> ## Documentation Index
> Fetch the complete documentation index at: https://api-docs.ollang.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Ollang Agentic STT

> Ollang's multi-agent transcription workflow — Agentic STT, Agentic STT-1.2, and Visual STT — for complex multimedia, noisy audio, and on-screen text.

**Ollang Agentic STT** is Ollang's own transcription workflow and the default provider for every `transcribe` step. Unlike a single-model ASR service, it runs a collaborative multi-agent pass: several interpretations of the same audio are produced and reconciled, speaker attribution is resolved across the whole timeline, and surrounding context — including non-speech audio cues — is used to choose between readings that a single model would have to guess at.

<Info>
  **Available in Ollang Workflows** — Closed Captions, Subtitle Translation, and AI Dubbing. See the [Speech-to-Text catalog](/providers/speech-to-text) for the full comparison.
</Info>

## Models

| Model           | Provider tag                   | Status       |
| --------------- | ------------------------------ | ------------ |
| Agentic STT     | `ollangAITranscribe`           | **Default**  |
| Agentic STT-1.2 | `ollang-stt-1.2-exp`           | Experimental |
| Visual STT      | `agentic-visual-transcription` | Experimental |

## Key capabilities

* **Multi-agent reconciliation** — competing transcription hypotheses are compared rather than accepted, which is what produces the accuracy gain on ambiguous audio.
* **Noise and accent robustness** — built for real-world source material: field recordings, conference audio, overlapping speakers, and wide accent diversity.
* **Multi-language streams** — handles video and audio where more than one language is present, without forcing a single language assumption across the file.
* **Contextual audio cues** — non-speech information is treated as signal, which matters when the transcript becomes a dubbing script rather than a reading transcript.
* **Stable speaker attribution** — speaker identity holds across long-form content instead of drifting between segments.

## Visual STT

Visual STT is a different modality in the same family. Instead of transcribing speech, it reads **on-screen text** from video frames and converts it into editable, timecoded text content.

It is the right provider when the message lives in the picture rather than the audio:

* silent video and sound-off social content,
* burned-in captions and subtitles,
* title cards, lower thirds, and kinetic typography,
* product and UI demos where the interface text carries the meaning.

Because it transcribes what is displayed rather than what is said, content with meaningful speech *and* meaningful on-screen text usually needs both an audio pass and a visual pass.

## Where it fits in a workflow

Agentic STT is the default because transcription errors do the most damage of any in the pipeline — everything downstream inherits them. A mistranscribed name becomes a mistranslated name and then a mispronounced one in the dub.

Choose it when the source audio is genuinely difficult. On clean, single-speaker studio narration, [AssemblyAI](/apis/stt-apis/assemblyai) or [ElevenLabs Scribe v2](/apis/stt-apis/elevenlabs-scribe) deliver comparable transcripts in a single pass.

## Trade-offs

* **Throughput** — a multi-agent pass does more work than a single model call, so it is slower on large batches. What it buys is robustness, not speed.
* **Experimental tiers** — Agentic STT-1.2 and Visual STT may change between releases. Run them in a benchmarking Folder alongside your production provider rather than switching a Global Workflow to them.

## Reference

* [Speech-to-Text provider catalog](/providers/speech-to-text)
* [Workflows and provider architecture](/workflows-provider-architecture)
* [Order types](/apis/ollang-api-reference/order-types)