fix: Ollama message sanitization and streaming think slugs

- Fix `invalid message content type: <nil>` error from Ollama
- Create TatlockOllamaProvider that sanitizes messages (null → "")
- Update all agents to use sanitized provider
- Fix repeating think messages by adding ReasoningSummaryDone signal

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

AGENTS.md

@@ -27,7 +27,17 @@ This document contains instructions and documentation references for AI assistan
 * **git.schweitz.net**: Access via `http://localhost:3002` (direct Gitea) to bypass Authentik SSO
   * Example: `curl http://localhost:3002/jpmschweitzer/library-desk/raw/branch/main/README.md`
   * Public repos are readable without authentication
-  * Related repos: `library-desk`, `scheduler`
+  * Related repos: `library-desk`, `scheduler`, `core-api`, `portainer-core`
+
+### 🐳 Deployment & Infrastructure
+* **Full stack documentation**: Available in the `portainer-core` repo
+  * Access: `curl http://localhost:3002/jpmschweitzer/portainer-core/raw/branch/main/CONTAINERS.md`
+  * Contains: All service ports, URLs, Redis DB allocations, external domains
+* **Tatlock deployment**:
+  * LAN: `http://192.168.86.149:8000`
+  * External: `tatlock.schweitz.net` (behind Authentik SSO)
+  * Redis DBs: 1 (memory), 6 (benchmarks)
+* **Health check**: `curl http://192.168.86.149:8000/health`
 
 ### 🛡️ Git Discipline
 * **NEVER commit to `main` or `master` directly.** Always create a feature branch: `feature/your-feature-name` or `fix/issue-description`.

CHANGELOG.md

@@ -7,6 +7,122 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [1.8.1] - 2025-12-16
+
+### Fixed
+
+#### Ollama Message Sanitization
+- **Fixed `invalid message content type: <nil>` error** from Ollama
+- Created custom `TatlockOllamaProvider` that sanitizes messages before sending to Ollama
+- Ollama rejects assistant messages with `content: null` (tool-only messages from PydanticAI)
+- Provider converts `null` content to empty string `""` for compatibility
+- Updated all agents (Librarian, Biographer, Housekeeper, Tatlock) to use sanitized provider
+- Added `src/ollama/provider.py` with reusable provider pattern
+
+#### Streaming Think Message Accumulation
+- **Fixed repeating think messages in frontend** (e.g., 10x "The Librarian has compiled...")
+- Frontend was accumulating `ReasoningSummaryDelta` events expecting concatenation
+- Added `ReasoningSummaryDone()` signal after each think message to indicate completion
+- Each think slug is now treated as a complete message, not a continuation
+
+## [1.8.0] - 2025-12-15
+
+### Fixed
+
+#### Steward Routing for Web Search
+- Updated Steward guidelines to route web searches, weather, news → Librarian with `search_web`
+- Added URL/article reading → Librarian with `read_url` to routing guidelines
+- Added examples showing `search_web` and `read_url` tool usage
+
+#### Librarian Agent Tool Registration
+- Registered `search_web`, `read_url`, `read_urls_batch` tools with the Librarian PydanticAI agent
+- Updated Librarian system prompt with Web Search & Content Extraction section
+- Fixed tool count in agent logger (11 → 14 tools)
+
+#### Query Enrichment Integration
+- Fixed enriched query (with location/timezone context) not being passed to delegations
+- Response service now uses `enriched_query` from Steward recommendation for all delegations
+- Weather queries now automatically include user's stored location
+
+#### Action Type Detection
+- Added "read", "fetch", "url", "http" keywords to RESEARCH action type for Librarian
+- Ensures proper think messages for URL reading tasks
+
+## [1.7.0] - 2025-12-15
+
+### Added
+
+#### Web Search Migration to Librarian
+- **`search_web()`** tool in Librarian for web search via library-desk `/rag/search` endpoint
+- **`read_url()`** tool for single URL content extraction via Trafilatura
+- **`read_urls_batch()`** tool for parallel batch URL extraction (max 20 URLs)
+- `WebSearchResult`, `WebSearchResponse` models in LibraryDeskClient
+- `ContentExtractionResult`, `BatchExtractionResponse` models for content extraction
+- `search_web()`, `extract_content()`, `extract_content_batch()` methods in LibraryDeskClient
+- Comprehensive unit tests for new Librarian tools (`tests/agents/librarian/test_tools.py`)
+
+### Changed
+
+- Librarian capability updated with web search domains: "web", "url", "internet"
+- Tatlock system prompt now delegates web search to Librarian
+- `tatlock_core` capability reduced to computation/datetime only (no longer requires network)
+
+### Removed
+
+- `search_web` function from `src/agents/tatlock_core/tools.py`
+- `web_search_tool` from `tatlock_core_tools` list
+- `search_web` from legacy `src/agents/tools.py`
+- Search tests from `tests/agents/test_tools.py` (moved to Librarian tests)
+
+## [1.6.0] - 2025-12-15
+
+### Added
+
+#### Two-Phase Tatlock Execution
+- **Phase 1: Orchestration** - Executes tool calls and expert delegations, returns structured results
+- **Phase 2: Synthesis** - Synthesizes butler-toned response from gathered results
+- `orchestrate_tool_calls()` method in TatlockAgent for coordination phase
+- `synthesize_from_results()` method in TatlockAgent for synthesis phase
+- Guarantees butler personality in all responses by separating coordination from response generation
+
+#### Automatic Think Slugs
+- **Deterministic butler-perspective messages** during expert delegation (no LLM involved)
+- `ActionType` enum: RETRIEVE, RESEARCH, CREATE, CONTROL, RECORD
+- `HOUSEHOLD_THINK_MESSAGES` mapping with butler-perspective messages for all experts:
+  - Librarian: "Allow me to consult the archives, sir." / "I'm having the Librarian prepare a new entry."
+  - Biographer: "Let me consult the household records." / "I've asked the Biographer to take note, sir."
+  - Housekeeper: "I'm instructing the household staff now, sir." / "Allow me to inquire with the household staff."
+- `_detect_action_type()` function for keyword-based action detection
+- `get_think_message()` helper for retrieving appropriate messages
+- Streaming delegation wrappers: `stream_delegate_to_librarian()`, `stream_delegate_to_biographer()`, `stream_delegate_to_housekeeper()`
+- `STREAMING_DELEGATION_WRAPPERS` mapping in delegation.py
+- `get_streaming_delegation_tools()` method in HouseholdRegistry
+
+#### Steward Query Enrichment
+- **Auto-fill user context** (location, timezone) when not specified in query
+- `_build_enriched_query()` function in steward service
+- Regex word boundary matching for accurate location detection (avoids false positives)
+- `enriched_query` field added to `StewardRecommendation` schema
+- Automatic enrichment for weather queries (location), time queries (timezone), temperature preferences
+
+#### Documentation
+- **ORCHESTRATION_SCENARIOS.md** completely rewritten with:
+  - Mermaid flow diagrams for two-phase execution
+  - 4 new Housekeeper scenarios (light control, device status, parallel delegation)
+  - Biographer memory recording scenario
+  - Complete think slug reference tables
+  - Action type detection tables
+  - Updated architecture mindmap
+- **TESTING_IMPROVEMENTS.md** - LLM testing best practices for future implementation
+
+### Changed
+
+- `create_response_with_steward()` now uses two-phase execution
+- `_direct_delegation()` routes through synthesis phase for consistent butler tone
+- `_execute_single_delegation()` now supports housekeeper
+- Streaming response handler integrated with think slug system
+- All 326 unit tests passing
+
 ## [1.5.0] - 2025-12-15
 
 ### Added
@@ -608,7 +724,9 @@ 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/v1.4.0...main
+[Unreleased]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.6.0...main
+[1.6.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.5.0...v1.6.0
+[1.5.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.4.0...v1.5.0
 [1.4.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.3.3...v1.4.0
 [1.3.3]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.3.2...v1.3.3
 [1.3.2]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.3.1...v1.3.2

TESTING_IMPROVEMENTS.md

@@ -0,0 +1,105 @@
+# Testing Improvements for LLM Outputs
+
+## Problem
+
+LLM outputs are non-deterministic. Tests checking for exact string matches fail when the LLM writes "thirty-seven" instead of "37".
+
+## Proposed Solutions
+
+### 1. LLM-as-Judge Pattern
+
+Use a smaller/faster model to evaluate semantic correctness:
+
+```python
+async def llm_judge(output: str, criteria: str) -> bool:
+    """Use LLM to evaluate if output meets criteria."""
+    prompt = f"""
+    Evaluate if this output is correct:
+    Output: {output}
+    Criteria: {criteria}
+    Answer only YES or NO.
+    """
+    result = await judge_model.run(prompt)
+    return "YES" in result.output.upper()
+
+# Usage in test:
+assert await llm_judge(
+    response,
+    "The answer correctly states that sqrt(144) + 25 = 37"
+)
+```
+
+### 2. Fuzzy/Regex Matching
+
+For numeric answers, accept multiple representations:
+
+```python
+import re
+
+def contains_number(text: str, number: int) -> bool:
+    """Check if text contains number in any form."""
+    patterns = [
+        rf'\b{number}\b',  # Digit form
+        number_to_words(number),  # Word form
+    ]
+    return any(re.search(p, text, re.I) for p in patterns)
+
+# Usage:
+assert contains_number(response, 37)  # Matches "37" or "thirty-seven"
+```
+
+### 3. DeepEval Framework
+
+```python
+from deepeval.metrics import AnswerRelevancyMetric
+from deepeval.test_case import LLMTestCase
+
+def test_calculation():
+    test_case = LLMTestCase(
+        input="What is sqrt(144) + 25?",
+        actual_output=response,
+        expected_output="37"
+    )
+    metric = AnswerRelevancyMetric(threshold=0.7)
+    assert metric.measure(test_case)
+```
+
+### 4. pytest-evals Plugin
+
+Minimal pytest plugin for LLM testing with metrics collection.
+
+```bash
+pip install pytest-evals
+```
+
+### 5. Multiple Runs with Threshold
+
+Run flaky tests multiple times and require majority pass:
+
+```python
+@pytest.mark.flaky(reruns=3, reruns_delay=1)
+def test_llm_response():
+    ...
+```
+
+Or custom:
+
+```python
+@pytest.mark.parametrize("run", range(3))
+def test_llm_response(run):
+    ...
+    # Aggregate results across runs
+```
+
+## Resources
+
+- [DeepEval](https://github.com/confident-ai/deepeval) - LLM evaluation framework
+- [pytest-evals](https://github.com/AlmogBaku/pytest-evals) - pytest plugin for LLM evals
+- [LLM Testing Guide 2025](https://www.confident-ai.com/blog/llm-testing-in-2024-top-methods-and-strategies)
+- [Testing LLM Applications - Langfuse](https://langfuse.com/blog/2025-10-21-testing-llm-applications)
+
+## Implementation Priority
+
+1. Add fuzzy number matching helper (quick win)
+2. Evaluate DeepEval for complex output testing
+3. Consider LLM-as-judge for semantic correctness

