> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lyzr.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Trace your first agent with Control Plane in under 10 minutes.

## Prerequisites

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

## Step 1: Start Control Plane Server

```bash theme={null}
curl -O https://raw.githubusercontent.com/LyzrCore/langship/main/docker-compose.yml
docker compose up -d
```

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

## Step 2: Install the SDK

```bash theme={null}
pip install langship
```

## Step 3: Instrument your agent

Add two lines to your agent code:

```python theme={null}
import langship

langship.init(
    endpoint="http://localhost:3000",
    project="my-first-agent"
)
```

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

```python theme={null}
from langchain.agents import initialize_agent, load_tools
from langchain.llms import OpenAI

langship.init(endpoint="http://localhost:3000", project="my-first-agent")

llm = OpenAI(temperature=0)
tools = load_tools(["serpapi"], llm=llm)
agent = initialize_agent(tools, llm, verbose=True)

agent.run("What is the capital of France?")
```

## 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:

```yaml theme={null}
project: my-first-agent

evals:
  - name: factual-accuracy
    type: llm-judge
    prompt: "Is this response factually correct? Answer yes or no."
    dataset: ./evals/golden-set.jsonl
    pass_threshold: 0.8
```

Create `evals/golden-set.jsonl`:

```jsonl theme={null}
{"input": "What is the capital of France?", "expected": "Paris"}
{"input": "Who wrote Hamlet?", "expected": "Shakespeare"}
```

Run the eval:

```bash theme={null}
langship eval run
```

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

* [How It Works](./how-it-works): pipeline architecture and core data model
* [Deployment](./deployment): Docker Compose, Kubernetes, and scaling options
* [GitHub Actions](./github-actions): run evals on every PR automatically
* [CLI Reference](./cli-reference): full command reference