Vision-Only Desktop Agents vs. API-Based: Running the Same Task Both Ways

📖 Overview | 🎯 Key Highlights | 🎬 Use Cases | 📊 Benchmark | 🔧 Skills | 🤖 Models | ⚡ Inference SDK | ⚗️ Approach | 🌟 Technical Advantages | 🔗 Applications | 📄 Citation | ❓ FAQ

Mano-P: "Mano" means "hand" in Spanish, and "P" stands for Private. We believe that both individuals and organizations can create their own Private AI, and a bright future of human-machine collaboration is on the horizon.

Mano-P is a GUI-VLA agent project designed specifically for edge devices. It serves both as an open-source project and a hardware product solution. As an open-source project, Mano-P is being released in a phased, progressive manner, targeting three distinct groups of developers. In the first phase, we will open-source the Mano-CUA Skills. This phase is aimed at Agent enthusiasts—such as users of OpenClaw or Claude Code—enabling them to leverage the capabilities of Mano-CUA Skills to construct more intelligent CUA task workflows and overcome the bottlenecks associated with human intervention. In the second phase, we will open-source the local-side models and SDK components of Mano-CUA. This phase targets developers with high security requirements, allowing them to directly utilize GUI-VLA models capable of running inference locally on a Mac mini to build their own custom Skills, Tools, and more; crucially, all your CUA operations will be executed entirely on your local Mac mini and will not be uploaded to external servers. In the third phase, we will open-source the training methodologies and the pruning and quantization techniques used for the Mano-P models. This phase is designed for developers with specific model training needs, empowering them to apply our training methods to create their own on-device GUI-VLA models tailored to their unique requirements.

Regarding our GUI-VLA models—which are capable of running inference directly on Mac mini and MacBook devices—we currently support two deployment methods: First, direct deployment on Mac mini or MacBook models equipped with an M4 chip and 32GB or more of RAM; and second, deployment utilizing a compute stick connected via a USB 4.0 port or higher. We will be releasing detailed instructions for both deployment methods in the near future, and we plan to expand our support to include additional deployment options in the future.

Complex GUI Automation: Autonomously complete complex interface operations containing hundreds of interactive elements

Cross-System Data Integration: Extract and integrate multi-source data through pure visual interaction without API interfaces

Long-Task Planning Execution: Support enterprise-level business process automation of dozens to hundreds of steps

Intelligent Report Generation: Automatically generate structured documents such as data analysis reports and work summaries

Edge-Native Inference: Efficient on-device execution on Apple Silicon via INT8 activation quantization (Cider)

Autonomous Application Construction: Drives end-to-end software construction pipelines through visual GUI operation (Mano-AFK)

Technical Background

Mano-P builds upon the complete technical framework of the Mano project (see Mano Technical Report), employing the Mano-Action bidirectional self-reinforcement learning method, three-stage progressive training (SFT → Offline Reinforcement Learning → Online Reinforcement Learning), "think-act-verify" loop reasoning mechanism, and a closed-loop data circulation system to achieve high-precision GUI understanding and operation capabilities. The edge version is optimized through mixed-precision quantization, visual token pruning, and edge inference adaptation, enabling large-scale parameter models to run efficiently on edge devices like Mac mini/MacBook/computing sticks.

#1 on OSWorld Benchmark: Mano-CUA 1.1 achieves 58.2% success rate on OSWorld, ranking first among all specialized GUI agent models, outperforming the second-place opencua-72b (45.0%) by 13.2 percentage points

Leading on WebRetriever Protocol I: Mano-CUA 1.1 scores 41.7 NavEval, surpassing Gemini 2.5 Pro Computer Use (40.9) and Claude 4.5 Computer Use (31.3)

Fully Local Execution: Runs inference locally on Apple M4 chip with 32GB RAM (Mac mini or MacBook). No cloud API calls required. All screenshots and task data stay on-device

High-Performance Inference: Mano-CUA-4B achieves ~80 tokens/s decode on Apple M5 Pro; with Cider's W8A8 activation quantization, prefill speeds up by ~12.7% over the W8A16 baseline

Autonomous Long-Task Execution: Supports complex business processes with end-to-end automation without internet connectivity

