fixes

.github/workflows/release.yml

@@ -139,7 +139,7 @@ jobs:
         path: hindsight-clients/typescript/*.tgz
         retention-days: 1
 
-  release-openclaw-integration:
+  release-moltbot-integration:
     runs-on: ubuntu-latest
     environment: npm
 
@@ -153,15 +153,15 @@ jobs:
         registry-url: 'https://registry.npmjs.org'
 
     - name: Install dependencies
-      working-directory: ./hindsight-integrations/openclaw
+      working-directory: ./hindsight-integrations/moltbot
       run: npm ci
 
     - name: Build
-      working-directory: ./hindsight-integrations/openclaw
+      working-directory: ./hindsight-integrations/moltbot
       run: npm run build
 
     - name: Publish to npm
-      working-directory: ./hindsight-integrations/openclaw
+      working-directory: ./hindsight-integrations/moltbot
       run: |
         set +e
         OUTPUT=$(npm publish --access public 2>&1)
@@ -178,14 +178,14 @@ jobs:
         NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
 
     - name: Pack for GitHub release
-      working-directory: ./hindsight-integrations/openclaw
+      working-directory: ./hindsight-integrations/moltbot
       run: npm pack
 
     - name: Upload artifacts
       uses: actions/upload-artifact@v4
       with:
-        name: openclaw-integration
-        path: hindsight-integrations/openclaw/*.tgz
+        name: moltbot-integration
+        path: hindsight-integrations/moltbot/*.tgz
         retention-days: 1
 
   release-control-plane:
@@ -415,7 +415,7 @@ jobs:
 
   create-github-release:
     runs-on: ubuntu-latest
-    needs: [release-python-packages, release-typescript-client, release-openclaw-integration, release-control-plane, release-rust-cli, release-docker-images, release-helm-chart]
+    needs: [release-python-packages, release-typescript-client, release-moltbot-integration, release-control-plane, release-rust-cli, release-docker-images, release-helm-chart]
     permissions:
       contents: write
 
@@ -438,11 +438,11 @@ jobs:
         name: typescript-client
         path: ./artifacts/typescript-client
 
-    - name: Download OpenClaw Integration
+    - name: Download Moltbot Integration
       uses: actions/download-artifact@v4
       with:
-        name: openclaw-integration
-        path: ./artifacts/openclaw-integration
+        name: moltbot-integration
+        path: ./artifacts/moltbot-integration
 
     - name: Download Control Plane
       uses: actions/download-artifact@v4
@@ -485,8 +485,8 @@ jobs:
         cp artifacts/python-packages/hindsight-embed/dist/* release-assets/ || true
         # TypeScript client
         cp artifacts/typescript-client/*.tgz release-assets/ || true
-        # OpenClaw Integration
-        cp artifacts/openclaw-integration/*.tgz release-assets/ || true
+        # Moltbot Integration
+        cp artifacts/moltbot-integration/*.tgz release-assets/ || true
         # Control Plane
         cp artifacts/control-plane/*.tgz release-assets/ || true
         # Rust CLI binaries

.github/workflows/test.yml

@@ -82,7 +82,7 @@ jobs:
     - name: Build TypeScript client
       run: npm run build --workspace=hindsight-clients/typescript
 
-  build-openclaw-integration:
+  build-moltbot-integration:
     runs-on: ubuntu-latest
 
     steps:
@@ -94,15 +94,15 @@ jobs:
         node-version: '22'
 
     - name: Install dependencies
-      working-directory: ./hindsight-integrations/openclaw
+      working-directory: ./hindsight-integrations/moltbot
       run: npm ci
 
     - name: Run tests
-      working-directory: ./hindsight-integrations/openclaw
+      working-directory: ./hindsight-integrations/moltbot
       run: npm test
 
     - name: Build
-      working-directory: ./hindsight-integrations/openclaw
+      working-directory: ./hindsight-integrations/moltbot
       run: npm run build
 
   build-control-plane:
@@ -958,18 +958,6 @@ jobs:
       working-directory: ./hindsight-dev
       run: uv run pytest upgrade_tests/ -v --tb=short
 
-    - name: Show upgrade test logs
-      if: always()
-      run: |
-        echo "=== Upgrade Test Server Logs ==="
-        for log in /tmp/upgrade-test-*.log; do
-          if [ -f "$log" ]; then
-            echo ""
-            echo "--- $log ---"
-            tail -500 "$log"
-          fi
-        done
-
   verify-generated-files:
     runs-on: ubuntu-latest
     env:

CONTRIBUTING.md

@@ -93,34 +93,6 @@ uv run ty check hindsight_api  # Type check
 3. Run tests to ensure nothing breaks
 4. Submit a PR with a clear description of changes
 
-## Release Process
-
-The project uses `scripts/release.sh` for creating releases. This script automates the entire release workflow:
-
-1. Bumps version in all components (API, clients, CLI, control plane, Helm)
-2. **Regenerates OpenAPI spec and client SDKs** (Python, TypeScript, Rust)
-3. Updates documentation versioning
-4. Creates a commit and git tag
-5. Pushes to GitHub (triggers CI/CD to publish packages)
-
-### Usage
-
-```bash
-./scripts/release.sh <version>
-```
-
-**Example:**
-```bash
-./scripts/release.sh 0.5.0
-```
-
-### Important for Developers
-
-- During development, version bumps in `__init__.py` do NOT require client regeneration
-- Clients are only regenerated during releases
-- Do not manually run `./scripts/generate-clients.sh` unless testing generation changes
-- Client version comments will reflect the API version from the latest release
-
 ## Reporting Issues
 
 Open an issue on GitHub with:

docker/standalone/Dockerfile

@@ -169,34 +169,16 @@ ENV PATH="/app/api/.venv/bin:${PATH}"
 
 # Pre-download ML models to avoid runtime download (conditional)
 # Only runs if both PRELOAD_ML_MODELS=true AND INCLUDE_LOCAL_MODELS=true
-# Includes retry logic with exponential backoff for transient network failures
 ARG PRELOAD_ML_MODELS
 ARG INCLUDE_LOCAL_MODELS
-ENV HF_HUB_DOWNLOAD_TIMEOUT=600
 RUN if [ "$PRELOAD_ML_MODELS" = "true" ] && [ "$INCLUDE_LOCAL_MODELS" = "true" ]; then \
-    MAX_RETRIES=3; \
-    RETRY_DELAY=10; \
-    for i in $(seq 1 $MAX_RETRIES); do \
-      echo "Attempt $i/$MAX_RETRIES: Downloading ML models..."; \
-      /app/api/.venv/bin/python -c "\
-import os; os.environ['HF_HUB_DOWNLOAD_TIMEOUT'] = '600'; \
+    /app/api/.venv/bin/python -c "\
 from sentence_transformers import SentenceTransformer, CrossEncoder; \
 print('Downloading embedding model...'); \
 SentenceTransformer('BAAI/bge-small-en-v1.5'); \
 print('Downloading cross-encoder model...'); \
 CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2'); \
-print('Downloading tiktoken encoding...'); import tiktoken; tiktoken.get_encoding('cl100k_base'); \
-print('Models cached successfully')" && break; \
-      if [ $i -lt $MAX_RETRIES ]; then \
-        echo "Attempt $i failed, retrying in ${RETRY_DELAY}s..."; \
-        sleep $RETRY_DELAY; \
-        RETRY_DELAY=$((RETRY_DELAY * 2)); \
-      fi; \
-    done; \
-    if [ $i -eq $MAX_RETRIES ] && ! /app/api/.venv/bin/python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('BAAI/bge-small-en-v1.5')" 2>/dev/null; then \
-      echo "ERROR: Failed to download models after $MAX_RETRIES attempts"; \
-      exit 1; \
-    fi; \
+print('Models cached successfully')"; \
     elif [ "$INCLUDE_LOCAL_MODELS" != "true" ]; then echo "Skipping ML model preload (local-models not included)"; \
     else echo "Skipping ML model preload"; fi
 
@@ -295,34 +277,16 @@ ENV PATH="/app/api/.venv/bin:${PATH}"
 
 # Pre-download ML models to avoid runtime download (conditional)
 # Only runs if both PRELOAD_ML_MODELS=true AND INCLUDE_LOCAL_MODELS=true
-# Includes retry logic with exponential backoff for transient network failures
 ARG PRELOAD_ML_MODELS
 ARG INCLUDE_LOCAL_MODELS
-ENV HF_HUB_DOWNLOAD_TIMEOUT=600
 RUN if [ "$PRELOAD_ML_MODELS" = "true" ] && [ "$INCLUDE_LOCAL_MODELS" = "true" ]; then \
-    MAX_RETRIES=3; \
-    RETRY_DELAY=10; \
-    for i in $(seq 1 $MAX_RETRIES); do \
-      echo "Attempt $i/$MAX_RETRIES: Downloading ML models..."; \
-      /app/api/.venv/bin/python -c "\
-import os; os.environ['HF_HUB_DOWNLOAD_TIMEOUT'] = '600'; \
+    /app/api/.venv/bin/python -c "\
 from sentence_transformers import SentenceTransformer, CrossEncoder; \
 print('Downloading embedding model...'); \
 SentenceTransformer('BAAI/bge-small-en-v1.5'); \
 print('Downloading cross-encoder model...'); \
 CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2'); \
-print('Downloading tiktoken encoding...'); import tiktoken; tiktoken.get_encoding('cl100k_base'); \
-print('Models cached successfully')" && break; \
-      if [ $i -lt $MAX_RETRIES ]; then \
-        echo "Attempt $i failed, retrying in ${RETRY_DELAY}s..."; \
-        sleep $RETRY_DELAY; \
-        RETRY_DELAY=$((RETRY_DELAY * 2)); \
-      fi; \
-    done; \
-    if [ $i -eq $MAX_RETRIES ] && ! /app/api/.venv/bin/python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('BAAI/bge-small-en-v1.5')" 2>/dev/null; then \
-      echo "ERROR: Failed to download models after $MAX_RETRIES attempts"; \
-      exit 1; \
-    fi; \
+print('Models cached successfully')"; \
     elif [ "$INCLUDE_LOCAL_MODELS" != "true" ]; then echo "Skipping ML model preload (local-models not included)"; \
     else echo "Skipping ML model preload"; fi
 

helm/hindsight/Chart.yaml

@@ -2,8 +2,8 @@ apiVersion: v2
 name: hindsight
 description: Hindsight helm chart
 type: application
-version: 0.4.7
-appVersion: "0.4.7"
+version: 0.4.2
+appVersion: "0.4.2"
 keywords:
   - ai
   - memory

hindsight-api/hindsight_api/__init__.py

@@ -46,4 +46,4 @@ __all__ = [
     "RemoteTEICrossEncoder",
     "LLMConfig",
 ]
-__version__ = "0.4.7"
+__version__ = "0.4.2"

hindsight-api/hindsight_api/api/http.py

@@ -1398,18 +1398,13 @@ def create_app(
 
         # Start worker poller if enabled (standalone mode)
         if config.worker_enabled and memory._pool is not None:
-            from ..config import DEFAULT_DATABASE_SCHEMA
-
             worker_id = config.worker_id or socket.gethostname()
-            # Convert default schema to None for SQL compatibility (no schema prefix)
-            schema = None if config.database_schema == DEFAULT_DATABASE_SCHEMA else config.database_schema
             poller = WorkerPoller(
                 pool=memory._pool,
                 worker_id=worker_id,
                 executor=memory.execute_task,
                 poll_interval_ms=config.worker_poll_interval_ms,
                 max_retries=config.worker_max_retries,
-                schema=schema,
                 tenant_extension=getattr(memory, "_tenant_extension", None),
                 max_slots=config.worker_max_slots,
                 consolidation_max_slots=config.worker_consolidation_max_slots,
@@ -2290,23 +2285,6 @@ def _register_routes(app: FastAPI):
     ):
         """Get a mental model by ID."""
         try:
-            # Pre-operation validation hook
-            validator = app.state.memory._operation_validator
-            if validator:
-                from hindsight_api.extensions.operation_validator import MentalModelGetContext
-
-                ctx = MentalModelGetContext(
-                    bank_id=bank_id,
-                    mental_model_id=mental_model_id,
-                    request_context=request_context,
-                )
-                validation = await validator.validate_mental_model_get(ctx)
-                if not validation.allowed:
-                    raise OperationValidationError(
-                        validation.reason or "Operation not allowed",
-                        status_code=validation.status_code,
-                    )
-
             mental_model = await app.state.memory.get_mental_model(
                 bank_id=bank_id,
                 mental_model_id=mental_model_id,
@@ -2314,31 +2292,9 @@ def _register_routes(app: FastAPI):
             )
             if mental_model is None:
                 raise HTTPException(status_code=404, detail=f"Mental model '{mental_model_id}' not found")
-
-            # Post-operation hook
-            if validator:
-                from hindsight_api.extensions.operation_validator import MentalModelGetResult
-
-                content = mental_model.get("content", "")
-                output_tokens = len(content) // 4 if content else 0
-
-                result_ctx = MentalModelGetResult(
-                    bank_id=bank_id,
-                    mental_model_id=mental_model_id,
-                    request_context=request_context,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-                try:
-                    await validator.on_mental_model_get_complete(result_ctx)
-                except Exception as hook_err:
-                    logger.warning(f"Post-mental-model-get hook error (non-fatal): {hook_err}")
-
             return MentalModelResponse(**mental_model)
         except (AuthenticationError, HTTPException):
             raise
-        except OperationValidationError as e:
-            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except Exception as e:
             import traceback
 
@@ -2363,23 +2319,6 @@ def _register_routes(app: FastAPI):
     ):
         """Create a mental model (async - returns operation_id)."""
         try:
-            # Pre-operation validation hook
-            validator = app.state.memory._operation_validator
-            if validator:
-                from hindsight_api.extensions.operation_validator import MentalModelRefreshContext
-
-                ctx = MentalModelRefreshContext(
-                    bank_id=bank_id,
-                    mental_model_id=None,  # Not yet created
-                    request_context=request_context,
-                )
-                validation = await validator.validate_mental_model_refresh(ctx)
-                if not validation.allowed:
-                    raise OperationValidationError(
-                        validation.reason or "Operation not allowed",
-                        status_code=validation.status_code,
-                    )
-
             # 1. Create the mental model with placeholder content
             mental_model = await app.state.memory.create_mental_model(
                 bank_id=bank_id,
@@ -2402,8 +2341,6 @@ def _register_routes(app: FastAPI):
             raise HTTPException(status_code=400, detail=str(e))
         except (AuthenticationError, HTTPException):
             raise
-        except OperationValidationError as e:
-            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except Exception as e:
             import traceback
 
@@ -2426,23 +2363,6 @@ def _register_routes(app: FastAPI):
     ):
         """Refresh a mental model by re-running its source query (async)."""
         try:
-            # Pre-operation validation hook
-            validator = app.state.memory._operation_validator
-            if validator:
-                from hindsight_api.extensions.operation_validator import MentalModelRefreshContext
-
-                ctx = MentalModelRefreshContext(
-                    bank_id=bank_id,
-                    mental_model_id=mental_model_id,
-                    request_context=request_context,
-                )
-                validation = await validator.validate_mental_model_refresh(ctx)
-                if not validation.allowed:
-                    raise OperationValidationError(
-                        validation.reason or "Operation not allowed",
-                        status_code=validation.status_code,
-                    )
-
             result = await app.state.memory.submit_async_refresh_mental_model(
                 bank_id=bank_id,
                 mental_model_id=mental_model_id,
@@ -2453,8 +2373,6 @@ def _register_routes(app: FastAPI):
             raise HTTPException(status_code=404, detail=str(e))
         except (AuthenticationError, HTTPException):
             raise
-        except OperationValidationError as e:
-            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except Exception as e:
             import traceback
 

hindsight-api/hindsight_api/banner.py

@@ -83,12 +83,9 @@ def print_startup_info(
     embeddings_provider: str,
     reranker_provider: str,
     mcp_enabled: bool = False,
-    version: str | None = None,
 ):
     """Print styled startup information."""
     print(color_start("Starting Hindsight API..."))
-    if version:
-        print(f"  {dim('Version:')} {color(f'v{version}', 0.1)}")
     print(f"  {dim('URL:')} {color(f'http://{host}:{port}', 0.2)}")
     print(f"  {dim('Database:')} {color(database_url, 0.4)}")
     print(f"  {dim('LLM:')} {color(f'{llm_provider} / {llm_model}', 0.6)}")

hindsight-api/hindsight_api/config.py

@@ -154,21 +154,7 @@ ENV_REFLECT_MAX_ITERATIONS = "HINDSIGHT_API_REFLECT_MAX_ITERATIONS"
 DEFAULT_DATABASE_URL = "pg0"
 DEFAULT_DATABASE_SCHEMA = "public"
 DEFAULT_LLM_PROVIDER = "openai"
-
-# Provider-specific default models
-PROVIDER_DEFAULT_MODELS = {
-    "openai": "o3-mini",
-    "anthropic": "claude-haiku-4-5-20251001",
-    "gemini": "gemini-2.5-flash",
-    "groq": "openai/gpt-oss-120b",
-    "ollama": "gemma3:12b",
-    "lmstudio": "local-model",
-    "vertexai": "gemini-2.0-flash-001",
-    "openai-codex": "gpt-5.2-codex",
-    "claude-code": "claude-sonnet-4-5-20250929",
-    "mock": "mock-model",
-}
-DEFAULT_LLM_MODEL = "o3-mini"  # Fallback if provider not in table
+DEFAULT_LLM_MODEL = "gpt-5-mini"
 DEFAULT_LLM_MAX_CONCURRENT = 32
 DEFAULT_LLM_MAX_RETRIES = 10  # Max retry attempts for LLM API calls
 DEFAULT_LLM_INITIAL_BACKOFF = 1.0  # Initial backoff in seconds for retry exponential backoff
@@ -317,11 +303,6 @@ def _validate_extraction_mode(mode: str) -> str:
     return mode_lower
 
