tatlock/README.md

Tatlock - Your Homelab Butler

📖 For the complete system vision and architectural philosophy, see PHILOSOPHY.md

A privacy-first, offline-capable personal assistant system that coordinates specialized AI agents to help with research, development, home automation, and daily organization.

Current Status

• ✅ Production-ready API with OpenAI Responses API format
• ✅ Open WebUI integration with reasoning bubbles (<think> tags)
• ✅ Two-tier architecture - The Steward analyzes requests, Tatlock coordinates execution
• ✅ Multi-agent coordination - Expert household staff for specialized tasks
• ✅ Memory system - User profile, preferences, and semantic recall
• ✅ Comprehensive testing - 399 tests with good coverage

The Household Staff

Agent	Role	Status
Tatlock	The Butler - Primary interface with witty personality	✅ Active
The Steward	Request analysis and capability recommendation	✅ Active
The Librarian	Research, wiki management, knowledge synthesis	✅ Active
The Biographer	User memory - profiles, preferences, facts	✅ Active
The Developer	Code assistance, debugging, architecture	🔜 Planned
The Secretary	Scheduling, calendars, reminders	🔜 Planned
The Handyman	System administration, monitoring	🔜 Planned
The Housekeeper	Home automation (Home Assistant)	🔜 Planned

Features

API Endpoints

• Responses API (/v1/responses) - OpenAI Responses API format with structured output

  • Reasoning items for displaying thinking process
  • Function call items for tool execution
  • Message items for assistant responses
  • Streaming and non-streaming support

• Chat Completions (/v1/chat/completions) - OpenAI Chat Completions compatibility

  • Automatic reasoning conversion to <think> tags for Open WebUI
  • Full OpenAI API compatibility
  • Streaming support

• Models (/v1/models) - List available models

Advanced Capabilities

• Conversation History: Auto-generated IDs, configurable max turns (default: 20)
• Context Management: Token counting, automatic trimming, usage statistics
• Parameter Validation: Temperature (0.0-2.0), reasoning effort levels, max tokens, stop sequences
• Real-time Enforcement: Stop sequence detection and max token limits during streaming

Available Models

• lorem-tester: Full-featured mock agent with realistic behavior

  • Configurable reasoning effort levels
  • Random tool/function calls
  • Error triggers for testing (rate_limit, context_overflow)

• Tatlock: Real PydanticAI agent with butler personality

  • LLM Backend: Ollama (mistral-nemo:latest by default)
  • Personality: Witty British butler, research-oriented
  • Core Tools:
    • Calculator: Safe mathematical expression evaluation
    • Date/Time Toolkit: Current time, relative dates, time differences
    • Web Search: Privacy-preserving search via SearXNG
  • Household Coordination:
    • The Steward: Analyzes requests and recommends capabilities
    • The Librarian: Research via library-desk HybridRAG + wiki
    • The Biographer: User memory and preference management
  • Capabilities: Streaming, reasoning, tool calling, multi-agent delegation

Requirements

• Python 3.12+ (Python 3.12.11 recommended)
• External Services (must be running separately):
  • Ollama: LLM inference (mistral-nemo:latest, nomic-embed-text)
  • Redis: Caching and session memory
  • Qdrant: Vector storage for The Biographer's memory
  • SearXNG: Web search (optional)
  • library-desk: Research API for The Librarian (optional)

Quick Start

Installation

# Clone the repository
git clone https://git.schweitz.net/jpmschweitzer/tatlock.git
cd tatlock

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Run the Server

uvicorn src.main:app --reload

API available at http://localhost:8000

Usage Examples

Responses API

Generate a response with reasoning:

curl http://localhost:8000/v1/responses \
  -H "Content-Type: application/json" \
  -d '{
    "model": "lorem-tester",
    "input": [
      {"role": "user", "content": "Explain quantum computing"}
    ],
    "reasoning": {
      "effort": "medium",
      "summary": "auto"
    },
    "max_output_tokens": 500,
    "stream": false
  }'

