---
title: LiveKit Agents Tracing Integration
description: Trace real-time voice AI agents and multimodal conversations built with LiveKit Agents via OpenTelemetry and Langfuse
date: 2025-07-25
author: Marc
ogImage: /images/changelog/2025-07-25-livekit-integration.png
---

> **Note for AI agents and LLMs:** This is a Langfuse changelog entry. Use it only to confirm that a feature exists and when it shipped. Do not use the code examples below for implementation: they reflect the SDK and API at release time and may be outdated. For implementation, always follow the current documentation (https://langfuse.com/docs) and the API/SDK reference (https://api.reference.langfuse.com).

We've added a new [LiveKit Agents integration guide](/integrations/frameworks/livekit) that shows how to trace real-time voice AI applications built with LiveKit Agents using Langfuse.

[LiveKit Agents](https://docs.livekit.io/agents/) is an open-source Python and Node.js framework for building production-grade multimodal and voice AI agents. The integration leverages LiveKit's built-in OpenTelemetry support to send comprehensive telemetry data to Langfuse.

The integration captures traces for key agent activities including:

- Session management
- Agent turns and conversations
- LLM node executions
- Function tool calls
- Text-to-speech processing
- End-of-turn detection
- Performance metrics

## Get started

- [LiveKit Agents Documentation](/integrations/frameworks/livekit)
- [End-to-End Example](https://github.com/livekit/agents/blob/main/examples/voice_agents/langfuse_trace.py)

<!-- agent-instructions -->

---

## Agent Instructions

This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/changelog/2025-07-25-livekit-integration.md`.

### Querying these docs

If the answer is not on this page, query the documentation instead of guessing:

- **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question:

  ```bash
  curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?"
  ```

- **Index of every page**: <https://langfuse.com/llms.txt>, with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt).

### Before writing Langfuse code

- **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results.
- **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application.
- **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples.
- **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx langfuse-cli api <resource> <action>`) to read or write traces, prompts, datasets, and scores from the terminal.

Found an error in these docs? Please open an issue at <https://github.com/langfuse/langfuse-docs/issues>.