VisionAgent
VisionAgent is a generative Visual AI application builder developed by LandingAI that uses agent frameworks and text prompts to generate code for computer vision tasks without requiring data labeling or model training.
https://landing.ai/agentic-object-detection?ref=aipure
Product Information
Updated:Feb 16, 2025
VisionAgent Monthly Traffic Trends
VisionAgent experienced a 21.8% increase in traffic, reaching 90,511 visits. This moderate growth could be attributed to the broader industry trend of fully-scaled, enterprise-wide AI adoption in 2025, as highlighted by CIO, and the increasing demand for AI-powered landing page builders that offer data-driven insights and personalization.
What is VisionAgent
VisionAgent is a library and framework created by Andrew Ng's LandingAI team that helps developers utilize agent frameworks to solve computer vision tasks. It acts as an orchestrator layer for specialized AI agents that can reason through vision problems and leverage a curated set of vision tools. The framework integrates state-of-the-art vision language models and combines them with an agentic framework to generate custom code for various use cases like object detection, image classification, segmentation, and counting.
Key Features of VisionAgent
VisionAgent is a generative Visual AI application builder developed by LandingAI that uses an agentic framework to simplify computer vision development. It enables text prompt-based object detection without requiring data labeling or model training, integrates various vision models, and supports both local and cloud deployment options while providing reasoning-driven detection capabilities for complex visual tasks.
Text Prompt-Based Detection: Uses natural language prompts to detect objects without requiring manual data labeling or model training
Advanced Reasoning Capabilities: Employs agent systems to reason about object attributes like color, shape, and texture for more precise recognition
Flexible Deployment Options: Supports both local development and cloud-hosted deployment with options for creating Streamlit apps and API endpoints
Integrated Tool Suite: Combines multiple computer vision models and tools for tasks like object detection, classification, and segmentation
Use Cases of VisionAgent
Manufacturing Quality Control: Detecting missing components, verifying assembly, and identifying defects in production lines
Retail Inventory Management: Counting products, monitoring shelf stock levels, and tracking empty spaces in stores
Workplace Safety Monitoring: Identifying workers without proper safety equipment like helmets and monitoring compliance with safety protocols
Agricultural Inspection: Detecting and analyzing crop conditions, identifying unripe produce, and monitoring agricultural yields
Pros
Eliminates need for manual data labeling and model training
High accuracy with F1 Score of 79.7% in benchmarks
Versatile application across multiple industries and use cases
Cons
Processing time of 20-30 seconds per image may be slow for some applications
Currently limited to 7-day deployment period for testing purposes
How to Use VisionAgent
Install VisionAgent: Install the VisionAgent library using pip or by cloning the GitHub repository (landing-ai/vision-agent)
Import Required Modules: Import VisionAgentCoderV2 from vision_agent.agent and AgentMessage from vision_agent.agent.types
Initialize the Agent: Create a VisionAgentCoderV2 instance with verbose=True to see detailed outputs: agent = VisionAgentCoderV2(verbose=True)
Prepare Your Task: Create an AgentMessage object with your task description and media files (images/videos). Example: AgentMessage(role='user', content='Count people in image', media=['image.png'])
Generate Code: Use agent.generate_code() with your AgentMessage to get code for your vision task. The agent will plan, test and select the best approach
Save or Execute Code: Either save the generated code to a file or execute it directly. The code will use VisionAgent's built-in tools for tasks like object detection
Deploy (Optional): Deploy your solution as either a cloud endpoint or Streamlit app using VisionAgent's deployment options
Test and Iterate: Test the results and refine your prompt if needed. You can use the Streamlit interface for quick testing without coding
Customize (Optional): Change LLM providers by modifying config.py in vision_agent/configs directory if desired. For example, switch to Anthropic by copying anthropic_config.py
VisionAgent FAQs
VisionAgent is a visual AI technology from LandingAI that uses agentic object detection to identify objects in images through text prompts, without requiring data labeling or model training. It can generate AI code and solve various vision tasks through a planning, testing, and judging workflow.
VisionAgent Video
Analytics of VisionAgent Website
VisionAgent Traffic & Rankings
90.5K
Monthly Visits
#412618
Global Rank
#5252
Category Rank
Traffic Trends: Jun 2024-Jan 2025
VisionAgent User Insights
00:01:14
Avg. Visit Duration
2.63
Pages Per Visit
43.61%
User Bounce Rate
Top Regions of VisionAgent
US: 25.99%
IN: 8.36%
SE: 6.62%
NG: 4.68%
VN: 3.87%
Others: 50.48%
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