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hey how to integrate this with langgraph graph,whi...
# ask-ai
m
hey how to integrate this with langgraph graph,which works after the graph has been constructed, how to make it run in another function which will eork as a livekit work flow
t
Thanks for reaching out, rakesh. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Hey there! If you want to integrate LiveKit with LangGraph and use it as a workflow after building your graph, here’s a friendly step-by-step guide to help you out: 1. Install the LiveKit LangChain Plugin: First up, just install the plugin with pip: 
pip install "livekit-plugins-langchain~=1.1"
2. Import Necessary Modules: Next, bring in the modules you’ll need from LiveKit and LangGraph:
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from langgraph.graph import StateGraph
from livekit.agents import Agent, AgentSession
from livekit.plugins import langchain
3. Create and Compile Your LangGraph Workflow: Now, define your LangGraph workflow by adding the nodes and edges you want, then compile it:
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def create_workflow():
    workflow = StateGraph(...)
    # Add nodes and edges as needed
    return workflow.compile()
4. Use the Workflow as an LLM: Wrap your compiled graph with 
LLMAdapter
and plug it into an `AgentSession`:
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graph = create_workflow()
session = AgentSession(
    llm=langchain.LLMAdapter(graph),
    # Add other necessary components like STT, TTS, etc.
)
5. Start the Agent Session: Finally, create the agent and kick off the session:
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agent = Agent(llm=langchain.LLMAdapter(graph))
await session.start(agent=agent)
Just a heads-up: make sure you have the right dependencies installed, like 
langgraph
and 
langchain-core
. The great thing about 
LLMAdapter
is that it smoothly converts LiveKit’s chat context into LangChain messages, so everything fits together nicely. If anything’s unclear or you want to dive deeper into any part, just let me know—I’m here to help!
m
i have tried this way,but it is not working
t
Hey there! Could you share a bit more about what’s not working for you? For example: • Are you seeing any error messages or anything acting up unexpectedly? • At what point does the problem pop up? • Have you tweaked any part of the workflow or integration steps? Once I have a clearer picture, I’ll be able to help you out much better!
m
i am desiging an interviwer using langgraph and livekit ,it should ask questions from langgraph flow only nothing additional,but its not even working at all.not reading outr questions one by one or not waiting for candidates to answer ,nothing can you hhelp me with that.
t
Let's walk through how to design an interviewer using LangGraph and LiveKit that asks questions from the LangGraph flow and patiently waits for the candidate's answers. Here's a friendly step-by-step guide: 1. Create a LangGraph Workflow: First, define the interview flow with your questions and their order. Then, compile the workflow like this:
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from langgraph.graph import StateGraph

def create_interview_workflow():
    workflow = StateGraph()
    workflow.add_node("welcome", lambda state: {"messages": ["Hello! Let's begin the interview."]})
    workflow.add_node("question1", lambda state: {"messages": ["What is your current role?"]})
    workflow.add_edge("START", "welcome")
    workflow.add_edge("welcome", "question1")
    return workflow.compile()
2. Integrate LangGraph with LiveKit: Next, use the LiveKit LangChain plugin to wrap the workflow as an LLM. Here's how you can do it:
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from livekit.agents import AgentSession
from livekit.plugins import langchain

graph = create_interview_workflow()

session = AgentSession(
    llm=langchain.LLMAdapter(graph),
    # Add other required components like STT, TTS, etc.
)
3. Set Up the LiveKit Agent: Now, initialize an agent session to run the interview smoothly:
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from livekit.agents.voice import Agent

class InterviewAgent(Agent):
    def __init__(self):
        super().__init__(
            instructions="You are an interviewer. Ask questions from the LangGraph flow and wait for the candidate's answers.",
            llm=langchain.LLMAdapter(create_interview_workflow())
        )

async def entrypoint(ctx):
    await session.start(
        agent=InterviewAgent(),
        room=ctx.room
    )
4. Handle Candidate Responses: Finally, process the candidate's responses and update the frontend in real-time to keep everything interactive:
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class InterviewAgent(Agent):
    @function_tool
    async def handle_response(self, context, response):
        await self.session.send_form_update_to_frontend("updateField", {
            "field": "currentRole",
            "value": response
        })
        await self.session.send_form_update_to_frontend("highlightSection", {
            "section": "experience"
        })
With this setup, your interviewer will ask questions one by one from the LangGraph flow, wait for answers, and keep the frontend updated dynamically. If you have any questions or need a hand with anything else, just let me know—I'm here to help! Sources: examples/voice_agents/langgraph_agent.py | avatars/tavus/tavus.py
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