docs/rag_search_implementation.md

@@ -0,0 +1,348 @@
+# Tatlock Integration Guide
+
+Implementation instructions for integrating Library Desk search and content extraction endpoints into the Tatlock project.
+
+## Base Configuration
+
+```
+BASE_URL: http://library-desk:8089  (or your deployment URL)
+AUTH_HEADER: Authorization: Bearer <LIBRARY_API_KEY>
+```
+
+---
+
+## 1. RAG Search Endpoint
+
+**Use case:** Librarian needs to research a topic by searching the web.
+
+### Endpoint
+
+```
+POST /rag/search
+```
+
+### Request
+
+```json
+{
+  "query": "Python async programming best practices",
+  "search_type": "web",
+  "limit": 10,
+  "user": "tatlock-librarian"
+}
+```
+
+| Field | Type | Default | Description |
+|-------|------|---------|-------------|
+| `query` | string | required | Search query (1-500 chars) |
+| `search_type` | enum | `"web"` | `"web"`, `"news"`, or `"images"` |
+| `limit` | int | 10 | Results to return (1-20) |
+| `user` | string | `"default"` | User identifier for tracking |
+
+### Response
+
+```json
+{
+  "query": "Python async programming best practices",
+  "search_type": "web",
+  "results": [
+    {
+      "title": "Async IO in Python: A Complete Walkthrough",
+      "url": "https://realpython.com/async-io-python/",
+      "content": "Full extracted article text via Trafilatura (~2000 chars max)...",
+      "snippet": "Original search engine snippet (150-300 chars)...",
+      "source": "realpython.com",
+      "published_date": "2023-05-15"
+    }
+  ],
+  "total_results": 10,
+  "search_time_ms": 2340,
+  "sources_summary": "## Sources\n- [Async IO in Python](https://realpython.com/async-io-python/)\n- ..."
+}
+```
+
+### Key Fields for Tatlock
+
+| Field | Usage |
+|-------|-------|
+| `results[].content` | Full extracted text - use this for LLM context |
+| `results[].snippet` | Fallback if content extraction failed |
+| `sources_summary` | Pre-formatted markdown for citations |
+
+### Error Handling
+
+| HTTP Code | Meaning | Action |
+|-----------|---------|--------|
+| 400 | Invalid query | Check query length/format |
+| 502 | SearXNG unavailable | Retry with backoff |
+| 504 | Search timeout | Retry or reduce limit |
+| 500 | Internal error | Log and notify |
+
+### Example Usage (Python)
+
+```python
+import httpx
+
+async def search_web(query: str, limit: int = 10) -> dict:
+    async with httpx.AsyncClient() as client:
+        response = await client.post(
+            f"{BASE_URL}/rag/search",
+            headers={"Authorization": f"Bearer {API_KEY}"},
+            json={
+                "query": query,
+                "search_type": "web",
+                "limit": limit,
+                "user": "tatlock-librarian"
+            },
+            timeout=30.0
+        )
+        response.raise_for_status()
+        return response.json()
+
+# Usage
+results = await search_web("machine learning transformers")
+for r in results["results"]:
+    # Prefer full content, fall back to snippet
+    text = r["content"] or r["snippet"]
+    print(f"{r['title']}: {len(text)} chars")
+```
+
+---
+
+## 2. Content Extraction Endpoint
+
+**Use case:** Librarian has a specific URL and needs to read its content.
+
+### Single URL Extraction
+
+```
+POST /content/extract
+```
+
+#### Request
+
+```json
+{
+  "url": "https://example.com/article",
+  "include_metadata": true,
+  "max_length": 2000
+}
+```
+
+#### Response
+
+```json
+{
+  "result": {
+    "url": "https://example.com/article",
+    "title": "Article Title",
+    "content": "Extracted main text content...",
+    "author": "John Doe",
+    "date": "2024-01-15",
+    "language": "en",
+    "success": true,
+    "error": null
+  },
+  "extraction_time_ms": 1250
+}
+```
+
+### Batch URL Extraction
+
+```
+POST /content/extract/batch
+```
+
+#### Request
+
+```json
+{
+  "urls": [
+    "https://example.com/article1",
+    "https://example.com/article2",
+    "https://example.com/article3"
+  ],
+  "include_metadata": true,
+  "max_length": 2000
+}
+```
+
+#### Response
+
+```json
+{
+  "results": [
+    {
+      "url": "https://example.com/article1",
+      "title": "Article 1",
+      "content": "Extracted content...",
+      "success": true,
+      "error": null
+    },
+    {
+      "url": "https://example.com/article2",
+      "title": null,
+      "content": "",
+      "success": false,
+      "error": "Connection timeout"
+    }
+  ],
+  "total_urls": 3,
+  "successful": 2,
+  "failed": 1,
+  "extraction_time_ms": 3500
+}
+```
+
+---
+
+## 3. Error Pattern: Soft Failures
+
+> **Important:** Content extraction uses a **soft failure pattern** - individual URL failures do NOT throw HTTP errors.
+
+### Why Soft Failures?
+
+When extracting content from multiple URLs (batch) or even single URLs:
+- Some sites block bots
+- Some URLs are temporarily down
+- Some pages have no extractable content
+
+Instead of failing the entire request, we return:
+- `success: true/false` per result
+- `error: "reason"` when failed
+- Empty `content: ""` on failure
+
+### Handling Soft Failures
+
+```python
+async def extract_with_fallback(url: str) -> str:
+    response = await client.post(
+        f"{BASE_URL}/content/extract",
+        headers={"Authorization": f"Bearer {API_KEY}"},
+        json={"url": url}
+    )
+    response.raise_for_status()  # Only throws on 4xx/5xx
+
+    data = response.json()
+    result = data["result"]
+
+    if result["success"]:
+        return result["content"]
+    else:
+        # Log the failure, return empty or handle gracefully
+        logger.warning(f"Extraction failed for {url}: {result['error']}")
+        return ""  # Or raise, or use cached version, etc.
+```
+
+### Batch Processing Example
+
+```python
+async def extract_batch_with_stats(urls: list[str]) -> dict:
+    response = await client.post(
+        f"{BASE_URL}/content/extract/batch",
+        headers={"Authorization": f"Bearer {API_KEY}"},
+        json={"urls": urls, "max_length": 3000}
+    )
+    response.raise_for_status()
+
+    data = response.json()
+
+    # Separate successful and failed
+    successful = [r for r in data["results"] if r["success"]]
+    failed = [r for r in data["results"] if not r["success"]]
+
+    if failed:
+        logger.warning(f"{len(failed)} URLs failed extraction:")
+        for f in failed:
+            logger.warning(f"  {f['url']}: {f['error']}")
+
+    return {
+        "contents": {r["url"]: r["content"] for r in successful},
+        "failed_urls": [f["url"] for f in failed],
+        "success_rate": data["successful"] / data["total_urls"]
+    }
+```
+
+---
+
+## 4. Recommended Patterns for Tatlock
+
+### Research Flow
+
+```python
+async def librarian_research(topic: str) -> dict:
+    """
+    Full research flow: search + extract additional context.
+    """
+    # 1. Search for relevant pages
+    search_results = await search_web(topic, limit=10)
+
+    # 2. RAG search already includes extracted content
+    #    Only extract more if you need deeper content
+
+    # 3. Build context for LLM
+    context_parts = []
+    for r in search_results["results"]:
+        content = r["content"] or r["snippet"]
+        if content:
+            context_parts.append(f"## {r['title']}\nSource: {r['url']}\n\n{content}")
+
+    return {
+        "context": "\n\n---\n\n".join(context_parts),
+        "sources": search_results["sources_summary"],
+        "result_count": search_results["total_results"]
+    }
+```
+
+### Reading a Specific Page
+
+```python
+async def librarian_read_page(url: str) -> str:
+    """
+    Read a specific URL the user provided.
+    """
+    response = await client.post(
+        f"{BASE_URL}/content/extract",
+        headers={"Authorization": f"Bearer {API_KEY}"},
+        json={"url": url, "max_length": 5000}  # Longer for deep reads
+    )
+    response.raise_for_status()
+
+    result = response.json()["result"]
+
+    if not result["success"]:
+        raise ValueError(f"Could not read page: {result['error']}")
+
+    # Format for LLM
+    header = f"# {result['title'] or 'Untitled'}\n"
+    if result["author"]:
+        header += f"Author: {result['author']}\n"
+    if result["date"]:
+        header += f"Date: {result['date']}\n"
+
+    return header + "\n" + result["content"]
+```
+
+---
+
+## 5. Rate Limits & Best Practices
+
+| Recommendation | Reason |
+|----------------|--------|
+| Use `limit: 5-10` for searches | More results = longer extraction time |
+| Batch URLs when possible | More efficient than sequential calls |
+| Max 20 URLs per batch | Server limit |
+| Set reasonable timeouts (30s) | Content extraction can be slow |
+| Cache results client-side | Same URL rarely changes content |
+| Use `user` parameter | Helps with debugging and rate limiting |
+
+---
+
+## 6. Quick Reference
+
+| Endpoint | Method | Use Case |
+|----------|--------|----------|
+| `/rag/search` | POST | Search web + get extracted content |
+| `/content/extract` | POST | Read a single URL |
+| `/content/extract/batch` | POST | Read multiple URLs |
+| `/health` | GET | Check service status |

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "tatlock"
-version = "1.5.0"
+version = "1.8.1"
 description = "OpenAI-compatible API with Ollama backend"
 requires-python = ">=3.12"
 dependencies = []

src/agents/biographer/agent.py

@@ -102,18 +102,15 @@ _biographer_agent: Optional[Agent[None, str]] = None
 
 def _create_biographer_agent() -> Agent[None, str]:
     """Create The Biographer PydanticAI agent."""
-    # Import required classes for Ollama configuration
     from pydantic_ai.models.openai import OpenAIChatModel
-    from pydantic_ai.providers.ollama import OllamaProvider
 
-    # PydanticAI expects Ollama base URL to end with /v1
-    clean_host = str(config.OLLAMA_HOST).rstrip('/')
-    base_url = f"{clean_host}/v1"
+    from src.ollama.provider import get_ollama_provider
 
-    # Create Ollama model with provider
+    # Create Ollama model with sanitized provider
+    # (fixes 'content: null' issue with tool calls)
     model = OpenAIChatModel(
         model_name=config.OLLAMA_DEFAULT_MODEL,
-        provider=OllamaProvider(base_url=base_url)
+        provider=get_ollama_provider(),
     )
 
     agent: Agent[None, str] = Agent(

src/agents/delegation.py

@@ -9,13 +9,139 @@ This implements the agent-as-tool pattern recommended by PydanticAI:
 agents call other agents via tool wrappers, keeping each agent focused.
 """
 from dataclasses import dataclass, field
-from typing import Callable, Optional, Any
+from enum import Enum
+from typing import AsyncGenerator, Callable, Optional, Any
 
 from src.core.logging_config import get_logger
 
 logger = get_logger(__name__)
 
 
+# =============================================================================
+# Action Types for Think Slug Selection
+# =============================================================================
+
+class ActionType(Enum):
+    """
+    Categories of actions for selecting appropriate think messages.
+
+    Each expert has different action types that warrant different
+    butler-perspective messages to the user.
+    """
+    RETRIEVE = "retrieve"     # Looking up existing information
+    RESEARCH = "research"     # Conducting new research (web search, etc.)
+    CREATE = "create"         # Creating new content (pages, notes)
+    CONTROL = "control"       # Controlling devices/automations
+    RECORD = "record"         # Recording memories/notes
+
+
+# =============================================================================
+# Household Think Messages (Butler's Perspective)
+# =============================================================================
+
+HOUSEHOLD_THINK_MESSAGES: dict[str, dict[ActionType, dict[str, str]]] = {
+    "librarian": {
+        ActionType.RETRIEVE: {
+            "start": "<think>Allow me to consult the archives, sir.</think>",
+            "success": "<think>The Librarian has compiled the relevant findings.</think>",
+            "error": "<think>I'm afraid the archives proved difficult to access.</think>",
+        },
+        ActionType.RESEARCH: {
+            "start": "<think>I've dispatched the Librarian to conduct some fresh research.</think>",
+            "success": "<think>The Librarian has returned with findings, sir.</think>",
+            "error": "<think>The research proved inconclusive, I'm afraid.</think>",
+        },
+        ActionType.CREATE: {
+            "start": "<think>I'm having the Librarian prepare a new entry.</think>",
+            "success": "<think>The new material has been properly catalogued, sir.</think>",
+            "error": "<think>I'm afraid there was difficulty filing the entry.</think>",
+        },
+    },
+    "biographer": {
+        ActionType.RETRIEVE: {
+            "start": "<think>Let me consult the household records.</think>",
+            "success": "<think>The Biographer has located the relevant information, sir.</think>",
+            "error": "<think>I'm unable to locate those particular records.</think>",
+        },
+        ActionType.RECORD: {
+            "start": "<think>I've asked the Biographer to take note of this, sir.</think>",
+            "success": "<think>The household records have been updated accordingly.</think>",
+            "error": "<think>I'm afraid there was difficulty recording the entry.</think>",
+        },
+    },
+    "housekeeper": {
+        ActionType.RETRIEVE: {
+            "start": "<think>Allow me to inquire with the household staff.</think>",
+            "success": "<think>The staff reports the current status, sir.</think>",
+            "error": "<think>The household staff is momentarily unavailable, I'm afraid.</think>",
+        },
+        ActionType.CONTROL: {
+            "start": "<think>I'm instructing the household staff now, sir.</think>",
+            "success": "<think>The household has been configured as requested.</think>",
+            "error": "<think>I'm afraid the staff reports an issue with that request.</think>",
+        },
+    },
+}
+
+
+def _detect_action_type(expert: str, task: str) -> ActionType:
+    """
+    Detect action type from expert name and task description.
+
+    Used to select appropriate butler-perspective think messages.
+
+    Args:
+        expert: Name of the expert (librarian, biographer, housekeeper)
+        task: Task description
+
+    Returns:
+        ActionType: Detected action type for message selection
+    """
+    task_lower = task.lower()
+
+    if expert == "librarian":
+        # Web search, URL reading = RESEARCH (fresh external data)
+        if any(w in task_lower for w in ["search", "find", "look up", "research"]):
+            if any(w in task_lower for w in ["web", "online", "internet"]):
+                return ActionType.RESEARCH
+            return ActionType.RETRIEVE
+        if any(w in task_lower for w in ["read", "fetch", "url", "http"]):
+            return ActionType.RESEARCH  # Reading URLs is research
+        if any(w in task_lower for w in ["create", "write", "add", "make", "new"]):
+            return ActionType.CREATE
+        return ActionType.RETRIEVE
+
+    elif expert == "biographer":
+        if any(w in task_lower for w in ["remember", "note", "record", "save", "store"]):
+            return ActionType.RECORD
+        return ActionType.RETRIEVE
+
+    elif expert == "housekeeper":
+        if any(w in task_lower for w in ["turn", "set", "activate", "enable", "disable", "toggle"]):
+            return ActionType.CONTROL
+        return ActionType.RETRIEVE
+
+    return ActionType.RETRIEVE
+
+
+def get_think_message(expert: str, task: str, phase: str) -> str:
+    """
+    Get the appropriate think message for an expert delegation.
+
+    Args:
+        expert: Name of the expert
+        task: Task description (used to detect action type)
+        phase: One of "start", "success", "error"
+
+    Returns:
+        str: Butler-perspective think message
+    """
+    action_type = _detect_action_type(expert, task)
+    expert_messages = HOUSEHOLD_THINK_MESSAGES.get(expert, {})
+    action_messages = expert_messages.get(action_type, expert_messages.get(ActionType.RETRIEVE, {}))
+    return action_messages.get(phase, f"<think>Consulting {expert}...</think>")
+
+
 @dataclass
 class DelegationTask:
     """
@@ -301,6 +427,103 @@ async def delegate_to_housekeeper(
         )
 
 
+# =============================================================================
+# Streaming Delegation Wrappers (with Think Messages)
+# =============================================================================
+
+async def stream_delegate_to_librarian(
+    task: str,
+    context: str = "",
+) -> AsyncGenerator[str, None]:
+    """
+    Stream delegation to Librarian with automatic think messages.
+
+    Yields butler-perspective think messages before and after the delegation,
+    allowing the UI to show progress to the user.
+
+    Args:
+        task: Task description
+        context: Additional context
+
+    Yields:
+        str: Think messages and final result marker
+    """
+    # Yield start message (deterministic)
+    yield get_think_message("librarian", task, "start") + "\n"
+
+    # Execute delegation
+    result = await delegate_to_librarian(task, context)
+
+    # Yield completion message (deterministic)
+    if result.success:
+        yield get_think_message("librarian", task, "success") + "\n"
+    else:
+        yield get_think_message("librarian", task, "error") + "\n"
+
+    # Yield result marker for extraction
+    yield f"__DELEGATION_RESULT__:librarian:{result.output}"
+
+
+async def stream_delegate_to_biographer(
+    task: str,
+    context: str = "",
+) -> AsyncGenerator[str, None]:
+    """
+    Stream delegation to Biographer with automatic think messages.
+
+    Args:
+        task: Task description
+        context: Additional context
+
+    Yields:
+        str: Think messages and final result marker
+    """
+    yield get_think_message("biographer", task, "start") + "\n"
+
+    result = await delegate_to_biographer(task, context)
+
+    if result.success:
+        yield get_think_message("biographer", task, "success") + "\n"
+    else:
+        yield get_think_message("biographer", task, "error") + "\n"
+
+    yield f"__DELEGATION_RESULT__:biographer:{result.output}"
+
+
+async def stream_delegate_to_housekeeper(
+    task: str,
+    context: str = "",
+) -> AsyncGenerator[str, None]:
+    """
+    Stream delegation to Housekeeper with automatic think messages.
+
+    Args:
+        task: Task description
+        context: Additional context
+
+    Yields:
+        str: Think messages and final result marker
+    """
+    yield get_think_message("housekeeper", task, "start") + "\n"
+
+    result = await delegate_to_housekeeper(task, context)
+
+    if result.success:
+        yield get_think_message("housekeeper", task, "success") + "\n"
+    else:
+        yield get_think_message("housekeeper", task, "error") + "\n"
+
+    yield f"__DELEGATION_RESULT__:housekeeper:{result.output}"
+
+
+# Mapping of streaming delegation wrappers
+STREAMING_DELEGATION_WRAPPERS = {
+    "librarian": stream_delegate_to_librarian,
+    "biographer": stream_delegate_to_biographer,
+    "housekeeper": stream_delegate_to_housekeeper,
+}
+
+
 # Future expert delegation wrappers will be added here:
 # - delegate_to_developer(task, context) -> DelegationResult
 # - delegate_to_secretary(task, context) -> DelegationResult

src/agents/housekeeper/agent.py

@@ -113,18 +113,15 @@ _housekeeper_agent: Optional[Agent[None, str]] = None
 
 def _create_housekeeper_agent() -> Agent[None, str]:
     """Create the Housekeeper PydanticAI agent."""
-    # Import required classes for Ollama configuration
     from pydantic_ai.models.openai import OpenAIChatModel
-    from pydantic_ai.providers.ollama import OllamaProvider
 
-    # PydanticAI expects Ollama base URL to end with /v1
-    clean_host = str(config.OLLAMA_HOST).rstrip("/")
-    base_url = f"{clean_host}/v1"
+    from src.ollama.provider import get_ollama_provider
 
-    # Create Ollama model with provider
+    # Create Ollama model with sanitized provider
+    # (fixes 'content: null' issue with tool calls)
     model = OpenAIChatModel(
         model_name=config.OLLAMA_DEFAULT_MODEL,
-        provider=OllamaProvider(base_url=base_url),
+        provider=get_ollama_provider(),
     )
 
     agent: Agent[None, str] = Agent(

src/agents/librarian/agent.py

@@ -19,6 +19,9 @@ from src.agents.librarian.tools import (
     get_wiki_page,
     hybrid_search,
     list_dossiers,
+    read_url,
+    read_urls_batch,
+    search_web,
     search_wiki,
     semantic_search,
     smart_create_wiki_page,
@@ -47,8 +50,17 @@ Your role is to help users find, understand, synthesize, and manage information
 
