fix: use StreamingResponse for chat completions SSE

sse_starlette's EventSourceResponse added \r\n line endings that Open WebUI couldn't parse. Switched to plain StreamingResponse with manual SSE formatting matching OpenAI's exact format. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
fix: remove Steward analysis from user-visible reasoning
2026-02-05 12:29:08 +01:00 · 2026-02-05 12:19:05 +01:00 · 2026-02-05 11:51:42 +01:00 · 2026-02-05 09:52:43 +01:00 · 2026-02-05 09:49:46 +01:00 · 2026-02-05 09:47:02 +01:00
23 changed files with 959 additions and 551 deletions
@@ -8,7 +8,14 @@ API_HOST=0.0.0.0
 API_PORT=8000
 API_PREFIX=/v1

-# Ollama Configuration
+# Anthropic Configuration (Claude - preferred backend)
+# Set ANTHROPIC_API_KEY to enable Claude as the default backend
+# Without an API key, Tatlock uses Ollama exclusively
+# ANTHROPIC_API_KEY=sk-ant-api03-your-key-here
+ANTHROPIC_MODEL=claude-sonnet-4-20250514
+PREFER_CLOUD_BACKEND=true
+
+# Ollama Configuration (local fallback when Claude unavailable)
 OLLAMA_HOST=http://localhost:11434
 OLLAMA_DEFAULT_MODEL=mistral-nemo:latest
 OLLAMA_TIMEOUT=120
@@ -21,7 +28,6 @@ SEARXNG_TIMEOUT=30
 REDIS_HOST=localhost
 REDIS_PORT=6379
 REDIS_MEMORY_DB=1
-REDIS_BENCHMARK_DB=6
 REDIS_TIMEOUT=5

 # Qdrant Configuration
@@ -33,7 +39,6 @@ QDRANT_PORT=6333
 #   - development: DEBUG (maximum verbosity)
 #   - production: WARNING (minimal noise)
 # Uncomment to override: LOG_LEVEL=INFO
-ENABLE_BENCHMARKS=true
 # Note: Log format is auto-selected based on ENVIRONMENT (console for dev, json for production)

 # User Configuration
@@ -1,10 +1,22 @@
 name: Build and Push

 on:
-  release:
-    types: [published]
+  push:
+    tags:
+      - 'v[0-9]*'

 jobs:
+  release:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Create Gitea Release
+        run: |
+          curl -sf -X POST \
+            -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" \
+            -H "Content-Type: application/json" \
+            -d '{"tag_name": "${{ github.ref_name }}", "name": "Release ${{ github.ref_name }}", "body": "Automated release for ${{ github.ref_name }}"}' \
+            "${{ github.server_url }}/api/v1/repos/${{ github.repository }}/releases"
+            
  build:
    runs-on: ubuntu-latest
    steps:
@@ -7,6 +7,90 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0

 ## [Unreleased]

+## [2.0.4] - 2026-02-05
+
+### Fixed
+
+- **Open WebUI streaming compatibility** - Replaced `sse_starlette` `EventSourceResponse` with plain `StreamingResponse` for chat completions; `sse_starlette` added `\r\n` line endings and extra SSE fields that Open WebUI couldn't parse
+
+## [2.0.3] - 2026-02-05
+
+### Fixed
+
+- **Steward analysis leaking into responses** - Removed internal routing analysis (`DELEGATE: tatlock_core...`) from user-visible reasoning in both streaming and non-streaming paths
+
+## [2.0.2] - 2026-02-05
+
+### Fixed
+
+- **tool_choice format incompatibility** - Removed `extra_body` tool_choice hack for Claude backend; PydanticAI handles tool_choice natively for Anthropic, preventing infinite tool call loops
+- **CI trigger** - Changed workflow trigger from `release:published` to `push:tags:v[0-9]*`
+
+## [2.0.1] - 2026-02-05
+
+### Fixed
+
+- **Expert agent registration failure** - `AnthropicModel` does not accept `api_key` directly; now passes it via `AnthropicProvider`
+
+## [2.0.0] - 2026-02-05
+
+### Added
+
+- **Claude backend support (Claudification Phase 1)** - All agents now prefer Claude over Ollama
+  - New `src/anthropic/` module with model selector and health check
+  - `get_model()` factory returns Claude if available, Ollama as fallback
+  - Startup health check caches Claude API availability
+  - Configuration: `ANTHROPIC_API_KEY`, `ANTHROPIC_MODEL`, `PREFER_CLOUD_BACKEND`
+  - 200k token context when using Claude backend
+
+- **Steward dual-backend support** - Direct API calls to Claude or Ollama
+  - `_call_claude()`: Anthropic Messages API path
+  - `_call_ollama()`: Existing Ollama generate API path (preserved)
+  - Automatic fallback: if Claude call fails mid-request, retries with Ollama
+
+- **Claudification project tracking** - `PROJECT_CLAUDIFICATION.md` with Phase 1/2 roadmap
+
+### Changed
+
+- **All PydanticAI agents refactored to use `get_model()`**:
+  - Tatlock (6 instantiation locations)
+  - Librarian
+  - Biographer
+  - Housekeeper
+- **`initialize_application()` is now async** - Supports async Claude health check at startup
+- **Dependencies**: `pydantic-ai-slim[openai,anthropic]` replaces `pydantic-ai-slim[openai]`
+- **Startup logging** now includes backend selection info (claude/ollama)
+- **Agent creation logging** now includes backend and model info
+
+### Removed
+
+- Stale `tests/core/test_benchmarks.py` (benchmark system was removed in v1.10.0)
+
+## [1.11.0] - 2025-12-30
+
+### Added
+
+- **Paperless document integration** - HybridRAG now includes indexed PDFs and scanned documents from Paperless-ngx
+  - New `include_documents` parameter in `hybrid_search` tool
+  - 📑 icon for document sources in search results
+  - Librarian prompt updated with document awareness
+
+- **Volatile cache integration** - HybridRAG now includes pre-fetched real-time data
+  - New `include_volatile` parameter in `hybrid_search` tool
+  - ⚡ icon for volatile sources in search results
+  - Supports weather, forecast, news, stock, crypto, sun, air_quality namespaces
+  - Librarian prompt updated with volatile cache awareness (user-configured items only)
+
+- **Biographer routing in Steward** - Personal memory queries now correctly route to The Biographer
+  - Added explicit routing rules for "where do I live", "what car do I drive", etc.
+  - Added biographer delegation examples to Steward prompt
+  - Location keywords ("live", "where", "home") now trigger profile pre-fetch
+
+### Changed
+
+- **LibraryDeskClient.hybrid_search** - Now passes full config including `document_limit`, `volatile_limit`, and enable flags
+- **Steward guidelines** - Clarified that research queries about TOPICS go to Librarian, queries about USER go to Biographer
+
 ## [1.10.1] - 2025-12-23

 ### Fixed
@@ -831,7 +915,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - CORS middleware
 - Exception handlers (OpenAI-compatible error format)

