}
+                    </div>
+                  </div>
+                )}
+
+                {/* IDs */}
+                <div className="p-3 bg-muted rounded-lg">
+                  <div className="text-xs font-bold text-muted-foreground uppercase mb-1">
+                    Memory ID
+                  </div>
+                  <code className="text-xs font-mono text-muted-foreground break-all">
+                    {memory.id}
+                  </code>
+                </div>
+              </TabsContent>
+
+              <TabsContent value="chunk" className="mt-0 space-y-4">
+                {loadingChunk ? (
+                  <div className="flex items-center justify-center py-12">
+                    <Loader2 className="w-6 h-6 animate-spin text-muted-foreground" />
+                  </div>
+                ) : chunk ? (
+                  <>
+                    <div className="grid grid-cols-2 gap-3">
+                      <div className="p-3 bg-muted rounded-lg">
+                        <div className="text-xs font-bold text-muted-foreground uppercase mb-1">
+                          Chunk Index
+                        </div>
+                        <div className="text-sm text-foreground">{chunk.chunk_index}</div>
+                      </div>
+                      {chunk.chunk_text && (
+                        <div className="p-3 bg-muted rounded-lg">
+                          <div className="text-xs font-bold text-muted-foreground uppercase mb-1">
+                            Text Length
+                          </div>
+                          <div className="text-sm text-foreground">
+                            {chunk.chunk_text.length.toLocaleString()} chars
+                          </div>
+                        </div>
+                      )}
+                    </div>
+
+                    {chunk.chunk_text && (
+                      <div>
+                        <div className="text-xs font-bold text-muted-foreground uppercase mb-2">
+                          Chunk Text
+                        </div>
+                        <div className="p-4 bg-muted rounded-lg border border-border max-h-[300px] overflow-y-auto">
+                          <pre className="text-sm whitespace-pre-wrap font-mono text-foreground">
+                            {chunk.chunk_text}
+                          </pre>
+                        </div>
+                      </div>
+                    )}
+
+                    <div className="p-3 bg-muted rounded-lg">
+                      <div className="text-xs font-bold text-muted-foreground uppercase mb-1">
+                        Chunk ID
+                      </div>
+                      <code className="text-xs font-mono text-muted-foreground break-all">
+                        {chunk.chunk_id}
+                      </code>
+                    </div>
+                  </>
+                ) : (
+                  <div className="text-center py-12 text-muted-foreground">
+                    No chunk data available
+                  </div>
+                )}
+              </TabsContent>
+
+              <TabsContent value="document" className="mt-0 space-y-4">
+                {loadingDocument ? (
+                  <div className="flex items-center justify-center py-12">
+                    <Loader2 className="w-6 h-6 animate-spin text-muted-foreground" />
+                  </div>
+                ) : document ? (
+                  <>
+                    <div className="grid grid-cols-2 gap-3">
+                      {document.created_at && (
+                        <div className="p-3 bg-muted rounded-lg">
+                          <div className="text-xs font-bold text-muted-foreground uppercase mb-1">
+                            Created
+                          </div>
+                          <div className="text-sm text-foreground">
+                            {new Date(document.created_at).toLocaleString()}
+                          </div>
+                        </div>
+                      )}
+                      <div className="p-3 bg-muted rounded-lg">
+                        <div className="text-xs font-bold text-muted-foreground uppercase mb-1">
+                          Memory Units
+                        </div>
+                        <div className="text-sm text-foreground">{document.memory_unit_count}</div>
+                      </div>
+                    </div>
+
+                    {document.original_text && (
+                      <>
+                        <div className="p-3 bg-muted rounded-lg">
+                          <div className="text-xs font-bold text-muted-foreground uppercase mb-1">
+                            Text Length
+                          </div>
+                          <div className="text-sm text-foreground">
+                            {document.original_text.length.toLocaleString()} chars
+                          </div>
+                        </div>
+
+                        <div>
+                          <div className="text-xs font-bold text-muted-foreground uppercase mb-2">
+                            Original Text
+                          </div>
+                          <div className="p-4 bg-muted rounded-lg border border-border max-h-[300px] overflow-y-auto">
+                            <pre className="text-sm whitespace-pre-wrap font-mono text-foreground">
+                              {document.original_text}
+                            </pre>
+                          </div>
+                        </div>
+                      </>
+                    )}
+
+                    <div className="p-3 bg-muted rounded-lg">
+                      <div className="text-xs font-bold text-muted-foreground uppercase mb-1">
+                        Document ID
+                      </div>
+                      <code className="text-xs font-mono text-muted-foreground break-all">
+                        {document.id}
+                      </code>
+                    </div>
+                  </>
+                ) : (
+                  <div className="text-center py-12 text-muted-foreground">
+                    No document data available
+                  </div>
+                )}
+              </TabsContent>
+            </div>
+          </Tabs>
+        ) : null}
+      </DialogContent>
+    </Dialog>
+  );
+}

hindsight-control-plane/src/components/think-view.tsx

@@ -14,9 +14,21 @@ import {
 } from "@/components/ui/select";
 import { Checkbox } from "@/components/ui/checkbox";
 import { Card, CardContent, CardDescription, CardHeader, CardTitle } from "@/components/ui/card";
-import { Sparkles, Info, Tag, Clock, Database, Brain } from "lucide-react";
+import {
+  Sparkles,
+  Info,
+  Tag,
+  Clock,
+  Database,
+  Brain,
+  MessageSquare,
+  Shield,
+  X,
+} from "lucide-react";
+import { Textarea } from "@/components/ui/textarea";
 import JsonView from "react18-json-view";
 import "react18-json-view/src/style.css";
+import { MemoryDetailPanel } from "./memory-detail-panel";
 
