Omega Memory
Omega Memory — Persistent memory for AI coding agents
Last verified:
What is Omega Memory?
Omega Memory is a persistent memory MCP server for AI coding agents that runs entirely on your local machine. It solves the problem of AI agents forgetting everything between sessions by providing local-first, sovereign memory that persists decisions, lessons, context, and institutional knowledge across every conversation. The system uses semantic search, entity extraction, relationship mapping, and cross-session learning to ensure your agent picks up where it left off.
Key features include 15 memory tools in the free Core tier, semantic search with auto-capture, checkpoint/resume functionality, local SQLite storage, GitHub Issues support, and 50ms retrieval latency. OMEGA scores 95.4% on LongMemEval (ICLR 2025), the standardized benchmark for long-term memory in AI agents, ranking #1 globally. Pro and Team plans add 45 coordination tools, 10 multi-LLM routing tools, entity management, knowledge base tools, oracle intelligence tools, secure profile tools, and cloud sync via Supabase.
OMEGA is designed for individual developers, multi-agent teams, institutional finance firms, compliance/audit teams, and software engineering teams who need their AI agents to accumulate institutional knowledge. It integrates with Claude Code, Cursor, Windsurf, Cline, Zed, OpenClaw, Codex, Claude Desktop, Antigravity, and Obsidian. The system supports AES-256-GCM encryption at rest, court-admissible signed audit chains with Ed25519 + Merkle trees, autonomous dreaming for self-maintaining memory, and federated exchange between sovereign nodes.
The platform is built for Python 3.11+, runs as a lightweight MCP server subprocess, and requires no cloud infrastructure, API keys, or external calls. Your memories never leave your laptop, making it air-gap compatible and eliminating third-party data processing agreements. The open-source Core is Apache-2.0 licensed and will never be relicensed.
Omega Memory pricing
Pricing model: Freemium
Core: Free, Apache-2.0 licensed forever, includes 15 memory tools, semantic search & auto-capture, checkpoint/resume, local SQLite storage, GitHub Issues support. No coordination tools, multi-LLM routing, entity management, or cloud sync. Pro: $19/month for individual developers, includes 15 memory tools, 45 coordination tools, 10 multi-LLM routing tools, 10 entity management tools, 6 knowledge base tools, 4 oracle intelligence tools, 4 secure profile tools, cloud sync via Supabase, priority support. Team: $39/month per user/month for multi-agent teams, includes all Pro features plus team coordination dashboard and custom onboarding. Enterprise: Self-hosted, SSO, audit logs, SLA available upon contact. 14-day money-back guarantee for Pro, cancel anytime.
Omega Memory pros
- 95.4% on LongMemEval benchmark, ranked #1 globally
- 50ms retrieval latency on local machine
- Local-first with zero cloud dependencies
- No API keys required
- Apache-2.0 open-source core forever free
- AES-256-GCM encryption at rest
- Court-admissible signed audit chain with Ed25519 + Merkle tree
- Works with any MCP-compatible client (Claude, Cursor, Windsurf, etc.)
- Up to 134 MCP tools in Pro plan
- 45 coordination tools for multi-agent pipelines
- 10 multi-LLM routing tools
- Cloud sync via Supabase in paid plans
- Autonomous dreaming for self-maintaining memory
- Federated exchange between sovereign nodes
- GitHub Issues support included
- Semantic search with auto-capture
- 100MB disk footprint (~31MB memory)
- Intelligent forgetting with audit trails
- Tunable surface to load only tools you use
- Priority support for Pro and Team plans
Omega Memory cons
- Requires Python 3.11+ (not compatible with older Python versions)
- Paid features require subscription ($19/month Pro, $39/month Team)
- Cloud sync only available in paid plans
- Team coordination dashboard only in Team plan
- Knowledge base tools require Pro or Team plan
- Setup requires command-line installation
- Local embeddings mean CPU usage for embedding computation
- Some advanced features like federated exchange may be overkill for solo developers
Frequently asked questions about Omega Memory
What is OMEGA and what problem does it solve?
OMEGA is a persistent memory MCP server for AI coding agents. It solves the problem of AI agents forgetting everything between sessions. Every day your agents run with OMEGA, your institutional knowledge compounds with decisions, analysis, and constraints persisting across sessions. Each session builds on every one before it, patterns and lessons accumulate, and the LLM is rented but the intelligence is owned locally on your machine.
How does OMEGA compare to Mem0?
Mem0 is cloud-first and requires an API key that sends your data to their servers, with your accumulated institutional context living on infrastructure you don't control. Graph features cost $249/mo with Mem0. OMEGA runs entirely on your machine with memory, multi-agent coordination, and learning. Embeddings are computed locally with ONNX, graph relationships are included free, and your IP never leaves your infrastructure. OMEGA scores 95.4% on LongMemEval while Mem0 hasn't published a score.
Does OMEGA require cloud or API keys?
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 and no third-party data processing agreements are required.
What is the performance impact of running OMEGA?
Minimal. Embedding a memory takes approximately 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.
Which clients and editors support OMEGA?
Any MCP-compatible client including Claude Code, Cursor, Windsurf, OpenClaw, Obsidian, Cline, and more. It works with any MCP-compatible agent framework your team deploys. Setup takes two commands.
Is OMEGA secure and compliant?
Yes. OMEGA runs entirely on your machine with zero cloud dependencies. Your data never leaves your infrastructure. Features include AES-256-GCM encryption at rest, full audit trails with provenance tracking, intelligent forgetting with configurable retention policies, and no third-party data processing agreements required. It is air-gap compatible and a FINRA 2026 compliance guide is available.
Can OMEGA index documents and PDFs?
Yes. OMEGA Pro includes a knowledge base that indexes PDFs, documents, and structured data as retrievable memories. Earnings transcripts, research papers, 10-Ks, and internal documentation become part of your agent's persistent intelligence, searchable by semantic similarity.
What is the LongMemEval benchmark?
LongMemEval (ICLR 2025) is the standard benchmark for AI memory systems with 500 questions testing extraction, reasoning, temporal understanding, and abstention. It covers six capabilities: single-session recall, single-session update, preference application, knowledge updates, multi-session reasoning, and temporal reasoning. Without any memory system, an LLM scores about 49.6%. OMEGA scores 95.4% task-averaged accuracy (466/500 raw).
What is the open-source commitment for OMEGA?
The free Core is Apache-2.0 licensed and will never be relicensed. Paid features are additive—they extend the platform, they do not gate existing functionality. Foundation governance ensures this commitment outlasts any individual. Users can leave OMEGA tomorrow and the signed artifacts still verify because the math is yours, not theirs.
What are the four stages of how OMEGA processes knowledge?
Stage 01 Capture: Zero effort where every decision is remembered automatically with high-value knowledge prioritized and noise filtered at ingestion. Stage 02 Understand: Semantic matching that finds what matters by understanding meaning not keywords, running entirely on your machine. Stage 03 Evolve: Self-refining knowledge where duplicate insights merge, related knowledge consolidates, stale decisions retire automatically, and contradictions are flagged. Stage 04 Retrieve: Instant recall with the right context in under 50ms using three parallel search strategies that blend results while suppressing irrelevant or low-confidence matches.