I'm building a conversational AI agent using the LiveKit Agents Python SDK, and I wanted to ask if there's a way to combine the OpenAI Realtime API with LangChain. From the documentation, it looks like LangChain only works in the typical pipeline (STT → LLM → TTS). Is there a way I can combine them, or at least use the OpenAI Realtime API just to capture voice input, pass it to a LangChain workflow, and then send the result back through the OpenAI Realtime API?

Thanks for reaching out, Achraf. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

• yes i'm hoping to use LangChain’s conversation memory or chain features with the OpenAI Realtime API

Hey there! To make sure I can help you smoothly integrate LangChain with LiveKit and the OpenAI Realtime API, could you share a bit more about your needs? • Which LangChain features are you interested in? For instance, are you looking at complex multi-step workflows, specific memory types like ConversationBufferMemory, or maybe LangChain’s tool ecosystem? • What kind of latency are you aiming for? Do you need the super quick response time of the Realtime API (~300ms), or would the usual voice pipeline latency work just fine? • How complex is your workflow? Are you thinking simple conversational flows or something more like multi-agent setups? • What kind of conversation memory are you hoping for, and how long do you want conversations to be remembered? Once I have these details, I’ll be able to suggest the best way to get your integration up and running. Looking forward to your reply! Sources: LangChain integration guide | LiveKit Docs | OpenAI Realtime API integration guide | LiveKit Docs | realtime/openai-realtime-tools.py | examples/voice_agents/realtime_load_chat_history.py | plugins/openai/src/realtime/api_proto.ts

• all of those features • less then 300ms, nah the usual voice pipline is too slow • very complex, i have a multi-agent setups • short and long memory depends

Hey there! If you're looking to integrate LangChain's multi-agent workflows with LiveKit and the OpenAI Realtime API for smooth, low-latency voice interactions, you're in the right place. LiveKit’s multi-agent and function-calling features can really help make this happen. Here are a few ways you might approach it: • Function-Based Integration: You can use LiveKit's function-calling to trigger LangChain chains and agents. This way, you get to handle complex workflows in LangChain while keeping your voice interactions real-time and responsive. • Agent Wrapper Pattern: Another way is to wrap LangChain agents inside LiveKit Agent classes. This lets you tap into LangChain’s reasoning and memory capabilities right within LiveKit’s voice agent workflows. • Hybrid Orchestration: You could also let LangChain take care of the heavy lifting with reasoning and memory management, while LiveKit focuses on the real-time voice side. Syncing the state between them as needed keeps everything running smoothly. LiveKit’s multi-agent framework is pretty neat—it supports things like agent handoffs, shared state management, and flexible model setups. Plus, it plays well with LangChain’s tools and memory systems. On top of that, LiveKit’s plugin architecture works with tools like LlamaIndex and Retrieval-Augmented Generation (RAG), which might be just what you need to boost your LangChain workflows. If you want to dive deeper into any of these ideas or need a hand with implementation, just give me a shout—I’m here to help! Sources: Working with plugins | LiveKit Docs | LiveKit Docs | examples/voice_agents/multi_agent.py | OpenAI and LiveKit partner to turn Advanced Voice into an API

I have implemented AgentSession with Realtime OpenAI and when it works with realtime tools and LangGraph functions agent takes more time to respond back. how to solve this latency issue ?

To help reduce latency in your AgentSession with OpenAI Realtime API and LangGraph functions, here are some friendly suggestions you might find useful: • Optimize Network Proximity: It’s a great idea to have your agent server located close to services like OpenAI Realtime API, STT, TTS, and LangGraph functions. This can really help minimize network latency and speed things up. • Leverage LiveKit’s Infrastructure: Using LiveKit's WebRTC-based global edge network is a smart move—it helps reduce transmission latency and keeps your connections reliable. • Enable Preemptive Generation: Try turning on the

preemptive_generation

feature in your AgentSession configuration. This lets your agent start processing as soon as it detects user input, which can make responses quicker. • Optimize Turn Detection: Consider using server-side Voice Activity Detection (VAD) or other efficient turn detection methods. You can also adjust settings like

silence_duration_ms

to better detect when someone finishes speaking. • Monitor Performance: Keep an eye on things with LiveKit’s metrics API. Tracking key indicators like Time To First Token (TTFT) and Time To First Byte (TTFB) can help you spot any bottlenecks and address them promptly. I hope these tips help improve your response times! If you have any questions or want to dive deeper into any of these steps, just let me know—I’m here to help. Sources: OpenAI Realtime API and LiveKit | LiveKit Docs | Agent speech and audio | LiveKit Docs | livekit-plugins/livekit-plugins-openai/livekit/plugins/openai/stt.py | Voice Assistants | livekit/livekit_composite | DeepWiki | Voice Assistants | livekit/livekit_composite | DeepWiki