 type TagsMatch = "any" | "all" | "any_strict" | "all_strict";
 type ViewMode = "answer" | "trace" | "json";
@@ -33,6 +45,94 @@ export function ThinkView() {
   const [loading, setLoading] = useState(false);
   const [tags, setTags] = useState("");
   const [tagsMatch, setTagsMatch] = useState<TagsMatch>("any");
+  const [feedback, setFeedback] = useState("");
+  const [feedbackSubmitting, setFeedbackSubmitting] = useState(false);
+  const [feedbackSubmitted, setFeedbackSubmitted] = useState(false);
+  const [selectedMemory, setSelectedMemory] = useState<any | null>(null);
+  const [selectedDirective, setSelectedDirective] = useState<any | null>(null);
+  const [fullDirective, setFullDirective] = useState<any | null>(null);
+  const [loadingDirective, setLoadingDirective] = useState(false);
+  const [selectedMentalModel, setSelectedMentalModel] = useState<any | null>(null);
+  const [fullMentalModel, setFullMentalModel] = useState<any | null>(null);
+  const [loadingMentalModel, setLoadingMentalModel] = useState(false);
+
+  const FEEDBACK_DIRECTIVE_NAME = "General Feedback";
+
+  // Load full directive data when one is selected
+  const handleSelectDirective = async (directive: any) => {
+    setSelectedDirective(directive);
+    setFullDirective(null);
+    if (!currentBank || !directive?.id) return;
+
+    setLoadingDirective(true);
+    try {
+      const directives = await client.listDirectives(currentBank);
+      const fullDir = directives.items?.find((d: any) => d.id === directive.id);
+      setFullDirective(fullDir || directive);
+    } catch (error) {
+      console.error("Failed to load directive:", error);
+      setFullDirective(directive); // Fall back to partial data
+    } finally {
+      setLoadingDirective(false);
+    }
+  };
+
+  // Load full mental model data when one is selected
+  const handleSelectMentalModel = async (model: any) => {
+    setSelectedMentalModel(model);
+    setFullMentalModel(null);
+    if (!currentBank || !model?.id) return;
+
+    setLoadingMentalModel(true);
+    try {
+      const models = await client.listMentalModels(currentBank);
+      const fullModel = models.items?.find((m: any) => m.id === model.id);
+      setFullMentalModel(fullModel || model);
+    } catch (error) {
+      console.error("Failed to load mental model:", error);
+      setFullMentalModel(model); // Fall back to partial data
+    } finally {
+      setLoadingMentalModel(false);
+    }
+  };
+
+  const submitFeedback = async () => {
+    if (!currentBank || !feedback.trim()) return;
+
+    setFeedbackSubmitting(true);
+    try {
+      // Find existing "General Feedback" directive
+      const directives = await client.listDirectives(currentBank);
+      const existingDirective = directives.items?.find((d) => d.name === FEEDBACK_DIRECTIVE_NAME);
+
+      if (existingDirective) {
+        // Append to existing directive description
+        const newDescription = existingDirective.description
+          ? `${existingDirective.description}\n${feedback.trim()}`
+          : feedback.trim();
+        await client.updateMentalModel(currentBank, existingDirective.id, {
+          description: newDescription,
+        });
+      } else {
+        // Create new directive with observation
+        await client.createMentalModel(currentBank, {
+          name: FEEDBACK_DIRECTIVE_NAME,
+          description: "User feedback for improving responses",
+          subtype: "directive",
+          observations: [{ title: "Feedback", content: feedback.trim() }],
+        });
+      }
+
+      setFeedback("");
+      setFeedbackSubmitted(true);
+      setTimeout(() => setFeedbackSubmitted(false), 3000);
+    } catch (error) {
+      console.error("Error submitting feedback:", error);
+      alert("Error submitting feedback: " + (error as Error).message);
+    } finally {
+      setFeedbackSubmitting(false);
+    }
+  };
 
   const runReflect = async () => {
     if (!currentBank || !query) return;
@@ -135,7 +235,7 @@ export function ThinkView() {
                   checked={includeFacts}
                   onCheckedChange={(c) => setIncludeFacts(c as boolean)}
                 />
-                <span className="text-sm">Include Facts</span>
+                <span className="text-sm">Include Source</span>
               </label>
               <label className="flex items-center gap-2 cursor-pointer">
                 <Checkbox
@@ -288,6 +388,50 @@ export function ThinkView() {
                   </CardContent>
                 </Card>
               )}
+
+              {/* Feedback */}
+              <Card className="border-blue-200 dark:border-blue-800">
+                <CardHeader className="py-4">
+                  <CardTitle className="flex items-center gap-2 text-base">
+                    <MessageSquare className="w-4 h-4" />
+                    Provide Feedback
+                  </CardTitle>
+                  <CardDescription className="text-xs">
+                    Your feedback will be saved as a directive to improve future responses
+                  </CardDescription>
+                </CardHeader>
+                <CardContent className="pt-0">
+                  {feedbackSubmitted ? (
+                    <div className="flex items-center gap-2 text-green-600 dark:text-green-400">
+                      <span className="text-lg">&#10003;</span>
+                      <span className="text-sm font-medium">
+                        Feedback saved to {FEEDBACK_DIRECTIVE_NAME}
+                      </span>
+                    </div>
+                  ) : (
+                    <div className="flex gap-3">
+                      <Textarea
+                        value={feedback}
+                        onChange={(e) => setFeedback(e.target.value)}
+                        placeholder="Enter your feedback here..."
+                        className="flex-1 min-h-[60px] resize-none"
+                        onKeyDown={(e) => {
+                          if (e.key === "Enter" && (e.metaKey || e.ctrlKey)) {
+                            submitFeedback();
+                          }
+                        }}
+                      />
+                      <Button
+                        onClick={submitFeedback}
+                        disabled={feedbackSubmitting || !feedback.trim()}
+                        className="self-end"
+                      >
+                        {feedbackSubmitting ? "Saving..." : "Save"}
+                      </Button>
+                    </div>
+                  )}
+                </CardContent>
+              </Card>
             </div>
           )}
 
@@ -377,7 +521,17 @@ export function ThinkView() {
                           }> = [];
 
                           llmCalls.forEach((lc: any, idx: number) => {
-                            const isFinal = lc.scope.includes("final");
+                            // Add tools for this iteration (using iteration field from tool trace)
+                            const iterTools = toolCalls.filter(
+                              (tc: any) => tc.iteration === idx + 1
+                            );
+                            // Determine if this is the final LLM call:
+                            // - scope includes "final", OR
+                            // - it's the last LLM call AND no tools were called after it
+                            const isLastLLMCall = idx === llmCalls.length - 1;
+                            const isFinal =
+                              lc.scope.includes("final") ||
+                              (isLastLLMCall && iterTools.length === 0);
                             const iterNum = isFinal ? llmCalls.length : idx + 1;
 
                             // Add LLM call
@@ -388,10 +542,6 @@ export function ThinkView() {
                               isFinal,
                             });
 
