what will be the latency of my agent if i am using livekit cloud llm as gpt 4.1 mini stt deepgram tts cartesia sonic 3

Good question — here’s a concise, realistic estimate and what affects it when you run an agent on LiveKit Cloud with GPT‑4.1‑mini (LLM), Deepgram (STT), and Cartesia Sonic‑3 (TTS). Short bottom line • Typical end‑to‑end round‑trip latency (user finishes speaking → agent speech starts) under good conditions: ~800 ms to 1.5 s. • With heavier network or regional differences it can be higher; with careful tuning you can push toward the lower end. Component breakdown (typical ranges) • STT (Deepgram): 300–500 ms from end of user utterance to a usable transcript (streaming STT can reduce perceived delay). • Voice activity detection / end‑of‑utterance detection: 100–400 ms depending on VAD sensitivity and model. • LLM (GPT‑4.1‑mini): ~300–600 ms Time‑To‑First‑Token for a short reply when using a low‑latency model and a nearby endpoint. • TTS (Cartesia Sonic‑3): 150–300 ms to start streaming audio (TTFB), plus additional time to render the full response if it’s long. • Extra processing (routing, serialization, any intermediate logic/tools): 50–200 ms. Putting it together • End of user speech → VAD confirms end: 100–400 ms • STT final transcript available: 300–500 ms (may overlap with VAD) • LLM generates first token: 300–600 ms • TTS begins streaming audio: 150–300 ms Total (not strictly additive because some overlap occurs): ~800 ms → 1.5 s typical. Factors that increase latency • Geographic distance between your agent process and the AI providers (LLM/STT/TTS). • Cold starts / un‑prewarmed models or VAD. • Long STT or LLM responses (generation time scales with length). • Serializing many tool/HTTP calls between STT and LLM. • Network congestion, proxy/firewall overhead, or TLS handshake delays. How to reduce latency (practical steps) • Region co‑location: deploy your agent close to the LLM/STT/TTS endpoints. • Prewarm models and VAD on startup so you avoid cold initialization during calls. • Use streaming for STT, LLM, and TTS so partial results can flow and reduce perceived latency. • Keep system prompts concise and use low temperature / smaller model variants when appropriate (GPT‑4.1‑mini is already optimized for speed). • Avoid blocking synchronous work before calling connect/session start; callouts and I/O should be async. • Monitor metrics (TTFT, STT latency, TTS TTFB) and instrument end‑to‑end timing to find hotspots. What to measure in production • Time from user end → STT final transcript. • LLM Time‑to‑First‑Token and total generation time. • TTS Time‑to‑First‑Byte and time to first audible word. • End‑to‑end: user end → agent audio starts. Use these to set realistic SLOs. If you want, I can: • Suggest exact telemetry to instrument (metrics/events to capture). • Provide a deployment checklist to minimize latency for your particular region and provider choices. Which would you like next? Sources: LLM Metrics | LiveKit Documentation | Deepgram STT | LiveKit Documentation | OpenAI LLM models | LiveKit Documentation | examples/voice_agents | livekit/livekit_composite | DeepWiki | Cartesia TTS plugin guide | LiveKit Documentation

i am getting around 1200 to 1500 my llm is giving alot Total Latency: 1425.58ms (EOU Delay: 0.00ms, LLM TTFT: 1227.43ms, TTS TTFB: 198.15ms)

Thanks — this is a clear set of numbers and a good place to optimize. Your metrics show the LLM TTFT (1227 ms) is the dominant contributor, with TTS very small and EOU delay zero. That means the bottleneck is the model inference/round‑trip rather than VAD or TTS. What this tells us • LLM TTFT = 1227 ms is the main driver of the ~1.4 s total. That’s within the typical production range for a reasonably loaded model/endpoint, but there are a few practical ways to reduce it. • TTS TTFB = 198 ms is fine. • EOU Delay = 0 ms means end‑of‑utterance detection isn’t adding delay. Targeted actions you can try (priority order) 1. Region co‑location (fast win) ◦ Deploy your agent process in the same cloud region as the LLM endpoint (OpenAI/Azure/Groq) and STT/TTS providers. Network RTT is often the largest single contributor to TTFT. 1. Try a faster LLM variant for latency-sensitive turns ◦ Test GPT‑4.1‑nano or other lower‑latency models for short responses or fallback replies. If quality is acceptable for some responses, it can cut TTFT substantially. 1. Preemptive / speculative generation ◦ Enable preemptive generation so LLM work begins before final EOU (this overlaps speech detection and inference). This can significantly reduce perceived latency for brief exchanges. 1. Shorten and optimize prompts ◦ Reduce system prompt length, remove non‑essential context, and limit

max_tokens

so the model does less work to produce the first token. ◦ Ensure the model receives only necessary state; summarize longer history. 1. Streaming & incremental output ◦ Use streaming LLM output and start piping partial tokens into downstream TTS (if supported) to reduce time-to-audible. ◦ Ensure streaming is enabled end-to-end (STT→LLM→TTS). 1. Prewarm and provision capacity ◦ Prewarm any models (and VAD) at process start to avoid cold starts. ◦ If your provider supports reserved/provisioned capacity, use it to reduce queuing and variability. 1. Measure and monitor deeper ◦ Instrument these metrics per request: DNS lookup, TCP/TLS handshake, request send time, provider queue time, model TTFT, first token receive, TTS TTFB. ◦ That will tell if the delay is network, provider queueing, or model computation. Quick experiments to run now 1. Run the same prompt/model from the same host with curl/SDK to the LLM endpoint and measure single-request latency (isolates network vs provider). 2. Switch to a faster model (e.g., 4.1‑nano) briefly and compare TTFT. 3. Try preemptive_generation=True and measure end‑to‑end again. 4. Reduce the prompt/system content to a minimal test prompt and re-measure. If you want I can: • Suggest exact telemetry (span points) to add so you can tell whether the delay is network vs model queue vs compute. • Provide a short actionable checklist and code snippet for enabling preemptive generation and streaming in your session config. • Help pick which model to test first based on your quality/latency tradeoff. Which would you like next: telemetry snippet, preemptive generation code, or model switching test steps? Sources: How can I reduce latency in voice agents using STT, TTS and LLM? | LiveKit | examples/drive-thru | livekit/livekit_composite | DeepWiki | examples/frontdesk | livekit/livekit_composite | DeepWiki | examples/bank-ivr | livekit/livekit_composite | DeepWiki | Introducing LiveKit Inference: A unified model interface for voice AI

