Specialised Agents

Use specialised agent types (AudioAgent, VideoAgent, ImageAgent, OCRAgent) in YAML workflows

Specialised agents give your workflows domain-specific capabilities — speech, vision, video, and document extraction — from a single agent: field in YAML or a few lines of Python.

from praisonaiagents import Agent, ImageAgent

artist = ImageAgent(llm="openai/dall-e-3")
artist.generate("A mountain landscape at sunset", output="sunset.png")

The user requests media or OCR in a workflow; the specialised agent type handles that step.

How It Works

Quick Start

1. Text-to-Speech (YAML)

   agents:
     speaker:
       agent: AudioAgent
       llm: openai/tts-1
       role: Text-to-Speech Agent
       goal: Convert text to speech
   
   steps:
     - agent: speaker
       action: speech
       text: "Hello, welcome to PraisonAI!"
       output: "hello.mp3"
   
2. Python API

   from praisonaiagents import AudioAgent, ImageAgent, VideoAgent, OCRAgent
   
   audio = AudioAgent(llm="openai/tts-1")
   audio.speech("Hello world!", output="hello.mp3")
   
   image = ImageAgent(llm="openai/dall-e-3")
   image.generate("A mountain landscape", output="sunset.png")
   
   video = VideoAgent(llm="openai/sora-2")
   video.generate("A sunset timelapse", output="timelapse.mp4")
   
   ocr = OCRAgent(llm="mistral/mistral-ocr-latest")
   text = ocr.extract("document.pdf")
   

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Supported Agent Types

Agent Type	Purpose	Key Methods
AudioAgent	Text-to-Speech (TTS) and Speech-to-Text (STT)	speech(), transcribe()
VideoAgent	Video generation	generate()
ImageAgent	Image generation, editing, variations	generate(), edit()
OCRAgent	Text extraction from documents/images	extract()
DeepResearchAgent	Automated research with citations	research()

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YAML Examples

Speech-to-Text

agents:
  transcriber:
    agent: AudioAgent
    llm: openai/whisper-1
    role: Transcriber
    goal: Transcribe audio to text

steps:
  - agent: transcriber
    action: transcribe
    input: "recording.mp3"

Document OCR

agents:
  reader:
    agent: OCRAgent
    llm: mistral/mistral-ocr-latest
    role: Document Reader
    goal: Extract text from documents

steps:
  - agent: reader
    action: extract
    source: "document.pdf"

---

CLI Usage

praisonai recipe run ai-text-to-speech --var text="Hello world"
praisonai recipe run ai-speech-to-text --var audio=recording.mp3
praisonai recipe run ai-generate-image --var prompt="A sunset"
praisonai recipe run ai-generate-video --var prompt="A cat playing"
praisonai recipe run ai-document-ocr --var source=document.pdf

---

Best Practices

Choose the right model

Use tts-1-hd for higher-quality audio, dall-e-3 for detailed images, and provider-specific OCR models for scanned PDFs.

Set explicit output paths

Specialised agents produce files — always specify output or source paths so downstream steps can chain results.

Chain with standard agents

Combine specialised agents with a standard Agent for analysis, summarisation, or formatting of media outputs.

Pass context between steps

Use {{previous_output}} in YAML workflows to feed transcribed text or extracted content into the next agent step.

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Related

Multi-Agent Pipelines

Chain specialised agents together

OCR

Detailed OCR documentation