
Open-source enterprise framework for multi-user agents, governed execution, webhooks, Skills, and provider-based integrations.
AtlasClaw is an open-source enterprise AI agent framework for building governed, multi-user conversational agents that can interact with multiple enterprise systems. The official documentation describes a core runtime with API layer, agent engine, sessions, tools, and docs, plus reusable provider packages and reference implementations. AtlasClaw organizes integrations as provider folders with Skills, scripts, and references, and supports webhook AI integration and deployment in embedded or standalone modes. The project is published with Apache-2.0 licensing references on the official site and GitHub.
Product overview
AtlasClaw is an Apache-2.0 enterprise agent framework, not a ready-made end-user agent. It provides a core runtime, API layer, sessions, memory, skills, tools, channels, and a provider registry for embedding an assistant into one enterprise system or coordinating work across several systems.
Information as of September 3, 2026
Teams can add AtlasClaw as an in-product assistant for one enterprise application or run it as an independent conversational layer spanning multiple systems.[1]
Providers encapsulate platform authentication, skills, scripts, context resolution, and tool actions while the core remains reusable.[1], [2]
The framework is designed to inherit authenticated-user permissions and leave authorization and audit sources of truth in the connected enterprise systems.[1]
AtlasClaw's core and documentation are published under Apache-2.0 with no listed software subscription. Deployments still require infrastructure, an external model provider, and any connected enterprise services.[1], [2]
Platforms: Web UI, Embedded enterprise panels, Chat-platform adapters, REST, streaming, WebSocket, and webhook interfaces[1]
Access: Self-hosted embedded mode, Self-hosted standalone mode[1]
80/100
Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.
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