 ## Your Tools
 
-### Research Tools
-- **hybrid_search**: Your primary research tool - searches all sources at once
+### Web Search & Content Extraction
+- **search_web**: Search the internet for current information (weather, news, facts)
+  - Use for: weather forecasts, current events, recent developments, external facts
+  - Returns extracted content from search results, not just snippets
+- **read_url**: Read and extract content from a specific URL
+  - Use when: user provides a URL or you need to read a specific webpage
+- **read_urls_batch**: Read multiple URLs in parallel (up to 20)
+  - 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
 - **search_wiki**: Find specific wiki pages by keyword
 - **semantic_search**: Find conceptually similar content
 - **explore_knowledge_graph** / **find_related_entities**: Discover connections
@@ -110,18 +122,15 @@ _librarian_agent: Optional[Agent[None, str]] = None
 
 def _create_librarian_agent() -> Agent[None, str]:
     """Create the Librarian PydanticAI agent."""
-    # Import required classes for Ollama configuration
     from pydantic_ai.models.openai import OpenAIChatModel
-    from pydantic_ai.providers.ollama import OllamaProvider
 
-    # PydanticAI expects Ollama base URL to end with /v1
-    clean_host = str(config.OLLAMA_HOST).rstrip('/')
-    base_url = f"{clean_host}/v1"
+    from src.ollama.provider import get_ollama_provider
 
-    # Create Ollama model with provider
+    # Create Ollama model with sanitized provider
+    # (fixes 'content: null' issue with tool calls)
     model = OpenAIChatModel(
         model_name=config.OLLAMA_DEFAULT_MODEL,
-        provider=OllamaProvider(base_url=base_url)
+        provider=get_ollama_provider(),
     )
 
     agent: Agent[None, str] = Agent(
@@ -130,7 +139,7 @@ def _create_librarian_agent() -> Agent[None, str]:
         retries=2,
     )
 
-    # Register research tools
+    # Register research tools (internal knowledge)
     agent.tool_plain(hybrid_search)
     agent.tool_plain(search_wiki)
     agent.tool_plain(semantic_search)
@@ -139,6 +148,11 @@ def _create_librarian_agent() -> Agent[None, str]:
     agent.tool_plain(explore_knowledge_graph)
     agent.tool_plain(find_related_entities)
 
+    # Register web search & content extraction tools
+    agent.tool_plain(search_web)
+    agent.tool_plain(read_url)
+    agent.tool_plain(read_urls_batch)
+
     # Register wiki read tools
     agent.tool_plain(get_wiki_page)
 
@@ -150,7 +164,7 @@ def _create_librarian_agent() -> Agent[None, str]:
     logger.info(
         "librarian_agent_created",
         model=config.OLLAMA_DEFAULT_MODEL,
-        tool_count=11,
+        tool_count=14,  # 7 research + 3 web + 1 wiki read + 3 wiki write
     )
 
     return agent

src/agents/librarian/capability.py

@@ -21,10 +21,11 @@ LIBRARIAN_CAPABILITY = HouseholdCapability(
     role="The Librarian",
     category="research",
     description=(
-        "Research and wiki management: can CREATE wiki pages about topics "
+        "Research, web search, and wiki management: can SEARCH the web for current "
+        "information, READ URLs/articles, CREATE wiki pages about topics "
         "(with automatic HybridRAG research), UPDATE existing pages, "
-        "SEARCH wiki/knowledge graph/web, and synthesize information. "
-        "Use for: 'create a page about X', 'update wiki', 'find info on X'"
+        "and synthesize information from multiple sources. "
+        "Use for: 'search for X', 'what is X', 'create a page about X', 'read this URL'"
     ),
     domains=[
         "research",
@@ -33,6 +34,9 @@ LIBRARIAN_CAPABILITY = HouseholdCapability(
         "wiki",
         "documents",
         "search",
+        "web",
+        "url",
+        "internet",
         "synthesis",
         "create",
         "write",

src/agents/librarian/client.py

@@ -98,6 +98,47 @@ class ResearchSummary(BaseModel):
     timing_ms: int = 0
 
 
+class WebSearchResult(BaseModel):
+    """Result from web search via /rag/search."""
+    title: str
+    url: str
+    content: str = ""  # Full extracted text via Trafilatura
+    snippet: str = ""  # Original search engine snippet
+    source: str = ""  # Domain name
+    published_date: Optional[str] = None
+
+
+class WebSearchResponse(BaseModel):
+    """Response from /rag/search endpoint."""
+    query: str
+    search_type: str
+    results: list[WebSearchResult] = Field(default_factory=list)
+    total_results: int = 0
+    search_time_ms: int = 0
+    sources_summary: str = ""  # Pre-formatted markdown citations
+
+
+class ContentExtractionResult(BaseModel):
+    """Result from content extraction."""
+    url: str
+    title: Optional[str] = None
+    content: str = ""
+    author: Optional[str] = None
+    date: Optional[str] = None
+    language: Optional[str] = None
+    success: bool = True
+    error: Optional[str] = None
+
+
+class BatchExtractionResponse(BaseModel):
+    """Response from batch content extraction."""
+    results: list[ContentExtractionResult] = Field(default_factory=list)
+    total_urls: int = 0
+    successful: int = 0
+    failed: int = 0
+    extraction_time_ms: int = 0
+
+
 class EntityLinking(BaseModel):
     """Entity linking results from smart-create."""
     forward_links: int = 0
@@ -685,6 +726,186 @@ class LibraryDeskClient:
             logger.warning("library_desk_health_check_failed", error=str(e))
             return False
 
+    # ========================================================================
+    # RAG Search (Web Search with Content Extraction)
+    # ========================================================================
+
+    async def search_web(
+        self,
+        query: str,
+        user: str | None = None,
+        search_type: str = "web",
+        limit: int = 10,
+    ) -> WebSearchResponse:
+        """
+        Search the web and extract content from results.
+
+        Uses SearXNG for search and Trafilatura for content extraction.
+        Returns both snippets and full extracted text.
+
+        Args:
+            query: Search query (1-500 chars)
+            user: User identifier for tracking
+            search_type: "web", "news", or "images"
+            limit: Number of results (1-20)
+
+        Returns:
+            WebSearchResponse with results and pre-formatted sources
+        """
+        user = user or get_user()
+        client = self._ensure_client()
+
+        payload = {
+            "query": query,
+            "search_type": search_type,
+            "limit": limit,
+            "user": user or "tatlock-librarian",
+        }
+
+        logger.info("library_desk_web_search", query=query, limit=limit)
+
+        response = await client.post("/rag/search", json=payload, timeout=30.0)
+        response.raise_for_status()
+
+        data = response.json()
+
+        results = [
+            WebSearchResult(
+                title=r.get("title", ""),
+                url=r.get("url", ""),
+                content=r.get("content", ""),
+                snippet=r.get("snippet", ""),
+                source=r.get("source", ""),
+                published_date=r.get("published_date"),
+            )
+            for r in data.get("results", [])
+        ]
+
+        return WebSearchResponse(
+            query=data.get("query", query),
+            search_type=data.get("search_type", search_type),
+            results=results,
+            total_results=data.get("total_results", len(results)),
+            search_time_ms=data.get("search_time_ms", 0),
+            sources_summary=data.get("sources_summary", ""),
+        )
+
+    # ========================================================================
+    # Content Extraction
+    # ========================================================================
+
+    async def extract_content(
+        self,
+        url: str,
+        include_metadata: bool = True,
+        max_length: int = 5000,
+    ) -> ContentExtractionResult:
+        """
+        Extract main content from a URL.
+
+        Uses Trafilatura for intelligent content extraction,
+        removing boilerplate, ads, and navigation.
+
+        Note: Uses soft failure pattern - check result.success field.
+
+        Args:
+            url: URL to extract content from
+            include_metadata: Whether to extract author, date, etc.
+            max_length: Maximum content length
+
+        Returns:
+            ContentExtractionResult (check .success and .error fields)
+        """
+        client = self._ensure_client()
+
+        payload = {
+            "url": url,
+            "include_metadata": include_metadata,
+            "max_length": max_length,
+        }
+
+        logger.debug("library_desk_extract_content", url=url)
+
+        response = await client.post("/content/extract", json=payload, timeout=30.0)
+        response.raise_for_status()
+
+        data = response.json()
+        result = data.get("result", {})
+
+        return ContentExtractionResult(
+            url=result.get("url", url),
+            title=result.get("title"),
+            content=result.get("content", ""),
+            author=result.get("author"),
+            date=result.get("date"),
+            language=result.get("language"),
+            success=result.get("success", False),
+            error=result.get("error"),
+        )
+
+    async def extract_content_batch(
+        self,
+        urls: list[str],
+        include_metadata: bool = True,
+        max_length: int = 2000,
+    ) -> BatchExtractionResponse:
+        """
+        Extract content from multiple URLs in parallel.
+
+        More efficient than sequential calls. Max 20 URLs per batch.
+
+        Note: Uses soft failure pattern - individual failures don't
+        throw errors, check each result's .success field.
+
+        Args:
+            urls: List of URLs to extract (max 20)
+            include_metadata: Whether to extract author, date, etc.
+            max_length: Maximum content length per URL
+
+        Returns:
+            BatchExtractionResponse with results and stats
+        """
+        client = self._ensure_client()
+
+        payload = {
+            "urls": urls[:20],  # Server limit
+            "include_metadata": include_metadata,
+            "max_length": max_length,
+        }
+
+        logger.info("library_desk_extract_batch", url_count=len(urls))
+
+        response = await client.post(
+            "/content/extract/batch",
+            json=payload,
+            timeout=60.0,  # Longer timeout for batch
+        )
+        response.raise_for_status()
+
+        data = response.json()
+
+        results = [
+            ContentExtractionResult(
+                url=r.get("url", ""),
+                title=r.get("title"),
+                content=r.get("content", ""),
+                author=r.get("author"),
+                date=r.get("date"),
+                language=r.get("language"),
+                success=r.get("success", False),
+                error=r.get("error"),
+            )
+            for r in data.get("results", [])
+        ]
+
+        return BatchExtractionResponse(
+            results=results,
+            total_urls=data.get("total_urls", len(urls)),
+            successful=data.get("successful", 0),
+            failed=data.get("failed", 0),
+            extraction_time_ms=data.get("extraction_time_ms", 0),
+        )
+
 
 # Global client factory
 async def get_library_client() -> LibraryDeskClient:

src/agents/librarian/tools.py

@@ -432,6 +432,239 @@ async def find_related_entities(
         return f"Error finding related entities: {str(e)}"
 
 
+# ============================================================================
+# Web Search & Content Extraction
+# ============================================================================
+
+async def search_web(
+    query: str,
+    limit: int = 10,
+    search_type: str = "web",
+) -> str:
+    """
+    Search the web and extract content from results.
+
+    This is the primary tool for finding current information online.
+    Results include both snippets and full extracted text from pages.
+
+    Search types:
+    - "web": General web search (default)
+    - "news": News articles
+    - "images": Image search
+
+    Args:
+        query: Search query (1-500 chars)
+        limit: Number of results (1-20, default: 10)
+        search_type: Type of search ("web", "news", or "images")
+
+    Returns:
+        Formatted search results with sources and extracted content
+
+    Examples:
+        search_web("Python 3.12 new features")
+        search_web("latest tech news", search_type="news", limit=5)
+    """
+    try:
+        async with LibraryDeskClient() as client:
+            response = await client.search_web(
+                query=query,
+                limit=limit,
+                search_type=search_type,
+            )
+
+            if not response.results:
+                return f"No results found for '{query}'"
+
+            output_parts = [f"## Web Search: {query}\n"]
+            output_parts.append(f"*Found {response.total_results} results in {response.search_time_ms}ms*\n")
+
+            for i, result in enumerate(response.results, 1):
+                output_parts.append(f"### {i}. {result.title}")
+                output_parts.append(f"**Source:** {result.source}")
+                output_parts.append(f"**URL:** {result.url}")
+
+                if result.published_date:
+                    output_parts.append(f"**Date:** {result.published_date}")
+
+                # Use full content if available, otherwise snippet
+                content = result.content or result.snippet
+                if content:
+                    # Truncate for readability
+                    if len(content) > 500:
+                        content = content[:500] + "..."
+                    output_parts.append(f"\n{content}")
+
+                output_parts.append("")
+
+            # Add pre-formatted sources for citations
+            if response.sources_summary:
+                output_parts.append("---")
+                output_parts.append(response.sources_summary)
+
+            logger.info(
+                "librarian_web_search",
+                query=query,
+                result_count=response.total_results,
+                search_type=search_type,
+            )
+
+            return "\n".join(output_parts)
+
+    except Exception as e:
+        logger.error("librarian_web_search_error", error=str(e), query=query)
+        return f"Error searching web: {str(e)}"
+
+
+async def read_url(
+    url: str,
+    max_length: int = 5000,
+) -> str:
+    """
+    Read and extract the main content from a URL.
+
+    Use this when you have a specific URL to read, such as:
+    - A link the user provided
+    - A URL from search results you want to read in full
+    - Documentation or article pages
+
+    Extracts the main content, removing ads, navigation, and boilerplate.
+
+    Args:
+        url: The URL to read
+        max_length: Maximum content length (default: 5000)
+
+    Returns:
+        Extracted page content with metadata
+
+    Examples:
+        read_url("https://docs.python.org/3/library/asyncio.html")
+        read_url("https://example.com/article", max_length=10000)
+    """
+    try:
+        async with LibraryDeskClient() as client:
+            result = await client.extract_content(
+                url=url,
+                include_metadata=True,
+                max_length=max_length,
+            )
+
+            if not result.success:
+                return f"Could not read page: {result.error or 'Unknown error'}"
+
+            output_parts = []
+
+            # Header with metadata
+            if result.title:
+                output_parts.append(f"# {result.title}")
+            else:
+                output_parts.append(f"# Content from {url}")
+
+            output_parts.append(f"**URL:** {url}")
+
+            if result.author:
+                output_parts.append(f"**Author:** {result.author}")
+
+            if result.date:
+                output_parts.append(f"**Date:** {result.date}")
+
+            if result.language and result.language != "en":
+                output_parts.append(f"**Language:** {result.language}")
+
+            output_parts.append("")
+
+            # Main content
+            if result.content:
+                output_parts.append(result.content)
+            else:
+                output_parts.append("(No content could be extracted)")
+
+            logger.info(
+                "librarian_read_url",
+                url=url,
+                content_length=len(result.content) if result.content else 0,
+            )
+
+            return "\n".join(output_parts)
+
+    except Exception as e:
+        logger.error("librarian_read_url_error", error=str(e), url=url)
+        return f"Error reading URL: {str(e)}"
+
+
+async def read_urls_batch(
+    urls: list[str],
+    max_length: int = 2000,
+) -> str:
+    """
+    Read and extract content from multiple URLs in parallel.
+
+    More efficient than calling read_url multiple times.
+    Max 20 URLs per batch.
+
+    Note: Individual failures don't fail the entire batch -
+    failed URLs are reported but other content is still returned.
+
+    Args:
+        urls: List of URLs to read (max 20)
+        max_length: Maximum content length per URL (default: 2000)
+
+    Returns:
+        Extracted content from all successful URLs with failure report
+
+    Examples:
+        read_urls_batch(["https://example.com/1", "https://example.com/2"])
+    """
+    try:
+        async with LibraryDeskClient() as client:
+            response = await client.extract_content_batch(
+                urls=urls,
+                include_metadata=True,
+                max_length=max_length,
+            )
+
+            output_parts = [
+                f"## Batch Content Extraction",
+                f"*Extracted {response.successful}/{response.total_urls} URLs in {response.extraction_time_ms}ms*\n",
+            ]
+
+            # Show successful extractions
+            for result in response.results:
+                if result.success:
+                    title = result.title or result.url
+                    output_parts.append(f"### {title}")
+                    output_parts.append(f"**URL:** {result.url}")
+
+                    if result.content:
+                        # Truncate for readability in batch mode
+                        content = result.content
+                        if len(content) > max_length:
+                            content = content[:max_length] + "..."
+                        output_parts.append(f"\n{content}")
+
+                    output_parts.append("")
+
+            # Report failures
+            failed = [r for r in response.results if not r.success]
+            if failed:
+                output_parts.append("---")
+                output_parts.append("### Failed Extractions")
+                for result in failed:
+                    output_parts.append(f"- {result.url}: {result.error}")
+
+            logger.info(
+                "librarian_read_urls_batch",
+                total=response.total_urls,
+                successful=response.successful,
+                failed=response.failed,
+            )
+
+            return "\n".join(output_parts)
+
+    except Exception as e:
+        logger.error("librarian_read_urls_batch_error", error=str(e))
+        return f"Error reading URLs: {str(e)}"
+
+
 # ============================================================================
 # Wiki Write Operations
 # ============================================================================
@@ -685,7 +918,7 @@ async def smart_create_wiki_page(
 