Total Latency: 1482.08ms (EOU Delay: 564.04ms, LLM TTFT: 727.41ms, TTS TTFB: 190.62ms) this is correct

Thanks — that latency breakdown looks valid and the numbers make sense. I’ll confirm what each part means, why EOU is now the largest contributor, and give focused next steps to reduce overall latency. What your numbers mean • Total Latency: 1482 ms — the full time from user end → agent audio starts. • EOU Delay: 564 ms — time spent detecting the end of user speech (VAD/turn detection). This is now the biggest contributor. • LLM TTFT: 727 ms — time from sending prompt to the LLM to receiving the first token. • TTS TTFB: 191 ms — time for TTS to start streaming audio (good). Why EOU Delay can be large • VAD/endpointing waits to be confident the user finished speaking. Conservative VAD settings (to avoid cutting off users) increase this delay. • Silence thresholds, VAD frame sizes, or multi-stage VAD + activity smoothing can add hundreds of ms. • If you enabled aggressive noise cancellation or used telephony VAD tuned for 8k, detection may be slower for certain audio profiles. Recommended quick fixes (prioritized) 1. Tune VAD / endpointing sensitivity ◦ Reduce silence timeout or lower detection thresholds so the system classifies end-of-utterance faster. ◦ If you use Silero or another VAD, try faster models or lower frame aggregation windows. ◦ Test with different configs to find the sweet spot (faster EOU with minimal false cuts). 1. Use partial/streaming LLM input (preemptive generation) ◦ Start LLM work on partial transcript (or preemptive_generation) before final EOU if possible. This overlaps EOU and LLM work and reduces perceived latency. ◦ Ensure your pipeline supports streaming tokens into TTS incrementally. 1. Shorten prompt and localize context ◦ Keep the LLM prompt compact so TTFT stays low. Make sure injected context is minimal and summarized. 1. Keep TTS streaming ◦ Your TTS TTFB is fine; use streaming TTS so you begin playback as soon as tokens arrive. 1. Measurement & A/B testing ◦ Run A/B tests with different VAD timeouts and preemptive settings and measure end-to-end latency and user experience (false cut vs latency). ◦ Instrument EOU decision times, VAD confidence traces, and partial transcript events so you can correlate false truncation vs latency. Suggested experiments to run now (fast feedback) 1. Reduce VAD silence timeout by 100–200 ms and measure total latency and user cutoffs. 2. Enable preemptive_generation and measure again — you should see LLM TTFT overlap with EOU, cutting total latency. 3. Test smaller prompts or a faster LLM variant to see how LLM TTFT behaves (helps confirm whether optimization focus should be EOU or LLM). If you want, I can: • Provide exact Silero/VAD parameter names and sample values to try. • Share a short code snippet to enable preemptive generation and streaming in your AgentSession config. • Help design an A/B test plan (VAD timeouts vs. preemptive generation) and the metrics to collect. Which would you like next? (A) VAD parameter suggestions, (B) preemptive_generation code snippet, or (C) A/B test plan and telemetry to collect.

can i make a script that can test and give me report of the latencies

Great — you can absolutely automate latency testing and reporting. Below is a practical plan plus a runnable Python script template you can adapt to your LiveKit agent. The script measures the key components you care about: • EOU Delay (end‑of‑utterance detection / VAD latency) • STT latency (streaming transcript finalization) • LLM TTFT (time to first token) • TTS TTFB (time to first byte / first audio chunk) • End-to-end total (user speech end → agent audio start) How it works (overview) 1. Play or stream a prerecorded test utterance into the agent (simulates user speech). 2. Instrument timestamps at key points: when playback ends (user EOU), when STT final transcript is received, when request is sent to LLM, when first token arrives, when TTS first chunk arrives, and when agent audio playback starts. 3. Calculate deltas for each metric and log them. 4. Repeat N times and produce a summary report (mean, p50, p90, p99). What you need • A way to inject audio into your LiveKit room (automated publisher or a helper client that publishes the test WAV). • Access to your agent code so you can emit structured logs or events when STT/LLM/TTS milestones occur (or instrument the agent to POST timing events to a local collector). • Network reachability to the services (run the script in the same region as the agent where possible). Recommended measurement approach • Best: instrument the agent to emit structured timing events (JSON) to an HTTP endpoint your script runs. This gives precise internal timing (no log parsing). • If you can’t instrument the agent, parse structured logs (JSON) emitted by the agent and correlate by a unique test_id per run. Key events to capture (per test run) • test_id: unique id for this run • playback_start_ts (when test audio started) • playback_end_ts (when test audio ended) — define this as user EOU time • stt_final_ts (when STT delivered final transcript) • llm_request_ts (when LLM request sent) • llm_first_token_ts (when first token from LLM received) • tts_first_byte_ts (when first TTS audio chunk received) • agent_playout_start_ts (when agent audio starts playing in room, optional if you can measure) Derived metrics • EOU Delay = stt_final_ts - playback_end_ts (or better: time from EOU to STT final) • LLM TTFT = llm_first_token_ts - llm_request_ts • TTS TTFB = tts_first_byte_ts - llm_first_token_ts (or - llm_response_complete_ts if you use full text) • Total Latency = agent_playout_start_ts - playback_end_ts (or tts_first_byte_ts - playback_end_ts if agent_playout not measurable) Example architecture choices • Agent emits events via HTTP POST to your collector endpoint (preferred). • Script publishes audio to LiveKit room, waits for event callbacks, computes metrics, and repeats. • Store measurements in CSV and compute summary stats. Python script template (collector + runner) • This template assumes you can: (A) publish test audio into the LiveKit room (function stub provided), and (B) the agent will POST JSON events to the collector endpoint with the test_id and event name. If your agent logs JSON instead, adapt the collector to tail/parse logs. Save as latency_test.py and run on a host reachable by agent.

import asyncio
import aiohttp
import time
import uuid
import statistics
from aiohttp import web

# CONFIG
COLLECTOR_PORT = 9001
AGENT_CALLBACK_URL = f"<http://localhost:{COLLECTOR_PORT}/event>"  # agent posts events here
TEST_RUNS = 20
ROOM_NAME = "test_room_123"
TEST_AUDIO_PATH = "test_utterance.wav"  # short WAV file to publish as user speech
PUBLISHER_DELAY_BEFORE_START = 0.5  # seconds before starting playback to let session settle

