Pipecat (tracing)

Pipecat is an open-source framework for building real-time, multimodal AI applications. It provides a pipeline architecture for voice agents, video processing, and other real-time experiences with support for speech-to-text, LLMs, and text-to-speech. Respan gives you full observability over every Pipecat turn, LLM call, STT step, TTS step, and tool execution, and gateway routing through the OpenAI-compatible Respan endpoint.

Set up Respan

Create an account at platform.respan.ai and grab an API key .

Paste this into your coding agent:


Help me set up Respan tracing with the Pipecat integration.
Follow https://www.respan.ai/docs/integrations/pipecat.md

Setup

Install packages

Terminal


pip install respan-ai respan-instrumentation-pipecat

Set environment variables

Terminal


export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"

OPENAI_API_KEY is used by Pipecat’s OpenAI service. RESPAN_API_KEY exports traces to Respan. If you use different Pipecat services, set their provider keys as usual.

Initialize and run

Python


import asyncio
import os

from dotenv import load_dotenv
from pipecat.frames.frames import EndFrame, LLMContextFrame, LLMFullResponseEndFrame, LLMTextFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.openai.llm import OpenAILLMService
from respan import Respan
from respan_instrumentation_pipecat import PipecatInstrumentor

load_dotenv()

respan = Respan(instrumentations=[PipecatInstrumentor()])

class TextCollector(FrameProcessor):
    def __init__(self):
        super().__init__(name="text_collector", enable_direct_mode=True)
        self.text = []
        self.done = asyncio.Event()

    async def process_frame(self, frame, direction: FrameDirection):
        await super().process_frame(frame, direction)
        if isinstance(frame, LLMTextFrame):
            self.text.append(frame.text)
        elif isinstance(frame, LLMFullResponseEndFrame):
            self.done.set()
        await self.push_frame(frame, direction)

async def main():
    collector = TextCollector()
    llm = OpenAILLMService(
        api_key=os.environ["OPENAI_API_KEY"],
        settings=OpenAILLMService.Settings(model=os.getenv("OPENAI_MODEL", "gpt-4.1-nano")),
    )

    pipeline = Pipeline([llm, collector])
    task = PipelineTask(pipeline, cancel_on_idle_timeout=False, enable_rtvi=False)

    async def push_frames():
        await asyncio.sleep(0.05)
        context = LLMContext(
            messages=[{"role": "user", "content": "Reply in one short sentence about Pipecat tracing."}]
        )
        await task.queue_frame(LLMContextFrame(context))
        await asyncio.wait_for(collector.done.wait(), timeout=30)
        await asyncio.sleep(0.2)
        await task.queue_frame(EndFrame())

    await asyncio.gather(PipelineRunner(handle_sigint=False).run(task), push_frames())
    print("".join(collector.text))

    await asyncio.sleep(1)

asyncio.run(main())

View your trace

Open the Traces page to see your Pipecat turn with child LLM, STT, TTS, and tool spans.

Configuration

Parameter	Type	Default	Description
`api_key`	`str \| None`	`None`	Falls back to `RESPAN_API_KEY` env var.
`base_url`	`str \| None`	`None`	Falls back to `RESPAN_BASE_URL` env var.
`instrumentations`	`list`	`[]`	Plugin instrumentations to activate (e.g. `PipecatInstrumentor()`).
`customer_identifier`	`str \| None`	`None`	Default customer identifier for all spans.
`thread_identifier`	`str \| None`	`None`	Default thread identifier for all spans.
`metadata`	`dict \| None`	`None`	Default metadata attached to all spans.
`environment`	`str \| None`	`None`	Environment tag (e.g. `"production"`).

PipecatInstrumentor forwards keyword arguments to the upstream OpenInference Pipecat instrumentor, including options such as debug_log_filename and config.

Attributes

In Respan()

Set defaults at initialization. These apply to all spans.

Python


from respan import Respan
from respan_instrumentation_pipecat import PipecatInstrumentor

respan = Respan(
    instrumentations=[PipecatInstrumentor()],
    customer_identifier="user_123",
    thread_identifier="session_abc_123",
    metadata={"service": "voice-agent", "version": "1.0.0"},
)

With propagate_attributes

Override per-request using a context scope.

Python


from respan import Respan, propagate_attributes
from respan_instrumentation_pipecat import PipecatInstrumentor

respan = Respan(instrumentations=[PipecatInstrumentor()])

async def handle_session(user_id: str):
    with propagate_attributes(
        customer_identifier=user_id,
        thread_identifier="session_abc_123",
        metadata={"plan": "pro"},
    ):
        await runner.run(task)

Attribute	Type	Description
`customer_identifier`	`str`	Identifies the end user in Respan analytics.
`thread_identifier`	`str`	Groups related messages into a conversation.
`metadata`	`dict`	Custom key-value pairs. Merged with default metadata.

Decorators (optional)

Decorators are not required. Pipecat turns, LLM calls, STT, TTS, and tools are auto-traced by the instrumentor. Use @workflow and @task to add application-level structure around a Pipecat session.

Python


from respan import Respan, workflow, task
from respan_instrumentation_pipecat import PipecatInstrumentor

respan = Respan(instrumentations=[PipecatInstrumentor()])

@task(name="run_pipecat_task")
async def run_pipecat_task(task):
    await runner.run(task)

@workflow(name="voice_session")
async def voice_session(task):
    await run_pipecat_task(task)