Build an LLM Agent That Can Write and Run Code

In this article, we will explore how to build an LLM (Large Language Model) agent that can write and run code using the OpenAI Agents SDK and Docker. This walkthrough will provide a step-by-step guide on how to create an agent that can execute code and interact with its environment.

Introduction to LLM Agents

LLM agents are a type of artificial intelligence that uses natural language processing to interact with their environment. These agents can be trained to perform various tasks, such as writing code, answering questions, and even controlling robots. With the OpenAI Agents SDK, we can create custom LLM agents that can be integrated with various applications and systems.

Prerequisites

To build an LLM agent that can write and run code, you will need:

• A basic understanding of Python programming
• Familiarity with Docker and containerization
• An OpenAI account and access to the Agents SDK
• A code editor or IDE of your choice

Step 1: Setting up the Environment

To start, we need to set up our environment by installing the necessary dependencies. First, we need to install Docker on our system. Docker is a containerization platform that allows us to run our agent in a isolated environment.

Once Docker is installed, we can create a new Docker container using the OpenAI Agents SDK image. This image provides a pre-configured environment for our agent to run in.

	docker pull openai/agents-sdk
	docker run -it --name llm-agent openai/agents-sdk
	

Step 2: Creating the Agent

Now that our environment is set up, we can create our LLM agent. We will use the OpenAI Agents SDK to define our agent’s behavior and capabilities.

We start by importing the necessary libraries and defining our agent’s class. In this example, we will create an agent that can write and run Python code.

	import openai
	from openai.agents import Agent

	class CodeAgent(Agent):
		def __init__(self):
			super().__init__()
			self.model = openai.Model("code-davinci-002")

		def write_code(self, prompt):
			response = self.model.complete(prompt, max_tokens=1024)
			return response["text"]

		def run_code(self, code):
			# Run the code using the Docker container
			docker_container = "llm-agent"
			docker_exec = f"docker exec -it {docker_container} python -c '{code}'"
			subprocess.run(docker_exec, shell=True)
	

Step 3: Training the Agent

Now that our agent is defined, we need to train it on a dataset of code examples. This will allow our agent to learn the patterns and structures of code and generate new code based on a given prompt.

We can use a dataset of open-source code repositories to train our agent. We will use the GitHub repository dataset, which provides a large collection of open-source code in various programming languages.

We will fine-tune our agent’s model using the dataset and evaluate its performance using a validation set.

	dataset = ...
	validation_set = ...

	# Fine-tune the model
	self.model.fine_tune(dataset, validation_set)
	

Step 4: Interacting with the Agent

Now that our agent is trained, we can interact with it using natural language. We can ask our agent to write code based on a prompt, and then run the generated code using the Docker container.

For example, we can ask our agent to write a Python function that calculates the factorial of a given number.

	prompt = "Write a Python function to calculate the factorial of a given number"
	code = agent.write_code(prompt)
	print(code)
	

This will generate the following code:

	def factorial(n):
		if n == 0:
			return 1
		else:
			return n * factorial(n-1)
	

We can then run the generated code using the Docker container.

	agent.run_code(code)
	

Conclusion

In this article, we have explored how to build an LLM agent that can write and run code using the OpenAI Agents SDK and Docker. We have walked through the steps of setting up the environment, creating the agent, training the agent, and interacting with the agent.

This is just the beginning of what is possible with LLM agents. With further development and training, these agents can be used to automate a wide range of tasks, from code generation to data analysis and more.

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