 # All tools available to The Librarian
 LIBRARIAN_TOOLS = [
-    # Research tools
+    # Research tools (internal knowledge)
     hybrid_search,
     search_wiki,
     get_wiki_page,
@@ -694,6 +927,10 @@ LIBRARIAN_TOOLS = [
     semantic_search,
     explore_knowledge_graph,
     find_related_entities,
+    # Web search & content extraction
+    search_web,
+    read_url,
+    read_urls_batch,
     # Write tools
     create_wiki_page,
     update_wiki_page,

src/agents/steward/agent.py

@@ -58,8 +58,9 @@ GUIDELINES:
 - 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)
 - Math/calculations → tatlock_core
-- Quick web searches → tatlock_core
 - Time/date queries → tatlock_core
+- 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
@@ -74,8 +75,10 @@ COMPLEXITY: [simple/moderate/complex]
 CONTEXT: [any relevant conversation context, or "none"]
 
 EXAMPLES:
+- "DELEGATE: librarian to search_web for tomorrow's weather forecast"
 - "DELEGATE: librarian to create a wiki page about CI/CD pipelines"
-- "DELEGATE: librarian to search for information about Docker networking"
+- "DELEGATE: librarian to hybrid_search for information about Docker networking"
+- "DELEGATE: librarian to read_url https://example.com/article"
 - "DELEGATE: tatlock_core to calculate the result"
 - "DELEGATE: none (conversational response only)"
 

src/agents/steward/schemas.py

@@ -60,6 +60,10 @@ class StewardRecommendation(BaseModel):
         default_factory=dict,
         description="Pre-fetched user context from memory (profile, preferences)"
     )
+    enriched_query: str = Field(
+        default="",
+        description="User query with auto-filled context (location, timezone) when not specified"
+    )
 
     def format_for_butler(self) -> str:
         """

src/agents/steward/service.py

@@ -149,6 +149,68 @@ def _extract_missing_capabilities(text: str) -> Optional[str]:
     return None
 
 
+def _build_enriched_query(user_request: str, memory_context: dict[str, Any]) -> str:
+    """
+    Build an enriched query by appending user context when not specified.
+
+    When the user asks location-dependent questions (weather, nearby, etc.)
+    without specifying a location, this appends their known location.
+    Similarly for timezone-dependent queries.
+
+    Args:
+        user_request: The user's original request
+        memory_context: Pre-fetched memory context with profile/preferences
+
+    Returns:
+        str: Query with context appended, or original query if no enrichment needed
+
+    Example:
+        >>> query = _build_enriched_query(
+        ...     "What's the weather?",
+        ...     {"profile": {"location": "Amsterdam", "timezone": "Europe/Amsterdam"}}
+        ... )
+        >>> query
+        "What's the weather?\n\n[User Context: location=Amsterdam, timezone=Europe/Amsterdam]"
+    """
+    if not memory_context:
+        return user_request
+
+    request_lower = user_request.lower()
+    profile = memory_context.get("profile", {})
+    preferences = memory_context.get("preferences", {})
+
+    context_parts = []
+
+    # Check if location is needed and not specified
+    location_keywords = ["weather", "temperature", "forecast", "nearby", "local", "here"]
+    # Use word boundary pattern to avoid false positives like "at" in "what"
+    location_prepositions = [r'\bin\b', r'\bat\b', r'\bnear\b', r'\baround\b', r'\bfor\b']
+    location_specified = any(re.search(p, request_lower) for p in location_prepositions)
+
+    if any(word in request_lower for word in location_keywords):
+        if not location_specified and profile.get("location"):
+            context_parts.append(f"location={profile['location']}")
+
+    # Check if timezone is needed and not specified
+    time_keywords = ["time", "schedule", "meeting", "appointment", "when", "today", "tomorrow"]
+    timezone_specified = any(word in request_lower for word in ["timezone", "tz", "utc", "gmt"])
+
+    if any(word in request_lower for word in time_keywords):
+        if not timezone_specified and profile.get("timezone"):
+            context_parts.append(f"timezone={profile['timezone']}")
+
+    # Add preferences if relevant
+    if preferences.get("temperature_unit") and "weather" in request_lower:
+        context_parts.append(f"temperature_unit={preferences['temperature_unit']}")
+
+    # Build enriched query
+    if context_parts:
+        context_str = ", ".join(context_parts)
+        return f"{user_request}\n\n[User Context: {context_str}]"
+
+    return user_request
+
+
 async def _prefetch_memory_context(user_request: str) -> dict[str, Any]:
     """
     Pre-fetch user context that might be needed for this request.
@@ -277,6 +339,9 @@ async def analyze_request(
             context = _extract_conversation_context(analysis_text, conversation_history)
             missing = _extract_missing_capabilities(analysis_text)
 
+            # Build enriched query with auto-filled context
+            enriched_query = _build_enriched_query(user_request, memory_context)
+
             recommendation = StewardRecommendation(
                 recommended_capabilities=capabilities,
                 reasoning=analysis_text,
@@ -284,6 +349,7 @@ async def analyze_request(
                 conversation_context=context,
                 missing_capabilities=missing,
                 memory_context=memory_context,
+                enriched_query=enriched_query,
             )
 
             # Update log context with results

src/agents/tatlock.py

@@ -17,7 +17,6 @@ from src.agents.tatlock_core.tools import (
     get_current_datetime,
     calculate_time_offset,
     time_difference,
-    search_web,
 )
 from src.core.config import config
 from src.core.logging_config import get_logger
@@ -76,18 +75,17 @@ You have direct access to several permanent tools that you should USE whenever a
    - time_difference: Calculate the time between two dates
    - Use these for ANY date/time queries - never guess at dates or times
 
-3. **Web Search** (search_web): Search for current, volatile, or factual information
-   - Use this for ANY information that might be current, factual, or outside your training data
+3. **Web Search** (via Librarian): For current, volatile, or factual information
+   - Delegate to the Librarian for web searches and research
    - Examples: news, current events, recent developments, specific facts, technical documentation
-   - Always prefer searching over guessing or using potentially outdated knowledge
-   - For extensive research questions, note that this will later be delegated to the librarian
+   - Use: delegate_to_librarian(task="search the web for ...")
 
 ## Tool Usage Guidelines
 
 - **Mathematics**: ALWAYS use the calculator tool, even for simple arithmetic
 - **Dates/Times**: ALWAYS use the date/time tools, never guess or estimate
-- **Current Information**: ALWAYS search for facts, news, or volatile information
-- **Verification**: When facts are important, use search to verify rather than rely on memory alone
+- **Current Information**: Delegate web searches to the Librarian
+- **Verification**: When facts are important, delegate to Librarian for research
 - When you use a tool, explain what you're doing in a butler-appropriate manner
 - Present tool results naturally in your response
 
@@ -145,7 +143,7 @@ class TatlockAgent(AgentInterface):
 
         # Import required classes for Ollama configuration
         from pydantic_ai.models.openai import OpenAIChatModel
-        from pydantic_ai.providers.ollama import OllamaProvider
+        from src.ollama.provider import get_ollama_provider
 
         # PydanticAI expects Ollama base URL to end with /v1
         # Remove trailing slash from ollama_host if present
@@ -155,7 +153,7 @@ class TatlockAgent(AgentInterface):
         # Create Ollama model with provider
         ollama_model = OpenAIChatModel(
             model_name=self.model_name,
-            provider=OllamaProvider(base_url=base_url)
+            provider=get_ollama_provider()
         )
 
         # Create PydanticAI agent with Ollama model
@@ -238,28 +236,8 @@ class TatlockAgent(AgentInterface):
                 ctx.deps.log_call(f"🕐 Calculating time difference between {date1_str} and {date2_str}")
             return time_difference(date1_str, date2_str)
 
-        # Web search tool
-        @self._agent.tool
-        async def web_search(ctx: RunContext[ToolCallTracker], query: str, num_results: int = 5) -> str:
-            """
-            Search the web using SearXNG for current information.
-
-            Use this tool for ANY information that might be:
-            - Current or time-sensitive (news, events, recent developments)
-            - Factual and verifiable (statistics, technical specs, definitions)
-            - Outside your training data or knowledge cutoff
-
-            Args:
-                query: Search query string
-                num_results: Number of results to return (default: 5, max: 10)
-
-            Returns:
-                Formatted search results with titles, URLs, and snippets
-            """
-            # Log the search query to reasoning output
-            if ctx.deps:
-                ctx.deps.log_call(f"🔍 Searching for: '{query}'")
-            return await search_web(query, num_results)
+        # NOTE: Web search has been moved to The Librarian agent.
+        # Use delegate_to_librarian(task="search web for ...") for web search.
 
     @property
     def agent(self):
@@ -470,7 +448,7 @@ class TatlockAgent(AgentInterface):
             ... )
         """
         from pydantic_ai.models.openai import OpenAIChatModel
-        from pydantic_ai.providers.ollama import OllamaProvider
+        from src.ollama.provider import get_ollama_provider
 
         logger.info(
             "tatlock_run_with_scoped_tools",
@@ -486,7 +464,7 @@ class TatlockAgent(AgentInterface):
 
         ollama_model = OpenAIChatModel(
             model_name=self.model_name,
-            provider=OllamaProvider(base_url=base_url)
+            provider=get_ollama_provider()
         )
 
         # Create agent with scoped tools
@@ -564,7 +542,7 @@ class TatlockAgent(AgentInterface):
             Text chunks from the streaming response
         """
         from pydantic_ai.models.openai import OpenAIChatModel
-        from pydantic_ai.providers.ollama import OllamaProvider
+        from src.ollama.provider import get_ollama_provider
 
         logger.info(
             "tatlock_run_with_scoped_tools_stream",
@@ -579,7 +557,7 @@ class TatlockAgent(AgentInterface):
 
         ollama_model = OpenAIChatModel(
             model_name=self.model_name,
-            provider=OllamaProvider(base_url=base_url)
+            provider=get_ollama_provider()
         )
 
         # Create agent with scoped tools
@@ -630,6 +608,241 @@ class TatlockAgent(AgentInterface):
 
         logger.info("tatlock_scoped_run_complete")
 
+    async def orchestrate_tool_calls(
+        self,
+        user_message: str,
+        steward_note: str,
+        scoped_tools: list[Any],
+        message_history: list[dict],
+        tool_tracker: Any = None,
+    ) -> dict[str, Any]:
+        """
+        Phase 1: Execute tool calls and delegations, return structured results.
+
+        This is the coordination phase where Tatlock orchestrates tool calls
+        and expert delegations. The raw output is captured for Phase 2 synthesis.
+
+        Args:
+            user_message: The user's original message
+            steward_note: Note from Steward (invisible to user)
+            scoped_tools: List of tool definitions from household registry
+            message_history: Conversation history
+            tool_tracker: Optional tool call tracker for benchmarking
+
+        Returns:
+            dict with:
+                - tools_called: List of tool names that were called
+                - expert_results: Dict mapping expert names to their outputs
+                - 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,
+            UserPromptPart,
+            TextPart,
+            ToolCallPart,
+            ToolReturnPart,
+        )
+
+        logger.info(
+            "tatlock_orchestrate_tool_calls",
+            user_message_preview=user_message[:100],
+            scoped_tool_count=len(scoped_tools),
+            history_length=len(message_history),
+        )
+
+        # 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()
+        )
+
+        # Create agent with scoped tools
+        scoped_agent = Agent(
+            ollama_model,
+            system_prompt=TATLOCK_SYSTEM_PROMPT,
+            tools=scoped_tools,
+        )
+
+        # Prepend Steward's note to the request
+        enriched_message = f"{steward_note}\n\n{user_message}"
+
+        # Convert message history to PydanticAI format
+        pydantic_history = []
+        for msg in message_history:
+            role = msg.get("role")
+            content = msg.get("content", "")
+
+            if not content or not content.strip():
+                continue
+
+            if role == "user":
+                pydantic_history.append(
+                    ModelRequest(parts=[UserPromptPart(content=content)])
+                )
+            elif role == "assistant":
+                pydantic_history.append(
+                    ModelResponse(parts=[TextPart(content=content)])
+                )
+
+        # Run with scoped tools and tracker
+        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"})
+        )
+
+        # Extract tool calls and results from the agent's messages
+        tools_called = []
+        expert_results = {}
+        tool_outputs = {}
+
+        # Parse through new messages to find tool calls and returns
+        for msg in result.new_messages():
+            if isinstance(msg, ModelResponse):
+                for part in msg.parts:
+                    if isinstance(part, ToolCallPart):
+                        tools_called.append(part.tool_name)
+            elif isinstance(msg, ModelRequest):
+                for part in msg.parts:
+                    if isinstance(part, ToolReturnPart):
+                        tool_name = part.tool_name
+                        content = part.content
+
+                        # Categorize as expert result or tool output
+                        if tool_name.startswith("delegate_to_"):
+                            expert_name = tool_name.replace("delegate_to_", "")
+                            expert_results[expert_name] = content
+                        else:
+                            tool_outputs[tool_name] = content
+
+        logger.info(
+            "tatlock_orchestration_complete",
+            tools_called=tools_called,
+            expert_count=len(expert_results),
+            tool_output_count=len(tool_outputs),
+        )
+
+        return {
+            "tools_called": tools_called,
+            "expert_results": expert_results,
+            "tool_outputs": tool_outputs,
+            "raw_output": result.output,
+        }
+
+    async def synthesize_from_results(
+        self,
+        user_message: str,
+        orchestration_results: dict[str, Any],
+        message_history: list[dict],
+    ) -> str:
+        """
+        Phase 2: Synthesize butler-toned response from gathered results.
+
+        This is the synthesis phase where Tatlock takes the coordination
+        results and produces a properly butler-toned response.
+
+        Args:
+            user_message: The user's original message
+            orchestration_results: Results from orchestrate_tool_calls()
+            message_history: Conversation history
+
+        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
+
+        logger.info(
+            "tatlock_synthesize_from_results",
+            user_message_preview=user_message[:100],
+            expert_count=len(orchestration_results.get("expert_results", {})),
+            tool_count=len(orchestration_results.get("tool_outputs", {})),
+        )
+
+        # Build synthesis prompt with all available information
+        synthesis_parts = []
+        synthesis_parts.append(f"The user asked: {user_message}")
+        synthesis_parts.append("")
+
+        # Add expert findings if any
+        if orchestration_results.get("expert_results"):
+            synthesis_parts.append("Expert findings:")
+            for expert, result in orchestration_results["expert_results"].items():
+                synthesis_parts.append(f"- {expert.title()}: {result}")
+            synthesis_parts.append("")
+
+        # Add tool outputs if any
+        if orchestration_results.get("tool_outputs"):
+            synthesis_parts.append("Tool results:")
+            for tool, result in orchestration_results["tool_outputs"].items():
+                synthesis_parts.append(f"- {tool}: {result}")
+            synthesis_parts.append("")
+
+        synthesis_parts.append(
+            "Based on this information, provide a response to the user. "
+            "Maintain your butler personality - address them as 'sir', "
+            "use formal but personable language, and be helpful."
+        )
+
+        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()
+        )
+
+        # Synthesis agent uses butler prompt but no tools
+        synthesis_agent = Agent(
+            ollama_model,
+            system_prompt=TATLOCK_SYSTEM_PROMPT,
+            # No tools for synthesis phase
+        )
+
+        # Convert message history to PydanticAI format
+        pydantic_history = []
+        for msg in message_history:
+            role = msg.get("role")
+            content = msg.get("content", "")
+
+            if not content or not content.strip():
+                continue
+
+            if role == "user":
+                pydantic_history.append(
+                    ModelRequest(parts=[UserPromptPart(content=content)])
+                )
+            elif role == "assistant":
+                pydantic_history.append(
+                    ModelResponse(parts=[TextPart(content=content)])
+                )
+
+        # Run synthesis
+        result = await synthesis_agent.run(
+            synthesis_prompt,
+            message_history=pydantic_history if pydantic_history else None,
+        )
+
+        logger.info(
+            "tatlock_synthesis_complete",
+            response_preview=result.output[:100],
+        )
+
+        return result.output
+
     async def get_capabilities(self) -> dict:
         """Return current capabilities."""
         return {

