Video Agent

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

from praisonaiagents import Agent, Task, Agents

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

praisonai "Summarize this video" --image video.mp4

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

praisonai agents.yaml

from praisonaiagents import Agent

agent = Agent(
    name="VideoAnalyst",
    instructions="You are a video analysis expert.",
    llm="gpt-4o-mini"
)

agent.launch(port=8080)

curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Analyze this video: https://example.com/video.mp4"}'

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

from praisonaiagents import Agent, Task, Agents, 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 = Agents(
    agents=[agent],
    tasks=[task],
    memory=True
)

result = agents.start()
print(result)

praisonai "Analyze this video" --image video.mp4 --memory --verbose

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

praisonai agents.yaml --verbose

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)

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

praisonai "test video" --image test.mp4 --verbose

rm -f videos.db