redis / agent-memory-server

Redis Agent Memory Server A memory layer for AI agents. **Documentation** • **GitHub** • **Docker** Features • **Dual Interface**: REST API and Model Context Protocol (MCP) server • **Two-Tier Memory**: Working memory (session-scoped) and long-term memory (persistent) • **Configurable Memory Strategies**: Customize how memories are extracted (discrete, summary, preferences, custom) • **Semantic Search**: Vector-based similarity search with metadata filtering • **Flexible Backends**: Pluggable memory vector database factory system • **Multi-Provider LLM Support**: OpenAI, Anthropic, AWS Bedrock, Ollama, Azure, Gemini via LiteLLM • **AI Integration**: Automatic topic extraction, entity recognition, and conversation summarization • **Python SDK**: Easy integration with AI applications Quick Start • Installation Using Docker Pre-built Docker images are available from: • **Docker Hub**: redislabs/agent-memory-server • **GitHub Packages**: ghcr.io/redis/agent-memory-server **Quick Start (Development Mode)**: By default, the image runs the API with the **Docket** task backend, which expects a separate process for non-blocking background tasks. The example above shows how to override this to use the asyncio backend for a single-container development setup. If you are connecting to a Redis Cluster and want Docket-backed workers, set to a or URL. AMS will translate that URL for its other Redis clients internally. **Production Deployment**: For production, run separate containers for the API and background workers: From Source • Python SDK Allowing the server to extract memories from working memory is easiest. However, you can also manually create memories: > **Note**: While you can call client functions directly as shown above, using **MCP or SDK-provided tool calls** is recommended for AI agents as it provides better integration, automatic context management, and follows AI-native patterns. For the best performance, you can add messages to working memory and allow the server to extract memories in the background. See **Memory Integration Patterns** for guidance on when to use each approach. LangChain Integration For LangChain users, the SDK provides automatic conversion of memory client tools to LangChain-compatible tools, eliminating the need for manual wrapping with decorators. • MCP Integration MCP config via uvx (recommended) Use this in your MCP tool configuration (e.g., Claude Desktop mcp.json): Notes: • API keys: Set either (default models use OpenAI) or switch to Anthropic by setting and to an Anthropic model (e.g., ). • Make sure your MCP host can find (on its PATH or by using an absolute command path). • macOS: • If not on PATH, set to the absolute path (e.g., on Apple Silicon, on Intel macOS). On Linux, is common. See https://docs.astral.sh/uv/getting-started/ • For production, remove and configure proper authentication. LLM Provider Configuration The server uses LiteLLM to support 100+ LLM providers. Configure via environment variables: See **LLM Providers** for complete configuration options. Documentation 📚 **Full Documentation** - Complete guides, API reference, and examples Key Documentation Sections: • **Quick Start Guide** - Get up and running in minutes • **Python SDK** - Complete SDK reference with examples • **LangChain Integration** - Automatic tool conversion for LangChain • **LLM Providers** - Configure OpenAI, Anthropic, AWS Bedrock, Ollama, and more • **Embedding Providers** - Configure embedding models for semantic search • **Custom Memory Vector Databases** - Configure custom memory vector databases • **Authentication** - OAuth2/JWT setup for production • **Memory Types** - Understanding semantic vs episodic memory • **API Reference** - REST API endpoints • **MCP Protocol** - Model Context Protocol integration Architecture Use Cases • **AI Assistants**: Persistent memory across conversations • **Customer Support**: Context from previous interactions • **Personal AI**: Learning user preferences and history • **Research Assistants**: Accumulating knowledge over time • **Chatbots**: Maintaining context and personalization Development License Apache License 2.0 - see LICENSE file for details. Contributing We welcome contributions! Please see the development documentation for guidelines.