RRepoGEO

REPOGEO REPORT · LITE

microsoft/fara

Default branch main · commit 44908264 · scanned 5/11/2026, 11:57:15 PM

GitHub: 5,053 stars · 483 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface microsoft/fara, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Add a clear, concise positioning statement to the README's introduction

    Why:

    COPY-PASTE FIX
    Fara-7B is an efficient, agentic large language model specifically designed for complex computer and browser interactions. It is *not* a forensic analysis tool, nor is it a general-purpose Retrieval-Augmented Language Model (RALM). Instead, Fara-7B focuses on enabling autonomous computer use and provides critical benchmarks like CUAVerifierBench and WebTailBench for reliable, human-annotated evaluation of AI agents.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add the official project homepage URL (e.g., `https://aka.ms/msaif/fara`) to the repository's 'About' section.
  • mediumreadme#3
    Add a 'Key Features' section to highlight Fara-7B's specific capabilities

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Key Features' immediately after the introductory paragraph, detailing Fara-7B's unique aspects such as:
*   Efficient agentic model for computer use
*   Specialized for browser and desktop interactions
*   Includes human-annotated benchmarks (CUAVerifierBench, WebTailBench) for agent evaluation
*   Designed for AI developers and researchers working on autonomous agents

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface microsoft/fara
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-4
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-4 · recommended 1×
  2. Selenium · recommended 1×
  3. Playwright · recommended 1×
  4. Google Gemini · recommended 1×
  5. Microsoft Copilot Studio · recommended 1×
  • CATEGORY QUERY
    What AI models can help automate complex computer and browser interactions?
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Selenium
    3. Playwright
    4. Google Gemini
    5. Microsoft Copilot Studio
    6. Power Automate Desktop
    7. Robocorp
    8. OpenCV
    9. Tesseract
    10. Google Cloud Vision API
    11. Tome.app
    12. Applitools Eyes
    13. Testim.io

    AI recommended 13 alternatives but never named microsoft/fara. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to reliably evaluate the performance of AI agents for desktop automation?
    you: not recommended
    AI recommended (in order):
    1. OpenRPA (open-rpa/openrpa)
    2. Robot Framework (robotframework/robotframework)
    3. UIPath Test Suite
    4. Playwright (microsoft/playwright)
    5. PyAutoGUI (asweigart/pyautogui)
    6. SikuliX (RaiMan/SikuliX1)
    7. TestComplete
    8. Ranorex Studio
    9. Python
    10. time module
    11. PowerShell
    12. Measure-Command
    13. Selenium (SeleniumHQ/selenium)
    14. Appium Desktop (appium/appium-desktop)

    AI recommended 14 alternatives but never named microsoft/fara. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of microsoft/fara?
    pass
    AI named microsoft/fara explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts microsoft/fara in production, what risks or prerequisites should they evaluate first?
    pass
    AI named microsoft/fara explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo microsoft/fara solve, and who is the primary audience?
    pass
    AI named microsoft/fara explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of microsoft/fara. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/microsoft/fara.svg)](https://repogeo.com/en/r/microsoft/fara)
HTML
<a href="https://repogeo.com/en/r/microsoft/fara"><img src="https://repogeo.com/badge/microsoft/fara.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

microsoft/fara — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite