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caura-memclaw
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caura-memclaw

Active·★ 339·Apache-2.0·Updated 2026-07-19

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 is currently grouped under Memory & Context, which makes it easier to evaluate through workflow fit instead of isolated features alone. Based on the available data, it leans most heavily toward Row-level tenant isolation with PII detection and quarantine. and Enabling large fleets of AI agents within an enterprise to share learned knowledge under strict governance.. The listed license is Apache-2.0, which is useful when adoption constraints matter. It also shows measurable community traction with 339 GitHub stars.

#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.
+Enabling large fleets of AI agents within an enterprise to share learned knowledge under strict governance.
+Covers 5 supported environments or platforms, which is helpful for broader deployment needs.
+Ships with a public repository and a Apache-2.0 license, which makes adoption and review easier.
03

Trade-offs

!There are at least 8 related tools in the same category, so the best choice is easier to make after side-by-side comparison.
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

How it compares

≈caura-memclaw sits in the Memory & Context category, so it makes more sense to evaluate it alongside tools like headroom instead of in isolation.
≈If your main need is closer to "Enabling large fleets of AI agents within an enterprise to share learned knowledge under strict governance.", that use case is a better lens for comparison than broad feature checklists alone.
≈caura-memclaw uses a Apache-2.0 license, and community traction are both easier to judge in category context.
08

Alternatives

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On this page
01Features02Why choose it03Trade-offs04Compatibility05Quick start06Use cases07How it compares08Alternatives
Stats
GitHub Stars★ 339
Last commit1d ago
StatusActive
LicenseApache-2.0
CategoryMemory & Context
Trend (30d)
+13.5↑ 2.5%
Links
Documentation↗Discussion↗Issues↗Releases↗

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