-                            // Add tools for this iteration (using iteration field from tool trace)
-                            const iterTools = toolCalls.filter(
-                              (tc: any) => tc.iteration === idx + 1
-                            );
                             if (iterTools.length > 0) {
                               timeline.push({
                                 type: "tools",
@@ -517,7 +667,11 @@ export function ThinkView() {
                     <CardTitle className="text-base">Based On</CardTitle>
                     <CardDescription className="text-xs">
                       {(result.based_on?.memories?.length || 0) +
-                        (result.based_on?.mental_models?.length || 0)}{" "}
+                        (result.based_on?.mental_models?.filter(
+                          (m: any) => m.subtype !== "directive"
+                        )?.length || 0) +
+                        (result.trace?.mental_models?.filter((m: any) => m.subtype === "directive")
+                          ?.length || 0)}{" "}
                       items used
                     </CardDescription>
                   </CardHeader>
@@ -528,7 +682,7 @@ export function ThinkView() {
                         <div>
                           <p className="font-medium text-sm text-foreground">Not included</p>
                           <p className="text-xs text-muted-foreground mt-0.5">
-                            Enable "Include Facts" to see memories.
+                            Enable "Include Source" to see memories.
                           </p>
                         </div>
                       </div>
@@ -543,10 +697,53 @@ export function ThinkView() {
                             (f: any) => f.type === "experience"
                           );
                           const opinionFacts = memories.filter((f: any) => f.type === "opinion");
-                          const mentalModels = result.based_on?.mental_models || [];
+                          const mentalModels = (result.based_on?.mental_models || []).filter(
+                            (m: any) => m.subtype !== "directive"
+                          );
+                          const directives =
+                            result.trace?.mental_models?.filter(
+                              (m: any) => m.subtype === "directive"
+                            ) || [];
 