# In-memory store for events per test_id
EVENTS = {}  # test_id -> { event_name: timestamp_seconds, ... }

async def handle_event(request):
    """
    Agent posts JSON: { "test_id": "<uuid>", "event": "stt_final" , "ts": 167... }
    If ts not provided, collector will use now().
    """
    data = await request.json()
    test_id = data.get("test_id")
    event = data.get("event")
    ts = data.get("ts") or time.time()
    if not test_id or not event:
        return web.json_response({"error": "missing test_id or event"}, status=400)
    EVENTS.setdefault(test_id, {})[event] = ts
    return web.json_response({"status": "ok"})

async def start_collector():
    app = web.Application()
    app.router.add_post("/event", handle_event)
    runner = web.AppRunner(app)
    await runner.setup()
    site = web.TCPSite(runner, "0.0.0.0", COLLECTOR_PORT)
    await site.start()
    print(f"Collector listening on port {COLLECTOR_PORT}")

# Helper to publish test audio into the LiveKit room
# You must implement this for your environment. This stub should:
#  - connect a publisher client to the LiveKit room
#  - publish the WAV audio, and return (test_id, playback_start_ts, playback_end_ts)
async def publish_test_audio(test_id):
    # Example pseudo-implementation:
    # connect = await connect_to_livekit(room=ROOM_NAME)
    # await sleep(PUBLISHER_DELAY_BEFORE_START)
    # playback_start = time.time()
    # await stream_wav_to_room(TEST_AUDIO_PATH)
    # playback_end = time.time()
    # disconnect publisher
    # return test_id, playback_start, playback_end
    await asyncio.sleep(0.1)
    now = time.time()
    # Simulate 800ms utterance
    playback_start = now
    playback_end = now + 0.8
    # Simulate agent receiving events by letting agent post real events; if not instrumented, you can simulate
    return test_id, playback_start, playback_end

def compute_metrics_for_run(events, playback_end_ts):
    # events: dict of event_name -> ts
    # required events: stt_final, llm_request, llm_first_token, tts_first_byte, playout_start (optional)
    out = {}
    out["playback_end_ts"] = playback_end_ts
    if "stt_final" in events:
        out["eou_to_stt_final_ms"] = (events["stt_final"] - playback_end_ts) * 1000
    if "llm_request" in events and "llm_first_token" in events:
        out["llm_ttft_ms"] = (events["llm_first_token"] - events["llm_request"]) * 1000
    if "llm_first_token" in events and "tts_first_byte" in events:
        out["tts_ttfb_ms"] = (events["tts_first_byte"] - events["llm_first_token"]) * 1000
    if "playout_start" in events:
        out["total_ms"] = (events["playout_start"] - playback_end_ts) * 1000
    elif "tts_first_byte" in events:
        out["total_ms"] = (events["tts_first_byte"] - playback_end_ts) * 1000
    return out

def summarize(results, key):
    values = [r[key] for r in results if key in r]
    if not values:
        return None
    return {
        "count": len(values),
        "mean": statistics.mean(values),
        "p50": statistics.median(values),
        "p90": percentile(values, 90),
        "p99": percentile(values, 99),
        "min": min(values),
        "max": max(values),
    }

def percentile(data, p):
    if not data:
        return None
    data = sorted(data)
    k = (len(data)-1) * (p/100.0)
    f = int(k)
    c = min(f+1, len(data)-1)
    if f == c:
        return data[int(k)]
    d0 = data[f] * (c - k)
    d1 = data[c] * (k - f)
    return d0 + d1

async def run_tests():
    results = []
    for i in range(TEST_RUNS):
        test_id = str(uuid.uuid4())
        # clear any prior events
        EVENTS.pop(test_id, None)
        print(f"Starting test {i+1}/{TEST_RUNS} id={test_id}")
        # start publisher shortly after giving agent time to connect
        await asyncio.sleep(0.2)
        test_id, playback_start, playback_end = await publish_test_audio(test_id)
        # Wait for agent events to be posted (polling with timeout)
        timeout = 10.0
        poll_interval = 0.05
        waited = 0.0
        while waited < timeout:
            ev = EVENTS.get(test_id)
            if ev and ("llm_first_token" in ev or "tts_first_byte" in ev or "playout_start" in ev):
                # got enough to compute
                metrics = compute_metrics_for_run(ev, playback_end)
                metrics["test_id"] = test_id
                results.append(metrics)

