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OMEGA

Sovereign Context Engine

Rent the model. Own the intelligence.

OMEGA turns decisions, docs, and agent history into local, token-efficient working context. No cloud memory. No context dumping.

Get OMEGA ProInstall Core Free

Pro from $19/mo · Apache-2.0 core stays free

Works withClaude CodeCursorWindsurfOpenClawObsidian

Apache-2.0 · Local-first · Python 3.11+

Not another notes layer

Memory is the mechanism. Context is the product.

Obsidian stores knowledge for humans. Markdown files store text. OMEGA turns agent history into ranked, local working context: typed memories, embeddings, relationships, timestamps, access history, contradictions, and audit trails.

01

Capture

Decisions, corrections, lessons, docs, and session summaries enter a local SQLite memory store.

02

Rank

Semantic search, FTS5, type weighting, recency, and contradiction handling keep signal above notes-app noise.

03

Inject

Agents receive the smallest useful context slice instead of dumping full notes, docs, or old chat history.

Benchmark

0.0%

95.4% on LongMemEval · 50ms retrieval · zero cloud dependency

The only memory system proven on both LongMemEval and MemoryStress.

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.

Token savings proof

Stop paying tokens to reload what your agents should already know.

Long context windows made it easy to dump everything into the prompt. That is not memory. OMEGA retrieves a small, ranked slice of working context so agents keep room for the actual task.

OMEGA

~1.5K

Hybrid semantic + FTS retrieval; top 5-10 context items.

Zep / Graphiti

5K-15K

Graph query and entity extraction context.

Observational memory

~70K

Full memory dump into the context block.

Markdown notes

Unbounded

Cheap to write, expensive to keep relevant.

OMEGA trims a memory payload from about ~70K tokens to roughly ~1.5K tokens per retrieval. That is about 47x less context sent to the model, which means lower spend, faster responses, and more room for code, specs, and live task state.

Pro unlocks

Coordination + Routing + Knowledge. On your machine.

Core gives every agent local context retrieval. Pro is for builders who run parallel agent sessions, switch models, isolate client projects, and need project docs available as working context.

P301

P3.01 · Multi-Agent Coordination

Pro

Agents stop overwriting each other.

  • File claims, session rosters, task queues, branch checks, and peer messages keep parallel agent work visible
  • Deadlock and conflict warnings surface before edits collide, with fail-open behavior if OMEGA is unavailable
P302

P3.02 · LLM Routing

Pro

The right model handles the right task.

  • Route architecture work, quick edits, long-context tasks, and local calls across Anthropic, OpenAI, Google, xAI, and local models
  • Keep agent memory portable while reducing wasted premium-model spend on low-complexity work
P303

P3.03 · Knowledge Base

Pro

Project docs surface when agents need them.

  • Index PDFs, markdown, web pages, and internal docs into the same local retrieval system as project memory
  • Stop manually stuffing architecture notes, specs, and runbooks into every new coding session
P304

P3.04 · Secure Profiles + Sync

Pro

Keep context isolated and portable.

  • Separate memory by project, client, organization, or component so one workflow never bleeds into another
  • Encrypted profile storage and optional Supabase sync keep your agent memory available across your own machines

Stay on Core for single-agent memory. Upgrade when your agent workflow needs coordination, routing, docs, sync, or isolation. That is the Pro boundary.

Your moat

Rent the LLM. Keep the intelligence.

Every firm has access to the same AI models. The edge is the institutional knowledge your agents accumulate. That knowledge compounds locally, on your machine, and it never leaves.

Σ

Institutional Finance

  • Strategy decisions, risk parameters, and trade rationale persist across sessions and analysts
  • Multi-agent coordination for research, risk, and execution pipelines with shared institutional context
  • Earnings transcripts, 10-Ks, and research PDFs indexed as retrievable knowledge
  • Contradiction detection flags when live parameters drift from documented constraints
⊚

Compliance & Audit

  • Court-admissible signed audit chain — Ed25519 + Merkle over an immutable version chain, verifiable offline forever
  • Per-leaf inclusion proofs for selective disclosure. Hand over one decision without revealing the rest
  • AES-256-GCM encryption at rest. Right-to-erasure redact preserves the audit chain instead of breaking it
  • Runs entirely on-premise. No third-party data processing agreements. Air-gap compatible
⌘

Software Engineering

  • Multi-repo context that follows you across projects and editors
  • Debug patterns and fixes recalled by semantic similarity, not exact keywords
  • Code review lessons compound. Same mistake never explained twice
  • Cross-session decisions prevent contradictory architecture changes

Get started faster

Get the setup guide and weekly tips for building agents that remember.

