tuner-pipecat-sdk

tuner-pipecat-sdk is a lightweight observer SDK for pipecat. It captures latency signals, transcript segments, and usage metadata, then sends a structured CallPayload to the Tuner API when a call ends.

Requirements

• Python 3.11–3.13.
• Do not use Python 3.14 for installs yet: Pipecat pulls onnxruntime~=1.23.2 and numba without 3.14 wheels → errors like No matching distribution found for onnxruntime.
• This SDK depends on pipecat-ai>=1.0.0.
• LangChain/LangGraph support (Observer.wrap_graph/wrap_chain) is fully optional — see LangChain / LangGraph observability.

Installation

pip install tuner-pipecat-sdk

Quick Start Example

---

Plain Pipecat — Observer

import uuid
from tuner_pipecat_sdk import Observer

observer = Observer(
    api_key="YOUR_TUNER_API_KEY",
    workspace_id=42,
    agent_id="my-agent",
    call_id=str(uuid.uuid4()),
    base_url="https://api.usetuner.ai",
    asr_model="deepgram/nova-3",
    llm_model="gpt-4o-mini",
    tts_model="cartesia/sonic",
)

---

Pipeline Setup

Observer is a pipeline-level observer — register it on the PipelineTask (observers=[...]), not as a processor inside Pipeline([...]). It then sees every frame at every processor boundary and stays out of the audio path:

Pipeline([
    transport.input(),
    stt,
    context_aggregator.user(),
    llm,
    tts,
    transport.output(),
    context_aggregator.assistant(),
])

Enable metrics on the pipeline task so latency and usage fields are populated, and add the observer to observers=[...]:

from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.observers.turn_tracking_observer import TurnTrackingObserver

turn_tracker = TurnTrackingObserver()
observer.attach_turn_tracking_observer(turn_tracker)

task = PipelineTask(
    pipeline,
    params=PipelineParams(
        enable_metrics=True,
        enable_usage_metrics=True,
    ),
    observers=[observer, observer.latency_observer, turn_tracker],
)

Without these flags the observer will log warnings and LLM/TTS metric fields will be absent from the payload. For more example check https://github.com/usetuner/tuner-pipecat-sdk-python/tree/main/examples

Trace view (OpenTelemetry)

Pipecat already emits OpenTelemetry spans for a conversation — STT, LLM, TTS, tool calls, turn boundaries. The SDK forwards them to Tuner, which shows them as a trace tree on the call details page: what ran, in what order, and how long each step took.

Setup

Two installs and one flag.

pip install 'pipecat-ai[tracing]'          # lets Pipecat emit spans at all
pip install 'tuner-pipecat-sdk[traces]'    # lets this SDK forward them to Tuner

task = PipelineTask(
    pipeline,
    params=PipelineParams(enable_metrics=True, enable_usage_metrics=True),
    observers=[observer, observer.latency_observer, turn_tracker],
    enable_tracing=True,        # <-- the one thing you must set yourself
    enable_turn_tracking=True,  # recommended: gives the trace its turn structure
)

enable_tracing=True is Pipecat's own switch for emitting spans. You have to set it because you build the PipelineTask — the Observer is constructed before the task exists and is handed to it, so the SDK has no way to reach that argument.

Everything else is automatic. You do not need setup_tracing(...), a TracerProvider, an exporter, or any OTEL_* environment variable — the SDK builds all of that from the API key and base URL you already pass to Observer, and tags every span with the call id so the trace lands on the right call.

Turning it off

Observer(..., forward_traces=False)

Leaves the rest of the SDK untouched — calls are still reported to Tuner, only the spans stop being forwarded. Note this is our switch; Pipecat's enable_tracing above is a separate one that controls whether Pipecat produces spans at all.

Notes

• Already exporting traces elsewhere? Your setup is preserved. If you call Pipecat's setup_tracing(...), the SDK adds Tuner as an extra destination instead of replacing it.
• No traces extra installed? Trace forwarding becomes a no-op with a debug log. It can never fail a call.
• Use the HTTP exporter if you configure one yourself. Tuner accepts OTLP over HTTP with protobuf; gRPC is not supported. Pipecat's own tracing example uses the gRPC exporter, so this is the one line to change if you started from it.

What is stored

The span tree — names, timings, parent relationships, status — plus metrics and configuration: model names, time-to-first-byte, token counts, turn latency, endpointing settings, and tool names and outcomes.

