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How to run the local alert investigation stack in one command with Agent Py

Start the API, worker, frontend, and Docker services to investigate alerts locally.

Credentials. The local JSON config holds your model API key and CLS credentials, so keep config/project.json and config/user.project.json out of version control.

Who it is forAn engineer who wants to test alert diagnosis on their own machine before trusting it with real alert data.

The problemRunning the diagnosis pipeline needs Kafka, Redis, Milvus, the API, the event worker, and the frontend all started and wired to model and CLS settings.

The outcomeA running local workspace where you can open a chat or diagnosis session and watch progress stream in the browser.

What you need first

  • Python 3.10+, uv, Node.js 22+, and Docker Compose installed locally
  • Access to a configured Qwen-compatible model service and its API key
  • Tencent CLS credentials if you use the CLS integration
  • Git clone of kkjiangk/agent-py

Steps

  1. Run npm ci at the repository root.
  2. Run cp config/project.template.json config/project.json and cp config/user.project.template.json config/user.project.json.
  3. Edit both local JSON files to set the model API key, chat and embedding model names, and the required CLS fields.
  4. Run ./scripts/start-local.sh.
  5. Open http://127.0.0.1:5173 in a browser to use the workspace.

Prompt for your agent

In the kkjiangk/agent-py repository, follow the local development steps in its README to start the stack with ./scripts/start-local.sh. Report which of the frontend, API, and Alertmanager addresses responded. Verify that http://127.0.0.1:8000/docs loads before you finish.

Caveat. The workspace UI is in Chinese, and the setup needs a Qwen-compatible model service and, for CLS, Tencent credentials.

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