-[Unreleased]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.10.0...main
+[Unreleased]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v2.0.0...main
+[2.0.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.11.0...v2.0.0
+[1.11.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.10.0...v1.11.0
 [1.10.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.9.0...v1.10.0
 [1.9.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.8.6...v1.9.0
 [1.8.6]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.8.5...v1.8.6
@@ -0,0 +1,401 @@
+# Tatlock Enhancement Plan: Bidirectional Claude Integration
+
+## Executive Summary
+
+Implement a **bidirectional architecture** that:
+1. **Superpowers Tatlock** by swapping Ollama→Claude backend (200k context, better reasoning, same butler personality)
+2. **Exposes Tatlock as MCP server** for Claude instances on any device (phone, browser, desktop)
+
+This gives you the flexibility to use whichever AI is best/most accessible at any moment.
+
+## Key Insight: Blanket Backend Swap (Simpler Than Sidecar)
+
+Instead of adding a Claude "Analyst" sidecar agent, **swap the underlying model for ALL agents**:
+
+```
+CURRENT:  TatlockAgent → OpenAIChatModel → OllamaProvider → Ollama (mistral-nemo)
+PROPOSED: TatlockAgent → AnthropicModel → AnthropicProvider → Claude API
+                      ↘ (fallback when offline) → OllamaProvider → Ollama
+```
+
+**Why this works:**
+- PydanticAI natively supports Anthropic via `AnthropicModel` + `AnthropicProvider`
+- The same `TATLOCK_SYSTEM_PROMPT` is passed to Claude - butler personality preserved
+- Claude is **better** at following system prompts than mistral-nemo
+- 200k context for ALL queries, not just "complex" ones
+- Simpler architecture: no routing logic, no sidecar delegation
+
+---
+
+## Research Findings
+
+### Industry Best Practices (2025-2026)
+
+**MCP Protocol Updates** ([MCP Spec Updates June 2025](https://auth0.com/blog/mcp-specs-update-all-about-auth/)):
+- Streamable HTTP replaced SSE (March 2025) - better for cloud deployment
+- OAuth 2.0 required for remote servers - MCP servers are OAuth Resource Servers
+- Tool Output Schemas now available - better structured data handling
+- MCP Registry launched (Sept 2025) - community server discovery
+
+**Community Patterns** ([Claude Code Router](https://github.com/musistudio/claude-code-router)):
+- Task-based routing is becoming standard: route simple→local, complex→cloud
+- Translation proxies bridge Anthropic Messages API ↔ OpenAI format
+- Cost savings of up to 98% reported with smart routing
+
+**Home Automation MCP** ([ha-mcp](https://github.com/homeassistant-ai/ha-mcp)):
+- Production-ready MCP servers exist for Home Assistant
+- Support Claude Code, Gemini CLI, Open WebUI, VSCode, Cursor
+- Pattern: expose local tools securely to remote AI clients
+
+**Remote MCP Access** ([mcp-remote](https://www.npmjs.com/package/mcp-remote)):
+- Bridge local MCP servers to Claude Desktop/Browser via proxy
+- Supports authentication headers for security
+- Works with ngrok/Cloudflare Tunnel for HTTPS
+
+---
+
+## Recommended Architecture
+
+```
+┌─────────────────────────────────────────────────────────────────────────────────────┐
+│                    BIDIRECTIONAL TATLOCK-CLAUDE ARCHITECTURE                        │
+├─────────────────────────────────────────────────────────────────────────────────────┤
+│                                                                                     │
+│  ╔═══════════════════════════════════════════════════════════════════════════════╗  │
+│  ║  SCENARIO A: Using Tatlock (Open WebUI, local apps)                           ║  │
+│  ║  ─────────────────────────────────────────────────                            ║  │
+│  ║                                                                               ║  │
+│  ║    Request → Steward → Tatlock → Tools + Expert Delegation                    ║  │
+│  ║                            │                                                  ║  │
+│  ║                            ├─→ Librarian (Claude) → research, wiki, RAG       ║  │
+│  ║                            ├─→ Biographer (Claude) → memory, preferences      ║  │
+│  ║                            ├─→ Housekeeper (Claude) → home automation         ║  │
+│  ║                            └─→ All powered by Claude with Ollama fallback     ║  │
+│  ║                                                                               ║  │
+│  ║    Butler personality preserved, 200k context for all queries                 ║  │
+│  ╚═══════════════════════════════════════════════════════════════════════════════╝  │
+│                                                                                     │
+│  ╔═══════════════════════════════════════════════════════════════════════════════╗  │
+│  ║  SCENARIO B: Using Claude.ai / Claude Desktop / Phone                         ║  │
+│  ║  ────────────────────────────────────────────────────                         ║  │
+│  ║                                                                               ║  │
+│  ║    Claude ──[MCP over HTTPS]──► Tatlock MCP Server → Household Tools          ║  │
+│  ║                                        │                                      ║  │
+│  ║                                        ├─→ calculator, datetime               ║  │
+│  ║                                        ├─→ web_search, wiki_search            ║  │
+│  ║                                        ├─→ hybrid_search (RAG)                ║  │
+│  ║                                        ├─→ memory_recall, store_insight       ║  │
+│  ║                                        └─→ home_control (lights, climate)     ║  │
+│  ║                                                                               ║  │
+│  ║    Full 200k context, your local tools accessible from anywhere               ║  │
+│  ╚═══════════════════════════════════════════════════════════════════════════════╝  │
+│                                                                                     │
+│  ╔═══════════════════════════════════════════════════════════════════════════════╗  │
+│  ║  SCENARIO C: Offline (internet down)                                          ║  │
+│  ║  ──────────────────────────────────                                           ║  │
+│  ║                                                                               ║  │
+│  ║    Tatlock operates fully locally with Ollama                                 ║  │
+│  ║    • All tools work (except web search)                                       ║  │
+│  ║    • Graceful degradation with same butler personality                        ║  │
+│  ╚═══════════════════════════════════════════════════════════════════════════════╝  │
+│                                                                                     │
+└─────────────────────────────────────────────────────────────────────────────────────┘
+```
+
+---
+
+## Implementation Plan
+
+### Phase 1: Blanket Backend Swap (Claude for All Agents)
+
+Replace Ollama with Claude as the default backend for all PydanticAI agents, with automatic offline fallback.
+
+**New Files:**
+```
+src/anthropic/
+├── __init__.py
+├── provider.py       # Claude provider with health check
+└── model_selector.py # Chooses Claude or Ollama based on availability
+```
+
+**Key Implementation (`src/anthropic/provider.py`):**
+```python
+from pydantic_ai.models.anthropic import AnthropicModel
+from pydantic_ai.providers.anthropic import AnthropicProvider
+from pydantic_ai.models.openai import OpenAIChatModel
+from src.ollama.provider import get_ollama_provider
+from src.core.config import config
+
+_anthropic_available: bool | None = None
+
+async def check_anthropic_health() -> bool:
+    """Check if Anthropic API is reachable."""
+    global _anthropic_available
+    try:
+        from anthropic import AsyncAnthropic
+        client = AsyncAnthropic(api_key=config.ANTHROPIC_API_KEY)
+        await client.messages.create(
+            model=config.ANTHROPIC_MODEL,
+            max_tokens=1,
+            messages=[{"role": "user", "content": "hi"}]
+        )
+        _anthropic_available = True
+    except Exception:
+        _anthropic_available = False
+    return _anthropic_available
+
+def get_model(prefer_cloud: bool = True):
+    """Get the best available model. Returns Claude if available, otherwise Ollama."""
+    if prefer_cloud and config.ANTHROPIC_API_KEY and _anthropic_available:
+        provider = AnthropicProvider(api_key=config.ANTHROPIC_API_KEY)
+        return AnthropicModel(
+            model_name=config.ANTHROPIC_MODEL,
+            provider=provider,
+        )
+    else:
+        return OpenAIChatModel(
+            model_name=config.OLLAMA_DEFAULT_MODEL,
+            provider=get_ollama_provider()
+        )
+```
+
+**Modify TatlockAgent (`src/agents/tatlock.py`):**
+```python
+def _ensure_agent(self):
+    if self._agent is not None:
+        return
+
+    from src.anthropic.model_selector import get_model
+
+    model = get_model(prefer_cloud=True)
+
+    self._agent = Agent(
+        model,
+        system_prompt=TATLOCK_SYSTEM_PROMPT,  # Same butler personality!
+    )
+    self._register_tools()
+```
+
+---
+
+### Phase 2: MCP Server (Expose Tools to Claude)
+
+Create an MCP server that exposes Tatlock's household tools to external Claude instances.
+
+**New Files:**
+```
+src/mcp/
+├── __init__.py
+├── server.py         # MCP server using mcp Python SDK
+├── tool_adapters.py  # Convert PydanticAI tools → MCP schemas
+├── auth.py           # API key authentication
+└── transport.py      # Streamable HTTP transport
+```
+
+**Docker Stack Addition (`stacks/agents.yml`):**
+```yaml
+tatlock-mcp:
+  image: git.schweitz.internal/jpmschweitzer/tatlock:latest
+  command: ["python", "-m", "src.mcp.server"]
+  ports:
+    - "8002:8002"
+  environment:
+    - MCP_AUTH_TOKEN=${MCP_AUTH_TOKEN}
+  networks:
+    - docker-dataplane
+```
+
+**Claude Desktop Configuration:**
+```json
+{
+  "mcpServers": {
+    "tatlock": {
+      "command": "npx",
+      "args": ["mcp-remote", "https://mcp.schweitz.net/sse", "--header", "Authorization: Bearer ${MCP_AUTH_TOKEN}"]
+    }
+  }
+}
+```
+
+---
+
+## Files to Modify
+
+### Phase 1 - Backend Swap
+
+**New Files:**
+| File | Purpose |
+|------|---------|
+| `src/anthropic/__init__.py` | Package init |
+| `src/anthropic/provider.py` | Claude provider with health check |
+| `src/anthropic/model_selector.py` | Choose Claude or Ollama based on availability |
+
+**Modified Files:**
+| File | Changes |
+|------|---------|
+| `src/core/config.py` | Add `ANTHROPIC_API_KEY`, `ANTHROPIC_MODEL`, `PREFER_CLOUD_BACKEND` |
+| `src/agents/tatlock.py` | Use `get_model()` instead of hardcoded Ollama |
+| `src/agents/librarian/agent.py` | Use `get_model()` instead of hardcoded Ollama |
+| `src/agents/biographer/agent.py` | Use `get_model()` instead of hardcoded Ollama |
+| `src/agents/steward/agent.py` | Convert to PydanticAI or add Anthropic API support |
+| `src/core/startup.py` | Add Anthropic health check on startup |
+| `requirements.txt` | Add `anthropic>=0.40.0` |
+| `.env.example` | Document new environment variables |
+
+### Phase 2 - MCP Server
+
+**New Files:**
+| File | Purpose |
+|------|---------|
+| `src/mcp/__init__.py` | Package init |
+| `src/mcp/server.py` | MCP server implementation |
+| `src/mcp/tool_adapters.py` | PydanticAI → MCP schema conversion |
+| `src/mcp/auth.py` | Token-based authentication |
+
+---
+
+## Cost Analysis
+
+- **Claude API**: $5-30/month (10-50 calls/day, ~2k input + 1k output tokens/call)
+- **MCP via Claude Pro**: Included in subscription
+- **Total**: ~$10-80/month for full bidirectional integration
+
+---
+
+## Verification Plan
+
+### Phase 1 Testing
+```bash
+# 1. Run with Claude backend
+ANTHROPIC_API_KEY=your-key docker-compose up -d tatlock
+
+# 2. Verify Claude is being used
+docker logs tatlock 2>&1 | grep -i "anthropic\|claude"
+
+# 3. Test butler personality
+curl -X POST http://tatlock.schweitz.internal:8000/v1/responses \
+  -H "Content-Type: application/json" \
+  -d '{"model": "Tatlock", "input": "Hello, who are you?"}'
+
+# 4. Test offline fallback
+ANTHROPIC_API_KEY="" docker-compose up -d tatlock
+docker logs tatlock 2>&1 | grep -i "ollama\|fallback"
+```
+
+### Phase 2 Testing
+```bash
+# 1. Start MCP server
+docker-compose up -d tatlock-mcp
+
+# 2. Test MCP endpoint
+curl -X POST https://mcp.schweitz.net/tools/list \
+  -H "Authorization: Bearer $MCP_AUTH_TOKEN"
+```
+
+---
+
+## Implementation Priority
+
+1. **Phase 1: Backend Swap** (~1 week)
+   - Immediate value: 200k context for ALL queries
+   - Low risk: provider abstraction, graceful offline fallback
+
+2. **Phase 2: MCP Server** (~2-3 weeks)
+   - Enables cross-device access
+   - Bidirectional: Tatlock superpowered by Claude AND accessible to Claude
+
+---
+
+## Future Phases (Optional)
+
+- **Phase 3: LiteLLM Gateway** - Unified endpoint for all models, config-driven routing
+- **Phase 4: Multi-Provider** - Add OpenAI, Vertex AI, etc.
+- **Phase 5: Smart Routing** - Context-aware model selection, cost ceiling enforcement
+
+---
+
+## Offline Behavior
+
+| Scenario | Behavior |
+|----------|----------|
+| No API key | Use Ollama exclusively |
+| API unreachable | Use Ollama, log warning |
+| API rate limited | Fallback to Ollama |
+
+| Aspect | Claude | Ollama |
+|--------|--------|--------|
+| Context | 200k tokens | ~8k tokens |
+| Latency | 1-3s (network) | 0.5-1s (local) |
+| Personality | Preserved | Preserved |
+| Tools | All work | All work |
+| Cost | API charges | Free |
+
+---
+
+## Implementation Status
+
+### Phase 1: Backend Swap - CODE COMPLETE (awaiting API access)
+
+- [x] Add Anthropic config settings to `src/core/config.py`
+- [x] Add `pydantic-ai-slim[openai,anthropic]` to requirements.txt
+- [x] Create `src/anthropic/` module (model_selector.py)
+- [x] Add Claude health check to startup.py
+- [x] Refactor all PydanticAI agents to use `get_model()`
+  - [x] Librarian
+  - [x] Biographer
+  - [x] Housekeeper
+  - [x] Tatlock (6 locations)
+- [x] Add Claude API path to Steward agent (direct API calls)
+- [x] Update `.env.example` with new variables
+- [x] Test Ollama fallback (working)
+- [ ] Test with Claude API key (blocked: no API access currently)
+
+**Note:** Implementation complete. Currently runs in Ollama-only mode. Will automatically use Claude when `ANTHROPIC_API_KEY` is configured.
+
+### Phase 2: MCP Server - NOT STARTED
+
+- [ ] Create `src/mcp/` module
+- [ ] Tool adapters (PydanticAI → MCP schema)
+- [ ] Authentication middleware
+- [ ] Streamable HTTP transport
+- [ ] Docker stack configuration
+
+---
+
+## Related Repository Handovers
+
+Handover documents created in each repo: `PROJECT_CLAUDIFICATION_HANDOVER.md`
+
+### library-desk - HANDOVER CREATED
+
+- [x] Write handover document
+- [ ] Review HybridRAG response size limits
+- [ ] Review smart_create endpoint for Claude optimization
+- [ ] Evaluate response formats for LLM consumption
+
+### core-api - HANDOVER CREATED
+
+- [x] Write handover document
+- [ ] Review list_devices response format
+- [ ] Review error messages for LLM consumption
+- [ ] Evaluate rate limiting for faster Claude processing
+
+### portainer-core - HANDOVER CREATED (blocking for production)
+
+- [x] Write handover document
+- [ ] Update stack with new environment variables
+- [ ] Configure secrets management for API key
+- [ ] Update CONTAINERS.md documentation
+
+### webber - HANDOVER CREATED
+
+- [x] Write handover document
+- [ ] Review content truncation limits
+- [ ] Evaluate extraction quality for LLM consumption
+
+### tatlock-ui - HANDOVER CREATED
+
+- [x] Write handover document
+- [ ] Test streaming responses with Claude backend
+- [ ] Test conversation history with larger context
+- [ ] Verify tool call display and reasoning rendering
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "tatlock"
-version = "1.10.1"
+version = "2.0.4"
 description = "OpenAI-compatible API with Ollama backend"
 requires-python = ">=3.12"
 dependencies = []
@@ -21,10 +21,9 @@ pydantic-settings>=2.12,<2.13