                           return (
                             <>
+                              {/* Directives */}
+                              {directives.length > 0 && (
+                                <div className="space-y-1.5">
+                                  <div className="flex items-center gap-2 text-xs font-semibold text-foreground">
+                                    <Shield className="w-3 h-3" />
+                                    Directives ({directives.length})
+                                  </div>
+                                  <div className="space-y-1.5">
+                                    {directives.map((directive: any, i: number) => (
+                                      <div
+                                        key={i}
+                                        className="p-2 bg-muted rounded text-xs cursor-pointer hover:bg-muted/80 transition-colors"
+                                        onClick={() => handleSelectDirective(directive)}
+                                      >
+                                        <div className="font-medium">{directive.name}</div>
+                                        {directive.observations &&
+                                          directive.observations.length > 0 && (
+                                            <ul className="mt-1 space-y-0.5">
+                                              {directive.observations.map(
+                                                (obs: string, j: number) => (
+                                                  <li
+                                                    key={j}
+                                                    className="text-[10px] text-muted-foreground flex items-start gap-1"
+                                                  >
+                                                    <span>•</span>
+                                                    <span>{obs}</span>
+                                                  </li>
+                                                )
+                                              )}
+                                            </ul>
+                                          )}
+                                      </div>
+                                    ))}
+                                  </div>
+                                </div>
+                              )}
+
                               {/* Mental Models */}
                               {mentalModels.length > 0 && (
                                 <div className="space-y-1.5">
@@ -556,13 +753,12 @@ export function ThinkView() {
                                   </div>
                                   <div className="space-y-1.5">
                                     {mentalModels.map((model: any, i: number) => (
-                                      <div key={i} className="p-2 bg-muted rounded text-xs">
+                                      <div
+                                        key={i}
+                                        className="p-2 bg-muted rounded text-xs cursor-pointer hover:bg-muted/80 transition-colors"
+                                        onClick={() => handleSelectMentalModel(model)}
+                                      >
                                         <div className="font-medium">{model.name}</div>
-                                        {model.description && (
-                                          <div className="text-[10px] text-muted-foreground mt-1">
-                                            {model.description}
-                                          </div>
-                                        )}
                                       </div>
                                     ))}
                                   </div>
@@ -578,7 +774,11 @@ export function ThinkView() {
                                   </div>
                                   <div className="space-y-1.5">
                                     {worldFacts.map((fact: any, i: number) => (
-                                      <div key={i} className="p-2 bg-muted rounded text-xs">
+                                      <div
+                                        key={i}
+                                        className="p-2 bg-muted rounded text-xs cursor-pointer hover:bg-muted/80 transition-colors"
+                                        onClick={() => setSelectedMemory(fact)}
+                                      >
                                         {fact.text}
                                         {fact.context && (
                                           <div className="text-[10px] text-muted-foreground mt-1">
@@ -600,7 +800,11 @@ export function ThinkView() {
                                   </div>
                                   <div className="space-y-1.5">
                                     {experienceFacts.map((fact: any, i: number) => (
-                                      <div key={i} className="p-2 bg-muted rounded text-xs">
+                                      <div
+                                        key={i}
+                                        className="p-2 bg-muted rounded text-xs cursor-pointer hover:bg-muted/80 transition-colors"
+                                        onClick={() => setSelectedMemory(fact)}
+                                      >
                                         {fact.text}
                                         {fact.context && (
                                           <div className="text-[10px] text-muted-foreground mt-1">
@@ -622,7 +826,11 @@ export function ThinkView() {
                                   </div>
                                   <div className="space-y-1.5">
                                     {opinionFacts.map((fact: any, i: number) => (
-                                      <div key={i} className="p-2 bg-muted rounded text-xs">
+                                      <div
+                                        key={i}
+                                        className="p-2 bg-muted rounded text-xs cursor-pointer hover:bg-muted/80 transition-colors"
+                                        onClick={() => setSelectedMemory(fact)}
+                                      >
                                         {fact.text}
                                         {fact.context && (
                                           <div className="text-[10px] text-muted-foreground mt-1">
@@ -686,6 +894,224 @@ export function ThinkView() {
           </CardContent>
         </Card>
       )}
+
+      {/* Memory Detail Panel */}
+      {selectedMemory && (
+        <div className="fixed right-0 top-0 h-screen w-[420px] bg-card border-l shadow-2xl z-50 overflow-y-auto">
+          <MemoryDetailPanel
+            memory={selectedMemory}
+            onClose={() => setSelectedMemory(null)}
+            inPanel
+            bankId={currentBank || undefined}
+          />
+        </div>
+      )}
+
+      {/* Directive Detail Panel */}
+      {selectedDirective && (
+        <div className="fixed right-0 top-0 h-screen w-[420px] bg-card border-l shadow-2xl z-50 overflow-y-auto">
+          <div className="p-6">
+            <div className="flex items-center justify-between mb-6">
+              <div className="flex items-center gap-2">
+                <Shield className="w-5 h-5" />
+                <h2 className="text-lg font-semibold">Directive</h2>
+              </div>
+              <Button
+                variant="ghost"
+                size="icon"
+                onClick={() => {
+                  setSelectedDirective(null);
+                  setFullDirective(null);
+                }}
+              >
+                <X className="w-4 h-4" />
+              </Button>
+            </div>
+            {loadingDirective ? (
+              <div className="flex items-center justify-center py-8">
+                <div className="animate-spin rounded-full h-8 w-8 border-b-2 border-primary"></div>
+              </div>
+            ) : (
+              <div className="space-y-4">
+                <div>
+                  <h3 className="text-sm font-medium text-muted-foreground">Name</h3>
+                  <p className="mt-1 font-medium">
+                    {fullDirective?.name || selectedDirective.name}
+                  </p>
+                </div>
+                {fullDirective?.description && (
+                  <div>
+                    <h3 className="text-sm font-medium text-muted-foreground">Description</h3>
+                    <p className="mt-1 text-sm">{fullDirective.description}</p>
+                  </div>
+                )}
+                {fullDirective?.tags && fullDirective.tags.length > 0 && (
+                  <div>
+                    <h3 className="text-sm font-medium text-muted-foreground mb-1">Tags</h3>
+                    <div className="flex flex-wrap gap-1">
+                      {fullDirective.tags.map((tag: string) => (
+                        <span
+                          key={tag}
+                          className="text-xs px-2 py-0.5 rounded bg-muted text-muted-foreground flex items-center gap-1"
+                        >
+                          <Tag className="w-2.5 h-2.5" />
+                          {tag}
+                        </span>
+                      ))}
+                    </div>
+                  </div>
+                )}
+                {(fullDirective?.observations || selectedDirective.observations) && (
+                  <div>
+                    <h3 className="text-sm font-medium text-muted-foreground mb-2">
+                      Observations (
+                      {(fullDirective?.observations || selectedDirective.observations)?.length || 0}
+                      )
+                    </h3>
+                    <div className="space-y-2">
+                      {(fullDirective?.observations || selectedDirective.observations)?.map(
+                        (obs: any, i: number) => (
+                          <div key={i} className="p-3 bg-muted rounded-lg">
+                            {obs.title && (
+                              <div className="font-medium text-sm mb-1">{obs.title}</div>
+                            )}
+                            <div className="text-sm text-muted-foreground whitespace-pre-wrap">
+                              {obs.content || obs.text || (typeof obs === "string" ? obs : "")}
+                            </div>
+                            {obs.memory_ids && obs.memory_ids.length > 0 && (
+                              <div className="mt-2 text-xs text-muted-foreground">
+                                Based on {obs.memory_ids.length} memories
+                              </div>
+                            )}
+                          </div>
+                        )
+                      )}
+                    </div>
+                  </div>
+                )}
+                <div className="pt-2 border-t">
+                  <h3 className="text-sm font-medium text-muted-foreground">ID</h3>
+                  <p className="mt-1 font-mono text-xs text-muted-foreground">
+                    {selectedDirective.id}
+                  </p>
+                </div>
+              </div>
+            )}
+          </div>
+        </div>
+      )}
+
+      {/* Mental Model Detail Panel */}
+      {selectedMentalModel && (
+        <div className="fixed right-0 top-0 h-screen w-[420px] bg-card border-l shadow-2xl z-50 overflow-y-auto">
+          <div className="p-6">
+            <div className="flex items-center justify-between mb-6">
+              <div className="flex items-center gap-2">
+                <Brain className="w-5 h-5" />
+                <h2 className="text-lg font-semibold">Mental Model</h2>
+              </div>
+              <Button
+                variant="ghost"
+                size="icon"
+                onClick={() => {
+                  setSelectedMentalModel(null);
+                  setFullMentalModel(null);
+                }}
+              >
+                <X className="w-4 h-4" />
+              </Button>
+            </div>
+            {loadingMentalModel ? (
+              <div className="flex items-center justify-center py-8">
+                <div className="animate-spin rounded-full h-8 w-8 border-b-2 border-primary"></div>
+              </div>
+            ) : (
+              <div className="space-y-4">
+                <div>
+                  <h3 className="text-sm font-medium text-muted-foreground">Name</h3>
+                  <p className="mt-1 font-medium">
+                    {fullMentalModel?.name || selectedMentalModel.name}
+                  </p>
+                </div>
+                {fullMentalModel?.description && (
+                  <div>
+                    <h3 className="text-sm font-medium text-muted-foreground">Description</h3>
+                    <p className="mt-1 text-sm">{fullMentalModel.description}</p>
+                  </div>
+                )}
+                <div className="flex gap-4">
+                  <div>
+                    <h3 className="text-sm font-medium text-muted-foreground">Type</h3>
+                    <p className="mt-1 text-sm">{selectedMentalModel.type}</p>
+                  </div>
+                  <div>
+                    <h3 className="text-sm font-medium text-muted-foreground">Subtype</h3>
+                    <span
+                      className={`inline-block mt-1 text-xs px-2 py-0.5 rounded ${
+                        selectedMentalModel.subtype === "structural"
+                          ? "bg-blue-500/10 text-blue-600"
+                          : selectedMentalModel.subtype === "emergent"
+                            ? "bg-emerald-500/10 text-emerald-600"
+                            : selectedMentalModel.subtype === "learned"
+                              ? "bg-violet-500/10 text-violet-600"
+                              : selectedMentalModel.subtype === "directive"
+                                ? "bg-rose-500/10 text-rose-600"
+                                : "bg-muted"
+                      }`}
+                    >
+                      {selectedMentalModel.subtype}
+                    </span>
+                  </div>
+                </div>
+                {fullMentalModel?.tags && fullMentalModel.tags.length > 0 && (
+                  <div>
+                    <h3 className="text-sm font-medium text-muted-foreground mb-1">Tags</h3>
+                    <div className="flex flex-wrap gap-1">
+                      {fullMentalModel.tags.map((tag: string) => (
+                        <span
+                          key={tag}
+                          className="text-xs px-2 py-0.5 rounded bg-muted text-muted-foreground flex items-center gap-1"
+                        >
+                          <Tag className="w-2.5 h-2.5" />
+                          {tag}
+                        </span>
+                      ))}
+                    </div>
+                  </div>
+                )}
+                {fullMentalModel?.observations && fullMentalModel.observations.length > 0 && (
+                  <div>
+                    <h3 className="text-sm font-medium text-muted-foreground mb-2">
+                      Observations ({fullMentalModel.observations.length})
+                    </h3>
+                    <div className="space-y-2">
+                      {fullMentalModel.observations.map((obs: any, i: number) => (
+                        <div key={i} className="p-3 bg-muted rounded-lg">
+                          {obs.title && <div className="font-medium text-sm mb-1">{obs.title}</div>}
+                          <div className="text-sm text-muted-foreground whitespace-pre-wrap">
+                            {obs.content || obs.text || (typeof obs === "string" ? obs : "")}
+                          </div>
+                          {obs.memory_ids && obs.memory_ids.length > 0 && (
+                            <div className="mt-2 text-xs text-muted-foreground">
+                              Based on {obs.memory_ids.length} memories
+                            </div>
+                          )}
+                        </div>
+                      ))}
+                    </div>
+                  </div>
+                )}
+                <div className="pt-2 border-t">
+                  <h3 className="text-sm font-medium text-muted-foreground">ID</h3>
+                  <p className="mt-1 font-mono text-xs text-muted-foreground">
+                    {selectedMentalModel.id}
+                  </p>
+                </div>
+              </div>
+            )}
+          </div>
+        </div>
+      )}
     </div>
   );
 }

