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

# Quick Start

> Get your first simulation running in under 10 minutes

Get OneRun running locally and create your first agent evaluation in minutes.

## Prerequisites

* **Docker** installed on your machine
* **Anthropic API key** for running AI agents

## Step 1: Start Temporal Server

OneRun uses Temporal for orchestrating simulations. First, start the Temporal server:

```bash theme={null}
# Clone the OneRun repository
git clone https://github.com/onerun-ai/onerun.git
cd onerun

# Navigate to temporal directory and start services
cd docker/temporal
docker compose up
```

<Note>
  Keep this terminal running. Temporal needs to stay active for OneRun to work.
</Note>

## Step 2: Configure Environment

In a new terminal, set up your environment configuration:

```bash theme={null}
# Navigate to docker directory
cd docker

# Copy environment template
cp .env.example .env

# Edit the .env file with your API key
# Replace YOUR_ANTHROPIC_API_KEY with your actual key
```

Update your `.env` file:

```bash theme={null}
ANTHROPIC_API_KEY=your-actual-anthropic-api-key-here
```

<Tip>
  Support for other AI models and better configuration management is coming in future releases.
</Tip>

## Step 3: Start OneRun Platform

```bash theme={null}
# From the docker directory
docker compose up
```

Your OneRun instance will be available at `http://localhost:3000`

## Step 4: Create Your First Evaluation

1. **Open OneRun** in your browser at `http://localhost:3000`
2. **Sign Up** for a new account or log in if you already have one
3. **Create a Project** to organize your evaluations
4. **Define an Agent** that represents your AI system
5. **Set Objectives** to measure what success looks like
6. **Create a Simulation** to test specific scenarios

<Note>
  OneRun will automatically generate diverse personas and orchestrate conversations once your worker is running.
</Note>

## Step 5: Run Your Worker

To handle actual conversations, you need to run a worker that implements your agent logic:

```bash theme={null}
# Navigate to the Python SDK examples
cd python-sdk/examples

# Install dependencies (requires uv)
uv pip install .

# Set up environment variables
cp .env.example .env

# Edit .env with your OneRun configuration
# ONERUN_API_BASE_URL=http://localhost:3000
# ONERUN_API_KEY=your-api-key-from-onerun-ui
# ONERUN_PROJECT_ID=your-project-id (found in Project Settings page)
# ONERUN_AGENT_ID=your-agent-id (found in Agent Details page)
# ANTHROPIC_API_KEY=your-anthropic-key

# Start the worker
uv run multi-turn-agent/main.py
```

Your worker will:

* Poll for assigned conversations during active simulations
* Handle turn-by-turn exchanges with generated personas
* Use your AI model to generate agent responses
* Report results back to OneRun for evaluation

## Next Steps

Now that OneRun is running, explore these key concepts:

<CardGroup cols={2}>
  <Card title="Core Concepts" href="/concepts/projects">
    Understand projects, agents, and objectives
  </Card>

  <Card title="Workers" href="/concepts/workers">
    Learn how to integrate your AI agent
  </Card>

  <Card title="Use Cases" href="/use-cases">
    See how teams use OneRun in production
  </Card>

  <Card title="API Reference" href="/api-reference/introduction">
    Explore the complete API documentation
  </Card>
</CardGroup>

<Note>
  Your OneRun instance is completely self-hosted. All your evaluation data stays on your infrastructure with no external dependencies except for AI model API calls.
</Note>