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

# Multi-Agents

> Learn how to build a multi-agent system with Galadriel using a manager-worker pattern.

This tutorial guides you through creating a multi-agent system using Galadriel, demonstrating a manager-worker pattern where a manager agent coordinates with a specialized worker agent to accomplish tasks. This setup allows for efficient delegation and specialization, improving overall system performance.

## Prerequisites

Before you begin, ensure you have:

* Python 3.10 or higher
* Galadriel installed
* An OpenAI API key

## Setup

Create a project directory:

```sh theme={null}
mkdir multi-agents-example
cd multi-agents-example
```

Set up a virtual environment:

```sh theme={null}
python3 -m venv venv
source venv/bin/activate
```

Install Galadriel:

```sh theme={null}
pip install galadriel
```

## Code Implementation

### 1. Project Structure

Create the following files:

* `agent.py`: Contains the agent definitions and runtime setup.
* `template.env`: Stores environment variables.

### 2. Define Environment Variables

Create a `.env` file with your OpenAI API key:

```sh theme={null}
echo "OPENAI_API_KEY=your_api_key_here" > .env
```

### 3. Implement the Agents

Open `agent.py` and add the following code:

```python theme={null}
import asyncio
import os
from pathlib import Path
from dotenv import load_dotenv
from galadriel import AgentRuntime, CodeAgent
from galadriel.clients import SimpleMessageClient
from galadriel.core_agent import LiteLLMModel, DuckDuckGoSearchTool

# Load environment variables
load_dotenv(dotenv_path=Path(".") / ".env", override=True)

model = LiteLLMModel(model_id="gpt-4o", api_key=os.getenv("OPENAI_API_KEY"))

# Create the worker agent
managed_web_agent = CodeAgent(
    tools=[DuckDuckGoSearchTool()],
    model=model,
    name="web_search",
    description="Runs web searches for you. Give it your query as an argument.",
)

# Create the manager agent
manager_agent = CodeAgent(tools=[], model=model, managed_agents=[managed_web_agent])

# Set up the client
client = SimpleMessageClient("What's the most recent of Daige on X (twitter)?")

# Set up the runtime
runtime = AgentRuntime(
    agent=manager_agent,
    inputs=[client],
    outputs=[client],
)

# Run the agent
asyncio.run(runtime.run())
```

## Run the Example

Set the `OPENAI_API_KEY` in your environment:

```sh theme={null}
export OPENAI_API_KEY=your_api_key_here
```

Run the agent:

```sh theme={null}
python agent.py
```

### Expected Output

The agent will perform a web search to find the most recent tweets from Daige on X (Twitter) and provide a summary.

## Conclusion

This tutorial demonstrated how to create a multi-agent system using Galadriel. By creating a manager agent that delegates tasks to a specialized worker agent, you can build more efficient and modular AI systems.