# Building Multi-Agent Systems with LangGraph or CrewAI

Hello, fellow AI enthusiasts! Today, we're diving into the fascinating world of multi-agent systems using two powerful tools: LangGraph and CrewAI. By the end of this post, you'll have a solid understanding of how to set up, train, and deploy these systems using Python. Let's get started!

Prerequisites

Before we dive into the code, make sure you have:

  1. A modern version of Python (3.6+) installed on your machine.
  2. Familiarity with Python libraries such as NumPy, Pandas, and TensorFlow or PyTorch.
  3. Git to clone repositories and manage your project.
  4. Docker for easier environment setup and deployment.

Introduction to LangGraph and CrewAI

Both LangGraph and CrewAI are tools designed to simplify the development of multi-agent systems. LangGraph provides a flexible framework for defining, training, and deploying agents in various environments, while CrewAI offers a more user-friendly interface for creating and managing multi-agent simulations.

Setting Up LangGraph

To get started with LangGraph, first, clone the repository from GitHub:

git clone https://github.com/langtechai/langgraph.git
cd langgraph
pip install .

Next, let's create a simple environment for our agent to navigate using the EnvBuilder class:

from langgraph import EnvBuilder, Agent, ActionSpace, ObservationSpace

class MyEnvironment(EnvBuilder):
    def __init__(self, width=80, height=60):
        self.width = width
        self.height = height
        super().__init__()

    def _reset(self):
        self.observation_space = ObservationSpace({'x': 0, 'y': 0})
        self.action_space = ActionSpace(['up', 'down', 'left', 'right'])
        self._reset_env()

    def _step(self, action):
        # Implement the logic of your environment here.
        pass

Now, create an agent that can move around our environment:

class MyAgent(Agent):
    def __init__(self, observation_space, action_space):
        super().__init__(observation_space, action_space)

    def choose_action(self, observation):
        # Implement your agent's decision-making logic here.
        pass

Finally, let's run a training session for our agent:

from langgraph import Trainer

def main():
    environment = MyEnvironment()
    agent = MyAgent(environment.observation_space, environment.action_space)
    trainer = Trainer(agent, environment, nb_episodes=1000)
    trainer.train()

if __name__ == "__main__":
    main()

Setting Up CrewAI

To use CrewAI, first, sign up for an account and create a new project at CrewAI. Clone the provided starter repository to your local machine:

git clone https://github.com//.git
cd 
pip install -r requirements.txt

Next, open the config.yaml file and modify it according to your project's needs. This includes defining the environment, agents, training parameters, and more.

Finally, run the training script using Docker:

docker build -t  .
docker run -it --rm --name

Conclusion

In this post, we explored how to build multi-agent systems using LangGraph and CrewAI. Both tools provide a powerful foundation for creating complex AI simulations, and their Python-based nature makes them accessible to developers familiar with the language. By leveraging these tools, you can focus on developing intelligent agents and leave the environment setup and training to the frameworks. Happy coding!