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Fara-7B: An Efficient Agentic Model for Computer Use

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Overview

Fara-7B is Microsoft's first agentic small language model (SLM) designed specifically for computer use. With only 7 billion parameters, Fara-7B is an ultra-compact Computer Use Agent (CUA) that achieves state-of-the-art performance within its size class and is competitive with larger, more resource-intensive agentic systems.

Try Fara-7B locally as follows (see Installation for detailed instructions on Windows ) or via Magentic-UI:

# 1. Clone repository
git clone https://github.com/microsoft/fara.git
cd fara

# 2. Setup environment
python3 -m venv .venv 
source .venv/bin/activate
pip install -e .
playwright install

Then in one process, host the model:

vllm serve "microsoft/Fara-7B" --port 5000 --dtype auto 

Then you can iteratively query it with:

fara-cli --task "whats the weather in new york now"

To try Fara-7B inside Magentic-UI, please follow the instructions here Magentic-UI + Fara-7B. You will need to serve the model as before, but instead of fara-cli you can use Magentic-UI which has a nice UI (see video demos below).

Notes:

• If you're using Windows, we highly recommend using WSL2 (Windows Subsystem for Linux). Please see the Windows instructions in the Installation section.
• You might need to do --tensor-parallel-size 2 with vllm command if you run out of memory

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Shopping

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GitHub Issues

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Directions with Cheese

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What Makes Fara-7B Unique

Unlike traditional chat models that generate text-based responses, Fara-7B leverages computer interfaces—mouse and keyboard—to perform multi-step tasks on behalf of users. The model:

• Operates visually by perceiving webpages and taking actions like scrolling, typing, and clicking on directly predicted coordinates without accessibility trees or separate parsing models
• Enables on-device deployment due to its compact 7B parameter size, resulting in reduced latency and improved privacy as user data remains local
• Completes tasks efficiently, averaging only ~16 steps per task compared to ~41 for comparable models

Fara-7B is trained using a novel synthetic data generation pipeline built on the Magentic-One multi-agent framework, with 145K trajectories covering diverse websites, task types, and difficulty levels. The model is based on Qwen2.5-VL-7B and trained with supervised fine-tuning.

Key Capabilities

Fara-7B can automate everyday web tasks including:

• Searching for information and summarizing results
• Filling out forms and managing accounts
• Booking travel, movie tickets, and restaurant reservations
• Shopping and comparing prices across retailers
• Finding job postings and real estate listings

Performance Highlights

Fara-7B achieves state-of-the-art results across multiple web agent benchmarks, outperforming both comparable-sized models and larger systems:

| Model | Params | WebVoyager | Online-M2W | DeepShop | WebTailBench | |-------|--------|------------|------------|----------|--------------| | SoM Agents | | | | | | | SoM Agent (GPT-4o-0513) | - | 90.6 | 57.7 | 49.1 | 60.4 | | SoM Agent (o3-mini) | - | 79.3 | 55.4 | 49.7 | 52.7 | | SoM Agent (GPT-4o) | - | 65.1 | 34.6 | 16.0 | 30.8 | | GLM-4.1V-9B-Thinking | 9B | 66.8 | 33.9 | 32.0 | 22.4 | | Computer Use Models | | | | | | | OpenAI computer-use-preview | - | 70.9 | 42.9 | 24.7 | 25.7 | | UI-TARS-1.5-7B | 7B | 66.4 | 31.3 | 11.6 | 19.5 | | Fara-7B | 7B | 73.5 | 34.1 | 26.2 | 38.4 |

Table: Online agent evaluation results showing success rates (%) across four web benchmarks. Results are averaged over 3 runs.

WebTailBench: A New Benchmark for Real-World Web Tasks

We are releasing WebTailBench, a new evaluation benchmark focusing on 11 real-world task types that are underrepresented or missing in existing benchmarks. The benchmark includes 609 tasks across diverse categories, with the first 8 segments testing single skills or objectives (usually on a single website), and the remaining 3 evaluating more difficult multi-step or cross-site tasks.

