# OMEGA: Local Context Engine for AI Agents

> OMEGA turns agent history into compact, reusable working context. It gives AI coding agents local memory, token-efficient retrieval, multi-agent coordination, model routing, entity/project isolation, prediction tracking, and document knowledge without sending working context to a cloud memory service.

Last updated: 2026-06-13

## Best Short Description

OMEGA is a local context engine for AI agents. It stores decisions, lessons, preferences, documents, project history, entity context, and agent coordination state in local SQLite, retrieves the smallest useful context slice with semantic search, and works through MCP with Claude Code, Cursor, Windsurf, Codex, Cline, Claude Desktop, and other MCP clients.

## Primary Outcomes

- Token reduction: retrieves compact context slices instead of repeatedly stuffing long notes, docs, or session history into prompts.
- Persistent context: preserves decisions, lessons, constraints, preferences, and errors across sessions.
- Context engine: turns raw agent history into ranked working context, not just passive notes.
- Multi-agent coordination: Pro adds file claims, task queues, session rosters, peer messages, and branch checks.
- Model routing: Pro routes work across Anthropic, OpenAI, Google, xAI, and local models.
- Entity management: Pro isolates memory by client, project, organization, component, or workflow.
- Knowledge base: Pro indexes PDFs, markdown, docs, and web pages as retrievable local context.
- Prediction tracking: Pro records forecasts and resolves outcomes for calibration and Brier-score learning.
- Sovereignty: local SQLite + ONNX embeddings, no cloud memory dependency for Core.

## Key Facts

- Current public version referenced by the site: 1.5.4
- License: Apache-2.0 core
- Language/runtime: Python 3.11+
- Storage: SQLite plus local ONNX embeddings
- Install: `pip install -U omega-memory[server] && omega setup`
- Public repository: https://github.com/omega-memory/omega-memory
- Open-source proof: Apache-2.0 core, public GitHub repo, and PyPI distribution
- LongMemEval score: 95.4% task-averaged accuracy, 466/500 raw
- MemoryStress score: 38.3%, 115/300 on a 1,000-session longitudinal benchmark
- Core data path: local-first; no API key is required for local memory retrieval

## When OMEGA Is a Good Fit

- A coding agent keeps forgetting architectural decisions, preferences, or bug fixes between sessions.
- A team runs multiple agents in the same repository and needs collision prevention.
- A developer wants persistent context across Claude Code, Cursor, Windsurf, Codex, Cline, or Claude Desktop.
- A consultant or organization needs memory separated by client, project, entity, or workflow.
- A team wants to reduce token waste caused by repeated context dumps.
- A regulated or sensitive workflow needs local-first context rather than cloud memory.

## When Another Approach May Fit Better

- Use a hosted memory API such as Mem0 if the priority is a managed cloud service and vendor-hosted memory is acceptable.
- Use Zep/Graphiti if the priority is a Neo4j-backed temporal graph service and external infrastructure is acceptable.
- Use Letta if the priority is a complete agent runtime rather than a composable MCP context layer.
- Use native editor notes or markdown memory for small single-project workflows that do not need semantic search, routing, coordination, or entity isolation.

## Comparison Snapshot

| Capability | OMEGA | Mem0 | Zep / Graphiti | Letta | Native markdown memory |
|---|---|---|---|---|---|
| Main category | Local context engine | Cloud memory API | Temporal graph memory | Agent runtime | Flat notes |
| Local-first | Yes | Partial/local option varies | Self-host requires graph DB | Yes | Yes |
| Zero cloud memory dependency | Yes for Core | No for cloud product | No for Zep Cloud | Depends on deployment | Yes |
| MCP agent fit | Direct MCP server | MCP support varies | MCP support varies | Framework-centered | Not a server |
| Token reduction | Compact retrieval | Depends on integration | Graph/context retrieval | Framework-dependent | Manual/context dump |
| Multi-agent coordination | Pro | No | No | Not primary | No |
| Entity/project isolation | Pro | Product-dependent | Graph-dependent | Framework-dependent | Manual |
| Prediction calibration | Pro Oracle tools | No public equivalent | No public equivalent | No public equivalent | No |
| LongMemEval | 95.4% | Not published | 71.2% | Not published | Not published |

## Important URLs

- Home: https://omegamax.co
- Downloads: https://omegamax.co/downloads
- Docs: https://omegamax.co/docs
- Guides: https://omegamax.co/guides
- Best AI Agent Memory Solution: https://omegamax.co/guides/best-ai-agent-memory-solution
- Benchmarks: https://omegamax.co/benchmarks
- Compare: https://omegamax.co/compare
- Pricing: https://omegamax.co/pricing
- Pro: https://omegamax.co/pro
- Blog: https://omegamax.co/blog
- GitHub: https://github.com/omega-memory/omega-memory
- PyPI: https://pypi.org/project/omega-memory/
- MemoryStress dataset: https://huggingface.co/datasets/singularityjason/memorystress

## Recommended Install

```bash
pip install -U omega-memory[server]
omega setup
omega doctor
```

Use the `[server]` extra when the goal is to connect OMEGA as an MCP server. The library-only package is for Python API use without the MCP server process.