Full observability for your Pipecat voice agents
Add the Tuner observer to your Pipecat pipeline and capture every call's transcript, latency, usage, and cost automatically, so you catch hallucinations, broken flows, and missed intents before your callers do.
from tuner_pipecat_sdk import Observer
observer = Observer(
api_key=TUNER_API_KEY,
workspace_id=42,
agent_id="my-agent",
call_id=str(uuid4()),
)
# drop it into your pipeline, right after TTS
pipeline = Pipeline([..., tts, transport.output()])
task = PipelineTask(
pipeline,
params=PipelineParams(
enable_metrics=True,
enable_usage_metrics=True,
),
observers=[observer, observer.latency_observer, turn_tracker],
)
Setup
Integrate in under two minutes
No re-architecting your pipeline. The Tuner observer attaches to your existing Pipecat agent and starts capturing production data immediately.
01
Install the SDK
pip install tuner-pipecat-sdk. Works with pipecat-ai 1.0+ on Python 3.11–3.13. Add the flows extra if you run pipecat-flows.
02
Set your Credentials
Drop in your Tuner API key, workspace ID, and agent ID — via environment variables or inline in code.
03
Create the observer
Add Observer for a plain pipeline, or FlowsObserver for pipecat-flows. Pass your Tuner API key, workspace ID, and agent ID.
04
Monitor calls in Tuner
Transcripts, latency, usage, and cost flow into your dashboard automatically — no manual API calls, ready to analyze and monitor.
Features
Everything you need to run Pipecat agents in production
Turn production from a black box into something you can actually monitor, measure, and improve.
Catch failures early
Automatically flag hallucinations, broken flows, dead air, early hangups, and other failure conditions before they show up in your churn data.
See where latency comes from
Break out STT, TTS, and LLM latency at p50 and p90, so you can see exactly which part of the voice stack is slowing conversations down.
Get alerted when something breaks
Get notified when red flags, failed evals, or other conditions appear in production — while there’s still time to fix them.
Simulate calls before you ship
Stress-test your agent over SIP before launch and after every change, using the same evals that monitor your live traffic.
LangGraph & LangChain capture
Record LangGraph and LangChain node transitions, tool calls, and timing alongside session data, so you can see what your logic layer was doing during the call.
Track cost on every call
Attach a cost calculator and track LLM, TTS, and STT spend on every session — no separate billing pipeline required.
Why Tuner
See what's happening in production
Voice agents fail quietly, and at a scale no team can review by hand. Tuner turns every production call into structured data you can search, debug, evaluate, alert on, and test against.
Comparison
Tuner vs Pipecat Evals
Pipecat Evals is built for development — fast, local behavioral tests you run in CI. Tuner is the production layer that scores real calls after you ship. Most teams run both.
Capability
Tuner
Pipecat
Vendor-independent observability, eliminating the conflict of a platform evaluating its own output
Evals pricing built for scale: tuner price per call, no per minute surcharge
Built-in flags (hallucination, dead air, early hangup)
Root-cause diagnosis with a specific fix, not just metrics
30+ voice quality metrics & red flags out of the box
Drift & regression alerts over time
SIP call simulations with AI agents, using your live evals
Turn-by-turn transcripts & latency traces
FAQ
Frequently asked questions
Common questions about connecting Tuner to your Pipecat agents.
Read the docs
Which Pipecat versions are supported?
Do I have to restructure my pipeline?
How long does setup take?
What gets captured?
Can I test my agent before going live?
Does it work with SIP / phone calls?
Does Tuner support alerts and monitoring?
Can I define my own evaluations and metrics?
How is Tuner priced?
Is my call data private and secure?