import { Callout, Steps } from "nextra/components";

# Create Your First Harness

This tutorial shows you how to use the DeerFlow Harness programmatically — importing and using DeerFlow directly in your Python code rather than through the web interface.

## Prerequisites

- Python 3.12+
- `uv` installed
- DeerFlow repository cloned

## Install

```bash
cd deer-flow/backend
uv sync
```

## Create configuration

Create a minimal `config.yaml`:

```yaml
config_version: 6

models:
  - name: gpt-4o
    use: langchain_openai:ChatOpenAI
    model: gpt-4o
    api_key: $OPENAI_API_KEY

sandbox:
  use: deerflow.sandbox.local:LocalSandboxProvider

tools:
  - use: deerflow.community.ddg_search.tools:web_search_tool
  - use: deerflow.sandbox.tools:read_file_tool
  - use: deerflow.sandbox.tools:write_file_tool
```

## Write the code

<Steps>

### Create a Python file

Create `my_agent.py` in the `backend/` directory:

```python
import asyncio
import os
from deerflow.client import DeerFlowClient
from deerflow.config import load_config

os.environ["OPENAI_API_KEY"] = "sk-..."

# Load config.yaml
load_config()

client = DeerFlowClient()

async def main():
    async for event in client.astream(
        thread_id="my-first-thread",
        message="Write a Python fibonacci function with a docstring",
        config={
            "configurable": {
                "model_name": "gpt-4o",
            }
        },
    ):
        print(event)

asyncio.run(main())
```

### Run it

```bash
cd backend
uv run python my_agent.py
```

</Steps>

## What the events look like

The stream yields events like:

```python
{"type": "messages", "data": {"content": "def fibonacci..."}}
{"type": "thread_state", "data": {"title": "Python Fibonacci Function"}}
```

## Next steps

- [Use Tools and Skills](/docs/tutorials/use-tools-and-skills)
- [Harness Quick Start](/docs/harness/quick-start)