VisionAgent
LandingAI · Image · Coding
VisionAgent is LandingAI's open-source visual AI agent. Give it a prompt and an image, and it picks the right computer vision models and writes runnable Python code for detection, OCR, and tracking.
About VisionAgent
What Is VisionAgent
VisionAgent is a Python library from LandingAI, the computer vision company founded by Andrew Ng. It acts as a coding partner for visual tasks. You describe what you want in plain language and attach an image or video, then the agent plans the work, chooses models from its tool library, and hands back a Python script you can run.
The pitch is speed. Building a pipeline for something like "count the people in this photo" normally means hunting for a detection model, learning its API, and gluing preprocessing and visualization code together. VisionAgent skips most of that. It writes the code, tests it against your input, and keeps fixing it until the test passes.
The catch is scope. The agent is built for developers who can read Python and manage API keys. It needs your own Anthropic and Google keys to run, and results depend on those underlying models. LandingAI now routes most of this work toward its newer Agentic Document Extraction platform, and the original VisionAgent repository is marked deprecated, but the library still installs and runs.
Getting Started
- Create a LandingAI account at va.landing.ai and generate your VisionAgent API key.
- Grab an Anthropic API key and a Google AI Studio API key, since the agent uses Claude and Gemini to plan and write code.
- Install the library with
pip install vision-agentoruv add vision-agent. - Set the three keys as environment variables, then call the agent with a prompt and an image file.
- Run the script. VisionAgent saves its output as
generated_code.py, ready to inspect and reuse.
Product Information
A quick look at VisionAgent's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Python developers
- Machine learning engineers
- Data teams without vision specialists
Tasks
- Object detection and counting
- Text extraction from images
- Image segmentation
- Video object tracking
Scenarios
- Early prototyping of a vision feature
- Automating repetitive image review
- Learning computer vision hands-on
Key features
Prompt-to-Code Workflow
You send a natural language request with an attached file, and VisionAgent returns a full Python script. The agent plans the steps, writes the code, and creates a matching test case, so you start from something that already runs rather than a blank file. Need image segmentation code for a specific object? Describe it and skip the model hunt.
Automatic Model Selection
This computer vision code generator ships with a catalog of vision models covering detection, segmentation, OCR, and tracking. Rather than comparing options yourself, you let the agent match the task to the right tool, which is the part that usually slows people down. It is agentic vision in practice: the model plans ahead instead of reacting one step at a time.
Self-Debugging Loop
After generating code, VisionAgent tests it against your input. If the test fails, the agent revises the code and tries again until it passes. This iterative cycle is what separates it from a single-shot code prompt.
Standalone Tool Library
The underlying tools work outside the agent too. You can import vision_agent.tools and call functions like countgd_object_detection or owlv2_sam2_video_tracking directly in your own scripts. This open source vision agent doubles as a plain image analysis tool for anyone who would rather wire up the pieces by hand.
Multi-Model Backend
VisionAgent relies on Anthropic Claude and Google Gemini to plan and write code, and you can swap in a different provider by editing the config file. Bring your own keys and the rate limits you hit are the ones on your own accounts.
Local Web Playground
The repository includes a small web app in the examples/chat folder, so you can experiment with prompts and images in a browser before wiring the agent into a larger project.
Pros and cons
Pros
- Turns a plain-language request and an image into working Python code in minutes.
- Handles the model-selection step for detection, segmentation, OCR, and video tracking.
- The generated code comes with a test, and the agent iterates until it passes.
- The tool library is usable on its own, so you can call individual functions in your own scripts.
- Free to install as an open-source package.
Cons
- It needs separate Anthropic and Google API keys and basic Python skills, so it is not a point-and-click tool.
- The original open-source project is deprecated, and LandingAI now points users toward its Agentic Document Extraction platform, which means long-term support is uncertain.
- Output quality depends on the underlying models, so complex prompts can need manual cleanup.
Frequently asked questions
The library itself is open source and free to install. What you pay for is the underlying model usage, since you supply your own Anthropic and Google API keys and those providers bill for their models.
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