hindsight-control-plane/src/components/ui/tabs.tsx

@@ -0,0 +1,55 @@
+"use client";
+
+import * as React from "react";
+import * as TabsPrimitive from "@radix-ui/react-tabs";
+
+import { cn } from "@/lib/utils";
+
+const Tabs = TabsPrimitive.Root;
+
+const TabsList = React.forwardRef<
+  React.ElementRef<typeof TabsPrimitive.List>,
+  React.ComponentPropsWithoutRef<typeof TabsPrimitive.List>
+>(({ className, ...props }, ref) => (
+  <TabsPrimitive.List
+    ref={ref}
+    className={cn(
+      "inline-flex h-10 items-center justify-center rounded-md bg-muted p-1 text-muted-foreground",
+      className
+    )}
+    {...props}
+  />
+));
+TabsList.displayName = TabsPrimitive.List.displayName;
+
+const TabsTrigger = React.forwardRef<
+  React.ElementRef<typeof TabsPrimitive.Trigger>,
+  React.ComponentPropsWithoutRef<typeof TabsPrimitive.Trigger>
+>(({ className, ...props }, ref) => (
+  <TabsPrimitive.Trigger
+    ref={ref}
+    className={cn(
+      "inline-flex items-center justify-center whitespace-nowrap rounded-sm px-3 py-1.5 text-sm font-medium ring-offset-background transition-all focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50 data-[state=active]:bg-background data-[state=active]:text-foreground data-[state=active]:shadow-sm",
+      className
+    )}
+    {...props}
+  />
+));
+TabsTrigger.displayName = TabsPrimitive.Trigger.displayName;
+
+const TabsContent = React.forwardRef<
+  React.ElementRef<typeof TabsPrimitive.Content>,
+  React.ComponentPropsWithoutRef<typeof TabsPrimitive.Content>
+>(({ className, ...props }, ref) => (
+  <TabsPrimitive.Content
+    ref={ref}
+    className={cn(
+      "mt-2 ring-offset-background focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2",
+      className
+    )}
+    {...props}
+  />
+));
+TabsContent.displayName = TabsPrimitive.Content.displayName;
+
+export { Tabs, TabsList, TabsTrigger, TabsContent };

hindsight-control-plane/src/components/ui/tooltip.tsx

@@ -0,0 +1,32 @@
+"use client";
+
+import * as React from "react";
+import * as TooltipPrimitive from "@radix-ui/react-tooltip";
+
+import { cn } from "@/lib/utils";
+
+const TooltipProvider = TooltipPrimitive.Provider;
+
+const Tooltip = TooltipPrimitive.Root;
+
+const TooltipTrigger = TooltipPrimitive.Trigger;
+
+const TooltipContent = React.forwardRef<
+  React.ElementRef<typeof TooltipPrimitive.Content>,
+  React.ComponentPropsWithoutRef<typeof TooltipPrimitive.Content>
+>(({ className, sideOffset = 4, ...props }, ref) => (
+  <TooltipPrimitive.Portal>
+    <TooltipPrimitive.Content
+      ref={ref}
+      sideOffset={sideOffset}
+      className={cn(
+        "z-50 overflow-hidden rounded-md bg-primary px-3 py-1.5 text-xs text-primary-foreground animate-in fade-in-0 zoom-in-95 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2",
+        className
+      )}
+      {...props}
+    />
+  </TooltipPrimitive.Portal>
+));
+TooltipContent.displayName = TooltipPrimitive.Content.displayName;
+
+export { Tooltip, TooltipTrigger, TooltipContent, TooltipProvider };

hindsight-control-plane/src/lib/api.ts

@@ -272,7 +272,7 @@ export class ControlPlaneClient {
         subtype: string;
         name: string;
         description: string;
-        observations?: Array<{ title: string; text: string; based_on: string[] }>;
+        observations?: Array<{ title: string; content: string; based_on: string[] }>;
         entity_id: string | null;
         links: string[];
         tags?: string[];
@@ -308,6 +308,32 @@ export class ControlPlaneClient {
     });
   }
 
+  /**
+   * Update a mental model (name and/or description)
+   */
+  async updateMentalModel(
+    bankId: string,
+    modelId: string,
+    params: { name?: string; description?: string }
+  ) {
+    return this.fetchApi<{
+      id: string;
+      bank_id: string;
+      subtype: string;
+      name: string;
+      description: string;
+      observations?: Array<{ title: string; content: string; based_on: string[] }>;
+      entity_id: string | null;
+      links: string[];
+      tags?: string[];
+      last_updated: string | null;
+      created_at: string;
+    }>(`/api/banks/${bankId}/mental-models/${modelId}`, {
+      method: "PATCH",
+      body: JSON.stringify(params),
+    });
+  }
+
   /**
    * Get operation status
    */
@@ -324,11 +350,11 @@ export class ControlPlaneClient {
   }
 