import asyncio import aiohttp import time import uuid import statistics from aiohttp import web # CONFIG COLLECTOR_PORT = 9001 AGENT_CALLBACK_URL = f"http//localhost{COLLECTOR_PORT}/event" # agent posts events here TEST_RUNS = 20 ROOM_NAME = "test_room_123" TEST_AUDIO_PATH = "test_utterance.wav" # short WAV file to publish as user speech PUBLISHER_DELAY_BEFORE_START = 0.5 # seconds before starting playback to let session settle # In-memory store for events per test_id EVENTS = {} # test_id -> { event_name: timestamp_seconds, ... } async def handle_event(request): """ Agent posts JSON: { "test_id": "<uuid>", "event": "stt_final" , "ts": 167... } If ts not provided, collector will use now(). """ data = await request.json() test_id = data.get("test_id") event = data.get("event") ts = data.get("ts") or time.time() if not test_id or not event: return web.json_response({"error": "missing test_id or event"}, status=400) EVENTS.setdefault(test_id, {})[event] = ts return web.json_response({"status": "ok"}) async def start_collector(): app = web.Application() app.router.add_post("/event", handle_event) runner = web.AppRunner(app) await runner.setup() site = web.TCPSite(runner, "0.0.0.0", COLLECTOR_PORT) await site.start() print(f"Collector listening on port {COLLECTOR_PORT}") # Helper to publish test audio into the LiveKit room # You must implement this for your environment. This stub should: # - connect a publisher client to the LiveKit room # - publish the WAV audio, and return (test_id, playback_start_ts, playback_end_ts) async def publish_test_audio(test_id): # Example pseudo-implementation: # connect = await connect_to_livekit(room=ROOM_NAME) # await sleep(PUBLISHER_DELAY_BEFORE_START) # playback_start = time.time() # await stream_wav_to_room(TEST_AUDIO_PATH) # playback_end = time.time() # disconnect publisher # return test_id, playback_start, playback_end await asyncio.sleep(0.1) now = time.time() # Simulate 800ms utterance playback_start = now playback_end = now + 0.8 # Simulate agent receiving events by letting agent post real events; if not instrumented, you can simulate return test_id, playback_start, playback_end def compute_metrics_for_run(events, playback_end_ts): # events: dict of event_name -> ts # required events: stt_final, llm_request, llm_first_token, tts_first_byte, playout_start (optional) out = {} out["playback_end_ts"] = playback_end_ts if "stt_final" in events: out["eou_to_stt_final_ms"] = (events["stt_final"] - playback_end_ts) * 1000 if "llm_request" in events and "llm_first_token" in events: out["llm_ttft_ms"] = (events["llm_first_token"] - events["llm_request"]) * 1000 if "llm_first_token" in events and "tts_first_byte" in events: out["tts_ttfb_ms"] = (events["tts_first_byte"] - events["llm_first_token"]) * 1000 if "playout_start" in events: out["total_ms"] = (events["playout_start"] - playback_end_ts) * 1000 elif "tts_first_byte" in events: out["total_ms"] = (events["tts_first_byte"] - playback_end_ts) * 1000 return out def summarize(results, key): values = [r[key] for r in results if key in r] if not values: return None return { "count": len(values), "mean": statistics.mean(values), "p50": statistics.median(values), "p90": percentile(values, 90), "p99": percentile(values, 99), "min": min(values), "max": max(values), } def percentile(data, p): if not data: return None data = sorted(data) k = (len(data)-1) * (p/100.0) f = int(k) c = min(f+1, len(data)-1) if f == c: return data[int(k)] d0 = data[f] * (c - k) d1 = data[c] * (k - f) return d0 + d1 async def run_tests(): results = [] for i in range(TEST_RUNS): test_id = str(uuid.uuid4()) # clear any prior events EVENTS.pop(test_id, None) print(f"Starting test {i+1}/{TEST_RUNS} id={test_id}") # start publisher shortly after giving agent time to connect await asyncio.sleep(0.2) test_id, playback_start, playback_end = await publish_test_audio(test_id) # Wait for agent events to be posted (polling with timeout) timeout = 10.0 poll_interval = 0.05 waited = 0.0 while waited < timeout: ev = EVENTS.get(test_id) if ev and ("llm_first_token" in ev or "tts_first_byte" in ev or "playout_start" in ev): # got enough to compute metrics = compute_metrics_for_run(ev, playback_end) metrics["test_id"] = test_id results.append(metrics)

when the user disconnects the call on the whatsapp will my whatsapp sidecar recieve anything

Short answer: it depends on the connector/sidecar. LiveKit itself will not magically notify your WhatsApp gateway when the remote WhatsApp user hangs up; the connector or gateway is the piece that must surface that hangup event to you. What typically happens • If the WhatsApp call is bridged via a SIP gateway: the gateway receives a SIP BYE from the WhatsApp leg (or its provider). Your gateway should expose that BYE as either (a) a webhook/event to your backend or (b) an internal callback in the sidecar that you can observe. • If the WhatsApp call is handled by a CPaaS or custom WhatsApp gateway (Twilio, Vonage, third‑party): they normally send a call‑status webhook (e.g., status=completed/call.ended) to a URL you provided. • If you are using a custom “sidecar” process that both bridges WhatsApp → LiveKit and provides signaling, that sidecar must explicitly emit a hangup/terminated event to your backend when WhatsApp signals disconnect. What LiveKit does/does not do • LiveKit will emit participant lifecycle events (participant_left, etc.) for the LiveKit room side when participants disconnect from LiveKit. That does not guarantee the WhatsApp leg was closed unless the connector also tears down the external call. • Removing participants or deleting the LiveKit room stops media inside LiveKit but does not automatically hang up the WhatsApp call on the connector side — you must explicitly tell the connector to hang up. How to make sure you get notified when the WhatsApp user disconnects • If using a SIP gateway: ◦ Ensure your SIP gateway/bridge propagates the BYE/terminated event to your backend (webhook, message queue, or logs). ◦ Keep a mapping call_id_ ↔️ _livekit_room so you can delete the correct room when you receive the BYE. • If using a CPaaS (Twilio/Vonage/etc.): ◦ Configure their call status webhook (status=completed) to post to your backend. ◦ In the webhook handler, map the provider call SID to your LiveKit room and delete/close the room. • If using a custom sidecar: ◦ Add code to the sidecar to POST a hangup event to your backend (or call your internal API) when WhatsApp signals the end of the call. ◦ Include the connector call/session id and the LiveKit room or participant id in the event so your backend can correlate and clean up. • If you cannot get push events from the connector: ◦ Poll the connector session API periodically for call state (fallback, less real-time). What I need from you to give exact steps • Which connector/gateway are you using? (SIP trunk, Twilio/Vonage CPaaS, WhatsApp Cloud API bridger, or a custom sidecar) • How is the call bridged into LiveKit today (SIP leg, CPaaS media bridge, or a custom publisher)? • Do you currently receive any webhooks from the connector? If yes, share a sample event or event name for inbound call updates. If you tell me that, I’ll give: • The exact webhook event to listen for (or SIP event to watch), an example webhook payload, and a small backend snippet to map call → LiveKit room and delete the room on hangup. Sources: Agents telephony integration | LiveKit Documentation | Server-side | livekit/livekit_composite | DeepWiki

