tuner-livekit-sdk

Automatically ingest LiveKit Agents session data into the Tuner observability API.

Drop it into your entrypoint and every session — transcript, tool calls, timing metrics, usage, cost — is submitted to Tuner when the call ends. No manual event wiring required.

Features

• Zero-friction wiring — one line after AgentSession creation; the plugin hooks itself into session lifecycle events.
• Rich per-turn timing — STT/LLM/TTS latency, end-of-turn delay, and end-to-end latency captured per transcript segment.
• Tool call tracking — function calls and their results are merged into the timeline with real timing data.
• LangGraph / LangChain support — via the bundled tuner-langchain package, capture node transitions and tool calls straight from your graph.
• SIP simulation correlation — match Tuner-initiated simulation calls to your production SIP trunk.
• Cost calculation — plug in your own pricing function; Tuner reports cost per call.
• Resilient delivery — configurable timeouts and retries on transient failures.

Table of Contents

• Installation
• Quickstart
• Configuration
• Options
• Data captured
• LangGraph / LangChain observability
• Simulation correlation (SIP)
• Privacy & data handling
• Requirements
• Support
• Development
• License

Installation of the Library into your Livekit project

pip install tuner-livekit-sdk

Quickstart

Set credentials via environment variables:

export TUNER_API_KEY="tr_api_..."
export TUNER_WORKSPACE_ID="123"
export TUNER_AGENT_ID="my-agent"

Then drop the plugin in right after creating your AgentSession:

from tuner import TunerPlugin

async def entrypoint(ctx: JobContext):
    session = AgentSession(...)
    TunerPlugin(session, ctx)   # wires itself automatically
    await session.start(...)

That's it. The plugin listens to session events and submits call data to Tuner when the session ends.

Configuration

Environment variables

Variable	Required	Description
TUNER_API_KEY	✅	Bearer token (starts with tr_api_)
TUNER_WORKSPACE_ID	✅	Integer workspace ID
TUNER_AGENT_ID	✅	Agent identifier from Tuner Agent Settings
TUNER_BASE_URL	—	API base URL (default: https://api.usetuner.ai)

Credentials from code

Pass credentials directly instead of (or to override) environment variables:

TunerPlugin(
    session, ctx,
    api_key="tr_api_...",
    workspace_id=123,
    agent_id="my-agent",
)

Options

Call type

By default the plugin auto-detects the call type (phone_call for SIP participants, web_call otherwise). Override it explicitly:

TunerPlugin(session, ctx, call_type="phone_call")
TunerPlugin(session, ctx, call_type="web_call")

Recipient (callee)

Pass the phone number or SIP URL of the called party when your agent initiates or routes outbound calls. This field is not auto-collected — supply it explicitly when known:

# E.164 phone number
TunerPlugin(session, ctx, recipient="+15551234567")

# SIP URI
TunerPlugin(session, ctx, recipient="sip:alice@example.com")

recipient is optional. If omitted it is simply not included in the call record.

Recording URL

Tuner requires a recording_url for every call. If you don't provide a resolver the plugin logs a warning and submits "pending" as a placeholder:

# Static URL
async def my_resolver(room_name: str, job_id: str) -> str:
    return f"https://cdn.example.com/recordings/{job_id}.ogg"

TunerPlugin(session, ctx, recording_url_resolver=my_resolver)

# LiveKit Egress → S3
async def egress_resolver(room_name: str, job_id: str) -> str:
    url = await my_egress_db.get_recording_url(room_name)
    return url or "pending"

TunerPlugin(session, ctx, recording_url_resolver=egress_resolver)

Cost calculation

Provide a callable that receives a UsageSummary and returns the call cost in cents:

def calculate_cost(usage) -> float:
    llm_cost  = usage.llm_prompt_tokens     * 0.000_003
    llm_cost += usage.llm_completion_tokens * 0.000_015
    tts_cost  = usage.tts_characters_count  * 0.000_030
    stt_cost  = usage.stt_audio_duration    * 0.000_006
    total_dollars = llm_cost + tts_cost + stt_cost
    return round(total_dollars * 100, 2)  # dollars -> cents

TunerPlugin(session, ctx, cost_calculator=calculate_cost)

Extra metadata

Attach arbitrary key-value data to every call record:

TunerPlugin(
    session, ctx,
    extra_metadata={
        "env": "production",
        "region": "us-east-1",
        "deployment": "v2.3.1",
    },
)

Retry and timeout

TunerPlugin(
    session, ctx,
    timeout_seconds=15.0,   # per-request timeout (default: 30.0)
    max_retries=5,          # retries on 5xx / 429 / network errors (default: 3)
)

Agent version tracking

Track which version of your agent handled each call — useful when you update a prompt, swap a model, or change your pipeline:

AGENT_VERSION=42 python agent.py start

Tuner reads it automatically. Bump the number on every deployment.

