Multi-Agent Memory
Your AI agents forget everything between sessions. This fixes that.
Your agents don't talk to each other
You don't run one AI agent — you run many. Claude Code on your laptop, an autonomous agent on a server, n8n workflows on another machine. Each one maintains its own context. When one discovers something important, the others never learn about it.
Multi-Agent Memory is the shared brain that connects them all.
Store, search, and share memories
A REST API sits between your agents and a dual storage layer. Agents store memories with type, importance, and source metadata. The system handles embeddings, credential scrubbing, deduplication, and decay automatically.
4 memory types, each with different behavior:
| Type | Purpose | Decays? | Example |
|---|---|---|---|
| event | Something happened | No | "Deploy completed at 14:30" |
| fact | Persistent knowledge | Yes | "Client prefers formal tone" |
| status | Current state (supersedes previous) | Yes | "API rate limit: 100/min" |
| decision | Choices made with reasoning | No | "Chose Postgres over MySQL for X" |
How it all connects
Hover over any node to learn what it does. Data flows left to right — agents store and search memories through a central API backed by dual storage.
↑ Hover a node to see details
Up and running in 60 seconds
git clone https://github.com/ZenSystemAI/multi-agent-memory.git cd multi-agent-memory cp .env.example .env # Edit .env with your API keys docker compose up -d
That's it. Qdrant + the Memory API are running. Point your agents at localhost:8084.
Built in production
This isn't a weekend project. Multi-Agent Memory runs in production coordinating 10+ AI agents across 3 machines, powering client deliverables for a digital agency. It was built because nothing on the market handled cross-machine agent memory well.