 
-def _get_default_model_for_provider(provider: str) -> str:
-    """Get the default model for a given provider."""
-    return PROVIDER_DEFAULT_MODELS.get(provider.lower(), DEFAULT_LLM_MODEL)
-
-
 @dataclass
 class HindsightConfig:
     """Configuration container for Hindsight API."""
@@ -450,18 +431,14 @@ class HindsightConfig:
     @classmethod
     def from_env(cls) -> "HindsightConfig":
         """Create configuration from environment variables."""
-        # Get provider first to determine default model
-        llm_provider = os.getenv(ENV_LLM_PROVIDER, DEFAULT_LLM_PROVIDER)
-        llm_model = os.getenv(ENV_LLM_MODEL) or _get_default_model_for_provider(llm_provider)
-
         return cls(
             # Database
             database_url=os.getenv(ENV_DATABASE_URL, DEFAULT_DATABASE_URL),
             database_schema=os.getenv(ENV_DATABASE_SCHEMA, DEFAULT_DATABASE_SCHEMA),
             # LLM
-            llm_provider=llm_provider,
+            llm_provider=os.getenv(ENV_LLM_PROVIDER, DEFAULT_LLM_PROVIDER),
             llm_api_key=os.getenv(ENV_LLM_API_KEY),
-            llm_model=llm_model,
+            llm_model=os.getenv(ENV_LLM_MODEL, DEFAULT_LLM_MODEL),
             llm_base_url=os.getenv(ENV_LLM_BASE_URL) or None,
             llm_max_concurrent=int(os.getenv(ENV_LLM_MAX_CONCURRENT, str(DEFAULT_LLM_MAX_CONCURRENT))),
             llm_max_retries=int(os.getenv(ENV_LLM_MAX_RETRIES, str(DEFAULT_LLM_MAX_RETRIES))),
@@ -476,12 +453,7 @@ class HindsightConfig:
             # Per-operation LLM config (None = use default)
             retain_llm_provider=os.getenv(ENV_RETAIN_LLM_PROVIDER) or None,
             retain_llm_api_key=os.getenv(ENV_RETAIN_LLM_API_KEY) or None,
-            retain_llm_model=os.getenv(ENV_RETAIN_LLM_MODEL)
-            or (
-                _get_default_model_for_provider(os.getenv(ENV_RETAIN_LLM_PROVIDER))
-                if os.getenv(ENV_RETAIN_LLM_PROVIDER)
-                else None
-            ),
+            retain_llm_model=os.getenv(ENV_RETAIN_LLM_MODEL) or None,
             retain_llm_base_url=os.getenv(ENV_RETAIN_LLM_BASE_URL) or None,
             retain_llm_max_concurrent=int(os.getenv(ENV_RETAIN_LLM_MAX_CONCURRENT))
             if os.getenv(ENV_RETAIN_LLM_MAX_CONCURRENT)
@@ -498,12 +470,7 @@ class HindsightConfig:
             retain_llm_timeout=float(os.getenv(ENV_RETAIN_LLM_TIMEOUT)) if os.getenv(ENV_RETAIN_LLM_TIMEOUT) else None,
             reflect_llm_provider=os.getenv(ENV_REFLECT_LLM_PROVIDER) or None,
             reflect_llm_api_key=os.getenv(ENV_REFLECT_LLM_API_KEY) or None,
-            reflect_llm_model=os.getenv(ENV_REFLECT_LLM_MODEL)
-            or (
-                _get_default_model_for_provider(os.getenv(ENV_REFLECT_LLM_PROVIDER))
-                if os.getenv(ENV_REFLECT_LLM_PROVIDER)
-                else None
-            ),
+            reflect_llm_model=os.getenv(ENV_REFLECT_LLM_MODEL) or None,
             reflect_llm_base_url=os.getenv(ENV_REFLECT_LLM_BASE_URL) or None,
             reflect_llm_max_concurrent=int(os.getenv(ENV_REFLECT_LLM_MAX_CONCURRENT))
             if os.getenv(ENV_REFLECT_LLM_MAX_CONCURRENT)
@@ -522,12 +489,7 @@ class HindsightConfig:
             else None,
             consolidation_llm_provider=os.getenv(ENV_CONSOLIDATION_LLM_PROVIDER) or None,
             consolidation_llm_api_key=os.getenv(ENV_CONSOLIDATION_LLM_API_KEY) or None,
-            consolidation_llm_model=os.getenv(ENV_CONSOLIDATION_LLM_MODEL)
-            or (
-                _get_default_model_for_provider(os.getenv(ENV_CONSOLIDATION_LLM_PROVIDER))
-                if os.getenv(ENV_CONSOLIDATION_LLM_PROVIDER)
-                else None
-            ),
+            consolidation_llm_model=os.getenv(ENV_CONSOLIDATION_LLM_MODEL) or None,
             consolidation_llm_base_url=os.getenv(ENV_CONSOLIDATION_LLM_BASE_URL) or None,
             consolidation_llm_max_concurrent=int(os.getenv(ENV_CONSOLIDATION_LLM_MAX_CONCURRENT))
             if os.getenv(ENV_CONSOLIDATION_LLM_MAX_CONCURRENT)

hindsight-api/hindsight_api/daemon.py

@@ -15,7 +15,7 @@ from pathlib import Path
 logger = logging.getLogger(__name__)
 
 # Default daemon configuration
-DEFAULT_DAEMON_PORT = 8888
+DEFAULT_DAEMON_PORT = 8889
 DEFAULT_IDLE_TIMEOUT = 0  # 0 = no auto-exit (hindsight-embed passes its own timeout)
 LOCKFILE_PATH = Path.home() / ".hindsight" / "daemon.lock"
 DAEMON_LOG_PATH = Path.home() / ".hindsight" / "daemon.log"

hindsight-api/hindsight_api/engine/llm_interface.py

@@ -1,146 +0,0 @@
-"""
-Abstract interface for LLM providers.
-
-This module defines the interface that all LLM providers must implement,
-enabling support for multiple LLM backends (OpenAI, Anthropic, Gemini, Codex, etc.)
-"""
-
-from abc import ABC, abstractmethod
-from typing import Any
-
-from .response_models import LLMToolCallResult, TokenUsage
-
-
-class LLMInterface(ABC):
-    """
-    Abstract interface for LLM providers.
-
-    All LLM provider implementations must inherit from this class and implement
-    the required methods.
-    """
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,
-        base_url: str,
-        model: str,
-        reasoning_effort: str = "low",
-        **kwargs: Any,
-    ):
-        """
-        Initialize LLM provider.
-
-        Args:
-            provider: Provider name (e.g., "openai", "codex", "anthropic", "gemini").
-            api_key: API key or authentication token.
-            base_url: Base URL for the API.
-            model: Model name.
-            reasoning_effort: Reasoning effort level for supported providers.
-            **kwargs: Additional provider-specific parameters.
-        """
-        self.provider = provider.lower()
-        self.api_key = api_key
-        self.base_url = base_url
-        self.model = model
-        self.reasoning_effort = reasoning_effort
-
-    @abstractmethod
-    async def verify_connection(self) -> None:
-        """
-        Verify that the LLM provider is configured correctly by making a simple test call.
-
-        Raises:
-            RuntimeError: If the connection test fails.
-        """
-        pass
-
-    @abstractmethod
-    async def call(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "memory",
-        max_retries: int = 10,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 60.0,
-        skip_validation: bool = False,
-        strict_schema: bool = False,
-        return_usage: bool = False,
-    ) -> Any:
-        """
-        Make an LLM API call with retry logic.
-
-        Args:
-            messages: List of message dicts with 'role' and 'content'.
-            response_format: Optional Pydantic model for structured output.
-            max_completion_tokens: Maximum tokens in response.
-            temperature: Sampling temperature (0.0-2.0).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            skip_validation: Return raw JSON without Pydantic validation.
-            strict_schema: Use strict JSON schema enforcement (OpenAI only).
-            return_usage: If True, return tuple (result, TokenUsage) instead of just result.
-
-        Returns:
-            If return_usage=False: Parsed response if response_format is provided, otherwise text content.
-            If return_usage=True: Tuple of (result, TokenUsage) with token counts.
-
-        Raises:
-            OutputTooLongError: If output exceeds token limits.
-            Exception: Re-raises API errors after retries exhausted.
-        """
-        pass
-
-    @abstractmethod
-    async def call_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "tools",
-        max_retries: int = 5,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 30.0,
-        tool_choice: str | dict[str, Any] = "auto",
-    ) -> LLMToolCallResult:
-        """
-        Make an LLM API call with tool/function calling support.
-
-        Args:
-            messages: List of message dicts. Can include tool results with role='tool'.
-            tools: List of tool definitions in OpenAI format.
-            max_completion_tokens: Maximum tokens in response.
-            temperature: Sampling temperature (0.0-2.0).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            tool_choice: How to choose tools - "auto", "none", "required", or specific function.
-
-        Returns:
-            LLMToolCallResult with content and/or tool_calls.
-        """
-        pass
-
-    @abstractmethod
-    async def cleanup(self) -> None:
-        """Clean up resources (close connections, etc.)."""
-        pass
-
-
-class OutputTooLongError(Exception):
-    """
-    Bridge exception raised when LLM output exceeds token limits.
-
-    This wraps provider-specific errors (e.g., OpenAI's LengthFinishReasonError)
-    to allow callers to handle output length issues without depending on
-    provider-specific implementations.
-    """
-
-    pass

hindsight-api/hindsight_api/engine/memory_engine.py

@@ -303,10 +303,8 @@ class MemoryEngine(MemoryEngineInterface):
         db_url = db_url or config.database_url
         memory_llm_provider = memory_llm_provider or config.llm_provider
         memory_llm_api_key = memory_llm_api_key or config.llm_api_key
-        # Ollama, openai-codex, claude-code, and mock don't require an API key
-        # openai-codex uses OAuth tokens from ~/.codex/auth.json
-        # claude-code uses OAuth tokens from macOS Keychain
-        if not memory_llm_api_key and memory_llm_provider not in ("ollama", "openai-codex", "claude-code", "mock"):
+        # Ollama and mock don't require an API key
+        if not memory_llm_api_key and memory_llm_provider not in ("ollama", "mock"):
             raise ValueError("LLM API key is required. Set HINDSIGHT_API_LLM_API_KEY environment variable.")
         memory_llm_model = memory_llm_model or config.llm_model
         memory_llm_base_url = memory_llm_base_url or config.get_llm_base_url() or None
@@ -538,15 +536,10 @@ class MemoryEngine(MemoryEngineInterface):
             f"[BATCH_RETAIN_TASK] Starting background batch retain for bank_id={bank_id}, {len(contents)} items"
         )
 
-        # Restore tenant_id/api_key_id from task payload so downstream operations
-        # (e.g., consolidation and mental model refreshes) can attribute usage.
+        # Use internal request context for background tasks (skips tenant auth when schema is pre-set)
         from hindsight_api.models import RequestContext
 
-        internal_context = RequestContext(
-            internal=True,
-            tenant_id=task_dict.get("_tenant_id"),
-            api_key_id=task_dict.get("_api_key_id"),
-        )
+        internal_context = RequestContext(internal=True)
         await self.retain_batch_async(bank_id=bank_id, contents=contents, request_context=internal_context)
 
         logger.info(f"[BATCH_RETAIN_TASK] Completed background batch retain for bank_id={bank_id}")
@@ -572,13 +565,7 @@ class MemoryEngine(MemoryEngineInterface):
 
         from .consolidation import run_consolidation_job
 
-        # Restore tenant_id/api_key_id from task payload so downstream operations
-        # (e.g., mental model refreshes) can attribute usage to the correct org.
-        internal_context = RequestContext(
-            internal=True,
-            tenant_id=task_dict.get("_tenant_id"),
-            api_key_id=task_dict.get("_api_key_id"),
-        )
+        internal_context = RequestContext(internal=True)
         result = await run_consolidation_job(
             memory_engine=self,
             bank_id=bank_id,
@@ -610,13 +597,7 @@ class MemoryEngine(MemoryEngineInterface):
 
         from hindsight_api.models import RequestContext
 
-        # Restore tenant_id/api_key_id from task payload so extensions can
-        # attribute the mental_model_refresh operation to the correct org.
-        internal_context = RequestContext(
-            internal=True,
-            tenant_id=task_dict.get("_tenant_id"),
-            api_key_id=task_dict.get("_api_key_id"),
-        )
+        internal_context = RequestContext(internal=True)
 
         # Get the current mental model to get source_query
         mental_model = await self.get_mental_model(bank_id, mental_model_id, request_context=internal_context)
@@ -660,42 +641,6 @@ class MemoryEngine(MemoryEngineInterface):
             request_context=internal_context,
         )
 
-        # Call post-operation hook if validator is configured
-        if self._operation_validator:
-            from hindsight_api.extensions.operation_validator import MentalModelRefreshResult
-
-            # Count facts and mental models from based_on
-            facts_used = 0
-            mental_models_used = 0
-            if reflect_result.based_on:
-                for fact_type, facts in reflect_result.based_on.items():
-                    if facts:
-                        if fact_type == "mental_models":
-                            mental_models_used += len(facts)
-                        else:
-                            facts_used += len(facts)
-
-            # Estimate tokens
-            query_tokens = len(source_query) // 4 if source_query else 0
-            output_tokens = len(generated_content) // 4 if generated_content else 0
-            context_tokens = 0  # refresh doesn't use additional context
-
-            result_ctx = MentalModelRefreshResult(
-                bank_id=bank_id,
-                mental_model_id=mental_model_id,
-                request_context=internal_context,
-                query_tokens=query_tokens,
-                output_tokens=output_tokens,
-                context_tokens=context_tokens,
-                facts_used=facts_used,
-                mental_models_used=mental_models_used,
-                success=True,
-            )
-            try:
-                await self._operation_validator.on_mental_model_refresh_complete(result_ctx)
-            except Exception as hook_err:
-                logger.warning(f"Post-mental-model-refresh hook error (non-fatal): {hook_err}")
-
         logger.info(f"[REFRESH_MENTAL_MODEL_TASK] Completed for bank_id={bank_id}, mental_model_id={mental_model_id}")
 
     async def execute_task(self, task_dict: dict[str, Any]):
@@ -5471,13 +5416,6 @@ class MemoryEngine(MemoryEngineInterface):
         task_payload: dict[str, Any] = {"contents": contents}
         if document_tags:
             task_payload["document_tags"] = document_tags
-        # Pass tenant_id and api_key_id through task payload so the worker
-        # can propagate request context to downstream operations (e.g.,
-        # consolidation and mental model refreshes triggered after retain).
-        if request_context.tenant_id:
-            task_payload["_tenant_id"] = request_context.tenant_id
-        if request_context.api_key_id:
-            task_payload["_api_key_id"] = request_context.api_key_id
 
         result = await self._submit_async_operation(
             bank_id=bank_id,
@@ -5510,21 +5448,11 @@ class MemoryEngine(MemoryEngineInterface):
             Dict with operation_id
         """
         await self._authenticate_tenant(request_context)
-
-        # Pass tenant_id and api_key_id through task payload so the worker
-        # can provide request context to extension hooks (e.g., usage metering
-        # for mental model refreshes triggered by consolidation).
-        task_payload: dict[str, Any] = {}
-        if request_context.tenant_id:
-            task_payload["_tenant_id"] = request_context.tenant_id
-        if request_context.api_key_id:
-            task_payload["_api_key_id"] = request_context.api_key_id
-
         return await self._submit_async_operation(
             bank_id=bank_id,
             operation_type="consolidation",
             task_type="consolidation",
-            task_payload=task_payload,
+            task_payload={},
             dedupe_by_bank=True,
         )
 
@@ -5554,21 +5482,13 @@ class MemoryEngine(MemoryEngineInterface):
         if not mental_model:
             raise ValueError(f"Mental model {mental_model_id} not found in bank {bank_id}")
 
-        # Pass tenant_id and api_key_id through task payload so the worker
-        # can provide request context to extension hooks.
-        task_payload: dict[str, Any] = {
-            "mental_model_id": mental_model_id,
-        }
-        if request_context.tenant_id:
-            task_payload["_tenant_id"] = request_context.tenant_id
-        if request_context.api_key_id:
-            task_payload["_api_key_id"] = request_context.api_key_id
-
         return await self._submit_async_operation(
             bank_id=bank_id,
             operation_type="refresh_mental_model",
             task_type="refresh_mental_model",
-            task_payload=task_payload,
+            task_payload={
+                "mental_model_id": mental_model_id,
+            },
             result_metadata={"mental_model_id": mental_model_id, "name": mental_model["name"]},
             dedupe_by_bank=False,
         )

hindsight-api/hindsight_api/engine/providers/__init__.py

@@ -1,14 +0,0 @@
-"""
-LLM provider implementations.
-
-This package contains concrete implementations of the LLMInterface for various providers.
-"""
-
-from .anthropic_llm import AnthropicLLM
-from .claude_code_llm import ClaudeCodeLLM
-from .codex_llm import CodexLLM
-from .gemini_llm import GeminiLLM
-from .mock_llm import MockLLM
-from .openai_compatible_llm import OpenAICompatibleLLM
-
-__all__ = ["AnthropicLLM", "ClaudeCodeLLM", "CodexLLM", "GeminiLLM", "MockLLM", "OpenAICompatibleLLM"]

