​
Quick Start

1

Install Package

First, install the PraisonAI Agents package:

pip install praisonaiagents opencv-python moviepy
2

Set API Key

Set your OpenAI API key as an environment variable in your terminal:

export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
3

Create a file

Create a new file app.py with the basic setup:

from praisonaiagents import Agent, Task, PraisonAIAgents

# Create Vision Analysis Agent
vision_agent = Agent(
    name="VisionAnalyst",
    role="Computer Vision Specialist",
    goal="Analyze images and videos to extract meaningful information",
    backstory="""You are an expert in computer vision and image analysis.
    You excel at describing images, detecting objects, and understanding visual content.""",
    llm="gpt-4o-mini",
    self_reflect=False
)

# Create tasks with different media types
task = Task(
    name="analyze_landmark",
    description="Describe this famous landmark and its architectural features.",
    expected_output="Detailed description of the landmark's architecture and significance",
    agent=vision_agent,
    images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
)

# Run the agents
agents = PraisonAIAgents(
    agents=[vision_agent],
    tasks=[task],
    process="sequential",
    verbose=True
)

agents.start()
4

Start Agents

Type this in your terminal to run your agents:

python app.py

Requirements

  • Python 3.10 or higher
  • OpenAI API key with vision model access
  • Basic understanding of Python and media handling

​
Understanding Multimodal Agents

What are Multimodal Agents?

Multimodal agents are designed to:

  • Process multiple types of data (text, images, videos)
  • Understand context across different modalities
  • Generate insights from diverse media sources
  • Handle complex multimedia tasks

​
Features

Vision Processing

Analyze images, detect objects, and understand visual content.

Video Analysis

Process video content for events and actions.

Text Extraction

Extract and analyze text from images and documents.

Cross-Modal Understanding

Integrate insights across different media types.

​
Multi-Agent Media Processing

from praisonaiagents import Agent, Task, PraisonAIAgents

# Create first agent for image analysis
vision_agent = Agent(
    role="Image Analyst",
    goal="Analyze visual content and extract key information",
    backstory="Expert in visual analysis and image understanding",
    llm="gpt-4o-mini",
    self_reflect=False
)

# Create second agent for content writing
writer_agent = Agent(
    role="Content Writer",
    goal="Create engaging content based on image analysis",
    backstory="Expert in creating compelling content from visual insights",
    llm="gpt-4o-mini"
)

# Create tasks for different media types
document_task = Task(
    description="Extract and summarize text from this document image",
    expected_output="Structured text content with key information highlighted",
    agent=vision_agent,
    images=["document.jpg"]
)

writing_task = Task(
    description="Create engaging content based on image analysis",
    expected_output="Compelling article incorporating visual insights",
    agent=writer_agent
)

# Create and start the agents
agents = PraisonAIAgents(
    agents=[vision_agent, writer_agent],
    tasks=[document_task, writing_task],
    process="sequential"
)

result = agents.start()

​
Configuration Options

# Create an agent with multimodal configuration
agent = Agent(
    role="Media Analyst",
    goal="Process multiple types of media",
    backstory="Expert in multimedia analysis",
    llm="gpt-4o-mini",  # Must support vision capabilities
    verbose=True,  # Enable detailed logging
    self_reflect=False  # Optional: disable self-reflection
)

# Task with media requirements
task = Task(
    description="Analyze media content",
    expected_output="Comprehensive analysis",
    agent=agent,
    images=[  # Support multiple media sources
        "https://example.com/image1.jpg",
        "path/to/local/image.jpg",
        "path/to/video.mp4"
    ]
)

​
Best Practices

Media Handling

  • Use supported formats (JPEG, PNG)
  • Keep reasonable file sizes
  • Provide high-quality media
  • Validate files before processing

Task Design

  • Write clear descriptions
  • Break down complex analyses
  • Specify expected outputs
  • Handle errors gracefully

​
Example Use Cases

Document Analysis

Extract and analyze text from document images.

Security Monitoring

Monitor security feeds for suspicious activity.

Medical Imaging

Analyze medical scans for abnormalities.

Architectural Analysis

Study architectural features and designs.

​
Next Steps

AutoAgents

Learn about automatically created and managed AI agents

Mini Agents

Explore lightweight, focused AI agents

For optimal results, ensure your media files are in supported formats and sizes for processing.

