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Prerequisites

  • Docker and Docker Compose
  • Python 3.9+
  • An agent project (LangChain, LlamaIndex, or raw OpenAI)

Step 1: Start Control Plane Server

Control Plane Server starts at http://localhost:3000. Open it in a browser to confirm the dashboard loads.

Step 2: Install the SDK

Step 3: Instrument your agent

Add two lines to your agent code:
Control Plane auto-patches LangChain, LlamaIndex, and the OpenAI client. Every LLM call and tool invocation is traced automatically, without manual spans required.

Step 4: Run your agent

Step 5: View the trace

Open http://localhost:3000 → select your project → click the latest run. You’ll see the full trace tree:
  • Agent decision step
  • LLM call with token count, cost, and latency
  • Tool invocations with inputs and outputs
  • Final response

Step 6: Add your first eval

Create langship.yaml in your project root:
Create evals/golden-set.jsonl:
Run the eval:
You’ll see per-example pass/fail and an overall score. If the score drops below pass_threshold, the command exits with code 1, blocking CI.

What’s next