Caura

Caura — Shared governed memory for AI agents

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

MemClaw is now Caura — same product, one name. Existing memclaw_* tool calls and supported MEMCLAW_* environment aliases continue to work; use caura_* names and current Caura URLs for new configuration. The PyPI name memclaw-client is kept only as a redirect shell (0.5.1) that installs caura-client; it provides no memclaw_client import and no MemClaw class. The npm package @caura/memclaw-client was never published.

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Caura (formerly MemClaw) — the shared governed memory layer for AI agent fleets

Caura — formerly 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. Caura 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. One agent works, and that's where most teams start — nothing below changes for a single-agent setup. What Caura adds is headroom: scoped memory, cross-agent outcome propagation, and fleet-wide trust tiers are there from the first write, and they keep paying off as agents multiply. Public agent-memory benchmarks (LoCoMo, LongMemEval) measure one agent, one user, one long conversation — the single-chatbot shape. Fleet governance and workload-specific latency still need validation in the environment where they matter. See the generated Performance block and evidence registry for approved benchmark results, or read the benchmarks write-up.

Production case study: eToro uses Caura as a governed shared-memory layer for its AI-agent work. Architecture deep-dive →

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Quick Start

Try it locally — no API key, no signup

The fastest way to see Caura work. Standalone mode runs single-tenant with auth bypassed — start Caura, write a memory, and find it again. (With no key there's nothing to configure: memories are stored without embeddings and found by keyword. Add an AI provider key for semantic search — see Self-Hosted below.)

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

# Write a memory — no API key needed
curl -X POST http://localhost:8000/api/v1/memories \
  -H "X-API-Key: standalone" -H "Content-Type: application/json" \
  -d '{"tenant_id": "default", "agent_id": "quickstart", "write_mode": "strong", "content": "Our auth service uses JWT with 15-minute expiry."}'