   /**
-   * Generate/refresh content for a specific mental model (async)
+   * Refresh content for a specific mental model (async)
    */
-  async generateMentalModel(bankId: string, modelId: string) {
+  async refreshMentalModel(bankId: string, modelId: string) {
     return this.fetchApi<{ operation_id: string; message: string }>(
-      `/api/banks/${bankId}/mental-models/${modelId}/generate`,
+      `/api/banks/${bankId}/mental-models/${modelId}/refresh`,
       {
         method: "POST",
       }
@@ -336,13 +362,15 @@ export class ControlPlaneClient {
   }
 
   /**
-   * Create a pinned mental model
+   * Create a mental model (pinned or directive)
    */
   async createMentalModel(
     bankId: string,
     params: {
       name: string;
       description: string;
+      subtype?: "pinned" | "directive";
+      observations?: Array<{ title: string; content: string }>;
       tags?: string[];
     }
   ) {
@@ -352,7 +380,7 @@ export class ControlPlaneClient {
       subtype: string;
       name: string;
       description: string;
-      observations?: Array<{ title: string; text: string; based_on: string[] }>;
+      observations?: Array<{ title: string; content: string; based_on: string[] }>;
       entity_id: string | null;
       links: string[];
       tags?: string[];
@@ -384,6 +412,64 @@ export class ControlPlaneClient {
       body: JSON.stringify(profile),
     });
   }
+
+  /**
+   * List directives for a bank
+   */
+  async listDirectives(bankId: string) {
+    return this.fetchApi<{
+      items: Array<{
+        id: string;
+        bank_id: string;
+        subtype: string;
+        name: string;
+        description: string;
+        observations?: Array<{ title: string; content: string; based_on: string[] }>;
+        entity_id: string | null;
+        links: string[];
+        tags?: string[];
+        last_updated: string | null;
+        created_at: string;
+      }>;
+    }>(`/api/banks/${bankId}/mental-models?subtype=directive`);
+  }
+
+  /**
+   * List version history for a mental model
+   */
+  async listMentalModelVersions(bankId: string, modelId: string) {
+    return this.fetchApi<{
+      versions: Array<{
+        version: number;
+        created_at: string | null;
+        observation_count: number;
+      }>;
+    }>(`/api/banks/${bankId}/mental-models/${modelId}/versions`);
+  }
+
+  /**
+   * Get a specific version of a mental model
+   */
+  async getMentalModelVersion(bankId: string, modelId: string, version: number) {
+    return this.fetchApi<{
+      version: number;
+      observations: Array<{
+        title: string;
+        content: string;
+        evidence: Array<{
+          memory_id: string;
+          quote: string;
+          relevance: string;
+          timestamp: string;
+        }>;
+        created_at: string;
+        trend: string;
+        evidence_count: number;
+        evidence_span: { from: string | null; to: string | null };
+      }>;
+      created_at: string | null;
+    }>(`/api/banks/${bankId}/mental-models/${modelId}/versions/${version}`);
+  }
 }
 
