Saa Sdk
SAA is a pre-ASR attention gate that decides whether speech in the room is addressed to your voice agent — before any transcription runs.
If your voice agent answers side conversations, SAA is attacking the right layer — it decides whether the expensive pipeline should run at all, and it does so before ASR, LLM, and TTS ever fire. The free cloud tier means you can test that on your own room audio this week. The trade is real: the hosted decision adds a round trip of about 150ms before ASR, on top of your existing latency budget, so latency-critical products should price the on-device license at under 9ms instead. SAA is not a VAD replacement and not speaker ID — it answers who is being spoken to, with no enrollment. If you already ship push-to-talk, or your agent only ever hears one close mic, you have no false-trigger
Verified 4d ago · liveness 72/100 · cite: rightaichoice.com/tools/saa-sdk
- Voice agent developers on LiveKit, Pipecat, Twilio, or OpenAI Realtime
- Teams building open-microphone smart displays, robots, or XR devices
- Hardware and OEM teams needing on-device inference with no cloud round trip
- Regulated or privacy-sensitive deployments where raw audio cannot leave the device
- Push-to-talk, touch-tone, or static IVR systems that never over-trigger
- Purely text-based chatbots with no audio input
- Teams that need production non-English support today — English is the primary evaluation set
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Skip SAA if you already ship push-to-talk or a close-mic single-speaker setup, or if your latency budget cannot absorb an extra ~150ms hosted round trip before ASR.
The free tier is a starting allowance — beyond it the hosted Cloud SDK bills usage-based, so high-volume streaming is a per-utterance line item to model before launch.
The free cloud account — no card required, with usage-based billing after — puts SAA within reach of a solo developer testing an idea this week, which is cheap for a gate that sits in front of every ASR and LLM call you would otherwise pay for. The on-device per-device license is an OEM and regulated-hardware purchase priced on volume, in the same bracket as other embedded voice licensing rather than the SaaS tools around it. Compare against the cost of the ASR, LLM, and TTS seconds SAA
In short
Saa Sdk — SAA is a pre-ASR attention gate that decides whether speech in the room is addressed to your voice agent — before any transcription runs. Best for Voice agent developers on LiveKit, Pipecat, Twilio, or OpenAI Realtime, Teams building open-microphone smart displays, robots, or XR devices, Hardware and OEM teams needing on-device inference with no cloud round trip. Free to use.
What people actually say about Saa Sdk — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
37 mentions across 3 sources (Hacker News, YouTube, Lemmy) · researched Jul 29, 2026.
Average across the 3 sources that answered — each source counts once, not each post.
- +Solves a real problem: voice agents mishearing background speech and TTS echo.
- +Model-agnostic — works with any STT engine and major voice platforms.
- +No wake-word required, enabling more natural conversational flow.
- +Real-time on-device inference under 9ms for low latency.
- +Adjustable sensitivity thresholds for different acoustic environments.
- −Extremely limited community feedback — no production validation or reviews.
- −On-device deployment requires enterprise license, limiting access.
- −Multi-language support still in development; English only for now.
- −Pricing beyond free tier is not publicly documented.
- −No independent benchmarks to verify latency or accuracy claims.
- • Usage-based pricing can escalate quickly with high audio volume (no published per-call rates)
- • Enterprise license cost undisclosed — may be prohibitive for small teams
Viability Score
How well maintained and how widely used is Saa Sdk? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Pre-ASR addressee detection that gates audio before speech-to-text runs
- Three-class output per frame: 0 silent, 1 human-directed, 2 device-directed
- prediction event fires on every frame with confidence and face count
- turnReady event carries the addressed PCM16 segment at 16 kHz
- Adjustable engagement threshold (initialThreshold default 0.7) via setThreshold() / set_threshold()
- Fails closed: inconclusive predictions are treated as human-directed and not forwarded
- No wake word, no enrollment, no speaker identification or diarization
- Audio-only or audio plus webcam video fusion for spatial context
- JavaScript SDK @attenlabs/saa-js using getUserMedia, AudioWorklet, canvas, WebSocket
- Python SDK attenlabs-saa for 3.10+, installs sounddevice and opencv-python
- Cloud SDK decision in about 150ms end to end, before ASR, streaming and REST interfaces
- On-device inference via ONNX runtime, CPU-only, no GPU, under 9ms
- Broker-based connection: POST /allocate returns a WebSocket URL, or pass ws:// to skip the broker
- Model-agnostic handoff of addressed audio to OpenAI, Gemini, Pipecat, LiveKit, or your own model
- Raw audio and video not retained after inference, encrypted in transit and at rest, never used for training
About Saa Sdk
SAA (Selective Auditory Attention) from attention labs is an engagement-control layer for voice AI. It sits between voice activity detection and speech-to-text and answers one question VAD cannot: is this speech directed at the device, or at someone else in the room? Every frame is classified as silent (class 0), human-directed (class 1), or device-directed (class 2), scored with a confidence value and a face count. On a positive gate the SDK fires a turnReady event carrying the addressed PCM16 segment at 16 kHz, which you hand to OpenAI, Gemini, Pipecat, LiveKit, or your own model. Everything else is suppressed. There is no wake word, no speaker enrollment, and no diarization. It works from audio alone or from audio plus webcam frames for spatial context, and it is deliberately model-agnostic — the gate is the product, not the LLM behind it. Four core concepts run through the docs: the clients are thin and the model is hosted (nothing runs in your process), three classes resolved by one adjustable threshold (initialThreshold defaults to 0.7), prediction events fire per frame while turnReady fires per completed utterance, and the system fails closed — an inconclusive prediction is treated like human-directed and the gate stays shut. Two deployment paths. The Cloud SDK is self-serve: npm install @attenlabs/saa-js for the browser (getUserMedia, AudioWorklet, canvas, WebSocket) or pip install attenlabs-saa for Python 3.10+ (sounddevice and opencv-python install automatically). It returns an addressee decision in about 150ms end to end before ASR, with streaming and REST interfaces, and the new account starts free with no card. The enterprise per-device license runs inference on the hardware itself on a CPU-only ONNX runtime at under 9ms, so raw audio never leaves the device — the version for regulated deployments, robots, smart displays, and XR headsets. That path is licensed per device on volume and includes custom acoustic profiles arranged on a call. Accuracy in the published evaluation is 95% F1 for the audio-visual model, with lighter embedded models around 90% depending on whether vision is available. English is the primary evaluation set; multi-language work is underway but not production-ready. Raw audio and video are not retained after inference and are not used for training, with encryption in transit and at rest.
Behind the Verdict
SAA is the narrowest product in the voice AI stack, and that is the point. Teams building always-on agents spend real money running ASR, an LLM, and TTS on audio that was never meant for the device — hallway chatter, a colleague across the desk, the agent's own TTS echo. SAA inserts a gate before that spend and returns a per-frame class plus confidence, so you can hold a stream closed on prediction events and pull the addressed clip off turnReady when it opens. The docs make the integration contract unusually clear: two clients, one shared event surface, a three-class output (0 silent, 1 human-directed, 2 device-directed), an adjustable threshold you can retune mid-session with setThreshold() or set_threshold(), and an explicit fail-closed rule — if the socket drops or a prediction is ambiguous, keep the gate shut rather than forward audio you are unsure about. Missing a turn is the accepted cost of never leaking a side conversation to your model. Strengths are architectural. The gate is model-agnostic, so it does not lock your LLM choice — turnReady hands addressed audio to OpenAI, Gemini, Pipecat, LiveKit, or your own model. It needs no wake word, no enrollment, and no speaker identification, which removes