why i see o data in agent insights

Thanks — I can help with that. I’ll explain the common reasons Agent Insights can show no data, what to check quickly, and which logs/config values would help me diagnose your specific case. Quick summary of likely causes • Metrics/insights not enabled or not configured for your deployment. • Agent process not instrumented to emit the insight metrics/events (missing telemetry hooks). • Using a custom or unsupported LLM/STT/TTS pipeline where LiveKit’s automatic metrics aren’t collected. • Events are being emitted but your metrics collector/ingestion (Prometheus/Cloud/agent) is dropping them or misconfigured (API key, endpoint, or network). • Timing: you’re looking before any sessions completed (no completed jobs → no insights). • Version mismatch: older agent/runtime that doesn’t include the insight instrumentation. What to check now (fast diagnostics) 1) Are metrics/insights enabled in your deployment? • Check config/env for flags like ENABLE_METRICS, INFERENCE_METRICS, or INSIGHTS_ENABLED (or your cloud LiveKit control panel). If disabled, turn them on and restart. 2) Confirm the agent emits MetricsCollected / Insights events • Search agent logs for “MetricsCollected”, “LLMMetrics”, “STTMetrics”, or “Insights” around a call. If you see none, telemetry isn’t being emitted. • If logs are JSON, grep for event names or look for job/session lifecycle logs (session start/close). 3) Check provider use and compatibility • Are you using LiveKit Inference (LiveKit-managed LLMs/STT/TTS) or custom providers? LiveKit Inference and certain plugins auto-emit richer metrics. Custom plugins may require manual instrumentation. • If you use a third-party plugin for LLM/STT/TTS, verify the plugin exposes metrics or that you added instrumentation. 4) Verify telemetry ingestion • If your metrics are sent to an external collector (Prometheus, Datadog, cloud), check that the collector endpoint is reachable and credentials are valid. • Look for dropped/failed requests in agent logs (network errors, auth failures). 5) Confirm you ran sessions that complete measurable events • Agent Insights often summarize completed sessions. Do at least one full call (STT→LLM→TTS) and then check again. 6) Version check • Ensure your agent runtime and any agent SDKs are up-to-date with the instrumentation that populates Insights. If you prefer quick commands • Tail your agent logs and look for these strings: ◦ "MetricsCollected" ◦ "LLMMetrics" / "STTMetrics" / "TTSMetrics" ◦ "job_id" events and session start/stop messages • Check your LiveKit admin or cloud console for enabled analytics/insights toggles. What to send me so I can help further • A short snippet (or pasted lines) of recent agent logs around one test call (session start → user speech → LLM call → playback) — redact secrets. I’ll look for telemetry emission or errors. • Which LLM/STT/TTS providers you’re using (LiveKit Inference vs custom). • How you deployed the agent (LiveKit Cloud, self-hosted) and whether you enabled metrics in config. If you want, I can: • Give the exact log lines to search for based on your agent version. • Provide a small code snippet to add minimal metrics emission (LLM TTFT, STT latency, TTS TTFB) if you’re using custom providers. Which would you like next — help checking logs, or a snippet to add instrumentation?

do i have to enable anything in the ode

Yes — you may need to enable something, but it depends where you want insights to appear. Short answer • Agent Observability (Agent Insights / metrics) is controlled at the project level in the LiveKit Cloud Dashboard. Enable "Record agent sessions" there to have insights collected. • In code you do not have to add extra instrumentation to get the built-in insights — but you must not opt out. If you set

record=False

on your AgentSession, that will disable observability for that session even if the project setting is enabled. Action checklist to make sure you see data in Agent Insights 1. Dashboard: enable recording ◦ Go to your LiveKit Cloud project → Settings → Data & privacy (or Agent Observability) → enable “Record agent sessions” (or similar toggle). 1. Agent code: do not opt out ◦ Ensure you are not creating sessions with

record=False

. Either omit

record

or set

record=True

if you want to be explicit. ◦ Example (Python):

session = AgentSession(
         llm=..., stt=..., tts=..., 
         record=True  # ensure not disabled
     )

1. SDK version ◦ Make sure your Agents SDK version supports observability (Agent Observability added in v1.3+). If you are running an older SDK, upgrade. 1. Run a complete session ◦ Insights often appear after a completed session (calls that include STT → LLM → TTS). Run a test call and wait for it to finish. 1. Verify ingestion / network ◦ If dashboard is enabled and code is correct but you still see no data: ▪︎ Check agent logs for telemetry emission lines (search for events like

MetricsCollected

,

LLMMetrics

,

STTMetrics

, or

INSIGHTS

). ▪︎ Confirm the agent host can reach LiveKit telemetry endpoints (no blocked egress, proxies, or auth errors). 1. Optional: add minimal logging if you use custom providers ◦ If you use custom LLM/STT/TTS plugins that don’t auto-emit LiveKit metrics, consider adding small instrumentation to emit the key metrics (TTFT, TTFB, STT latency) so Insights can show useful numbers. Diagnostics to run now (fast) • In the agent logs for a test call, grep for: ◦ "MetricsCollected" or "LLMMetrics" ◦ job/session start and stop messages • If you find no metrics logs: ◦ Confirm

record

is not set to False ◦ Confirm project-level recording is enabled in the dashboard ◦ Confirm SDK version >= 1.3 If you want, I can: • Provide the exact log strings to search for in your agent version. • Give a small snippet to explicitly emit a minimal metrics event if you’re using custom plugins. • Walk through verifying the dashboard setting step-by-step if you tell me where you look in the UI. Sources: Streamline troubleshooting with Agent Observability

record=True

so in this livekit egress is used will this cost us?

Short answer: yes — setting

record=True

causes session data to be recorded, and that can incur storage and egress costs depending on your LiveKit plan and how you use the recordings. What enabling

record=True

does • It instructs the agent/session to allow observability and recording of session artifacts: transcripts, audio segments, LLM traces/metrics, and other telemetry captured by Agent Observability. • Those recorded artifacts are stored and made available in your LiveKit project’s insights/recording backend. Cost implications to consider • Storage: recorded audio, transcripts, and logs are persisted and consume storage. Longer calls and more frequent recordings increase storage usage. • Egress/Bandwidth: if you download or stream recordings out of LiveKit (for analysis, backup, or copy to other services), that may count as egress bandwidth billed by your provider. • Processing: features that post-process recordings (transcoding, analytics, exporting to another service) can generate additional compute or transfer costs depending on your configuration. • Retention: longer retention periods mean more storage costs over time. How to control and minimize costs • Enable recording only when needed: ◦ Use

record=True

for environments where you need insights (staging/production debugging) and

record=False

for short tests or dev runs. • Use selective recording: ◦ If you can, record only certain sessions (e.g., when debug flag set or on-demand) instead of every session. • Shorten retention: ◦ If dashboard/LiveKit settings let you configure retention, reduce retention time for non-critical sessions. • Trim what's recorded: ◦ Avoid recording full raw audio if you only need transcripts or metrics; choose the smallest set of artifacts required. • Batch/aggregate exports: ◦ Instead of continuously streaming recordings to another service, batch exports at intervals to reduce repeated egress. • Monitor usage: ◦ Track storage and egress consumption so you can detect spikes and tune recording policy. Practical steps you can take now 1. Check your LiveKit project billing/plan docs or console to see: ◦ Which items are billed (storage, egress, processing). ◦ Current usage and retention settings. 1. If cost is a concern, set

record=True

only for specific sessions and keep

record=False

for routine/dev sessions:

session = AgentSession(..., record=False)  # dev/test
   # or
   session = AgentSession(..., record=True)   # when you want insights/recordings

