> ## 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.

# Video Agent

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

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

Video analysis agent using vision models for content understanding.

***

## Simple

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

### Workflow

1. Receive video file
2. Process frames with vision model
3. Generate comprehensive analysis

### 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="VideoAnalyst",
    instructions="Describe video content in detail.",
    llm="gpt-4o-mini"
)

task = Task(
    description="Analyze this video and summarize key events",
    expected_output="Video analysis",
    agent=agent,
    images=["video.mp4"]
)

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

### Run — CLI

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "Summarize this video" --image video.mp4
```

### Run — agents.yaml

```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
framework: praisonai
topic: Video Analysis
roles:
  video_analyst:
    role: Video Analysis Specialist
    goal: Analyze videos and describe content
    backstory: You are an expert in video analysis
    llm: gpt-4o-mini
    tasks:
      analyze:
        description: Analyze this video and summarize key events
        expected_output: Video analysis
        images:
          - video.mp4
```

```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="VideoAnalyst",
    instructions="You are a video 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": "Analyze this video: https://example.com/video.mp4"}'
```

***

## Advanced Workflow (All Features)

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

### Workflow

1. Initialize session for video tracking
2. Configure SQLite persistence for analysis history
3. Analyze video 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 VideoAnalysis(BaseModel):
    duration: str
    scenes: list[str]
    key_events: list[str]
    summary: str

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

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

task = Task(
    description="Analyze this video in detail",
    expected_output="Structured video analysis",
    agent=agent,
    images=["video.mp4"],
    output_pydantic=VideoAnalysis
)

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 video" --image video.mp4 --memory --verbose
```

### Run — agents.yaml

```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
framework: praisonai
topic: Video Analysis
memory: true
memory_config:
  provider: sqlite
  db_path: videos.db
roles:
  video_analyst:
    role: Video Analysis Specialist
    goal: Analyze videos with structured output
    backstory: You are an expert in video analysis
    llm: gpt-4o-mini
    memory: true
    tasks:
      analyze:
        description: Analyze this video in detail
        expected_output: Structured video analysis
        images:
          - video.mp4
        output_json:
          duration: string
          scenes: array
          key_events: array
          summary: 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="VideoAnalyst",
    instructions="Analyze videos 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 video", "session_id": "video-001"}'
```

***

## Monitor / Verify

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "test video" --image test.mp4 --verbose
```

## Cleanup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
rm -f videos.db
```

## Features Demonstrated

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

## Next Steps

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