hindsight-api/hindsight_api/engine/providers/anthropic_llm.py

@@ -1,434 +0,0 @@
-"""
-Anthropic LLM provider using the Anthropic Python SDK.
-
-This provider enables using Claude models from Anthropic with support for:
-- Structured JSON output
-- Tool/function calling with proper format conversion
-- Extended thinking mode
-- Retry logic with exponential backoff
-"""
-
-import asyncio
-import json
-import logging
-import time
-from typing import Any
-
-from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
-from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
-from hindsight_api.metrics import get_metrics_collector
-
-logger = logging.getLogger(__name__)
-
-
-class AnthropicLLM(LLMInterface):
-    """
-    LLM provider using Anthropic's Claude models.
-
-    Supports structured output, tool calling, and extended thinking mode.
-    Handles format conversion between OpenAI-style messages and Anthropic's format.
-    """
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,
-        base_url: str,
-        model: str,
-        reasoning_effort: str = "low",
-        timeout: float = 300.0,
-        **kwargs: Any,
-    ):
-        """
-        Initialize Anthropic LLM provider.
-
-        Args:
-            provider: Provider name (should be "anthropic").
-            api_key: Anthropic API key.
-            base_url: Base URL for the API (optional, uses Anthropic default if empty).
-            model: Model name (e.g., "claude-sonnet-4-20250514").
-            reasoning_effort: Reasoning effort level (not used by Anthropic).
-            timeout: Request timeout in seconds.
-            **kwargs: Additional provider-specific parameters.
-        """
-        super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
-
-        if not self.api_key:
-            raise ValueError("API key is required for Anthropic provider")
-
-        # Import and initialize Anthropic client
-        try:
-            from anthropic import AsyncAnthropic
-
-            client_kwargs: dict[str, Any] = {"api_key": self.api_key}
-            if self.base_url:
-                client_kwargs["base_url"] = self.base_url
-            if timeout:
-                client_kwargs["timeout"] = timeout
-
-            self._client = AsyncAnthropic(**client_kwargs)
-            logger.info(f"Anthropic client initialized for model: {self.model}")
-        except ImportError as e:
-            raise RuntimeError("Anthropic SDK not installed. Run: uv add anthropic or pip install anthropic") from e
-
-    async def verify_connection(self) -> None:
-        """
-        Verify that the Anthropic provider is configured correctly by making a simple test call.
-
-        Raises:
-            RuntimeError: If the connection test fails.
-        """
-        try:
-            test_messages = [{"role": "user", "content": "test"}]
-            await self.call(
-                messages=test_messages,
-                max_completion_tokens=10,
-                temperature=0.0,
-                scope="test",
-                max_retries=0,
-            )
-            logger.info("Anthropic connection verified successfully")
-        except Exception as e:
-            logger.error(f"Anthropic connection verification failed: {e}")
-            raise RuntimeError(f"Failed to verify Anthropic connection: {e}") from e
-
-    async def call(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "memory",
-        max_retries: int = 10,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 60.0,
-        skip_validation: bool = False,
-        strict_schema: bool = False,
-        return_usage: bool = False,
-    ) -> Any:
-        """
-        Make an LLM API call with retry logic.
-
-        Args:
-            messages: List of message dicts with 'role' and 'content'.
-            response_format: Optional Pydantic model for structured output.
-            max_completion_tokens: Maximum tokens in response.
-            temperature: Sampling temperature (0.0-2.0).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            skip_validation: Return raw JSON without Pydantic validation.
-            strict_schema: Use strict JSON schema enforcement (not supported by Anthropic).
-            return_usage: If True, return tuple (result, TokenUsage) instead of just result.
-
-        Returns:
-            If return_usage=False: Parsed response if response_format is provided, otherwise text content.
-            If return_usage=True: Tuple of (result, TokenUsage) with token counts.
-
-        Raises:
-            OutputTooLongError: If output exceeds token limits.
-            Exception: Re-raises API errors after retries exhausted.
-        """
-        from anthropic import APIConnectionError, APIStatusError, RateLimitError
-
-        start_time = time.time()
-
-        # Convert OpenAI-style messages to Anthropic format
-        system_prompt = None
-        anthropic_messages = []
-
-        for msg in messages:
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-
-            if role == "system":
-                if system_prompt:
-                    system_prompt += "\n\n" + content
-                else:
-                    system_prompt = content
-            else:
-                anthropic_messages.append({"role": role, "content": content})
-
-        # Add JSON schema instruction if response_format is provided
-        if response_format is not None and hasattr(response_format, "model_json_schema"):
-            schema = response_format.model_json_schema()
-            schema_msg = f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}"
-            if system_prompt:
-                system_prompt += schema_msg
-            else:
-                system_prompt = schema_msg
-
-        # Prepare parameters
-        call_params: dict[str, Any] = {
-            "model": self.model,
-            "messages": anthropic_messages,
-            "max_tokens": max_completion_tokens if max_completion_tokens is not None else 4096,
-        }
-
-        if system_prompt:
-            call_params["system"] = system_prompt
-
-        if temperature is not None:
-            call_params["temperature"] = temperature
-
-        last_exception = None
-
-        for attempt in range(max_retries + 1):
-            try:
-                response = await self._client.messages.create(**call_params)
-
-                # Anthropic response content is a list of blocks
-                content = ""
-                for block in response.content:
-                    if block.type == "text":
-                        content += block.text
-
-                if response_format is not None:
-                    # Models may wrap JSON in markdown code blocks
-                    clean_content = content
-                    if "```json" in content:
-                        clean_content = content.split("```json")[1].split("```")[0].strip()
-                    elif "```" in content:
-                        clean_content = content.split("```")[1].split("```")[0].strip()
-
-                    try:
-                        json_data = json.loads(clean_content)
-                    except json.JSONDecodeError:
-                        # Fallback to parsing raw content if markdown stripping failed
-                        json_data = json.loads(content)
-
-                    if skip_validation:
-                        result = json_data
-                    else:
-                        result = response_format.model_validate(json_data)
-                else:
-                    result = content
-
-                # Record metrics and log slow calls
-                duration = time.time() - start_time
-                input_tokens = response.usage.input_tokens or 0 if response.usage else 0
-                output_tokens = response.usage.output_tokens or 0 if response.usage else 0
-                total_tokens = input_tokens + output_tokens
-
-                # Record LLM metrics
-                metrics = get_metrics_collector()
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-
-                # Log slow calls
-                if duration > 10.0:
-                    logger.info(
-                        f"slow llm call: scope={scope}, model={self.provider}/{self.model}, "
-                        f"input_tokens={input_tokens}, output_tokens={output_tokens}, "
-                        f"time={duration:.3f}s"
-                    )
-
-                if return_usage:
-                    token_usage = TokenUsage(
-                        input_tokens=input_tokens,
-                        output_tokens=output_tokens,
-                        total_tokens=total_tokens,
-                    )
-                    return result, token_usage
-                return result
-
-            except json.JSONDecodeError as e:
-                last_exception = e
-                if attempt < max_retries:
-                    logger.warning("Anthropic returned invalid JSON, retrying...")
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    await asyncio.sleep(backoff)
-                    continue
-                else:
-                    logger.error(f"Anthropic returned invalid JSON after {max_retries + 1} attempts")
-                    raise
-
-            except (APIConnectionError, RateLimitError, APIStatusError) as e:
-                # Fast fail on 401/403
-                if isinstance(e, APIStatusError) and e.status_code in (401, 403):
-                    logger.error(f"Anthropic auth error (HTTP {e.status_code}), not retrying: {str(e)}")
-                    raise
-
-                last_exception = e
-                if attempt < max_retries:
-                    # Check if it's a rate limit or server error
-                    should_retry = isinstance(e, (APIConnectionError, RateLimitError)) or (
-                        isinstance(e, APIStatusError) and e.status_code >= 500
-                    )
-
-                    if should_retry:
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
-                        jitter = backoff * 0.2 * (2 * (time.time() % 1) - 1)
-                        await asyncio.sleep(backoff + jitter)
-                        continue
-
-                logger.error(f"Anthropic API error after {max_retries + 1} attempts: {str(e)}")
-                raise
-
-            except Exception as e:
-                logger.error(f"Unexpected error during Anthropic call: {type(e).__name__}: {str(e)}")
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Anthropic call failed after all retries")
-
-    async def call_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "tools",
-        max_retries: int = 5,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 30.0,
-        tool_choice: str | dict[str, Any] = "auto",
-    ) -> LLMToolCallResult:
-        """
-        Make an LLM API call with tool/function calling support.
-
-        Args:
-            messages: List of message dicts. Can include tool results with role='tool'.
-            tools: List of tool definitions in OpenAI format.
-            max_completion_tokens: Maximum tokens in response.
-            temperature: Sampling temperature (0.0-2.0).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            tool_choice: How to choose tools - "auto", "none", "required", or specific function.
-
-        Returns:
-            LLMToolCallResult with content and/or tool_calls.
-        """
-        from anthropic import APIConnectionError, APIStatusError
-
-        start_time = time.time()
-
-        # Convert OpenAI tool format to Anthropic format
-        anthropic_tools = []
-        for tool in tools:
-            func = tool.get("function", {})
-            anthropic_tools.append(
-                {
-                    "name": func.get("name", ""),
-                    "description": func.get("description", ""),
-                    "input_schema": func.get("parameters", {"type": "object", "properties": {}}),
-                }
-            )
-
-        # Convert messages - handle tool results
-        system_prompt = None
-        anthropic_messages = []
-        for msg in messages:
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-
-            if role == "system":
-                system_prompt = (system_prompt + "\n\n" + content) if system_prompt else content
-            elif role == "tool":
-                # Anthropic uses tool_result blocks
-                anthropic_messages.append(
-                    {
-                        "role": "user",
-                        "content": [
-                            {"type": "tool_result", "tool_use_id": msg.get("tool_call_id", ""), "content": content}
-                        ],
-                    }
-                )
-            elif role == "assistant" and msg.get("tool_calls"):
-                # Convert assistant tool calls
-                tool_use_blocks = []
-                for tc in msg["tool_calls"]:
-                    tool_use_blocks.append(
-                        {
-                            "type": "tool_use",
-                            "id": tc.get("id", ""),
-                            "name": tc.get("function", {}).get("name", ""),
-                            "input": json.loads(tc.get("function", {}).get("arguments", "{}")),
-                        }
-                    )
-                anthropic_messages.append({"role": "assistant", "content": tool_use_blocks})
-            else:
-                anthropic_messages.append({"role": role, "content": content})
-
-        call_params: dict[str, Any] = {
-            "model": self.model,
-            "messages": anthropic_messages,
-            "tools": anthropic_tools,
-            "max_tokens": max_completion_tokens or 4096,
-        }
-        if system_prompt:
-            call_params["system"] = system_prompt
-
-        if temperature is not None:
-            call_params["temperature"] = temperature
-
-        last_exception = None
-        for attempt in range(max_retries + 1):
-            try:
-                response = await self._client.messages.create(**call_params)
-
-                # Extract content and tool calls
-                content_parts = []
-                tool_calls: list[LLMToolCall] = []
-
-                for block in response.content:
-                    if block.type == "text":
-                        content_parts.append(block.text)
-                    elif block.type == "tool_use":
-                        tool_calls.append(LLMToolCall(id=block.id, name=block.name, arguments=block.input or {}))
-
-                content = "".join(content_parts) if content_parts else None
-                finish_reason = "tool_calls" if tool_calls else "stop"
-
-                # Extract token usage
-                input_tokens = response.usage.input_tokens or 0
-                output_tokens = response.usage.output_tokens or 0
-
-                # Record metrics
-                metrics = get_metrics_collector()
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=time.time() - start_time,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-
-                return LLMToolCallResult(
-                    content=content,
-                    tool_calls=tool_calls,
-                    finish_reason=finish_reason,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                )
-
-            except (APIConnectionError, APIStatusError) as e:
-                if isinstance(e, APIStatusError) and e.status_code in (401, 403):
-                    raise
-                last_exception = e
-                if attempt < max_retries:
-                    await asyncio.sleep(min(initial_backoff * (2**attempt), max_backoff))
-                    continue
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Anthropic tool call failed")
-
-    async def cleanup(self) -> None:
-        """Clean up resources (close Anthropic client connections)."""
-        if hasattr(self, "_client") and self._client:
-            await self._client.close()

hindsight-api/hindsight_api/engine/providers/claude_code_llm.py

@@ -1,352 +0,0 @@
-"""
-Claude Code LLM provider using Claude Agent SDK.
-
-This provider enables using Claude Pro/Max subscriptions for API calls
-via the Claude CLI authentication. It uses the Claude Agent SDK which
-automatically handles authentication via `claude auth login` credentials.
-"""
-
-import asyncio
-import json
-import logging
-import time
-from typing import Any
-
-from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
-from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
-from hindsight_api.metrics import get_metrics_collector
-
-logger = logging.getLogger(__name__)
-
-
-class ClaudeCodeLLM(LLMInterface):
-    """
-    LLM provider using Claude Code authentication.
-
-    Authenticates using Claude Pro/Max credentials via `claude auth login`
-    and makes API calls through the Claude Agent SDK.
-    """
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,  # Will be ignored, uses CLI auth
-        base_url: str,
-        model: str,
-        reasoning_effort: str = "low",
-        **kwargs: Any,
-    ):
-        """Initialize Claude Code LLM provider."""
-        super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
-
-        # Verify Claude Agent SDK is available
-        try:
-            self._verify_claude_code_available()
-            logger.info("Claude Code: Using Claude Agent SDK (authentication via claude auth login)")
-        except Exception as e:
-            raise RuntimeError(
-                f"Failed to initialize Claude Code provider: {e}\n\n"
-                "To set up Claude Code authentication:\n"
-                "1. Install Claude Code CLI: npm install -g @anthropics/claude-code\n"
-                "2. Login with your Pro/Max plan: claude auth login\n"
-                "3. Verify authentication: claude --version\n\n"
-                "Or use a different provider (anthropic, openai, gemini) with API keys."
-            ) from e
-
-        # Metrics collector is imported at module level
-
-    def _verify_claude_code_available(self) -> None:
-        """
-        Verify that Claude Agent SDK can be imported and is properly configured.
-
-        Raises:
-            ImportError: If Claude Agent SDK is not installed.
-            RuntimeError: If Claude Code is not authenticated.
-        """
-        try:
-            # Import Claude Agent SDK
-            # Reduce Claude Agent SDK logging verbosity
-            import logging as sdk_logging
-
-            from claude_agent_sdk import query  # noqa: F401
-
-            sdk_logging.getLogger("claude_agent_sdk").setLevel(sdk_logging.WARNING)
-            sdk_logging.getLogger("claude_agent_sdk._internal").setLevel(sdk_logging.WARNING)
-
-            logger.debug("Claude Agent SDK imported successfully")
-        except ImportError as e:
-            raise ImportError(
-                "Claude Agent SDK not installed. Run: uv add claude-agent-sdk or pip install claude-agent-sdk"
-            ) from e
-
-        # SDK will automatically check for authentication when first used
-        # No need to verify here - let it fail gracefully on first call with helpful error
-
-    async def verify_connection(self) -> None:
-        """
-        Verify that the Claude Code provider is configured correctly by making a simple test call.
-
-        Raises:
-            RuntimeError: If the connection test fails.
-        """
-        try:
-            test_messages = [{"role": "user", "content": "test"}]
-            await self.call(
-                messages=test_messages,
-                max_completion_tokens=10,
-                temperature=0.0,
-                scope="test",
-                max_retries=0,
-            )
-            logger.info("Claude Code connection verified successfully")
-        except Exception as e:
-            logger.error(f"Claude Code connection verification failed: {e}")
-            raise RuntimeError(f"Failed to verify Claude Code connection: {e}") from e
-
-    async def call(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "memory",
-        max_retries: int = 10,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 60.0,
-        skip_validation: bool = False,
-        strict_schema: bool = False,
-        return_usage: bool = False,
-    ) -> Any:
-        """
-        Make an LLM API call with retry logic.
-
-        Args:
-            messages: List of message dicts with 'role' and 'content'.
-            response_format: Optional Pydantic model for structured output.
-            max_completion_tokens: Maximum tokens in response (ignored by Claude Agent SDK).
-            temperature: Sampling temperature (ignored by Claude Agent SDK).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            skip_validation: Return raw JSON without Pydantic validation.
-            strict_schema: Use strict JSON schema enforcement (not supported).
-            return_usage: If True, return tuple (result, TokenUsage) instead of just result.
-
-        Returns:
-            If return_usage=False: Parsed response if response_format is provided, otherwise text content.
-            If return_usage=True: Tuple of (result, TokenUsage) with estimated token counts.
-
-        Raises:
-            OutputTooLongError: If output exceeds token limits (not supported by Claude Agent SDK).
-            Exception: Re-raises API errors after retries exhausted.
-        """
-        from claude_agent_sdk import AssistantMessage, ClaudeAgentOptions, TextBlock, query
-
-        start_time = time.time()
-
-        # Build system prompt
-        system_prompt = ""
-        user_content = ""
-
-        for msg in messages:
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-
-            if role == "system":
-                system_prompt += ("\n\n" + content) if system_prompt else content
-            elif role == "user":
-                user_content += ("\n\n" + content) if user_content else content
-            elif role == "assistant":
-                # Claude Agent SDK doesn't support multi-turn easily in query()
-                # For now, prepend assistant messages to user content
-                user_content += f"\n\n[Previous assistant response: {content}]"
-
-        # Add JSON schema instruction if response_format is provided
-        if response_format is not None and hasattr(response_format, "model_json_schema"):
-            schema = response_format.model_json_schema()
-            schema_instruction = (
-                f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}\n\n"
-                "Respond with ONLY the JSON, no markdown formatting."
-            )
-            user_content += schema_instruction
-
-        # Configure SDK options
-        options = ClaudeAgentOptions(
-            system_prompt=system_prompt if system_prompt else None,
-            max_turns=1,  # Single-turn for API-style interactions
-            allowed_tools=[],  # Disable tools for standard LLM calls
-        )
-
-        # Call Claude Agent SDK
-        last_exception = None
-        for attempt in range(max_retries + 1):
-            try:
-                # Collect streaming response
-                full_text = ""
-
-                async for message in query(prompt=user_content, options=options):
-                    if isinstance(message, AssistantMessage):
-                        for block in message.content:
-                            if isinstance(block, TextBlock):
-                                full_text += block.text
-
-                # Handle structured output
-                if response_format is not None:
-                    # Models may wrap JSON in markdown
-                    clean_text = full_text
-                    if "```json" in full_text:
-                        clean_text = full_text.split("```json")[1].split("```")[0].strip()
-                    elif "```" in full_text:
-                        clean_text = full_text.split("```")[1].split("```")[0].strip()
-
-                    try:
-                        json_data = json.loads(clean_text)
-                    except json.JSONDecodeError as e:
-                        logger.warning(f"Claude Code JSON parse error (attempt {attempt + 1}/{max_retries + 1}): {e}")
-                        if attempt < max_retries:
-                            backoff = min(initial_backoff * (2**attempt), max_backoff)
-                            await asyncio.sleep(backoff)
-                            last_exception = e
-                            continue
-                        raise
-
-                    if skip_validation:
-                        result = json_data
-                    else:
-                        result = response_format.model_validate(json_data)
-                else:
-                    result = full_text
-
-                # Record metrics
-                duration = time.time() - start_time
-                metrics = get_metrics_collector()
-
-                # Estimate token usage (Claude Agent SDK doesn't report exact counts)
-                # Use character count / 4 as rough estimate (1 token ≈ 4 characters)
-                estimated_input = sum(len(m.get("content", "")) for m in messages) // 4
-                estimated_output = len(full_text) // 4
-
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=estimated_input,
-                    output_tokens=estimated_output,
-                    success=True,
-                )
-
-                # Log slow calls
-                if duration > 10.0:
-                    logger.info(
-                        f"slow llm call: scope={scope}, model={self.provider}/{self.model}, time={duration:.3f}s"
-                    )
-
-                if return_usage:
-                    token_usage = TokenUsage(
-                        input_tokens=estimated_input,
-                        output_tokens=estimated_output,
-                        total_tokens=estimated_input + estimated_output,
-                    )
-                    return result, token_usage
-
-                return result
-
-            except Exception as e:
-                last_exception = e
-
-                # Check for authentication errors
-                error_str = str(e).lower()
-                if "auth" in error_str or "login" in error_str or "credential" in error_str:
-                    logger.error(f"Claude Code authentication error: {e}")
-                    raise RuntimeError(
-                        f"Claude Code authentication failed: {e}\n\n"
-                        "Run 'claude auth login' to authenticate with Claude Pro/Max."
-                    ) from e
-
-                if attempt < max_retries:
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    logger.warning(f"Claude Code error (attempt {attempt + 1}/{max_retries + 1}): {e}")
-                    await asyncio.sleep(backoff)
-                    continue
-                else:
-                    logger.error(f"Claude Code error after {max_retries + 1} attempts: {e}")
-                    raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Claude Code call failed after all retries")
-
-    async def call_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "tools",
-        max_retries: int = 5,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 30.0,
-        tool_choice: str | dict[str, Any] = "auto",
-    ) -> LLMToolCallResult:
-        """
-        Make an LLM API call with tool/function calling support.
-
-        Note: This is a simplified implementation. Full tool support would require
-        integrating with Claude Agent SDK's tool system.
-
-        Args:
-            messages: List of message dicts. Can include tool results with role='tool'.
-            tools: List of tool definitions in OpenAI format.
-            max_completion_tokens: Maximum tokens in response.
-            temperature: Sampling temperature.
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            tool_choice: How to choose tools - "auto", "none", "required", or specific function.
-
-        Returns:
-            LLMToolCallResult with content and/or tool_calls.
-        """
-        # For now, use regular call without tools
-        # Full implementation would require mapping OpenAI tool format to Claude Agent SDK tools
-        logger.warning(
-            "Claude Code provider does not fully support tool calling yet. Falling back to regular text completion."
-        )
-
-        result = await self.call(
-            messages=messages,
-            response_format=None,
-            max_completion_tokens=max_completion_tokens,
-            temperature=temperature,
-            scope=scope,
-            max_retries=max_retries,
-            initial_backoff=initial_backoff,
-            max_backoff=max_backoff,
-            return_usage=True,
-        )
-
-        if isinstance(result, tuple):
-            text, usage = result
-            return LLMToolCallResult(
-                content=text,
-                tool_calls=[],
-                finish_reason="stop",
-                input_tokens=usage.input_tokens,
-                output_tokens=usage.output_tokens,
-            )
-        else:
-            # Fallback if return_usage didn't work as expected
-            return LLMToolCallResult(
-                content=str(result),
-                tool_calls=[],
-                finish_reason="stop",
-                input_tokens=0,
-                output_tokens=0,
-            )
-
-    async def cleanup(self) -> None:
-        """Clean up resources (no HTTP client to close for Claude Agent SDK)."""
-        pass