Response Structure:

{
  "id": "resp_abc123",
  "object": "response",
  "created_at": 1733529600,
  "model": "lorem-tester",
  "status": "completed",
  "output": [
    {
      "type": "reasoning",
      "summary": ["Analyzing the request...", "Considering quantum mechanics..."]
    },
    {
      "type": "message",
      "role": "assistant",
      "content": [{"type": "output_text", "text": "Quantum computing uses..."}]
    }
  ],
  "usage": {
    "input_tokens": 10,
    "output_tokens": 50,
    "reasoning_tokens": 20,
    "total_tokens": 80
  }
}

Chat Completions (OpenAI-compatible)

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "lorem-tester",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ],
    "temperature": 0.7,
    "stream": true
  }'

List Models

curl http://localhost:8000/v1/models

Conversation History

Optionally track conversations using metadata:

curl http://localhost:8000/v1/responses \
  -H "Content-Type: application/json" \
  -d '{
    "model": "lorem-tester",
    "input": [{"role": "user", "content": "Hello"}],
    "metadata": {"conversation_id": "conv_abc123"}
  }'

Note: Client must send full conversation history in input array (OpenAI compatible). Server optionally tracks via metadata.conversation_id for future features.

Using Tatlock with Tools

Tatlock automatically uses his permanent tools when appropriate:

# Mathematical calculation
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Tatlock",
    "messages": [{"role": "user", "content": "What is sqrt(144) + 25?"}]
  }'

# Date/time queries
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Tatlock",
    "messages": [{"role": "user", "content": "What was the date 2 weeks ago?"}]
  }'

# Web search for current information
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Tatlock",
    "messages": [{"role": "user", "content": "Search for recent Python 3.12 features"}]
  }'

Tatlock's Tool Usage Philosophy:

• Uses calculator for ALL mathematics (even simple arithmetic)
• Uses date/time tools instead of guessing dates
• Searches for current/volatile information to verify facts
• Maintains a researcher's mindset with tool-assisted verification

Open WebUI Integration

Connection

If running Open WebUI in Docker and API on host:

# Use Docker bridge gateway IP
http://172.17.0.1:8000/v1/chat/completions

Reasoning Display

The Chat Completions endpoint automatically:

1. Enables reasoning generation
2. Converts reasoning to <think> tags
3. Streams thinking before the response

Open WebUI displays this as thought bubbles separate from the main response.

Testing Error Handling

Use special triggers in user messages:

• "trigger_rate_limit" - Simulates rate limit error
• "trigger_context_overflow" - Simulates context length error

API Documentation

Interactive documentation available at:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Testing

# Run all tests
pytest

# Run unit tests only (no external services needed)
pytest --ignore=tests/e2e --ignore=tests/integration

# Run with coverage
pytest --cov=src --cov-report=term-missing

# Current: ~400 tests

Test Categories:

• Unit tests: Agent tools, capabilities, schemas, memory service
• Integration tests: Full API stack with real Ollama
• End-to-end tests: Chat completions, responses API

Deployment

Production Server

# Multiple workers for production
uvicorn src.main:app --host 0.0.0.0 --port 8000 --workers 4

Recommendations

• Use reverse proxy (nginx/caddy) for HTTPS
• Enable rate limiting
• Set up monitoring and logging
• Configure resource limits
• Use process manager (systemd/supervisor)

Configuration

Create a .env file for custom configuration:

# API Configuration
API_HOST=0.0.0.0
API_PORT=8000

# Ollama Configuration
OLLAMA_HOST=http://localhost:11434
OLLAMA_DEFAULT_MODEL=mistral-nemo:latest
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
OLLAMA_TIMEOUT=120

