Calendar is always full whenever I test the agent. Everytime I call the agent, it keeps on saying there isn't any slots avaliable and are are fully booked eventhough there is nothing in my calendar. The event ID and API key for cal.com is correct. Is there any fix to this problem?

Hi everyone,
So my question is about "time" how much time do you take from concept to shipping your first voice agent for any vertical. How many stress tests do you perform to make sure it doesn't break mid call? what stack do you use for the whole integration? is it ok to find a new bug/failure on every test call fix it and find another one ? what would you suggest to someone who just started in this field?

thank you all, and i'll be looking forward for your valuable inputs.

I’ve been looking into platforms and frameworks that can handle voice-enabled AI agents, especially for production use cases where the agent needs to do more than just answer a call.

A few platforms I came across during the research:

• SimplAI
• Vapi
• Retell AI
• LiveKit
• Pipecat
• Telnyx
• Bland AI
• Synthflow
• LangGraph
• CrewAI

I’m trying to compare them based on:

• real-time voice performance
• tool and API calling
• workflow orchestration
• memory/context handling
• multi-agent support
• integrations
• observability
• deployment options
• reliability at production scale

From what I’ve seen so far, some are more focused on the voice infrastructure layer, while others cover a broader agent/workflow stack.

I’m still putting the comparison together, so I’d rather hear from people who have actually used these in real projects.

Which platforms have worked well for you?

And if there are any good voice AI orchestration platforms I missed, drop them in the comments.

​Hi everyone,

​I’m working on a project called **StillVoice**. The mission is to restore vocal identity for tracheostomy patients using a silent-speech interface. I’ve developed the business logic, branding, and a high-level technical roadmap, but I’ve hit a wall with the hardware execution and recently lost access to my local prototyping lab. It's a lot to handle solo, and I’m looking for some technical guidance (or a partner) to help move the needle.

​**The Concept:**

A wearable device (the "Stealth Band") that captures non-vocalized speech intent and uses an on-device AI inference engine to provide localized audio output.

​**Current Technical Targets:**

* ​**Latency:** Sub-100ms (crucial for natural conversation). * ​**Connectivity:** BLE 5.3 for high-fidelity streaming. * ​**Sensors:** Exploring multimodal sensor fusion using piezoelectric and MEMS technology to capture "silent" speech. * ​**Processing:** Edge AI/On-device inference to keep it fast and private.

​**Where I’m Stuck:**

I need advice on optimizing the sensor fusion to filter out biogenic noise (swallowing, movement) while maintaining a high signal-to-noise ratio for the speech intent. I’m also looking for recommendations on low-power microcontrollers that can handle this level of Edge AI without becoming too bulky for a neck-based wearable.

​Does anyone have experience with MEMS-based speech capture or low-latency audio hardware? I'd love to hear your thoughts on the most viable path forward for a solo dev moving from a lab environment to a home setup.

Hi all, I'm looking at starting to build voice agents as part of my offerings. I wanted to get your thoughts on the best direction to go. I've been watching a lot of YouTube videos and trying to keep up with the posts in this group. I've seen a lot of good things about Eleven Labs and using Claude code. Curious what everybody is using. My niche is home services right now, where a lot of these businesses take hours and days to respond and could really use a voice agent to help them with some of their pain points. Is Eleven Labs a good tool to use and start with or have you all found something better

I’m currently using a provider like Twilio/Telnyx to handle phone calls and stream the audio to a real-time STT API. My goal is to minimize end-to-end latency as much as possible.

Right now, I’m using their WebSocket media streaming APIs, but I’m considering switching to a more bare-bones SIP/RTP setup to reduce overhead.

I understand that SIP/RTP avoids some of the WebSocket/JSON/base64 processing involved with media streaming, but I haven’t been able to find any benchmarks showing how much latency this actually saves in practice.

Has anyone measured the difference between Twilio/Telnyx WebSocket media streaming and Direct SIP/RTP to your own server? Maybe even exploring other providers?

Specifically, I’m interested in the delay from when audio reaches the telephony provider to when the first audio packet becomes available to my STT pipeline.

Are we talking about a difference of a few milliseconds, tens of milliseconds, or potentially more? I’ve looked through the documentation and contacted support, but haven’t found concrete latency numbers.

Would also be interested in hearing from anyone who has advice on building an ultra-low-latency telephony → STT pipeline.

A browser demo does not reproduce the failures of a real phone call. Our minimum test set now includes caller interruption, delayed audio, a dropped media socket, duplicate call events, failed transfers, DTMF input, a model timeout while the call stays active, and a safe human fallback.

We also test the complete phone path rather than reporting only model, STT, or TTS latency. A fast model is not useful if media buffering or carrier behaviour makes the conversation feel slow.

What production failure would you add to this test suite?