hindsight-api/hindsight_api/engine/providers/codex_llm.py

@@ -1,527 +0,0 @@
-"""
-OpenAI Codex LLM provider using ChatGPT Plus/Pro OAuth authentication.
-
-This provider enables using ChatGPT Plus/Pro subscriptions for API calls
-without separate OpenAI Platform API credits. It uses OAuth tokens from
-~/.codex/auth.json and communicates with the ChatGPT backend API.
-"""
-
-import asyncio
-import json
-import logging
-import os
-import time
-import uuid
-from pathlib import Path
-from typing import Any
-
-import httpx
-
-from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
-from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
-from hindsight_api.metrics import get_metrics_collector
-
-logger = logging.getLogger(__name__)
-
-
-class CodexLLM(LLMInterface):
-    """
-    LLM provider using OpenAI Codex OAuth authentication.
-
-    Authenticates using ChatGPT Plus/Pro credentials stored in ~/.codex/auth.json
-    and makes API calls to chatgpt.com/backend-api/codex/responses.
-    """
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,  # Will be ignored, reads from ~/.codex/auth.json
-        base_url: str,
-        model: str,
-        reasoning_effort: str = "low",
-        **kwargs: Any,
-    ):
-        """Initialize Codex LLM provider."""
-        super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
-
-        # Load Codex OAuth credentials
-        try:
-            self.access_token, self.account_id = self._load_codex_auth()
-            logger.info(f"Loaded Codex OAuth credentials for account: {self.account_id}")
-        except Exception as e:
-            raise RuntimeError(
-                f"Failed to load Codex OAuth credentials from ~/.codex/auth.json: {e}\n\n"
-                "To set up Codex authentication:\n"
-                "1. Install Codex CLI: npm install -g @openai/codex\n"
-                "2. Login: codex auth login\n"
-                "3. Verify: ls ~/.codex/auth.json\n\n"
-                "Or use a different provider (openai, anthropic, gemini) with API keys."
-            ) from e
-
-        # Use ChatGPT backend API endpoint
-        if not self.base_url:
-            self.base_url = "https://chatgpt.com/backend-api"
-
-        # Normalize model name (strip openai/ prefix if present)
-        if self.model.startswith("openai/"):
-            self.model = self.model[len("openai/") :]
-
-        # Map reasoning effort to Codex reasoning summary format
-        # Codex supports: "auto", "concise", "detailed"
-        self.reasoning_summary = self._map_reasoning_effort(reasoning_effort)
-
-        # HTTP client for SSE streaming
-        self._client = httpx.AsyncClient(timeout=120.0)
-
-    def _load_codex_auth(self) -> tuple[str, str]:
-        """
-        Load OAuth credentials from ~/.codex/auth.json.
-
-        Returns:
-            Tuple of (access_token, account_id).
-
-        Raises:
-            FileNotFoundError: If auth file doesn't exist.
-            ValueError: If auth file is invalid.
-        """
-        auth_file = Path.home() / ".codex" / "auth.json"
-
-        if not auth_file.exists():
-            raise FileNotFoundError(
-                f"Codex auth file not found: {auth_file}\nRun 'codex auth login' to authenticate with ChatGPT Plus/Pro."
-            )
-
-        with open(auth_file) as f:
-            data = json.load(f)
-
-        # Validate auth structure
-        auth_mode = data.get("auth_mode")
-        if auth_mode != "chatgpt":
-            raise ValueError(f"Expected auth_mode='chatgpt', got: {auth_mode}")
-
-        tokens = data.get("tokens", {})
-        access_token = tokens.get("access_token")
-        account_id = tokens.get("account_id")
-
-        if not access_token:
-            raise ValueError("No access_token found in Codex auth file. Run 'codex auth login' again.")
-
-        return access_token, account_id
-
-    def _map_reasoning_effort(self, effort: str) -> str:
-        """
-        Map standard reasoning effort to Codex reasoning summary format.
-
-        Args:
-            effort: Standard effort level ("low", "medium", "high", "xhigh").
-
-        Returns:
-            Codex reasoning summary: "concise", "detailed", or "auto".
-        """
-        mapping = {
-            "low": "concise",
-            "medium": "auto",
-            "high": "detailed",
-            "xhigh": "detailed",
-        }
-        return mapping.get(effort.lower(), "auto")
-
-    async def verify_connection(self) -> None:
-        """Verify Codex connection by making a simple test call."""
-        try:
-            logger.info(f"Verifying Codex LLM: model={self.model}, account={self.account_id}...")
-            await self.call(
-                messages=[{"role": "user", "content": "Say 'ok'"}],
-                max_completion_tokens=10,
-                max_retries=2,
-                initial_backoff=0.5,
-                max_backoff=2.0,
-            )
-            logger.info(f"Codex LLM verified: {self.model}")
-        except Exception as e:
-            raise RuntimeError(f"Codex LLM connection verification failed for {self.model}: {e}") from e
-
-    async def call(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "memory",
-        max_retries: int = 10,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 60.0,
-        skip_validation: bool = False,
-        strict_schema: bool = False,
-        return_usage: bool = False,
-    ) -> Any:
-        """Make API call to Codex backend with SSE streaming."""
-        start_time = time.time()
-
-        # Prepare system instructions
-        system_instruction = ""
-        user_messages = []
-
-        for msg in messages:
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-
-            if role == "system":
-                system_instruction += ("\n\n" + content) if system_instruction else content
-            else:
-                user_messages.append(msg)
-
-        # Add JSON schema instruction if response_format is provided
-        if response_format is not None and hasattr(response_format, "model_json_schema"):
-            schema = response_format.model_json_schema()
-            schema_msg = f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}"
-            system_instruction += schema_msg
-
-        # Build Codex request payload
-        payload = {
-            "model": self.model,
-            "instructions": system_instruction,
-            "input": [
-                {
-                    "type": "message",
-                    "role": msg.get("role", "user"),
-                    "content": msg.get("content", ""),
-                }
-                for msg in user_messages
-            ],
-            "tools": [],
-            "tool_choice": "auto",
-            "parallel_tool_calls": True,
-            "reasoning": {"summary": self.reasoning_summary},
-            "store": False,  # Codex uses stateless mode
-            "stream": True,  # SSE streaming
-            "include": ["reasoning.encrypted_content"],
-            "prompt_cache_key": str(uuid.uuid4()),
-        }
-
-        headers = {
-            "Authorization": f"Bearer {self.access_token}",
-            "Content-Type": "application/json",
-            "OpenAI-Account-ID": self.account_id,
-            "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)",
-            "Origin": "https://chatgpt.com",
-        }
-
-        url = f"{self.base_url}/codex/responses"
-        last_exception = None
-
-        for attempt in range(max_retries + 1):
-            try:
-                response = await self._client.post(url, json=payload, headers=headers, timeout=120.0)
-                response.raise_for_status()
-
-                # Parse SSE stream
-                content = await self._parse_sse_stream(response)
-
-                # Handle structured output
-                if response_format is not None:
-                    # Models may wrap JSON in markdown
-                    clean_content = content
-                    if "```json" in content:
-                        clean_content = content.split("```json")[1].split("```")[0].strip()
-                    elif "```" in content:
-                        clean_content = content.split("```")[1].split("```")[0].strip()
-
-                    try:
-                        json_data = json.loads(clean_content)
-                    except json.JSONDecodeError as e:
-                        logger.warning(f"Codex JSON parse error (attempt {attempt + 1}/{max_retries + 1}): {e}")
-                        if attempt < max_retries:
-                            backoff = min(initial_backoff * (2**attempt), max_backoff)
-                            await asyncio.sleep(backoff)
-                            last_exception = e
-                            continue
-                        raise
-
-                    if skip_validation:
-                        result = json_data
-                    else:
-                        result = response_format.model_validate(json_data)
-                else:
-                    result = content
-
-                # Record metrics
-                duration = time.time() - start_time
-                metrics = get_metrics_collector()
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=0,  # Codex doesn't report token counts in SSE
-                    output_tokens=0,
-                    success=True,
-                )
-
-                if return_usage:
-                    # Codex doesn't provide token counts, estimate based on content
-                    estimated_input = sum(len(m.get("content", "")) for m in messages) // 4
-                    estimated_output = len(content) // 4
-                    token_usage = TokenUsage(
-                        input_tokens=estimated_input,
-                        output_tokens=estimated_output,
-                        total_tokens=estimated_input + estimated_output,
-                    )
-                    return result, token_usage
-
-                return result
-
-            except httpx.HTTPStatusError as e:
-                last_exception = e
-                status_code = e.response.status_code
-
-                # Fast fail on auth errors
-                if status_code in (401, 403):
-                    logger.error(f"Codex auth error (HTTP {status_code}): {e.response.text[:200]}")
-                    raise RuntimeError(
-                        "Codex authentication failed. Your OAuth token may have expired.\n"
-                        "Run 'codex auth login' to re-authenticate."
-                    ) from e
-
-                if attempt < max_retries:
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    logger.warning(f"Codex HTTP error {status_code} (attempt {attempt + 1}/{max_retries + 1})")
-                    await asyncio.sleep(backoff)
-                    continue
-                else:
-                    logger.error(f"Codex HTTP error after {max_retries + 1} attempts: {e}")
-                    raise
-
-            except httpx.RequestError as e:
-                last_exception = e
-                if attempt < max_retries:
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    logger.warning(f"Codex connection error (attempt {attempt + 1}/{max_retries + 1}): {e}")
-                    await asyncio.sleep(backoff)
-                    continue
-                else:
-                    logger.error(f"Codex connection error after {max_retries + 1} attempts: {e}")
-                    raise
-
-            except Exception as e:
-                logger.error(f"Unexpected Codex error: {type(e).__name__}: {e}")
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Codex call failed after all retries")
-
-    async def _parse_sse_stream(self, response: httpx.Response) -> str:
-        """
-        Parse Server-Sent Events (SSE) stream from Codex API.
-
-        Args:
-            response: HTTP response with SSE stream.
-
-        Returns:
-            Extracted text content from stream.
-        """
-        full_text = ""
-        event_type = None
-
-        async for line in response.aiter_lines():
-            if not line:
-                continue
-
-            # Track event type
-            if line.startswith("event: "):
-                event_type = line[7:]
-
-            # Parse data
-            elif line.startswith("data: "):
-                data_str = line[6:]
-                if data_str == "[DONE]":
-                    break
-
-                try:
-                    data = json.loads(data_str)
-
-                    # Extract content based on event type
-                    if event_type == "response.text.delta" and "delta" in data:
-                        full_text += data["delta"]
-                    elif event_type == "response.content_part.delta" and "delta" in data:
-                        full_text += data["delta"]
-                    # Check for item content
-                    elif "item" in data:
-                        item = data["item"]
-                        if "content" in item:
-                            content = item["content"]
-                            if isinstance(content, list):
-                                for part in content:
-                                    if isinstance(part, dict) and "text" in part:
-                                        full_text += part["text"]
-                            elif isinstance(content, str):
-                                full_text += content
-
-                except json.JSONDecodeError:
-                    # Skip malformed JSON events
-                    pass
-
-        return full_text
-
-    async def call_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "tools",
-        max_retries: int = 5,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 30.0,
-        tool_choice: str | dict[str, Any] = "auto",
-    ) -> LLMToolCallResult:
-        """
-        Make API call with tool calling support.
-
-        Note: This is a basic implementation. Full tool calling support for Codex
-        may require additional SSE event parsing.
-        """
-        start_time = time.time()
-
-        # Prepare system instructions
-        system_instruction = ""
-        user_messages = []
-
-        for msg in messages:
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-
-            if role == "system":
-                system_instruction += ("\n\n" + content) if system_instruction else content
-            elif role == "tool":
-                # Handle tool results
-                user_messages.append(
-                    {
-                        "type": "message",
-                        "role": "user",
-                        "content": f"Tool result: {content}",
-                    }
-                )
-            else:
-                user_messages.append(
-                    {
-                        "type": "message",
-                        "role": role,
-                        "content": content,
-                    }
-                )
-
-        # Convert tools to Codex format
-        codex_tools = []
-        for tool in tools:
-            func = tool.get("function", {})
-            codex_tools.append(
-                {
-                    "type": "function",
-                    "function": {
-                        "name": func.get("name", ""),
-                        "description": func.get("description", ""),
-                        "parameters": func.get("parameters", {}),
-                    },
-                }
-            )
-
-        payload = {
-            "model": self.model,
-            "instructions": system_instruction,
-            "input": user_messages,
-            "tools": codex_tools,
-            "tool_choice": tool_choice,
-            "parallel_tool_calls": True,
-            "reasoning": {"summary": self.reasoning_summary},
-            "store": False,
-            "stream": True,
-            "include": ["reasoning.encrypted_content"],
-            "prompt_cache_key": str(uuid.uuid4()),
-        }
-
-        headers = {
-            "Authorization": f"Bearer {self.access_token}",
-            "Content-Type": "application/json",
-            "OpenAI-Account-ID": self.account_id,
-            "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)",
-            "Origin": "https://chatgpt.com",
-        }
-
-        url = f"{self.base_url}/codex/responses"
-
-        try:
-            response = await self._client.post(url, json=payload, headers=headers, timeout=120.0)
-            response.raise_for_status()
-
-            # Parse SSE for tool calls and content
-            content, tool_calls = await self._parse_sse_tool_stream(response)
-
-            duration = time.time() - start_time
-            metrics = get_metrics_collector()
-            metrics.record_llm_call(
-                provider=self.provider,
-                model=self.model,
-                scope=scope,
-                duration=duration,
-                input_tokens=0,
-                output_tokens=0,
-                success=True,
-            )
-
-            return LLMToolCallResult(
-                content=content,
-                tool_calls=tool_calls,
-                finish_reason="tool_calls" if tool_calls else "stop",
-                input_tokens=0,
-                output_tokens=0,
-            )
-
-        except Exception as e:
-            logger.error(f"Codex tool call error: {e}")
-            raise
-
-    async def _parse_sse_tool_stream(self, response: httpx.Response) -> tuple[str | None, list[LLMToolCall]]:
-        """
-        Parse SSE stream for tool calls and content.
-
-        Returns:
-            Tuple of (content, tool_calls).
-        """
-        content = ""
-        tool_calls: list[LLMToolCall] = []
-        event_type = None
-
-        async for line in response.aiter_lines():
-            if not line:
-                continue
-
-            if line.startswith("event: "):
-                event_type = line[7:]
-
-            elif line.startswith("data: "):
-                data_str = line[6:]
-                if data_str == "[DONE]":
-                    break
-
-                try:
-                    data = json.loads(data_str)
-
-                    # Extract text content
-                    if event_type == "response.text.delta" and "delta" in data:
-                        content += data["delta"]
-
-                    # Extract tool calls
-                    elif event_type == "response.function_call_arguments.delta":
-                        # Handle tool call events (implementation depends on actual Codex SSE format)
-                        pass
-
-                except json.JSONDecodeError:
-                    pass
-
-        return content if content else None, tool_calls
-
-    async def cleanup(self) -> None:
-        """Clean up HTTP client."""
-        await self._client.aclose()

hindsight-api/hindsight_api/engine/providers/gemini_llm.py

@@ -1,502 +0,0 @@
-"""
-Google Gemini/VertexAI LLM provider.
-
-This provider supports both:
-1. Gemini API (api.generativeai.google.com) with API key authentication
-2. Vertex AI with service account or Application Default Credentials (ADC)
-"""
-
-import asyncio
-import json
-import logging
-import os
-import time
-from typing import Any
-
-from google import genai
-from google.genai import errors as genai_errors
-from google.genai import types as genai_types
-
-from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
-from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
-from hindsight_api.metrics import get_metrics_collector
-
-logger = logging.getLogger(__name__)
-
-# Vertex AI imports (optional)
-try:
-    import google.auth
-    from google.oauth2 import service_account
-
-    VERTEXAI_AVAILABLE = True
-except ImportError:
-    VERTEXAI_AVAILABLE = False
-
-
-class GeminiLLM(LLMInterface):
-    """
-    LLM provider for Google Gemini and Vertex AI.
-
-    Supports:
-    - Gemini API: provider="gemini", requires api_key
-    - Vertex AI: provider="vertexai", requires project_id and region, uses ADC or service account
-    """
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,
-        base_url: str,
-        model: str,
-        reasoning_effort: str = "low",
-        **kwargs: Any,
-    ):
-        """Initialize Gemini/VertexAI LLM provider."""
-        super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
-
-        self._client = None
-        self._is_vertexai = self.provider == "vertexai"
-
-        if self._is_vertexai:
-            self._init_vertexai(**kwargs)
-        else:
-            self._init_gemini()
-
-    def _init_gemini(self) -> None:
-        """Initialize Gemini API client."""
-        if not self.api_key:
-            raise ValueError("Gemini provider requires api_key")
-
-        self._client = genai.Client(api_key=self.api_key)
-        logger.info(f"Gemini API: model={self.model}")
-
-    def _init_vertexai(self, **kwargs: Any) -> None:
-        """Initialize Vertex AI client with project, region, and credentials."""
-        # Extract Vertex AI config from kwargs
-        project_id = kwargs.get("vertexai_project_id")
-        region = kwargs.get("vertexai_region", "us-central1")
-        service_account_key = kwargs.get("vertexai_service_account_key")
-        credentials = kwargs.get("vertexai_credentials")  # Pre-loaded credentials object
-
-        if not project_id:
-            raise ValueError(
-                "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID is required for Vertex AI provider. "
-                "Set it to your GCP project ID."
-            )
-
-        auth_method = "ADC"
-
-        # Use pre-loaded credentials if provided (passed from LLMProvider)
-        if credentials is not None:
-            auth_method = "service_account"
-        # Otherwise, load explicit service account credentials if path provided
-        elif service_account_key:
-            if not VERTEXAI_AVAILABLE:
-                raise ValueError(
-                    "Vertex AI service account auth requires 'google-auth' package. "
-                    "Install with: pip install google-auth"
-                )
-            credentials = service_account.Credentials.from_service_account_file(
-                service_account_key,
-                scopes=["https://www.googleapis.com/auth/cloud-platform"],
-            )
-            auth_method = "service_account"
-            logger.info(f"Vertex AI: Using service account key: {service_account_key}")
-
-        # Strip google/ prefix from model name — native SDK uses bare names
-        # e.g. "google/gemini-2.0-flash-lite-001" -> "gemini-2.0-flash-lite-001"
-        if self.model.startswith("google/"):
-            self.model = self.model[len("google/") :]
-
-        # Create Vertex AI client
-        client_kwargs: dict[str, Any] = {
-            "vertexai": True,
-            "project": project_id,
-            "location": region,
-        }
-        if credentials is not None:
-            client_kwargs["credentials"] = credentials
-
-        self._client = genai.Client(**client_kwargs)
-
-        logger.info(f"Vertex AI: project={project_id}, region={region}, model={self.model}, auth={auth_method}")
-
-    async def verify_connection(self) -> None:
-        """
-        Verify that the Gemini/VertexAI provider is configured correctly.
-
-        Raises:
-            RuntimeError: If the connection test fails.
-        """
-        try:
-            logger.info(f"Verifying {self.provider.upper()}: model={self.model}...")
-            await self.call(
-                messages=[{"role": "user", "content": "Say 'ok'"}],
-                max_completion_tokens=100,
-                max_retries=2,
-                initial_backoff=0.5,
-                max_backoff=2.0,
-            )
-            logger.info(f"{self.provider.upper()} connection verified successfully")
-        except Exception as e:
-            raise RuntimeError(f"Failed to verify {self.provider.upper()} connection: {e}") from e
-
-    async def call(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "memory",
-        max_retries: int = 10,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 60.0,
-        skip_validation: bool = False,
-        strict_schema: bool = False,
-        return_usage: bool = False,
-    ) -> Any:
-        """
-        Make a Gemini/VertexAI API call with retry logic.
-
-        Args:
-            messages: List of message dicts with 'role' and 'content'.
-            response_format: Optional Pydantic model for structured output.
-            max_completion_tokens: Maximum tokens in response (not supported by Gemini).
-            temperature: Sampling temperature (0.0-2.0).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            skip_validation: Return raw JSON without Pydantic validation.
-            strict_schema: Use strict JSON schema enforcement (not supported by Gemini).
-            return_usage: If True, return tuple (result, TokenUsage).
-
-        Returns:
-            If return_usage=False: Parsed response if response_format provided, else text.
-            If return_usage=True: Tuple of (result, TokenUsage).
-        """
-        start_time = time.time()
-
-        # Convert OpenAI-style messages to Gemini format
-        system_instruction = None
-        gemini_contents = []
-
-        for msg in messages:
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-
-            if role == "system":
-                if system_instruction:
-                    system_instruction += "\n\n" + content
-                else:
-                    system_instruction = content
-            elif role == "assistant":
-                gemini_contents.append(genai_types.Content(role="model", parts=[genai_types.Part(text=content)]))
-            else:
-                gemini_contents.append(genai_types.Content(role="user", parts=[genai_types.Part(text=content)]))
-
-        # Add JSON schema instruction if response_format is provided
-        if response_format is not None and hasattr(response_format, "model_json_schema"):
-            schema = response_format.model_json_schema()
-            schema_msg = f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}"
-            if system_instruction:
-                system_instruction += schema_msg
-            else:
-                system_instruction = schema_msg
-
-        # Build generation config
-        config_kwargs: dict[str, Any] = {}
-        if system_instruction:
-            config_kwargs["system_instruction"] = system_instruction
-        if response_format is not None:
-            config_kwargs["response_mime_type"] = "application/json"
-            config_kwargs["response_schema"] = response_format
-        if temperature is not None:
-            config_kwargs["temperature"] = temperature
-
-        generation_config = genai_types.GenerateContentConfig(**config_kwargs) if config_kwargs else None
-
-        last_exception = None
-
-        for attempt in range(max_retries + 1):
-            try:
-                response = await self._client.aio.models.generate_content(
-                    model=self.model,
-                    contents=gemini_contents,
-                    config=generation_config,
-                )
-
-                content = response.text
-
-                # Handle empty response
-                if content is None:
-                    block_reason = None
-                    if hasattr(response, "candidates") and response.candidates:
-                        candidate = response.candidates[0]
-                        if hasattr(candidate, "finish_reason"):
-                            block_reason = candidate.finish_reason
-
-                    if attempt < max_retries:
-                        logger.warning(f"Gemini returned empty response (reason: {block_reason}), retrying...")
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
-                        await asyncio.sleep(backoff)
-                        continue
-                    else:
-                        raise RuntimeError(f"Gemini returned empty response after {max_retries + 1} attempts")
-
-                # Parse structured output if requested
-                if response_format is not None:
-                    json_data = json.loads(content)
-                    if skip_validation:
-                        result = json_data
-                    else:
-                        result = response_format.model_validate(json_data)
-                else:
-                    result = content
-
-                # Extract token usage
-                input_tokens = 0
-                output_tokens = 0
-                if hasattr(response, "usage_metadata") and response.usage_metadata:
-                    usage = response.usage_metadata
-                    input_tokens = usage.prompt_token_count or 0
-                    output_tokens = usage.candidates_token_count or 0
-
-                # Record metrics
-                duration = time.time() - start_time
-                metrics = get_metrics_collector()
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-
-                # Log slow calls
-                if duration > 10.0 and input_tokens > 0:
-                    logger.info(
-                        f"slow llm call: scope={scope}, model={self.provider}/{self.model}, "
-                        f"input_tokens={input_tokens}, output_tokens={output_tokens}, "
-                        f"time={duration:.3f}s"
-                    )
-
-                if return_usage:
-                    token_usage = TokenUsage(
-                        input_tokens=input_tokens,
-                        output_tokens=output_tokens,
-                        total_tokens=input_tokens + output_tokens,
-                    )
-                    return result, token_usage
-                return result
-
-            except json.JSONDecodeError as e:
-                last_exception = e
-                if attempt < max_retries:
-                    logger.warning("Gemini returned invalid JSON, retrying...")
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    await asyncio.sleep(backoff)
-                    continue
-                else:
-                    logger.error(f"Gemini returned invalid JSON after {max_retries + 1} attempts")
-                    raise
-
-            except genai_errors.APIError as e:
-                # Fast fail on auth errors - these won't recover with retries
-                if e.code in (401, 403):
-                    logger.error(f"Gemini auth error (HTTP {e.code}), not retrying: {str(e)}")
-                    raise
-
-                # Retry on retryable errors (rate limits, server errors, client errors)
-                if e.code in (400, 429, 500, 502, 503, 504) or (e.code and e.code >= 500):
-                    last_exception = e
-                    if attempt < max_retries:
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
-                        jitter = backoff * 0.2 * (2 * (time.time() % 1) - 1)
-                        await asyncio.sleep(backoff + jitter)
-                    else:
-                        logger.error(f"Gemini API error after {max_retries + 1} attempts: {str(e)}")
-                        raise
-                else:
-                    logger.error(f"Gemini API error: {type(e).__name__}: {str(e)}")
-                    raise
-
-            except Exception as e:
-                logger.error(f"Unexpected error during Gemini call: {type(e).__name__}: {str(e)}")
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Gemini call failed after all retries")
-
-    async def call_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "tools",
-        max_retries: int = 5,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 30.0,
-        tool_choice: str | dict[str, Any] = "auto",
-    ) -> LLMToolCallResult:
-        """
-        Make a Gemini/VertexAI API call with tool/function calling support.
-
-        Args:
-            messages: List of message dicts. Can include tool results with role='tool'.
-            tools: List of tool definitions in OpenAI format.
-            max_completion_tokens: Maximum tokens (not supported by Gemini).
-            temperature: Sampling temperature.
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            tool_choice: How to choose tools (Gemini uses "auto" only).
-
-        Returns:
-            LLMToolCallResult with content and/or tool_calls.
-        """
-        start_time = time.time()
-
-        # Convert tools to Gemini format
-        gemini_tools = []
-        for tool in tools:
-            func = tool.get("function", {})
-            gemini_tools.append(
-                genai_types.Tool(
-                    function_declarations=[
-                        genai_types.FunctionDeclaration(
-                            name=func.get("name", ""),
-                            description=func.get("description", ""),
-                            parameters=func.get("parameters"),
-                        )
-                    ]
-                )
-            )
-
-        # Convert messages
-        system_instruction = None
-        gemini_contents = []
-        for msg in messages:
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-
-            if role == "system":
-                system_instruction = (system_instruction + "\n\n" + content) if system_instruction else content
-            elif role == "tool":
-                # Gemini uses function_response
-                gemini_contents.append(
-                    genai_types.Content(
-                        role="user",
-                        parts=[
-                            genai_types.Part(
-                                function_response=genai_types.FunctionResponse(
-                                    name=msg.get("name", ""),
-                                    response={"result": content},
-                                )
-                            )
-                        ],
-                    )
-                )
-            elif role == "assistant":
-                gemini_contents.append(genai_types.Content(role="model", parts=[genai_types.Part(text=content)]))
-            else:
-                gemini_contents.append(genai_types.Content(role="user", parts=[genai_types.Part(text=content)]))
-
-        config_kwargs: dict[str, Any] = {"tools": gemini_tools}
-        if system_instruction:
-            config_kwargs["system_instruction"] = system_instruction
-        if temperature is not None:
-            config_kwargs["temperature"] = temperature
-
-        config = genai_types.GenerateContentConfig(**config_kwargs)
-
-        last_exception = None
-        for attempt in range(max_retries + 1):
-            try:
-                response = await self._client.aio.models.generate_content(
-                    model=self.model,
-                    contents=gemini_contents,
-                    config=config,
-                )
-
-                # Extract content and tool calls
-                content = None
-                tool_calls: list[LLMToolCall] = []
-
-                if response.candidates and response.candidates[0].content:
-                    parts = response.candidates[0].content.parts
-                    if parts:
-                        for part in parts:
-                            if hasattr(part, "text") and part.text:
-                                content = part.text
-                            if hasattr(part, "function_call") and part.function_call:
-                                fc = part.function_call
-                                tool_calls.append(
-                                    LLMToolCall(
-                                        id=f"gemini_{len(tool_calls)}",
-                                        name=fc.name,
-                                        arguments=dict(fc.args) if fc.args else {},
-                                    )
-                                )
-
-                finish_reason = "tool_calls" if tool_calls else "stop"
-
-                # Extract token usage
-                input_tokens = 0
-                output_tokens = 0
-                if response.usage_metadata:
-                    input_tokens = response.usage_metadata.prompt_token_count or 0
-                    output_tokens = response.usage_metadata.candidates_token_count or 0
-
-                # Record metrics
-                duration = time.time() - start_time
-                metrics = get_metrics_collector()
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-
-                return LLMToolCallResult(
-                    content=content,
-                    tool_calls=tool_calls,
-                    finish_reason=finish_reason,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                )
-
-            except genai_errors.APIError as e:
-                # Fast fail on auth errors
-                if e.code in (401, 403):
-                    logger.error(f"Gemini auth error (HTTP {e.code}), not retrying: {str(e)}")
-                    raise
-
-                # Retry on retryable errors
-                last_exception = e
-                if attempt < max_retries:
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    await asyncio.sleep(backoff)
-                    continue
-                raise
-
-            except Exception as e:
-                logger.error(f"Unexpected error during Gemini tool call: {type(e).__name__}: {str(e)}")
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Gemini tool call failed")
-
-    async def cleanup(self) -> None:
-        """Clean up resources (close connections, etc.)."""
-        # Gemini client doesn't require explicit cleanup
-        pass

