DashClaw — MCP Server by ucsandman

by ucsandman · MCP Server · ★ 275

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About DashClaw

DashClaw Govern AI agents before they act. DashClaw is the governance layer for AI agents that touch real systems. It sits between agents and the world, evaluates policy on every risky action, routes human approval where it is required, records verifiable evidence, and tracks terminal outcomes so a retried agent never silently double-executes. Plugs into the agents you already run: Claude Code, Codex, Hermes Agent, OpenClaw, Claude Desktop, and Claude Managed Agents. Framework integrations for LangChain, CrewAI, AutoGen, LangGraph, and OpenAI Agents SDK. Any other runtime over MCP, the Node/Python SDK, or direct REST. <img src="https://img.shields.io/b

agent-frameworkagent-governanceagent-runtimeai-agentsai-governanceai-infrastructureai-opsautogenclaude-codecrew-ai

Quick Facts

Stars275
Forks48
LanguageTypeScript
CategoryMCP Server
LicenseMIT
Quality Score48.182/100
Open Issues2
Last Updated2026-06-18
Created2026-02-08
Platformsclaude-code, mcp, node
Est. Tokens~20k

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Frequently Asked Questions

What is DashClaw?

DashClaw is 🛡️The governance runtime for AI agents. Intercept actions, enforce guard policies, require approvals, and produce audit-ready decision trails.. It is categorized as a MCP Server with 275 GitHub stars.

What programming language is DashClaw written in?

DashClaw is primarily written in TypeScript. It covers topics such as agent-framework, agent-governance, agent-runtime.

How do I install or use DashClaw?

You can find installation instructions and usage details in the DashClaw GitHub repository at github.com/ucsandman/DashClaw. The project has 275 stars and 48 forks, indicating an active community.

What license does DashClaw use?

DashClaw is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to DashClaw?

The top alternatives to DashClaw on Agent Skills Hub include Network-AI, swarmclaw, claude-emporium. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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