 # AI/LLM integration
 # PydanticAI: Agent framework for using Pydantic with LLMs
-# Using slim version with only openai extra (Ollama uses OpenAI-compatible API)
-# This avoids installing SDKs for anthropic, cohere, google, groq, huggingface, etc.
+# Using slim version with openai (Ollama) and anthropic (Claude) extras
 # See DEPENDENCY_SLIM.md for rollback instructions if this breaks
-pydantic-ai-slim[openai]>=1.27,<1.28
+pydantic-ai-slim[openai,anthropic]>=1.27,<1.28

 # HTTP client for Ollama communication
 # Latest: 0.28.1 - No known CVEs
@@ -102,16 +102,10 @@ _biographer_agent: Optional[Agent[None, str]] = None

 def _create_biographer_agent() -> Agent[None, str]:
    """Create The Biographer PydanticAI agent."""
-    from pydantic_ai.models.openai import OpenAIChatModel
+    from src.anthropic.model_selector import get_model

-    from src.ollama.provider import get_ollama_provider
-
-    # Create Ollama model with sanitized provider
-    # (fixes 'content: null' issue with tool calls)
-    model = OpenAIChatModel(
-        model_name=config.OLLAMA_DEFAULT_MODEL,
-        provider=get_ollama_provider(),
-    )
+    # Get best available model (Claude if available, else Ollama)
+    model = get_model()

    agent: Agent[None, str] = Agent(
        model=model,
@@ -131,9 +125,12 @@ def _create_biographer_agent() -> Agent[None, str]:
    # Register management tools
    agent.tool_plain(forget_memory)

+    from src.anthropic.model_selector import get_model_info
+    model_info = get_model_info()
    logger.info(
        "biographer_agent_created",
-        model=config.OLLAMA_DEFAULT_MODEL,
+        backend=model_info["backend"],
+        model=model_info["model"],
        tool_count=6,
    )

@@ -103,16 +103,10 @@ _housekeeper_agent: Optional[Agent[None, str]] = None

 def _create_housekeeper_agent() -> Agent[None, str]:
    """Create the Housekeeper PydanticAI agent."""
-    from pydantic_ai.models.openai import OpenAIChatModel
+    from src.anthropic.model_selector import get_model

-    from src.ollama.provider import get_ollama_provider
-
-    # Create Ollama model with sanitized provider
-    # (fixes 'content: null' issue with tool calls)
-    model = OpenAIChatModel(
-        model_name=config.OLLAMA_DEFAULT_MODEL,
-        provider=get_ollama_provider(),
-    )
+    # Get best available model (Claude if available, else Ollama)
+    model = get_model()

    agent: Agent[None, str] = Agent(
        model=model,
@@ -145,9 +139,12 @@ def _create_housekeeper_agent() -> Agent[None, str]:
    # Register history tools
    agent.tool_plain(get_history)

+    from src.anthropic.model_selector import get_model_info
+    model_info = get_model_info()
    logger.info(
        "housekeeper_agent_created",
-        model=config.OLLAMA_DEFAULT_MODEL,
+        backend=model_info["backend"],
+        model=model_info["model"],
        tool_count=13,
    )

@@ -39,7 +39,14 @@ Your role is to help users find, understand, synthesize, and manage information
 - The personal wiki (Wiki.js) containing documentation and notes
 - The knowledge graph (Neo4j) with entities and relationships
 - Vector embeddings (Qdrant) for semantic search
- Web search (SearXNG) for current information
+- Paperless documents (📑) - indexed PDFs, scanned documents, invoices, receipts from the user's document archive
+- Volatile cache (⚡) - pre-fetched real-time data for user-relevant locations and items:
+  - weather/forecast: conditions and forecasts for user's configured cities
+  - news: headlines from user's preferred sources
+  - stock/crypto: quotes for user's watched symbols
+  - sun/air_quality: data for user's locations
+  - Note: volatile data may not exist for arbitrary queries - falls back to web search
+- Web search (SearXNG) for current information not available in cache

 ## Your Personality
 - Scholarly and thorough in your research
@@ -60,7 +67,13 @@ Your role is to help users find, understand, synthesize, and manage information
  - Use for: comparing multiple sources, gathering info from several pages

 ### Internal Research Tools
- **hybrid_search**: Your primary research tool - searches wiki, graph, and web at once
+- **hybrid_search**: Your primary research tool - searches ALL sources at once:
+  - Wiki pages (vector similarity)
+  - Knowledge graph (entity relationships)
+  - Paperless documents (📑 indexed PDFs, scans)
+  - Volatile cache (⚡ weather, news, stocks - when available)
+  - Web search (current information)
+  Results are fused and re-ranked by relevance. Volatile data gets priority when fresh.
 - **search_wiki**: Find specific wiki pages by keyword
 - **semantic_search**: Find conceptually similar content
 - **explore_knowledge_graph** / **find_related_entities**: Discover connections
@@ -130,16 +143,10 @@ _librarian_agent: Optional[Agent[None, str]] = None

 def _create_librarian_agent() -> Agent[None, str]:
    """Create the Librarian PydanticAI agent."""
-    from pydantic_ai.models.openai import OpenAIChatModel
+    from src.anthropic.model_selector import get_model

-    from src.ollama.provider import get_ollama_provider
-
-    # Create Ollama model with sanitized provider
-    # (fixes 'content: null' issue with tool calls)
-    model = OpenAIChatModel(
-        model_name=config.OLLAMA_DEFAULT_MODEL,
-        provider=get_ollama_provider(),
-    )
+    # Get best available model (Claude if available, else Ollama)
+    model = get_model()

    agent: Agent[None, str] = Agent(
        model=model,
@@ -169,9 +176,12 @@ def _create_librarian_agent() -> Agent[None, str]:
    agent.tool_plain(update_wiki_page)
    agent.tool_plain(smart_create_wiki_page)

+    from src.anthropic.model_selector import get_model_info
+    model_info = get_model_info()
    logger.info(
        "librarian_agent_created",
-        model=config.OLLAMA_DEFAULT_MODEL,
+        backend=model_info["backend"],
+        model=model_info["model"],
        tool_count=14,  # 7 research + 3 web + 1 wiki read + 3 wiki write
    )

@@ -225,18 +225,22 @@ class LibraryDeskClient:
        vector_limit: int = 10,
        graph_limit: int = 10,
        web_limit: int = 5,
+        document_limit: int = 5,
+        volatile_limit: int = 3,
        enable_reranking: bool = True,
        final_result_count: int = 10,
    ) -> HybridRAGResponse:
        """
-        Execute HybridRAG search combining vector, graph, and web results.
+        Execute HybridRAG search combining vector, graph, documents, volatile, and web.