hindsight-api/hindsight_api/engine/providers/mock_llm.py

@@ -1,234 +0,0 @@
-"""
-Mock LLM provider for testing.
-
-This provider allows tests to record LLM calls and return configurable mock responses
-without making actual API calls to external LLM services.
-"""
-
-import logging
-from typing import Any
-
-from ..llm_interface import LLMInterface
-from ..response_models import LLMToolCall, LLMToolCallResult, TokenUsage
-
-logger = logging.getLogger(__name__)
-
-
-class MockLLM(LLMInterface):
-    """
-    Mock LLM provider for testing.
-
-    This provider records all calls and returns configurable mock responses,
-    enabling tests to verify LLM interactions without making real API calls.
-
-    Example:
-        # Create mock provider
-        mock_llm = MockLLM(provider="mock", api_key="", base_url="", model="mock-model")
-
-        # Set mock response
-        mock_llm.set_mock_response({"answer": "test"})
-
-        # Make calls
-        result = await mock_llm.call(
-            messages=[{"role": "user", "content": "test"}],
-            response_format=MyResponseModel
-        )
-
-        # Verify calls
-        calls = mock_llm.get_mock_calls()
-        assert len(calls) == 1
-        assert calls[0]["scope"] == "memory"
-    """
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,
-        base_url: str,
-        model: str,
-        reasoning_effort: str = "low",
-        **kwargs: Any,
-    ):
-        """
-        Initialize mock LLM provider.
-
-        Args:
-            provider: Provider name (should be "mock").
-            api_key: Not used for mock provider.
-            base_url: Not used for mock provider.
-            model: Model name for tracking.
-            reasoning_effort: Not used for mock provider.
-            **kwargs: Additional parameters (not used).
-        """
-        super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
-
-        # Storage for test verification
-        self._mock_calls: list[dict] = []
-        self._mock_response: Any = None
-
-    async def verify_connection(self) -> None:
-        """
-        Verify mock provider (always succeeds).
-
-        Mock provider doesn't need connection verification since it doesn't
-        make real API calls.
-        """
-        logger.debug("Mock LLM: connection verification (always succeeds)")
-
-    async def call(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "memory",
-        max_retries: int = 10,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 60.0,
-        skip_validation: bool = False,
-        strict_schema: bool = False,
-        return_usage: bool = False,
-    ) -> Any:
-        """
-        Make a mock LLM API call.
-
-        Records the call for test verification and returns the configured mock response.
-
-        Args:
-            messages: List of message dicts with 'role' and 'content'.
-            response_format: Optional Pydantic model for structured output.
-            max_completion_tokens: Not used in mock.
-            temperature: Not used in mock.
-            scope: Scope identifier for tracking.
-            max_retries: Not used in mock.
-            initial_backoff: Not used in mock.
-            max_backoff: Not used in mock.
-            skip_validation: Return raw JSON without Pydantic validation.
-            strict_schema: Not used in mock.
-            return_usage: If True, return tuple (result, TokenUsage) instead of just result.
-
-        Returns:
-            If return_usage=False: Parsed response if response_format is provided, otherwise text content.
-            If return_usage=True: Tuple of (result, TokenUsage) with mock token counts.
-        """
-        # Record the call for test verification
-        call_record = {
-            "provider": self.provider,
-            "model": self.model,
-            "messages": messages,
-            "response_format": response_format.__name__
-            if response_format and hasattr(response_format, "__name__")
-            else str(response_format),
-            "scope": scope,
-        }
-        self._mock_calls.append(call_record)
-        logger.debug(f"Mock LLM call recorded: scope={scope}, model={self.model}")
-
-        # Return mock response
-        if self._mock_response is not None:
-            result = self._mock_response
-        elif response_format is not None:
-            # Try to create a minimal valid instance of the response format
-            try:
-                # For Pydantic models, try to create with minimal valid data
-                result = {"mock": True}
-            except Exception:
-                result = {"mock": True}
-        else:
-            result = "mock response"
-
-        if return_usage:
-            token_usage = TokenUsage(input_tokens=10, output_tokens=5, total_tokens=15)
-            return result, token_usage
-        return result
-
-    async def call_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "tools",
-        max_retries: int = 5,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 30.0,
-        tool_choice: str | dict[str, Any] = "auto",
-    ) -> LLMToolCallResult:
-        """
-        Make a mock LLM API call with tool/function calling support.
-
-        Records the call for test verification and returns the configured mock response.
-
-        Args:
-            messages: List of message dicts. Can include tool results with role='tool'.
-            tools: List of tool definitions in OpenAI format.
-            max_completion_tokens: Not used in mock.
-            temperature: Not used in mock.
-            scope: Scope identifier for tracking.
-            max_retries: Not used in mock.
-            initial_backoff: Not used in mock.
-            max_backoff: Not used in mock.
-            tool_choice: Not used in mock.
-
-        Returns:
-            LLMToolCallResult with content and/or tool_calls.
-        """
-        # Record the call for test verification
-        call_record = {
-            "provider": self.provider,
-            "model": self.model,
-            "messages": messages,
-            "tools": [t.get("function", {}).get("name") for t in tools],
-            "scope": scope,
-        }
-        self._mock_calls.append(call_record)
-
-        if self._mock_response is not None:
-            if isinstance(self._mock_response, LLMToolCallResult):
-                return self._mock_response
-            # Allow setting just tool calls as a list
-            if isinstance(self._mock_response, list):
-                return LLMToolCallResult(
-                    tool_calls=[
-                        LLMToolCall(id=f"mock_{i}", name=tc["name"], arguments=tc.get("arguments", {}))
-                        for i, tc in enumerate(self._mock_response)
-                    ],
-                    finish_reason="tool_calls",
-                )
-
-        return LLMToolCallResult(content="mock response", finish_reason="stop")
-
-    async def cleanup(self) -> None:
-        """Clean up resources (no-op for mock provider)."""
-        pass
-
-    def set_mock_response(self, response: Any) -> None:
-        """
-        Set the response to return from mock calls.
-
-        Args:
-            response: The response to return. Can be:
-                - A dict/Pydantic model for regular calls
-                - An LLMToolCallResult for tool calls
-                - A list of tool call dicts for tool calls
-                - Any other value to return as-is
-        """
-        self._mock_response = response
-
-    def get_mock_calls(self) -> list[dict]:
-        """
-        Get the list of recorded mock calls.
-
-        Returns:
-            List of call records, each containing:
-                - provider: Provider name
-                - model: Model name
-                - messages: Messages sent
-                - response_format/tools: Format or tools used
-                - scope: Call scope
-        """
-        return self._mock_calls
-
-    def clear_mock_calls(self) -> None:
-        """Clear the recorded mock calls."""
-        self._mock_calls = []

