Connect your GitHub account - Composio

🦜🕸️ Using Composio With LangGraph

Integrate Composio with LangGraph Agentic workflows & enable them to interact seamlessly with external apps, enhancing their functionality and reach.

Goal

Star a repository on GitHub using natural language commands through a LangGraph Agent.

Installation and Setup

Ensure you have the necessary packages installed and connect your GitHub account to allow your agents to utilize GitHub functionalities.

bash

# Install Composio LangGraph package
pip install composio-langgraph

# Connect your GitHub account
composio-cli add github

# View available applications you can connect with
composio-cli show-apps

Usage Steps

1. Import Base Packages

Prepare your environment by initializing necessary imports from LangGraph & LangChain for setting up your agent.

python

from typing import Literal

from langchain_openai import ChatOpenAI
from langgraph.graph import MessagesState, StateGraph
from langgraph.prebuilt import ToolNode

2. Fetch GitHub LangGraph Tools via Composio

Access GitHub tools provided by Composio for LangGraph, initialize a ToolNode with necessary tools obtaned from ComposioToolSet.

python

from composio_langgraph import Action, ComposioToolSet

# Initialize the toolset for GitHub
composio_toolset = ComposioToolSet()
tools = composio_toolset.get_actions(
    actions=[
        Action.GITHUB_ACTIVITY_STAR_REPO_FOR_AUTHENTICATED_USER,
        Action.GITHUB_USERS_GET_AUTHENTICATED,
    ])
tool_node = ToolNode(tools)

3. Prepare the model

Initialize the LLM class and bind obtained tools to the model.

python

model = ChatOpenAI(temperature=0, streaming=True)
model_with_tools = model.bind_tools(functions)

4. Define the Graph Nodes

LangGraph expects you to define different nodes of the agentic workflow as separate functions. Here we define a node for calling the LLM model.

python

def call_model(state: MessagesState):
    messages = state["messages"]
    response = model_with_tools.invoke(messages)
    return {"messages": [response]}

5. Define the Graph Nodes and Edges

To establish the agent's workflow, we begin by initializing the workflow with agent and tools node, followed by specifying the connecting edges between nodes, finally compiling the workflow. These edges can be straightforward or conditional, depending on the workflow requirements.

python

def should_continue(state: MessagesState) -> Literal["tools", "__end__"]:
    messages = state["messages"]
    last_message = messages[-1]
    if last_message.tool_calls:
        return "tools"
    return "__end__"


workflow = StateGraph(MessagesState)

# Define the two nodes we will cycle between
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()

6. Invoke & Check Response

After the compilation of workflow, we invoke the LLM with a task, and stream the response.

python

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