How I Gave My AI Coding Agent Persistent Memory in 2 Minutes

A developer built Engram, a tool that gives AI coding assistants like ChatGPT, Claude Code, Cursor, and Windsurf persistent memory across sessions. Engram stores conversations verbatim and enables semantic search, allowing AI agents to recall past context without manual re-explanation. The tool uses the Model Context Protocol and runs on Cloudflare's global network.

Every AI coding session starts the same way: you explain your project, your conventions, your preferences, and the context from last week's debugging session. Then you do it all again tomorrow. I built Engram https://getengram.app to fix this. It gives ChatGPT, Claude Code, Cursor, and Windsurf a persistent memory that survives across sessions — so your AI actually remembers what you discussed yesterday. AI assistants are stateless. Each session starts from zero. There are workarounds: None of these capture the full arc of a conversation — the trade-offs you evaluated, the bugs you investigated, the reasoning behind decisions. Engram stores your conversations verbatim and makes them searchable by meaning. When your AI starts a new session, it can search your history and pull in relevant context from days or weeks ago. It works through MCP https://modelcontextprotocol.io Model Context Protocol , so any compatible client can use it. Your AI gets three core tools: search — "what did we decide about the auth flow?" finds the relevant conversation even if you never used those exact words create conversation / append messages — stores new conversations automatically vault set / vault get — key-value store for structured factsUnder the hood, conversations are chunked and embedded using vector embeddings. Search is semantic — it matches by meaning, not keywords. https://mcp.getengram.app/mcp That's it. No CLI, no API keys, no install. ChatGPT can now search your history and store new conversations. Try asking: "What did we discuss about topic last week?" Add to your MCP config ~/.claude/settings.json : { "mcpServers": { "engram": { "type": "http", "url": "https://mcp.getengram.app/mcp", "headers": { "Authorization": "Bearer YOUR API KEY" } } } } Then add a few lines to your CLAUDE.md telling Claude to search on session start: Memory On session start, search Engram for context relevant to the current task: - search query: "<summary of what the user is asking about " When important decisions or context are established, store them in Engram. For zero-friction capture, install the CLI: npm i -g @getengram/cli engram daemon start The daemon runs in the background and auto-captures every Claude Code session. You don't have to prompt Claude to remember things — it just happens. Same as Claude — add the MCP server URL + API key to your MCP config. Both support MCP natively. After a few weeks of use, here's the kind of context Engram surfaces for me: The key difference from CLAUDE.md: I don't maintain any of this manually. Conversations are stored as they happen, and the right context surfaces when it's relevant. The backend runs entirely on Cloudflare: When you store a conversation, Engram: When you search, it: The whole thing is globally distributed with sub-100ms cold starts. The source is on GitHub under BSL-1.1 converts to Apache 2.0 after the change date . You can self-host the whole thing on Cloudflare's free tier with wrangler deploy . GitHub: github.com/get-engram/engram https://github.com/get-engram/engram I'm working on: If you're tired of re-explaining yourself to your AI every session: https://mcp.getengram.app/mcp @getengram/cli for auto-captureThe free tier is enough to see if it's useful for your workflow. I'd love to hear what context you find yourself repeating the most. Engram https://getengram.app is built by Get Engram Inc https://github.com/get-engram . Questions? hello@getengram.app mailto:hello@getengram.app