Memclaw

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<h3 align="center">Fleet memory for AI agents &mdash; governed, shared, self-improving.</h3>

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<a href="LICENSE"></a>
<a href="https://github.com/caura-ai/caura-memclaw/stargazers"></a>
<a href="https://github.com/caura-ai/caura-memclaw/actions"></a>
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<a href="https://discord.com/invite/aNfpgfpj"></a>
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<a href="#quick-start">Quick Start</a> &middot;
<a href="#features">Features</a> &middot;
<a href="#performance">Performance</a> &middot;
<a href="#mcp-model-context-protocol">MCP</a> &middot;
<a href="#api-reference">API Reference</a> &middot;
<a href="static/docs/integration-guide.md">Plugin Docs</a> &middot;
<a href="CONTRIBUTING.md">Contributing</a> &middot;
<a href="https://discord.com/invite/aNfpgfpj">Discord</a>
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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)
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", "content": "Our auth service uses JWT with 15-minute expiry."}'
Search for it
curl -X POST http://localhost:8000/api/v1/search \
  -H "X-API-Key: standalone" -H "Content-Type: application/json" \
  -d '{"tenant_id": "default", "query": "authentication token lifetime"}'

The write response comes back enriched with an LLM-inferred memory_type, title, summary, tags, status, and weight — all from a single content field.

Ready for semantic recall, multi-tenant, a managed host, or an OpenClaw fleet? Pick a path below.

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Three paths — pick the one that matches your setup:

| Path | When | Time to first memory |
|---|---|---|
| Managed platform | Quickest. We host the DB + scaling. | ~2 min |
| Self-hosted (Docker) | Privacy / on-prem / air-gapped. | ~5 min |
| OpenClaw plugin | You already run an OpenClaw fleet — install MemClaw as a plugin against any of the above. | ~3 min |

Managed Platform

Get up and running in minutes — no infrastructure, automatic updates, usage analytics, and enterprise-grade security included.

1. Sign up free on memclaw.net
2. Grab your API key from the dashboard
3. Connect via MCP or REST:

{
  "mcpServers": {
    "memclaw": {
      "url": "https://memclaw.net/mcp",
      "headers": { "X-API-Key": "mc_your_api_key_here" }
    }
  }
}

> Production / team use: the quickstart key above is a tenant-scoped credential — fine for personal use, but a fleet of agents should bind each one to its own agent-scoped credential for trust gating, fleet membership, and per-agent keystones. Provision agent-scoped credentials atomically via POST /api/v1/admin/agent-keys/provision, or through the dashboard at /settings/organization/api-credentials. Both kinds use the mc_ prefix on the wire — scope is bound at mint time on the credential itself. The MCP server accepts the credential on either X-API-Key: mc_… or Authorization: Bearer mc_…. (Pre-existing mca_… and mci_… keys continue to authenticate via back-compat.)
>
> Using a tenant-scoped credential? Pass an explicit agent_id on every MCP tool call — the gateway refuses the reserved default (mcp-agent) on the tenant-scoped path.

Self-Hosted (Open Source)

The fastest path is Docker Compose — one command brings up Postgres + pgvector + Redis + the API.

> Prefer not to use Docker? Skip to Manual deployment (Python + Postgres) below for the bare-Python path.
>
> No cloud API key, no external calls? v2.0+ supports a self-hosted local embedder (BAAI/bge-m3 via HuggingFace TEI) — see docs/local-embedder.md. The setup below walks through the OpenAI default; the local-embedder doc walks through the alternative.

Prerequisites

- Docker Engine 24+ (Linux) or Docker Desktop (macOS / Windows). Confirm with docker --version.
- Docker Compose v2 (built into modern Docker). Confirm with docker compose version.
- Git for cloning.
- ~2 GB free disk for images + Postgres data volume.

1. Clone and configure

git clone https://github.com/caura-ai/caura-memclaw.git
cd caura-memclaw
cp .env.example .env

Set your AI provider in .env — minimal setup with OpenAI:

EMBEDDING_PROVIDER=openai
ENTITY_EXTRACTION_PROVIDER=openai
USE_LLM_FOR_MEMORY_CREATION=true
OPENAI_API_KEY=sk-...

> Without any AI keys the stack still starts — dummy providers return non-semantic embeddings, useful for testing the API surface.

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