 // Export singleton instance

hindsight-docs/static/openapi.json

@@ -901,7 +901,7 @@
           "Mental Models"
         ],
         "summary": "Create mental model",
-        "description": "Create a pinned mental model. Pinned models are user-defined and persist across refreshes.",
+        "description": "Create a mental model. Supports two subtypes:\n- 'pinned' (default): User-defined topic, observations are LLM-generated on refresh\n- 'directive': User-defined hard rules, observations are provided at creation and never regenerated",
         "operationId": "create_mental_model",
         "parameters": [
           {
@@ -1096,6 +1096,82 @@
             }
           }
         }
+      },
+      "patch": {
+        "tags": [
+          "Mental Models"
+        ],
+        "summary": "Update mental model",
+        "description": "Update a mental model's name and/or description. Useful for editing directives.",
+        "operationId": "update_mental_model",
+        "parameters": [
+          {
+            "name": "bank_id",
+            "in": "path",
+            "required": true,
+            "schema": {
+              "type": "string",
+              "title": "Bank Id"
+            }
+          },
+          {
+            "name": "model_id",
+            "in": "path",
+            "required": true,
+            "schema": {
+              "type": "string",
+              "title": "Model Id"
+            }
+          },
+          {
+            "name": "authorization",
+            "in": "header",
+            "required": false,
+            "schema": {
+              "anyOf": [
+                {
+                  "type": "string"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "title": "Authorization"
+            }
+          }
+        ],
+        "requestBody": {
+          "required": true,
+          "content": {
+            "application/json": {
+              "schema": {
+                "$ref": "#/components/schemas/UpdateMentalModelRequest"
+              }
+            }
+          }
+        },
+        "responses": {
+          "200": {
+            "description": "Successful Response",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/MentalModelResponse"
+                }
+              }
+            }
+          },
+          "422": {
+            "description": "Validation Error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/HTTPValidationError"
+                }
+              }
+            }
+          }
+        }
       }
     },
     "/v1/default/banks/{bank_id}/mental-models/refresh": {
@@ -1174,14 +1250,14 @@
         }
       }
     },
-    "/v1/default/banks/{bank_id}/mental-models/{model_id}/generate": {
+    "/v1/default/banks/{bank_id}/mental-models/{model_id}/refresh": {
       "post": {
         "tags": [
           "Mental Models"
         ],
-        "summary": "Generate mental model content (async)",
-        "description": "Submit a background job to generate/refresh content for a specific mental model. This is useful for newly created learned models or to regenerate content for any model.",
-        "operationId": "generate_mental_model",
+        "summary": "Refresh mental model content (async)",
+        "description": "Submit a background job to refresh content for a specific mental model. This is useful for newly created learned models or to refresh content for any model.",
+        "operationId": "refresh_mental_model",
         "parameters": [
           {
             "name": "bank_id",
@@ -1242,6 +1318,147 @@
         }
       }
     },
+    "/v1/default/banks/{bank_id}/mental-models/{model_id}/versions": {
+      "get": {
+        "tags": [
+          "Mental Models"
+        ],
+        "summary": "List mental model version history",
+        "description": "List all saved versions of a mental model's observations, ordered by version descending.",
+        "operationId": "list_mental_model_versions",
+        "parameters": [
+          {
+            "name": "bank_id",
+            "in": "path",
+            "required": true,
+            "schema": {
+              "type": "string",
+              "title": "Bank Id"
+            }
+          },
+          {
+            "name": "model_id",
+            "in": "path",
+            "required": true,
+            "schema": {
+              "type": "string",
+              "title": "Model Id"
+            }
+          },
+          {
+            "name": "authorization",
+            "in": "header",
+            "required": false,
+            "schema": {
+              "anyOf": [
+                {
+                  "type": "string"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "title": "Authorization"
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "Successful Response",
+            "content": {
+              "application/json": {
+                "schema": {}
+              }
+            }
+          },
+          "422": {
+            "description": "Validation Error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/HTTPValidationError"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
+    "/v1/default/banks/{bank_id}/mental-models/{model_id}/versions/{version}": {
+      "get": {
+        "tags": [
+          "Mental Models"
+        ],
+        "summary": "Get specific mental model version",
+        "description": "Get observations from a specific version of a mental model.",
+        "operationId": "get_mental_model_version",
+        "parameters": [
+          {
+            "name": "bank_id",
+            "in": "path",
+            "required": true,
+            "schema": {
+              "type": "string",
+              "title": "Bank Id"
+            }
+          },
+          {
+            "name": "model_id",
+            "in": "path",
+            "required": true,
+            "schema": {
+              "type": "string",
+              "title": "Model Id"
+            }
+          },
+          {
+            "name": "version",
+            "in": "path",
+            "required": true,
+            "schema": {
+              "type": "integer",
+              "title": "Version"
+            }
+          },
+          {
+            "name": "authorization",
+            "in": "header",
+            "required": false,
+            "schema": {
+              "anyOf": [
+                {
+                  "type": "string"
+                },
+                {
+                  "type": "null"
+                }
+              ],
+              "title": "Authorization"
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "Successful Response",
+            "content": {
+              "application/json": {
+                "schema": {}
+              }
+            }
+          },
+          "422": {
+            "description": "Validation Error",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/HTTPValidationError"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
     "/v1/default/banks/{bank_id}/documents": {
       "get": {
         "tags": [
@@ -2899,6 +3116,27 @@
             "title": "Description",
             "description": "One-liner description for quick scanning"
           },
+          "subtype": {
+            "type": "string",
+            "title": "Subtype",
+            "description": "Type of mental model: 'pinned' (observations LLM-generated) or 'directive' (observations user-provided)",
+            "default": "pinned"
+          },
+          "observations": {
+            "anyOf": [
+              {
+                "items": {
+                  "$ref": "#/components/schemas/ObservationInput"
+                },
+                "type": "array"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Observations",
+            "description": "For directives only: list of user-provided observations. Required when subtype='directive'."
+          },
           "tags": {
             "items": {
               "type": "string"
@@ -2914,14 +3152,27 @@
           "description"
         ],
         "title": "CreateMentalModelRequest",
-        "description": "Request model for creating a pinned mental model.",
-        "example": {
-          "description": "Key product priorities and upcoming features",
-          "name": "Product Roadmap",
-          "tags": [
-            "project-x"
-          ]
-        }
+        "description": "Request model for creating a mental model.",
+        "examples": [
+          {
+            "description": "Key product priorities and upcoming features",
+            "name": "Product Roadmap",
+            "tags": [
+              "project-x"
+            ]
+          },
+          {
+            "description": "Rules about scheduling meetings",
+            "name": "Meeting Rules",
+            "observations": [
+              {
+                "content": "Never schedule meetings before 10am",
+                "title": "Morning meetings"
+              }
+            ],
+            "subtype": "directive"
+          }
+        ]
       },
       "CreatedMentalModel": {
         "properties": {
@@ -3812,6 +4063,48 @@
           "timestamp": "2024-01-15T10:30:00Z"
         }
       },
+      "MentalModelFreshnessResponse": {
+        "properties": {
+          "is_up_to_date": {
+            "type": "boolean",
+            "title": "Is Up To Date",
+            "description": "Whether the model has been refreshed since the last memory was added"
+          },
+          "last_refresh_at": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Last Refresh At",
+            "description": "When the model was last refreshed (ISO format)"
+          },
+          "memories_since_refresh": {
+            "type": "integer",
+            "title": "Memories Since Refresh",
+            "description": "Number of memories added since last refresh"
+          },
+          "reasons": {
+            "items": {
+              "type": "string"
+            },
+            "type": "array",
+            "title": "Reasons",
+            "description": "Reasons why the model needs refresh (empty if up to date). Possible values: never_refreshed, new_memories, mission_changed, disposition_changed, directives_changed"
+          }
+        },
+        "type": "object",
+        "required": [
+          "is_up_to_date",
+          "last_refresh_at",
+          "memories_since_refresh"
+        ],
+        "title": "MentalModelFreshnessResponse",
