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Persistent Memory for AI

Your AI forgets.
OMEGA
remembers.

Persistent memory for AI coding agents. Decisions, lessons, and context survive across sessions. Your agent picks up where it left off.

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Works withClaude CodeCursorWindsurf

Apache-2.0 · Local-first · Python 3.11+

The problem

Every session begins in darkness.

×

Context lost

Architecture decisions vanish when the window closes. You re-explain the same codebase every session.

✓

Context preserved

Decisions, preferences, and architecture choices persist across every session, automatically.

tap to reveal
↻

Bugs re-solved

Your agent fixed that bug last week. Today it starts from scratch, no memory of what worked.

→

Lessons recalled

Past fixes surface automatically. Your agent never re-solves the same problem twice.

tap to reveal
?

Questions repeated

“What auth method?” gets asked every session. You become the bottleneck for your own tools.

≡

Answers remembered

Preferences, constraints, and prior answers are stored once and recalled forever. You stop repeating yourself.

tap to reveal

How it works

OMEGA remembers.

01

Capture

Hooks detect decisions, lessons, and errors during normal work. Nothing to tag manually.

02

Store & Index

Memories get embedded, indexed for full-text and semantic search, and stored locally in SQLite.

03

Surface

Next session, relevant context appears automatically. The agent picks up where you left off.

$omega_query("that webpack thing that broke prod")
[reranking]47 candidates → top 3:
1. "CJS/ESM mismatch in stripe lib" (0.96) Nov 12
FIX: dynamic import() + moduleResolution: 'bundler'
2. "Webpack 5 broke Firebase init" (0.89) Oct 28
3. "Can't resolve 'crypto'" (0.84) Oct 15
>Check for contradictions before I deploy
[OMEGA]Scanning 230 memories...
[⚠️ conflict]You said "never use Redis for sessions"
on Dec 3, but redis-session-store was just added.
Reason from Dec 3: "session data must survive crashes"

Benchmark

0.0%

#1 on LongMemEval - ICLR 2025 - 500 questions

OMEGA
95.4%
Mastra OM
94.87%
Zep / Graphiti*
71.2%
No Memory
49.6%

LongMemEval (ICLR 2025) is the standard benchmark for AI memory systems. 500 questions testing extraction, reasoning, temporal understanding, and abstention.

OMEGA uses category-tuned prompts (different answer prompts per question type); Mastra does not. Different methodologies, not directly comparable. Tested with GPT-4.1 + OMEGA v1.0.0. Full methodology and source available in the repo. *Zep/Graphiti score from their published evaluation. Mastra OM score (gpt-5-mini actor) from their published research.

Questions

Frequently asked.

Mem0 is cloud-first — it requires an API key and sends your data to their servers. Graph features cost $249/mo. OMEGA is local-first: everything runs on your machine, embeddings are computed locally with ONNX, and graph relationships are included free. OMEGA also scores 95.4% on LongMemEval; Mem0 hasn’t published a score.

Yes. OMEGA v1.0 ships with 12 MCP tools, AES-256 encryption at rest, intelligent forgetting with audit trails, and multi-agent coordination. It’s been tested across thousands of sessions and is the #1 ranked system on the LongMemEval benchmark (ICLR 2025).

No. OMEGA uses a local ONNX embedding model (bge-small-en-v1.5) and SQLite for storage. Zero API keys, zero cloud dependencies, zero external calls. Your data never leaves your machine.

Minimal. Embedding a memory takes ~8ms on CPU. Queries return in under 50ms. The SQLite database and ONNX model add about 100MB to disk. OMEGA runs as a lightweight subprocess managed by your editor via MCP.

Any MCP-compatible client: Claude Code, Cursor, Windsurf, Cline, and more. If your tool supports the Model Context Protocol, OMEGA works with it. Setup takes two commands.

Stay in the loop

Get updates on OMEGA.

New features, benchmarks, and launch news. No spam.

Ready?

Give your agent memory.

Two commands. No cloud. No API keys. Your agent starts remembering in under a minute.

$ pip install omega-memory && omega setup
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Apache 2.0 · Foundation Governed · Local-first · Python 3.11+