Retrieval Augmented Generation
See how AutoMem enhances the tools you're already using with the power of memory and RAG
What is RAG?
Retrieval Augmented Generation (RAG) is a technique that enhances AI responses by retrieving relevant context from a knowledge base before generating answers.
Instead of relying solely on the AI's training data, RAG systems:
- Retrieve relevant memories, preferences, and context from a database
- Augment the AI's prompt with this personalized information
- Generate responses that are contextually aware and tailored to you
AutoMem's RAG Advantage
AutoMem automatically injects relevant memories at the beginning of every conversation with your AI tools. This means:
- Claude Desktop remembers your coding preferences from last week
- Cursor recalls your project architecture decisions
- ChatGPT knows your communication style and priorities
- Every AI tool you use shares the same contextual memory
RAG Across Platforms
Click any screenshot to view full-size. See how AutoMem seamlessly integrates with your favorite AI tools. View technical documentation →
Claude Desktop
Claude automatically recalls memories at conversation start using custom instructions. Your preferences and context are injected before the first message.
Cursor IDE
Cursor uses automem.mdc rules to automatically recall and store memories during coding sessions.
ChatGPT with SSE Connector
ChatGPT accesses AutoMem memories via the SSE sidecar in developer mode. Cross-platform memory without vendor lock-in.
Claude Web
Claude.ai website connected to AutoMem via remote MCP (SSE). Your memories work everywhere, not just desktop.
Claude Code
Git commits, builds, and deployments automatically stored to memory. Context persists across development sessions.
Claude Mobile (iOS)
Claude Mobile connected to AutoMem via remote MCP. Same memories across desktop, web, and mobile.
Ready to Try AutoMem?
Deploy in under 5 minutes and start experiencing RAG across all your AI tools