+        "description": "Freshness information for a mental model."
+      },
       "MentalModelListResponse": {
         "properties": {
           "items": {
@@ -3834,29 +4127,54 @@
           "title": {
             "type": "string",
             "title": "Title",
-            "description": "Observation header (empty for intro)"
+            "description": "Short summary title for the observation"
           },
-          "text": {
+          "content": {
             "type": "string",
-            "title": "Text",
-            "description": "Observation content"
+            "title": "Content",
+            "description": "The observation content - detailed explanation"
           },
-          "based_on": {
+          "evidence": {
             "items": {
-              "type": "string"
+              "$ref": "#/components/schemas/ObservationEvidenceResponse"
             },
             "type": "array",
-            "title": "Based On",
-            "description": "Memory IDs supporting this observation"
+            "title": "Evidence",
+            "description": "Supporting evidence with quotes"
+          },
+          "created_at": {
+            "type": "string",
+            "title": "Created At",
+            "description": "When this observation was first created (ISO format)"
+          },
+          "trend": {
+            "type": "string",
+            "title": "Trend",
+            "description": "Computed trend: stable, strengthening, weakening, new, stale"
+          },
+          "evidence_count": {
+            "type": "integer",
+            "title": "Evidence Count",
+            "description": "Number of evidence items supporting this observation"
+          },
+          "evidence_span": {
+            "additionalProperties": true,
+            "type": "object",
+            "title": "Evidence Span",
+            "description": "Time span of evidence: {from: iso_date, to: iso_date}"
           }
         },
         "type": "object",
         "required": [
           "title",
-          "text"
+          "content",
+          "created_at",
+          "trend",
+          "evidence_count",
+          "evidence_span"
         ],
         "title": "MentalModelObservationResponse",
-        "description": "An observation within a mental model with its supporting memories."
+        "description": "An observation within a mental model with its supporting evidence."
       },
       "MentalModelResponse": {
         "properties": {
@@ -3888,6 +4206,12 @@
             "title": "Observations",
             "description": "Structured observations with per-observation fact attribution"
           },
+          "version": {
+            "type": "integer",
+            "title": "Version",
+            "description": "Version number of the mental model observations",
+            "default": 0
+          },
           "entity_id": {
             "anyOf": [
               {
@@ -3926,6 +4250,29 @@
             ],
             "title": "Last Updated"
           },
+          "last_refresh_at": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Last Refresh At",
+            "description": "When observations were last refreshed (ISO format)"
+          },
+          "freshness": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/MentalModelFreshnessResponse"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "description": "Freshness info (null for directive subtypes which don't need refresh)"
+          },
           "created_at": {
             "type": "string",
             "title": "Created At"
@@ -3946,25 +4293,99 @@
           "bank_id": "test-bank",
           "created_at": "2024-01-10T08:00:00Z",
           "description": "Who's on the team and their roles",
+          "freshness": {
+            "is_up_to_date": true,
+            "last_refresh_at": "2024-01-15T10:30:00Z",
+            "memories_since_refresh": 0,
+            "reasons": []
+          },
           "id": "team-structure",
+          "last_refresh_at": "2024-01-15T10:30:00Z",
           "last_updated": "2024-01-15T10:30:00Z",
           "links": [],
           "name": "Team Structure",
           "observations": [
             {
-              "based_on": [
-                "uuid1"
+              "content": "The team prefers async communication over synchronous meetings",
+              "created_at": "2024-01-15T10:30:00Z",
+              "evidence": [
+                {
+                  "memory_id": "uuid1",
+                  "quote": "I prefer Slack over meetings",
+                  "relevance": "Shows async preference",
+                  "timestamp": "2024-01-10T08:00:00Z"
+                }
               ],
-              "text": "The team consists of...",
-              "title": "Overview"
+              "evidence_count": 1,
+              "evidence_span": {
+                "from": "2024-01-10T08:00:00Z",
+                "to": "2024-01-10T08:00:00Z"
+              },
+              "title": "Prefers async communication",
+              "trend": "stable"
             }
           ],
           "subtype": "structural",
           "tags": [
             "project-x"
-          ]
+          ],
+          "version": 1
         }
       },
+      "ObservationEvidenceResponse": {
+        "properties": {
+          "memory_id": {
+            "type": "string",
+            "title": "Memory Id",
+            "description": "ID of the memory unit this evidence comes from"
+          },
+          "quote": {
+            "type": "string",
+            "title": "Quote",
+            "description": "Exact quote from the memory supporting the observation"
+          },
+          "relevance": {
+            "type": "string",
+            "title": "Relevance",
+            "description": "Brief explanation of how this quote supports the observation"
+          },
+          "timestamp": {
+            "type": "string",
+            "title": "Timestamp",
+            "description": "When the source memory was created (ISO format)"
+          }
+        },
+        "type": "object",
+        "required": [
+          "memory_id",
+          "quote",
+          "relevance",
+          "timestamp"
+        ],
+        "title": "ObservationEvidenceResponse",
+        "description": "A single piece of evidence supporting an observation."
+      },
+      "ObservationInput": {
+        "properties": {
+          "title": {
+            "type": "string",
+            "title": "Title",
+            "description": "Short title/header for the observation"
+          },
+          "content": {
+            "type": "string",
+            "title": "Content",
+            "description": "Content of the observation"
+          }
+        },
+        "type": "object",
+        "required": [
+          "title",
+          "content"
+        ],
+        "title": "ObservationInput",
+        "description": "Input model for a single observation."
+      },
       "OperationResponse": {
         "properties": {
           "id": {
@@ -4692,24 +5113,22 @@
           "subtype": {
             "type": "string",
             "title": "Subtype",
-            "description": "Mental model subtype: structural, emergent, learned"
+            "description": "Mental model subtype: structural, emergent, learned, directive"
           },
-          "description": {
-            "type": "string",
-            "title": "Description",
-            "description": "Brief description"
-          },
-          "summary": {
+          "observations": {
             "anyOf": [
               {
-                "type": "string"
+                "items": {
+                  "type": "string"
+                },
+                "type": "array"
               },
               {
                 "type": "null"
               }
             ],
-            "title": "Summary",
-            "description": "Full summary (when looked up in detail)"
+            "title": "Observations",
+            "description": "Observations for directive mental models (subtype='directive')"
           }
         },
         "type": "object",
@@ -4717,8 +5136,7 @@
           "id",
           "name",
           "type",
-          "subtype",
-          "description"
+          "subtype"
         ],
         "title": "ReflectMentalModel",
         "description": "A mental model accessed during reflect."
@@ -5028,6 +5446,14 @@
             "type": "array",
             "title": "Llm Calls",
             "description": "LLM calls made during reflection"
+          },
+          "mental_models": {
+            "items": {
+              "$ref": "#/components/schemas/ReflectMentalModel"
+            },
+            "type": "array",
+            "title": "Mental Models",
+            "description": "Mental models used during reflection (includes directives with subtype='directive')"
           }
         },
         "type": "object",
@@ -5278,6 +5704,41 @@
         "title": "UpdateDispositionRequest",
         "description": "Request model for updating disposition traits."
       },
+      "UpdateMentalModelRequest": {
+        "properties": {
+          "name": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Name",
+            "description": "New name for the mental model"
+          },
+          "description": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Description",
+            "description": "New description/rule text"
+          }
+        },
+        "type": "object",
+        "title": "UpdateMentalModelRequest",
+        "description": "Request model for updating a mental model.",
+        "example": {
+          "description": "Updated description with new rules",
+          "name": "Updated Name"
+        }
+      },
       "ValidationError": {
         "properties": {
           "loc": {

package-lock.json

@@ -145,6 +145,8 @@
         "@radix-ui/react-slider": "^1.3.6",
         "@radix-ui/react-slot": "^1.2.4",
         "@radix-ui/react-switch": "^1.2.6",
+        "@radix-ui/react-tabs": "^1.1.13",
+        "@radix-ui/react-tooltip": "^1.2.8",
         "@tailwindcss/postcss": "^4.1.17",
         "@tailwindcss/typography": "^0.5.19",
         "@types/cytoscape": "^3.21.9",
@@ -7588,6 +7590,88 @@
         }
       }
     },
+    "node_modules/@radix-ui/react-tabs": {
+      "version": "1.1.13",
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