No conversation content. Transcripts, LLM inputs and outputs, TTS text, system prompts, and tool call arguments and results are all dropped on arrival and never stored. The filter is an allowlist, so anything not explicitly approved is discarded by default.

Roughly 30 KB per call, for a five-minute call with twenty turns.

End-of-Utterance (EOU) Delay

Wire the user context aggregator to capture how long the pipeline took to decide the user was done talking — the gap between the user going silent and the turn actually closing. This lands on every user row's eou_delay, eou_confidence, and eou_reason metadata fields.

user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
    context,
    user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
)
observer.attach_user_aggregator(user_aggregator)

eou_reason tells you which signal closed the turn, and eou_delay is calculated differently depending on that signal: for VAD-driven turns the SDK measures it directly from the frame stream, while for model-driven turns it's the turn-detection model's own reported figure.

eou_reason	What closed the turn	eou_delay measures	Source
silence_timeout	Local VAD silence threshold	ms from the last VAD-detected speech-end to the turn closing	Measured by the SDK from the frame stream (wall-clock)
model_verdict	A turn-completion model (e.g. Smart Turn) decided the turn was done	the model's own end-to-end decision time; eou_confidence carries its probability	Reported by pipecat (TurnMetricsData.e2e_processing_time_ms) — the model's internal timing, not a wall-clock measurement
stt_endpoint	Server-side STT (e.g. Deepgram Flux) decided end-of-turn on its own clock	not populated — the decision happens server-side, independent of local VAD	—

All three fields are simply omitted from a row's metadata when nothing decided that turn's end (e.g. a pipeline that doesn't use Pipecat's turn-strategy stack yet)

What Happens at Call End

When Pipecat emits EndFrame or CancelFrame, the observer:

1. Builds a typed CallPayload from everything captured during the call.
2. Sends it to the Tuner API in the background — this does not block your pipeline from shutting down.

If the request fails (network error, timeout, bad API key, Tuner API down), the SDK logs the failure and moves on. It never raises and never blocks or crashes your pipeline. That also means a failed delivery is easy to miss — if a call isn't showing up in Tuner, check your logs for [tuner] failed to deliver call.

Observer Parameters

Observer accepts the following constructor parameters:

Parameter	Type	Default	Description
api_key	str	—	Tuner API key
workspace_id	int	—	Tuner workspace ID
agent_id	str	—	Agent identifier
call_id	str	—	Unique call ID (use uuid4())
base_url	str	https://api.usetuner.ai	Tuner API base URL
call_type	str	"web_call"	Call type label
recording_url	str	"pipecat://no-recording"	Recording URL if available
asr_model	str	""	ASR model name (e.g. deepgram/nova-3)
llm_model	str	""	LLM model name (e.g. gpt-4o-mini)
tts_model	str	""	TTS model name (e.g. cartesia/sonic)
sip_call_id	str | None	None	SIP provider call identifier (see SIP section below)
sip_headers	dict[str, str] | None	None	SIP INVITE headers as a flat dict
debug	bool	False	Log full transcript at flush
agent_version	int | None	None	Deployment version number — overrides APP_VERSION env var
extra_metadata	dict | None	None	Arbitrary key-value data merged into every call's metadata (see Extra Metadata)
forward_traces	bool	True	Forward Pipecat's OpenTelemetry spans to Tuner (see Trace view)

Extra Metadata

Attach arbitrary key-value data to every call record:

Observer(
    api_key=..., workspace_id=..., agent_id=..., call_id=...,
    extra_metadata={
        "env": "production",
        "region": "us-east-1",
        "deployment": "v2.3.1",
    },
)

Agent Version Tracking

Set APP_VERSION in your environment to track which version of your agent handled each call:

APP_VERSION=42 python agent.py start

Tuner reads it automatically — bump the number on every deployment.

Using GitHub Actions or CircleCI? No setup needed — Tuner reads the build number from GITHUB_RUN_NUMBER / CIRCLE_BUILD_NUM automatically.

Manual override — takes priority over all env vars:

Observer(
    ...
    agent_version=42,
)

agent_version is omitted from the payload entirely when no value is found.

---

SIP / Telephony Calls

The observer can capture the SIP Call-ID and SIP headers and forward them to Tuner. The SDK call is one line per provider — your server passes its raw payload straight through and the SDK handles all SIP-field extraction internally.

