Breaking

Three-layer hybrid memory system for OpenClaw released as open source

June 30, 2026 at 08:22 EDT

AI Agents
Open Source

A "three-layer hybrid memory system" combining files and a temporal knowledge graph has been released as open source for OpenClaw's multi-agent environments. By bundling private notes, shared documents, and a cross-agent knowledge graph, it offers a reference implementation that lets multiple AI agents collaborate while sharing context.

June 30, 2026 · MIT License

A Three-Layer Hybrid Memory System for Multi-Agent OpenClaw

"openclaw-graphiti-memory" combines private files, shared files, and a temporal knowledge graph — balancing the transparency of human-readable memory with graph-based cross-cutting search to fight forgetting and context contamination.

memory layers: files, shared files, graph

<$1

per month at 20-agent scale (gpt-4.1)

search cost — local Neo4j queries

The Three Layers, Stacked

Layer 1 · Private Files QMD — vector + BM25

Each agent's own memory/ directory, queried with hybrid full-text and vector search.

↓

Layer 2 · Shared Files read-only, orchestrator-managed

A shared/ directory — user profiles, agent roster, infrastructure info — mounted to each agent via symlinks.

↓

Layer 3 · Knowledge Graph Graphiti + Neo4j

A shared graph strong on temporal facts; each agent writes to its own group but can search across groups.

Why isolate per-agent groups?

Separating workspace from memory is the key to clean multi-agent operation — each agent lives in a dedicated group to prevent cross-mixing of information.

Forgetting

No more per-session zero-starts

Contamination

Per-group isolation across agents

Cross-cutting

Temporal & invoice search across groups

Strengths

Human-readable, editable file memory
Temporal tracking of config changes
Near-zero running cost; free local search

Limitations

Cost of Graphiti entity extraction
Overhead of symlink management
Cross-group accuracy depends on the model

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