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All models by microsoft →Fara1.5-4B is a multimodal computer use agent (CUA) for web browsers, from Microsoft Research AI Frontiers. It observes the browser through screenshots and acts on the user's behalf by emitting structured tool calls — click, type, scroll, visit URL, web search, and so on — to complete tasks end-to-end.
The model is vision-only at perception time: it sees the browser through screenshots, not the DOM or accessibility tree. Internal reasoning and trajectory history are tracked as text. Given the latest screenshot and prior actions, it predicts the next action with grounded arguments (e.g., pixel coordinates for a click).
Fara1.5-4B is supervised fine-tuned from Qwen3.5-4B on data generated by FaraGen1.5, our multi-agent pipeline that synthesizes web tasks, executes trajectories to solve them, and verifies the results before training.
Please use only with the harness developped in Github/fara CLI or Magentic-Lite.
It's co-designed with MagenticLite, and that's the recommended deployment for both research and production.
The safest way to run Fara1.5-4B is inside MagenticLite, which provides:
If you integrate Fara1.5-4B directly, you're responsible for these controls. Don't run this model with unrestricted browser access on a machine that has anything sensitive on it.
torch >= 2.11.0transformers >= 5.2.0vllm >= 0.19.1Fara1.5-4B is trained against a specific system prompt. Use it verbatim for best results:
The full system prompt, including the complete computer_use tool schema, ships with the model in MagenticLite.
Fara1.5-4B emits actions as <tool_call>...</tool_call> XML blocks containing a JSON object that calls the computer_use function. Supported actions:
The screen resolution Fara is most commonly trained with is 1440×900. Match this in your sandbox for the most reliable grounding.
Note: We strongly recommend to use only with the harness developped in Github/fara or Magentic-Lite.
A reference implementation of the full agent loop, including screenshot capture and Docker sandboxing, is in MagenticLite.
Fara1.5-4B is trained to pause and ask the user at three types of critical points:
Concrete actions Fara is trained to stop on without explicit authorization:
If the user grants authorization, the model proceeds. The principle is that costly, irreversible actions require a human in the loop.
We evaluate Fara1.5-4B on end-to-end web agent benchmarks. For WebTailBench, we report outcome success.
Supervised Fine-Tuning on top of Qwen3.5-4B. Training mix is generated by FaraGen1.5, our multi-agent data pipeline, supplemented with curated public datasets for grounding and UI understanding.
The corpus is static. Future updates will ship as separately versioned models with their own model cards.
Automating repetitive web tasks: filling forms, shopping, booking travel, restaurant reservations, information seeking, account workflows. Fara1.5-4B can also serve as a grounding model for other agents that need pixel-accurate action prediction.
Computer use is a powerful capability. An agent that can click, type, and submit in a real browser can also do those things wrong. We strongly recommend:
Fara1.5-4B was evaluated via automated red-teaming on Azure. Coverage included groundedness, jailbreak resistance, harmful content (hate, violence, self-harm), and copyright violations. Safety post-training includes both refusal data for malicious tasks and the critical-points framework described above.
Like all language models, Fara1.5-4B can produce unfair, unreliable, or offensive outputs. Specific concerns:
Developers deploying Fara1.5-4B should apply responsible AI best practices and ensure compliance with applicable laws and regulations. Using safety services like Azure AI Content Safety is recommended.
Released under the MIT License.
For information requests under the EU AI Act and related inquiries: MSFTAIActRequest@microsoft.com
Authorized representative: Microsoft Ireland Operations Limited, 70 Sir John Rogerson's Quay, Dublin 2, D02 R296, Ireland.
vllm serve microsoft/Fara1.5-4B \ --dtype bfloat16 \ --max-model-len 262144 \ --limit-mm-per-prompt image=10You are Fara, a computer use agent (CUA) specialized for web browsers. You are developed by Microsoft AI Frontiers. You assist users with completing and automating tasks that require the use of a web browser.
The model was trained in the timeframe of January - April 2026. You can effectively perform tasks even beyond this range by accessing the web browser and using the latest information on the live web. But your knowledge cutoff is limited to early 2026, so you may not be aware of events or developments that occurred after that time, without explicitly browsing and searching for latest information on the web.
This edition of the model was trained using SFT on top of Qwen3.5-4B, using a synthetic data mixture generated and developed by Microsoft AI Frontiers.
A critical point is a situation where we must pause and request information or confirmation from the user before proceeding. There are three types:
Case 1: Missing User Information — The task requires personal information that the user has not provided (e.g., email, phone number, address, payment details). Never fabricate or assume personal information. Fill in only what the user has explicitly provided, then pause and ask for any missing required fields.
Case 2: Underspecified Task — The task description is ambiguous or missing details needed to make a decision at the current step. Pause and ask for clarification.
Case 3: Irreversible Action — We are about to perform an action that cannot be undone (e.g., submitting a form, completing a purchase, sending a message, deleting data). If the user explicitly authorized the action, proceed. Otherwise, stop and ask for confirmation.
Only stop at a critical point if (1) required information is missing, (2) the task is ambiguous, OR (3) an irreversible action lacks explicit user authorization.# Pseudocode for a single-step interaction. Use vLLM's OpenAI-compatible# endpoint and pass the screenshot as an image content part.
screenshot_b64 = capture_browser_screenshot() # base64 PNG
messages = [ {"role": "system", "content": FARA_SYSTEM_PROMPT}, {"role": "user", "content": [ {"type": "text", "text": "Book a table for 2 at a sushi place in Sunnyvale for Friday 7pm."}, {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{screenshot_b64}"}}, ]},]
response = client.chat.completions.create( model="microsoft/Fara1.5-4B", messages=messages, temperature=0.0, max_tokens=2048,)
# Parse the assistant's reply: it will contain chain-of-thought text# followed by <tool_call>{"name": "computer_use", "arguments": {...}}</tool_call>.# Execute the action in your sandboxed browser, capture the new screenshot,# append the assistant turn and a tool/observation turn, and loop until# the model emits action="terminate". We only keep the most recent 3 screenshots# in the chat history.