two onboarding steps and a privacy surface from anything always-on. The two deployment modes map cleanly onto two buyer types: the hosted Cloud SDK is a few lines of JavaScript or Python with API keys issued self-serve from the dashboard, and the on-device enterprise license runs CPU-only ONNX inference at under 9ms so raw audio never leaves the hardware. Raw audio and video are not retained past the moment of processing and are never used for training, with encryption in transit and at rest — the detail that makes this sellable into healthcare and regulated hardware. Weaknesses are equally clear. The hosted path costs about 150ms end to end before ASR; that is a genuine line item on a real-time voice budget, not a footnote, and it sits in front of every turn your agent takes. Accuracy is conditional: 95% F1 for the audio-visual model in the published evaluation, around 90% for lighter embedded models, and the number moves depending on whether you can supply video and how much model you can afford to run. The on-device path is licensed per device on volume with acoustic profiles arranged on a call, so it is not something a solo developer ships without a vendor conversation. And language coverage is English-first — multi-language support is underway, not done, which rules SAA out for production deployments outside English today. Fail-closed behaviour is the right default but it is also a recall trade: expect to miss some addressed turns, and do not deploy this into a workflow where a dropped turn is worse than a leaked one. Where it fits: open-microphone products, robots like the Reachy Mini demonstration, XR and spatial-computing setups where several agents share a room and each must know who is addressing it, smart displays, PCs with
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Real-world workflow fit
Concrete scenarios for the personas Saa Sdk actually fits — and what changes day-one when you adopt it.
You npm install @attenlabs/saa-js or pip install attenlabs-saa, create an API key from the dashboard, and pass it to AttentionClient with initialThreshold set to 0.7. You listen for prediction to hold the gate and turnReady to pick up the addressed PCM16 segment.
Outcome: Side conversations in the open-plan office stop reaching your LLM. Only class 2 audio is forwarded; if the socket drops or a prediction is inconclusive the gate stays closed.
You place SAA between VAD and your ASR on a caller-line agent, so the caller talking to a colleague in the room is tagged human-directed while speech aimed at the bot is tagged device-directed and emitted on turnReady.
Outcome: The bot stops firing on cross-talk and background chatter, and your ASR, LLM, and TTS spend drops on audio that was never addressed to the agent.
You deploy the enterprise per-device license, running CPU-only ONNX inference under 9ms on the device, and arrange custom acoustic profiles with attention labs for your enclosure and mic array.
Outcome: Addressee detection runs with no cloud round trip, raw audio never leaves the hardware, and the robot attends only to speech directed at it — no wake word.
Use Cases
- Deploy addressee detection on a LiveKit voice agent so it ignores side conversations in an open-plan office.
- Filter out your own agent's TTS echo so it never triggers itself during self-loop testing.
- In a call centre bot, respond only when the caller speaks to the bot, not when talking to a colleague.
- Give each agent in a shared XR or spatial-computing room the ability to know who is addressing it, with automatic turn-taking.
- Prototype a smart home or PC assistant that activates only for device-directed speech, without a wake word.
- Integrate with Pipecat to separate user commands from background noise in a multi-person environment.
- Run on-device addressee detection on robot or OEM hardware where raw audio cannot leave the device.
Models Under the Hood
as of 2026-10-17
Limitations
- SAA answers one question — is this speech addressed to the device — and adds about 150ms end to end on the hosted cloud path before ASR runs, which is a real cost on a real-time latency budget.
- It fails closed under uncertainty, so ambiguous audio is not forwarded and you should expect to miss some addressed turns.
- Accuracy depends on signals and model size: the published evaluation puts the audio-visual model at 95% F1, with lighter embedded models around 90%.
- English is the primary evaluation set and multi-language support is still underway.
- The on-device path is an enterprise per-device license, not a self-serve product, and requires arranging custom acoustic profiles on a call.
as of 2026-10-07
Verification history
We have re-verified Saa Sdk 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Saa Sdk tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
A developer or small team prototyping an always-on voice agent and wanting to test addressee detection on their own room audio before committing.
What this tier adds
Starting tier: free cloud SDK access with an API key, JavaScript and Python SDKs, streaming and REST interfaces, typed per-frame events, and an adjustable decision threshold.