        Args:
            query: Search query
            user: User identifier for multi-tenancy (defaults to request context)
-            vector_limit: Max results from vector search
+            vector_limit: Max results from vector search (wiki pages)
            graph_limit: Max results from graph search
-            web_limit: Max results from web search
+            web_limit: Max results from web search (0 to disable)
+            document_limit: Max results from Paperless documents (0 to disable)
+            volatile_limit: Max results from volatile cache (0 to disable)
            enable_reranking: Whether to rerank with LLM
            final_result_count: Number of final results after fusion

@@ -252,6 +256,11 @@ class LibraryDeskClient:
                "vector_limit": vector_limit,
                "graph_limit": graph_limit,
                "web_limit": web_limit,
+                "document_limit": document_limit,
+                "volatile_limit": volatile_limit,
+                "enable_documents": document_limit > 0,
+                "enable_volatile": volatile_limit > 0,
+                "enable_web": web_limit > 0,
                "enable_reranking": enable_reranking,
                "final_result_count": final_result_count,
            },
@@ -17,20 +17,26 @@ logger = get_logger(__name__)
 async def hybrid_search(
    query: str,
    include_web: bool = True,
+    include_documents: bool = True,
+    include_volatile: bool = True,
 ) -> str:
    """
    Search across all knowledge sources using HybridRAG.

    This is the primary research tool, combining:
-    - Vector search (semantic similarity over documents)
+    - Vector search (semantic similarity over wiki pages)
    - Knowledge graph (entities and relationships)
+    - Paperless documents (📑 indexed PDFs, scans, invoices)
+    - Volatile cache (⚡ weather, news, stocks - for user's configured items)
    - Web search (current information from SearXNG)

-    Results are fused and re-ranked by relevance.
+    Results are fused and re-ranked by relevance. Volatile data gets priority when fresh.

    Args:
        query: Natural language research query
        include_web: Whether to include web results (default: True)
+        include_documents: Whether to include Paperless documents (default: True)
+        include_volatile: Whether to include volatile cache data (default: True)

    Returns:
        Formatted search results with sources and context
@@ -38,12 +44,16 @@ async def hybrid_search(
    Examples:
        hybrid_search("How does Docker orchestration work with Kubernetes?")
        hybrid_search("What projects use Neo4j?", include_web=False)
+        hybrid_search("Find my electricity invoices", include_web=False, include_volatile=False)
+        hybrid_search("What's the weather in Rotterdam?")  # May hit volatile cache
    """
    try:
        async with LibraryDeskClient() as client:
            response = await client.hybrid_search(
                query=query,
                web_limit=5 if include_web else 0,
+                document_limit=5 if include_documents else 0,
+                volatile_limit=3 if include_volatile else 0,
            )

            if not response.results:
@@ -70,6 +80,8 @@ async def hybrid_search(
                    "vector": "📄",
                    "graph": "🔗",
                    "web": "🌐",
+                    "document": "📑",
+                    "volatile": "⚡",
                }.get(result.source, "•")

                output_parts.append(
@@ -5,11 +5,13 @@ The Steward analyzes incoming requests, identifies relevant household
 capabilities, and provides focused recommendations to Tatlock (the Butler).
 This creates a two-tier architecture that prevents cognitive overload.

-Uses plain text output (not JSON) for reliability with Ollama models.
+Uses plain text output (not JSON) for reliability. Supports both Claude
+(preferred) and Ollama (fallback) backends via direct API calls.
 """
 import httpx
 from typing import Optional

+from src.anthropic.model_selector import is_claude_available, get_model_info
 from src.core.config import config
 from src.core.household_registry import get_household_registry
 from src.core.logging_config import get_logger
@@ -56,14 +58,22 @@ USER QUERY: {query}
 GUIDELINES:
 - Be conservative - only recommend truly necessary capabilities
 - Simple greetings/chat → no capabilities needed (conversational response only)
- Questions about prior conversation ("what did I say", "my name", "what we discussed") → no capabilities (Tatlock has full history)
+- Questions about prior conversation ("what did I say", "what we discussed") → no capabilities (Tatlock has full history)
 - Math/calculations → tatlock_core
 - Time/date queries → tatlock_core
+- PERSONAL MEMORY queries → biographer to recall (ALWAYS use for questions about the user themselves):
+  - "where do I live", "what's my location", "my address" → biographer to recall location
+  - "what's my name", "who am I" → biographer to recall name
+  - "what car do I drive", "my vehicle" → biographer to recall car
+  - "what do you know about me", "what have I told you" → biographer to recall or list_memories
+  - "remember that I...", "store that..." → biographer to store_insight
+  - "forget my...", "delete..." → biographer to forget_memory
+  - "my timezone", "my preferences" → biographer to recall preferences
 - Web searches, weather, news, current information → librarian with search_web
 - Read a URL or article → librarian with read_url
 - Wiki creation ("create a page about X", "add X to wiki") → librarian with smart_create
 - Wiki updates ("update the page", "add to dossier") → librarian with update
- Research queries ("find info", "what do we know about", "search for") → librarian with hybrid_search
+- Research queries about TOPICS (not about the user) → librarian with hybrid_search
 - In-depth research, knowledge synthesis, document lookup → librarian with hybrid_search
 - If conversation history is relevant, note which previous turns matter
 - Assess complexity: simple (1 tool), moderate (2-3 tools), complex (multiple steps)
@@ -75,6 +85,10 @@ COMPLEXITY: [simple/moderate/complex]
 CONTEXT: [any relevant conversation context, or "none"]

 EXAMPLES:
+- "DELEGATE: biographer to recall the user's location" (for "where do I live?")
+- "DELEGATE: biographer to recall the user's car" (for "what car do I drive?")
+- "DELEGATE: biographer to list_memories about the user" (for "what do you know about me?")
+- "DELEGATE: biographer to store_insight about user's pet" (for "remember that I have a dog named Max")
 - "DELEGATE: librarian to search_web for tomorrow's weather forecast"
 - "DELEGATE: librarian to create a wiki page about CI/CD pipelines"
 - "DELEGATE: librarian to hybrid_search for information about Docker networking"
@@ -93,22 +107,74 @@ class StewardAgent:
    Analyzes requests with full conversation context and recommends
    which household capabilities the Butler should use.

-    Uses plain text output for reliability with Ollama models.
+    Uses plain text output for reliability. Supports both Claude
+    (preferred) and Ollama (fallback) backends via direct API calls.
    """

    def __init__(self):
-        """Initialize Steward with Ollama model (same as Tatlock for VRAM efficiency)."""
+        """Initialize Steward with backend selection based on availability."""
+        # Ollama config (fallback)
        self.ollama_host = str(config.OLLAMA_HOST).rstrip('/')
-        self.model_name = config.OLLAMA_DEFAULT_MODEL
+        self.ollama_model = config.OLLAMA_DEFAULT_MODEL
+
+        # Claude config (preferred)
+        self.claude_model = config.ANTHROPIC_MODEL
+        self._anthropic_client = None
+
+        # Determine which backend to use
+        self._use_claude = config.PREFER_CLOUD_BACKEND and is_claude_available()
+
        self.timeout = 30.0  # 30 second timeout for analysis

+        model_info = get_model_info()
        logger.info(
            "steward_agent_created",
-            ollama_host=self.ollama_host,
-            model=self.model_name,
+            backend=model_info["backend"],
+            model=model_info["model"],
            timeout=self.timeout,
        )

+    def _get_anthropic_client(self):
+        """Get or create Anthropic client (lazy initialization)."""
+        if self._anthropic_client is None:
+            from anthropic import AsyncAnthropic
+            self._anthropic_client = AsyncAnthropic(api_key=config.ANTHROPIC_API_KEY)
+        return self._anthropic_client
+
+    async def _call_claude(self, system_prompt: str, user_message: str) -> str:
+        """Call Claude API directly for plain text generation."""
+        client = self._get_anthropic_client()
+
+        response = await client.messages.create(
+            model=self.claude_model,
+            max_tokens=1024,
+            system=system_prompt,
+            messages=[{"role": "user", "content": user_message}],
+            temperature=0.3,  # Lower = more consistent
+        )
+
+        return response.content[0].text.strip()
+
+    async def _call_ollama(self, prompt: str) -> str:
+        """Call Ollama API directly for plain text generation."""
+        async with httpx.AsyncClient(timeout=self.timeout) as client:
+            response = await client.post(
+                f"{self.ollama_host}/api/generate",
+                json={
+                    "model": self.ollama_model,
+                    "prompt": prompt,
+                    "stream": False,
+                    "options": {
+                        "temperature": 0.3,  # Lower = more consistent
+                        "top_p": 0.9
+                    }
+                }
+            )
+
+            response.raise_for_status()
+            result = response.json()
+            return result["response"].strip()
+
    async def analyze(
        self,
        query: str,
@@ -117,6 +183,8 @@ class StewardAgent:
        """
        Analyze query and return plain text recommendation.