1. If you need consistent observability in production but want to cap cost: ◦ Record only a sample of calls (e.g., 5–10%). ◦ Or record metadata + transcripts but avoid storing raw audio long-term. 1. Ask your LiveKit account rep or consult the LiveKit Cloud billing docs for exact pricing and which operations count as billable egress/storage in your plan. If you want, I can: • Help you design a recording policy (when to enable recording, sampling strategy, retention) tailored to your usage pattern. • Draft code examples to toggle

record

based on environment, a debug flag, or a per-call sampling rate. • Point you to the exact billing docs or the console path to view current storage/egress usage (if you tell me which LiveKit plan you are on).

so is there a chance i can do without recording

Yes — you can run your agent without recording. It’s straightforward to disable and common in cost‑sensitive setups, but there are tradeoffs. I’ll summarize what you gain/lose and give practical alternatives so you can keep visibility without full recording. How to disable • In code: create the session with

record=False

(or omit

record=True

). Example:

session = AgentSession(..., record=False)

• In the LiveKit Cloud dashboard: make sure project‑level recording/observability is off (or keep it on but opt out per session with

record=False

). What you lose when you disable recording • Agent Insights: dashboard metrics, per‑session traces, transcripts, and stored audio will not be available for that session. • Historical debugging artifacts: you won’t have stored transcripts/audio to review later. • Automatic analytics that rely on stored data (call summaries, session replay). What you keep / can still do • Live, real‑time behavior still works: STT, LLM, and TTS operate normally and your agent can serve users in real time. • Runtime state management (e.g.,

session.userdata

) still functions for the duration of the session. • You can still emit custom telemetry (metrics/logs) to your own monitoring stack without enabling recording. Cost‑friendly alternatives to full recording • Selective recording: only set

record=True

for a sample of sessions (e.g., 1–5%) or for sessions flagged for review. • Transcripts only: if available in your plan, record/store transcripts and metrics instead of raw audio (lower storage). • Short retention: enable recording but set short retention (if dashboard supports it) so artifacts are deleted quickly. • Emit lightweight telemetry: instrument and send essential metrics (LLM TTFT, STT latency, total latency, errors) to Prometheus/Datadog—this captures performance without storing audio. • On‑demand recording: start the session with

record=False

, then flip to

record=True

programmatically only when you need to capture a specific interaction (if your workflow supports it). If you need observability but want to limit cost, recommended approach 1. Keep

record=False

for most calls. 2. Implement a sampling policy that sets

record=True

for a small percentage or for calls meeting criteria (errors, escalations, high value). 3. Add lightweight metrics emission for every call (TTFT, TTFB, EOU delay, errors) so you have continuous monitoring. 4. For debugging, reproduce with

record=True

or use the sampled recordings. If you’d like, I can: • Provide a small snippet to implement sampled recording (e.g., 5% of calls set

record=True

). • Provide example telemetry (metric names and where to emit) so you get observability without recordings. Which would help you next — sample recording code or a minimal telemetry plan?

no no i mean just all agent insights just without recording

Short answer: No — Agent Insights depend on recorded session artifacts, so you cannot get the full Insights experience without enabling recording. Why: Agent Insights (traces, synchronized transcripts/playback, turn‑by‑turn logs and the Insights UI) are built from the recorded session data (audio, transcripts, LLM traces). If you disable recording for a session (or at the project level), those underlying artifacts are not uploaded/stored, so the Insights UI has nothing to show. What you can do instead (keep visibility while minimizing cost/privacy exposure) • Sample recordings: enable recording for only a percentage of sessions (e.g., 1–5%) so you still get representative Insights without recording everything. • On‑demand recording: start sessions with recording off and flip to record=True only when you need to capture a problem or special case. • Lightweight telemetry (no recording): emit metrics and structured logs from your agent to your own monitoring stack (Prometheus, Datadog, CloudWatch). Track the key numbers Insights shows: STT latency, LLM TTFT, TTS TTFB, EOU delay, errors, and success/failure counts. This gives you continuous observability without storing audio/transcripts. • Persist only transcripts/metadata: if your plan or implementation allows, record/store transcripts and metrics but avoid storing raw audio (lower storage/egress). Check your LiveKit plan for options. • Short retention: if you must record, set a short retention window for non-critical sessions so storage costs stay low. Practical suggestions • Implement sampling logic in your session startup (example): set record=True for 1 in N sessions, or when a debug flag is present. • Instrument these metrics per session and send them to your metrics backend so you can build dashboards similar to Insights (TTFT, TTFB, EOU, total latency, error rates). • Use a small subset of recorded sessions to debug or to feed into the Insights UI when you need full trace & playback. If you want, I can: • Provide a short code snippet to implement sampled/on‑demand recording in your agent startup. • List exact metrics and event names to emit so you can build comparable dashboards outside of Agent Insights. • Help you design a sampling policy (percentages, triggers) that balances debugging needs and cost/privacy. Which would you like next — a sampling example or the telemetry metric list? Sources: Handling payments with PCI compliance | LiveKit | Streamline troubleshooting with Agent Observability

if i want to pass a json

Thanks — do you mean passing a JSON object into the agent/LLM flow, into a tool call, to the connector API, or somewhere else? A few common cases and what I need from you to give a concrete example: • Pass JSON as LLM context (inject into ChatContext/system message): I can show how to stringify/sanitize and add it as a

system

message or structured JSON block. • Pass JSON to a tool (function tool / RawFunctionTool): I can show how to build a JSON schema for a dynamic tool and how the LLM should call it. • Send JSON to an external API (webhook/connector): I can show how to form the request body and include call/session metadata. • Store JSON in session state (

ctx.proc.userdata

): I can show safe validation and persistence patterns. Tell me: 1) Which of the above (or another) is your goal? 2) If it’s for the LLM or tool, do you want the model to read the JSON, or to call a tool with JSON parameters? 3) If you have a sample JSON payload you want to pass, paste it (redact secrets) and I’ll give the exact code snippet for Python (or Node) using LiveKit agent patterns. Once you confirm, I’ll provide a compact, runnable example tailored to your case.

