Get concurrency in use in real time?

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pull it programmatically via an API/metrics endpoint so you can display/alert on it yourself

Thanks — I’m responding to your request about pulling real‑time concurrency (active sessions) programmatically so you can display or alert on it yourself. Short answer: There isn’t a documented single API that directly returns LiveKit Cloud “real‑time concurrency usage” for LiveKit Inference. You can, however, obtain the same information by using available server APIs/SDKs and observability options to infer current concurrency, or by using webhook events to maintain a live count. To give you practical next steps, I need to know whether you’re on LiveKit Cloud or self‑hosted LiveKit. Which are you using? While you confirm, here are the recommended programmatic approaches you can choose from (pick one and I’ll give code/examples tailored to your environment): • Use the Server SDK / server APIs to list active rooms and participants ◦ What it gives: ability to query current rooms and participants and compute active session/concurrency counts in real time. ◦ When to use: Self‑hosted or when you run a trusted backend that can call LiveKit server APIs. ◦ Work required: Poll the API on a cadence that suits your needs (for near‑real‑time use, every 15–60s) and aggregate counts per app/room/feature you care about. • Subscribe to webhook events (participant_joined, participant_left, room_started, room_finished) ◦ What it gives: Event-driven, near‑real‑time updates that let you maintain an accurate running count without polling. ◦ When to use: Recommended if you want lower latency and fewer API calls; works for both Cloud and self‑hosted (if webhooks are enabled). ◦ Work required: Implement a server endpoint to receive events, update an in‑memory or persistent counter/store, and emit alerts or metrics to your monitoring system. • Export telemetry/observability metrics (Prometheus / metrics pipeline) or query your metrics system ◦ What it gives: If you have access to exported metrics (Prometheus or another metrics store) you can query real‑time metrics and build alerts/dashboards. ◦ When to use: Best when you already use Prometheus/Grafana or want time‑series history and alerting rules. ◦ Work required: Ensure LiveKit metrics are being scraped/exported, then create queries/alerts in your monitoring system. Which approach is best depends on your setup and needs: • If you want the simplest, most accurate event‑driven method, use webhooks to maintain a live counter and push to your alerting/dashboard system. • If webhooks aren’t possible and you control a backend, polling the Server SDK API to list rooms/participants is straightforward. • If you already run Prometheus/Grafana, query the metrics store for active session metrics and alert from there. If you tell me: 1) Cloud or self‑hosted? 2) Which language/runtime for your backend (Node, Go, Python, etc.)? 3) Prefer polling or event-driven (webhooks)? I’ll give a step‑by‑step example (including sample code) for the best option for your environment. Sources: Server-side | livekit/livekit_composite | DeepWiki | server-sdk-go | livekit/livekit_composite | DeepWiki | How much can LiveKit scale? | LiveKit | Say hello to improved Cloud analytics

Cloud, polling and event-driven both

Thanks — I’ll provide concrete examples for both polling (Server SDK/API) and event‑driven (webhooks) for LiveKit Cloud. Which programming language/runtime would you like the sample code in? Pick one: Node.js, Go, or Python. Also helpful to know: • Do you already have a backend server (yes/no)? • Do you want sample alerting integration (e.g., send to Slack, email, or push metrics to Prometheus/Grafana)? Once you tell me the language and those preferences, I’ll send step‑by‑step instructions and runnable sample code for both polling and webhook approaches.

I have both

Thanks — since you have both a backend and want both polling and event-driven examples, I can provide concrete, runnable guidance for each approach. Quick clarifying question before I send code and step‑by‑step instructions: • Which language/runtime do you prefer for the sample code: Node.js, Go, or Python? (I’ll provide both polling and webhook examples in that language.) Also tell me: • Do you want sample alerting integration? Choose one: Slack, email, or Prometheus/Grafana (or “none — just the counts”). • Do you already have a LiveKit API key that your backend can use to call server APIs and receive webhooks? (yes/no) Answer those three items and I’ll send tailored instructions and sample code for both polling and webhooks on LiveKit Cloud.

how to handle dynamic variables in prompt ?

Thanks — I’m responding to your question about handling dynamic variables in prompts. You can inject dynamic data into agent prompts in several reliable ways depending on whether you’re using the SDK (code) or the Agent Builder (no‑code). Below are the practical options and short examples so you can pick what fits your setup. 1) Job metadata (recommended for one‑time / per‑job variables) • When you dispatch a job/session, include a JSON metadata object with the variables you need (name, user preferences, session-specific values). • In code, format your prompt with those values before starting the agent so the agent receives fully rendered instructions. Example (Python-style pseudocode):

context_variables = {"name": "Shayne", "age": 35, "city": "Toronto"}
instructions = f"""
You are a helpful agent. The user's name is {context_variables['name']}.
They are {context_variables['age']} years old and live in {context_variables['city']}.
"""
agent = ContextAgent(context_vars=context_variables)
await session.start(agent=agent, room=ctx.room)

2) Agent Builder variable placeholders (no-code) • In Agent Builder prompts use the

{{metadata.variable_name}}

syntax. • When you dispatch the agent, pass metadata like

{"user_name":"Alice","city":"Seattle"}

and the builder will render those placeholders automatically. Example prompt in Builder: You are assisting {{metadata.user_name}} from {{metadata.city}}. 3) Webhook / runtime injection for live values • For values that change during a call (e.g., live metrics, external API results), fetch the data at runtime and update the session instructions or call a function/tool that the agent can use. • Use server-side code to fetch the external value, then call the agent with updated context or invoke a function tool exposing that data. 4) session.userdata for multi-turn state • Store conversation state or user preferences in

session.userdata

so later turns can reference/update those variables. • Use

session.userdata

to keep personalization across turns without re-sending all metadata each time. Example pattern:

state = context.session.userdata
state.user_name = "Shayne"
# Later:
instructions = f"Welcome back, {state.user_name}. How can I help today?"

5) Function tools / RunContext for structured data • If your agent uses tools/functions, pass dynamic data into those tools via a RunContext so the agent can call them and receive structured responses to incorporate into replies. When to use which: • Use job metadata or Agent Builder placeholders for initial personalization and static per‑session data. • Use webhooks/runtime injection or function tools for data that changes frequently or must be fetched live. • Use

session.userdata

to preserve state across multiple turns or sessions. Security and formatting tips: • Sanitize and validate variables before injecting into prompts to avoid prompt injection or malformed prompts. • Prefer templating (f-strings or .format) on the server side so placeholders are resolved before sending instructions. • For large or sensitive data, pass a reference (ID) and fetch it securely at runtime rather than embedding raw secrets in prompts. If you tell me: • Are you using the SDK (which language: Node, Go, Python) or Agent Builder? • Are the variables static at session start or do they need live updates during the session? I’ll give a concrete, runnable example in your language (including secure templating and a sample webhook/function integration) tailored to your exact workflow. Sources: Context Variables | LiveKit Documentation | Agent Builder | LiveKit Documentation | Survey Calling Agent | LiveKit Documentation | Building multi-agent architectures with LiveKit agents | LiveKit