Edge-Native INT8 Acceleration: Companion Cider SDK adds the W8A8 / W4A8 activation-quantization primitives MLX lacks natively, delivering 1.4x–2.2x prefill speedup over MLX W4A16 on Apple M5 Pro — works with any MLX model, not just Mano-P

Autonomous Software Construction: Mano-AFK drives a full PRD → code → deploy → test → fix loop using Mano-P as its local vision model for real-browser E2E testing — from a single natural-language prompt to a deployed, tested application, no human in the loop

🎬 Use Case Demonstrations

Scenario 1: Mano-AFK Fully automated application construction

Mano-P_AFK_EN.mp4

We demonstrated the fully automated application construction process of Mano-AFK. After receiving natural language requirements, the system sequentially completes requirement clarification, technical architecture design, code generation, local deployment, and multi-level testing (API interface testing, LLM based page visual inspection, and end-to-end GUI automation testing driven by VLA model). When the test fails, the system automatically locates the root cause of the problem, fixes the code, and deploys verification again, iterating until all test cases pass. The entire process does not require manual intervention, and ultimately delivers a runnable application with complete requirement documents and build reports.

Mano-P.Commercial_video_intelligent_system_EN.mp4

We fully demonstrated the actual workflow of a commercial video intelligent system. Starting from the user's command, the system automatically completes the entire process of video generation, uploading, analysis, editing, and secondary evaluation. During the process, the system can autonomously operate web pages and editing software, complete fine operations such as file processing and subtitle modification, and generate analysis reports containing subjective evaluations and objective indicators. By comparing the differences between the initial and refined versions, visually present the overall capabilities and application effects of the system.

Mano-P._EN.mp4

Mano-P, The small-sized end side GUI-VLA model can run directly on your computer, supporting direct inference operation on Macmini/Macbook with M4 chip and above, as well as direct operation on plug and play computing power sticks. In the CUA scenario, break through the bottleneck of human participation in the Agent workflow. Mano-P, The first step in leading Private AI.

Mano_._EN.mp4

Mano-P excels not only in enterprise-level business automation but also integrates seamlessly into daily life. This video demonstrates the system's application in Mahjong gameplay: through pure visual understanding of the game interface, it autonomously completes tile recognition, analysis, and decision-making. This case validates Mano-P's general-purpose capabilities beyond work scenarios—from office automation to leisure entertainment, from structured data processing to unstructured game interactions, truly realizing the vision of "Private AI." One model, adapting to every aspect of life and work.

0429_Mano-AFK+Cider_EN_compressed.mp4

This video demonstrates the combined capability of Mano-AFK and the Cider inference acceleration SDK. Starting from a single natural-language requirement, Mano-AFK autonomously performs requirement clarification, architecture design, code generation, and local deployment. In the E2E testing stage it drives a real browser through the local Mano-P vision model — accelerated by Cider — to run GUI automation tests; on failure, it localizes the defect, patches the code, and re-verifies until a runnable application is delivered. Cider supplies the INT8 activation-quantization primitives that give Mano-P a substantial prefill speedup on Apple Silicon, so the entire build–test–fix loop runs fully on-device, balancing autonomy, privacy, and performance.

Performance of the Mano series models in multiple benchmarks:

📊 Expand Evaluation Data OSWorld - Specialized Models

📊 Expand Evaluation Data Video-SME-2

Models Protocol CA CV PAR Saliency

Acc F1 Acc F1 Acc F1 KL↓ CC↑ SIM↑ NSS↑ AUC↑

Random P1 10.42 11.03 10.76 10.95 15.94 16.00 2.1789 0.0452 0.2852 0.1081 0.5340

P2 10.01 10.74 10.32 10.50 14.39 15.04 4.3378 0.0270 0.2274 0.0665 0.5273

Zero-shot for MLLMs

GPT4o P1 15.17 6.57 16.11 9.58 16.71 10.34 1.9423 0.4660 0.4602 1.2842 0.7848

P2 10.26 4.77 12.16 7.66 15.00 8.55 2.2650 0.4097 0.4028 1.2418 0.7807

Gemini 2.0 Flash P1 17.18 5.13 25.06 8.39 24.94 9.52 1.4726 0.3380 0.3751 0.8629 0.7296