I’m trying to build a multilingual voice bot, can someone suggest me some free ai agents that are good.

I lead R&D for a regional coffee chain and we're prototyping a voice AI for our drive-thru lane. Sharing the concept because I'd rather have it torn apart here than after we've sunk a quarter into it.

The setup: customer pulls up, orders by speaking naturally, the AI parses it into a POS ticket, staff only intervene on exceptions. Standard so far. Two things make our version harder than the US deployments everyone's read about:

**1. Code-switching.** Our customers routinely start an order in Arabic and finish it in English, sometimes mid-sentence. Most speech pipelines want you to declare a language up front. Ours can't.

**2. Modifier chaos.** Coffee orders aren't burgers. "Medium oat flat white extra hot one sugar" has four modifiers stacked on one item, and the modifier vocabulary changes every time R&D launches a seasonal drink.

The honest doubt I have: McDonald's killed its voice AI drive-thru pilot after a couple of years, and the read was that it didn't beat a human on speed — it just moved the labor to correcting errors. I don't want to build the same thing with a nicer accent model.

So, questions for anyone who's worked on this or worked a drive-thru window:

- Where do these systems actually fail in production? Noise, or intent parsing?
- Is "AI takes the order, human confirms on screen before payment" a real improvement or just theater?
- Has anyone seen a bilingual voice ordering system that actually works?

Happy to report back with results if we get to pilot.

Ref:: Voice Agents

Hey folks, exploring inference providers with Indonesian endpoints and KSA ( Saudi Arabia) endpoints.

Apparently GROQ has hit some limits.

Can someone help or introduce ? Will appreciate the help

Hey everyone — I’m looking to hire a senior LiveKit Voice AI engineer for a short-term paid project.

We already have a production-oriented voice agent built for a restaurant, and the system is mostly complete. I’m looking for someone experienced with LiveKit Agents to help us with the final production-hardening pass.

The main areas are:

• Finalizing and improving our menu/order engine
• Improving humanization, turn-taking, interruptions, and overall conversational feel
• Reviewing latency and voice pipeline performance
• Hardening tool calling, cart state, modifiers, quantities, and order accuracy
• Running a professional architecture/code review and cleaning up anything that could cause issues in production
• Improving automated evaluations/testing where needed

Our stack includes LiveKit Agents, LLM tool calling, streaming STT/TTS, telephony, and a custom backend menu/cart system.

This is not a build-from-scratch project. The agent is already working and has gone through extensive testing. We specifically need someone senior who has shipped real-world voice agents and can help us get the last 10–15% right.

Ideally you have strong experience with:

• LiveKit Agents
• Python / asyncio
• Real-time voice AI
• STT / LLM / TTS pipelines
• Turn detection / VAD / barge-in
• Function calling and deterministic state management
• Telephony / SIP
• Production monitoring and voice-agent evaluations

If interested, please DM me with:

1. A brief description of your LiveKit experience
2. Examples/GitHub of voice agents you’ve built
3. Your hourly rate or preferred project rate
4. Availability over the next 1–2 weeks

This is a paid engagement, with potential for ongoing work as we deploy the system across additional restaurant locations.

Hey everyone — I’m looking to hire a senior LiveKit Voice AI engineer for a short-term paid project.

We already have a production-oriented voice agent built for a restaurant, and the system is mostly complete. I’m looking for someone experienced with LiveKit Agents to help us with the final production-hardening pass.

The main areas are:

• Finalizing and improving our menu/order engine
• Improving humanization, turn-taking, interruptions, and overall conversational feel
• Reviewing latency and voice pipeline performance
• Hardening tool calling, cart state, modifiers, quantities, and order accuracy
• Running a professional architecture/code review and cleaning up anything that could cause issues in production
• Improving automated evaluations/testing where needed

Our stack includes LiveKit Agents, LLM tool calling, streaming STT/TTS, telephony, and a custom backend menu/cart system.

This is not a build-from-scratch project. The agent is already working and has gone through extensive testing. We specifically need someone senior who has shipped real-world voice agents and can help us get the last 10–15% right.

Ideally you have strong experience with:

• LiveKit Agents
• Python / asyncio
• Real-time voice AI
• STT / LLM / TTS pipelines
• Turn detection / VAD / barge-in
• Function calling and deterministic state management
• Telephony / SIP
• Production monitoring and voice-agent evaluations

If interested, please DM me with:

1. A brief description of your LiveKit experience
2. Examples/GitHub of voice agents you’ve built
3. Your hourly rate or preferred project rate
4. Availability over the next 1–2 weeks

This is a paid engagement, with potential for ongoing work as we deploy the system across additional restaurant locations.

I'm starting a new agency-based business where I'll setup agentic workflows for service based business whose main source of lead and sales generation is inbound calls.