​
Quick Start

1

Install Package

First, install the PraisonAI Agents package:

pip install praisonaiagents opencv-python moviepy
2

Set API Key

Set your OpenAI API key as an environment variable in your terminal:

export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
3

Create a file

Create a new file app.py with the basic setup:

from praisonaiagents import Agent, Task, PraisonAIAgents

# Create Vision Analysis Agent
vision_agent = Agent(
    name="VisionAnalyst",
    role="Computer Vision Specialist",
    goal="Analyze images and videos to extract meaningful information",
    backstory="""You are an expert in computer vision and image analysis.
    You excel at describing images, detecting objects, and understanding visual content.""",
    llm="gpt-4o-mini",
    self_reflect=False
)

# Create tasks with different media types
task = Task(
    name="analyze_landmark",
    description="Describe this famous landmark and its architectural features.",
    expected_output="Detailed description of the landmark's architecture and significance",
    agent=vision_agent,
    images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
)

# Run the agents
agents = PraisonAIAgents(
    agents=[vision_agent],
    tasks=[task],
    process="sequential",
    verbose=True
)

agents.start()
4

Start Agents

Type this in your terminal to run your agents:

python app.py

Requirements

  • Python 3.10 or higher
  • OpenAI API key with vision model access
  • Basic understanding of Python and media handling

​
Understanding Multimodal Agents

What are Multimodal Agents?

Multimodal agents are designed to:

  • Process multiple types of data (text, images, videos)
  • Understand context across different modalities
  • Generate insights from diverse media sources
  • Handle complex multimedia tasks

​
Features

Vision Processing

Analyze images, detect objects, and understand visual content.

Video Analysis

Process video content for events and actions.

Text Extraction

Extract and analyze text from images and documents.

Cross-Modal Understanding

Integrate insights across different media types.

​
Multi-Agent Media Processing

from praisonaiagents import Agent, Task, PraisonAIAgents

# Create first agent for image analysis
vision_agent = Agent(
    role="Image Analyst",
    goal="Analyze visual content and extract key information",
    backstory="Expert in visual analysis and image understanding",
    llm="gpt-4o-mini",
    self_reflect=False
)

# Create second agent for content writing
writer_agent = Agent(
    role="Content Writer",
    goal="Create engaging content based on image analysis",
    backstory="Expert in creating compelling content from visual insights",
    llm="gpt-4o-mini"
)

# Create tasks for different media types
document_task = Task(
    description="Extract and summarize text from this document image",
    expected_output="Structured text content with key information highlighted",
    agent=vision_agent,
    images=["document.jpg"]
)

writing_task = Task(
    description="Create engaging content based on image analysis",
    expected_output="Compelling article incorporating visual insights",
    agent=writer_agent
)

# Create and start the agents
agents = PraisonAIAgents(
    agents=[vision_agent, writer_agent],
    tasks=[document_task, writing_task],
    process="sequential"
)

result = agents.start()

​
Configuration Options

# Create an agent with multimodal configuration
agent = Agent(
    role="Media Analyst",
    goal="Process multiple types of media",
    backstory="Expert in multimedia analysis",
    llm="gpt-4o-mini",  # Must support vision capabilities
    verbose=True,  # Enable detailed logging
    self_reflect=False  # Optional: disable self-reflection
)

# Task with media requirements
task = Task(
    description="Analyze media content",
    expected_output="Comprehensive analysis",
    agent=agent,
    images=[  # Support multiple media sources
        "https://example.com/image1.jpg",
        "path/to/local/image.jpg",
        "path/to/video.mp4"
    ]
)

​
Best Practices

Media Handling

  • Use supported formats (JPEG, PNG)
  • Keep reasonable file sizes
  • Provide high-quality media
  • Validate files before processing

Task Design

  • Write clear descriptions
  • Break down complex analyses
  • Specify expected outputs
  • Handle errors gracefully

​
Example Use Cases

Document Analysis

Extract and analyze text from document images.

Security Monitoring

Monitor security feeds for suspicious activity.

Medical Imaging

Analyze medical scans for abnormalities.

Architectural Analysis

Study architectural features and designs.

​
Next Steps

AutoAgents

Learn about automatically created and managed AI agents

Mini Agents

Explore lightweight, focused AI agents

For optimal results, ensure your media files are in supported formats and sizes for processing.