hindsight-api/hindsight_api/engine/providers/openai_compatible_llm.py

@@ -1,745 +0,0 @@
-"""
-OpenAI-compatible LLM provider supporting OpenAI, Groq, Ollama, and LMStudio.
-
-This provider handles all OpenAI API-compatible models including:
-- OpenAI: GPT-4, GPT-4o, GPT-5, o1, o3 (reasoning models)
-- Groq: Fast inference with seed control and service tiers
-- Ollama: Local models with native streaming API support
-- LMStudio: Local models with OpenAI-compatible API
-
-Features:
-- Reasoning models with extended thinking (o1, o3, GPT-5 families)
-- Strict JSON schema enforcement (OpenAI)
-- Provider-specific parameters (Groq seed, service tier)
-- Native Ollama streaming for better structured output
-- Automatic token limit handling per model family
-"""
-
-import asyncio
-import json
-import logging
-import os
-import re
-import time
-from typing import Any
-
-import httpx
-from openai import APIConnectionError, APIStatusError, AsyncOpenAI, LengthFinishReasonError
-
-from hindsight_api.config import DEFAULT_LLM_TIMEOUT, ENV_LLM_TIMEOUT
-from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
-from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
-from hindsight_api.metrics import get_metrics_collector
-
-logger = logging.getLogger(__name__)
-
-# Seed applied to every Groq request for deterministic behavior
-DEFAULT_LLM_SEED = 4242
-
-
-class OpenAICompatibleLLM(LLMInterface):
-    """
-    LLM provider for OpenAI-compatible APIs.
-
-    Supports:
-    - OpenAI: Standard models (GPT-4, GPT-4o) and reasoning models (o1, o3, GPT-5)
-    - Groq: Fast inference with seed control and service tiers
-    - Ollama: Local models with native streaming API for better structured output
-    - LMStudio: Local models with OpenAI-compatible API
-    """
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,
-        base_url: str,
-        model: str,
-        reasoning_effort: str = "low",
-        timeout: float | None = None,
-        groq_service_tier: str | None = None,
-        **kwargs: Any,
-    ):
-        """
-        Initialize OpenAI-compatible LLM provider.
-
-        Args:
-            provider: Provider name ("openai", "groq", "ollama", "lmstudio").
-            api_key: API key (optional for ollama/lmstudio).
-            base_url: Base URL for the API (uses defaults for groq/ollama/lmstudio if empty).
-            model: Model name.
-            reasoning_effort: Reasoning effort level for supported models ("low", "medium", "high").
-            timeout: Request timeout in seconds (uses env var or 300s default).
-            groq_service_tier: Groq service tier ("on_demand", "flex", "auto").
-            **kwargs: Additional provider-specific parameters.
-        """
-        super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
-
-        # Validate provider
-        valid_providers = ["openai", "groq", "ollama", "lmstudio"]
-        if self.provider not in valid_providers:
-            raise ValueError(f"OpenAICompatibleLLM only supports: {', '.join(valid_providers)}. Got: {self.provider}")
-
-        # Set default base URLs
-        if not self.base_url:
-            if self.provider == "groq":
-                self.base_url = "https://api.groq.com/openai/v1"
-            elif self.provider == "ollama":
-                self.base_url = "http://localhost:11434/v1"
-            elif self.provider == "lmstudio":
-                self.base_url = "http://localhost:1234/v1"
-
-        # For ollama/lmstudio, use dummy key if not provided
-        if self.provider in ("ollama", "lmstudio") and not self.api_key:
-            self.api_key = "local"
-
-        # Validate API key for cloud providers
-        if self.provider in ("openai", "groq") and not self.api_key:
-            raise ValueError(f"API key is required for {self.provider}")
-
-        # Groq service tier configuration
-        self.groq_service_tier = groq_service_tier or os.getenv("HINDSIGHT_API_LLM_GROQ_SERVICE_TIER", "auto")
-
-        # Get timeout config
-        self.timeout = timeout or float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT)))
-
-        # Create OpenAI client
-        client_kwargs: dict[str, Any] = {"api_key": self.api_key, "max_retries": 0}
-        if self.base_url:
-            client_kwargs["base_url"] = self.base_url
-        if self.timeout:
-            client_kwargs["timeout"] = self.timeout
-
-        self._client = AsyncOpenAI(**client_kwargs)
-        logger.info(
-            f"OpenAI-compatible client initialized: provider={self.provider}, model={self.model}, "
-            f"base_url={self.base_url or 'default'}"
-        )
-
-    async def verify_connection(self) -> None:
-        """
-        Verify that the provider is configured correctly by making a simple test call.
-
-        Raises:
-            RuntimeError: If the connection test fails.
-        """
-        try:
-            logger.info(f"Verifying connection: {self.provider}/{self.model}")
-            await self.call(
-                messages=[{"role": "user", "content": "Say 'ok'"}],
-                max_completion_tokens=100,
-                max_retries=2,
-                initial_backoff=0.5,
-                max_backoff=2.0,
-            )
-            logger.info(f"Connection verified: {self.provider}/{self.model}")
-        except Exception as e:
-            raise RuntimeError(f"Connection verification failed for {self.provider}/{self.model}: {e}") from e
-
-    def _supports_reasoning_model(self) -> bool:
-        """Check if the current model is a reasoning model (o1, o3, GPT-5, DeepSeek)."""
-        model_lower = self.model.lower()
-        return any(x in model_lower for x in ["gpt-5", "o1", "o3", "deepseek"])
-
-    def _get_max_reasoning_tokens(self) -> int | None:
-        """Get max reasoning tokens for reasoning models."""
-        model_lower = self.model.lower()
-
-        # GPT-4 and GPT-4.1 models have different caps
-        if any(x in model_lower for x in ["gpt-4.1", "gpt-4-"]):
-            return 32000
-        elif "gpt-4o" in model_lower:
-            return 16384
-
-        return None
-
-    async def call(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "memory",
-        max_retries: int = 10,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 60.0,
-        skip_validation: bool = False,
-        strict_schema: bool = False,
-        return_usage: bool = False,
-    ) -> Any:
-        """
-        Make an LLM API call with retry logic.
-
-        Args:
-            messages: List of message dicts with 'role' and 'content'.
-            response_format: Optional Pydantic model for structured output.
-            max_completion_tokens: Maximum tokens in response.
-            temperature: Sampling temperature (0.0-2.0).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            skip_validation: Return raw JSON without Pydantic validation.
-            strict_schema: Use strict JSON schema enforcement (OpenAI only).
-            return_usage: If True, return tuple (result, TokenUsage) instead of just result.
-
-        Returns:
-            If return_usage=False: Parsed response if response_format is provided, otherwise text content.
-            If return_usage=True: Tuple of (result, TokenUsage) with token counts.
-
-        Raises:
-            OutputTooLongError: If output exceeds token limits.
-            Exception: Re-raises API errors after retries exhausted.
-        """
-        # Handle Ollama with native API for structured output (better schema enforcement)
-        if self.provider == "ollama" and response_format is not None:
-            return await self._call_ollama_native(
-                messages=messages,
-                response_format=response_format,
-                max_completion_tokens=max_completion_tokens,
-                temperature=temperature,
-                max_retries=max_retries,
-                initial_backoff=initial_backoff,
-                max_backoff=max_backoff,
-                skip_validation=skip_validation,
-                scope=scope,
-                return_usage=return_usage,
-            )
-
-        start_time = time.time()
-
-        # Build call parameters
-        call_params: dict[str, Any] = {
-            "model": self.model,
-            "messages": messages,
-        }
-
-        # Check if model supports reasoning parameter
-        is_reasoning_model = self._supports_reasoning_model()
-
-        # Apply model-specific token limits
-        if max_completion_tokens is not None:
-            max_tokens_cap = self._get_max_reasoning_tokens()
-            if max_tokens_cap and max_completion_tokens > max_tokens_cap:
-                max_completion_tokens = max_tokens_cap
-            # For reasoning models, enforce minimum to ensure space for reasoning + output
-            if is_reasoning_model and max_completion_tokens < 16000:
-                max_completion_tokens = 16000
-            call_params["max_completion_tokens"] = max_completion_tokens
-
-        # Temperature - reasoning models don't support custom temperature
-        if temperature is not None and not is_reasoning_model:
-            call_params["temperature"] = temperature
-
-        # Set reasoning_effort for reasoning models
-        if is_reasoning_model:
-            call_params["reasoning_effort"] = self.reasoning_effort
-
-        # Provider-specific parameters
-        if self.provider == "groq":
-            call_params["seed"] = DEFAULT_LLM_SEED
-            extra_body: dict[str, Any] = {}
-            # Add service_tier if configured
-            if self.groq_service_tier:
-                extra_body["service_tier"] = self.groq_service_tier
-            # Add reasoning parameters for reasoning models
-            if is_reasoning_model:
-                extra_body["include_reasoning"] = False
-            if extra_body:
-                call_params["extra_body"] = extra_body
-
-        # Prepare response format ONCE before retry loop
-        if response_format is not None:
-            schema = None
-            if hasattr(response_format, "model_json_schema"):
-                schema = response_format.model_json_schema()
-
-            if strict_schema and schema is not None:
-                # Use OpenAI's strict JSON schema enforcement
-                call_params["response_format"] = {
-                    "type": "json_schema",
-                    "json_schema": {
-                        "name": "response",
-                        "strict": True,
-                        "schema": schema,
-                    },
-                }
-            else:
-                # Soft enforcement: add schema to prompt and use json_object mode
-                if schema is not None:
-                    schema_msg = (
-                        f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}"
-                    )
-
-                    if call_params["messages"] and call_params["messages"][0].get("role") == "system":
-                        first_msg = call_params["messages"][0]
-                        if isinstance(first_msg, dict) and isinstance(first_msg.get("content"), str):
-                            first_msg["content"] += schema_msg
-                    elif call_params["messages"]:
-                        first_msg = call_params["messages"][0]
-                        if isinstance(first_msg, dict) and isinstance(first_msg.get("content"), str):
-                            first_msg["content"] = schema_msg + "\n\n" + first_msg["content"]
-                if self.provider not in ("lmstudio", "ollama"):
-                    # LM Studio and Ollama don't support json_object response format reliably
-                    call_params["response_format"] = {"type": "json_object"}
-
-        last_exception = None
-
-        for attempt in range(max_retries + 1):
-            try:
-                if response_format is not None:
-                    response = await self._client.chat.completions.create(**call_params)
-
-                    content = response.choices[0].message.content
-
-                    # Strip reasoning model thinking tags
-                    # Supports: <think>, <thinking>, <reasoning>, |startthink|/|endthink|
-                    if content:
-                        original_len = len(content)
-                        content = re.sub(r"<think>.*?</think>", "", content, flags=re.DOTALL)
-                        content = re.sub(r"<thinking>.*?</thinking>", "", content, flags=re.DOTALL)
-                        content = re.sub(r"<reasoning>.*?</reasoning>", "", content, flags=re.DOTALL)
-                        content = re.sub(r"\|startthink\|.*?\|endthink\|", "", content, flags=re.DOTALL)
-                        content = content.strip()
-                        if len(content) < original_len:
-                            logger.debug(f"Stripped {original_len - len(content)} chars of reasoning tokens")
-
-                    # For local models, they may wrap JSON in markdown code blocks
-                    if self.provider in ("lmstudio", "ollama"):
-                        clean_content = content
-                        if "```json" in content:
-                            clean_content = content.split("```json")[1].split("```")[0].strip()
-                        elif "```" in content:
-                            clean_content = content.split("```")[1].split("```")[0].strip()
-                        try:
-                            json_data = json.loads(clean_content)
-                        except json.JSONDecodeError:
-                            # Fallback to parsing raw content
-                            json_data = json.loads(content)
-                    else:
-                        # Log raw LLM response for debugging JSON parse issues
-                        try:
-                            json_data = json.loads(content)
-                        except json.JSONDecodeError as json_err:
-                            # Truncate content for logging
-                            content_preview = content[:500] if content else "<empty>"
-                            if content and len(content) > 700:
-                                content_preview = f"{content[:500]}...TRUNCATED...{content[-200:]}"
-                            logger.warning(
-                                f"JSON parse error from LLM response (attempt {attempt + 1}/{max_retries + 1}): {json_err}\n"
-                                f"  Model: {self.provider}/{self.model}\n"
-                                f"  Content length: {len(content) if content else 0} chars\n"
-                                f"  Content preview: {content_preview!r}\n"
-                                f"  Finish reason: {response.choices[0].finish_reason if response.choices else 'unknown'}"
-                            )
-                            # Retry on JSON parse errors
-                            if attempt < max_retries:
-                                backoff = min(initial_backoff * (2**attempt), max_backoff)
-                                await asyncio.sleep(backoff)
-                                last_exception = json_err
-                                continue
-                            else:
-                                logger.error(f"JSON parse error after {max_retries + 1} attempts, giving up")
-                                raise
-
-                    if skip_validation:
-                        result = json_data
-                    else:
-                        result = response_format.model_validate(json_data)
-                else:
-                    response = await self._client.chat.completions.create(**call_params)
-                    result = response.choices[0].message.content
-
-                # Record token usage metrics
-                duration = time.time() - start_time
-                usage = response.usage
-                input_tokens = usage.prompt_tokens or 0 if usage else 0
-                output_tokens = usage.completion_tokens or 0 if usage else 0
-                total_tokens = usage.total_tokens or 0 if usage else 0
-
-                # Record LLM metrics
-                metrics = get_metrics_collector()
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-
-                # Log slow calls
-                if duration > 10.0 and usage:
-                    ratio = max(1, output_tokens) / max(1, input_tokens)
-                    cached_tokens = 0
-                    if hasattr(usage, "prompt_tokens_details") and usage.prompt_tokens_details:
-                        cached_tokens = getattr(usage.prompt_tokens_details, "cached_tokens", 0) or 0
-                    cache_info = f", cached_tokens={cached_tokens}" if cached_tokens > 0 else ""
-                    logger.info(
-                        f"slow llm call: scope={scope}, model={self.provider}/{self.model}, "
-                        f"input_tokens={input_tokens}, output_tokens={output_tokens}, "
-                        f"total_tokens={total_tokens}{cache_info}, time={duration:.3f}s, ratio out/in={ratio:.2f}"
-                    )
-
-                if return_usage:
-                    token_usage = TokenUsage(
-                        input_tokens=input_tokens,
-                        output_tokens=output_tokens,
-                        total_tokens=total_tokens,
-                    )
-                    return result, token_usage
-                return result
-
-            except LengthFinishReasonError as e:
-                logger.warning(f"LLM output exceeded token limits: {str(e)}")
-                raise OutputTooLongError(
-                    "LLM output exceeded token limits. Input may need to be split into smaller chunks."
-                ) from e
-
-            except APIConnectionError as e:
-                last_exception = e
-                status_code = getattr(e, "status_code", None) or getattr(
-                    getattr(e, "response", None), "status_code", None
-                )
-                logger.warning(f"APIConnectionError (HTTP {status_code}), attempt {attempt + 1}: {str(e)[:200]}")
-                if attempt < max_retries:
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    await asyncio.sleep(backoff)
-                    continue
-                else:
-                    logger.error(f"Connection error after {max_retries + 1} attempts: {str(e)}")
-                    raise
-
-            except APIStatusError as e:
-                # Fast fail only on 401 (unauthorized) and 403 (forbidden)
-                if e.status_code in (401, 403):
-                    logger.error(f"Auth error (HTTP {e.status_code}), not retrying: {str(e)}")
-                    raise
-
-                # Handle tool_use_failed error - model outputted in tool call format
-                if e.status_code == 400 and response_format is not None:
-                    try:
-                        error_body = e.body if hasattr(e, "body") else {}
-                        if isinstance(error_body, dict):
-                            error_info: dict[str, Any] = error_body.get("error") or {}
-                            if error_info.get("code") == "tool_use_failed":
-                                failed_gen = error_info.get("failed_generation", "")
-                                if failed_gen:
-                                    # Parse tool call format and convert to expected format
-                                    tool_call = json.loads(failed_gen)
-                                    tool_name = tool_call.get("name", "")
-                                    tool_args = tool_call.get("arguments", {})
-                                    converted = {"actions": [{"tool": tool_name, **tool_args}]}
-                                    if skip_validation:
-                                        result = converted
-                                    else:
-                                        result = response_format.model_validate(converted)
-
-                                    # Record metrics
-                                    duration = time.time() - start_time
-                                    metrics = get_metrics_collector()
-                                    metrics.record_llm_call(
-                                        provider=self.provider,
-                                        model=self.model,
-                                        scope=scope,
-                                        duration=duration,
-                                        input_tokens=0,
-                                        output_tokens=0,
-                                        success=True,
-                                    )
-                                    if return_usage:
-                                        return result, TokenUsage(input_tokens=0, output_tokens=0, total_tokens=0)
-                                    return result
-                    except (json.JSONDecodeError, KeyError, TypeError):
-                        pass  # Failed to parse tool_use_failed, continue with normal retry
-
-                last_exception = e
-                if attempt < max_retries:
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    jitter = backoff * 0.2 * (2 * (time.time() % 1) - 1)
-                    sleep_time = backoff + jitter
-                    await asyncio.sleep(sleep_time)
-                else:
-                    logger.error(f"API error after {max_retries + 1} attempts: {str(e)}")
-                    raise
-
-            except Exception:
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("LLM call failed after all retries with no exception captured")
-
-    async def call_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "tools",
-        max_retries: int = 5,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 30.0,
-        tool_choice: str | dict[str, Any] = "auto",
-    ) -> LLMToolCallResult:
-        """
-        Make an LLM API call with tool/function calling support.
-
-        Args:
-            messages: List of message dicts. Can include tool results with role='tool'.
-            tools: List of tool definitions in OpenAI format.
-            max_completion_tokens: Maximum tokens in response.
-            temperature: Sampling temperature (0.0-2.0).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            tool_choice: How to choose tools - "auto", "none", "required", or specific function.
-
-        Returns:
-            LLMToolCallResult with content and/or tool_calls.
-        """
-        start_time = time.time()
-
-        # Build call parameters
-        call_params: dict[str, Any] = {
-            "model": self.model,
-            "messages": messages,
-            "tools": tools,
-            "tool_choice": tool_choice,
-        }
-
-        if max_completion_tokens is not None:
-            call_params["max_completion_tokens"] = max_completion_tokens
-        if temperature is not None:
-            call_params["temperature"] = temperature
-
-        # Provider-specific parameters
-        if self.provider == "groq":
-            call_params["seed"] = DEFAULT_LLM_SEED
-
-        last_exception = None
-
-        for attempt in range(max_retries + 1):
-            try:
-                response = await self._client.chat.completions.create(**call_params)
-
-                message = response.choices[0].message
-                finish_reason = response.choices[0].finish_reason
-
-                # Extract tool calls if present
-                tool_calls: list[LLMToolCall] = []
-                if message.tool_calls:
-                    for tc in message.tool_calls:
-                        try:
-                            args = json.loads(tc.function.arguments) if tc.function.arguments else {}
-                        except json.JSONDecodeError:
-                            args = {"_raw": tc.function.arguments}
-                        tool_calls.append(LLMToolCall(id=tc.id, name=tc.function.name, arguments=args))
-
-                content = message.content
-
-                # Record metrics
-                duration = time.time() - start_time
-                usage = response.usage
-                input_tokens = usage.prompt_tokens or 0 if usage else 0
-                output_tokens = usage.completion_tokens or 0 if usage else 0
-
-                metrics = get_metrics_collector()
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-
-                return LLMToolCallResult(
-                    content=content,
-                    tool_calls=tool_calls,
-                    finish_reason=finish_reason,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                )
-
-            except APIConnectionError as e:
-                last_exception = e
-                if attempt < max_retries:
-                    await asyncio.sleep(min(initial_backoff * (2**attempt), max_backoff))
-                    continue
-                raise
-
-            except APIStatusError as e:
-                if e.status_code in (401, 403):
-                    raise
-                last_exception = e
-                if attempt < max_retries:
-                    await asyncio.sleep(min(initial_backoff * (2**attempt), max_backoff))
-                    continue
-                raise
-
-            except Exception:
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Tool call failed after all retries")
-
-    async def _call_ollama_native(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any,
-        max_completion_tokens: int | None,
-        temperature: float | None,
-        max_retries: int,
-        initial_backoff: float,
-        max_backoff: float,
-        skip_validation: bool,
-        scope: str = "memory",
-        return_usage: bool = False,
-    ) -> Any:
-        """
-        Call Ollama using native API with JSON schema enforcement.
-
-        Ollama's native API supports passing a full JSON schema in the 'format' parameter,
-        which provides better structured output control than the OpenAI-compatible API.
-        """
-        start_time = time.time()
-
-        # Get the JSON schema from the Pydantic model
-        schema = response_format.model_json_schema() if hasattr(response_format, "model_json_schema") else None
-
-        # Build the base URL for Ollama's native API
-        # Default OpenAI-compatible URL is http://localhost:11434/v1
-        # Native API is at http://localhost:11434/api/chat
-        base_url = self.base_url or "http://localhost:11434/v1"
-        if base_url.endswith("/v1"):
-            native_url = base_url[:-3] + "/api/chat"
-        else:
-            native_url = base_url.rstrip("/") + "/api/chat"
-
-        # Build request payload
-        payload: dict[str, Any] = {
-            "model": self.model,
-            "messages": messages,
-            "stream": False,
-        }
-
-        # Add schema as format parameter for structured output
-        if schema:
-            payload["format"] = schema
-
-        # Add optional parameters with optimized defaults for Ollama
-        options: dict[str, Any] = {
-            "num_ctx": 16384,  # 16k context window for larger prompts
-            "num_batch": 512,  # Optimal batch size for prompt processing
-        }
-        if max_completion_tokens:
-            options["num_predict"] = max_completion_tokens
-        if temperature is not None:
-            options["temperature"] = temperature
-        payload["options"] = options
-
-        last_exception = None
-
-        async with httpx.AsyncClient(timeout=300.0) as client:
-            for attempt in range(max_retries + 1):
-                try:
-                    response = await client.post(native_url, json=payload)
-                    response.raise_for_status()
-
-                    result = response.json()
-                    content = result.get("message", {}).get("content", "")
-
-                    # Parse JSON response
-                    try:
-                        json_data = json.loads(content)
-                    except json.JSONDecodeError as json_err:
-                        content_preview = content[:500] if content else "<empty>"
-                        if content and len(content) > 700:
-                            content_preview = f"{content[:500]}...TRUNCATED...{content[-200:]}"
-                        logger.warning(
-                            f"Ollama JSON parse error (attempt {attempt + 1}/{max_retries + 1}): {json_err}\n"
-                            f"  Model: ollama/{self.model}\n"
-                            f"  Content length: {len(content) if content else 0} chars\n"
-                            f"  Content preview: {content_preview!r}"
-                        )
-                        if attempt < max_retries:
-                            backoff = min(initial_backoff * (2**attempt), max_backoff)
-                            await asyncio.sleep(backoff)
-                            last_exception = json_err
-                            continue
-                        else:
-                            raise
-
-                    # Extract token usage from Ollama response
-                    duration = time.time() - start_time
-                    input_tokens = result.get("prompt_eval_count", 0) or 0
-                    output_tokens = result.get("eval_count", 0) or 0
-                    total_tokens = input_tokens + output_tokens
-
-                    # Record LLM metrics
-                    metrics = get_metrics_collector()
-                    metrics.record_llm_call(
-                        provider=self.provider,
-                        model=self.model,
-                        scope=scope,
-                        duration=duration,
-                        input_tokens=input_tokens,
-                        output_tokens=output_tokens,
-                        success=True,
-                    )
-
-                    # Validate against Pydantic model or return raw JSON
-                    if skip_validation:
-                        validated_result = json_data
-                    else:
-                        validated_result = response_format.model_validate(json_data)
-
-                    if return_usage:
-                        token_usage = TokenUsage(
-                            input_tokens=input_tokens,
-                            output_tokens=output_tokens,
-                            total_tokens=total_tokens,
-                        )
-                        return validated_result, token_usage
-                    return validated_result
-
-                except httpx.HTTPStatusError as e:
-                    last_exception = e
-                    if attempt < max_retries:
-                        logger.warning(
-                            f"Ollama HTTP error (attempt {attempt + 1}/{max_retries + 1}): {e.response.status_code}"
-                        )
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
-                        await asyncio.sleep(backoff)
-                        continue
-                    else:
-                        logger.error(f"Ollama HTTP error after {max_retries + 1} attempts: {e}")
-                        raise
-
-                except httpx.RequestError as e:
-                    last_exception = e
-                    if attempt < max_retries:
-                        logger.warning(f"Ollama connection error (attempt {attempt + 1}/{max_retries + 1}): {e}")
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
-                        await asyncio.sleep(backoff)
-                        continue
-                    else:
-                        logger.error(f"Ollama connection error after {max_retries + 1} attempts: {e}")
-                        raise
-
-                except Exception as e:
-                    logger.error(f"Unexpected error during Ollama call: {type(e).__name__}: {e}")
-                    raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Ollama call failed after all retries")
-
-    async def cleanup(self) -> None:
-        """Clean up resources (close OpenAI client connections)."""
-        if hasattr(self, "_client") and self._client:
-            await self._client.close()

hindsight-api/hindsight_api/engine/retain/fact_extraction.py

@@ -57,25 +57,21 @@ def _infer_temporal_date(fact_text: str, event_date: datetime) -> str | None:
     return None
 
 
-def _sanitize_text(text: str | None) -> str | None:
+def _sanitize_text(text: str) -> str:
     """
-    Sanitize text by removing characters that break downstream systems.
+    Sanitize text by removing invalid Unicode surrogate characters.
 
-    Removes:
-    - Null bytes (\\x00): Invalid in PostgreSQL UTF-8 encoding
-    - Unicode surrogates (U+D800-U+DFFF): Invalid in UTF-8, break LLM APIs
+    Surrogate characters (U+D800 to U+DFFF) are used in UTF-16 encoding
+    but cannot be encoded in UTF-8. They can appear in Python strings
+    from improperly decoded data (e.g., from JavaScript or broken files).
 
-    Surrogate characters are used in UTF-16 encoding but cannot be encoded
-    in UTF-8. They can appear in Python strings from improperly decoded data
-    (e.g., from JavaScript or broken files). Null bytes commonly appear in
-    OCR output, PDF extraction, or copy-paste from binary sources.
+    This function removes unpaired surrogates to prevent UnicodeEncodeError
+    when the text is sent to the LLM API.
     """
-    if text is None:
-        return None
     if not text:
         return text
-    # Remove null bytes and surrogate characters
-    text = text.replace("\x00", "")
+    # Remove surrogate characters (U+D800 to U+DFFF) using regex
+    # These are invalid in UTF-8 and cause encoding errors
     return re.sub(r"[\ud800-\udfff]", "", text)
 
 
@@ -763,10 +759,6 @@ async def _extract_facts_from_chunk(
 
     # Build user message with metadata and chunk content in a clear format
     # Format event_date with day of week for better temporal reasoning
-    # Handle both datetime objects and ISO string formats (from deserialized async tasks)
-    from .orchestrator import parse_datetime_flexible
-
-    event_date = parse_datetime_flexible(event_date)
     event_date_formatted = event_date.strftime("%A, %B %d, %Y")  # e.g., "Monday, June 10, 2024"
     user_message = f"""Extract facts from the following text chunk.
 