WebTailBench Detailed Results

| Task Segment | Tasks | SoM GPT-4o-0513 | SoM o3-mini | SoM GPT-4o | GLM-4.1V-9B | OAI Comp-Use | UI-TARS-1.5 | Fara-7B | |--------------|-------|-----------------|-------------|------------|-------------|--------------|-------------|-------------| | Single-Site Tasks | | Shopping | 56 | 62.5 | 71.4 | 38.1 | 31.0 | 42.3 | 41.1 | 52.4 | | Flights | 51 | 60.1 | 39.2 | 11.1 | 10.5 | 17.6 | 10.5 | 37.9 | | Hotels | 52 | 68.6 | 56.4 | 31.4 | 19.9 | 26.9 | 35.3 | 53.8 | | Restaurants | 52 | 67.9 | 59.6 | 47.4 | 32.1 | 35.9 | 22.4 | 47.4 | | Activities | 80 | 70.4 | 62.9 | 41.7 | 26.3 | 30.4 | 9.6 | 36.3 | | Ticketing | 57 | 58.5 | 56.7 | 37.4 | 35.7 | 49.7 | 30.4 | 38.6 | | Real Estate | 48 | 34.0 | 17.4 | 20.1 | 16.0 | 9.0 | 9.7 | 23.6 | | Jobs/Careers | 50 | 49.3 | 44.0 | 32.7 | 22.7 | 20.7 | 20.7 | 28.0 | | Multi-Step Tasks | | Shopping List (2 items) | 51 | 66.0 | 62.7 | 17.0 | 7.8 | 34.0 | 20.9 | 49.0 | | Comparison Shopping | 57 | 67.3 | 59.1 | 27.5 | 22.8 | 1.2 | 8.8 | 32.7 | | Compositional Tasks | 55 | 51.5 | 39.4 | 26.7 | 17.0 | 10.3 | 9.1 | 23.0 | | Overall | | Macro Average | 609 | 59.7 | 51.7 | 30.1 | 22.0 | 25.3 | 19.9 | 38.4 | | Micro Average | 609 | 60.4 | 52.7 | 30.8 | 22.4 | 25.7 | 19.5 | 38.4 |

Table: Breakdown of WebTailBench results across all 11 segments. Success rates (%) are averaged over 3 independent runs. Fara-7B achieves the highest performance among computer-use models across all task categories.

Coming Soon:

• Task Verification pipeline for LLM-as-a-judge evaluation
• Official human annotations of WebTailBench (in partnership with BrowserBase)

Evaluation Infrastructure

Our evaluation setup leverages:

1. Playwright - A cross-browser automation framework that replicates browser environments
2. Abstract Web Agent Interface - Allows integration of any model from any source into the evaluation environment
3. Fara-Agent Class - Reference implementation for running the Fara model

Note: Fara-7B is an experimental release designed to invite hands-on exploration and feedback from the community. We recommend running it in a sandboxed environment, monitoring its execution, and avoiding sensitive data or high-risk domains.

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Installation

Linux

The following instructions are for Linux systems, see the Windows section below for Windows instructions.

Install the package using pip and set up the environment with Playwright:

# 1. Clone repository
git clone https://github.com/microsoft/fara.git
cd fara

# 2. Setup environment
python3 -m venv .venv 
source .venv/bin/activate
pip install -e .[vllm]
playwright install

Note: If you plan on hosting with Azure Foundry only, you can skip the [vllm] and just do pip install -e .

Windows

For Windows, we highly recommend using WSL2 (Windows Subsystem for Linux) to provide a Linux-like environment. However, if you prefer to run natively on Windows, follow these steps:

# 1. Clone repository
git clone https://github.com/microsoft/fara.git
cd fara

# 2. Setup environment
python3 -m venv .venv
.venv\Scripts\activate
pip install -e .
python3 -m playwright install

Hosting the Model

Recommended: The easiest way to get started is using Azure Foundry hosting, which requires no GPU hardware or model downloads. Alternatively, you can self-host with vLLM if you have GPU resources available.

Azure Foundry Hosting (Recommended)

Deploy Fara-7B on Azure Foundry without needing to download weights or manage GPU infrastructure.

Setup:

1. Deploy the Fara-7B model on Azure Foundry and obtain your endpoint URL and API key

Then create a endpoint configuration JSON file (e.g., azure_foundry_config.json):

{
    "model": "Fara-7B",
    "base_url": "https://your-endpoint.inference.ml.azure.com/",
    "api_key": "YOUR_API_KEY_HERE"
}

Then you can run Fara-7B using this endpoint configuration.

2. Run the Fara agent:

fara-cli --task "how many pages does wikipedia have" --endpoint_config azure_foundry_config.json [--headful]

Note: you can also specify the endpoint con