Override in code (takes priority over the env var):

TunerPlugin(session, ctx, agent_version=42, ...)

Disable the plugin

Useful for local development or test environments:

import os

TunerPlugin(
    session, ctx,
    enabled=os.getenv("ENV") == "production",
)

Data captured

Each item in session.history is mapped to a transcript segment (role, text, start_ms, end_ms, metadata). For user / agent segments, metadata includes the following per-turn timing and quality fields:

Field	Type	Description
id	string	LiveKit ChatContext item ID
interrupted	bool	Whether this turn was interrupted before completing
transcript_confidence	float | null	STT confidence score for user turns
stt_node_ttfb	int (ms) | null	Time to obtain the transcript after the user stopped speaking
eou_delay	int (ms) | null	Time between end of user speech and the decision to end their turn
llm_node_ttft	int (ms) | null	Time for the LLM node to return its first token
tts_node_ttfb	int (ms) | null	Time for the TTS node to return its first audio chunk
e2e_latency	int (ms) | null	Time from end of user speech to the agent beginning its response

All timing fields above are taken directly from LiveKit's own per-turn metrics (EOUMetrics, STT/LLM/TTS metrics) and passed through unchanged — the SDK does not recompute them.

Tool calls (agent_function / agent_result roles) and, when LangGraph/LangChain instrumentation is enabled, node_transition segments are interleaved into the same timeline, sorted by start_ms.

LangGraph / LangChain observability

tuner-langchain ships as a dependency of tuner-livekit-sdk, so no separate install step is needed. If your agent uses LangGraph or LangChain as the orchestration layer, wire it in with wrap_graph() or wrap_chain(). Each returns a drop-in replacement for the graph/chain you pass in — hand it straight to LLMAdapter, no callbacks to wire up:

from tuner import TunerPlugin
from livekit.plugins import langchain

plugin = TunerPlugin(session, ctx)

llm = langchain.LLMAdapter(
    plugin.wrap_graph(my_graph),
    stream_mode="messages",
)

Use wrap_chain() instead of wrap_graph() for a plain (non-graph) LangChain runnable.

To limit what data is forwarded to Tuner, pass a CaptureConfig:

from tuner import TunerPlugin
from tuner_langchain import CaptureConfig

plugin = TunerPlugin(session, ctx)
wrapped_graph = plugin.wrap_graph(
    my_graph,
    capture=CaptureConfig(
        tool_inputs=False,
        node_instructions=False,
    ),
)

Simulation correlation (SIP)

Tuner simulations dial into your agent through the same SIP trunk that handles production phone calls. To match a simulation run with the session your agent submits, the SDK forwards LiveKit's sip.callIDFull attribute as a sip_call_id.

This section covers the SDK wiring only. For LiveKit platform setup (SIP URI, inbound trunk, dispatch rule, Tuner SIP settings), see:

→ docs.usetuner.ai/docs/api-and-integrations/connecting-to-livekit/simulation-setup

Requirements

• tuner-livekit-sdk >= 0.1.5 (the sip_call_id argument was added in 0.1.5)

Step 1 — The _extract_sip_call_id helper

This helper scans the LiveKit room for the SIP caller and returns their sip.callIDFull — the value Tuner uses to match a simulation run to the session your agent submits.

from livekit import rtc


def _extract_sip_call_id(ctx: JobContext) -> str | None:
    for participant in ctx.room.remote_participants.values():
        if participant.kind != rtc.ParticipantKind.PARTICIPANT_KIND_SIP:
            continue
        attributes = dict(getattr(participant, "attributes", {}) or {})
        sip_call_id_full = attributes.get("sip.callIDFull")
        if isinstance(sip_call_id_full, str) and sip_call_id_full:
            return sip_call_id_full
    return None

How it works:

• Loops through remote participants and keeps only the SIP one (rooms can hold web clients, observers, etc.).
• Reads sip.callIDFull from that participant's attributes — this is the full SIP Call-ID Tuner stamps on its outbound leg (not the shorter sip.callID).
• Returns None for web calls or non-simulation SIP calls; TunerPlugin accepts None and simply skips correlation.

