[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-70990":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":19,"rankGlobal":10,"rankLanguage":10,"license":20,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":23,"hasPages":21,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":28,"readmeContent":29,"aiSummary":30,"trendingCount":16,"starSnapshotCount":16,"syncStatus":31,"lastSyncTime":32,"discoverSource":33},70990,"self-operating-computer","OthersideAI\u002Fself-operating-computer","OthersideAI","A framework to enable a multimodal model to operate a computer.","https:\u002F\u002Fwww.hyperwriteai.com\u002Fself-operating-computer",null,"Python",10248,1423,132,80,0,1,7,44.46,"MIT License",false,"main",true,[25,26,27],"automation","openai","pyautogui","2026-06-12 02:02:46","ome\n\u003Ch1 align=\"center\">Self-Operating Computer Framework\u003C\u002Fh1>\n\n\u003Cp align=\"center\">\n  \u003Cstrong>A framework to enable multimodal models to operate a computer.\u003C\u002Fstrong>\n\u003C\u002Fp>\n\u003Cp align=\"center\">\n  Using the same inputs and outputs as a human operator, the model views the screen and decides on a series of mouse and keyboard actions to reach an objective. Released Nov 2023, the Self-Operating Computer Framework was one of the first examples of full computer-use. \n\u003C\u002Fp>\n\n\u003Cdiv align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002FOthersideAI\u002Fself-operating-computer\u002Fblob\u002Fmain\u002Freadme\u002Fself-operating-computer.png\" width=\"750\"  style=\"margin: 10px;\"\u002F>\n\u003C\u002Fdiv>\n\n\u003C!--\n:rotating_light: **OUTAGE NOTIFICATION: gpt-4o**\n**This model is currently experiencing an outage so the self-operating computer may not work as expected.**\n-->\n\n\n## Key Features\n- **Compatibility**: Designed for various multimodal models.\n- **Integration**: Currently integrated with **GPT-4o, GPT-4.1, o1, Gemini Pro Vision, Claude 3, Qwen-VL and LLaVa.**\n- **Future Plans**: Support for additional models.\n\n## Demo\nhttps:\u002F\u002Fgithub.com\u002FOthersideAI\u002Fself-operating-computer\u002Fassets\u002F42594239\u002F9e8abc96-c76a-46fb-9b13-03678b3c67e0\n\n\n## Run `Self-Operating Computer`\n\n1. **Install the project**\n```\npip install self-operating-computer\n```\n2. **Run the project**\n```\noperate\n```\n3. **Enter your OpenAI Key**: If you don't have one, you can obtain an OpenAI key [here](https:\u002F\u002Fplatform.openai.com\u002Faccount\u002Fapi-keys). If you need you change your key at a later point, run `vim .env` to open the `.env` and replace the old key. \n\n\u003Cdiv align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002FOthersideAI\u002Fself-operating-computer\u002Fblob\u002Fmain\u002Freadme\u002Fkey.png\" width=\"300\"  style=\"margin: 10px;\"\u002F>\n\u003C\u002Fdiv>\n\n4. **Give Terminal app the required permissions**: As a last step, the Terminal app will ask for permission for \"Screen Recording\" and \"Accessibility\" in the \"Security & Privacy\" page of Mac's \"System Preferences\".\n\n\u003Cdiv align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002FOthersideAI\u002Fself-operating-computer\u002Fblob\u002Fmain\u002Freadme\u002Fterminal-access-1.png\" width=\"300\"  style=\"margin: 10px;\"\u002F>\n  \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002FOthersideAI\u002Fself-operating-computer\u002Fblob\u002Fmain\u002Freadme\u002Fterminal-access-2.png\" width=\"300\"  style=\"margin: 10px;\"\u002F>\n\u003C\u002Fdiv>\n\n## Using `operate` Modes\n\n#### OpenAI models\n\nThe default model for the project is gpt-4o which you can use by simply typing `operate`. To try running OpenAI's new `o1` model, use the command below.\n\n```\noperate -m o1-with-ocr\n```\n\nTo experiment with OpenAI's latest `gpt-4.1` model, run:\n\n```\noperate -m gpt-4.1-with-ocr\n```\n\n\n### Multimodal Models  `-m`\nTry Google's `gemini-pro-vision` by following the instructions below. Start `operate` with the Gemini model\n```\noperate -m gemini-pro-vision\n```\n\n**Enter your Google AI Studio API key when terminal prompts you for it** If you don't have one, you can obtain a key [here](https:\u002F\u002Fmakersuite.google.com\u002Fapp\u002Fapikey) after setting up your Google AI Studio account. You may also need [authorize credentials for a desktop application](https:\u002F\u002Fai.google.dev\u002Fpalm_docs\u002Foauth_quickstart). It took me a bit of time to get it working, if anyone knows a simpler way, please make a PR.\n\n#### Try Claude `-m claude-3`\nUse Claude 3 with Vision to see how it stacks up to GPT-4-Vision at operating a computer. Navigate to the [Claude dashboard](https:\u002F\u002Fconsole.anthropic.com\u002Fdashboard) to get an API key and run the command below to try it. \n\n```\noperate -m claude-3\n```\n\n#### Try qwen `-m qwen-vl`\nUse Qwen-vl with Vision to see how it stacks up to GPT-4-Vision at operating a computer. Navigate to the [Qwen dashboard](https:\u002F\u002Fbailian.console.aliyun.com\u002F) to get an API key and run the command below to try it. \n\n```\noperate -m qwen-vl\n```\n\n#### Try LLaVa Hosted Through Ollama `-m llava`\nIf you wish to experiment with the Self-Operating Computer Framework using LLaVA on your own machine, you can with Ollama!   \n*Note: Ollama currently only supports MacOS and Linux. Windows now in Preview*   \n\nFirst, install Ollama on your machine from https:\u002F\u002Follama.ai\u002Fdownload.   \n\nOnce Ollama is installed, pull the LLaVA model:\n```\nollama pull llava\n```\nThis will download the model on your machine which takes approximately 5 GB of storage.   \n\nWhen Ollama has finished pulling LLaVA, start the server:\n```\nollama serve\n```\n\nThat's it! Now start `operate` and select the LLaVA model:\n```\noperate -m llava\n```   \n**Important:** Error rates when using LLaVA are very high. This is simply intended to be a base to build off of as local multimodal models improve over time.\n\nLearn more about Ollama at its [GitHub Repository](https:\u002F\u002Fwww.github.com\u002Follama\u002Follama)\n\n### Voice Mode `--voice`\nThe framework supports voice inputs for the objective. Try voice by following the instructions below. \n**Clone the repo** to a directory on your computer:\n```\ngit clone https:\u002F\u002Fgithub.com\u002FOthersideAI\u002Fself-operating-computer.git\n```\n**Cd into directory**:\n```\ncd self-operating-computer\n```\nInstall the additional `requirements-audio.txt`\n```\npip install -r requirements-audio.txt\n```\n**Install device requirements**\nFor mac users:\n```\nbrew install portaudio\n```\nFor Linux users:\n```\nsudo apt install portaudio19-dev python3-pyaudio\n```\nRun with voice mode\n```\noperate --voice\n```\n\n### Optical Character Recognition Mode `-m gpt-4-with-ocr`\nThe Self-Operating Computer Framework now integrates Optical Character Recognition (OCR) capabilities with the `gpt-4-with-ocr` mode. This mode gives GPT-4 a hash map of clickable elements by coordinates. GPT-4 can decide to `click` elements by text and then the code references the hash map to get the coordinates for that element GPT-4 wanted to click. \n\nBased on recent tests, OCR performs better than `som` and vanilla GPT-4 so we made it the default for the project. To use the OCR mode you can simply write: \n\n `operate` or `operate -m gpt-4-with-ocr` will also work. \n\n### Set-of-Mark Prompting `-m gpt-4-with-som`\nThe Self-Operating Computer Framework now supports Set-of-Mark (SoM) Prompting with the `gpt-4-with-som` command. This new visual prompting method enhances the visual grounding capabilities of large multimodal models.\n\nLearn more about SoM Prompting in the detailed arXiv paper: [here](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.11441).\n\nFor this initial version, a simple YOLOv8 model is trained for button detection, and the `best.pt` file is included under `model\u002Fweights\u002F`. Users are encouraged to swap in their `best.pt` file to evaluate performance improvements. If your model outperforms the existing one, please contribute by creating a pull request (PR).\n\nStart `operate` with the SoM model\n\n```\noperate -m gpt-4-with-som\n```\n\n\n\n## Contributions are Welcomed!:\n\nIf you want to contribute yourself, see [CONTRIBUTING.md](https:\u002F\u002Fgithub.com\u002FOthersideAI\u002Fself-operating-computer\u002Fblob\u002Fmain\u002FCONTRIBUTING.md).\n\n## Feedback\n\nFor any input on improving this project, feel free to reach out to [Josh](https:\u002F\u002Ftwitter.com\u002Fjosh_bickett) on Twitter. \n\n## Join Our Discord Community\n\nFor real-time discussions and community support, join our Discord server. \n- If you're already a member, join the discussion in [#self-operating-computer](https:\u002F\u002Fdiscord.com\u002Fchannels\u002F877638638001877052\u002F1181241785834541157).\n- If you're new, first [join our Discord Server](https:\u002F\u002Fdiscord.gg\u002FYqaKtyBEzM) and then navigate to the [#self-operating-computer](https:\u002F\u002Fdiscord.com\u002Fchannels\u002F877638638001877052\u002F1181241785834541157).\n\n## Follow HyperWriteAI for More Updates\n\nStay updated with the latest developments:\n- Follow HyperWriteAI on [Twitter](https:\u002F\u002Ftwitter.com\u002FHyperWriteAI).\n- Follow HyperWriteAI on [LinkedIn](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fothersideai\u002F).\n\n## Compatibility\n- This project is compatible with Mac OS, Windows, and Linux (with X server installed).\n\n## OpenAI Rate Limiting Note\nThe ```gpt-4o``` model is required. To unlock access to this model, your account needs to spend at least \\$5 in API credits. Pre-paying for these credits will unlock access if you haven't already spent the minimum \\$5.   \nLearn more **[here](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Frate-limits?context=tier-one)**\n","Self-Operating Computer Framework 是一个使多模态模型能够操作计算机的框架。它通过模拟人类操作员的输入和输出，让模型能够查看屏幕并决定一系列鼠标和键盘动作以达成目标。该框架支持多种多模态模型，包括GPT-4o、Gemini Pro Vision等，并且易于集成。其主要技术特点包括兼容性强、与主流AI模型集成以及未来将支持更多模型。适用于需要自动化桌面任务的场景，如自动化测试、数据处理或日常办公任务自动化。",2,"2026-06-11 03:35:21","high_star"]