Usage-Based
Usage-based
Ideal for
Software teams shipping a hosted voice agent on LiveKit, Pipecat, Twilio, or OpenAI Realtime who need per-utterance addressee decisions in production.
What this tier adds
Adds production hosted capacity on every platform: a pre-ASR addressee decision in about 150ms end to end, device-directed output with confidence, the turnReady PCM16 segment, and engineering support on Slack.
Enterprise Per-Device License
Custom
Ideal for
OEM, robotics, XR, and regulated hardware teams that cannot send raw audio off the device and need the gate to run locally.
What this tier adds
Moves inference onto the hardware: ONNX runtime, CPU-only, under 9ms before ASR, audio or audio-plus-video fusion, per-device volume licensing, and custom acoustic profiles arranged on a call.
Where the pricing makes sense
The company stage and team size where Saa Sdk's pricing actually pencils out — and where peers do it cheaper.
The free cloud account — no card required, with usage-based billing after — puts SAA within reach of a solo developer testing an idea this week, which is cheap for a gate that sits in front of every ASR and LLM call you would otherwise pay for. The on-device per-device license is an OEM and regulated-hardware purchase priced on volume, in the same bracket as other embedded voice licensing rather than the SaaS tools around it. Compare against the cost of the ASR, LLM, and TTS seconds SAA
Setup time & first value
How long it actually takes to get something useful out of Saa Sdk — broken out by persona, not the marketing-page minute.
Cloud SDK: install one package, create a self-serve API key, and stream your first prediction events in a few lines — most teams have audio flowing the same day, then spend the real time tuning initialThreshold against their own room audio. Enterprise on-device: expect a procurement and integration cycle, since the license is per device, priced on volume, with acoustic profiles arranged on a call.
Switching to or from Saa Sdk
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From wake-word gating: drop the hotword trigger and let SAA's class 2 gate decide when your agent is addressed.
- →From plain VAD: keep VAD upstream and route its output into SAA, which adds the who-is-being-addressed decision VAD cannot make.
- →From speaker identification or diarization: replace enrollment-based speaker ID with addressee classification that needs no enrollment.
- →From a cloud-hosted gate to on-device: move to the enterprise per-device license with ONNX CPU inference when raw audio cannot leave the hardware.
- ↗To another pre-ASR gate: remove the AttentionClient listeners and swap in the replacement, keeping your existing turnReady-to-LLM handoff shape.
- ↗To bare VAD: accept that your agent will answer speech that was not addressed to it unless another gate is added.
- ↗To a fully managed voice platform with native turn detection: hand the addressee decision to the platform and drop the extra ~150ms pre-ASR hop.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Saa Sdk”, and we withheld 6: 6 did not mention Saa Sdk. We are showing none, because we could not prove any of them are about Saa Sdk.
Official links
Featured Head-to-Head Comparisons
Saa Sdk vs Spider Cloud
Choose Saa Sdk if you build voice agents that must ignore background speech and TTS echo — it's a specialized addressee detection layer. Choose Spider Cloud if your AI agent needs to crawl and scrape the web at scale for RAG or LLM context. They solve different problems; the decision hinges on whether your bottleneck is audio directionality or web data extraction.
Saa Sdk vs Voyage Ai
Voyage AI and Saa SDK are not direct competitors—they solve different problems. Choose Voyage AI if you need high-quality embeddings and rerankers for enterprise RAG, especially on finance/legal documents. Choose Saa SDK if you're building a voice agent that must ignore background speech and TTS echo, and you want a free tier to start quickly. For most buyers, the choice is driven by whether your bottleneck is retrieval accuracy or voice addressee detection, not price.
Saa Sdk vs Temporal Ai
If you need to build reliable, fault-tolerant AI agents or multi-step microservices that survive crashes, Temporal is the clear choice with its durable execution and extensive SDK support. If your biggest pain point is voice agents triggering on background noise or TTS echo, Saa SDK solves that specific problem with real-time addressee detection. They are complementary tools — use Temporal to orchestrate complex workflows and Saa SDK to clean up audio input for voice interfaces.
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