src/agents/tatlock_core/__init__.py

@@ -1,7 +1,8 @@
 """
 Tatlock's core tools package.
 
-Provides calculator, date/time, and web search capabilities.
+Provides calculator and date/time capabilities.
+Web search has been moved to The Librarian agent.
 Organized as a household member with toolset and capability registration.
 """
 from .capability import TATLOCK_CORE_CAPABILITY, get_capability
@@ -10,7 +11,6 @@ from .tools import (
     calculate,
     calculate_time_offset,
     get_current_datetime,
-    search_web,
     time_difference,
 )
 
@@ -20,7 +20,6 @@ __all__ = [
     "get_current_datetime",
     "calculate_time_offset",
     "time_difference",
-    "search_web",
     # Toolset
     "tatlock_core_tools",
     "get_core_tools",

src/agents/tatlock_core/capability.py

@@ -11,10 +11,10 @@ TATLOCK_CORE_CAPABILITY = HouseholdCapability(
     name="tatlock_core",
     role="Butler's Core Tools",
     category="core",
-    description="Essential tools for computation, date/time operations, and web searches",
-    domains=["computation", "datetime", "information", "research"],
+    description="Essential tools for computation and date/time operations",
+    domains=["computation", "datetime", "math", "calculator"],
     cost="low",
-    requires_network=True,  # For web search
+    requires_network=False,  # Web search moved to Librarian
 )
 
 

src/agents/tatlock_core/tools.py

@@ -256,96 +256,5 @@ def time_difference(date1_str: str, date2_str: str = "now") -> str:
         return f"Error calculating time difference: {str(e)}"
 
 
-# ============================================================================
-# SearXNG Search Tool
-# ============================================================================
-
-async def search_web(query: str, num_results: int = 5) -> str:
-    """
-    Search the web using SearXNG.
-
-    Args:
-        query: Search query string
-        num_results: Number of results to return (default: 5, max: 10)
-
-    Returns:
-        Formatted search results as a string with titles, URLs, and snippets
-
-    Examples:
-        search_web("Python async programming") -> "1. Title: ...\n   URL: ...\n   ..."
-    """
-    try:
-        # Limit results
-        num_results = min(num_results, 10)
-
-        # Get SearXNG host with fallback logic
-        searxng_host = str(config.SEARXNG_HOST)
-
-        # Try production host first, fall back to localhost in development
-        hosts_to_try = [searxng_host]
-        if config.ENVIRONMENT.value == "development" and "localhost" not in searxng_host:
-            # Add localhost fallback for development
-            hosts_to_try.append("http://localhost:8087")
-
-        last_error = None
-
-        for host in hosts_to_try:
-            try:
-                logger.debug("searxng_search_attempt", host=host, query=query)
-
-                async with httpx.AsyncClient(timeout=config.SEARXNG_TIMEOUT) as client:
-                    response = await client.get(
-                        f"{host}/search",
-                        params={
-                            "q": query,
-                            "format": "json",
-                            "pageno": 1,
-                        }
-                    )
-
-                    if response.status_code == 200:
-                        data = response.json()
-                        results = data.get("results", [])
-
-                        if not results:
-                            return f"No results found for '{query}'"
-
-                        # Format results
-                        formatted_results = []
-                        for i, result in enumerate(results[:num_results], 1):
-                            title = result.get("title", "No title")
-                            url = result.get("url", "")
-                            content = result.get("content", "No description available")
-
-                            formatted_results.append(
-                                f"{i}. {title}\n"
-                                f"   URL: {url}\n"
-                                f"   {content}\n"
-                            )
-
-                        logger.info(
-                            "searxng_search_success",
-                            host=host,
-                            query=query,
-                            result_count=len(results),
-                        )
-                        return "\n".join(formatted_results)
-                    else:
-                        last_error = f"SearXNG returned status {response.status_code}"
-
-            except httpx.ConnectError:
-                last_error = f"Cannot connect to SearXNG at {host}"
-                logger.warning("searxng_connection_failed", host=host)
-                continue
-            except Exception as e:
-                last_error = str(e)
-                logger.warning("searxng_error", host=host, error=str(e))
-                continue
-
-        # All hosts failed
-        logger.error("searxng_all_hosts_failed", error=last_error)
-        return f"Error searching: {last_error}. Please check that SearXNG is running."
-
-    except Exception as e:
-        logger.error("searxng_unexpected_error", error=str(e), exc_info=True)
-        return f"Error searching: {str(e)}"
+# NOTE: Web search has been moved to The Librarian agent.
+# Use delegate_to_librarian(task="search web for ...") for web search.

src/agents/tatlock_core/toolset.py

@@ -55,17 +55,8 @@ time_difference_tool = Tool(
     ),
 )
 
-web_search_tool = Tool(
-    function=tools.search_web,
-    name="search_web",
-    description=(
-        "Search the web using SearXNG for current information. "
-        "Use this to find recent events, current data, or verify facts. "
-        "Returns formatted results with titles, URLs, and snippets. "
-        "Useful for information that may have changed since training data."
-    ),
-    takes_ctx=False,
-)
+# NOTE: Web search has been moved to The Librarian agent.
+# Use delegate_to_librarian(task="search web for ...") for web search.
 
 
 # Combined toolset of all core tools
@@ -74,7 +65,6 @@ tatlock_core_tools = [
     current_datetime_tool,
     time_offset_tool,
     time_difference_tool,
-    web_search_tool,
 ]
 
 

src/agents/tools.py

@@ -4,20 +4,14 @@ Tatlock's permanent tools.
 These tools are always available to the butler agent:
 - Calculator: For all mathematical operations
 - Date/Time toolkit: For current time and time calculations
-- SearXNG search: For searching the web for current information
+
+Note: Web search has been moved to The Librarian agent.
+See src/agents/librarian/tools.py for search_web functionality.
 """
 
-import logging
 import math
 import re
 from datetime import datetime, timedelta
-from typing import Any
-
-import httpx
-
-from src.core.config import config
-
-logger = logging.getLogger(__name__)
 
 
 # ============================================================================
@@ -256,91 +250,3 @@ def time_difference(date1_str: str, date2_str: str = "now") -> str:
 
     except Exception as e:
         return f"Error calculating time difference: {str(e)}"
-
-
-# ============================================================================
-# SearXNG Search Tool
-# ============================================================================
-
-async def search_web(query: str, num_results: int = 5) -> str:
-    """
-    Search the web using SearXNG.
-
-    Args:
-        query: Search query string
-        num_results: Number of results to return (default: 5, max: 10)
-
-    Returns:
-        Formatted search results as a string with titles, URLs, and snippets
-
-    Examples:
-        search_web("Python async programming") -> "1. Title: ...\n   URL: ...\n   ..."
-    """
-    try:
-        # Limit results
-        num_results = min(num_results, 10)
-
-        # Get SearXNG host with fallback logic
-        searxng_host = str(config.SEARXNG_HOST)
-
-        # Try production host first, fall back to localhost in development
-        hosts_to_try = [searxng_host]
-        if config.ENVIRONMENT.value == "development" and "localhost" not in searxng_host:
-            # Add localhost fallback for development
-            hosts_to_try.append("http://localhost:8087")
-
-        last_error = None
-
-        for host in hosts_to_try:
-            try:
-                logger.info(f"Attempting SearXNG search at {host}")
-
-                async with httpx.AsyncClient(timeout=config.SEARXNG_TIMEOUT) as client:
-                    response = await client.get(
-                        f"{host}/search",
-                        params={
-                            "q": query,
-                            "format": "json",
-                            "pageno": 1,
-                        }
-                    )
-
-                    if response.status_code == 200:
-                        data = response.json()
-                        results = data.get("results", [])
-
-                        if not results:
-                            return f"No results found for '{query}'"
-
-                        # Format results
-                        formatted_results = []
-                        for i, result in enumerate(results[:num_results], 1):
-                            title = result.get("title", "No title")
-                            url = result.get("url", "")
-                            content = result.get("content", "No description available")
-
-                            formatted_results.append(
-                                f"{i}. {title}\n"
-                                f"   URL: {url}\n"
-                                f"   {content}\n"
-                            )
-
-                        return "\n".join(formatted_results)
-                    else:
-                        last_error = f"SearXNG returned status {response.status_code}"
-
-            except httpx.ConnectError:
-                last_error = f"Cannot connect to SearXNG at {host}"
-                logger.warning(f"SearXNG connection failed at {host}, trying next host if available")
-                continue
-            except Exception as e:
-                last_error = str(e)
-                logger.warning(f"SearXNG error at {host}: {e}")
-                continue
-
-        # All hosts failed
-        return f"Error searching: {last_error}. Please check that SearXNG is running."
-
-    except Exception as e:
-        logger.error(f"Unexpected error in search_web: {e}", exc_info=True)
-        return f"Error searching: {str(e)}"

src/core/household_registry.py

@@ -273,6 +273,65 @@ class HouseholdRegistry:
 
         return tools
 
+    def get_streaming_delegation_tools(self, names: list[str]) -> list[Any]:
+        """
+        Get streaming delegation wrapper tools for specified capabilities.
+
+        Similar to get_delegation_tools() but returns streaming wrappers
+        that yield butler-perspective think messages during execution.
+
+        These wrappers emit think slugs like:
+        - "Allow me to consult the archives, sir."
+        - "The Librarian has compiled the relevant findings."
+
+        Args:
+            names: List of member names to include
+
+        Returns:
+            List of streaming delegation wrappers and/or raw tools
+
+        Example:
+            >>> tools = registry.get_streaming_delegation_tools(["librarian"])
+            >>> async for chunk in tools[0](task="Search for Docker"):
+            ...     print(chunk)  # Yields think messages then result
+        """
+        from src.agents.delegation import STREAMING_DELEGATION_WRAPPERS
+
+        tools = []
+        for name in names:
+            member = self._members.get(name)
+            if not member:
+                logger.warning(
+                    "household_member_not_found",
+                    requested_name=name,
+                    available_names=list(self._members.keys()),
+                )
+                continue
+
+            # Check if this member has a streaming delegation wrapper
+            if name in STREAMING_DELEGATION_WRAPPERS and member.agent is not None:
+                tools.append(STREAMING_DELEGATION_WRAPPERS[name])
+                logger.debug(
+                    "streaming_delegation_wrapper_added",
+                    member=name,
+                )
+            else:
+                # No agent = direct tools (e.g., tatlock_core)
+                tools.extend(member.tools)
+                logger.debug(
+                    "raw_tools_added",
+                    member=name,
+                    tool_count=len(member.tools),
+                )
+
+        logger.info(
+            "streaming_delegation_tools_created",
+            requested_members=names,
+            total_tools=len(tools),
+        )
+
+        return tools
+
     def list_members(self) -> list[str]:
         """
         List all registered member names.

src/ollama/provider.py

@@ -0,0 +1,130 @@
+"""
+PydanticAI provider for Ollama with message sanitization.
+
+Ollama's OpenAI-compatible API rejects messages with `content: null`,
+which PydanticAI sends for assistant messages that only contain tool calls.
+This provider sanitizes messages to use empty strings instead of null.
+"""
+from typing import Any
+
+from openai import AsyncOpenAI
+from pydantic_ai.providers.ollama import OllamaProvider
+
+from src.core.config import config
+from src.core.logging_config import get_logger
+
+logger = get_logger(__name__)
+
+
+class TatlockOllamaProvider(OllamaProvider):
+    """
+    Custom OllamaProvider with message sanitization for Tatlock agents.
+
+    Fixes the 'invalid message content type: <nil>' error that occurs
+    when assistant messages have `content: null` with tool calls.
+    """
+
+    def __init__(self, base_url: str | None = None):
+        """
+        Initialize provider with Ollama base URL.
+
+        Args:
+            base_url: Ollama API URL (defaults to config.OLLAMA_HOST/v1)
+        """
+        if base_url is None:
+            clean_host = str(config.OLLAMA_HOST).rstrip("/")
+            base_url = f"{clean_host}/v1"
+
+        super().__init__(base_url=base_url)
+
+        # Override the client with our sanitized version
+        self._openai_client = _SanitizedAsyncOpenAI(base_url=base_url)
+
+        logger.debug("tatlock_ollama_provider_created", base_url=base_url)
+
+
+class _SanitizedAsyncOpenAI(AsyncOpenAI):
+    """AsyncOpenAI client that sanitizes messages before sending."""
+
+    def __init__(self, **kwargs: Any):
+        # Ollama doesn't need an API key
+        super().__init__(api_key="ollama", **kwargs)
+
+    @property
+    def chat(self) -> "_SanitizedChat":
+        """Return sanitized chat interface."""
+        return _SanitizedChat(self)
+
+
+class _SanitizedChat:
+    """Chat interface wrapper with sanitized completions."""
+
+    def __init__(self, client: _SanitizedAsyncOpenAI):
+        self._client = client
+        self._original_chat = AsyncOpenAI.chat.fget(client)  # type: ignore
+
+    @property
+    def completions(self) -> "_SanitizedCompletions":
+        """Return sanitized completions interface."""
+        return _SanitizedCompletions(self._original_chat.completions)
+
+
+class _SanitizedCompletions:
+    """Completions wrapper that sanitizes messages before API calls."""
+
+    def __init__(self, original_completions: Any):
+        self._original = original_completions
+
+    async def create(self, **kwargs: Any) -> Any:
+        """
+        Create chat completion with sanitized messages.
+
+        Converts `content: null` to `content: ""` in assistant messages
+        to prevent Ollama's 'invalid message content type: <nil>' error.
+        """
+        if "messages" in kwargs:
+            kwargs["messages"] = _sanitize_messages(kwargs["messages"])
+
+        return await self._original.create(**kwargs)
+
+
+def _sanitize_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    """
+    Sanitize messages to fix null content issues.
+
+    When an assistant message has tool_calls but no text content,
+    PydanticAI sets content to None. Ollama rejects this.
+    We convert None to empty string.
+
+    Args:
+        messages: List of chat messages
+
+    Returns:
+        Sanitized messages with null content replaced by empty strings
+    """
+    sanitized = []
+    for msg in messages:
+        msg_copy = dict(msg)
+
+        # Fix null content in assistant messages with tool calls
+        if msg_copy.get("role") == "assistant":
+            if msg_copy.get("content") is None and msg_copy.get("tool_calls"):
+                msg_copy["content"] = ""
+                logger.debug(
+                    "sanitized_null_content",
+                    tool_call_count=len(msg_copy["tool_calls"]),
+                )
+
+        sanitized.append(msg_copy)
+
+    return sanitized
+
+
+def get_ollama_provider() -> TatlockOllamaProvider:
+    """
+    Get a configured Ollama provider for PydanticAI agents.
+
+    Returns:
+        TatlockOllamaProvider configured with sanitization
+    """
+    return TatlockOllamaProvider()

src/responses/service.py

@@ -43,7 +43,7 @@ async def _execute_single_delegation(
     Execute a single delegation to an agent.
 
