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Local-First Agent Intelligence

Sovereign Intelligence
for AI Agents.

The LLM is rented. The intelligence is owned. OMEGA runs entirely on your machine.

$ pip install omega-memory && omega setup
Install FreeStar on GitHub
Works withClaude CodeCursorWindsurfOpenClawObsidian

Apache-2.0 · Local-first · Python 3.11+

Your memory across every provider.
On your machine.

Switch LLMs. Switch editors. Your agent's memory stays. Sovereign, local, yours.

$ pip install omega-memory && omega setup
Install FreeView on GitHub

95.4%

LONGMEMEVAL

50ms

RETRIEVAL

AES-256

ENCRYPTED

Claude Code
Cursor
Windsurf
Cline
Zed
OpenClaw
Codex
Claude Desktop
Antigravity
Obsidian

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.

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

  • Full decision audit trail with timestamps, provenance, and reasoning chains
  • AES-256-GCM encryption at rest. Zero cloud dependency. Zero data exfiltration
  • Intelligent forgetting with retention policies per data type
  • Runs entirely on-premise. No third-party data processing agreements needed
⌘

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

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.

Questions

Frequently asked. questions about OMEGA memory for AI agents

Every day your agents run with OMEGA, your institutional knowledge compounds. A competitor can clone any software overnight. They cannot replicate 12 months of accumulated decisions, constraints, corrections, and domain context. The moat isn’t the tool. It’s the time.

Mem0 is cloud-first. It requires an API key and sends your data to their servers. Your accumulated institutional context lives on infrastructure you don’t control. Graph features cost $249/mo. OMEGA runs entirely on your machine: 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; Mem0 hasn’t published a score.

Yes. OMEGA ships with 95+ MCP tools covering memory, coordination, and learning. AES-256-GCM encryption at rest, intelligent forgetting with audit trails, and multi-agent coordination with file claims and deadlock detection. 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.

Intelligence updates

How firms are building compounding knowledge systems with AI agents.

Weekly, max.

Ready?

Start compounding today.

Two commands. No cloud. No API keys. Every day you wait is institutional knowledge you don't accumulate.

$ pip install omega-memory && omega setup

Apache 2.0 · Foundation Governed · Local-first · Python 3.11+