The SDK call:

# Any built-in provider — pass the raw payload your server already has:
observer.attach_sip_from_telephony(payload, provider="twilio")
observer.attach_sip_from_telephony(payload, provider="telnyx")
observer.attach_sip_from_telephony(payload, provider="plivo")
observer.attach_sip_from_telephony(payload, provider="exotel")
observer.attach_sip_from_telephony(payload, provider="jambonz")

# Unlisted provider — pass your own extractor:
def my_extractor(payload):
    return payload["my_id"], payload.get("my_headers")
observer.attach_sip_from_telephony(payload, provider=my_extractor)

# Daily PSTN/SIP dial-in:
observer.attach_sip_from_dialin(runner_args.body["dialin_settings"])

# Anything else (you already have the values):
observer.attach_sip_info(sip_call_id="...", sip_headers={...})

provider= is required for attach_sip_from_telephony. Built-in strings: "twilio", "telnyx", "plivo", "exotel", "jambonz". An unknown string raises ValueError — pass a callable instead for any provider not in the list.

What the SDK does automatically

The built-in extractors look for a SIP-layer Call-ID in the provider-specific location, then fall back to the provider's native call id when no SIP-layer key was forwarded:

Provider	Expected payload	SIP-layer source	Native fallback
Twilio	parse_telephony_websocket output	body[SipCallId] (case-insensitive)	call_id (CallSid)
Telnyx	Call Control event or WS call_data	data.payload.sip_call_id / sip_headers list / customParameters	call_control_id
Plivo	parse_telephony_websocket output	customParameters / extra_headers	call_id
Exotel	parse_telephony_websocket output	custom_parameters	call_id
Jambonz	call-hook webhook JSON body	sip.headers[X-CID] → Call-ID → SipCallId → sip.call_id	call_sid

Headers in the same location populate sip_headers automatically; override with the sip_headers= kwarg when you want to send something different.

For the SIP-layer Call-ID to actually appear, your trunk/webhook must forward it. The per-provider configuration is below.

Twilio Media Streams (most common case)

Twilio's HTTP voice webhook receives SIP context (SipCallId, Caller, every SipHeader_*) but the Media Streams WebSocket drops those fields by default. The SDK ships two helpers so your integration stays short and correct:

• build_sip_forwarding_twiml(form, ws_url=...) — returns the TwiML response that forwards every SIP-relevant field as a <Parameter> tag. Handles XML escaping and SipHeader_* auto-forwarding.
• TwilioCallContext — typed dataclass with .sip_call_id, .from_number, .to_number, .stream_sid, .raw_headers. Built via TwilioCallContext.from_call_data(call_data) where call_data is the output of Pipecat's parse_telephony_websocket.

Server wiring (in your application):

from tuner_pipecat_sdk.providers.twilio import (
    TwilioCallContext,
    build_sip_forwarding_twiml,
)

@app.post("/twiml")
async def twiml(request):
    form = await request.form()
    xml = build_sip_forwarding_twiml(form, ws_url="wss://your-host/ws")
    return Response(content=xml, media_type="application/xml")

@app.websocket("/ws")
async def ws(websocket):
    _, call_data = await parse_telephony_websocket(websocket)
    ctx = TwilioCallContext.from_call_data(call_data)
    await run_bot(transport, sip_context=ctx)

Bot — single line:

observer.attach_sip_from_context(ctx)

Point Twilio's Voice URL at https://your-host/twiml. See examples/nova_clinic_pipecat/twilio_server.py for the full pattern.

Why this matters: Pipecat's built-in dev runner ships a hardcoded TwiML response with no <Parameter> tags, so the SIP Call-ID never reaches the bot — Tuner sees only the Twilio CallSid and falls back to it. You must run your own webhook server using build_sip_forwarding_twiml (or equivalent) to get the real SIP id.

Telnyx, Plivo, Exotel

Bot side stays one line:

observer.attach_sip_from_telephony(payload, provider="telnyx")  # or "plivo" / "exotel"

To capture the actual SIP Call-ID, configure your trunk/XML to forward it as a customParameter:

• Telnyx — add <CustomParameter name="SipCallId" value="{{sip_call_id}}"/> to your <Stream> element. With Call Control (JSON webhook + answer command), cache the inbound event payload and pass it through verbatim — the SDK reads sip_headers / custom_headers lists directly.
• Plivo — add extraHeaders="SipCallId={{sip_call_id}}" to <Stream>.
• Exotel — add custom_parameters: SipCallId=<%CallSid%> in the App Bazaar Voicebot applet.

