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caura-memclaw
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caura-memclaw

Active·★ 504·Apache-2.0·Updated 2026-10-21
★ Trending

MemClaw is an open-source, multi-tenant, multi-agent AI fleet memory system designed for agents to store what they learn, find fleet knowledge, and improve with every interaction. It transforms plain text into searchable, governed, and self-improving memory, enabling agents to learn from each other and avoid repeating mistakes.

caura-memclaw sits in the Memory & Context category, so it is usually better judged by workflow fit, deployment style, and team needs than by isolated features alone. Based on the available metadata, it leans most clearly toward Row-level tenant isolation with PII detection and quarantine.. A representative use case is Enabling large fleets of AI agents within an enterprise to share learned knowledge under strict governance.. The listed license is Apache-2.0. GitHub stars are currently 504. Relevant tags include AI Agent Memory, Multi-Agent System.

This write-up is based on the project's README and public repository data. We have not hand-tested the tool.

#AI Agent Memory#Multi-Agent System#Knowledge Graph#Vector Search#Data Governance#LLM Context Management#Workflow Automation
$ Install
$ git clone https://github.com/caura-ai/caura-memclaw.git && cd caura-memclaw && cp .env.example .env && docker compose up -d
↗ Visit site★ GitHub
01

Features

01Row-level tenant isolation with PII detection and quarantine.
02Hybrid search combining semantic similarity, full-text, and knowledge graph expansion.
03Outcome-based learning (Karpathy Loop) to reinforce successful actions and learn from failures.
04Contradiction detection and supersession for conflicting memories.
05Built-in MCP (Model Context Protocol) server for client integrations.
02

Why choose it

+Row-level tenant isolation with PII detection and quarantine.
+Hybrid search combining semantic similarity, full-text, and knowledge graph expansion.
+Enabling large fleets of AI agents within an enterprise to share learned knowledge under strict governance.
+Supports 5 platforms or runtime environments.
03

Trade-offs

!The final decision should still be made through side-by-side comparison with similar tools.
04

Compatibility

Python
Runtime
Verified via docs
Docker
Deployment
Verified via docs
PostgreSQL
Database
Verified via docs
Redis
Cache
Verified via docs
MCP Clients
Integration
Verified via docs
05

Quick start

1
$ git clone https://github.com/caura-ai/caura-memclaw.git
2
$ cd caura-memclaw
3
$ cp .env.example .env
4
$ docker compose up -d
06

Use cases

↳Enabling large fleets of AI agents within an enterprise to share learned knowledge under strict governance.
↳Deploying a unified, governed memory system for hundreds of production AI agents to share skills and memories efficiently.
↳Providing an auditable memory plane for multiple agents, teams, and even different vendors to collaborate and share information securely.
07

Alternatives

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On this page
01Features02Why choose it03Trade-offs04Compatibility05Quick start06Use cases07Alternatives
caura-ai /
caura-memclaw
↗

Governed shared memory for AI agent fleets — multi-agent, multi-tenant, MCP-native. Trust tiers, keystone policies, audit trails, knowledge graph, self-improving retrieval. Apache 2.0.

504Apache-2.0
Last commit1d ago
StatusActive
CategoryMemory & Context
Links
Documentation↗Discussion↗Issues↗Releases↗
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