Research Agent

This guide provides detailed steps to create a Demo Assistant using Composio and OpenAI. You will build a system capable of interacting with GitHub to create issues and fetch user information.

This project is an example that uses Composio to seamlessly interact with GitHub through an AI assistant. It automatically creates issues and retrieves user information based on user input.

Python

Research Agent GitHub Repository

Explore the complete source code for the Demo Assistant project. This repository contains all the necessary files and scripts to set up and run the Demo Assistant using Composio and OpenAI.

1. Import base packages

   Next, we’ll import the essential libraries for our project.

   Import statements

   import os
   import dotenv
   from composio_llamaindex import Action, ComposioToolSet  # pylint: disable=import-error
   from llama_index.core.llms import ChatMessage  # pylint: disable=import-error
   from llama_index.llms.openai import OpenAI  # pylint: disable=import-error
   from llama_index.agent.openai import OpenAIAgent
   from llama_index.tools.arxiv.base import ArxivToolSpec
   
2. Initialize LLM

   We’ll initialize our LLM and set up the necessary configurations.

   Python - Initialize LLM

   dotenv.load_dotenv()
   
   llm = OpenAI(model="gpt-4o")
   
   research_topic = "LLM agents function calling"
   target_repo = "composiohq/composio"
   n_issues = 3
   
   
3. Define Main Function

   Define the main function for Python that will handle incoming requests and interact with the OpenAI API.

   Define Main

   def main():
       # Get All the tools
       composio_toolset = ComposioToolSet()
       tools = composio_toolset.get_actions(actions=[Action.GITHUB_CREATE_AN_ISSUE])
       arxiv_tool = ArxivToolSpec()
   
       prefix_messages = [
           ChatMessage(
               role="system",
               content=(
                   "You are now a integration agent, and whatever you are "
                   "requested, you will try to execute utilizing your tools."
               ),
           )
       ]
   
       agent = OpenAIAgent.from_tools(
           tools=tools + arxiv_tool.to_tool_list(),
           llm=llm,
           prefix_messages=prefix_messages,
           max_function_calls=10,
           allow_parallel_tool_calls=False,
           verbose=True,
       )
   
       response = agent.chat(
           f"Please research on Arxiv about `{research_topic}`, Organize "
           f"the top {n_issues} results as {n_issues} issues for "
           f"a github repository, finally raise those issues with proper, "
           f"title, body, implementation guidance and reference in "
           f"{target_repo} repo,  as well as relevant tags and assignee as "
           "the repo owner."
       )
   
       print("Response:", response)
   
   
4. Start the Server

   Finally, we’ll Run the main on Python to execute the Agent

   Main function

   if __name__ == "__main__":
       main()
   
   

Putting it All Together

Python Final code

import os
import dotenv
from composio_llamaindex import Action, ComposioToolSet  # pylint: disable=import-error
from llama_index.core.llms import ChatMessage  # pylint: disable=import-error
from llama_index.llms.openai import OpenAI  # pylint: disable=import-error
from llama_index.agent.openai import OpenAIAgent
from llama_index.tools.arxiv.base import ArxivToolSpec

# Load environment variables from .env
dotenv.load_dotenv()

llm = OpenAI(model="gpt-4o")

research_topic = "LLM agents function calling"
target_repo = "composiohq/composio"
n_issues = 3


def main():
    # Get All the tools
    composio_toolset = ComposioToolSet()
    tools = composio_toolset.get_actions(actions=[Action.GITHUB_CREATE_AN_ISSUE])
    arxiv_tool = ArxivToolSpec()

    prefix_messages = [
        ChatMessage(
            role="system",
            content=(
                "You are now a integration agent, and what  ever you are "
                "requested, you will try to execute utilizing your tools."
            ),
        )
    ]

    agent = OpenAIAgent.from_tools(
        tools=tools + arxiv_tool.to_tool_list(),
        llm=llm,
        prefix_messages=prefix_messages,
        max_function_calls=10,
        allow_parallel_tool_calls=False,
        verbose=True,
    )

    response = agent.chat(
        f"Please research on Arxiv about `{research_topic}`, Organize "
        f"the top {n_issues} results as {n_issues} issues for "
        f"a github repository, finally raise those issues with proper, "
        f"title, body, implementation guidance and reference in "
        f"{target_repo} repo,  as well as relevant tags and assignee as "
        "the repo owner."
    )

    print("Response:", response)


if __name__ == "__main__":
    main()