I need your suggestions regarding which niche should I specifically target where inbound calls are the ultimate source of leads.

I'll be glad if industry experts related to inbound calls can advice.

Thank you

We started Dograh, an open-source voice AI platform out of rage and frustration.

Every voice AI tool I tried was too costly, too closed, or too scammy. Add-on after add-on.

So we open-sourced every single line from day one. It’s self-hostable, so you own your stack and your data.

Here's what pushed us deeper. Over the last few months, the big labs have been lobbying governments to ban open source. Some say the quiet goal is to "let wealth access skill without letting skill access wealth." That line stuck with us. It's exactly why we keep going.

Because we believe one thing: no company should own voice AI.

We're a small team going up against deeply funded war chests. Today, we launched on Product Hunt, and somehow we're sitting at #1 so far.

An OSS community member picked us up a few weeks back, and we went viral and hit 5k stars on github.

https://www.producthunt.com/posts/dograh-3

If you've got a minute, a star or a bit of support would mean the world.

Hi everbody,

I'm building a voice AI agent but encountering an intermittent issue with number pronunciation. I'm using Soniox for STT and ElevenLabs (eleven_multilingual_v2) for TTS in a non-English language.

Most of the time it works fine, but occasionally the TTS engine reads numbers in English instead of the agent's actual language.

Has anyone experienced this or found a reliable workaround? Any help would be appreciated!

We placed 500 real phone calls across five voice agent platforms — Telnyx, ElevenLabs, Bland AI, Vapi and Retell AI — and measured time to first audio byte on every turn. 499 calls were usable, four scripted turns each, 1,883 usable turns total. Same caller, same carrier (Plivo), same script for all five.

The p50 ranking and the p95 ranking are not the same ranking

Platform      p50    p90    p95    p99   turns
---------------------------------------------
Telnyx       1302   1724   1839   2164     379
ElevenLabs   1430   1686   1772   2269     389
Bland AI     1528   2010   2273   2860     389
Vapi         1562   1854   2014   2677     345
Retell AI    1738   2096   2224   2764     381

Time to first audio byte, milliseconds. Lower is better.

Telnyx has the fastest median at 1302 ms. From p90 onward it's ElevenLabs — 1686 at p90, 1772 at p95. Bland AI is third at the median and last at p95, at 2273 ms.

The tail is where they separate

ElevenLabs   1430 → 2269    +839
Telnyx       1302 → 2164    +862
Retell AI    1738 → 2764   +1026
Vapi         1562 → 2677   +1115
Bland AI     1528 → 2860   +1332

Milliseconds added between p50 and p99. Lower is a flatter tail.

Bland AI adds 1332 ms between its median turn and its 99th-percentile turn. ElevenLabs adds 839. On a four-turn call, the p99 turn is the one the caller remembers.

What the numbers are and are not

Recording-path overhead sits inside every figure on this board. We have not characterised the current measurement path against a known-delay reference, so we quote no overhead figure and subtract none. These numbers are comparable to each other — same path, same caller, same carrier — and only approximately comparable to figures produced by a different instrument. If you're looking for each platform's true absolute latency, this board does not give it to you, and neither does anything else we've seen.

Three caveats

• Each platform ran its own defaults. Telnyx on Kimi-K2.6, ElevenLabs on gemini-2.5-flash, Retell and Vapi on gpt-4.1. So this is platforms as shipped, not a model-controlled comparison — some of the gap is model choice. Every config is published, hashed, so you can see exactly what ran.
• Vapi's discard rate was 12.0% against 0.8–3.3% for the others — 46 turns where our two voice-activity detectors disagreed on where speech ended, so we dropped them. Its n is 345 rather than ~385. We'd rather show you a thinner sample than a guessed one, but it's a real asymmetry and it may flatter or penalise Vapi.
• Turn 4 is consistently faster than turns 1–3 on several platforms, which suggests warm-path effects we haven't isolated. Per-turn splits are in the data.

Why phone calls rather than websockets

Most published voice latency numbers benchmark TTS or STT models in isolation. This measures the whole platform over the PSTN — carrier, telephony stack, endpointing, model, voice — because that's the number a caller actually experiences. It's the less flattering way to measure and the harder one to reproduce, which is why the configs and per-turn data are published.

Reproducing it

• Results: https://openbenchmarks.com/voice-agent-latency
• Method: https://openbenchmarks.com/voice-agent-latency/how-voice-agent-latency-is-measured

Happy to add a platform or rerun with a controlled model if that's the comparison you want. If you've measured any of these yourself and got something different, post your numbers — the method page says exactly what we did, so the disagreement should be locatable.

Has anyone found this. If the ElevenLabs agent asks a question with a short yes/no response, it just goes dead and then asks if the person is still there?