🦜🕸️ Using Composio With LangGraph

Star A Repository on GitHub

In this example, we will use LangGraph Agent to star a repository on GitHub using Composio Tools

Install Packages

$ pip install composio-langgraph

Import Libraries & Initialize ComposioToolSet

1 from typing import Literal
2 from langchain_openai import ChatOpenAI
3 from langgraph.graph import MessagesState, StateGraph
4 from langgraph.prebuilt import ToolNode
5 from composio_langgraph import Action, ComposioToolSet, App
6 
7 composio_toolset = ComposioToolSet()

Connect Your GitHub Account

You need to have an active GitHub Integration. Learn how to do this here

$ composio login
> composio add github

Don’t forget to set your COMPOSIO_API_KEY and OPENAI_API_KEY in your environment variables.

Get And Bind Tools

You can get all the tools for a given app as shown below, but you can get specific actions and filter actions using usecase & tags. Learn more here

1 tools = composio_toolset.get_tools(
2     apps=[App.GITHUB]
3 )
4 tool_node = ToolNode(tools)
5 model = ChatOpenAI(temperature=0, streaming=True)
6 model_with_tools = model.bind_tools(tools)

Define the model calling function

1 def call_model(state: MessagesState):
2     """
3     Process messages through the LLM and return the response
4     """
5     messages = state["messages"]
6     response = model_with_tools.invoke(messages)
7     return {"messages": [response]}

Define the decision function for workflow routing

1 def should_continue(state: MessagesState) -> Literal["tools", "__end__"]:
2     """
3     Determine if the conversation should continue to tools or end
4     Returns:
5         - "tools" if the last message contains tool calls
6         - "__end__" otherwise
7     """
8     messages = state["messages"]
9     last_message = messages[-1]
10     if last_message.tool_calls:
11         return "tools"
12     return "__end__"

Define the workflow graph

1 workflow = StateGraph(MessagesState)
2 
3 workflow.add_node("agent", call_model)
4 workflow.add_node("tools", tool_node)
5 workflow.add_edge("__start__", "agent")
6 workflow.add_conditional_edges(
7     "agent",
8     should_continue,
9 )
10 workflow.add_edge("tools", "agent")
11 
12 app = workflow.compile()

Execute the workflow

1 for chunk in app.stream(
2     {
3         "messages": [
4             (
5                 "human",
6                 "Star the GitHub Repository composiohq/composio",
7             )
8         ]
9     },
10     stream_mode="values",
11 ):
12     chunk["messages"][-1].pretty_print()

1	from typing import Literal
2	from langchain_openai import ChatOpenAI
3	from langgraph.graph import MessagesState, StateGraph
4	from langgraph.prebuilt import ToolNode
5	from composio_langgraph import Action, ComposioToolSet, App
6
7	composio_toolset = ComposioToolSet()

1	tools = composio_toolset.get_tools(
2	apps=[App.GITHUB]
3	)
4	tool_node = ToolNode(tools)
5	model = ChatOpenAI(temperature=0, streaming=True)
6	model_with_tools = model.bind_tools(tools)

1	def call_model(state: MessagesState):
2	"""
3	Process messages through the LLM and return the response
4	"""
5	messages = state["messages"]
6	response = model_with_tools.invoke(messages)
7	return {"messages": [response]}

1	def should_continue(state: MessagesState) -> Literal["tools", "__end__"]:
2	"""
3	Determine if the conversation should continue to tools or end
4	Returns:
5	- "tools" if the last message contains tool calls
6	- "__end__" otherwise
7	"""
8	messages = state["messages"]
9	last_message = messages[-1]
10	if last_message.tool_calls:
11	return "tools"
12	return "__end__"

1	workflow = StateGraph(MessagesState)
2
3	workflow.add_node("agent", call_model)
4	workflow.add_node("tools", tool_node)
5	workflow.add_edge("__start__", "agent")
6	workflow.add_conditional_edges(
7	"agent",
8	should_continue,
9	)
10	workflow.add_edge("tools", "agent")
11
12	app = workflow.compile()

1	for chunk in app.stream(
2	{
3	"messages": [
4	(
5	"human",
6	"Star the GitHub Repository composiohq/composio",
7	)
8	]
9	},
10	stream_mode="values",
11	):
12	chunk["messages"][-1].pretty_print()