Authorization notice. Dodoguard is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.
Who it is forA release owner who must show evidence of testing before an AI agent goes live.
The problemTeams often ship agents with no durable findings or reports to show what was tested.
The outcomeA set of exported reports for one agent that document the findings, the fixes, and the retest before go-live.
What you need first
- Node.js 22.12 or newer for the CLI
- An OpenAI API key for the OpenAI example, or a local Ollama setup for the local red-team example
- Docker with Docker Compose for the web console
- Access to the Dify, Coze, FastGPT, or n8n app you want to check, if you run platform checks
Steps
- Start the web console with
docker compose up -d --build - Open http://localhost:3000 and register the agent in the asset ledger
- Run evaluation, red-team, and platform detection tasks against the agent
- Work through findings in the remediation loop and rerun the tasks
- Export the reports and keep them in your own infrastructure
Prompt for your agent
Using the dodoguard repo, register the agent <agent-name> in the web console, run its detection tasks, and export the reports. Return the exported report list and the open findings count. Verify that the acknowledged state did not move the agent to released before the remediation loop passed.
Caveat. The README says acknowledge is not release, so confirm the agent is marked released only after your own sign-off in the console.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for dodoguardai/dodoguard and "Export release evidence to support a go-live decision"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
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