Fallback when nothing is forwarded:

Provider	Fallback id	Native key on call_data
Telnyx	call_control_id	call_control_id
Plivo	callId	call_id
Exotel	call_sid	call_id

Jambonz

Jambonz delivers SIP info on the call-hook webhook (the JSON POST to your application URL), not the WebSocket. The SDK ships two helpers so your integration is short and correct:

• JambonzCallContext — typed dataclass with .sip_call_id, .from_number, .to_number, .direction, .raw_headers. Built via JambonzCallContext.from_webhook(payload). SIP Call-ID resolution priority: X-CID → Call-ID → SipCallId → sip.call_id → call_sid.
• JambonzPendingStore — bridges webhook → WebSocket with a 30s TTL (configurable) and asyncio.Event await semantics, so the WS handler never races the webhook.

Server wiring (in your application):

from tuner_pipecat_sdk.providers.jambonz import (
    JambonzCallContext,
    JambonzPendingStore,
)

pending = JambonzPendingStore()

@app.post("/")
async def call_hook(request):
    data = await request.json()
    pending.park(JambonzCallContext.from_webhook(data))
    return _jambonz_verbs(...)

@app.websocket("/ws")
async def ws(websocket):
    ...  # read first frame to learn call_sid
    ctx = await pending.wait_and_pop(call_sid) or JambonzCallContext.fallback(call_sid)
    await run_bot(transport, sip_context=ctx)

Bot — single line:

observer.attach_sip_from_context(ctx)

attach_sip_from_context is duck-typed: any object exposing sip_call_id and raw_headers attributes works, so you can pass your own context type if you've wrapped Jambonz's payload further.

See examples/nova_clinic_pipecat/jambonz_server.py for the full pattern.

SIP Call-ID resolution priority: X-CID → Call-ID → SipCallId → sip.call_id → call_sid. The X-CID header is the canonical cross-system id for chains like LiveKit SIP → Jambonz, where Jambonz regenerates the SIP Call-ID on each hop. See examples/nova_clinic_pipecat/jambonz_server.py for a full pattern.

Daily PSTN/SIP dial-in

Daily delivers SIP info in the dial-in webhook payload — no XML required. Pass it straight to the SDK:

observer.attach_sip_from_dialin(runner_args.body["dialin_settings"])

DialinSettings.call_id and DialinSettings.sip_headers reach Tuner as-is.

Custom SIP trunk / direct termination

If you terminate SIP yourself (Asterisk, FreeSWITCH, Kamailio, …), pass the values straight in:

observer.attach_sip_info(
    sip_call_id=my_sip_session.call_id,
    sip_headers=my_sip_session.headers,
)

Debugging checklist

Both sip_call_id and sip_headers are optional in the final payload — they are omitted entirely when unset, so non-SIP web calls remain backward-compatible. If you expect SIP fields but see them missing:

Symptom in the final payload	Likely cause	Fix
sip_call_id is the Twilio CA… CallSid	TwiML did not include <Parameter name="SipCallId" .../>	Update your webhook TwiML (Twilio section above)
sip_call_id is null for a Twilio call	attach_sip_from_telephony(..., provider="twilio") never ran	Call it after parse_telephony_websocket returns
sip_call_id is the Jambonz call_sid	No X-CID / Call-ID header in the webhook	Configure your upstream trunk to add it, or use a custom callable extractor
sip_call_id is null for Daily PSTN	attach_sip_from_dialin() not wired	Call it from runner_args.body["dialin_settings"]
sip_headers is null but sip_call_id is set	Headers were not in customParameters	Pass them explicitly via attach_sip_info(sip_headers=...)
ValueError: unknown provider …	Misspelled built-in name	Use "twilio"/"telnyx"/"plivo"/"exotel"/"jambonz" or pass a callable

Disconnection Reason

Pass a disconnection_reason_resolver callable to the observer to record why a call ended. The resolver is called at flush time and should return a string or None.

from tuner_pipecat_sdk.models import DisconnectReason

observer = Observer(
    ...
    disconnection_reason_resolver=lambda: DisconnectReason.USER_HANGUP,
)

Use the built-in constants from DisconnectReason or pass a custom string:

Constant	Value
DisconnectReason.USER_HANGUP	"user_hangup"
DisconnectReason.AGENT_HANGUP	"agent_hangup"
DisconnectReason.ERROR	"error"
DisconnectReason.TIMEOUT	"timeout"
DisconnectReason.UNKNOWN	"unknown"

For dynamic resolution (e.g. when the reason is only known at call end):

_reason = None

def resolve_reason() -> str | None:
    return _reason

observer = Observer(..., disconnection_reason_resolver=resolve_reason)