     Args:
-        agent_name: Name of agent (biographer, librarian)
+        agent_name: Name of agent (biographer, librarian, housekeeper)
         task: Task description
         tracker: Tool call tracker
 
@@ -67,6 +67,13 @@ async def _execute_single_delegation(
         await tracker.track_call("delegate_to_librarian", duration)
         return (agent_name, result.output)
 
+    elif agent_name == "housekeeper":
+        from src.agents.delegation import delegate_to_housekeeper
+        result = await delegate_to_housekeeper(task=task)
+        duration = time.time() - start_time
+        await tracker.track_call("delegate_to_housekeeper", duration)
+        return (agent_name, result.output)
+
     else:
         return (agent_name, f"Unknown agent: {agent_name}")
 
@@ -244,6 +251,68 @@ async def _direct_delegation(
     return "\n\n".join(results) if results else "I apologize, sir. No delegation results available."
 
 
+async def _direct_delegation_with_results(
+    user_message: str,
+    recommendation: "StewardRecommendation",
+    tracker: "ToolCallTracker",
+    conversation_id: str,
+) -> dict:
+    """
+    Directly delegate to expert agents and return structured results.
+
+    This is the Phase 1 variant of direct delegation that returns results
+    in the same format as TatlockAgent.orchestrate_tool_calls() for
+    consistent Phase 2 synthesis.
+
+    Args:
+        user_message: User's request
+        recommendation: Steward's recommendation
+        tracker: Tool call tracker
+        conversation_id: Conversation ID
+
+    Returns:
+        dict: Orchestration results with expert_results, tool_outputs, etc.
+    """
+    logger.info(
+        "direct_delegation_with_results",
+        agents=recommendation.recommended_capabilities,
+        conversation_id=conversation_id,
+    )
+
+    expert_results = {}
+    tools_called = []
+
+    for agent in recommendation.recommended_capabilities:
+        try:
+            agent_name, result = await _execute_single_delegation(
+                agent, user_message, tracker
+            )
+            expert_results[agent_name] = result
+            tools_called.append(f"delegate_to_{agent_name}")
+
+            logger.info(
+                "direct_delegation_result",
+                agent=agent_name,
+                result_preview=result[:100] if result else "empty",
+                conversation_id=conversation_id,
+            )
+        except Exception as e:
+            logger.error(
+                "direct_delegation_failed",
+                agent=agent,
+                error=str(e),
+                conversation_id=conversation_id,
+            )
+            expert_results[agent] = f"Error: {e}"
+
+    return {
+        "tools_called": tools_called,
+        "expert_results": expert_results,
+        "tool_outputs": {},  # No tool outputs for direct delegation
+        "raw_output": "",  # No raw output for direct delegation
+    }
+
+
 # Global conversation history tracker
 # In production, this would be backed by a database or Redis
 _conversation_history = ConversationHistory(max_turns=20)
@@ -379,12 +448,13 @@ async def create_response(request: ResponseRequest) -> Response:
 
 async def create_response_with_steward(request: ResponseRequest) -> Response:
     """
-    Create response using Steward preprocessing (Phase 2 flow).
+    Create response using Steward preprocessing and two-phase Tatlock execution.
 
-    This is the two-tier architecture where:
+    This is the two-tier architecture with two-phase synthesis:
     1. Steward analyzes the request and recommends capabilities
-    2. Tatlock runs with scoped tools based on recommendations
-    3. Tool usage is tracked for benchmarking
+    2. Phase 1: Tatlock orchestrates tool calls and expert delegations
+    3. Phase 2: Tatlock synthesizes butler-toned response from results
+    4. Tool usage is tracked for benchmarking
 
     Args:
         request: Response request
@@ -420,52 +490,66 @@ async def create_response_with_steward(request: ResponseRequest) -> Response:
         conversation_id=conversation_id,
     )
 
-    # Phase 1: Steward preprocessing
+    # Steward preprocessing
     enriched = await preprocess_request(
         user_message,
         conversation_history=conversation_history,
         conversation_id=conversation_id,
     )
 
-    # Phase 2: Initialize tool tracker
+    # Initialize tool tracker
     tracker = ToolCallTracker(
         recommended_capabilities=enriched.recommendation.recommended_capabilities,
         conversation_id=conversation_id,
     )
 
-    # Phase 3: Check if direct delegation is recommended
-    # If Steward recommends ONLY delegation agents (biographer/librarian),
-    # skip Tatlock and delegate directly
+    # Check if direct delegation is recommended
+    # If Steward recommends ONLY delegation agents (biographer/librarian/housekeeper),
+    # we still use two-phase but delegate directly in Phase 1
+    delegation_agents = {"biographer", "librarian", "housekeeper"}
     delegation_only = all(
-        cap in ("biographer", "librarian")
+        cap in delegation_agents
         for cap in enriched.recommendation.recommended_capabilities
     ) and enriched.recommendation.recommended_capabilities
 
+    from src.agents.tatlock import TatlockAgent
+    tatlock = TatlockAgent()
+
+    # Use enriched query (with location/timezone context) if available
+    effective_query = enriched.recommendation.enriched_query or user_message
+
     if delegation_only:
-        tatlock_response = await _direct_delegation(
-            user_message, enriched.recommendation, tracker, conversation_id
+        # Direct delegation path - collect results then synthesize
+        orchestration_results = await _direct_delegation_with_results(
+            effective_query, enriched.recommendation, tracker, conversation_id
         )
     else:
-        # Phase 3a: Run Tatlock with scoped tools
-        from src.agents.tatlock import TatlockAgent
-        tatlock = TatlockAgent()
-
-        tatlock_response = await tatlock.run_with_scoped_tools(
-            user_message=user_message,
+        # Phase 1: Orchestrate tool calls
+        orchestration_results = await tatlock.orchestrate_tool_calls(
+            user_message=effective_query,
             steward_note=enriched.steward_note,
             scoped_tools=enriched.scoped_tools,
             message_history=conversation_history,
             tool_tracker=tracker,
         )
 
-        # Phase 3b: Check for text-based delegation fallback
-        # If Tatlock outputs [DELEGATE:...] instead of calling the function,
-        # we parse and execute it here
-        tatlock_response = await _handle_text_delegation(
-            tatlock_response, tracker, conversation_id
-        )
+        # Handle text-based delegation fallback if present
+        if "[DELEGATE:" in orchestration_results.get("raw_output", ""):
+            text_delegation_results = await _handle_text_delegation(
+                orchestration_results["raw_output"], tracker, conversation_id
+            )
+            # Add text delegation results to expert_results
+            if text_delegation_results != orchestration_results["raw_output"]:
+                orchestration_results["expert_results"]["text_delegation"] = text_delegation_results
 
-    # Phase 4: Finalize tool tracking
+    # Phase 2: Synthesize butler-toned response from all results
+    tatlock_response = await tatlock.synthesize_from_results(
+        user_message=user_message,
+        orchestration_results=orchestration_results,
+        message_history=conversation_history,
+    )
+
+    # Finalize tool tracking
     await tracker.finalize()
 
     # Build response output items

src/responses/streaming.py

@@ -118,11 +118,12 @@ class StreamingCoordinator:
         request: "ResponseRequest"  # type: ignore # Forward reference
     ) -> AsyncGenerator[StreamEvent, None]:
         """
-        Stream response with Steward preprocessing (Phase 2 flow).
+        Stream response with Steward preprocessing and two-phase Tatlock execution.
 
         Streams in order:
         1. Steward's analysis as reasoning summary
-        2. Tatlock's response as output text
+        2. Think slugs during expert delegation (butler-perspective messages)
+        3. Synthesized butler-toned response as output text
 
         Args:
             request: Response request
@@ -130,11 +131,17 @@ class StreamingCoordinator:
         Yields:
             StreamEvent: Stream of SSE events
         """
-        from src.responses.service import _calculate_usage, generate_id, _conversation_history
+        from src.responses.service import (
+            _calculate_usage,
+            generate_id,
+            _conversation_history,
+            _direct_delegation_with_results,
+        )
         from src.core.preprocessing import preprocess_request
         from src.core.tool_tracking import ToolCallTracker
         from src.responses.schemas import MessageOutputItem, ReasoningOutputItem, OutputTextContent
         from src.agents.tatlock import TatlockAgent
+        from src.agents.delegation import get_think_message, STREAMING_DELEGATION_WRAPPERS
         import asyncio
 
         output_items = []
@@ -152,7 +159,7 @@ class StreamingCoordinator:
 
             conversation_history = request.input[:-1] if len(request.input) > 1 else []
 
-            # Phase 1: Steward preprocessing
+            # Steward preprocessing
             enriched = await preprocess_request(
                 user_message,
                 conversation_history=conversation_history,
@@ -179,31 +186,62 @@ class StreamingCoordinator:
             )
             output_items.append(reasoning_item)
 
-            # Phase 2: Initialize tool tracker
+            # Initialize tool tracker
             tracker = ToolCallTracker(
                 recommended_capabilities=enriched.recommendation.recommended_capabilities,
                 conversation_id=conversation_id,
             )
 
-            # Phase 3: Stream Tatlock's response with scoped tools
-            tatlock = TatlockAgent()
-            tatlock_response_parts = []
+            # Check if direct delegation is recommended
+            delegation_agents = {"biographer", "librarian", "housekeeper"}
+            delegation_only = all(
+                cap in delegation_agents
+                for cap in enriched.recommendation.recommended_capabilities
+            ) and enriched.recommendation.recommended_capabilities
 
-            async for chunk in tatlock.run_with_scoped_tools_stream(
+            tatlock = TatlockAgent()
+
+            if delegation_only:
+                # Direct delegation path with streaming think slugs
+                orchestration_results = await self._stream_direct_delegation(
+                    user_message=user_message,
+                    recommendation=enriched.recommendation,
+                    tracker=tracker,
+                    conversation_id=conversation_id,
+                )
+
+                # Stream think slugs that were collected during delegation
+                # Each think message is complete, so we signal done after each
+                for think_msg in orchestration_results.get("think_messages", []):
+                    yield ReasoningSummaryDelta(delta=think_msg)
+                    yield ReasoningSummaryDone()
+                    await asyncio.sleep(0.05)
+
+            else:
+                # Phase 1: Orchestrate tool calls
+                orchestration_results = await tatlock.orchestrate_tool_calls(
+                    user_message=user_message,
+                    steward_note=enriched.steward_note,
+                    scoped_tools=enriched.scoped_tools,
+                    message_history=conversation_history,
+                    tool_tracker=tracker,
+                )
+
+            # Phase 2: Synthesize butler-toned response
+            tatlock_response = await tatlock.synthesize_from_results(
                 user_message=user_message,
-                steward_note=enriched.steward_note,
-                scoped_tools=enriched.scoped_tools,
+                orchestration_results=orchestration_results,
                 message_history=conversation_history,
-                tool_tracker=tracker,
-            ):
-                tatlock_response_parts.append(chunk)
-                yield OutputTextDelta(delta=chunk)
+            )
+
+            # Stream the synthesized response
+            chunk_size = 50
+            for i in range(0, len(tatlock_response), chunk_size):
+                yield OutputTextDelta(delta=tatlock_response[i:i + chunk_size])
+                await asyncio.sleep(0.02)
 
             yield OutputTextDone()
 
-            # Combine response for output item
-            tatlock_response = "".join(tatlock_response_parts)
-
             # Add Tatlock message to output items
             message_item = MessageOutputItem(
                 id=f"msg_{generate_id()}",
@@ -217,7 +255,7 @@ class StreamingCoordinator:
             )
             output_items.append(message_item)
 
-            # Phase 4: Finalize tool tracking
+            # Finalize tool tracking
             await tracker.finalize()
 
             # Calculate usage and build final response
@@ -241,6 +279,85 @@ class StreamingCoordinator:
             # Stream error event
             yield self._create_error_event(e)
 
