microsoft/fara
Fara
FARA is a visual computer-use agent model that controls a browser by predicting screen coordinates for clicking, typing, and scrolling, without relying on DOM or accessibility trees. It is designed to automate multi-step web tasks such as searching, form filling, booking, and shopping by reasoning over visual state and decomposing tasks into sequential actions.
The model uses a compact 7-billion-parameter decoder-only transformer that can run on consumer GPUs for low-latency on-device inference, or be deployed as a managed endpoint on Azure Foundry for cloud-based inference without local infrastructure. It also supports self-hosted serving via vLLM, LM Studio, or Ollama, giving users full control over the inference environment.
FARA includes a reproducible evaluation framework that runs agent benchmarks on 609 real, live web-browsing tasks with automatic retry handling for time-sensitive and error-prone scenarios. The framework provides standardized scoring rubrics to compare agent performance across different task descriptions and versions.
Features
- Browser and Web Agents - Controls a browser to complete multi-step web tasks through visual perception and coordinate-based actions.
- Visual Web Task Agents - Provides a visual computer-use agent that automates multi-step web tasks through pixel-level interface control.
- Task Decomposition - Breaks multi-step web tasks into sequential actions by reasoning over visual state.
- Visual Grounding Execution - Predicts click coordinates and scroll targets directly from pixel-level screen analysis without DOM reliance.
- Computer Use Agents - Controls a computer visually by predicting screen coordinates for clicking, typing, and scrolling.
- Compact Parameter Models - Uses a compact 7-billion-parameter transformer that fits on consumer GPUs for low-latency inference.
- On-Device GUI Agents - Runs a compact 7-billion-parameter model locally on consumer hardware for computer-use tasks with low latency.
- Visual Computer-Use Agents - Ships a visual computer-use agent model deployable via vLLM, LM Studio, or Ollama for full local control.
- Web Task Automations - Automates multi-step web tasks like searching, form filling, booking, and shopping via visual interface control.
- Visual Computer Controllers - Perceives webpages and performs scrolling, typing, and clicking on predicted coordinates without accessibility trees.
- Live-Website Agent Benchmarks - Includes a reproducible evaluation framework that runs agent benchmarks on 609 real, live web-browsing tasks.
- On-Device Deployments - Runs locally on consumer hardware with a compact 7-billion-parameter size for low latency and privacy.
- Local Model Inference Servers - Supports self-hosted serving via vLLM, LM Studio, or Ollama for full inference control.
- Agent Benchmarks - Provides a reproducible evaluation system for testing web-browsing agents across 609 tasks with live websites.
- Managed Model Endpoints - Ships a managed endpoint deployment option on Azure Foundry for API-based model inference.
- Managed Agent Endpoints - Deploys a computer-use model via Azure Foundry endpoint without managing infrastructure or downloading weights.
- Managed AI Endpoints - Deploys the model on Azure Foundry without downloading weights or managing GPU infrastructure.
- Self-Hosted Agent Inference - Runs the model on a GPU machine using vLLM, LM Studio, or Ollama for full inference control.
- Self-Hosted Inference Servers - Runs the model on a GPU machine using vLLM, LM Studio, or Ollama for full inference control.
- Live Website Agent Benchmarks - Provides a reproducible evaluation framework running agent benchmarks on 609 real, live web-browsing tasks.
- Agent Performance Benchmarks - Runs a benchmark of 609 web-browsing tasks comparing agent performance across scoring rubrics.
Star history
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