+        Uses Claude if available, falls back to Ollama.
+
        Args:
            query: User's query to analyze
            conversation_history: Previous conversation turns
@@ -132,35 +200,48 @@ class StewardAgent:
        history = conversation_history or []
        prompt = build_steward_prompt(query, history)

-        logger.debug("steward_calling_ollama", query_preview=query[:100])
+        backend = "claude" if self._use_claude else "ollama"
+        logger.debug(
+            "steward_calling_llm",
+            backend=backend,
+            query_preview=query[:100],
+        )

-        # Call Ollama API directly (more reliable than PydanticAI for plain text)
-        async with httpx.AsyncClient(timeout=self.timeout) as client:
-            response = await client.post(
-                f"{self.ollama_host}/api/generate",
-                json={
-                    "model": self.model_name,
-                    "prompt": prompt,
-                    "stream": False,
-                    "options": {
-                        "temperature": 0.3,  # Lower = more consistent
-                        "top_p": 0.9
-                    }
-                }
-            )
-
-            response.raise_for_status()
-            result = response.json()
-
-            analysis_text = result["response"].strip()
+        try:
+            if self._use_claude:
+                # For Claude, split into system + user message
+                # The prompt contains both, but Claude prefers explicit system
+                analysis_text = await self._call_claude(
+                    system_prompt="You are the Steward of the household, advising the Butler (Tatlock) on which capabilities to use. Be concise and specific.",
+                    user_message=prompt,
+                )
+            else:
+                analysis_text = await self._call_ollama(prompt)

            logger.debug(
                "steward_analysis_received",
-                text_preview=analysis_text[:150]
+                backend=backend,
+                text_preview=analysis_text[:150],
            )

            return analysis_text

+        except Exception as e:
+            # If Claude fails, try Ollama as fallback
+            if self._use_claude:
+                logger.warning(
+                    "steward_claude_fallback",
+                    error=str(e),
+                )
+                analysis_text = await self._call_ollama(prompt)
+                logger.debug(
+                    "steward_analysis_received",
+                    backend="ollama_fallback",
+                    text_preview=analysis_text[:150],
+                )
+                return analysis_text
+            raise
+

 # Global Steward instance
 _steward_agent = None
@@ -236,7 +236,9 @@ async def _prefetch_memory_context(user_request: str) -> dict[str, Any]:
    # Location-related queries
    if any(word in request_lower for word in [
        "weather", "temperature", "forecast", "nearby", "local",
-        "directions", "distance", "map", "here"
+        "directions", "distance", "map", "here",
+        # Direct location questions
+        "live", "where", "home", "reside", "location", "address",
    ]):
        profile_keys.append("location")

@@ -138,10 +138,7 @@ class TatlockAgent(AgentInterface):
    """

    def __init__(self):
-        """Initialize Tatlock configuration (lazy agent creation)."""
-        # Store Ollama configuration
-        self.ollama_host = str(config.OLLAMA_HOST)
-        self.model_name = config.OLLAMA_DEFAULT_MODEL
+        """Initialize Tatlock (lazy agent creation)."""
        self._agent = None  # Lazy initialization

    def _ensure_agent(self):
@@ -149,30 +146,21 @@ class TatlockAgent(AgentInterface):
        if self._agent is not None:
            return

+        from src.anthropic.model_selector import get_model, get_model_info
+
+        model_info = get_model_info()
        logger.info(
            "tatlock_agent_initializing",
-            ollama_host=self.ollama_host,
-            model=self.model_name,
+            backend=model_info["backend"],
+            model=model_info["model"],
        )

-        # Import required classes for Ollama configuration
-        from pydantic_ai.models.openai import OpenAIChatModel
-        from src.ollama.provider import get_ollama_provider
+        # Get best available model (Claude if available, else Ollama)
+        model = get_model()

-        # PydanticAI expects Ollama base URL to end with /v1
-        # Remove trailing slash from ollama_host if present
-        clean_host = self.ollama_host.rstrip('/')
-        base_url = f"{clean_host}/v1"
-
-        # Create Ollama model with provider
-        ollama_model = OpenAIChatModel(
-            model_name=self.model_name,
-            provider=get_ollama_provider()
-        )
-
-        # Create PydanticAI agent with Ollama model
+        # Create PydanticAI agent
        self._agent = Agent(
-            ollama_model,
+            model,
            system_prompt=TATLOCK_SYSTEM_PROMPT,
        )

@@ -461,8 +449,7 @@ class TatlockAgent(AgentInterface):
            ...     tool_tracker=tracker,
            ... )
        """
-        from pydantic_ai.models.openai import OpenAIChatModel
-        from src.ollama.provider import get_ollama_provider
+        from src.anthropic.model_selector import get_model

        logger.info(
            "tatlock_run_with_scoped_tools",
@@ -473,18 +460,12 @@ class TatlockAgent(AgentInterface):

        # Create a fresh agent instance with scoped tools only
        # This ensures Tatlock can ONLY use tools recommended by the Steward
-        clean_host = self.ollama_host.rstrip('/')
-        base_url = f"{clean_host}/v1"
-
-        ollama_model = OpenAIChatModel(
-            model_name=self.model_name,
-            provider=get_ollama_provider()
-        )
+        model = get_model()

        # Create agent with scoped tools
        # Tools from household registry are already PydanticAI Tool objects
        scoped_agent = Agent(
-            ollama_model,
+            model,
            system_prompt=TATLOCK_SYSTEM_PROMPT,
            tools=scoped_tools,  # Pass tools directly to Agent constructor
        )
@@ -513,13 +494,13 @@ class TatlockAgent(AgentInterface):
                )

        # Run with scoped tools and tracker
-        # Force tool_choice: required to make LLM actually call tools
-        from pydantic_ai.settings import ModelSettings
+        # Force tool_choice to make LLM actually call tools
+        from src.anthropic.model_selector import get_tool_choice_settings
        result = await scoped_agent.run(
            enriched_message,
            message_history=pydantic_history if pydantic_history else None,
            deps=tool_tracker,
-            model_settings=ModelSettings(extra_body={"tool_choice": "required"})
+            model_settings=get_tool_choice_settings(),
        )

        logger.info(
@@ -555,8 +536,7 @@ class TatlockAgent(AgentInterface):
        Yields:
            Text chunks from the streaming response
        """
-        from pydantic_ai.models.openai import OpenAIChatModel
-        from src.ollama.provider import get_ollama_provider
+        from src.anthropic.model_selector import get_model

        logger.info(
            "tatlock_run_with_scoped_tools_stream",
@@ -566,17 +546,11 @@ class TatlockAgent(AgentInterface):
        )

        # Create a fresh agent instance with scoped tools only
-        clean_host = self.ollama_host.rstrip('/')
-        base_url = f"{clean_host}/v1"
-
-        ollama_model = OpenAIChatModel(
-            model_name=self.model_name,
-            provider=get_ollama_provider()
-        )
+        model = get_model()

        # Create agent with scoped tools
        scoped_agent = Agent(
-            ollama_model,
+            model,
            system_prompt=TATLOCK_SYSTEM_PROMPT,
            tools=scoped_tools,
        )
@@ -650,9 +624,6 @@ class TatlockAgent(AgentInterface):
                - tool_outputs: Dict mapping tool names to their outputs
                - raw_output: The agent's raw text output
        """
-        from pydantic_ai.models.openai import OpenAIChatModel
-        from src.ollama.provider import get_ollama_provider
-        from pydantic_ai.settings import ModelSettings
        from pydantic_ai.messages import (
            ModelRequest,
            ModelResponse,
@@ -661,6 +632,7 @@ class TatlockAgent(AgentInterface):
            ToolCallPart,
            ToolReturnPart,
        )
+        from src.anthropic.model_selector import get_model

        logger.info(
            "tatlock_orchestrate_tool_calls",
@@ -680,17 +652,11 @@ class TatlockAgent(AgentInterface):
        )

        # Create a fresh agent instance with scoped tools only
-        clean_host = self.ollama_host.rstrip('/')
-        base_url = f"{clean_host}/v1"
-
-        ollama_model = OpenAIChatModel(
-            model_name=self.model_name,
-            provider=get_ollama_provider()
-        )
+        model = get_model()

        # Create agent with scoped tools
        scoped_agent = Agent(
-            ollama_model,
+            model,
            system_prompt=TATLOCK_SYSTEM_PROMPT,
            tools=scoped_tools,
        )
@@ -717,11 +683,12 @@ class TatlockAgent(AgentInterface):
                )

        # Run with scoped tools and tracker
+        from src.anthropic.model_selector import get_tool_choice_settings
        result = await scoped_agent.run(
            enriched_message,
            message_history=pydantic_history if pydantic_history else None,
            deps=tool_tracker,
-            model_settings=ModelSettings(extra_body={"tool_choice": "required"})
+            model_settings=get_tool_choice_settings(),
        )

