Auto Generated Agent Chat: GPTAssistant with Code Interpreter

The latest released Assistants API by OpenAI allows users to build AI assistants within their own applications. The Assistants API currently supports three types of tools: Code Interpreter, Retrieval, and Function calling. In this notebook, we demonstrate how to enable GPTAssistantAgent to use code interpreter.

Requirements

AG2 requires Python>=3.9. To run this notebook example, please install:

Requirements

Install autogen:

pip install autogen

For more information, please refer to the installation guide.

Set your API Endpoint

The config_list_from_json function loads a list of configurations from an environment variable or a json file.

import io
import os

from IPython.display import display
from PIL import Image

import autogen
from autogen.agentchat import UserProxyAgent
from autogen.agentchat.contrib.gpt_assistant_agent import GPTAssistantAgent

config_list = autogen.LLMConfig(
    config_list=[
        {
            "model": "gpt-5",
            "api_key": os.getenv("OPENAI_API_KEY"),
            "api_type": "openai",
        },
        {
            "model": "gpt-5-mini",
            "api_key": os.getenv("OPENAI_API_KEY"),
            "api_type": "openai",
        },
    ]
)

Tip: Learn more about configuring LLMs for agents here.

Perform Tasks Using Code Interpreter

We demonstrate task solving using GPTAssistantAgent with code interpreter. Pass code_interpreter in tools parameter to enable GPTAssistantAgent with code interpreter. It will write code and automatically execute it in a sandbox. The agent will receive the results from the sandbox environment and act accordingly.

Example 1: Math Problem Solving

In this example, we demonstrate how to use code interpreter to solve math problems.

# Initiate an agent equipped with code interpreter
gpt_assistant = GPTAssistantAgent(
    name="CoderAssistant",
    llm_config={
        "config_list": config_list,
    },
    assistant_config={
        "tools": [{"type": "code_interpreter"}],
    },
    instructions="You are an expert at solving math questions. Write code and run it to solve math problems. Reply TERMINATE when the task is solved and there is no problem.",
)

user_proxy = UserProxyAgent(
    name="user_proxy",
    is_termination_msg=lambda msg: "TERMINATE" in msg["content"],
    code_execution_config={
        "work_dir": "coding",
        "use_docker": False,  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
    },
    human_input_mode="NEVER",
)

# When all is set, initiate the chat.
user_proxy.initiate_chat(
    gpt_assistant, message="If $725x + 727y = 1500$ and $729x+ 731y = 1508$, what is the value of $x - y$ ?"
)
gpt_assistant.delete_assistant()

Example 2: Plotting with Code Interpreter

Code Interpreter can outputs files, such as generating image diagrams. In this example, we demonstrate how to draw figures and download it.

gpt_assistant = GPTAssistantAgent(
    name="CoderAssistant",
    llm_config={
        "config_list": config_list,
    },
    assistant_config={
        "tools": [{"type": "code_interpreter"}],
    },
    instructions="You are an expert at writing python code to solve problems. Reply TERMINATE when the task is solved and there is no problem.",
)

user_proxy.initiate_chat(
    gpt_assistant,
    message="Draw a line chart to show the population trend in US. Show how you solved it with code.",
    is_termination_msg=lambda msg: "TERMINATE" in msg["content"],
    human_input_mode="NEVER",
    clear_history=True,
    max_consecutive_auto_reply=1,
)

Now we have the file id. We can download and display it.

api_response = gpt_assistant.openai_client.files.with_raw_response.retrieve_content(
    "assistant-tvLtfOn6uAJ9kxmnxgK2OXID"
)

if api_response.status_code == 200:
    content = api_response.content
    image_data_bytes = io.BytesIO(content)
    image = Image.open(image_data_bytes)
    display(image)

gpt_assistant.delete_assistant()