fara README

The project's README from github.com/microsoft/fara. As one markdown file: README.md.

Fara1.5 – A family of frontier computer use agent models

Fara1.5 Performance

Blog Paper Foundry Foundry Foundry

Model Dataset Dataset

Updates

Overview

Fara1.5 is a family of native Computer Use Agents (CUAs) at three scales — Fara1.5-4B, Fara1.5-9B, and Fara1.5-27B — built on Qwen3.5 and trained with supervised finetuning on data from FaraGen1.5, our scalable data pipeline of environments, solvers, and verifiers. Each model sets a new state of the art for its size class on browser-use benchmarks, and Fara1.5-27B outperforms much larger proprietary systems such as OpenAI Operator and Gemini 2.5 Computer Use on Online-Mind2Web.

Fara1.5 models operate through an observe-think-act loop: given a screenshot of the browser and the conversation history, the model reasons about the state of the task and outputs an action — mouse and keyboard inputs on directly predicted coordinates, web searches, or context management operations — with no accessibility trees or separate parsing models.

Fara1.5 Observe-Think-Act Loop

All three models are available on Microsoft Foundry: Fara1.5-4B, Fara1.5-9B, and Fara1.5-27B.

Try Fara1.5-9B as follows (see Installation for detailed instructions):

# 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

Deploy Fara1.5-9B from the Microsoft Foundry catalog and put your endpoint in a config JSON (e.g. azure_foundry_config.json):

{
    "model": "Fara1.5-9B",
    "base_url": "https://your-endpoint.inference.ml.azure.com/",
    "api_key": "YOUR_API_KEY_HERE"
}

Then you can iteratively query it with:

fara-cli --task "whats the weather in new york now" --endpoint_config azure_foundry_config.json

To try Fara inside Magentic-UI — a sandboxed browser environment with auditable action logging and user prompts at critical points — follow the instructions in the Magentic-UI repo. You will need a model endpoint as before, but instead of fara-cli you can use Magentic-UI which has a nice UI (see video demos below).

Note: If you're using Windows, we highly recommend using WSL2 (Windows Subsystem for Linux). Please see the Windows instructions in the Installation section.

What Makes Fara1.5 Unique

Unlike traditional chat models that generate text-based responses, Fara1.5 leverages computer interfaces—mouse and keyboard—to perform multi-step tasks on behalf of users. The models:

FaraGen1.5: Scalable Learning Environments

Collecting computer use data from human demonstrations is expensive and slow. FaraGen1.5 is a scalable data pipeline composed of three modular components:

The resulting training mix contains roughly 2M samples: ~60% web trajectories, 12.8% synthetic environments, 12.5% form filling, 8.8% grounding, 4.9% VQA, and 0.8% GUI drag tasks.

Key Capabilities

Fara1.5 can automate everyday web tasks including:

Performance Highlights

Each Fara1.5 model sets a new state of the art for its size class. Fara1.5-9B improves over Fara-7B by +29.3 points on Online-Mind2Web, +13.1 points on WebVoyager, and +8.2 points on WebTailBench outcome success:

Model Size WebVoyager Online-Mind2Web WebTailBench v1.5 (Process) WebTailBench v1.5 (Outcome)
Larger and proprietary agents
o3 SoM - 79.3 55.4 69.5 35.0
GPT-5 SoM - 90.6 57.7 69.2 45.1
Gemini 2.5 Computer Use† - - 57.3 - -
OpenAI Operator† - 87.0 58.3 - -
Yutori Navigator (n1)† - - 64.7 - -
GUI-Owl-1.5† 32B 82.0 - - -
Holo2† 30B-A3B 83.0 - - -
Similarly sized agents
Fara-7B 7B 73.5 34.1 48.8 24.1
MolmoWeb† 8B 78.2 35.3 - -
Holo2† 8B 80.2 - - -
GUI-Owl-1.5† 8B 78.1 48.6 - -
Fara1.5 family (ours)
Fara1.5-4B 4B 80.8 57.3 60.3 27.4
Fara1.5-9B 9B 86.6 63.4 64.5 32.3
Fara1.5-27B 27B 89.3 72.3 72.9 40.2
FaraGen1.5 Solver (GPT-5.4) - 93.4 83.4 79.6 57.4

Table: Task success rate (%) on WebVoyager, Online-Mind2Web, and WebTailBench v1.5. For WebTailBench v1.5 we report both Process Success (correct intermediate steps) and Outcome Success (final task state correct). All Fara1.5 and Fara-7B numbers are averaged over three independent runs. The GPT-5.4-based FaraGen1.5 solver is an upper-bound reference for the SFT-based distillation. † denotes numbers sourced from the model's official release or leaderboard rather than re-run by us.