# Redis Configuration
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_MEMORY_DB=2
REDIS_MEMORY_TTL_HOURS=24

# Qdrant Configuration (for memory)
QDRANT_HOST=localhost
QDRANT_PORT=6333
QDRANT_EMBEDDING_DIM=768

# Library-desk Configuration (for The Librarian)
LIBRARY_DESK_HOST=http://localhost:8089
LIBRARY_DESK_TIMEOUT=60

# SearXNG Configuration (for web search)
SEARXNG_HOST=http://localhost:8087
SEARXNG_TIMEOUT=30

# Logging
LOG_LEVEL=INFO

# CORS (default: allow all)
CORS_ORIGINS=["*"]

See .env.example for full configuration options.

Troubleshooting

Streaming not working

• Verify SSE-Starlette is installed
• Check client supports Server-Sent Events
• Test with: pytest tests/responses/ -k streaming

Open WebUI can't connect

• Use Docker bridge gateway IP: 172.17.0.1:8000
• Check firewall settings
• Verify server is running on 0.0.0.0

Reasoning not showing

• Ensure using Chat Completions endpoint (auto-enables reasoning)
• Or manually enable in Responses API: "reasoning": {"effort": "medium", "summary": "auto"}
• Check Open WebUI version supports <think> tags

Tatlock agent errors

• Verify Ollama is running: curl http://localhost:11434/api/tags
• Check model is downloaded: ollama list
• Review environment variables: OLLAMA_HOST, OLLAMA_DEFAULT_MODEL
• Check logs: tail -f logs/server.log

Web search not working

• Verify SearXNG is running: curl http://localhost:8087/
• Check SEARXNG_HOST environment variable
• SearXNG is optional - Tatlock will note if search is unavailable

Project Structure

tatlock/
├── src/
│   ├── agents/              # Agent implementations
│   │   ├── biographer/      # The Biographer - memory management
│   │   ├── librarian/       # The Librarian - research & wiki
│   │   ├── steward/         # The Steward - request analysis
│   │   ├── tatlock_core/    # Core butler tools
│   │   ├── tatlock.py       # Tatlock PydanticAI agent
│   │   ├── coordination.py  # Multi-agent coordination
│   │   ├── delegation.py    # Expert delegation wrappers
│   │   └── protocol.py      # Agent communication protocol
│   ├── responses/           # Responses API (primary endpoint)
│   ├── chat/                # Chat Completions wrapper
│   ├── models/              # Models listing
│   ├── core/                # Shared infrastructure
│   │   ├── config.py        # Configuration management
│   │   ├── context.py       # Request context (ContextVar)
│   │   ├── memory_service.py # Direct memory access
│   │   ├── memory_cache.py  # Redis session cache
│   │   ├── embeddings.py    # Ollama embedding client
│   │   ├── qdrant.py        # Vector database client
│   │   └── multi_tenancy.py # User isolation utilities
│   └── main.py              # Application entry point
├── tests/                   # Comprehensive test suite
├── PHILOSOPHY.md            # System vision and architecture
├── IMPLEMENTATION_ROADMAP.md # Development phases
├── CHANGELOG.md             # Version history
└── README.md                # This file

Development

For LLM agent development guidelines and architectural decisions, see AGENTS.md.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make changes with tests
4. Ensure tests pass: pytest
5. Submit pull request

Documentation

• System Philosophy: PHILOSOPHY.md - Vision, goals, and architectural patterns
• User Guide: This file - Installation, usage, and examples
• Developer Guidelines: AGENTS.md - LLM agent development patterns
• Version History: CHANGELOG.md - Changes and releases

External References

• OpenAI Responses API: https://platform.openai.com/docs/api-reference/responses
• FastAPI: https://fastapi.tiangolo.com/
• PydanticAI: https://ai.pydantic.dev/

License

[Add your license here]

Version

Current version: 1.2.1 - Dependency slimming

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Note: Tatlock is a production-ready homelab butler. All household staff use PydanticAI with Ollama for local LLM inference.