getting the error using handoff tool

Thanks — I can help, but I’ll need a bit more detail to diagnose the handoff tool error and the JSON issue. Could you paste the following (redact secrets/tokens) so I can pinpoint the problem? 1. The exact error message and stack trace you see when calling the handoff tool. 2. The code for the handoff tool (function signature and body) that you’re calling. 3. The JSON you’re trying to pass (or an example payload) and how you’re passing it (tool call, API request, tool parameters). 4. How you register the handoff tool with the agent (tool schema / decorator / RawFunctionTool usage). 5. Any logs from the agent around the time of the handoff call (request/response logging if available). Quick checks you can run now • Ensure the JSON is valid (use an online linter or

json.loads

in Python). • If the tool expects typed params (e.g., booleans/strings), make sure the JSON fields match types exactly. • If you built a dynamic tool schema, confirm the parameter names in the schema match the keys in your JSON. • Check for missing required params — tools often error when required fields are absent. If you want, paste the error + the small code snippet now and I’ll point out the exact fix (schema mismatch, type coercion, missing registration, or JSON parsing issue).

these are the logs of the call i am making ultiagent workflow on this i am facing this issue

Thanks — I see you’re working an ultiagent workflow and hitting an error when invoking the handoff tool and passing JSON. I can help debug, but I’ll need a few specific items to pinpoint the root cause. Could you please share the following (redact any secrets or tokens): 1. Exact error message and any stack trace you see when the handoff tool fails. 2. The handoff tool code (function signature and body) or how you register it (decorator, RawFunctionTool schema, etc.). 3. The JSON payload you are trying to pass (or a representative example). 4. The code or call that invokes the tool (how you call it from the LLM or agent). 5. Any agent logs around the time of the call, particularly lines that show incoming tool calls, parameter parsing, or exceptions. While you gather that, here are the most common causes and quick checks you can run right now: • JSON validity: run

json.loads(your_payload)

(Python) or an online linter to confirm it’s valid JSON. • Schema mismatch: if the tool exposes typed parameters (boolean/string/etc.), ensure JSON keys exactly match parameter names and types. Tools often reject unknown keys or wrong types. • Required fields: confirm you’re providing required parameters; if your schema marks fields required, missing ones will cause errors. • Dynamic tool schema: if you build the tool schema at runtime, verify schema generation code matches the current connection config and that the tool registration happens before the LLM can call it. • Tool registration: ensure the tool is actually registered with the Assistant/plugin set used by the running agent session (check startup logs where tools are listed). • String vs JSON object: if the tool expects structured params but you pass a JSON string, parse it into an object first (or have the tool parse it). Conversely, if the tool expects a string, passing an object may fail. If you want a quick example of a robust handoff tool that accepts arbitrary JSON safely, here’s a pattern to consider (adapt to your framework): • Accept a single optional string param

payload_json

and parse inside the tool:

def handoff_tool(payload_json: Optional[str] = None):
    if payload_json:
        try:
            data = json.loads(payload_json)
        except Exception as e:
            raise ValueError(f"Invalid JSON: {e}")
    else:
        data = {}
    # validate and map data to expected fields, then perform handoff
    ...

This avoids schema mismatches when the LLM produces varying shapes. Send me the error + code + sample JSON and I’ll point out the exact fix (schema change, parsing code, or registration timing).

the issue i am facing agent is hallucinating during handoff saying on its own when it get transfered like repeating itself or something like this see the livekit docs and in the chat you have seen the problem that i am facing i tried calling with singlre agent it doesnt repeat but in squad when handoff happens it repeats User Okay. I like to hear this. Agent Glad you like this one. To move forward, let's schedule an in-person viewing so you can see all the details. Let me just pull up my calendar... Agent Is there anything else you'd like to explore or any other preferences you have in mind? Alright, I have my calendar open here. What day and time works best for you for the viewing? User Tomorrow, Agent What time works best for you tomorrow for the viewing? User 4PM. Agent Perfect, I can do tomorrow at 4 PM. Can I just get your full name, please? User Do you have any apartment in Dubai Marina? Agent Alright, I have my calendar open here. What day and time works best for you for the viewing?(this line) Agent I’m sorry, but currently, there are no apartments for sale in Dubai Marina within your budget. Would you like me to check other nearby areas or adjust your budget? ent Great. Are you looking to rent or purchase this apartment? User For purchase my Agent Okay, I have all those details. Give me just a second to pull up our system and see what the best options are... Agent Is there anything else you would like to know or any other preferences you'd like to share?(this line) User No Agent Alright, looking at the options right now... um, give me one second. Agent Alright, I have a 5-bedroom apartment in JVC at 1,332,000 dirhams, featuring a sea view, balcony, User Okay. I like to hear this.

I don't have the answer you're looking for. You could also try asking your question: • in one of the other Slack channels or • to https://deepwiki.com/livekit/livekit_composite which is trained on all LiveKit source code If you find the answer, please post it here to help others!

import logging import time import asyncio import re # <--- NEW IMPORT REQUIRED FOR STRICT MATCHING from livekit.agents import function_tool, RunContext, get_job_context from livekit.api import ( LiveKitAPI, RoomParticipantIdentity, ListParticipantsRequest, CreateSIPParticipantRequest, ) from core.services.status_service import CallStatus from core.entrypoint_components.identity import is_subscriber_identity from core.utils.utils import log_json_turn from datetime import timedelta from google.protobuf.duration_pb2 import Duration from core.models.api_models import get_farewell_message from core.entrypoint_components.function_tools.language_detection import get_current_user_language from core.entrypoint_components.whatsapp import notify_whatsapp_end_call logger = logging.getLogger("core.entrypoint.tools") @function_tool(name="end_call_tool") async def *end_call_tool*(_context_: RunContext, _user_input_: str): """End the call when the user says goodbye. This tool must ONLY be used for explicit farewells matching a configured keyword.""" start_time = time.time() job_ctx = get_job_context() config = job_ctx.proc.userdata.get("config") # === 1. GET THE REAL VALUE (can be null / None) === call_ending_keyword_mode = config.call_ending_keyword_mode # === 2. DISABLE IF null, None, or empty string (Strictly honors the configuration) === if call_ending_keyword_mode in (None, ""): logger.info("Farewell detection is DISABLED (call_ending_keyword_mode = null/empty)") return

