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

# Image Analysis Agent

> Learn how to create AI agents for image analysis and visual content understanding.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
flowchart LR
    In[Image] --> Agent[Image Agent]
    Agent --> Out[Analysis]
    
    style In fill:#8B0000,color:#fff
    style Agent fill:#2E8B57,color:#fff
    style Out fill:#8B0000,color:#fff
```

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

***

## Simple

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

### Workflow

1. Receive image (URL or local file)
2. Process with vision model
3. Generate detailed description

### Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonaiagents praisonai
export OPENAI_API_KEY="your-key"
```

### Run — Python

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "Describe this image" --image path/to/image.jpg
```

### Run — agents.yaml

```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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
```

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai agents.yaml
```

### Serve API

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent

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

agent.launch(port=8080)
```

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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

1. Initialize session for image analysis tracking
2. Configure SQLite persistence for analysis history
3. Analyze image with structured output
4. Store results in memory for comparison
5. Resume session for follow-up analysis

### Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonaiagents praisonai pydantic
export OPENAI_API_KEY="your-key"
```

### Run — Python

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "Analyze this image" --image image.jpg --memory --verbose
```

### Run — agents.yaml

```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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
```

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai agents.yaml --verbose
```

### Serve API

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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)
```

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Analyze image", "session_id": "image-001"}'
```

***

## Monitor / Verify

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "test image" --image test.jpg --verbose
```

## Cleanup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 |

## Next Steps

* [Video Agent](/agents/video) for video analysis
* [Image to Text](/agents/image-to-text) for OCR
* [Memory](/features/advanced-memory) for persistent context