# Later, when you know the reason:
_reason = DisconnectReason.AGENT_HANGUP

Cost Tracking

Pass a cost_calculator callable to compute call cost from usage data. The result is included in the payload sent to the Tuner API.

from tuner_pipecat_sdk import Observer, CallUsage

def calculate_cost(usage: CallUsage) -> float:
    llm_cost  = (usage.llm_prompt_tokens     or 0) * 0.000_003
    llm_cost += (usage.llm_completion_tokens or 0) * 0.000_015
    tts_cost  = (usage.tts_characters        or 0) * 0.000_030
    stt_cost  = usage.stt_audio_seconds            * 0.000_006
    return llm_cost + tts_cost + stt_cost

observer = Observer(
    ...,
    cost_calculator=calculate_cost,
)

CallUsage fields:

Field	Type	Description
llm_prompt_tokens	int | None	Input tokens sent to the LLM
llm_completion_tokens	int | None	Output tokens generated by the LLM
llm_total_tokens	int | None	Total LLM tokens (prompt + completion)
tts_characters	int | None	Characters sent to TTS
stt_audio_seconds	int	Call wall-clock duration in seconds (used as STT duration proxy)

Token and character counts are None when no metrics were collected for that service during the call.

The callable must return the cost in USD cents (e.g. 0.5 = half a cent, 1.0 = one cent).

LangChain / LangGraph observability

If your agent's "LLM step" is a LangChain runnable or LangGraph graph — driven through pipecat's built-in LangchainProcessor, or your own custom processor — install the optional langchain extra and wrap it with Observer.wrap_graph() / Observer.wrap_chain():

pip install tuner-pipecat-sdk[langchain]

from pipecat.processors.frameworks.langchain import LangchainProcessor
from tuner_pipecat_sdk import Observer

observer = Observer(api_key=..., workspace_id=..., agent_id=..., call_id=...)

wrapped_graph = observer.wrap_graph(my_compiled_graph)   # or observer.wrap_chain(my_chain)
lc = LangchainProcessor(chain=wrapped_graph)

pipeline = Pipeline([
    transport.input(),
    stt,
    user_aggregator,
    lc,                 # in place of a native LLM service
    tts,
    transport.output(),
    assistant_aggregator,
])

This captures tool calls, LangGraph node transitions, and LLM token usage from inside the wrapped runnable via tuner-langchain's callback handler, and merges them into the same transcript_with_tool_calls timeline used by plain pipecat pipelines — independent of whatever pipecat frames the driving processor does or doesn't emit.

Runnable examples: examples/pizza_order_langchain/ (wrap_chain(), a raw LangChain tool-calling chat model) and examples/nova_clinic_langgraph/ (wrap_graph(), a LangGraph create_react_agent — demonstrates node-transition tracking).

This extra is entirely optional. If tuner-langchain isn't installed, calling wrap_graph/wrap_chain raises a clear ImportError with install instructions; everything else about the SDK behaves identically to a plain pipecat pipeline.

Known limitations (inherited from pipecat, not fixed by this integration):

• Pipecat's built-in LangchainProcessor only forwards the latest user message to the chain/graph (no full conversation history) — multi-turn memory has to be handled by your LangChain/LangGraph state itself (e.g. LangGraph checkpointing).
• LangchainProcessor is a bare FrameProcessor, not a pipecat LLMService, so it never emits pipecat's own latency/processing metrics for the LLM step — the llm_node_ttft field would otherwise always be absent for LangChain-driven turns. Instead, it's populated from LangChain's own on_llm_start→on_llm_end call duration (via the same session hook as usage/cost below). This is a total call duration, not a strict time-to-first-token — if a turn makes multiple LLM calls (e.g. a tool-calling round-trip), only the last call's duration is reported, not a sum across the turn.

Public API

• tuner_pipecat_sdk.Observer — for pipecat pipelines
  • Observer.wrap_graph(graph, capture=None) / Observer.wrap_chain(chain, capture=None) — optional, requires the langchain extra (see above)
• tuner_pipecat_sdk.TunerConfig
• tuner_pipecat_sdk.CallUsage — usage data type for cost_calculator

Payload and transcript schemas are available under tuner_pipecat_sdk.models, including NodeInfo (LangGraph node transitions) and ToolInfo (tool calls/results, shared between native and LangChain-sourced rows).

Building this project

pip install -e ".[dev]"   # test, lint, type-check, and publish tooling
pytest                     # run tests
ruff check .               # lint

Full environment setup (Python version, examples, publishing to PyPI): see setup_guide.md.