{
  "status": "continue",
  "message": "Farewell detection disabled"
}

# === 3. Normal logic only runs if mode is set === call_ending_keywords = getattr(config, "call_ending_keywords", []) # Resolve current language for farewell message selection # Priority: detected user language > STT config language > default 'ar' current_language = None _try_: current_language = get_current_user_language() except Exception as e: logger.debug(f"Could not get detected language: {e}") # Fallback to STT config language if detected language not available if not current_language and config.stt and "options" in config.stt: stt_lang = config.stt["options"].get("language") if stt_lang: if isinstance(stt_lang, (list, tuple)): current_language = str(stt_lang[0]).lower().split("-")[0].split("_")[0] if stt_lang else "ar" _else_: current_language = str(stt_lang).lower().split("-")[0].split("_")[0] # Final fallback if not current_language: current_language = "en" # Get farewell message based on language agent_farewell_message_dict = getattr(config, "agent_farewell_message", None) agent_farewell_message = get_farewell_message(agent_farewell_message_dict, current_language) farewell_phrases = { "goodbye", "bye", "see you", "take care", "farewell", "later", "catch you later","wil call you later", "مع السلامة", "وداعا", "إلى اللقاء", "باي", "अलविदा", "नमस्ते", "फिर मिलेंगे", "बाय", "خدا حافظ", "اللہ حافظ", "پھر ملتے ہیں", "بائی", "adiós", "hasta luego", "nos vemos", "cuídate", "hasta pronto", "au revoir", "salut", "à bientôt", "prends soin de toi", "adieu", "വിട","ബൈ ബൈ","ഗുഡ് ബൈ","ഗുഡ് ബൈ ബൈ", } phrases_to_check: set[str] = set() if call_ending_keyword_mode == "default_backend": phrases_to_check = farewell_phrases elif call_ending_keyword_mode == "user_configured": phrases_to_check = set(call_ending_keywords) elif call_ending_keyword_mode == "default_and_user": phrases_to_check = farewell_phrases.union(set(call_ending_keywords)) _else_: logger.warning(f"Invalid mode: {call_ending_keyword_mode}, using default") phrases_to_check = farewell_phrases user_input_lower = _user_input_.lower() farewell_detected = False # === NEW: STRICT KEYWORD MATCHING WITH WORD BOUNDARIES === for phrase in phrases_to_check: # re.escape handles special characters in the phrase. # \b ensures that the phrase is matched as a whole word/phrase (word boundary). pattern = r"\b" + re.escape(phrase.lower()) + r"\b" if re.search(pattern, user_input_lower): farewell_detected = True logger.info(f"Detected strict farewell: '{phrase}' in '{_user_input_}'") break if farewell_detected: session = context.session logger.info(f"Detected farewell: '{_user_input_}'") goodbye_audio = job_ctx.proc.userdata.get("goodbye_audio") _try_: await session.say(goodbye_audio or agent_farewell_message, _allow_interruptions_=True) logger.info("Farewell message sent") except Exception as e: logger.error(f"Failed to say goodbye: {e}") await asyncio.sleep(1.0) logger.info("Ending call...") # === EARLY VALIDATION === if not job_ctx.room: logger.error("job_ctx.room is None — cannot end call") status_manager = job_ctx.proc.userdata.get("status_manager") if status_manager: _try_: await status_manager.send_status("unknown", CallStatus.REJECTED, "Room context missing") _except_: pass return {"status": "error", "message": "Room context missing"} room_id = job_ctx.room.name status_manager = job_ctx.proc.userdata.get("status_manager") lkapi = LiveKitAPI() _try_: _# === Notify WhatsApp end-call webhook (for WA_CALL_ rooms) ===_ await notify_whatsapp_end_call(room_id) # === Remove participants === _try_: participants = await lkapi.room.list_participants(ListParticipantsRequest(room=room_id)) if participants and participants.participants: for p in participants.participants: _try_: await lkapi.room.remove_participant( RoomParticipantIdentity(room=room_id, identity=p.identity) ) logger.info(f"Removed participant: {p.identity}") except Exception as e: logger.warning(f"Failed to remove {p.identity}: {e}") _else_: logger.info("No participants to remove") except Exception as e: logger.warning(f"Failed to list/remove participants: {e}") # === Send COMPLETED status === if status_manager: _# Check if we already sent TRANSFERRED or TRANSFER_FAILED_ if job_ctx.proc.userdata.get("final_status_sent", False): logger.info("end_call_tool: Skipping COMPLETED — final status already sent (TRANSFERRED or TRANSFER_FAILED)") _else_: await status_manager.send_status( room_id, CallStatus.COMPLETED, "User said goodbye to call ended" ) job_ctx.proc.userdata["final_status_sent"] = True logger.info("end_call_tool: Sent COMPLETED and marked final_status_sent=True") # === SAFE ROOM DISCONNECT (IDEMPOTENT) === disconnect_success = False if hasattr(job_ctx.room, "disconnect") and callable(job_ctx.room.disconnect): _try_: await job_ctx.room.disconnect() logger.info("Room disconnected successfully") disconnect_success = True except Exception as e: logger.warning(f"Room disconnect failed (possibly already disconnected): {e}") _else_: logger.info("job_ctx.room.disconnect not available — skipping") # === Shutdown job (if available) === # NOTE: Removed shutdown call - it was causing "NoneType can't be used in 'await' expression" errors # The agent will shutdown naturally when the session ends _# if hasattr(job_ctx, "shutdown") and callable(job_ctx.shutdown):_ # try: _# shutdown_result = job_ctx.shutdown()_ _# if shutdown_result is not None:_ _# await shutdown_result_ # logger.info("Job shutdown successfully") # except Exception as e: # logger.warning(f"Job shutdown failed: {e}") # === SUCCESS: Call ended cleanly === return

{
  "status": "call_ended",
  "message": "Call ended"
}

except Exception as e: error_msg = f"End call error: {e}" logger.error(error_msg) # === ONLY send REJECTED on REAL failures === if "NoneType" in str(e) or "already disconnected" in str(e).lower(): logger.info("Call already terminated elsewhere — treating as success") return

{
  "status": "call_ended",
  "message": "Call ended (idempotent)"
}

# Otherwise, real failure if status_manager: _try_: await status_manager.send_status(room_id, CallStatus.REJECTED, f"End failed: {e}") except Exception as send_err: logger.error(f"Failed to send REJECTED status: {send_err}") return {"status": "error", "message": str(e)} _finally_: # === Always close LiveKit API === _try_: await lkapi.aclose() _except_: pass logger.info(f"No farewell in: '{_user_input_}'") return {"status": "continue", "message": "No goodbye"} ahould i use your endcalll tool which is built in or should i use mine FYI mine is working perfectly fine is switching worth it ?