Image Analysis Agent

Describe images, detect objects, and read scenes with a single Agent backed by a vision model.

from praisonaiagents import Agent, Task, AgentTeam

agent = Agent(
    name="ImageAnalyst",
    instructions="Describe images in detail.",
    llm="gpt-4o-mini",
)

task = Task(
    description="Describe this image",
    expected_output="Detailed description",
    agent=agent,
    images=["image.jpg"],
)

AgentTeam(agents=[agent], tasks=[task]).start()

Image analysis agent using vision models for object detection and description.

Quick Start

Simple Usage

Pass an image to a task and let the vision model describe it.

from praisonaiagents import Agent, Task, AgentTeam

agent = Agent(
    name="ImageAnalyst",
    instructions="Describe images in detail.",
    llm="gpt-4o-mini",
)

task = Task(
    description="Describe this image",
    expected_output="Detailed description",
    agent=agent,
    images=["image.jpg"],
)

AgentTeam(agents=[agent], tasks=[task]).start()

With Configuration

Return structured fields with a Pydantic schema.

from praisonaiagents import Agent, Task, AgentTeam
from pydantic import BaseModel

class ImageAnalysis(BaseModel):
    objects: list[str]
    scene: str
    description: str

agent = Agent(
    name="ImageAnalyst",
    instructions="Analyze images and return structured results.",
    llm="gpt-4o-mini",
)

task = Task(
    description="Analyze this image",
    expected_output="Structured analysis",
    agent=agent,
    images=["image.jpg"],
    output_pydantic=ImageAnalysis,
)

AgentTeam(agents=[agent], tasks=[task]).start()

How It Works

Simple

Agents: 1 — Single agent with vision capabilities analyzes images.

Workflow

Receive image (URL or local file)
Process with vision model
Generate detailed description

Setup

pip install praisonaiagents praisonai
export OPENAI_API_KEY="your-key"

Run — Python

from praisonaiagents import Agent, Task, AgentTeam

agent = Agent(
    name="ImageAnalyst",
    instructions="Describe images in detail.",
    llm="gpt-4o-mini"
)

task = Task(
    description="Describe this image",
    expected_output="Detailed description",
    agent=agent,
    images=["image.jpg"]
)

agents = AgentTeam(agents=[agent], tasks=[task])
result = agents.start()
print(result)

Run — CLI

praisonai "Describe this image" --image path/to/image.jpg

Run — agents.yaml

framework: praisonai
topic: Image Analysis
roles:
  image_analyst:
    role: Image Analysis Specialist
    goal: Analyze images and describe content
    backstory: You are an expert in computer vision
    llm: gpt-4o-mini
    tasks:
      analyze:
        description: Describe this image in detail
        expected_output: Detailed description
        images:
          - image.jpg

praisonai agents.yaml

Serve API

from praisonaiagents import Agent

agent = Agent(
    name="ImageAnalyst",
    instructions="You are an image analysis expert.",
    llm="gpt-4o-mini"
)

agent.launch(port=8080)

curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Describe this: https://example.com/image.jpg"}'

Advanced Workflow (All Features)

Agents: 1 — Single agent with memory, persistence, structured output, and session resumability.

Workflow

Initialize session for image analysis tracking
Configure SQLite persistence for analysis history
Analyze image with structured output
Store results in memory for comparison
Resume session for follow-up analysis

Setup

pip install praisonaiagents praisonai pydantic
export OPENAI_API_KEY="your-key"

Run — Python

from praisonaiagents import Agent, Task, AgentTeam, Session
from pydantic import BaseModel

class ImageAnalysis(BaseModel):
    objects: list[str]
    scene: str
    colors: list[str]
    description: str

session = Session(session_id="image-001", user_id="user-1")

agent = Agent(
    name="ImageAnalyst",
    instructions="Analyze images and return structured results.",
    llm="gpt-4o-mini",
    memory=True
)

task = Task(
    description="Analyze this image in detail",
    expected_output="Structured image analysis",
    agent=agent,
    images=["image.jpg"],
    output_pydantic=ImageAnalysis
)

agents = AgentTeam(
    agents=[agent],
    tasks=[task],
    memory=True
)

result = agents.start()
print(result)

Run — CLI

praisonai "Analyze this image" --image image.jpg --memory --verbose

Run — agents.yaml

framework: praisonai
topic: Image Analysis
memory: true
memory_config:
  provider: sqlite
  db_path: images.db
roles:
  image_analyst:
    role: Image Analysis Specialist
    goal: Analyze images with structured output
    backstory: You are an expert in computer vision
    llm: gpt-4o-mini
    memory: true
    tasks:
      analyze:
        description: Analyze this image in detail
        expected_output: Structured image analysis
        images:
          - image.jpg
        output_json:
          objects: array
          scene: string
          colors: array
          description: string

praisonai agents.yaml --verbose

Serve API

from praisonaiagents import Agent

agent = Agent(
    name="ImageAnalyst",
    instructions="Analyze images and return structured results.",
    llm="gpt-4o-mini",
    memory=True
)

agent.launch(port=8080)

curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Analyze image", "session_id": "image-001"}'

Monitor / Verify

praisonai "test image" --image test.jpg --verbose

Cleanup

rm -f images.db

Features Demonstrated

Feature	Implementation
Workflow	Vision-based image analysis
DB Persistence	SQLite via `memory_config`
Observability	`--verbose` flag
Resumability	`Session` with `session_id`
Structured Output	Pydantic `ImageAnalysis` model

Best Practices

Pass images through the Task, not the prompt

Vision models read images from the images=[...] field on a Task. Embedding file paths in the instruction text does nothing — the model never sees the pixels.

Use a vision-capable model

Set llm="gpt-4o-mini" or another multimodal model. Text-only models silently ignore the image and describe nothing.

Request structured output for pipelines

When results feed a database or UI, add output_pydantic so objects, scene, and colours arrive as typed fields instead of free text.

Reach for a specialised agent when you need OCR or generation

For text extraction use the Image to Text Agent; to create new images from prompts use the Image generation flow rather than this analysis agent.

Extract text from images with an OCR-focused agent.

Analyze video content frame by frame.

​Quick Start

​How It Works

​Simple

​Workflow

​Setup

​Run — Python

​Run — CLI

​Run — agents.yaml

​Serve API

​Advanced Workflow (All Features)

​Workflow

​Setup

​Run — Python

​Run — CLI

​Run — agents.yaml

​Serve API

​Monitor / Verify

​Cleanup

​Features Demonstrated

​Best Practices

​Related

Quick Start

How It Works

Simple

Workflow

Setup

Run — Python

Run — CLI

Run — agents.yaml

Serve API

Advanced Workflow (All Features)

Workflow

Setup

Run — Python

Run — CLI

Run — agents.yaml

Serve API

Monitor / Verify

Cleanup

Features Demonstrated

Best Practices

Related