+    async def _stream_direct_delegation(
+        self,
+        user_message: str,
+        recommendation: "StewardRecommendation",  # type: ignore
+        tracker: "ToolCallTracker",  # type: ignore
+        conversation_id: str,
+    ) -> dict:
+        """
+        Execute direct delegation with streaming think messages.
+
+        Collects think messages as delegations execute for streaming to client.
+
+        Args:
+            user_message: User's request
+            recommendation: Steward's recommendation
+            tracker: Tool call tracker
+            conversation_id: Conversation ID
+
+        Returns:
+            dict: Orchestration results with think_messages list
+        """
+        from src.agents.delegation import (
+            get_think_message,
+            delegate_to_librarian,
+            delegate_to_biographer,
+            delegate_to_housekeeper,
+        )
+        import time as time_module
+
+        expert_results = {}
+        tools_called = []
+        think_messages = []
+
+        for agent in recommendation.recommended_capabilities:
+            # Emit start think message
+            start_msg = get_think_message(agent, user_message, "start")
+            think_messages.append(start_msg + "\n")
+
+            start_time = time_module.time()
+            try:
+                # Execute delegation
+                if agent == "librarian":
+                    result = await delegate_to_librarian(task=user_message)
+                elif agent == "biographer":
+                    result = await delegate_to_biographer(task=user_message)
+                elif agent == "housekeeper":
+                    result = await delegate_to_housekeeper(task=user_message)
+                else:
+                    result = None
+
+                duration = time_module.time() - start_time
+                await tracker.track_call(f"delegate_to_{agent}", duration)
+
+                if result and result.success:
+                    expert_results[agent] = result.output
+                    tools_called.append(f"delegate_to_{agent}")
+                    # Emit success think message
+                    success_msg = get_think_message(agent, user_message, "success")
+                    think_messages.append(success_msg + "\n")
+                else:
+                    error_msg = result.error if result else "Unknown error"
+                    expert_results[agent] = f"Error: {error_msg}"
+                    # Emit error think message
+                    error_think = get_think_message(agent, user_message, "error")
+                    think_messages.append(error_think + "\n")
+
+            except Exception as e:
+                expert_results[agent] = f"Error: {e}"
+                error_think = get_think_message(agent, user_message, "error")
+                think_messages.append(error_think + "\n")
+
+        return {
+            "tools_called": tools_called,
+            "expert_results": expert_results,
+            "tool_outputs": {},
+            "raw_output": "",
+            "think_messages": think_messages,
+        }
+
     async def stream_response(
         self,
         request: "ResponseRequest"  # type: ignore # Forward reference

tests/agents/librarian/test_tools.py

@@ -0,0 +1,426 @@
+"""
+Tests for Librarian tools.
+
+Tests the tool functions that wrap the Library-Desk API,
+including the new web search and content extraction tools.
+"""
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+
+from src.agents.librarian.tools import (
+    search_web,
+    read_url,
+    read_urls_batch,
+    hybrid_search,
+    search_wiki,
+)
+from src.agents.librarian.client import (
+    WebSearchResult,
+    WebSearchResponse,
+    ContentExtractionResult,
+    BatchExtractionResponse,
+)
+
+
+@pytest.fixture
+def mock_client():
+    """Create a mock LibraryDeskClient."""
+    client = AsyncMock()
+    return client
+
+
+# ============================================================================
+# Web Search Tests
+# ============================================================================
+
+@pytest.mark.unit
+class TestSearchWeb:
+    """Tests for search_web tool."""
+
+    @pytest.mark.asyncio
+    async def test_search_web_success(self, mock_client):
+        """Test successful web search."""
+        mock_response = WebSearchResponse(
+            query="Python async programming",
+            search_type="web",
+            results=[
+                WebSearchResult(
+                    title="Async Python Tutorial",
+                    url="https://example.com/async",
+                    content="Full content about async programming...",
+                    snippet="Learn async programming in Python",
+                    source="example.com",
+                ),
+                WebSearchResult(
+                    title="AsyncIO Documentation",
+                    url="https://docs.python.org/asyncio",
+                    content="Official asyncio docs content...",
+                    snippet="Python asyncio library reference",
+                    source="docs.python.org",
+                ),
+            ],
+            total_results=2,
+            search_time_ms=150,
+            sources_summary="**Sources:**\n- example.com\n- docs.python.org",
+        )
+        mock_client.search_web.return_value = mock_response
+
+        with patch(
+            "src.agents.librarian.tools.LibraryDeskClient"
+        ) as mock_client_class:
+            mock_client_class.return_value.__aenter__.return_value = mock_client
+            mock_client_class.return_value.__aexit__.return_value = None
+
+            result = await search_web("Python async programming")
+
+            assert "Python async programming" in result
+            assert "Async Python Tutorial" in result
+            assert "https://example.com/async" in result
+            assert "example.com" in result
+            assert "150ms" in result or "2 results" in result
+
+    @pytest.mark.asyncio
+    async def test_search_web_no_results(self, mock_client):
+        """Test web search with no results."""
+        mock_response = WebSearchResponse(
+            query="nonexistent query xyz123",
+            search_type="web",
+            results=[],
+            total_results=0,
+            search_time_ms=50,
+        )
+        mock_client.search_web.return_value = mock_response
+
+        with patch(
+            "src.agents.librarian.tools.LibraryDeskClient"
+        ) as mock_client_class:
+            mock_client_class.return_value.__aenter__.return_value = mock_client
+            mock_client_class.return_value.__aexit__.return_value = None
+
+            result = await search_web("nonexistent query xyz123")
+
+            assert "No results found" in result
+
+    @pytest.mark.asyncio
+    async def test_search_web_error_handling(self, mock_client):
+        """Test web search error handling."""
+        mock_client.search_web.side_effect = Exception("Connection failed")
+
+        with patch(
+            "src.agents.librarian.tools.LibraryDeskClient"
+        ) as mock_client_class:
+            mock_client_class.return_value.__aenter__.return_value = mock_client
+            mock_client_class.return_value.__aexit__.return_value = None
+
+            result = await search_web("test query")
+
+            assert "Error" in result
+            assert "Connection failed" in result
+
+    @pytest.mark.asyncio
+    async def test_search_web_with_news_type(self, mock_client):
+        """Test web search with news search type."""
+        mock_response = WebSearchResponse(
+            query="latest tech news",
+            search_type="news",
+            results=[
+                WebSearchResult(
+                    title="Tech News Today",
+                    url="https://news.example.com/tech",
+                    snippet="Breaking tech news",
+                    source="news.example.com",
+                    published_date="2024-01-15",
+                ),
+            ],
+            total_results=1,
+            search_time_ms=100,
+        )
+        mock_client.search_web.return_value = mock_response
+
+        with patch(
+            "src.agents.librarian.tools.LibraryDeskClient"
+        ) as mock_client_class:
+            mock_client_class.return_value.__aenter__.return_value = mock_client
+            mock_client_class.return_value.__aexit__.return_value = None
+
+            result = await search_web("latest tech news", search_type="news")
+
+            assert "Tech News Today" in result
+            mock_client.search_web.assert_called_with(
+                query="latest tech news",
+                limit=10,
+                search_type="news",
+            )
+
+
+# ============================================================================
+# Read URL Tests
+# ============================================================================
+
+@pytest.mark.unit
+class TestReadUrl:
+    """Tests for read_url tool."""
+
+    @pytest.mark.asyncio
+    async def test_read_url_success(self, mock_client):
+        """Test successful URL content extraction."""
+        mock_result = ContentExtractionResult(
+            url="https://example.com/article",
+            title="Great Article Title",
+            content="This is the full article content extracted from the page.",
+            author="John Doe",
+            date="2024-01-10",
+            language="en",
+            success=True,
+        )
+        mock_client.extract_content.return_value = mock_result
+
+        with patch(
+            "src.agents.librarian.tools.LibraryDeskClient"
+        ) as mock_client_class:
+            mock_client_class.return_value.__aenter__.return_value = mock_client
+            mock_client_class.return_value.__aexit__.return_value = None
+
+            result = await read_url("https://example.com/article")
+
+            assert "Great Article Title" in result
+            assert "https://example.com/article" in result
+            assert "John Doe" in result
+            assert "full article content" in result
+
+    @pytest.mark.asyncio
+    async def test_read_url_failure(self, mock_client):
+        """Test URL extraction failure."""
+        mock_result = ContentExtractionResult(
+            url="https://example.com/blocked",
+            success=False,
+            error="403 Forbidden",
+        )
+        mock_client.extract_content.return_value = mock_result
+
+        with patch(
+            "src.agents.librarian.tools.LibraryDeskClient"
+        ) as mock_client_class:
+            mock_client_class.return_value.__aenter__.return_value = mock_client
+            mock_client_class.return_value.__aexit__.return_value = None
+
+            result = await read_url("https://example.com/blocked")
+
+            assert "Could not read page" in result
+            assert "403 Forbidden" in result
+
+    @pytest.mark.asyncio
+    async def test_read_url_with_max_length(self, mock_client):
+        """Test URL extraction with custom max length."""
+        mock_result = ContentExtractionResult(
+            url="https://example.com/long",
+            title="Long Article",
+            content="X" * 10000,
+            success=True,
+        )
+        mock_client.extract_content.return_value = mock_result
+
+        with patch(
+            "src.agents.librarian.tools.LibraryDeskClient"
+        ) as mock_client_class:
+            mock_client_class.return_value.__aenter__.return_value = mock_client
+            mock_client_class.return_value.__aexit__.return_value = None
+
+            result = await read_url("https://example.com/long", max_length=2000)
+
+            mock_client.extract_content.assert_called_with(
+                url="https://example.com/long",
+                include_metadata=True,
+                max_length=2000,
+            )
+
+
+# ============================================================================
+# Batch URL Tests
+# ============================================================================
+
+@pytest.mark.unit
+class TestReadUrlsBatch:
+    """Tests for read_urls_batch tool."""
+
+    @pytest.mark.asyncio
+    async def test_batch_success(self, mock_client):
+        """Test successful batch extraction."""
+        mock_response = BatchExtractionResponse(
+            results=[
+                ContentExtractionResult(
+                    url="https://example.com/1",
+                    title="Article 1",
+                    content="Content from article 1",
+                    success=True,
+                ),
+                ContentExtractionResult(
+                    url="https://example.com/2",
+                    title="Article 2",
+                    content="Content from article 2",
+                    success=True,
+                ),
+            ],
+            total_urls=2,
+            successful=2,
+            failed=0,
+            extraction_time_ms=300,
+        )
+        mock_client.extract_content_batch.return_value = mock_response
+
+        with patch(
+            "src.agents.librarian.tools.LibraryDeskClient"
+        ) as mock_client_class:
+            mock_client_class.return_value.__aenter__.return_value = mock_client
+            mock_client_class.return_value.__aexit__.return_value = None
+
+            result = await read_urls_batch([
+                "https://example.com/1",
+                "https://example.com/2",
+            ])
+
+            assert "Article 1" in result
+            assert "Article 2" in result
+            assert "2/2" in result or "Extracted 2" in result
+
+    @pytest.mark.asyncio
+    async def test_batch_partial_failure(self, mock_client):
+        """Test batch extraction with some failures."""
+        mock_response = BatchExtractionResponse(
+            results=[
+                ContentExtractionResult(
+                    url="https://example.com/good",
+                    title="Good Article",
+                    content="Content extracted successfully",
+                    success=True,
+                ),
+                ContentExtractionResult(
+                    url="https://example.com/bad",
+                    success=False,
+                    error="Connection timeout",
+                ),
+            ],
+            total_urls=2,
+            successful=1,
+            failed=1,
+            extraction_time_ms=500,
+        )
+        mock_client.extract_content_batch.return_value = mock_response
+
+        with patch(
+            "src.agents.librarian.tools.LibraryDeskClient"
+        ) as mock_client_class:
+            mock_client_class.return_value.__aenter__.return_value = mock_client
+            mock_client_class.return_value.__aexit__.return_value = None
+
+            result = await read_urls_batch([
+                "https://example.com/good",
+                "https://example.com/bad",
+            ])
+
+            # Should contain successful result
+            assert "Good Article" in result
+            # Should report failure
+            assert "Failed" in result
+            assert "Connection timeout" in result
+
+
+# ============================================================================
+# Response Model Tests
+# ============================================================================
+
+@pytest.mark.unit
+class TestWebSearchModels:
+    """Tests for web search response models."""
+
+    def test_web_search_result_model(self):
+        """Test WebSearchResult model."""
+        result = WebSearchResult(
+            title="Test Title",
+            url="https://example.com",
+            content="Full content here",
+            snippet="Short snippet",
+            source="example.com",
+            published_date="2024-01-15",
+        )
+
+        assert result.title == "Test Title"
+        assert result.url == "https://example.com"
+        assert result.content == "Full content here"
+        assert result.source == "example.com"
+
+    def test_web_search_result_defaults(self):
+        """Test WebSearchResult default values."""
+        result = WebSearchResult(
+            title="Title",
+            url="https://example.com",
+        )
+
+        assert result.content == ""
+        assert result.snippet == ""
+        assert result.source == ""
+        assert result.published_date is None
+
+    def test_web_search_response_model(self):
+        """Test WebSearchResponse model."""
+        response = WebSearchResponse(
+            query="test query",
+            search_type="web",
+            results=[
+                WebSearchResult(title="R1", url="https://example.com/1"),
+                WebSearchResult(title="R2", url="https://example.com/2"),
+            ],
+            total_results=2,
+            search_time_ms=100,
+            sources_summary="**Sources:** example.com",
+        )
+
+        assert response.query == "test query"
+        assert len(response.results) == 2
+        assert response.total_results == 2
+
+    def test_content_extraction_result_model(self):
+        """Test ContentExtractionResult model."""
+        result = ContentExtractionResult(
+            url="https://example.com",
+            title="Title",
+            content="Content",
+            author="Author",
+            date="2024-01-01",
+            language="en",
+            success=True,
+        )
+
+        assert result.url == "https://example.com"
+        assert result.success is True
+        assert result.author == "Author"
+
+    def test_content_extraction_failure(self):
+        """Test ContentExtractionResult for failed extraction."""
+        result = ContentExtractionResult(
+            url="https://example.com",
+            success=False,
+            error="404 Not Found",
+        )
+
+        assert result.success is False
+        assert result.error == "404 Not Found"
+        assert result.content == ""
+
+    def test_batch_extraction_response_model(self):
+        """Test BatchExtractionResponse model."""
+        response = BatchExtractionResponse(
+            results=[
+                ContentExtractionResult(url="https://1.com", success=True),
+                ContentExtractionResult(url="https://2.com", success=False),
+            ],
+            total_urls=2,
+            successful=1,
+            failed=1,
+            extraction_time_ms=500,
+        )
+
+        assert response.total_urls == 2
+        assert response.successful == 1
+        assert response.failed == 1

tests/agents/steward/test_steward_service.py

@@ -8,7 +8,7 @@ from unittest.mock import AsyncMock, MagicMock, patch
 import pytest
 
 from src.agents.steward.schemas import ConversationContext, StewardRecommendation
-from src.agents.steward.service import analyze_request, format_steward_note
+from src.agents.steward.service import analyze_request, format_steward_note, _build_enriched_query
 from src.core.startup import initialize_application
 
 
@@ -199,3 +199,102 @@ class TestFormatStewardNote:
 
         assert "⚠️  Missing:" in note
         assert "Advanced research" in note
+
+
+@pytest.mark.unit
+class TestBuildEnrichedQuery:
+    """Tests for _build_enriched_query function."""
+
+    def test_no_enrichment_without_context(self):
+        """Test no enrichment when memory context is empty."""
+        query = "What's the weather?"
+        result = _build_enriched_query(query, {})
+
+        assert result == query
+
+    def test_enrichment_adds_location(self):
+        """Test location is appended for weather queries."""
+        query = "What's the weather?"
+        memory_context = {
+            "profile": {"location": "Amsterdam", "timezone": "Europe/Amsterdam"}
+        }
+
+        result = _build_enriched_query(query, memory_context)
+
+        assert "location=Amsterdam" in result
+        assert query in result
+        assert "[User Context:" in result
+
+    def test_no_location_when_specified(self):
+        """Test location is not appended when already specified."""
+        query = "What's the weather in London?"
+        memory_context = {
+            "profile": {"location": "Amsterdam"}
+        }
+
+        result = _build_enriched_query(query, memory_context)
+
+        # Should not add Amsterdam since location is specified
+        assert result == query
+
+    def test_enrichment_adds_timezone(self):
+        """Test timezone is appended for time queries."""
+        query = "What time is it?"
+        memory_context = {
+            "profile": {"timezone": "Europe/Amsterdam"}
+        }
+
+        result = _build_enriched_query(query, memory_context)
+
+        assert "timezone=Europe/Amsterdam" in result
+
+    def test_no_timezone_when_specified(self):
+        """Test timezone is not appended when already specified."""
+        query = "What time is it in UTC?"
+        memory_context = {
+            "profile": {"timezone": "Europe/Amsterdam"}
+        }
+
+        result = _build_enriched_query(query, memory_context)
+
+        assert result == query
+
+    def test_enrichment_adds_temperature_unit(self):
+        """Test temperature unit is appended for weather queries."""
+        query = "What's the weather?"
+        memory_context = {
+            "profile": {"location": "Amsterdam"},
+            "preferences": {"temperature_unit": "celsius"}
+        }
+
+        result = _build_enriched_query(query, memory_context)
+
+        assert "temperature_unit=celsius" in result
+
+    def test_multiple_context_fields(self):
+        """Test multiple context fields are appended."""
+        query = "What time and weather today?"
+        memory_context = {
+            "profile": {
+                "location": "Amsterdam",
+                "timezone": "Europe/Amsterdam"
+            },
+            "preferences": {"temperature_unit": "celsius"}
+        }
+
+        result = _build_enriched_query(query, memory_context)
+
+        assert "location=Amsterdam" in result
+        assert "timezone=Europe/Amsterdam" in result
+        assert "temperature_unit=celsius" in result
+
+    def test_no_enrichment_for_unrelated_query(self):
+        """Test no enrichment for queries that don't need context."""
+        query = "Tell me a joke"
+        memory_context = {
+            "profile": {"location": "Amsterdam", "timezone": "Europe/Amsterdam"}
+        }
+
+        result = _build_enriched_query(query, memory_context)
+
+        assert result == query

tests/agents/test_delegation.py

@@ -8,9 +8,14 @@ import pytest
 from unittest.mock import AsyncMock, patch, MagicMock
 
 from src.agents.delegation import (
+    ActionType,
     DelegationTask,
     DelegationResult,
+    HOUSEHOLD_THINK_MESSAGES,
+    STREAMING_DELEGATION_WRAPPERS,
     delegate_to_librarian,
+    get_think_message,
+    _detect_action_type,
 )
 