P2 10.45 4.26 12.60 4.95 15.96 7.90 1.6373 0.3542 0.3490 1.0027 0.7590

GPT-5.2 P1 17.83 7.67 22.22 12.55 16.17 9.74 1.3262 0.4852 0.4632 1.3078 0.7969

P2 15.31 5.14 19.88 10.27 13.56 7.42 1.5444 0.4379 0.4092 1.3006 0.7999

Claude Sonnet 4.5 P1 10.34 5.8 13.26 9.84 16.02 9.94 1.4235 0.4912 0.4213 1.2956 0.8042

P2 10.34 5.55 13.27 7.08 16.02 9.6 1.2855 0.4564 0.4781 1.3112 0.7915

Llama 4 Scout P1 13.98 9.96 10.25 6.51 13.27 8.11 3.7166 0.3331 0.3849 0.8828 0.7238

P2 10.00 7.33 11.10 8.49 14.35 7.42 3.7434 0.3019 0.3452 0.8848 0.7258

Qwen2.5-VL-7B P1 15.88 5.21 10.07 6.07 12.26 4.96 12.0586 0.0999 0.2154 0.2578 0.5852

P2 10.25 3.95 10.89 5.83 14.39 5.73 12.7596 0.0762 0.1855 0.2195 0.5753

InternVL3-8B P1 13.35 7.78 14.71 8.02 10.20 6.95 12.6480 0.0572 0.1895 0.1140 0.5769

P2 10.58 6.70 10.94 8.12 12.68 6.32 12.1385 0.0604 0.1819 0.1395 0.5859

Fine-tune for MLLMs

Qwen2.5-VL-7B P1 22.51 19.11 23.39 10.83 32.06 25.88 1.5091 0.6953 0.6118 1.8937 0.8579

P2 13.72 13.25 13.03 10.94 21.24 20.65 2.2496 0.5359 0.4793 1.6439 0.8221

InternVL3-8B P1 20.94 18.41 21.96 11.02 30.33 24.66 1.2551 0.7014 0.6340 1.9896 0.8670

P2 12.81 11.83 12.16 11.11 19.26 19.27 1.8759 0.6282 0.5467 2.0621 0.8627

Mano-CUA 1.1

Stage I P1 31.27 30.53 27.31 25.18 35.16 34.45 0.6794 0.7670 0.7015 2.1347 0.8710

P2 21.89 22.06 18.27 18.57 23.77 23.87 1.5759 0.6482 0.6167 2.1021 0.8627

Stage II P1 32.59 31.46 27.57 25.76 37.73 35.79 0.6736 0.7686 0.7120 2.1688 0.8853

P2 20.55 21.26 15.37 15.15 25.36 25.83 0.5617 0.6440 0.6130 2.1090 0.8602

Stage III P1 34.58 33.99 31.92 28.37 39.42 37.63 0.6073 0.7853 0.7248 2.2103 0.8938

P2 25.29 25.83 20.21 19.29 26.49 26.54 1.4617 0.6725 0.6330 2.1788 0.8776

MIT1003 & SalECI

Dataset Method Saliency

KL↓ CC↑ SIM↑ NSS↑ AUC↑

MIT1003 FastSal 1.036 0.590 0.478 2.008 0.875

SAM-Resnet 1.247 0.746 0.597 2.752 0.902

DAV 0.753 0.699 0.566 2.574 0.897

UNISAL 1.014 0.734 0.597 2.759 0.902

Transalnet 0.660 0.722 0.592 2.631 0.903

SUM 0.563 0.768 0.630 2.839 0.913

Mano-CUA 1.1 0.648 0.770 0.698 2.950 0.902

SalECI SSM 0.720 0.599 0.611 1.396 0.830

DeepGaze IIE 0.995 0.560 0.399 1.327 0.842

EML-NET 1.220 0.510 0.536 1.232 0.807

Transalnet 0.873 0.717 0.534 1.723 0.824

Temp-Sal 0.712 0.719 0.629 1.76