🦜🕸️ Using Composio With LangGraph

pip install composio-langgraph

from typing import Literal
from langchain_openai import ChatOpenAI
from langgraph.graph import MessagesState, StateGraph
from langgraph.prebuilt import ToolNode
from composio_langgraph import Action, ComposioToolSet, App

composio_toolset = ComposioToolSet()

composio login
composio add github

tools = composio_toolset.get_tools(
    apps=[App.GITHUB]
)
tool_node = ToolNode(tools)
model = ChatOpenAI(temperature=0, streaming=True)
model_with_tools = model.bind_tools(tools)

def call_model(state: MessagesState):
    """
    Process messages through the LLM and return the response
    """
    messages = state["messages"]
    response = model_with_tools.invoke(messages)
    return {"messages": [response]}

def should_continue(state: MessagesState) -> Literal["tools", "__end__"]:
    """
    Determine if the conversation should continue to tools or end
    Returns:
        - "tools" if the last message contains tool calls
        - "__end__" otherwise
    """
    messages = state["messages"]
    last_message = messages[-1]
    if last_message.tool_calls:
        return "tools"
    return "__end__"

workflow = StateGraph(MessagesState)

workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)
workflow.add_edge("__start__", "agent")
workflow.add_conditional_edges(
    "agent",
    should_continue,
)
workflow.add_edge("tools", "agent")

app = workflow.compile()

for chunk in app.stream(
    {
        "messages": [
            (
                "human",
                "Star the GitHub Repository composiohq/composio",
            )
        ]
    },
    stream_mode="values",
):
    chunk["messages"][-1].pretty_print()