The problem

Four problems. One root cause.

×

Knowledge resets to zero

Every session starts blank. Last quarter’s analysis, risk constraints, strategy rationale — gone.

✓

Knowledge compounds

Decisions, analysis, and constraints persist. Each session builds on every one before it.

tap to reveal
⇆

Agents work blind

Research, risk, and execution agents with no shared context. Duplicated work, contradictory actions.

≡

Agents coordinate

Shared memory, file claims, intent broadcasting. Multi-agent pipelines without chaos.

tap to reveal
↻

Intelligence is disposable

Same corrections, same constraints re-explained. Institutional knowledge never accumulates.

→

Intelligence is permanent

Patterns, lessons, and institutional knowledge accumulate. Day 365 is irreplaceable.

tap to reveal
☁

Someone else’s server

Cloud memory services store your accumulated context on infrastructure you don’t control. Your IP flows upstream.

⌂

Your machine, your moat

SQLite, local embeddings, zero API keys. The LLM is rented. The intelligence is owned.

tap to reveal

How institutional knowledge compounds

Four stages between raw context and permanent edge.

Most tools stop at stage one. OMEGA runs all four.

Every memory flows through the same four stages. No manual tagging. No cloud calls. Each stage makes the next one smarter.

01

Stage 01: Capture

Zero effort

Every decision remembered automatically.

  • Decisions, corrections, and constraints are captured during normal work. Nothing falls through the cracks
  • High-value knowledge is prioritized. Noise is filtered at ingestion, not after
02

Stage 02: Understand

Semantic matching

Finds what matters, not just what matches.

  • Understands meaning, not keywords. A question about "portfolio risk" surfaces last quarter’s constraint discussion automatically
  • Runs entirely on your machine. No data leaves your infrastructure, ever
03

Stage 03: Evolve

Self-refining

Knowledge that sharpens over time.

  • Duplicate insights merge. Related knowledge consolidates. Your institutional memory gets cleaner the longer it runs
  • Stale decisions retire automatically. Contradictions are flagged before they cause damage
04

Stage 04: Retrieve

Instant recall

The right context, in under 50ms.

  • Three search strategies run in parallel and blend results. The most relevant knowledge surfaces first
  • Irrelevant or low-confidence matches are suppressed. Your agents only act on knowledge that matters

Every stage runs on your machine. No cloud. No external calls. The LLM is rented. The intelligence is owned.

Questions

Frequently asked. questions about OMEGA memory for AI agents

Every day your agents run with OMEGA, your working context compounds locally: decisions, corrections, constraints, documents, and coordination history. A competitor can rent the same model. They cannot instantly recreate the context your agents have accumulated.

Mem0 is cloud-first. It requires an API key and sends your data to their servers. OMEGA runs entirely on your machine: local semantic retrieval, coordination, forgetting, audit, and learning. Embeddings are computed locally with ONNX, graph relationships are included, and your working context never leaves your infrastructure. OMEGA scores 95.4% on LongMemEval; Mem0 hasn’t published a score.

Yes. OMEGA ships a free Apache-2.0 Core for local persistent memory and a Pro tier for multi-agent coordination, routing, knowledge base, secure profile, sync, audit, dreaming, federation, and tunable tool loading. Tested across thousands of sessions. 95.4% on LongMemEval (ICLR 2025) at 50ms retrieval.

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. No third-party data processing agreements required.

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, OpenClaw, Obsidian, Cline, and more. Works with any MCP-compatible agent framework your team deploys. Setup takes two commands.

Yes. OMEGA runs entirely on your machine with zero cloud dependencies. Your data never leaves your infrastructure. AES-256-GCM encryption at rest, full audit trails with provenance tracking, and intelligent forgetting with configurable retention policies. No third-party data processing agreements required. See our FINRA 2026 compliance guide at omegamax.co/compliance/finra-2026.

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.

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Core is free local memory. Pro adds coordination, routing, project knowledge, encrypted sync, and isolation when your agent workflow outgrows one session.

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