MEDIA PROCESSING & DELIVERY · FOR AI AGENTS
AssemblyAI
Turn recorded or live audio into transcripts through hosted speech models.
Use AssemblyAI APIs and SDKs to transcribe audio, obtain speaker-labeled text where supported and pass verified transcripts into agent workflows.
Your integration checklist
- Capabilities
- What you need from your owner
- Create an account, obtain an API key and configure funds and alerts for the intended usage. Choose the audio workflow, model and optional features, and ensure the audio is authorized for processing.
- Authentication
- SDKs accept an API key, commonly loaded from ASSEMBLYAI_API_KEY. REST uses the authorization header with the raw key, without a Bearer prefix. The documentation MCP helps build integrations; transcription runs through the service APIs.
- Your first task
- Submit a short authorized recording, retain its transcript ID and wait for completed or error status. Compare task-critical words and speaker assignments against the audio.
- Billing & payment
- Pre-recorded transcription is billed by processed audio duration and selected model, with optional features charged separately. Streaming bills the time a WebSocket session remains open, including idle time. Account funds are drawn down as the service is used.
- Agent payment support
- Not verified. Programmatic usage does not establish autonomous checkout support.
- Operational limits
- Parallel-job limits queue excess pre-recorded work. HTTP request limits and per-transcript polling limits also apply. A completed transcript can still contain recognition errors; language, model and recording quality affect suitability.
ORIGINAL AI AGENT STORE INTEGRATION PLAYBOOK
Keep the audio job identity through transcription and downstream analysis
Editorial recommendation · Reviewed 2026-10-25
When to choose this service
Choose AssemblyAI when an agent needs actual speech recognition for recordings or live audio rather than reasoning over an already available transcript.
Trained speech models and managed audio-processing capacity supply information extraction from sound. Writing a transcript parser does not reproduce those models or operate their inference infrastructure.
Suggested workflow
- Choose the required language, model and speaker options. Submit a representative recording and retain the job ID with the source audio reference.
- Wait for a terminal status using bounded polling or a webhook integration. Review names, numbers and speaker attribution before triggering downstream actions.
- Record audio duration and enabled features for cost attribution. For streaming workflows, explicitly terminate every session when audio processing ends.
Acceptance checks before real work
- Do not treat queued or processing jobs as completed transcripts.
- Verify critical passages against the audio before relying on them.
- Distinguish streaming session duration from the amount of speech sent.
If the workflow fails
Retrieve the existing job before submitting the same audio again. Inspect the error and audio input, honor polling Retry-After responses, and close unused streams to avoid accumulating idle-session charges.