How to Add Persistent Memory
to Any AI Agent

Your AI agent starts every session from zero. It forgets the decisions it made yesterday, the mistakes it learned from, and the preferences you told it about. Context windows are runtime memory. They vanish when the session ends.

OMEGA is an open-source MCP server that gives any AI agent persistent memory. Two commands to install. Works with Claude Code, Cursor, Windsurf, and anything that speaks MCP.

That's it. omega setup writes the MCP configuration your agent needs. Next time your agent starts, it has memory.

AI coding agents are getting good at writing code in a single session. But ask one to remember what it decided yesterday and it draws a blank. Tell it your team uses Prisma over Drizzle, and it forgets by tomorrow. Fix a bug caused by force-pushing, and it will force-push again next week.

Context windows are getting larger (200K+ tokens), but they only last for one session. The agent that helped you refactor authentication yesterday has no idea it did that today.

This is the gap persistent memory fills. Not bigger context windows. Memory that survives across sessions, searches itself, and compounds knowledge over time.

Not all memories are equal. A session summary from three weeks ago is noise. A decision about your database schema is still relevant. OMEGA types every memory and treats each type differently:

This typed approach matters. Without it, old session summaries pile up and pollute retrieval. The agent searches 10,000 memories for “what database does this project use?” and gets buried in irrelevant session logs. With typed TTLs, stale context expires automatically and retrieval stays precise.

Storing memories is easy. Retrieving the right one at the right time is the hard part. OMEGA uses hybrid search: semantic embeddings for meaning, BM25 for keywords, and type-scoped filtering to narrow the search space before either runs.

When your agent needs to know your database preference, it doesn't search all memories. It filters to user_preference type first, then runs semantic search on a much smaller set. Result: faster, more accurate retrieval.

Everything runs on SQLite. Embeddings stored as BLOBs, full-text search via FTS5, custom similarity functions. No Postgres. No Redis. No vector database. No API keys.

If you run multiple AI agents on the same project, you know the problem: they overwrite each other's changes, duplicate work, and have no idea what the other agent is doing.

OMEGA includes coordination tools. Agents can claim files before editing, announce their intent, hand off tasks, and share context. Three Claude Code instances working on the same repo without conflicts.

OMEGA holds #1 on LongMemEval (95.4% task-averaged), the standard benchmark for long-term memory in AI agents. The funded competitors: Mastra ($13M YC, 94.87%), Mem0 ($24M), Letta ($10M), Emergence ($97M).

Full benchmark details on the benchmarks page.

OMEGA speaks Model Context Protocol (MCP). Any agent that supports MCP can use it. 25 tools for storing, querying, and managing memory. Install once, works everywhere.