WebTailBench: Evaluating Real-World Web Tasks

WebTailBench is our 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 (shopping lists, comparison shopping, and compositional tasks). Tasks and precomputed rubrics are on Hugging Face (use the refreshed test_v2 split), the Universal Verifier (MMRubricAgent) is the official judge, and the reproducibility CLI lives in webeval/scripts/webtailbench.py.

CUAVerifierBench: Evaluating the Verifiers Themselves

While WebTailBench measures agents, CUAVerifierBench measures the judges that score those agents. Each row pairs a Fara 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 Hugging Face configs joinable on task_id:

Config Granularity Contents
trajectories one row per task instruction, screenshots, web_surfer log, verifier outputs, task-level human aggregates
annotations one row per (task, judge) per-reviewer outcome / process labels and free-text justifications

Two splits ship today:

Split Source Trajectories Annotation rows
fara7b_om2w_browserbase Fara-7B trajectories on Online-Mind2Web tasks executed via Browserbase 106 215 (≈2 reviewers/task; UV-blind and UV-informed stages)
internal Heldout aurora-v2 task suite scored with the same WebSurfer + verifier stack 154 154 (1 reviewer/task; UV-blind only)

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 Hugging Face at microsoft/CUAVerifierBench; see the dataset README for the full column list.

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 models

Note: Fara1.5 is a research preview 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:

# 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 Microsoft 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 Microsoft Foundry hosting, which requires no GPU hardware or model downloads. All three Fara1.5 models are available on Foundry.

Microsoft Foundry Hosting

Deploy Fara1.5 models from the Microsoft Foundry catalog (Fara1.5-4B, Fara1.5-9B, Fara1.5-27B) without needing to download weights or manage GPU infrastructure.

Setup:

  1. Deploy the model on Microsoft Foundry and obtain your endpoint URL and API key

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

{
    "model": "Fara1.5-9B",
    "base_url": "https://your-endpoint.inference.ml.azure.com/",
    "api_key": "YOUR_API_KEY_HERE"
}
  1. 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 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:

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

If you have access to GPU resources, you can download the model weights from Hugging Face, self-host Fara models with vLLM, and point fara-cli at the resulting OpenAI-compatible endpoint:

vllm serve <model> --port 5000 --dtype auto

The previous-generation Fara-7B weights remain available on Hugging Face (with GGUF variants for LM Studio / Ollama). Pass --fara-7b to run the Fara-7B agent instead of Fara1.5:

fara-cli --fara-7b --task "how many pages does wikipedia have" --endpoint_config fara7b_config.json

If you didn't use vLLM to host, specify --base_url [your_base_url] --api_key [your_api_key] --model [your_model_name] when running fara-cli. Please ensure that context length is set to at least 15000 tokens and temperature to 0 for best results.

Runs save a full trajectory — per-step screenshots and a data_point.json (task, actions, observations, and outcome) — to the folder passed via --output_folder.

Reproducibility

Instructions to reproduce our benchmark results (WebVoyager, Online-Mind2Web, WebTailBench) with the webeval/ framework — including installation, the per-benchmark CLIs, BrowserBase setup, and result analysis — live in docs/eval_reproducibility.md.

Note: the webeval pipeline documented there produced the previous-generation (Fara-7B) numbers and is being updated for the Fara1.5 evaluation stack.

Citation

If you use Fara1.5 in your research, please use the following BibTeX entry.