        # Extract tool calls and results from the agent's messages
@@ -799,9 +766,8 @@ class TatlockAgent(AgentInterface):
        Returns:
            str: Butler-toned response synthesized from all results
        """
-        from pydantic_ai.models.openai import OpenAIChatModel
-        from src.ollama.provider import get_ollama_provider
        from pydantic_ai.messages import ModelRequest, ModelResponse, UserPromptPart, TextPart
+        from src.anthropic.model_selector import get_model

        logger.info(
            "tatlock_synthesize_from_results",
@@ -848,17 +814,11 @@ class TatlockAgent(AgentInterface):
        synthesis_prompt = "\n".join(synthesis_parts)

        # Create synthesis agent (no tools needed)
-        clean_host = self.ollama_host.rstrip('/')
-        base_url = f"{clean_host}/v1"
-
-        ollama_model = OpenAIChatModel(
-            model_name=self.model_name,
-            provider=get_ollama_provider()
-        )
+        model = get_model()

        # Synthesis agent uses butler prompt but no tools
        synthesis_agent = Agent(
-            ollama_model,
+            model,
            system_prompt=TATLOCK_SYSTEM_PROMPT,
            # No tools for synthesis phase
        )
@@ -0,0 +1,19 @@
+"""
+Anthropic/Claude integration module.
+
+Provides model selection with automatic fallback between Claude and Ollama.
+"""
+
+from src.anthropic.model_selector import (
+    check_claude_health,
+    get_model,
+    get_tool_choice_settings,
+    is_claude_available,
+)
+
+__all__ = [
+    "check_claude_health",
+    "get_model",
+    "get_tool_choice_settings",
+    "is_claude_available",
+]
@@ -0,0 +1,169 @@
+"""
+Model selector for Claude/Ollama backend switching.
+
+Provides automatic model selection with Claude as preferred backend
+and Ollama as offline fallback.
+"""
+
+from typing import Union
+
+from pydantic_ai.models.anthropic import AnthropicModel
+from pydantic_ai.models.openai import OpenAIChatModel
+from pydantic_ai.providers.anthropic import AnthropicProvider
+
+from src.core.config import config
+from src.core.logging_config import get_logger
+
+logger = get_logger(__name__)
+
+# Cached health check result (set once at startup)
+_claude_available: bool | None = None
+
+
+async def check_claude_health() -> bool:
+    """
+    Check if Claude API is reachable and working.
+
+    This should be called once at application startup.
+    The result is cached in `_claude_available`.
+
+    Returns:
+        True if Claude API is accessible, False otherwise.
+    """
+    global _claude_available
+
+    # No API key configured - Claude not available
+    if not config.ANTHROPIC_API_KEY:
+        logger.info(
+            "claude_health_check_skipped",
+            reason="no_api_key",
+        )
+        _claude_available = False
+        return False
+
+    try:
+        from anthropic import AsyncAnthropic
+
+        client = AsyncAnthropic(api_key=config.ANTHROPIC_API_KEY)
+
+        # Minimal API call to verify connectivity
+        # Using a tiny max_tokens to minimize cost
+        await client.messages.create(
+            model=config.ANTHROPIC_MODEL,
+            max_tokens=1,
+            messages=[{"role": "user", "content": "hi"}],
+        )
+
+        _claude_available = True
+        logger.info(
+            "claude_health_check_passed",
+            model=config.ANTHROPIC_MODEL,
+        )
+        return True
+
+    except Exception as e:
+        _claude_available = False
+        logger.warning(
+            "claude_health_check_failed",
+            error=str(e),
+            model=config.ANTHROPIC_MODEL,
+        )
+        return False
+
+
+def is_claude_available() -> bool:
+    """
+    Check if Claude is available (from cached health check result).
+
+    Returns:
+        True if Claude API was reachable at startup, False otherwise.
+
+    Note:
+        Returns False if health check hasn't been run yet.
+        Call `check_claude_health()` at startup first.
+    """
+    return _claude_available is True
+
+
+def get_model(prefer_cloud: bool | None = None) -> Union[AnthropicModel, OpenAIChatModel]:
+    """
+    Get the best available model.
+
+    Returns Claude if available and preferred, otherwise Ollama.
+
+    Args:
+        prefer_cloud: Override config.PREFER_CLOUD_BACKEND for this call.
+                     If None, uses the config value.
+
+    Returns:
+        PydanticAI model instance (AnthropicModel or OpenAIChatModel).
+
+    Example:
+        >>> model = get_model()
+        >>> agent = Agent(model, system_prompt="...")
+    """
+    # Determine preference
+    use_cloud = prefer_cloud if prefer_cloud is not None else config.PREFER_CLOUD_BACKEND
+
+    # Use Claude if available and preferred
+    if use_cloud and is_claude_available():
+        logger.debug(
+            "model_selected",
+            backend="claude",
+            model=config.ANTHROPIC_MODEL,
+        )
+        return AnthropicModel(
+            model_name=config.ANTHROPIC_MODEL,
+            provider=AnthropicProvider(api_key=config.ANTHROPIC_API_KEY),
+        )
+
+    # Fall back to Ollama
+    from src.ollama.provider import get_ollama_provider
+
+    logger.debug(
+        "model_selected",
+        backend="ollama",
+        model=config.OLLAMA_DEFAULT_MODEL,
+        reason="fallback" if use_cloud else "preferred_local",
+    )
+    return OpenAIChatModel(
+        model_name=config.OLLAMA_DEFAULT_MODEL,
+        provider=get_ollama_provider(),
+    )
+
+
+def get_tool_choice_settings() -> 'ModelSettings':
+    """
+    Get model_settings for forcing tool calls on the first request.
+
+    For Claude: PydanticAI handles tool_choice natively, so no extra_body needed.
+    For Ollama: Pass tool_choice="required" via extra_body to force tool calling.
+    """
+    from pydantic_ai.settings import ModelSettings
+
+    if is_claude_available() and config.PREFER_CLOUD_BACKEND:
+        # PydanticAI's Anthropic model handles tool_choice internally
+        return ModelSettings()
+    else:
+        # Ollama needs explicit tool_choice via extra_body
+        return ModelSettings(extra_body={"tool_choice": "required"})
+
+
+def get_model_info() -> dict:
+    """
+    Get information about the current model configuration.
+
+    Useful for health checks and debugging.
+
+    Returns:
+        Dict with backend, model name, and availability info.
+    """
+    use_cloud = config.PREFER_CLOUD_BACKEND and is_claude_available()
+
+    return {
+        "backend": "claude" if use_cloud else "ollama",
+        "model": config.ANTHROPIC_MODEL if use_cloud else config.OLLAMA_DEFAULT_MODEL,
+        "claude_available": is_claude_available(),
+        "claude_configured": bool(config.ANTHROPIC_API_KEY),
+        "prefer_cloud": config.PREFER_CLOUD_BACKEND,
+    }
@@ -7,7 +7,7 @@ import logging
 from typing import AsyncGenerator

 from fastapi import APIRouter
-from sse_starlette.sse import EventSourceResponse
+from starlette.responses import StreamingResponse

 from src.chat import service
 from src.chat.schemas import (
@@ -22,47 +22,51 @@ router = APIRouter(prefix="/chat", tags=["chat"])

 async def _stream_response(
    request: ChatCompletionRequest,
-) -> AsyncGenerator[dict, None]:
+) -> AsyncGenerator[str, None]:
    """
    Generate SSE stream for chat completion.

-    EventSourceResponse adds "data: " prefix automatically.
-    We just yield the dict/string content.
+    Yields raw SSE-formatted strings matching OpenAI's format exactly:
+    data: {json}\n\n
    """
    try:
        async for chunk in service.create_chat_completion_stream(request):
-            # Yield dict - EventSourceResponse will format as SSE
-            yield {"data": chunk.model_dump_json()}
+            yield f"data: {chunk.model_dump_json()}\n\n"

-        # Send [DONE] message
-        yield {"data": "[DONE]"}
+        yield "data: [DONE]\n\n"

    except Exception as e:
        logger.error(f"Error in streaming response: {e}")
-        error_data = {"error": {"message": str(e), "type": "internal_error"}}
-        yield {"data": json.dumps(error_data)}
+        error_data = json.dumps({"error": {"message": str(e), "type": "internal_error"}})
+        yield f"data: {error_data}\n\n"


@router.post("/completions", response_model=ChatCompletionResponse)
 async def create_chat_completion(
    request: ChatCompletionRequest,
-) -> ChatCompletionResponse | EventSourceResponse:
+) -> ChatCompletionResponse | StreamingResponse:
    """
    Create chat completion (OpenAI-compatible).
-    
+
    Supports both regular and streaming responses.
-    Currently returns mock lorem ipsum responses.
-    
+
    Args:
        request: Chat completion request
-        
+
    Returns:
        Chat completion response or SSE stream
    """
    logger.info(f"Chat completion request for model: {request.model}")
-    
+
    if request.stream:
        logger.info("Streaming response requested")
-        return EventSourceResponse(_stream_response(request))
-    
+        return StreamingResponse(
+            _stream_response(request),
+            media_type="text/event-stream",
+            headers={
+                "Cache-Control": "no-store",
+                "X-Accel-Buffering": "no",
+            },
+        )
+
    return await service.create_chat_completion(request)
@@ -64,7 +64,21 @@ class Config(BaseSettings):
    API_PORT: int = Field(default=8000, description="API port")
    API_PREFIX: str = Field(default="/v1", description="API route prefix")
    
-    # Ollama Configuration
+    # Anthropic Configuration (Claude - preferred backend)
+    ANTHROPIC_API_KEY: str | None = Field(
+        default=None,
+        description="Anthropic API key for Claude access"
+    )
+    ANTHROPIC_MODEL: str = Field(
+        default="claude-sonnet-4-20250514",
+        description="Claude model to use"
+    )
+    PREFER_CLOUD_BACKEND: bool = Field(
+        default=True,
+        description="Prefer Claude over Ollama when available"
+    )
+
+    # Ollama Configuration (local fallback)
    OLLAMA_HOST: HttpUrl = Field(
        default="http://localhost:11434",
        description="Ollama server URL"
@@ -9,6 +9,7 @@ from src.agents.biographer import register_biographer
 from src.agents.housekeeper import register_housekeeper
 from src.agents.librarian import register_librarian
 from src.agents.tatlock_core import TATLOCK_CORE_CAPABILITY, tatlock_core_tools
+from src.anthropic.model_selector import check_claude_health, get_model_info
 from src.core.household_registry import get_household_registry
 from src.core.logging_config import get_logger

@@ -81,19 +82,29 @@ def register_household_members():
    )


-def initialize_application():
+async def initialize_application():
    """
    Initialize the application.

