Multi-Agent Memory: Open Source Shared Memory System for AI Agents

Multi-Agent Memory is an open source project that gives AI agents a shared memory system that works across machines, tools, and frameworks. It was born from a production setup where OpenClaw agents, Claude Code, and n8n workflows needed to share memory across separate machines.

The Problem It Solves

When running multiple AI agents like Claude Code for development, OpenClaw for autonomous tasks, and n8n for automation, each maintains its own context and forgets everything between sessions. Existing solutions are either single-machine only, require paid cloud services, or treat memory as a flat key-value store without understanding that facts and events are fundamentally different.

Typed Memory with Mutation Semantics

The system understands four distinct memory types, each with its own lifecycle:

• event: Append-only, immutable historical record. Use case: "Deployment completed", "Workflow failed"
• fact: Upsert by key, new facts supersede old ones. Use case: "API status: healthy", "Client prefers dark mode"
• status: Update-in-place by subject, latest wins. Use case: "build-pipeline: passing", "migration: in-progress"
• decision: Append-only, records choices and reasoning. Use case: "Chose Postgres over MySQL because..."

Memory Lifecycle

The system follows this process: Store → Dedup Check → Supersedes Chain → Confidence Decay → LLM Consolidation

• Deduplication: Content is hashed on storage, exact duplicates return existing memory
• Supersedes: When storing a fact with the same key as an existing fact, the old one is marked inactive and the new one links back to it
• Confidence Decay: Facts and statuses lose confidence over time if not accessed (configurable, default 2%/day). Events and decisions don't decay
• LLM Consolidation: Periodic background process (configurable, default every 6 hours) sends unconsolidated memories to an LLM that finds duplicates to merge, contradictions to flag, connections between memories, and cross-memory insights

Security Features

• Credential Scrubbing: All content is scrubbed before storage. API keys, JWTs, SSH private keys, passwords, and base64-encoded secrets are automatically redacted
• Agent Isolation: The API acts as a gatekeeper between agents and data. Agents can only store and search memories, read briefings and stats. They cannot delete memories, drop tables, bypass credential scrubbing, access filesystem/database directly, or modify other agents' memories retroactively
• Security Implementation: Timing-safe authentication using cr****.timingSafeEqual(), startup validation requiring environment variables, and credential scrubbing before storage

Session Briefings

The system supports session briefings that summarize what happened since the last session, allowing agents to start with relevant context.