Step 2 — Pass it to TunerPlugin

Once you have the helper, the wiring in entrypoint is three lines: connect, extract, attach.

async def entrypoint(ctx: JobContext):
    session = AgentSession(...)

    await ctx.connect()
    sip_call_id = _extract_sip_call_id(ctx)

    TunerPlugin(
        session,
        ctx,
        sip_call_id=sip_call_id,
        # ...other options
    )

    await session.start(...)

⚠️ Order matters: ctx.room.remote_participants is empty until await ctx.connect() completes. If you call the helper too early it will always return None and you'll silently lose correlation for every simulation — no error, just missing data in Tuner. Always: build AgentSession → await ctx.connect() → extract ID → attach plugin → await session.start(...).

Step 3 — Full example

Putting the helper, the plugin wiring, and the usual options (cost, recording URL, metadata) together:

import os
from livekit import rtc
from livekit.agents import JobContext, AgentSession
from tuner import TunerPlugin


def _extract_sip_call_id(ctx: JobContext) -> str | None:
    for participant in ctx.room.remote_participants.values():
        if participant.kind != rtc.ParticipantKind.PARTICIPANT_KIND_SIP:
            continue
        attributes = dict(getattr(participant, "attributes", {}) or {})
        sip_call_id_full = attributes.get("sip.callIDFull")
        if isinstance(sip_call_id_full, str) and sip_call_id_full:
            return sip_call_id_full
    return None


def calculate_cost(usage) -> float:
    total_dollars = (
        usage.llm_prompt_tokens     * 0.000_003
        + usage.llm_completion_tokens * 0.000_015
        + usage.tts_characters_count  * 0.000_030
    )
    return round(total_dollars * 100, 2)  # cents — the Tuner API expects cents


async def get_recording_url(room_name: str, job_id: str) -> str:
    return await my_storage.get_url(job_id) or "pending"


async def entrypoint(ctx: JobContext):
    session = AgentSession(...)

    await ctx.connect()
    sip_call_id = _extract_sip_call_id(ctx)

    TunerPlugin(
        session,
        ctx,
        api_key=os.environ["TUNER_API_KEY"],
        workspace_id=int(os.environ["TUNER_WORKSPACE_ID"]),
        agent_id="customer-support-v3",
        call_type="phone_call",
        recording_url_resolver=get_recording_url,
        cost_calculator=calculate_cost,
        sip_call_id=sip_call_id,
        extra_metadata={"env": "prod", "region": "us-east-1"},
        timeout_seconds=20.0,
        max_retries=3,
        enabled=True,
    )

    await session.start(...)

Privacy & data handling

The plugin submits the following to the Tuner API for each call: the plain-text transcript, per-turn timing metadata (see Data captured), tool call names/arguments/results, token/character/audio usage counts, and any extra_metadata you configure. It does not access raw audio, video, or room recordings directly — recordings are only referenced by the URL your recording_url_resolver returns.

Use CaptureConfig (see LangGraph / LangChain observability) to exclude tool inputs or node instructions from LangGraph/LangChain traces, and enabled=False to disable submission entirely for local development or test environments.

Requirements

• Python ≥ 3.10
• livekit-agents >= 1.4
• tuner-livekit-sdk >= 0.1.5 (needed for sip_call_id / SIP correlation)
• aiohttp >= 3.9
• tuner-langchain >= 0.1.1 (installed automatically as a dependency; used by wrap_graph() / wrap_chain())

Support

• Docs: docs.usetuner.ai
• Issues / feature requests: GitHub Issues
• Email: support@usetuner.ai
• Release notes: CHANGELOG.md

Development

Set up a local environment:

uv sync --dev
source .venv/bin/activate

Run the test suite:

uv run pytest -v

Publishing to PyPI

pip install build twine
python -m build
twine upload dist/*

License

MIT