How to Add Persistent Memory to OpenClaw with OMEGA
OpenClaw is the most popular AI agent on earth - 194K+ GitHub stars and growing. But every session starts from scratch. Here's how to give it real memory in under five minutes.
OpenClaw (formerly Moltbot, originally Clawdbot) has taken the AI agent world by storm. It runs locally, connects to any LLM, and manages everything from messaging to DevOps through a growing ecosystem of 3,000+ skills.
But there's a gap: memory. OpenClaw's built-in memory system is a collection of markdown files on disk. No semantic search. No cross-session learning. No way to query “what did I decide about the auth architecture last week?” Every session, your agent wakes up with amnesia.
Third-party options exist - Mem0's plugin requires a cloud API key, and the community openclaw-memory project is still markdown-based - SQLite search is in progress (Phase 2) but not yet complete.
OMEGA solves this. It's a standalone memory server that speaks MCP (Model Context Protocol) - the open standard for tool integration. It runs locally, requires zero API keys, and is #1 on LongMemEval with 95.4% accuracy.
OpenClaw's Memory Today
Out of the box, OpenClaw stores context in flat markdown files: a MEMORY.md hot context file loaded at session start, daily note files in YYYY-MM-DD.md format, and an optional auto-consolidation cron that extracts headings every 6 hours.
This works for basic note-taking. But it breaks down when you need to:
- •Search semantically - "find everything related to the payment refactor"
- •Track decisions across sessions - "why did we choose PostgreSQL over MongoDB?"
- •Learn from mistakes - "what went wrong last time I deployed on Friday?"
- •Coordinate multiple agents working on the same project
- •Resume a complex task that spans multiple sessions
These aren't edge cases. They're the daily reality of using an AI agent for real software development.
What OMEGA Adds
OMEGA is a persistent memory server with 12 MCP tools. When connected to OpenClaw, your agent gains:
- •Semantic search - query by meaning, not just keywords, using a local embedding model (bge-small via ONNX)
- •Typed memory events - decisions, lessons learned, error patterns, user preferences, each with their own retrieval logic
- •Graph relationships - memories link to related memories, enabling traversal and cluster discovery
- •Intelligent forgetting - TTL expiry, decay curves, compaction, all with an auditable deletion log
- •Checkpoint/resume - save full task state (plan, progress, files touched, decisions) and restore it in a new session
- •Multi-agent coordination - file claims, task queues, and agent-to-agent messaging when running multiple OpenClaw instances
Everything runs locally. Your data never leaves your machine. No API keys, no Docker, no external databases.
Setup Guide
Step 1: Install OMEGA
OMEGA is a Python package on PyPI. Install it and run the setup wizard:
pip install omega-memory
omega setupThis creates a ~/.omega/ directory with a SQLite database and downloads the embedding model (~50 MB, one-time). The whole process takes about 30 seconds.
Step 2: Connect OpenClaw via MCP
OpenClaw connects to MCP servers through community bridges. The most straightforward approach is using the OpenClaw MCP Server pattern or the MCPorter skill. Create an OpenClaw skill that wraps OMEGA's MCP endpoint:
---
name: omega-memory
description: Persistent semantic memory via OMEGA MCP server
mcp:
command: omega
args: ["serve", "--stdio"]
---
# OMEGA Memory
Provides persistent memory across sessions via MCP.
## Available Tools
- omega_store - Save a memory (decision, lesson, error pattern, etc.)
- omega_query - Search memories by meaning
- omega_lessons - Retrieve cross-session lessons learned
- omega_checkpoint - Save full task state for later
- omega_resume_task - Restore a checkpointed task
- omega_welcome - Get a session briefing with recent contextAlternatively, if your OpenClaw setup uses the mcp_servers configuration in your agent YAML, add OMEGA directly:
mcp_servers:
omega:
command: omega
args:
- serve
- --stdioStep 3: Verify the Connection
Ask your OpenClaw agent to call the welcome tool:
You: Check if OMEGA memory is connected.
OpenClaw: I'll call omega_welcome to check.
> omega_welcome()
> ✓ OMEGA ready - 0 memories | Project: default
> Health: healthy | Router: balanced
Memory is connected and ready. I can now store and
retrieve memories across sessions.In Practice
1. Storing Architecture Decisions
After making a significant decision, tell your agent to remember it:
You: Remember that we chose Stripe for payments over
Paddle because we need multi-currency support
and Stripe's webhook reliability is better.
OpenClaw: Stored as a decision via omega_store.
This will be surfaced in future sessions when
you work on payments.Next session, when you ask “why did we choose Stripe?” - OMEGA finds the decision by semantic similarity, not keyword matching.
2. Learning from Debugging Sessions
You: We just spent 2 hours debugging that CORS issue.
The fix was adding the staging domain to the
allowed origins in the API gateway config.
Remember this as a lesson.
OpenClaw: Stored as a lesson_learned. Next time
CORS issues come up, omega_lessons will surface
this automatically.3. Checkpoint and Resume Multi-Day Tasks
Working on a large refactor? Save your progress before ending the session:
You: Checkpoint this task. We're migrating the user
service from REST to gRPC. Finished the proto
definitions and server stubs. Next: client SDK
and integration tests.
OpenClaw: Checkpointed "REST to gRPC migration".
Saved: plan, progress, 6 files touched, 3 decisions.
--- Next day ---
You: Resume the gRPC migration task.
OpenClaw: Restored checkpoint. Here's where we left off:
✓ Proto definitions (complete)
✓ Server stubs (complete)
→ Next: Client SDK and integration tests
Files: user.proto, server.go, handler.go...Built-in Memory vs OMEGA
Here's how OMEGA compares to OpenClaw's default memory system:
Why Not Mem0?
Mem0 has an OpenClaw plugin too. It works. But there are trade-offs worth considering:
- •Mem0 requires a cloud API key - your memories leave your machine
- •Graph memory (relationships between facts) requires Mem0 Pro at $249/month
- •No published LongMemEval benchmark score
- •No checkpoint/resume for multi-day tasks
- •No intelligent forgetting - memories accumulate without lifecycle management
If you want cloud-hosted memory and are okay with API keys, Mem0 is a solid choice. If you want local-first, zero-dependency memory with verified benchmarks, OMEGA is the better fit. I wrote a detailed comparison if you want the full breakdown.
There are other local-first options too - notably mcp-memory-service (1.3K+ stars, 12 tools) which provides solid persistent memory via MCP. OMEGA differentiates with typed memory events, graph relationships, intelligent forgetting with audit trails, checkpoint/resume for multi-day tasks, and a published LongMemEval benchmark score.
Sources
All data verified February 2026 from official documentation and public repositories.
Give your OpenClaw agent real memory
Two commands. Zero API keys. Your data stays on your machine.