Cross-Framework LLM Tool for CaptainAgent

In this tutorial, we demonstrate how to integrate LLM tools from LangChain Tools, CrewAI Tools into CaptainAgent. The developers just need to use one line of code to convert them into AG2 tools, and then pass it to CaptainAgent while instantiation, simple as that.

Langchain Tool Integration

Langchain readily provides a number of tools at hand. These tools can be integrated into AG2 framework through interoperability.

Installation

To integrate LangChain tools into the AG2 framework, install the required dependencies:

pip install -U ag2[openai,interop-langchain,duckduckgo]

Note: If you have been using autogen or ag2, all you need to do is upgrade it using:

pip install -U autogen[openai,interop-langchain,duckduckgo]

or

pip install -U ag2[openai,interop-langchain,duckduckgo]

as autogen, and ag2 are aliases for the same PyPI package.

Preparation

Import necessary modules and tools. - DuckDuckGoSearchRun and DuckDuckGoSearchAPIWrapper: Tools for querying DuckDuckGo. - Interoperability: This module acts as a bridge, making it easier to integrate LangChain tools with AG2’s architecture.

from langchain_community.tools import DuckDuckGoSearchRun
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper

from autogen.interop import Interoperability

Configure the agents

Load the config for LLM, which include API key and model.

import os

from dotenv import load_dotenv

import autogen

load_dotenv()

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

Tool Integration

We use Interoperability to convert the LangChain tool into a format compatible with the AG2 framework.

interop = Interoperability()

api_wrapper = DuckDuckGoSearchAPIWrapper()
langchain_tool = DuckDuckGoSearchRun(api_wrapper=api_wrapper)
ag2_tool = interop.convert_tool(tool=langchain_tool, type="langchain")

Then add the tools to CaptainAgent. This will let the agents within the nested chat created by CaptainAgent all equipped with the tools. They can write python code to call the tools and observe the results.

from autogen import UserProxyAgent
from autogen.agentchat.contrib.captainagent import CaptainAgent

# build agents
captain_agent = CaptainAgent(
    name="captain_agent",
    code_execution_config={"use_docker": False, "work_dir": "groupchat"},
    agent_lib="captainagent_expert_library.json",
    tool_lib=[ag2_tool],  # The main difference lies here: we pass the converted tool to the agent
    agent_config_save_path=None,
    llm_config=llm_config,
)

captain_user_proxy = UserProxyAgent(name="captain_user_proxy", human_input_mode="NEVER")

res = captain_user_proxy.initiate_chat(
    captain_agent,
    message="Call a group of experts and search for the word of the day Merriham Webster December 26, 2024",
)

CrewAI Tool Integration

CrewAI also provides a variety of powerful tools designed for tasks such as web scraping, search, code interpretation, and more. The full list of available tools in CrewAI can be observed here.

Installation

Install the required packages for integrating CrewAI tools into the AG2 framework. This ensures all dependencies for both frameworks are installed.

pip install -U ag2[openai,interop-crewai]

Note: If you have been using autogen or ag2, all you need to do is upgrade it using:

pip install -U autogen[openai,interop-crewai]

or

pip install -U ag2[openai,interop-crewai]

as autogen, and ag2 are aliases for the same PyPI package.

Tool Integration

Integrating CrewAI tools into AG2 framework follows a similar pipeline as shown below.

from crewai_tools import ScrapeWebsiteTool

from autogen.interop import Interoperability

interop = Interoperability()
crewai_tool = ScrapeWebsiteTool()
ag2_tool = interop.convert_tool(tool=crewai_tool, type="crewai")

Adding tools to CaptainAgent

The process is identical to the above, pass the converted tool to tool_lib argument, and all the agents created by CaptainAgent get access to the tools.

from autogen import UserProxyAgent
from autogen.agentchat.contrib.captainagent import CaptainAgent

# build agents
captain_agent = CaptainAgent(
    name="captain_agent",
    code_execution_config={"use_docker": False, "work_dir": "groupchat"},
    agent_lib="captainagent_expert_library.json",
    tool_lib=[ag2_tool],
    agent_config_save_path=None,
    llm_config=llm_config,
)

captain_user_proxy = UserProxyAgent(name="captain_user_proxy", human_input_mode="NEVER")

message = "Call experts and Scrape the website https://ag2.ai/, analyze the content and summarize it"
result = captain_user_proxy.initiate_chat(captain_agent, message=message)

print(result.summary)