    Performs all startup tasks:
-    1. Register household members
-    2. (Future) Initialize connections
-    3. (Future) Load configuration
+    1. Check Claude API health (for backend selection)
+    2. Register household members
+    3. (Future) Initialize connections

    This should be called once during application startup.
    """
    logger.info("application_initialization_starting")

+    # Check Claude API health for backend selection
+    await check_claude_health()
+    model_info = get_model_info()
+    logger.info(
+        "model_backend_configured",
+        backend=model_info["backend"],
+        model=model_info["model"],
+        claude_available=model_info["claude_available"],
+    )
+
    # Register household members
    register_household_members()

@@ -44,14 +44,16 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
        app_name=config.APP_NAME,
        version=config.APP_VERSION,
        environment=config.ENVIRONMENT.value,
+        prefer_cloud=config.PREFER_CLOUD_BACKEND,
+        anthropic_model=config.ANTHROPIC_MODEL,
        ollama_host=str(config.OLLAMA_HOST),
        ollama_model=config.OLLAMA_DEFAULT_MODEL,
        redis_url=config.redis_memory_url,
        log_format=config.log_format,
    )

-    # Initialize application (register household members, etc.)
-    initialize_application()
+    # Initialize application (check Claude health, register household members, etc.)
+    await initialize_application()

    yield

@@ -651,16 +651,6 @@ async def create_response_with_steward(request: ResponseRequest) -> Response:
        # Build response output items
        output_items = []

-        # Add Steward reasoning as a reasoning output item
-        output_items.append(ReasoningOutputItem(
-            id=f"reasoning_{generate_id()}",
-            summary=[
-                "🎩 Steward's Analysis:",
-                enriched.steward_reasoning,
-            ],
-            status="completed"
-        ))
-
        # Add Tatlock's message
        output_items.append(MessageOutputItem(
            id=f"msg_{generate_id()}",
@@ -166,26 +166,6 @@ class StreamingCoordinator:
                conversation_id=conversation_id,
            )

