+ P Act for LiveKit agents
Capture every LiveKit AgentSession as it actually runs — the same session lifecycle and track events the SDK uses to orchestrate a voice call, not a side channel bolted onto the audio pipeline.
Prefactor acts on your LiveKit agents at runtime — block, throttle, sandbox, or escalate a tool call or data access before it executes, stop an agent instantly with the kill switch, and delete or redact tagged PII the moment it's encountered.
How to add act to LiveKit
Same SDK, no extra package
The prefactor-core client you already initialized for tracing is what enforcement runs through.
Wrap the action in a span
withSpan() wraps the operation you want governed — a tool call, a data write, anything your own code defines.
Policy runs before your code does
Prefactor evaluates policy inside withSpan before calling the function you wrapped — block, throttle, or hold for approval, before the action executes.
# pip install prefactor-core
import os
from prefactor_core import PrefactorCoreClient, PrefactorCoreConfig
from prefactor_http import HttpClientConfig
config = PrefactorCoreConfig(
http_config=HttpClientConfig(
api_url="https://app.prefactorai.com",
api_token=os.environ["PREFACTOR_API_TOKEN"],
)
)
client = PrefactorCoreClient(config)
await client.initialize()
# then instrument your LiveKit run with spans — see docs.prefactor.aiShown with the Prefactor SDK — a first-class, working integration today.
How the LiveKit integration actually works
- AgentSession is LiveKit's own orchestrator — it collects user input, runs the voice pipeline, invokes the LLM, and sends output back; Prefactor spans attach at the same session boundary, not a separate hook layered on top.
- RoomIO — the utility LiveKit creates automatically when a session starts — bridges the AgentSession to the room's tracks and the linked participant; that's the same boundary Prefactor's spans use to capture each turn.
- A native LiveKit package (prefactor-livekit) is available — verify its snippet against docs.prefactor.ai before switching this page to it.
What Prefactor captures for LiveKit agents
Runtime policy enforcement
Every tool call, API request, or data access is evaluated against your rules at the point the agent tries to act — block, throttle, sandbox, or escalate, in sub-millisecond time.
Kill switch
Stop a single run, an agent, or every agent a team owns — immediately, no code deploy, triggered natively from Prefactor or programmatically from a custom span.
Human-in-the-loop approval
A flagged action pauses execution and routes to the right approver with full context — what the agent was trying to do, why, and its recent history — through Slack, Teams, or email.
PII deletion
Find every field carrying a tag and remove it in one action, wherever it appears — plus real-time redact, block, or escalate the moment PII is first encountered.
Trace anything — not just LLM calls
The SDK captures LLM, tool, and agent spans automatically. With withSpan you wrap any operation in your own span type — an API call, a database query, a quality check, a business action — each with its own payload and schema. It all flows into the same Observe, Evaluate, and cost views.
import { withSpan } from "@prefactor/core";
// Wrap ANY operation in a span you define — an API call, a quality
// check, a business action — with its own spanType, inputs and schema
await withSpan(
{
name: "research competitor",
spanType: "research_competitor",
inputs: { competitor },
},
async () => {
const results = await search(competitor); // your tool / API calls
return summarize(results); // captured as one span
},
);Nest spans to capture business-level actions, and start with permissive schemas you tighten over time. Instrumentation strategy →
An example run, span by span
Illustrative — a single LiveKit run as nested spans.
Illustrative example.
Manual logging vs DIY vs Prefactor
| Capability | Manual | DIY OpenTelemetry | Prefactor |
|---|---|---|---|
| LLM / tool / agent spans | Hand-rolled | ✓ build it | ✓ via the SDK |
| Token usage captured per call | — | Build it | ✓ |
| Configurable capture & sampling | — | Partial | ✓ |
| Hosted Admin UI (agents, instances) | — | — | ✓ |
| Risk profiles & audit trail | — | — | ✓ |
LiveKit act — FAQ
How do runtime policies work for LiveKit agents?+
A policy engine sits at the point a LiveKit agent calls a tool, queries an API, or touches data — the same interception point Prefactor uses across every framework. Every attempted action is evaluated against the agent's identity, permissions, and current context before it's allowed to proceed.
Does enforcement add latency to LiveKit runs?+
No meaningfully — policy evaluation runs in sub-millisecond time, so enforcement doesn't add noticeable latency to the agent's response.
Can I kill a single LiveKit run without stopping the whole agent?+
Yes — the kill switch scopes to a single run, a single agent, or every agent a team owns. Trigger it from the Prefactor dashboard directly, or programmatically via a custom span the moment your own code detects a problem.
How does human-in-the-loop approval work for LiveKit?+
When a policy flags a LiveKit agent's action for escalation, execution pauses and an approval request goes out with full context. The approver's decision is logged and either lets the action proceed or cancels it.
How is PII actually deleted from LiveKit agent data?+
Detection and tagging happen continuously; deletion is one action against the tag — find every field carrying it for a given person or record, and remove it, wherever it appears across your LiveKit runs.
Do I need a dedicated package for LiveKit?+
LiveKit has a native package (prefactor-livekit); you can also instrument it today with the framework-agnostic prefactor-core SDK.
What does Prefactor capture from LiveKit?+
Prefactor records voice-session turns, STT and TTS steps, tool calls and LLM calls as structured, timestamped spans — so every LiveKit run is captured as trace data you can reconstruct, search and export end to end.
Does Prefactor add latency or change how LiveKit runs?+
No. Observability capture is designed to stay off your agent's critical path, so it doesn't alter your LiveKit logic or your users' responses. The only part that acts inline is the optional runtime guardrails you enable per agent — by design, so a high-risk or low-confidence action can be held for human approval before it executes.
Can I evaluate agents built with LiveKit and catch regressions?+
Yes. Once runs are captured, eval suites score quality and groundedness on real traffic, drift detection flags behaviour changes after deployment, and versioned eval history catches regressions before they ship — the observe → evaluate → improve loop applied to your LiveKit agents.
Keep going
Act for other frameworks
LangChain →CrewAI →LangGraph →OpenAI Agents SDK →Claude Agent SDK →Microsoft AutoGen →See it on your LiveKit agents
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