Cross-Framework LLM Tool Integration with AG2 - AG2

Cross-Framework LLM Tool Integration with AG2

In this tutorial, we demonstrate how to integrate LLM tools from various frameworks—including LangChain Tools, CrewAI Tools, and PydanticAI Tools into the AG2 framework. This process enables smooth interoperability between these systems, allowing developers to leverage the unique capabilities of each toolset within AG2’s flexible agent-based architecture. By the end of this guide, you will understand how to configure agents, adapt these tools for use in AG2, and validate the integration through practical examples.

LangChain Tools Integration

LangChain is a popular framework that offers a wide range of tools to work with LLMs. LangChain has already implemented a variety of tools that can be easily integrated into AG2. You can explore the available tools in the LangChain Community Tools folder. These tools, such as those for querying APIs, web scraping, and text generation, can be quickly incorporated into AG2, providing powerful functionality for your agents.

Installation

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

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

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]

or

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

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

Additionally, this notebook uses LangChain’s Wikipedia Tool, which requires the wikipedia package. Install it with:

pip install wikipedia

Imports

Import necessary modules and tools.

import os

from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper

from autogen import AssistantAgent, UserProxyAgent
from autogen.interop import Interoperability

Agent Configuration

Configure the agents for the interaction.

config_list = [{"model": "gpt-5", "api_key": os.environ["OPENAI_API_KEY"]}]
user_proxy = UserProxyAgent(
    name="User",
    human_input_mode="NEVER",
)

chatbot = AssistantAgent(
    name="chatbot",
    llm_config={"config_list": config_list},
)

Tool Integration

api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=1000)
langchain_tool = WikipediaQueryRun(api_wrapper=api_wrapper)

interop = Interoperability()
ag2_tool = interop.convert_tool(tool=langchain_tool, type="langchain")

ag2_tool.register_for_execution(user_proxy)
ag2_tool.register_for_llm(chatbot)

message = "Tell me about the history of the United States"
user_proxy.initiate_chat(recipient=chatbot, message=message, max_turns=2)

CrewAI Tools Integration

CrewAI provides a variety of powerful tools designed for tasks such as web scraping, search, code interpretation, and more. These tools are easy to integrate into the AG2 framework, allowing you to enhance your agents with advanced capabilities. You can explore the full list of available tools in the CrewAI Tools repository.

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.

Imports

Import necessary modules and tools.

import os

from crewai_tools import ScrapeWebsiteTool

from autogen import AssistantAgent, UserProxyAgent
from autogen.interop import Interoperability

Agent Configuration

Configure the agents for the interaction.

config_list = [{"model": "gpt-5", "api_key": os.environ["OPENAI_API_KEY"]}]
user_proxy = UserProxyAgent(
    name="User",
    human_input_mode="NEVER",
)

chatbot = AssistantAgent(
    name="chatbot",
    llm_config={"config_list": config_list},
)

Tool Integration

Initialize and register the CrewAI tool with AG2.

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

ag2_tool.register_for_execution(user_proxy)
ag2_tool.register_for_llm(chatbot)

message = "Scrape the website https://ag2.ai/"

chat_result = user_proxy.initiate_chat(recipient=chatbot, message=message, max_turns=2)

print(chat_result.summary)

PydanticAI Tools Integration

PydanticAI is a newer framework that offers powerful capabilities for working with LLMs. Although it currently does not have a repository with pre-built tools, it provides features like dependency injection, allowing you to inject a “Context” into a tool for better execution without relying on LLMs. This context can be used for passing parameters or managing state during the execution of a tool. While the framework is still growing, you can integrate its tools into AG2 to enhance agent capabilities, especially for tasks that involve structured data and context-driven logic.

Installation

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

pip install -U ag2[openai,interop-pydantic-ai]

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

pip install -U autogen[openai,interop-pydantic-ai]

or

pip install -U ag2[openai,interop-pydantic-ai]

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

Imports

Import necessary modules and tools.

import os

from pydantic import BaseModel
from pydantic_ai import RunContext
from pydantic_ai.tools import Tool as PydanticAITool

from autogen import AssistantAgent, UserProxyAgent
from autogen.interop import Interoperability

Agent Configuration

Configure the agents for the interaction.

config_list = [{"model": "gpt-5", "api_key": os.environ["OPENAI_API_KEY"]}]
user_proxy = UserProxyAgent(
    name="User",
    human_input_mode="NEVER",
)

chatbot = AssistantAgent(
    name="chatbot",
    llm_config={"config_list": config_list},
)

Tool Integration

Integrate the PydanticAI tool with AG2.

class Player(BaseModel):
    name: str
    age: int

def get_player(ctx: RunContext[Player], additional_info: str | None = None) -> str:  # type: ignore[valid-type]
    """Get the player's name.

Args:
        ctx: Run context
        additional_info: Additional information which can be used.
    """
    return f"Name: {ctx.deps.name}, Age: {ctx.deps.age}, Additional info: {additional_info}"  # type: ignore[attr-defined]

interop = Interoperability()
pydantic_ai_tool = PydanticAITool(get_player, takes_ctx=True)

# player will be injected as a dependency
player = Player(name="Luka", age=25)
ag2_tool = interop.convert_tool(tool=pydantic_ai_tool, type="pydanticai", deps=player)

ag2_tool.register_for_execution(user_proxy)
ag2_tool.register_for_llm(chatbot)

Initiate a conversation between the UserProxyAgent and the AssistantAgent.

user_proxy.initiate_chat(
    recipient=chatbot, message="Get player, for additional information use 'goal keeper'", max_turns=3
)