-            # Stream Steward's analysis as reasoning summary
-            steward_lines = enriched.steward_reasoning.split('\n')
-            for line in steward_lines:
-                if line.strip():
-                    yield ReasoningSummaryDelta(delta=line + "\n")
-                    await asyncio.sleep(0.05)
-
-            yield ReasoningSummaryDone()
-
-            # Add Steward reasoning to output items
-            reasoning_item = ReasoningOutputItem(
-                id=f"reasoning_{generate_id()}",
-                summary=[
-                    "🎩 Steward's Analysis:",
-                    enriched.steward_reasoning,
-                ],
-                status="completed"
-            )
-            output_items.append(reasoning_item)
-
            # Initialize tool tracker
            tracker = ToolCallTracker(
                recommended_capabilities=enriched.recommendation.recommended_capabilities,
@@ -1,352 +0,0 @@
-"""
-Tests for benchmark storage.
-
-Tests performance tracking, Redis storage, and analytics features.
-"""
-import json
-from datetime import datetime, timedelta, timezone
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-from src.core.benchmarks import (
-    BenchmarkStore,
-    PerformanceBenchmark,
-    get_benchmark_store,
-)
-
-
-class TestPerformanceBenchmark:
-    """Test PerformanceBenchmark model."""
-
-    def test_benchmark_creation(self):
-        """Test creating a performance benchmark."""
-        benchmark = PerformanceBenchmark(
-            operation="steward_analysis",
-            duration_seconds=1.23,
-            success=True,
-            recommendation_count=3,
-        )
-
-        assert benchmark.operation == "steward_analysis"
-        assert benchmark.duration_seconds == 1.23
-        assert benchmark.success is True
-        assert benchmark.recommendation_count == 3
-        assert isinstance(benchmark.timestamp, datetime)
-
-    def test_benchmark_with_tool_fields(self):
-        """Test benchmark with tool-specific fields."""
-        benchmark = PerformanceBenchmark(
-            operation="tool_call",
-            duration_seconds=0.5,
-            success=True,
-            tool_name="calculate",
-            was_recommended=True,
-            was_actually_used=True,
-        )
-
-        assert benchmark.tool_name == "calculate"
-        assert benchmark.was_recommended is True
-        assert benchmark.was_actually_used is True
-
-    def test_benchmark_to_redis_dict(self):
-        """Test conversion to Redis dict."""
-        benchmark = PerformanceBenchmark(
-            operation="test_op",
-            duration_seconds=1.0,
-            success=True,
-            metadata={"key": "value"},
-        )
-
-        redis_dict = benchmark.to_redis_dict()
-        assert redis_dict["operation"] == "test_op"
-        assert redis_dict["duration_seconds"] == 1.0
-        assert redis_dict["success"] == "True"  # Booleans stored as strings in Redis
-        assert isinstance(redis_dict["timestamp"], str)
-        assert isinstance(redis_dict["metadata"], str)
-
-    def test_benchmark_from_redis_dict(self):
-        """Test reconstruction from Redis dict."""
-        now = datetime.now(timezone.utc)
-        redis_dict = {
-            "timestamp": now.isoformat(),
-            "operation": "test_op",
-            "duration_seconds": 1.5,
-            "success": "True",  # Booleans stored as strings in Redis
-            "metadata": json.dumps({"test": "data"}),
-            "recommendation_count": None,
-            "confidence": None,
-            "tool_name": None,
-            "was_recommended": None,
-            "was_actually_used": None,
-            "conversation_id": None,
-        }
-
-        benchmark = PerformanceBenchmark.from_redis_dict(redis_dict)
-        assert benchmark.operation == "test_op"
-        assert benchmark.duration_seconds == 1.5
-        assert benchmark.success is True  # Converted back to bool
-        assert benchmark.metadata == {"test": "data"}
-
-
-class TestBenchmarkStore:
-    """Test BenchmarkStore functionality."""
-
-    @pytest.fixture
-    def mock_redis(self):
-        """Create mock Redis client."""
-        mock = AsyncMock()
-        mock.hset = AsyncMock()
-        mock.expire = AsyncMock()
-        mock.zadd = AsyncMock()
-        mock.zrevrangebyscore = AsyncMock(return_value=[])
-        mock.hgetall = AsyncMock(return_value={})
-        mock.aclose = AsyncMock()
-        return mock
-
-    @pytest.fixture
-    def store(self, mock_redis):
-        """Create benchmark store with mock Redis."""
-        return BenchmarkStore(redis_client=mock_redis)
-
-    @pytest.mark.asyncio
-    async def test_record_benchmark(self, store, mock_redis):
-        """Test recording a benchmark."""
-        benchmark = PerformanceBenchmark(
-            operation="test_op",
-            duration_seconds=1.0,
-            success=True,
-        )
-
-        await store.record(benchmark)
-
-        # Verify Redis calls
-        mock_redis.hset.assert_called_once()
-        mock_redis.expire.assert_called()
-        mock_redis.zadd.assert_called_once()
-
-    @pytest.mark.asyncio
-    async def test_record_benchmark_disabled(self, mock_redis):
-        """Test recording when benchmarks are disabled."""
-        with patch("src.core.benchmarks.config.ENABLE_BENCHMARKS", False):
-            store = BenchmarkStore(redis_client=mock_redis)
-            benchmark = PerformanceBenchmark(
-                operation="test_op",
-                duration_seconds=1.0,
-                success=True,
-            )
-
-            await store.record(benchmark)
-
-            # Should not call Redis
-            mock_redis.hset.assert_not_called()
-
-    @pytest.mark.asyncio
-    async def test_record_benchmark_handles_errors(self, store, mock_redis):
-        """Test recording handles Redis errors gracefully."""
-        mock_redis.hset.side_effect = Exception("Redis error")
-
-        benchmark = PerformanceBenchmark(
-            operation="test_op",
-            duration_seconds=1.0,
-            success=True,
-        )
-
-        # Should not raise exception
-        await store.record(benchmark)
-
-    @pytest.mark.asyncio
-    async def test_query_benchmarks(self, store, mock_redis):
-        """Test querying benchmarks."""
-        # Setup mock data
-        now = datetime.now(timezone.utc)
-        mock_key = f"benchmark:test_op:{int(now.timestamp() * 1000)}"
-        mock_redis.zrevrangebyscore.return_value = [mock_key]
-
-        # Mock hgetall to return proper data (booleans as strings, like Redis)
-        mock_redis.hgetall.return_value = {
-            "timestamp": now.isoformat(),
-            "operation": "test_op",
-            "duration_seconds": 1.5,  # Numeric, not string
-            "success": "True",  # Booleans stored as strings in Redis
-            "metadata": "{}",
-            "recommendation_count": None,
-            "confidence": None,
-            "tool_name": None,
-            "was_recommended": None,
-            "was_actually_used": None,
-            "conversation_id": None,
-        }
-
-        results = await store.query("test_op", limit=10)
-
-        assert len(results) == 1
-        assert results[0].operation == "test_op"
-        mock_redis.zrevrangebyscore.assert_called_once()
-
-    @pytest.mark.asyncio
-    async def test_query_with_time_range(self, store, mock_redis):
-        """Test querying with time range."""
-        now = datetime.now(timezone.utc)
-        start_time = now - timedelta(hours=1)
-        end_time = now
-
-        await store.query("test_op", start_time=start_time, end_time=end_time)
-
-        # Verify time range was converted to timestamps
-        call_args = mock_redis.zrevrangebyscore.call_args
-        assert call_args is not None
-
-    @pytest.mark.asyncio
-    async def test_query_disabled_benchmarks(self, mock_redis):
-        """Test querying when benchmarks are disabled."""
-        with patch("src.core.benchmarks.config.ENABLE_BENCHMARKS", False):
-            store = BenchmarkStore(redis_client=mock_redis)
-            results = await store.query("test_op")
-            assert results == []
-
-    @pytest.mark.asyncio
-    async def test_query_handles_errors(self, store, mock_redis):
-        """Test query handles errors gracefully."""
-        mock_redis.zrevrangebyscore.side_effect = Exception("Redis error")
-
-        results = await store.query("test_op")
-        assert results == []
-
-    @pytest.mark.asyncio
-    async def test_get_statistics(self, store, mock_redis):
-        """Test getting statistics."""
-        # Setup mock data with multiple benchmarks
-        now = datetime.now(timezone.utc)
-        mock_keys = [
-            f"benchmark:test_op:{int((now - timedelta(seconds=i)).timestamp() * 1000)}"
-            for i in range(3)
-        ]
-        mock_redis.zrevrangebyscore.return_value = mock_keys
-
-        # Return different durations and success values
-        benchmarks_data = [
-            {"duration_seconds": "1.0", "success": "True"},
-            {"duration_seconds": "2.0", "success": "True"},
-            {"duration_seconds": "3.0", "success": "False"},
-        ]
-
-        async def mock_hgetall(key):
-            idx = mock_keys.index(key)
-            data = benchmarks_data[idx]
-            return {
-                "timestamp": now.isoformat(),
-                "operation": "test_op",
-                "duration_seconds": float(data["duration_seconds"]),
-                "success": data["success"],  # Pass string through, from_redis_dict converts
-                "metadata": "{}",
-                "recommendation_count": None,
-                "confidence": None,
-                "tool_name": None,
-                "was_recommended": None,
-                "was_actually_used": None,
-                "conversation_id": None,
-            }
-
-        mock_redis.hgetall.side_effect = mock_hgetall
-
-        stats = await store.get_statistics("test_op")
-
-        assert stats["count"] == 3
-        assert stats["avg_duration"] == 2.0  # (1 + 2 + 3) / 3
-        assert stats["min_duration"] == 1.0
-        assert stats["max_duration"] == 3.0
-        assert stats["success_rate"] == pytest.approx(66.67, rel=0.01)
-        assert stats["total_successes"] == 2
-        assert stats["total_failures"] == 1
-
-    @pytest.mark.asyncio
-    async def test_get_statistics_empty(self, store, mock_redis):
-        """Test statistics with no data."""
-        mock_redis.zrevrangebyscore.return_value = []
-
-        stats = await store.get_statistics("test_op")
-
-        assert stats["count"] == 0
-        assert stats["avg_duration"] == 0.0
-        assert stats["success_rate"] == 0.0
-
-    @pytest.mark.asyncio
-    async def test_get_tool_accuracy(self, store, mock_redis):
-        """Test tool accuracy calculation."""
-        # Setup mock data
-        now = datetime.now(timezone.utc)
-        mock_keys = [
-            f"benchmark:tool_call:{int((now - timedelta(seconds=i)).timestamp() * 1000)}"
-            for i in range(4)
-        ]
-        mock_redis.zrevrangebyscore.return_value = mock_keys
-
-        # Different combinations of recommended/used
-        tool_data = [
-            {"was_recommended": "True", "was_actually_used": "True"},  # Good
-            {"was_recommended": "True", "was_actually_used": "True"},  # Good
-            {"was_recommended": "False", "was_actually_used": "True"},  # Missed
-            {"was_recommended": "True", "was_actually_used": "False"},  # Not used
-        ]
-
-        async def mock_hgetall(key):
-            idx = mock_keys.index(key)
-            data = tool_data[idx]
-            return {
-                "timestamp": now.isoformat(),
-                "operation": "tool_call",
-                "duration_seconds": 1.0,
-                "success": "True",  # Booleans stored as strings in Redis
-                "metadata": "{}",
-                "recommendation_count": None,
-                "confidence": None,
-                "tool_name": "test_tool",
-                "conversation_id": None,
-                "was_recommended": data["was_recommended"],  # Already strings
-                "was_actually_used": data["was_actually_used"],  # Already strings
-            }
-
-        mock_redis.hgetall.side_effect = mock_hgetall
-
-        accuracy = await store.get_tool_accuracy()
-
-        assert accuracy["total_calls"] == 4
-        assert accuracy["total_used"] == 3
-        assert accuracy["recommended_and_used"] == 2
-        assert accuracy["not_recommended_but_used"] == 1
-        assert accuracy["precision"] == pytest.approx(66.67, rel=0.01)
-
-    @pytest.mark.asyncio
-    async def test_get_tool_accuracy_empty(self, store, mock_redis):
-        """Test tool accuracy with no data."""
-        mock_redis.zrevrangebyscore.return_value = []
-
-        accuracy = await store.get_tool_accuracy()
-
-        assert accuracy["total_calls"] == 0
-        assert accuracy["precision"] == 0.0
-
-    @pytest.mark.asyncio
-    async def test_close(self, store, mock_redis):
-        """Test closing the store."""
-        await store.close()
-        mock_redis.aclose.assert_called_once()
-
-        # Client should be None after close
-        assert store._client is None
-
-
-class TestGlobalBenchmarkStore:
-    """Test global benchmark store instance."""
-
-    def test_get_benchmark_store(self):
-        """Test getting global store instance."""
-        store = get_benchmark_store()
-        assert isinstance(store, BenchmarkStore)
-
-    def test_get_benchmark_store_singleton(self):
-        """Test store is singleton."""
-        store1 = get_benchmark_store()
-        store2 = get_benchmark_store()
-        assert store1 is store2
Author	SHA1	Message	Date
jpmschweitzerandClaude Opus 4.5	e469746f75	fix: use StreamingResponse for chat completions SSE Build and Push / release (push) Successful in 2s Details Build and Push / build (push) Successful in 1m22s Details sse_starlette's EventSourceResponse added \r\n line endings that Open WebUI couldn't parse. Switched to plain StreamingResponse with manual SSE formatting matching OpenAI's exact format. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>	2026-02-05 12:29:08 +01:00
jpmschweitzerandClaude Opus 4.5	31e7884d8f	fix: remove Steward analysis from user-visible reasoning Build and Push / release (push) Successful in 3s Details Build and Push / build (push) Successful in 1m21s Details The Steward's internal routing analysis (DELEGATE, COMPLEXITY, etc.) was being exposed in <think> blocks. This is implementation detail, not useful reasoning for the user. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>	2026-02-05 12:19:05 +01:00
jpmschweitzerandClaude Opus 4.5	e15def607d	fix: remove extra_body tool_choice hack for Claude backend Build and Push / build (push) Successful in 1m57s Details Build and Push / release (push) Successful in 3s Details PydanticAI handles tool_choice natively for Anthropic. The extra_body hack caused an infinite tool call loop where Claude kept calling the same tool because tool_choice was forced to "any". Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>	2026-02-05 11:51:42 +01:00
jpmschweitzerandClaude Opus 4.5	6dd1c2e2a9	fix: trigger CI on version tag push instead of release event Changed workflow trigger from release:published to push:tags:v[0-9]* so that pushing a version tag triggers the build pipeline. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>	2026-02-05 09:52:43 +01:00
jpmschweitzerandClaude Opus 4.5	3617218359	chore: release v2.0.1 Build and Push / release (release) Failing after 3s Details Build and Push / build (release) Successful in 1m21s Details Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>	2026-02-05 09:49:46 +01:00
jpmschweitzerandClaude Opus 4.5	c7a4012831	fix: use AnthropicProvider to pass api_key to PydanticAI model AnthropicModel doesn't accept api_key directly; it must be passed through an AnthropicProvider instance. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>	2026-02-05 09:47:02 +01:00
jpmschweitzerandClaude Opus 4.5	496f37a538	feat: add Claude backend with automatic Ollama fallback (Claudification Phase 1) Build and Push / release (release) Failing after 6s Details Build and Push / build (release) Successful in 3m5s Details All agents now prefer Claude API when ANTHROPIC_API_KEY is configured, with automatic fallback to Ollama when offline or unconfigured. New src/anthropic/ module provides model selection via get_model() factory. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>	2026-02-05 07:19:29 +01:00
jpmschweitzer	5d23bcae79	auto release/build on version tag	2026-01-03 20:39:51 +01:00
jpmschweitzerandClaude Opus 4.5	62eac3eb61	feat: integrate Paperless documents and volatile cache into Librarian Build and Push / build (release) Successful in 54s Details - Add Paperless document search to HybridRAG pipeline - Add volatile cache (weather, forecast, news, stocks) to HybridRAG - Add include_documents and include_volatile params to hybrid_search - Add 📑 and ⚡ icons for document/volatile sources - Update Librarian prompt with new data source awareness - Fix Biographer routing: personal memory queries now route correctly - Add location keywords to Steward pre-fetch logic 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>	2025-12-30 13:31:23 +01:00