DashClaw — Decision Infrastructure for AI Agents

Main

DashClaw is useful when ordinary logging is not enough. It sits on the action path and gives you a chance to inspect or block risky behavior before execution.

The repo is concrete about scope: 8 governance tools, 4 resources, stdio MCP for desktop-style hosts, and SDKs for explicit framework integration.

That makes it a pragmatic bridge between lightweight local agent experiments and regulated production automation, where an approval step needs to be part of the design rather than an afterthought.

Source-backed notes

• README lists MCP server and SDK integration paths.
• The MCP package exposes 8 governance tools and 4 resources.
• SDK install examples are npm install dashclaw and pip install dashclaw.

FAQ

Q: Does DashClaw require MCP? A: No. MCP is one path, but the project also offers Node and Python SDKs.

Q: What does the MCP package expose? A: The README says 8 governance tools and 4 resources.

Q: Why adopt it? A: Because governance is enforced on the action path, not only described in policy documents.