@@ -1354,8 +1346,6 @@ def _add_temporal_offsets(facts: list[ExtractedFactType], contents: list[RetainC
 
     Modifies facts in place.
     """
-    from .orchestrator import parse_datetime_flexible
-
     # Group facts by content_index
     current_content_idx = 0
     content_fact_start = 0
@@ -1370,10 +1360,10 @@ def _add_temporal_offsets(facts: list[ExtractedFactType], contents: list[RetainC
         fact_position = i - content_fact_start
         offset = timedelta(seconds=fact_position * SECONDS_PER_FACT)
 
-        # Apply offset to all temporal fields (handle both datetime objects and ISO strings)
+        # Apply offset to all temporal fields
         if fact.occurred_start:
-            fact.occurred_start = parse_datetime_flexible(fact.occurred_start) + offset
+            fact.occurred_start = fact.occurred_start + offset
         if fact.occurred_end:
-            fact.occurred_end = parse_datetime_flexible(fact.occurred_end) + offset
+            fact.occurred_end = fact.occurred_end + offset
         if fact.mentioned_at:
-            fact.mentioned_at = parse_datetime_flexible(fact.mentioned_at) + offset
+            fact.mentioned_at = fact.mentioned_at + offset

hindsight-api/hindsight_api/engine/retain/fact_storage.py

@@ -8,7 +8,6 @@ import json
 import logging
 
 from ..memory_engine import fq_table
-from .fact_extraction import _sanitize_text
 from .types import ProcessedFact
 
 logger = logging.getLogger(__name__)
@@ -48,7 +47,7 @@ async def insert_facts_batch(
     tags_list = []
 
     for fact in facts:
-        fact_texts.append(_sanitize_text(fact.fact_text))
+        fact_texts.append(fact.fact_text)
         # Convert embedding to string for asyncpg vector type
         embeddings.append(str(fact.embedding))
         # event_date: Use occurred_start if available, otherwise use mentioned_at
@@ -57,7 +56,7 @@ async def insert_facts_batch(
         occurred_starts.append(fact.occurred_start)
         occurred_ends.append(fact.occurred_end)
         mentioned_ats.append(fact.mentioned_at)
-        contexts.append(_sanitize_text(fact.context))
+        contexts.append(fact.context)
         fact_types.append(fact.fact_type)
         # confidence_score is only for opinion facts
         confidence_scores.append(1.0 if fact.fact_type == "opinion" else None)
@@ -158,8 +157,7 @@ async def handle_document_tracking(
     """
     import hashlib
 
-    # Sanitize and calculate content hash
-    combined_content = _sanitize_text(combined_content) or ""
+    # Calculate content hash
     content_hash = hashlib.sha256(combined_content.encode()).hexdigest()
 
     # Always delete old document first if it exists (cascades to units and links)

hindsight-api/hindsight_api/engine/retain/orchestrator.py

@@ -8,7 +8,6 @@ import logging
 import time
 import uuid
 from datetime import UTC, datetime
-from typing import Any
 
 from ..db_utils import acquire_with_retry
 from . import bank_utils
@@ -19,39 +18,6 @@ def utcnow():
     return datetime.now(UTC)
 
 
-def parse_datetime_flexible(value: Any) -> datetime:
-    """
-    Parse a datetime value that could be either a datetime object or an ISO string.
-
-    This handles datetime values from both direct Python calls and deserialized JSON
-    (where datetime objects are serialized as ISO strings).
-
-    Args:
-        value: Either a datetime object or an ISO format string
-
-    Returns:
-        datetime object (timezone-aware)
-
-    Raises:
-        TypeError: If value is neither datetime nor string
-        ValueError: If string is not a valid ISO datetime
-    """
-    if isinstance(value, datetime):
-        # Ensure timezone-aware
-        if value.tzinfo is None:
-            return value.replace(tzinfo=UTC)
-        return value
-    elif isinstance(value, str):
-        # Parse ISO format string (handles both 'Z' and '+00:00' timezone formats)
-        dt = datetime.fromisoformat(value.replace("Z", "+00:00"))
-        # Ensure timezone-aware
-        if dt.tzinfo is None:
-            return dt.replace(tzinfo=UTC)
-        return dt
-    else:
-        raise TypeError(f"Expected datetime or string, got {type(value).__name__}")
-
-
 from ..response_models import TokenUsage
 from . import (
     chunk_storage,
@@ -123,18 +89,10 @@ async def retain_batch(
         # Merge item-level tags with document-level tags
         item_tags = item.get("tags", []) or []
         merged_tags = list(set(item_tags + (document_tags or [])))
-
-        # Handle event_date: parse flexibly (handles both datetime objects and ISO strings)
-        event_date_value = item.get("event_date")
-        if event_date_value:
-            event_date_value = parse_datetime_flexible(event_date_value)
-        else:
-            event_date_value = utcnow()
-
         content = RetainContent(
             content=item["content"],
             context=item.get("context", ""),
-            event_date=event_date_value,
+            event_date=item.get("event_date") or utcnow(),
             metadata=item.get("metadata", {}),
             entities=item.get("entities", []),
             tags=merged_tags,

hindsight-api/hindsight_api/engine/task_backend.py

@@ -182,16 +182,7 @@ class BrokerTaskBackend(TaskBackend):
         operation_id = task_dict.get("operation_id")
         task_type = task_dict.get("type", "unknown")
         bank_id = task_dict.get("bank_id")
-
-        # Custom encoder to handle datetime objects
-        from datetime import datetime
-
-        def datetime_encoder(obj):
-            if isinstance(obj, datetime):
-                return obj.isoformat()
-            raise TypeError(f"Object of type {type(obj).__name__} is not JSON serializable")
-
-        payload_json = json.dumps(task_dict, default=datetime_encoder)
+        payload_json = json.dumps(task_dict)
 
         schema = self._schema_getter() if self._schema_getter else self._schema
         table = fq_table("async_operations", schema)

hindsight-api/hindsight_api/extensions/__init__.py

@@ -24,11 +24,6 @@ from hindsight_api.extensions.operation_validator import (
     # Consolidation operation
     ConsolidateContext,
     ConsolidateResult,
-    # Mental Model operations
-    MentalModelGetContext,
-    MentalModelGetResult,
-    MentalModelRefreshContext,
-    MentalModelRefreshResult,
     # Core operations
     OperationValidationError,
     OperationValidatorExtension,
@@ -70,11 +65,6 @@ __all__ = [
     # Operation Validator - Consolidation
     "ConsolidateContext",
     "ConsolidateResult",
-    # Operation Validator - Mental Model
-    "MentalModelGetContext",
-    "MentalModelGetResult",
-    "MentalModelRefreshContext",
-    "MentalModelRefreshResult",
     # Tenant/Auth
     "ApiKeyTenantExtension",
     "AuthenticationError",

hindsight-api/hindsight_api/extensions/operation_validator.py

@@ -196,57 +196,6 @@ class ConsolidateResult:
     error: str | None = None
 
 
-# =============================================================================
-# Mental Model Contexts
-# =============================================================================
-
-
-@dataclass
-class MentalModelGetContext:
-    """Context for a mental model GET operation validation (pre-operation)."""
-
-    bank_id: str
-    mental_model_id: str
-    request_context: "RequestContext"
-
-
-@dataclass
-class MentalModelRefreshContext:
-    """Context for a mental model refresh/create operation validation (pre-operation)."""
-
-    bank_id: str
-    mental_model_id: str | None  # None for create (not yet assigned)
-    request_context: "RequestContext"
-
-
-@dataclass
-class MentalModelGetResult:
-    """Result context for post-mental-model-GET hook."""
-
-    bank_id: str
-    mental_model_id: str
-    request_context: "RequestContext"
-    output_tokens: int  # tokens in the returned content
-    success: bool = True
-    error: str | None = None
-
-
-@dataclass
-class MentalModelRefreshResult:
-    """Result context for post-mental-model-refresh hook."""
-
-    bank_id: str
-    mental_model_id: str
-    request_context: "RequestContext"
-    query_tokens: int  # tokens in source_query
-    output_tokens: int  # tokens in generated content
-    context_tokens: int  # tokens in context (if any)
-    facts_used: int  # facts referenced in based_on
-    mental_models_used: int  # mental models referenced in based_on
-    success: bool = True
-    error: str | None = None
-
-
 class OperationValidatorExtension(Extension, ABC):
     """
     Validates and hooks into retain/recall/reflect/consolidate operations.
@@ -453,81 +402,3 @@ class OperationValidatorExtension(Extension, ABC):
                 - error: Error message (if failed)
         """
         pass
-
-    # =========================================================================
-    # Mental Model - Pre-operation validation hook (optional - override to implement)
-    # =========================================================================
-
-    async def validate_mental_model_get(self, ctx: MentalModelGetContext) -> ValidationResult:
-        """
-        Validate a mental model GET operation before execution.
-
-        Override to implement custom validation logic for mental model retrieval.
-
-        Args:
-            ctx: Context containing:
-                - bank_id: Bank identifier
-                - mental_model_id: Mental model identifier
-                - request_context: Request context with auth info
-
-        Returns:
-            ValidationResult indicating whether the operation is allowed.
-        """
-        return ValidationResult.accept()
-
-    async def validate_mental_model_refresh(self, ctx: MentalModelRefreshContext) -> ValidationResult:
-        """
-        Validate a mental model refresh/create operation before execution.
-
-        Override to implement custom validation logic for mental model refresh.
-
-        Args:
-            ctx: Context containing:
-                - bank_id: Bank identifier
-                - mental_model_id: Mental model identifier (None for create)
-                - request_context: Request context with auth info
-
-        Returns:
-            ValidationResult indicating whether the operation is allowed.
-        """
-        return ValidationResult.accept()
-
-    # =========================================================================
-    # Mental Model - Post-operation hooks (optional - override to implement)
-    # =========================================================================
-
-    async def on_mental_model_get_complete(self, result: MentalModelGetResult) -> None:
-        """
-        Called after a mental model GET operation completes (success or failure).
-
-        Override to implement post-operation logic such as tracking or audit logging.
-
-        Args:
-            result: Result context containing:
-                - bank_id: Bank identifier
-                - mental_model_id: Mental model identifier
-                - output_tokens: Token count of the returned content
-                - success: Whether the operation succeeded
-                - error: Error message (if failed)
-        """
-        pass
-
-    async def on_mental_model_refresh_complete(self, result: MentalModelRefreshResult) -> None:
-        """
-        Called after a mental model refresh operation completes (success or failure).
-
-        Override to implement post-operation logic such as tracking or audit logging.
-
-        Args:
-            result: Result context containing:
-                - bank_id: Bank identifier
-                - mental_model_id: Mental model identifier
-                - query_tokens: Tokens in source_query
-                - output_tokens: Tokens in generated content
-                - context_tokens: Tokens in context
-                - facts_used: Number of facts referenced
-                - mental_models_used: Number of mental models referenced
-                - success: Whether the operation succeeded
-                - error: Error message (if failed)
-        """
-        pass

hindsight-api/hindsight_api/main.py

@@ -20,7 +20,7 @@ import warnings
 
 import uvicorn
 
-from . import MemoryEngine, __version__
+from . import MemoryEngine
 from .api import create_app
 from .banner import print_banner
 from .config import DEFAULT_WORKERS, ENV_WORKERS, HindsightConfig, get_config
@@ -362,7 +362,6 @@ def main():
             embeddings_provider=config.embeddings_provider,
             reranker_provider=config.reranker_provider,
             mcp_enabled=config.mcp_enabled,
-            version=__version__,
         )
 
     # Start idle checker in daemon mode

hindsight-api/hindsight_api/worker/main.py

@@ -200,20 +200,15 @@ def main():
         if tenant_extension:
             print("Tenant extension loaded - schemas will be discovered dynamically on each poll")
         else:
-            print(f"No tenant extension configured, using schema: {config.database_schema}")
+            print("No tenant extension configured, using public schema only")
 
         # Create a single poller that handles all schemas dynamically
-        # Convert default schema to None for SQL compatibility (no schema prefix)
-        from hindsight_api.config import DEFAULT_DATABASE_SCHEMA
-
-        schema = None if config.database_schema == DEFAULT_DATABASE_SCHEMA else config.database_schema
         poller = WorkerPoller(
             pool=memory._pool,
             worker_id=args.worker_id,
             executor=memory.execute_task,
             poll_interval_ms=args.poll_interval,
             max_retries=args.max_retries,
-            schema=schema,
             tenant_extension=tenant_extension,
             max_slots=config.worker_max_slots,
             consolidation_max_slots=config.worker_consolidation_max_slots,

hindsight-api/hindsight_api/worker/poller.py

@@ -99,13 +99,11 @@ class WorkerPoller:
         self._in_flight_by_type: dict[str, int] = {}
 
     async def _get_schemas(self) -> list[str | None]:
-        """Get list of schemas to poll. Returns [None] for default schema (no prefix)."""
+        """Get list of schemas to poll. Returns [None] for public schema."""
         if self._tenant_extension is not None:
-            from ..config import DEFAULT_DATABASE_SCHEMA
-
             tenants = await self._tenant_extension.list_tenants()
-            # Convert default schema to None for SQL compatibility (no prefix), keep others as-is
-            return [t.schema if t.schema != DEFAULT_DATABASE_SCHEMA else None for t in tenants]
+            # Convert "public" to None for SQL compatibility, keep others as-is
+            return [t.schema if t.schema != "public" else None for t in tenants]
         # Single schema mode
         return [self._schema]
 
@@ -196,9 +194,7 @@ class WorkerPoller:
         try:
             return await self._claim_batch_for_schema_inner(schema, limit, consolidation_limit)
         except Exception as e:
-            # Format schema for logging: custom schemas in quotes, None as-is
-            schema_display = f'"{schema}"' if schema else str(schema)
-            logger.warning(f"Worker {self._worker_id} failed to claim tasks for schema {schema_display}: {e}")
+            logger.warning(f"Worker {self._worker_id} failed to claim tasks for schema {schema or 'public'}: {e}")
             return []
 
     async def _claim_batch_for_schema_inner(
@@ -422,9 +418,7 @@ class WorkerPoller:
                 count = int(result.split()[-1]) if result else 0
                 total_count += count
             except Exception as e:
-                # Format schema for logging: custom schemas in quotes, None as-is
-                schema_display = f'"{schema}"' if schema else str(schema)
-                logger.warning(f"Worker {self._worker_id} failed to recover tasks for schema {schema_display}: {e}")
+                logger.warning(f"Worker {self._worker_id} failed to recover tasks for schema {schema or 'public'}: {e}")
 
         if total_count > 0:
             logger.info(f"Worker {self._worker_id} recovered {total_count} stale tasks from previous run")
@@ -463,8 +457,7 @@ class WorkerPoller:
                             consolidation_count += 1
 
                     types_str = ", ".join(f"{k}:{v}" for k, v in task_types.items())
-                    # Display None as "default" in logs
-                    schemas_str = ", ".join(s if s else "default" for s in schemas_seen)
+                    schemas_str = ", ".join(s or "public" for s in schemas_seen)
                     logger.info(
                         f"Worker {self._worker_id} claimed {len(tasks)} tasks "
                         f"({consolidation_count} consolidation): {types_str} (schemas: {schemas_str})"
@@ -598,8 +591,7 @@ class WorkerPoller:
                     other_workers.append(f"{wid}:{cnt}")
             others_str = ", ".join(other_workers) if other_workers else "none"
 
-            # Display None as "default" in logs
-            schemas_str = ", ".join(s if s else "default" for s in schemas)
+            schemas_str = ", ".join(s or "public" for s in schemas)
             logger.info(
                 f"[WORKER_STATS] worker={self._worker_id} "
                 f"slots={in_flight}/{self._max_slots} (consolidation={consolidation_count}/{self._consolidation_max_slots}) | "

hindsight-api/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "hindsight-api"
-version = "0.4.7"
+version = "0.4.2"
 description = "Hindsight: Agent Memory That Works Like Human Memory"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -25,7 +25,7 @@ dependencies = [
     "psycopg2-binary>=2.9.11",
     "tiktoken>=0.12.0",
     "httpx>=0.27.0",
-    "fastmcp>=2.14.0", # CVE-2025-66416
+    "fastmcp>=2.14.0",  # CVE-2025-66416
     "pg0-embedded>=0.11.0",
     "python-dateutil>=2.8.0",
     "opentelemetry-api>=1.20.0",
@@ -41,17 +41,16 @@ dependencies = [
     "flashrank>=0.2.0",
     # Local ML models for embeddings/reranking - can be excluded in Docker with INCLUDE_LOCAL_MODELS=false
     "sentence-transformers>=3.3.0",
-    "transformers>=4.53.0", # Security fixes for ReDoS vulnerabilities
-    "torch>=2.6.0", # CVE fix for remote code execution
+    "transformers>=4.53.0",  # Security fixes for ReDoS vulnerabilities
+    "torch>=2.6.0",  # CVE fix for remote code execution
     "uvloop>=0.22.1",
     # Transitive dependency security fixes
-    "pyasn1>=0.6.2", # DoS vulnerability fix
-    "urllib3>=2.6.3", # Decompression-bomb safeguards bypass fix
-    "langchain-core>=1.2.5", # Serialization injection vulnerability fix
-    "filelock>=3.20.1", # TOCTOU race condition fix
-    "authlib>=1.6.6", # Account takeover vulnerability fix
-    "aiohttp>=3.13.3", # Multiple DoS vulnerabilities
-    "claude-agent-sdk>=0.1.27",
+    "pyasn1>=0.6.2",  # DoS vulnerability fix
+    "urllib3>=2.6.3",  # Decompression-bomb safeguards bypass fix
+    "langchain-core>=1.2.5",  # Serialization injection vulnerability fix
+    "filelock>=3.20.1",  # TOCTOU race condition fix
+    "authlib>=1.6.6",  # Account takeover vulnerability fix
+    "aiohttp>=3.13.3",  # Multiple DoS vulnerabilities
 ]
 
