Research Agent

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import os
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import dotenv
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from composio_llamaindex import Action, ComposioToolSet
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from llama_index.core.llms import ChatMessage
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from llama_index.llms.openai import OpenAI
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from llama_index.agent.openai import OpenAIAgent
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from llama_index.tools.arxiv.base import ArxivToolSpec
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# Load environment variables
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dotenv.load_dotenv()
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# Initialize the language model
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llm = OpenAI(model="gpt-4o")
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# Set research parameters
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research_topic = "LLM agents function calling"
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target_repo = "composiohq/composio"
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n_issues = 3
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def main():
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    # Get Composio toolset and add ArXiv tools
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    composio_toolset = ComposioToolSet()
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    github_tools = composio_toolset.get_actions(actions=[Action.GITHUB_CREATE_AN_ISSUE])
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    arxiv_tool = ArxivToolSpec()
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    # Define system message
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    prefix_messages = [
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        ChatMessage(
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            role="system",
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            content=(
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                "You are now a research agent, and whatever you are "
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                "requested, you will try to execute utilizing your tools."
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            ),
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        )
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    ]
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    # Create the agent with tools
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    agent = OpenAIAgent.from_tools(
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        tools=github_tools + arxiv_tool.to_tool_list(),
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        llm=llm,
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        prefix_messages=prefix_messages,
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        max_function_calls=10,
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        allow_parallel_tool_calls=False,
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        verbose=True,
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    )
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    # Create the research prompt
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    prompt = (
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        f"Please research on Arxiv about `{research_topic}`, Organize "
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        f"the top {n_issues} results as {n_issues} issues for "
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        f"a github repository, finally raise those issues with proper, "
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        f"title, body, implementation guidance and reference in "
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        f"{target_repo} repo, as well as relevant tags and assignee as "
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        "the repo owner."
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    )
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    # Execute the agent
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    response = agent.chat(prompt)
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    # Print the result
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    print("Response:", response)
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if __name__ == "__main__":
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    main()