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mem0-owui
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Mem0 Integration with OpenWebUI
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# mem0-owui [](https://opensource.org/licenses/MIT) A memory filter integration for OpenWebUI using mem0 to persist and retrieve conversation context. This integration enhances your LLM conversations by automatically injecting relevant historical context. ## Overview mem0-owui provides two deployment options: 1. **Managed Version**: Simple setup using mem0's cloud service - just provide an API key 2. **Self-Hosted Version**: Complete control with your own vector database and embedding infrastructure ## Features - **Smart Context Injection**: Automatically inject relevant conversation history into prompts - **User Isolation**: Maintain separate memory spaces for different users - **LLM-Optimized Summarization**: Generate concise memory summaries using LLMs - **Dashboard Integration**: Seamless integration with OpenWebUI's interface - **Flexible Configuration**: Fine-tune behavior through environment variables - **Asynchronous Processing**: Self-hosted version uses async operations for better performance ## Requirements - **Managed Version**: - mem0ai Python package - pydantic 2.11.4 - mem0 API key - **Self-Hosted Version**: - mem0ai 0.1.96 - pydantic 2.7.4 - Qdrant vector database (can be run via Docker) - LLM provider (OpenAI, OpenRouter, etc.) - Embedding model provider ## Installation ### Managed Version (Recommended) 1. Ensure pipelines are installed in your OpenWebUI instance 2. Download `mem0-owui-managed.py` 3. Upload to OpenWebUI via: `Settings > Admin Settings > Pipelines > Upload` Select the "mem0-owui-managed.py" file 4. Set your mem0 API key in the configuration section 5. Enable the pipeline and set appropriate priority ### Self-Hosted Version 1. Clone the repository: `git clone https://github.com/mem0ai/mem0-owui.git` 2. Configure Docker: ```bash cp docker-compose.example.yml docker-compose.yml # Edit docker-compose.yml with your configuration ``` 3. Start the service: `docker-compose up -d` 4. Configure OpenWebUI to use your self-hosted endpoint 5. Upload `mem0-owui-selfhosted.py` through the dashboard ## Configuration ### Managed Version Parameters | Parameter | Required | Default | Description | |----------|----------|---------|-------------| | `api_key` | ✅ | - | Your mem0 API key | | `user_id` | ❌ | "default_user" | Default user ID for memory storage | | `pipelines` | ❌ | ["*"] | Pipeline IDs to apply the filter to | | `priority` | ❌ | 0 | Filter execution order (lower = earlier) | ### Self-Hosted Version Parameters #### Basic Configuration | Parameter | Required | Default | Description | |----------|----------|---------|-------------| | `user_id` | ❌ | "default_user" | Default user ID for memory storage | | `pipelines` | ❌ | ["*"] | Pipeline IDs to apply the filter to | | `priority` | ❌ | 0 | Filter execution order (lower = earlier) | #### Vector Store Configuration | Parameter | Required | Default | Description | |----------|----------|---------|-------------| | `qdrant_host` | ✅ | "qdrant" | Qdrant vector database host | | `qdrant_port` | ✅ | "6333" | Qdrant vector database port | | `collection_name` | ✅ | "mem1536" | Qdrant collection name | #### LLM Configuration | Parameter | Required | Default | Description | |----------|----------|---------|-------------| | `llm_provider` | ✅ | "openai" | LLM provider (openai, etc) | | `llm_api_key` | ✅ | "placeholder" | LLM API key | | `llm_model` | ✅ | "GPT-4.1" | LLM model name | | `llm_base_url` | ✅ | "https://openrouter.ai/api/v1" | LLM API base URL | #### Embedder Configuration | Parameter | Required | Default | Description | |----------|----------|---------|-------------| | `embedder_provider` | ✅ | "openai" | Embedding provider | | `embedder_api_key` | ✅ | "placeholder" | Embedding API key | | `embedder_model` | ✅ | "text-embedding-3-small" | Embedding model name | ## How It Works ### Memory Workflow 1. **Input Processing**: - When a user sends a message, the filter intercepts it before it reaches the LLM - The filter queries mem0 for relevant memories based on the user's message - These memories are injected into the system message to provide context 2. **Response Processing**: - After the LLM generates a response, the user's message is stored in mem0 - In the self-hosted version, the assistant's response is also stored - These memories are vectorized and stored for future retrieval 3. **Memory Retrieval**: - When the user sends a new message, the system searches for semantically similar memories - The most relevant memories are injected into the prompt - This allows the LLM to maintain context across multiple conversations ### Technical Implementation - **Managed Version**: Uses `MemoryClient` from mem0 for a simple, synchronous implementation - **Self-Hosted Version**: Uses `AsyncMemory` for asynchronous operations with more configuration options ## Troubleshooting ### Common Issues 1. **Memory Not Being Retrieved**: - Check that your API key is correct (managed version) - Verify vector store connection (self-hosted version) - Ensure user IDs are consistent across sessions 2. **Pipeline Not Running**: - Check pipeline priority - it may be overridden by other pipelines - Verify that the pipeline is enabled in OpenWebUI - Check OpenWebUI logs for any errors 3. **Self-Hosted Version Connection Issues**: - Verify Qdrant is running and accessible - Check that embedding service is operational - Ensure all required environment variables are set correctly ### Debugging For detailed debugging: ```bash # Check Docker logs for self-hosted version docker logs mem0-owui-container # Check OpenWebUI logs # Location depends on your OpenWebUI installation ``` ## Contributing ### Contribution Process 1. Fork the repository 2. Create a feature branch (`git checkout -b feature/your-feature`) 3. Commit changes with descriptive messages 4. Push to your fork (`git push origin feature/your-feature`) 5. Open a pull request with detailed description ### Development Setup ```bash # Clone the repository git clone https://github.com/mem0ai/mem0-owui.git # Install development dependencies pip install -r requirements.txt # Run tests pytest ``` ## License MIT License - see [LICENSE](LICENSE) file ## FAQ **Q: What's the difference between managed and self-hosted versions?** A: The managed version uses mem0's cloud service with a simple API key, while the self-hosted version gives you complete control over the vector database, embedding model, and LLM configuration. **Q: How do I reset my API key?** A: Generate a new key in your mem0 dashboard and update the configuration. **Q: Can I use this with multiple OpenWebUI instances?** A: Yes, use unique user IDs for each instance to maintain separate memory contexts. **Q: What happens if the mem0 service is unavailable?** A: The filter will fail gracefully, allowing normal OpenWebUI operation without memory context. **Q: Which version should I choose?** A: The managed version is simpler to set up and maintain, while the self-hosted version offers more control and customization options. Choose based on your needs for privacy, control, and ease of maintenance. **Q: How can I customize the memory retrieval process?** A: The self-hosted version allows you to configure the vector database, embedding model, and LLM settings to fine-tune the memory retrieval process.