 [project.optional-dependencies]

hindsight-api/tests/test_http_api_integration.py

@@ -1098,134 +1098,3 @@ async def test_version_endpoint_returns_correct_version(api_client):
     assert isinstance(features["worker"], bool)
 
     print(f"Version endpoint returned: api_version={result['api_version']}, features={features}")
-
-
-@pytest.mark.asyncio
-async def test_retain_with_timestamp_async(api_client, test_bank_id):
-    """Test that async retain accepts timestamp field and serializes correctly."""
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/memories",
-        json={
-            "items": [
-                {
-                    "content": "Test memory with timestamp",
-                    "context": "test",
-                    "timestamp": "2026-01-30T11:45:00Z"
-                }
-            ],
-            "async": True
-        }
-    )
-    
-    assert response.status_code == 200, f"Expected 200, got {response.status_code}: {response.text}"
-    data = response.json()
-    assert data["success"] is True
-    assert data["async"] is True
-    assert "operation_id" in data
-    
-
-@pytest.mark.asyncio
-async def test_retain_with_timestamp_sync(api_client, test_bank_id):
-    """Test that sync retain accepts timestamp field."""
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/memories",
-        json={
-            "items": [
-                {
-                    "content": "Test memory with timestamp sync",
-                    "context": "test", 
-                    "timestamp": "2026-01-30T11:45:00Z"
-                }
-            ],
-            "async": False
-        }
-    )
-    
-    assert response.status_code == 200, f"Expected 200, got {response.status_code}: {response.text}"
-    data = response.json()
-    assert data["success"] is True
-    assert data["async"] is False
-    
-
-@pytest.mark.asyncio
-async def test_retain_with_multiple_timestamps(api_client, test_bank_id):
-    """Test that multiple items with different timestamp formats work."""
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/memories",
-        json={
-            "items": [
-                {
-                    "content": "Event 1",
-                    "timestamp": "2026-01-30T11:45:00Z"  # With Z
-                },
-                {
-                    "content": "Event 2", 
-                    "timestamp": "2026-01-30T12:00:00+00:00"  # With timezone
-                },
-                {
-                    "content": "Event 3"  # No timestamp
-                }
-            ],
-            "async": True
-        }
-    )
-    
-    assert response.status_code == 200, f"Expected 200, got {response.status_code}: {response.text}"
-    data = response.json()
-    assert data["success"] is True
-    assert data["items_count"] == 3
-
-
-@pytest.mark.asyncio
-async def test_retain_with_timestamp_async_complete_processing(api_client, test_bank_id):
-    """Test that async retain with timestamp completes full processing including fact extraction."""
-    # Submit async retain with timestamp
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/memories",
-        json={
-            "items": [
-                {
-                    "content": "The quarterly meeting was held on January 30th 2026",
-                    "context": "meetings",
-                    "timestamp": "2026-01-30T11:45:00Z"
-                }
-            ],
-            "async": True
-        }
-    )
-    
-    assert response.status_code == 200, f"Expected 200, got {response.status_code}: {response.text}"
-    data = response.json()
-    assert data["success"] is True
-    assert data["async"] is True
-    operation_id = data["operation_id"]
-    
-    # Wait for async processing to complete (poll operation status)
-    max_wait_seconds = 30
-    poll_interval = 0.5
-    elapsed = 0
-    operation_completed = False
-    
-    while elapsed < max_wait_seconds:
-        response = await api_client.get(f"/v1/default/banks/{test_bank_id}/operations/{operation_id}")
-        if response.status_code == 200:
-            op_status = response.json()
-            if op_status.get("status") == "completed":
-                operation_completed = True
-                break
-            elif op_status.get("status") == "failed":
-                raise AssertionError(f"Operation failed: {op_status.get('error_message')}")
-        
-        await asyncio.sleep(poll_interval)
-        elapsed += poll_interval
-    
-    assert operation_completed, f"Async operation did not complete within {max_wait_seconds} seconds"
-    
-    # Verify memories were actually stored
-    response = await api_client.get(
-        f"/v1/default/banks/{test_bank_id}/memories/list",
-        params={"limit": 10}
-    )
-    assert response.status_code == 200
-    items = response.json()["items"]
-    assert len(items) > 0, "Should have stored memories after async processing"

hindsight-api/tests/test_llm_provider.py

@@ -19,10 +19,6 @@ MODEL_MATRIX = [
     ("openai", "gpt-5-nano"),
     ("openai", "gpt-5"),
     ("openai", "gpt-5.2"),
-    # Anthropic models
-    ("anthropic", "claude-sonnet-4-20250514"),
-    ("anthropic", "claude-opus-4-5-20251101"),
-    ("anthropic", "claude-haiku-4-20250514"),
     # Groq models
     ("groq", "openai/gpt-oss-120b"),
     ("groq", "openai/gpt-oss-20b"),
@@ -40,7 +36,6 @@ def get_api_key_for_provider(provider: str) -> str | None:
     """Get API key for provider from environment variables."""
     provider_key_map = {
         "openai": "OPENAI_API_KEY",
-        "anthropic": "ANTHROPIC_API_KEY",
         "groq": "GROQ_API_KEY",
         "gemini": "GEMINI_API_KEY",
     }

hindsight-api/tests/test_llm_token_metrics.py

@@ -30,8 +30,7 @@ async def test_llm_metrics_recorded_for_groq():
     # Create a mock metrics collector to track record_llm_call calls
     mock_collector = MagicMock(spec=MetricsCollector)
 
-    # Patch the provider module where get_metrics_collector is actually called
-    with patch("hindsight_api.engine.providers.openai_compatible_llm.get_metrics_collector", return_value=mock_collector):
+    with patch("hindsight_api.engine.llm_wrapper.get_metrics_collector", return_value=mock_collector):
         llm = LLMProvider(
             provider="groq",
             api_key=api_key,
@@ -91,8 +90,7 @@ async def test_llm_metrics_recorded_for_structured_output():
 
     mock_collector = MagicMock(spec=MetricsCollector)
 
-    # Patch the provider module where get_metrics_collector is actually called
-    with patch("hindsight_api.engine.providers.openai_compatible_llm.get_metrics_collector", return_value=mock_collector):
+    with patch("hindsight_api.engine.llm_wrapper.get_metrics_collector", return_value=mock_collector):
         llm = LLMProvider(
             provider="groq",
             api_key=api_key,

hindsight-api/tests/test_mental_model_hooks.py

@@ -1,206 +0,0 @@
-"""Unit tests for mental model operation validator hooks.
-
-Tests that the operation validator hooks are called correctly for
-mental model GET and refresh operations.
-"""
-
-import pytest
-
-from hindsight_api.extensions.operation_validator import (
-    MentalModelGetContext,
-    MentalModelGetResult,
-    MentalModelRefreshResult,
-    OperationValidatorExtension,
-    ValidationResult,
-)
-
-
-class TestMentalModelGetContextDataclass:
-    """Tests for MentalModelGetContext dataclass."""
-
-    def test_create_context(self):
-        """Test creating a MentalModelGetContext."""
-        from unittest.mock import MagicMock
-
-        request_context = MagicMock()
-        ctx = MentalModelGetContext(
-            bank_id="bank-1",
-            mental_model_id="mm-1",
-            request_context=request_context,
-        )
-
-        assert ctx.bank_id == "bank-1"
-        assert ctx.mental_model_id == "mm-1"
-        assert ctx.request_context is request_context
-
-
-class TestMentalModelGetResultDataclass:
-    """Tests for MentalModelGetResult dataclass."""
-
-    def test_create_result_success(self):
-        """Test creating a successful MentalModelGetResult."""
-        from unittest.mock import MagicMock
-
-        request_context = MagicMock()
-        result = MentalModelGetResult(
-            bank_id="bank-1",
-            mental_model_id="mm-1",
-            request_context=request_context,
-            output_tokens=250,
-        )
-
-        assert result.bank_id == "bank-1"
-        assert result.mental_model_id == "mm-1"
-        assert result.output_tokens == 250
-        assert result.success is True
-        assert result.error is None
-
-    def test_create_result_failure(self):
-        """Test creating a failed MentalModelGetResult."""
-        from unittest.mock import MagicMock
-
-        result = MentalModelGetResult(
-            bank_id="bank-1",
-            mental_model_id="mm-1",
-            request_context=MagicMock(),
-            output_tokens=0,
-            success=False,
-            error="Not found",
-        )
-
-        assert result.success is False
-        assert result.error == "Not found"
-
-
-class TestMentalModelRefreshResultDataclass:
-    """Tests for MentalModelRefreshResult dataclass."""
-
-    def test_create_result_with_all_fields(self):
-        """Test creating a MentalModelRefreshResult with all fields."""
-        from unittest.mock import MagicMock
-
-        result = MentalModelRefreshResult(
-            bank_id="bank-1",
-            mental_model_id="mm-1",
-            request_context=MagicMock(),
-            query_tokens=50,
-            output_tokens=500,
-            context_tokens=0,
-            facts_used=10,
-            mental_models_used=2,
-        )
-
-        assert result.query_tokens == 50
-        assert result.output_tokens == 500
-        assert result.context_tokens == 0
-        assert result.facts_used == 10
-        assert result.mental_models_used == 2
-        assert result.success is True
-        assert result.error is None
-
-    def test_create_result_failure(self):
-        """Test creating a failed MentalModelRefreshResult."""
-        from unittest.mock import MagicMock
-
-        result = MentalModelRefreshResult(
-            bank_id="bank-1",
-            mental_model_id="mm-1",
-            request_context=MagicMock(),
-            query_tokens=50,
-            output_tokens=0,
-            context_tokens=0,
-            facts_used=0,
-            mental_models_used=0,
-            success=False,
-            error="Reflect failed",
-        )
-
-        assert result.success is False
-        assert result.error == "Reflect failed"
-
-
-class TestDefaultHookBehavior:
-    """Tests for default (no-op) behavior of mental model hooks on OperationValidatorExtension."""
-
-    @pytest.fixture
-    def validator(self):
-        """Create a concrete subclass for testing default behavior."""
-        from unittest.mock import MagicMock
-
-        # Create a concrete subclass that implements the abstract methods
-        class TestValidator(OperationValidatorExtension):
-            async def validate_retain(self, ctx):
-                return ValidationResult.accept()
-
-            async def validate_recall(self, ctx):
-                return ValidationResult.accept()
-
-            async def validate_reflect(self, ctx):
-                return ValidationResult.accept()
-
-        return TestValidator(config={})
-
-    @pytest.mark.asyncio
-    async def test_validate_mental_model_get_default_accepts(self, validator):
-        """Test that default validate_mental_model_get accepts."""
-        from unittest.mock import MagicMock
-
-        ctx = MentalModelGetContext(
-            bank_id="bank-1",
-            mental_model_id="mm-1",
-            request_context=MagicMock(),
-        )
-
-        result = await validator.validate_mental_model_get(ctx)
-
-        assert result.allowed is True
-
-    @pytest.mark.asyncio
-    async def test_on_mental_model_get_complete_default_noop(self, validator):
-        """Test that default on_mental_model_get_complete is a no-op."""
-        from unittest.mock import MagicMock
-
-        result = MentalModelGetResult(
-            bank_id="bank-1",
-            mental_model_id="mm-1",
-            request_context=MagicMock(),
-            output_tokens=100,
-        )
-
-        # Should not raise
-        await validator.on_mental_model_get_complete(result)
-
-    @pytest.mark.asyncio
-    async def test_on_mental_model_refresh_complete_default_noop(self, validator):
-        """Test that default on_mental_model_refresh_complete is a no-op."""
-        from unittest.mock import MagicMock
-
-        result = MentalModelRefreshResult(
-            bank_id="bank-1",
-            mental_model_id="mm-1",
-            request_context=MagicMock(),
-            query_tokens=50,
-            output_tokens=500,
-            context_tokens=0,
-            facts_used=5,
-            mental_models_used=1,
-        )
-
-        # Should not raise
-        await validator.on_mental_model_refresh_complete(result)
-
-
-class TestExportsAvailable:
-    """Test that mental model hooks are properly exported."""
-
-    def test_imports_from_extensions_package(self):
-        """Test that all mental model types can be imported from hindsight_api.extensions."""
-        from hindsight_api.extensions import (
-            MentalModelGetContext,
-            MentalModelGetResult,
-            MentalModelRefreshResult,
-        )
-
-        assert MentalModelGetContext is not None
-        assert MentalModelGetResult is not None
-        assert MentalModelRefreshResult is not None

hindsight-api/tests/test_provider_default_models.py

@@ -1,123 +0,0 @@
-"""Test provider-specific default models in config."""
-
-import os
-
-import pytest
-
-
-def test_provider_default_models():
-    """Test that each provider has a default model and it's used when model is not explicitly set."""
-    from hindsight_api.config import PROVIDER_DEFAULT_MODELS, HindsightConfig, clear_config_cache
-
-    # Save original env vars
-    original_provider = os.environ.get("HINDSIGHT_API_LLM_PROVIDER")
-    original_model = os.environ.get("HINDSIGHT_API_LLM_MODEL")
-
-    try:
-        # Test each provider has a default
-        for provider, expected_model in PROVIDER_DEFAULT_MODELS.items():
-            clear_config_cache()
-            os.environ["HINDSIGHT_API_LLM_PROVIDER"] = provider
-            # Remove explicit model setting to test default
-            if "HINDSIGHT_API_LLM_MODEL" in os.environ:
-                del os.environ["HINDSIGHT_API_LLM_MODEL"]
-
-            config = HindsightConfig.from_env()
-            assert config.llm_provider == provider, f"Provider mismatch for {provider}"
-            assert config.llm_model == expected_model, f"Expected {expected_model} for {provider}, got {config.llm_model}"
-
-    finally:
-        # Restore original env vars
-        clear_config_cache()
-        if original_provider:
-            os.environ["HINDSIGHT_API_LLM_PROVIDER"] = original_provider
-        elif "HINDSIGHT_API_LLM_PROVIDER" in os.environ:
-            del os.environ["HINDSIGHT_API_LLM_PROVIDER"]
-
-        if original_model:
-            os.environ["HINDSIGHT_API_LLM_MODEL"] = original_model
-        elif "HINDSIGHT_API_LLM_MODEL" in os.environ:
-            del os.environ["HINDSIGHT_API_LLM_MODEL"]
-
-
-def test_explicit_model_overrides_provider_default():
-    """Test that explicit model setting overrides provider default."""
-    from hindsight_api.config import HindsightConfig, clear_config_cache
-
-    original_provider = os.environ.get("HINDSIGHT_API_LLM_PROVIDER")
-    original_model = os.environ.get("HINDSIGHT_API_LLM_MODEL")
-
-    try:
-        clear_config_cache()
-        os.environ["HINDSIGHT_API_LLM_PROVIDER"] = "anthropic"
-        os.environ["HINDSIGHT_API_LLM_MODEL"] = "claude-sonnet-4-5-20250929"
-
-        config = HindsightConfig.from_env()
-        assert config.llm_provider == "anthropic"
-        assert config.llm_model == "claude-sonnet-4-5-20250929", "Explicit model should override default"
-
-    finally:
-        clear_config_cache()
-        if original_provider:
-            os.environ["HINDSIGHT_API_LLM_PROVIDER"] = original_provider
-        elif "HINDSIGHT_API_LLM_PROVIDER" in os.environ:
-            del os.environ["HINDSIGHT_API_LLM_PROVIDER"]
-
-        if original_model:
-            os.environ["HINDSIGHT_API_LLM_MODEL"] = original_model
-        elif "HINDSIGHT_API_LLM_MODEL" in os.environ:
-            del os.environ["HINDSIGHT_API_LLM_MODEL"]
-
-
-def test_per_operation_provider_default_model():
-    """Test that per-operation providers use their own default models."""
-    from hindsight_api.config import HindsightConfig, clear_config_cache
-
-    original_provider = os.environ.get("HINDSIGHT_API_LLM_PROVIDER")
-    original_model = os.environ.get("HINDSIGHT_API_LLM_MODEL")
-    original_retain_provider = os.environ.get("HINDSIGHT_API_RETAIN_LLM_PROVIDER")
-    original_retain_model = os.environ.get("HINDSIGHT_API_RETAIN_LLM_MODEL")
-
-    try:
-        clear_config_cache()
-        os.environ["HINDSIGHT_API_LLM_PROVIDER"] = "openai"
-        # Remove explicit model to use provider default
-        if "HINDSIGHT_API_LLM_MODEL" in os.environ:
-            del os.environ["HINDSIGHT_API_LLM_MODEL"]
-
-        # Set retain-specific provider but not model
-        os.environ["HINDSIGHT_API_RETAIN_LLM_PROVIDER"] = "anthropic"
-        if "HINDSIGHT_API_RETAIN_LLM_MODEL" in os.environ:
-            del os.environ["HINDSIGHT_API_RETAIN_LLM_MODEL"]
-
-        config = HindsightConfig.from_env()
-
-        # Global LLM should use OpenAI default
-        assert config.llm_model == "o3-mini", f"Expected o3-mini, got {config.llm_model}"
-
-        # Retain should use Anthropic default
-        assert (
-            config.retain_llm_model == "claude-haiku-4-5-20251001"
-        ), f"Expected claude-haiku-4-5-20251001, got {config.retain_llm_model}"
-
-    finally:
-        clear_config_cache()
-        if original_provider:
-            os.environ["HINDSIGHT_API_LLM_PROVIDER"] = original_provider
-        elif "HINDSIGHT_API_LLM_PROVIDER" in os.environ:
-            del os.environ["HINDSIGHT_API_LLM_PROVIDER"]
-
-        if original_model:
-            os.environ["HINDSIGHT_API_LLM_MODEL"] = original_model
-        elif "HINDSIGHT_API_LLM_MODEL" in os.environ:
-            del os.environ["HINDSIGHT_API_LLM_MODEL"]
-
-        if original_retain_provider:
-            os.environ["HINDSIGHT_API_RETAIN_LLM_PROVIDER"] = original_retain_provider
-        elif "HINDSIGHT_API_RETAIN_LLM_PROVIDER" in os.environ:
-            del os.environ["HINDSIGHT_API_RETAIN_LLM_PROVIDER"]
-
-        if original_retain_model:
-            os.environ["HINDSIGHT_API_RETAIN_LLM_MODEL"] = original_retain_model
-        elif "HINDSIGHT_API_RETAIN_LLM_MODEL" in os.environ:
-            del os.environ["HINDSIGHT_API_RETAIN_LLM_MODEL"]

hindsight-api/tests/test_worker.py

@@ -1020,81 +1020,6 @@ class TestDynamicTenantDiscovery:
         for task in claimed:
             assert task.schema is None
 
-    @pytest.mark.asyncio
-    async def test_poller_with_custom_schema(self, pool):
-        """Test that poller uses custom schema when schema parameter is provided."""
-        from hindsight_api.worker import WorkerPoller
-
-        # Create a custom schema for testing
-        test_schema = "test_custom_schema"
-
-        try:
-            # Create schema and copy table structure
-            await pool.execute(f'CREATE SCHEMA IF NOT EXISTS "{test_schema}"')
-            await pool.execute(
-                f"""
-                CREATE TABLE "{test_schema}".async_operations (
-                    LIKE public.async_operations INCLUDING ALL
-                )
-                """
-            )
-
-            # Create pending tasks in the custom schema
-            bank_id = f"test-worker-{uuid.uuid4().hex[:8]}"
-            task_ids = []
-            for i in range(3):
-                op_id = uuid.uuid4()
-                task_ids.append(str(op_id))
-                payload = json.dumps({"type": "test_task", "index": i, "bank_id": bank_id})
-                await pool.execute(
-                    f"""
-                    INSERT INTO "{test_schema}".async_operations (operation_id, bank_id, operation_type, status, task_payload)
-                    VALUES ($1, $2, 'test', 'pending', $3::jsonb)
-                    """,
-                    op_id,
-                    bank_id,
-                    payload,
-                )
-
-            # Create poller with custom schema
-            poller = WorkerPoller(
-                pool=pool,
-                worker_id="test-worker-custom-schema",
-                executor=lambda x: None,
-                schema=test_schema,
-            )
-
-            # Claim tasks
-            claimed = await poller.claim_batch()
-            assert len(claimed) == 3, f"Expected 3 tasks, got {len(claimed)}"
-
-            # All tasks should have schema=test_schema
-            claimed_ids = []
-            for task in claimed:
-                assert task.schema == test_schema, f"Expected schema '{test_schema}', got '{task.schema}'"
-                claimed_ids.append(task.operation_id)
-
-            # Verify claimed tasks match what we inserted
-            assert set(claimed_ids) == set(task_ids)
-
-            # Verify tasks are marked as processing in the custom schema
-            rows = await pool.fetch(
-                f"""
-                SELECT operation_id, status, worker_id
-                FROM "{test_schema}".async_operations
-                WHERE operation_id = ANY($1)
-                """,
-                [uuid.UUID(tid) for tid in task_ids],
-            )
-            assert len(rows) == 3
-            for row in rows:
-                assert row["status"] == "processing"
-                assert row["worker_id"] == "test-worker-custom-schema"
-
-        finally:
-            # Clean up: drop the custom schema
-            await pool.execute(f'DROP SCHEMA IF EXISTS "{test_schema}" CASCADE')
-
 
 async def test_worker_fire_and_forget_nonblocking(pool, clean_operations):
     """

hindsight-cli/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "hindsight-cli"
-version = "0.4.7"
+version = "0.4.2"
 edition = "2021"
 authors = ["Hindsight Team"]
 description = "A beautiful CLI for Hindsight - semantic memory system"

hindsight-clients/python/hindsight_client_api/__init__.py

@@ -7,7 +7,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api/banks_api.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api/directives_api.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api/documents_api.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api/entities_api.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api/memory_api.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api/mental_models_api.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api/monitoring_api.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api/operations_api.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api_client.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/configuration.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.
@@ -489,7 +489,7 @@ class Configuration:
         return "Python SDK Debug Report:\n"\
                "OS: {env}\n"\
                "Python Version: {pyversion}\n"\
-               "Version of the API: 0.4.7\n"\
+               "Version of the API: 0.4.2\n"\
                "SDK Package Version: 0.0.7".\
                format(env=sys.platform, pyversion=sys.version)
 

hindsight-clients/python/hindsight_client_api/exceptions.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -6,7 +6,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.7
+    The version of the OpenAPI document: 0.4.2
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.