caura-memclaw

Fleet memory for AI agents — governed, shared, self-improving.

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MemClaw — Fleet memory for AI agents

MemClaw is open-source memory for multi-tenant, multi-agent AI fleets. Your agents store what they learn, find what the fleet knows, and get smarter with every interaction — learning from each other instead of repeating mistakes.

Agents write plain text. MemClaw turns it into searchable, governed, self-improving memory.

One loop, three pillars: write, recall, compound — every interaction makes the next one smarter.

Built for fleets, not single agents. Public agent-memory benchmarks (LoCoMo, LongMemEval) measure one agent, one user, one long conversation — the single-chatbot shape. The deployment shape we see in production is the opposite: dozens or thousands of agents working on behalf of a company, sharing what they learn under governance. MemClaw is architected around that shape from day one — scoped memory, cross-agent outcome propagation, fleet-wide trust tiers — and competes on the axes that compound with agent count: latency, token efficiency, and governance. See Performance for the numbers, or read the benchmarks write-up.

In production at eToro (NASDAQ: ETOR): 300+ AI agents on one governed memory — 26,500+ memories, 1,372 shared skills, 23 ms p50 search. Architecture deep-dive →

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Quick Start

Try it locally — no API key, no signup

The fastest way to see MemClaw work. Standalone mode runs single-tenant with auth bypassed — write and recall a memory in four commands. (It boots with dummy embeddings so there's nothing to configure; add an AI provider key for semantic search — see Self-Hosted below.)

git clone https://github.com/caura-ai/caura-memclaw.git
cd caura-memclaw
cp .env.example .env && echo "IS_STANDALONE=true" >> .env   # single-tenant, no API key
docker compose up -d                                        # Postgres + pgvector + Redis + API (~30s)