@article{fara152026,
  title={Fara1.5: Scalable Learning Environments for Computer Use Agents},
  author={Awadallah, Ahmed and Gupta, Sahil and Lara, Yash and Lu, Yadong and Mozannar, Hussein and Nambi, Akshay and Nussbaum, Zach and Pandya, Yash and Rajeswaran, Aravind and Rosset, Corby and Taymanov, Alexey and do Valle, Luiz and Vineet, Vibhav and Whitehead, Spencer and Zhao, Andrew},
  journal={arXiv:2606.20785},
  year={2026}
}

For the previous generation, Fara-7B:

@article{fara7b2025,
  title={Fara-7B: An Efficient Agentic Model for Computer Use},
  author={Awadallah, Ahmed and Lara, Yash and Magazine, Raghav and Mozannar, Hussein and Nambi, Akshay and Pandya, Yash and Rajeswaran, Aravind and Rosset, Corby and Taymanov, Alexey and Vineet, Vibhav and Whitehead, Spencer and Zhao, Andrew},
  journal={arXiv:2511.19663},
  year={2025}
}

fara (microsoft/fara)

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6.2K ·GitHub
Fara
这是做什么的？这是一系列开放权重计算机操作（computer-use）智能体模型——Fara1.5-4B、9B 和 27B，基于 Qwen3.5 构建——它们能像人一样控制鼠标和键盘，替你完成多步骤的网页任务。
那这是需要安装的软件，还是下载的模型？它是一个研究代码库，不是做好的成品应用：包含开放的模型权重、一个 Python 命令行工具（fara-cli）以及评测工具。你是把 CLI 指向一个模型接口——托管在 Microsoft Foundry 上或自己部署——而不是安装一个独立程序。
怎么把它跑起来？先用 pip 安装 Python 包并配置好 Playwright，从 Microsoft Foundry 目录部署一个 Fara1.5 模型，把接口地址和 API 密钥填进配置 JSON（例如 azurefoundryconfig.json），然后运行 fara-cli。
让它在浏览器里自己操作安全吗？目前只是研究预览版：模型会拒绝有害任务，遇到信息缺失、任务含糊或不可逆操作时会暂停询问，微软建议在沙盒环境中运行，远离敏感数据和高风险网站。
在 Windows 上能用吗？主要面向 Linux；在 Windows 上强烈建议使用 WSL2，不过 README 也提供了在 Windows 上直接安装的步骤。
运行它需要我自己的显卡吗？不一定：用 Microsoft Foundry 托管不需要 GPU 硬件，也不用下载模型。如果想用 vLLM 自己部署，就需要 GPU 资源——从 Hugging Face 拉取权重，然后对外提供一个 OpenAI 兼容的接口。
它能自动化哪些网页任务？都是日常的网上事务：搜索信息并整理摘要、填表和管理账户、预订行程、电影票和餐厅、购物并在不同商店之间比价，以及找工作职位或看房源信息。
和那些大型商业智能体相比，它表现怎么样？以它的体量来说相当强：Fara1.5-9B 在 WebVoyager 上得分 86.6%，在 Online-Mind2Web 上得分 63.4%，都是同级别中最先进的；27B 版本在 Online-Mind2Web 上达到 72.3%，超过了 OpenAI Operator 和 Gemini 2.5 Computer Use 这类大得多的系统。
FaraGen1.5 是什么？是这些模型背后的训练数据生成流程，分三部分：环境（真实网站加上 Mail、Calendar 等六个模拟克隆站）、求解器（可以运行在 GPT-5.4 这类前沿模型上）和验证器。它生成了大约 200 万条训练样本，其中约 60% 是网页操作轨迹。
可以不用命令行，用图形界面操作它吗？可以——Magentic-UI 提供一个沙盒浏览器环境，带可审计的操作日志，并在关键节点向你确认。你提供同样的模型接口，用它来代替 fara-cli 即可。
What are similar projects?Agent-S, magentic-ui, DeepAnalyze, LoongForge, verl, open-r1, UI-TARS, midscene
Which topics is it in?cua, computer-use-agent, sft, browser-use, vlm, computer-use, microsoft, playwright, vllm, qwen, fine-tuning, multimodal
Creator
microsoft
Size
9.9 MB
Stars
6.2K
Updated
CommitJul 22
Age
11mo
Commits · 90 Days