 
@@ -193,3 +198,158 @@ class TestDelegateToLibrarian:
             result = await delegate_to_librarian(task=original_task)
 
             assert result.task == original_task
+
+
+@pytest.mark.unit
+class TestActionType:
+    """Tests for the ActionType enum."""
+
+    def test_action_type_values(self):
+        """Test ActionType enum values."""
+        assert ActionType.RETRIEVE.value == "retrieve"
+        assert ActionType.RESEARCH.value == "research"
+        assert ActionType.CREATE.value == "create"
+        assert ActionType.CONTROL.value == "control"
+        assert ActionType.RECORD.value == "record"
+
+    def test_action_type_is_enum(self):
+        """Test ActionType is proper enum."""
+        assert len(ActionType) == 5
+
+
+@pytest.mark.unit
+class TestHouseholdThinkMessages:
+    """Tests for HOUSEHOLD_THINK_MESSAGES mapping."""
+
+    def test_librarian_has_messages(self):
+        """Test librarian has think messages."""
+        assert "librarian" in HOUSEHOLD_THINK_MESSAGES
+        assert ActionType.RETRIEVE in HOUSEHOLD_THINK_MESSAGES["librarian"]
+        assert ActionType.RESEARCH in HOUSEHOLD_THINK_MESSAGES["librarian"]
+        assert ActionType.CREATE in HOUSEHOLD_THINK_MESSAGES["librarian"]
+
+    def test_biographer_has_messages(self):
+        """Test biographer has think messages."""
+        assert "biographer" in HOUSEHOLD_THINK_MESSAGES
+        assert ActionType.RETRIEVE in HOUSEHOLD_THINK_MESSAGES["biographer"]
+        assert ActionType.RECORD in HOUSEHOLD_THINK_MESSAGES["biographer"]
+
+    def test_housekeeper_has_messages(self):
+        """Test housekeeper has think messages."""
+        assert "housekeeper" in HOUSEHOLD_THINK_MESSAGES
+        assert ActionType.RETRIEVE in HOUSEHOLD_THINK_MESSAGES["housekeeper"]
+        assert ActionType.CONTROL in HOUSEHOLD_THINK_MESSAGES["housekeeper"]
+
+    def test_messages_have_phases(self):
+        """Test each action type has start/success/error messages."""
+        for expert, action_types in HOUSEHOLD_THINK_MESSAGES.items():
+            for action_type, messages in action_types.items():
+                assert "start" in messages, f"{expert}/{action_type} missing 'start'"
+                assert "success" in messages, f"{expert}/{action_type} missing 'success'"
+                assert "error" in messages, f"{expert}/{action_type} missing 'error'"
+
+    def test_messages_are_think_tags(self):
+        """Test messages are wrapped in <think> tags."""
+        for expert, action_types in HOUSEHOLD_THINK_MESSAGES.items():
+            for action_type, messages in action_types.items():
+                for phase, msg in messages.items():
+                    assert msg.startswith("<think>"), f"{expert}/{action_type}/{phase}"
+                    assert msg.endswith("</think>"), f"{expert}/{action_type}/{phase}"
+
+
+@pytest.mark.unit
+class TestDetectActionType:
+    """Tests for _detect_action_type function."""
+
+    def test_librarian_search_is_retrieve(self):
+        """Test librarian search tasks are RETRIEVE."""
+        assert _detect_action_type("librarian", "search for Docker info") == ActionType.RETRIEVE
+        assert _detect_action_type("librarian", "find information about CI/CD") == ActionType.RETRIEVE
+        assert _detect_action_type("librarian", "look up Kubernetes docs") == ActionType.RETRIEVE
+
+    def test_librarian_web_search_is_research(self):
+        """Test librarian web search tasks are RESEARCH."""
+        assert _detect_action_type("librarian", "search the web for news") == ActionType.RESEARCH
+        assert _detect_action_type("librarian", "find online resources") == ActionType.RESEARCH
+        assert _detect_action_type("librarian", "research internet sources") == ActionType.RESEARCH
+
+    def test_librarian_create_is_create(self):
+        """Test librarian creation tasks are CREATE."""
+        assert _detect_action_type("librarian", "create a wiki page") == ActionType.CREATE
+        assert _detect_action_type("librarian", "write a new article") == ActionType.CREATE
+        assert _detect_action_type("librarian", "add a new entry") == ActionType.CREATE
+
+    def test_biographer_recall_is_retrieve(self):
+        """Test biographer recall tasks are RETRIEVE."""
+        assert _detect_action_type("biographer", "what car do I drive?") == ActionType.RETRIEVE
+        assert _detect_action_type("biographer", "what is my job?") == ActionType.RETRIEVE
+
+    def test_biographer_record_is_record(self):
+        """Test biographer record tasks are RECORD."""
+        assert _detect_action_type("biographer", "remember that I work at Acme") == ActionType.RECORD
+        assert _detect_action_type("biographer", "note that my car is a Tesla") == ActionType.RECORD
+        assert _detect_action_type("biographer", "save my preference for dark mode") == ActionType.RECORD
+
+    def test_housekeeper_status_is_retrieve(self):
+        """Test housekeeper status tasks are RETRIEVE."""
+        assert _detect_action_type("housekeeper", "what devices are in the bedroom?") == ActionType.RETRIEVE
+        assert _detect_action_type("housekeeper", "is the living room light on?") == ActionType.RETRIEVE
+
+    def test_housekeeper_control_is_control(self):
+        """Test housekeeper control tasks are CONTROL."""
+        assert _detect_action_type("housekeeper", "turn on the lights") == ActionType.CONTROL
+        assert _detect_action_type("housekeeper", "set brightness to 50%") == ActionType.CONTROL
+        assert _detect_action_type("housekeeper", "activate the movie scene") == ActionType.CONTROL
+        assert _detect_action_type("housekeeper", "toggle the fan") == ActionType.CONTROL
+
+
+@pytest.mark.unit
+class TestGetThinkMessage:
+    """Tests for get_think_message function."""
+
+    def test_librarian_retrieve_start(self):
+        """Test getting librarian retrieve start message."""
+        msg = get_think_message("librarian", "search for Docker", "start")
+        assert "<think>" in msg
+        assert "</think>" in msg
+
+    def test_librarian_create_success(self):
+        """Test getting librarian create success message."""
+        msg = get_think_message("librarian", "create a wiki page", "success")
+        assert "<think>" in msg
+        assert "catalogued" in msg.lower()
+
+    def test_biographer_record_start(self):
+        """Test getting biographer record start message."""
+        msg = get_think_message("biographer", "remember my preference", "start")
+        assert "<think>" in msg
+        assert "note" in msg.lower() or "biographer" in msg.lower()
+
+    def test_housekeeper_control_success(self):
+        """Test getting housekeeper control success message."""
+        msg = get_think_message("housekeeper", "turn on the lights", "success")
+        assert "<think>" in msg
+        assert "configured" in msg.lower()
+
+    def test_unknown_expert_fallback(self):
+        """Test unknown expert gets fallback message."""
+        msg = get_think_message("unknown_expert", "some task", "start")
+        assert "<think>" in msg
+        assert "unknown_expert" in msg.lower()
+
+
+@pytest.mark.unit
+class TestStreamingDelegationWrappers:
+    """Tests for streaming delegation wrapper mapping."""
+
+    def test_streaming_wrappers_exist(self):
+        """Test streaming wrappers mapping has all experts."""
+        assert "librarian" in STREAMING_DELEGATION_WRAPPERS
+        assert "biographer" in STREAMING_DELEGATION_WRAPPERS
+        assert "housekeeper" in STREAMING_DELEGATION_WRAPPERS
+
+    def test_streaming_wrappers_are_async_generators(self):
+        """Test streaming wrappers are async generator functions."""
+        import inspect
+        for name, wrapper in STREAMING_DELEGATION_WRAPPERS.items():
+            assert inspect.isasyncgenfunction(wrapper), f"{name} is not an async generator"

tests/agents/test_tools.py

@@ -1,17 +1,18 @@
 """
-Tests for Tatlock's permanent tools (calculator, date/time, search).
+Tests for Tatlock's permanent tools (calculator, date/time).
+
+Note: Web search has been moved to The Librarian agent.
+See tests/agents/librarian/test_tools.py for search tests.
 """
 
 import pytest
 from datetime import datetime
-from unittest.mock import AsyncMock, patch
 
 from src.agents.tools import (
     calculate,
     get_current_datetime,
     calculate_time_offset,
     time_difference,
-    search_web,
 )
 
 
@@ -188,184 +189,3 @@ class TestDateTime:
         """Test error handling for invalid dates."""
         result = time_difference("invalid-date", "now")
         assert "Error" in result
-
-
-# ============================================================================
-# Search Tests
-# ============================================================================
-
-class TestSearch:
-    """Tests for web search tool."""
-
-    @pytest.mark.asyncio
-    async def test_search_web_success(self):
-        """Test successful web search."""
-        mock_response = {
-            "results": [
-                {
-                    "title": "Test Result 1",
-                    "url": "https://example.com/1",
-                    "content": "This is a test result"
-                },
-                {
-                    "title": "Test Result 2",
-                    "url": "https://example.com/2",
-                    "content": "Another test result"
-                }
-            ]
-        }
-
-        with patch("src.agents.tools.httpx.AsyncClient") as mock_client_class:
-            # Create mock response
-            mock_response_obj = type('MockResponse', (), {
-                'status_code': 200,
-                'json': lambda *args, **kwargs: mock_response
-            })()
-
-            # Create mock client with async get method
-            async def mock_get(*args, **kwargs):
-                return mock_response_obj
-
-            mock_client_instance = type('MockClient', (), {
-                'get': mock_get
-            })()
-
-            # Setup async context manager
-            async def mock_aenter(*args, **kwargs):
-                return mock_client_instance
-
-            async def mock_aexit(*args, **kwargs):
-                return None
-
-            mock_client_class.return_value.__aenter__ = mock_aenter
-            mock_client_class.return_value.__aexit__ = mock_aexit
-
-            result = await search_web("test query", num_results=2)
-
-            assert "Test Result 1" in result
-            assert "https://example.com/1" in result
-            assert "Test Result 2" in result
-            assert "https://example.com/2" in result
-
-    @pytest.mark.asyncio
-    async def test_search_web_no_results(self):
-        """Test web search with no results."""
-        mock_response_data = {"results": []}
-
-        with patch("src.agents.tools.httpx.AsyncClient") as mock_client_class:
-            mock_response_obj = type('MockResponse', (), {
-                'status_code': 200,
-                'json': lambda *args, **kwargs: mock_response_data
-            })()
-
-            async def mock_get(*args, **kwargs):
-                return mock_response_obj
-
-            mock_client_instance = type('MockClient', (), {
-                'get': mock_get
-            })()
-
-            async def mock_aenter(*args, **kwargs):
-                return mock_client_instance
-
-            async def mock_aexit(*args, **kwargs):
-                return None
-
-            mock_client_class.return_value.__aenter__ = mock_aenter
-            mock_client_class.return_value.__aexit__ = mock_aexit
-
-            result = await search_web("test query")
-
-            assert "No results found" in result
-
-    @pytest.mark.asyncio
-    async def test_search_web_connection_error(self):
-        """Test web search with connection error."""
-        with patch("httpx.AsyncClient") as mock_client:
-            mock_client_instance = AsyncMock()
-            mock_client_instance.get.side_effect = Exception("Connection failed")
-            mock_client.return_value.__aenter__.return_value = mock_client_instance
-
-            result = await search_web("test query")
-
-            assert "Error searching" in result
-
-    @pytest.mark.asyncio
-    async def test_search_web_limits_results(self):
-        """Test that search limits results to max 10."""
-        mock_response_data = {
-            "results": [
-                {"title": f"Result {i}", "url": f"https://example.com/{i}", "content": "Test"}
-                for i in range(20)
-            ]
-        }
-
-        with patch("src.agents.tools.httpx.AsyncClient") as mock_client_class:
-            mock_response_obj = type('MockResponse', (), {
-                'status_code': 200,
-                'json': lambda *args, **kwargs: mock_response_data
-            })()
-
-            async def mock_get(*args, **kwargs):
-                return mock_response_obj
-
-            mock_client_instance = type('MockClient', (), {
-                'get': mock_get
-            })()
-
-            async def mock_aenter(*args, **kwargs):
-                return mock_client_instance
-
-            async def mock_aexit(*args, **kwargs):
-                return None
-
-            mock_client_class.return_value.__aenter__ = mock_aenter
-            mock_client_class.return_value.__aexit__ = mock_aexit
-
-            result = await search_web("test query", num_results=15)
-
-            # Should only return 10 results (max limit)
-            result_count = result.count("URL:")
-            assert result_count == 10
-
-    @pytest.mark.asyncio
-    async def test_search_web_formats_results(self):
-        """Test that search results are properly formatted."""
-        mock_response_data = {
-            "results": [
-                {
-                    "title": "Test Title",
-                    "url": "https://example.com",
-                    "content": "Test content description"
-                }
-            ]
-        }
-
-        with patch("src.agents.tools.httpx.AsyncClient") as mock_client_class:
-            mock_response_obj = type('MockResponse', (), {
-                'status_code': 200,
-                'json': lambda *args, **kwargs: mock_response_data
-            })()
-
-            async def mock_get(*args, **kwargs):
-                return mock_response_obj
-
-            mock_client_instance = type('MockClient', (), {
-                'get': mock_get
-            })()
-
-            async def mock_aenter(*args, **kwargs):
-                return mock_client_instance
-
-            async def mock_aexit(*args, **kwargs):
-                return None
-
-            mock_client_class.return_value.__aenter__ = mock_aenter
-            mock_client_class.return_value.__aexit__ = mock_aexit
-
-            result = await search_web("test query")
-
-            # Check formatting
-            assert "1. Test Title" in result
-            assert "URL: https://example.com" in result
-            assert "Test content description" in result