Microsoft recently launched a practical, hands-on tool that lets a 7B model take over your mouse and keyboard! FARA ditches pointless chat and focuses purely on local desktop automation. Its core strengths boil down to two words: obedient and cost-effective:

1. Pure desktop execution: It opens web pages, fills out forms, and automatically runs through all repetitive mechanical workflows.
2. Extremely low barrier: With only 7B parameters, it runs locally on a normal computer — no token burning, no internet dependency.

microsoft/fara

Source: https://github.com/microsoft/fara

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

Microsoft (https://aka.ms/msaif/fara)
Hugging Face Model (https://huggingface.co/microsoft/Fara-7b)
Foundry (https://aka.ms/foundry-fara-7b)
Dataset (https://huggingface.co/datasets/microsoft/WebTailBench)
Dataset (https://huggingface.co/datasets/microsoft/CUAVerifierBench)
Paper (https://arxiv.org/abs/2511.19663)

Updates

• 2026-05-21 - Fara1.5 agent harness coming soon!
• 2026-05-12 — Refreshed WebTailBench (V2) tasks and rubrics. Many V1 tasks had calendar-bound dates that expired (Nov 2025); V2 rolls those forward and revises the precomputed rubrics for the full 609-task suite. Available now as the test_v2 split on microsoft/WebTailBench (https://huggingface.co/datasets/microsoft/WebTailBench). A side-by-side V1↔V2 diff (task strings and rubric JSON) is hosted here (https://microsoft.github.io/fara/docs/webtailbench_v1_v2_diff.html).
• 2026-04-19 — Released CUAVerifierBench (https://huggingface.co/datasets/microsoft/CUAVerifierBench), a human-annotated benchmark for evaluating CUA verifiers (i.e. judges that score agent trajectories). Two splits — fara7b_om2w_browserbase (106 Fara-7B Online-Mind2Web/Browserbase trajectories, ~2 reviewers each) and internal (154 trajectories from a heldout aurora-v2 task suite) — with per-judge UV-blind / UV-informed labels, Universal Verifier outputs, and legacy verifier outputs side-by-side. The build script that produced the dataset lives alongside the data on HuggingFace.
• 2026-04-18 — Removed the autogen-core / autogen-ext dependency from webeval; chat completion clients are now self-contained under webeval/src/webeval/oai_clients/. No more autogen submodule install step; just pip install -e .[vllm] then cd webeval; pip install -e ..
• 2026-04-18 — Incorporated WebTailBench (initial / now-stale version) directly into the repo as a first-class benchmark. The loader auto-downloads WebTailBench-v1-rubrics.tsv from microsoft/WebTailBench (https://huggingface.co/datasets/microsoft/WebTailBench) and threads each task’s published precomputed_rubric through to the verifier. Reproducibility CLI lives in webeval/scripts/webtailbench.py.
• 2026-04-18 — Released the Universal Verifier (MMRubricAgent) as the official verifier for WebTailBench. Multimodal, rubric-grounded, two-model ensemble (gpt-5.2 + o4-mini) with per-criterion scoring, outcome verification, and first-point-of-failure analysis. A stand-alone parallel runner is at webeval/scripts/verify_trajectories.py for re-scoring any directory of webeval trajectories without touching the solver.

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:

``bash

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:

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

Then you can iteratively query it with:

bash 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 (https://github.com/microsoft/magentic-ui/blob/main/README.md#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

Shopping
GitHub Issues
Directions with Cheese

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 (https://www.microsoft.com/en-us/research/articles/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks/) multi-agent framework, with 145K trajectories covering diverse websites, task types, and difficulty levels. The model is based on Qwen2.5-VL-7B (https://arxiv.org/abs/2502.13923) 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:

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 (https://huggingface.co/datasets/microsoft/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

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)

CUAVerifierBench: Evaluating the Verifiers Themselves

While WebTailBench measures agents, CUAVerifierBench (https://huggingface.co/datasets/microsoft/CUAVerifierBench) measures the judges that score those agents. Each row pairs a Fara-7B agent trajectory (instruction, screenshots, web_surfer log, final answer) with one human reviewer’s verdict, plus the verdicts produced by the Universal Verifier (MMRubricAgent) and several legacy verifiers — so researchers can compute verifier–human agreement (Cohen’s κ, accuracy, F1) on a fixed corpus and iterate on new judge prompts / architectures against a frozen ground-truth set.

The dataset is exposed as two HuggingFace configs joinable on task_id:

Two splits ship today:

Reviewer identities are anonymized as Judge1 … JudgeN using a single shared map across both splits. The build script that produced the dataset (with full schema + provenance) ships alongside the data on HuggingFace at microsoft/CUAVerifierBench (https://huggingface.co/datasets/microsoft/CUAVerifierBench); see the dataset README (https://huggingface.co/datasets/microsoft/CUAVerifierBench/blob/main/README.md) for the full column list.

``python
from datasets import load_dataset

trajs = load_dataset(“microsoft/CUAVerifierBench”, “trajectories”, split=“fara7b_om2w_browserbase”)
anns = load_dataset(“microsoft/CUAVerifierBench”, “annotations”, split=“fara7b_om2w_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.

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:

``bash

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:

``bash

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 (https://ai.azure.com/explore/models/Fara-7B/version/2/registry/azureml-msr) 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):

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:

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

Note: you can also specify the endpoint config with the args --base_url [your_base_url] --api_key [your_api_key] --model [your_model_name] instead of using a config JSON file.

Note: If you see an error that the fara-cli command is not found, then try:

bash python -m fara.run_fara --task "what is the weather in new york now"

That’s it! No GPU or model downloads required.

Self-hosting with vLLM or LM Studio / Ollama

If you have access to GPU resources, you can self-host Fara-7B using vLLM. This requires a GPU machine with sufficient VRAM (e.g., 24GB or more). Only on Linux: all that is required is to run the following command to start the vLLM server:

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

For quantized models or lower VRAM GPUs, please see Fara-7B GGUF on HuggingFace (https://huggingface.co/bartowski/microsoft_Fara-7B-GGUF).

For Windows/Mac, vLLM is not natively supported. You can use WSL2 on Windows to run the above command or LM Studio / Ollama as described below.

Otherwise, you can use LM Studio (https://lmstudio.ai/) or Ollama (https://ollama.com/) to host the model locally. We currently recommend the following GGUF versions of our models Fara-7B GGUF on HuggingFace (https://huggingface.co/bartowski/microsoft_Fara-7B-GGUF) for use with LM Studio or Ollama. Select the largest model that fits your GPU. Please ensure that context length is set to at least 15000 tokens and temperature to 0 for best results.

Then you can run Fara-7B pointing to your local server:

Run the test script to see Fara in action:

bash fara-cli --task "what is the weather in new york now"

If you didn’t use vLLM to host, please specify the correct --base_url [your_base_url] --api_key [your_api_key] --model [your_model_name]

If you see an error that the fara-cli command is not found, then try:

bash python -m fara.run_fara --task "what is the weather in new york now"

Reproducibility

We provide a framework in webeval/ to reproduce our results on WebVoyager and OnlineMind2Web. Agentic evaluations on live websites present unique challenges due to day-to-day changes. We implement several measures to ensure reliable and comparable evaluations:

BrowserBase Integration
We employ BrowserBase to manage browser session hosting, enabling reliable browser instance management.

Time-sensitive Task Updates
Tasks in benchmarks like WebVoyager can become stale or impossible. We:

• Removed ~48 impossible tasks from the o