{"data":{"slug":"tanjeffreyz-auto-maple","name":"auto-maple","tagline":"Artificial intelligence for MapleStory that uses machine learning and computer vision to navigate challenging in-game environments","github_url":"https://github.com/tanjeffreyz/auto-maple","owner":"tanjeffreyz","repo":"auto-maple","owner_avatar_url":"https://avatars.githubusercontent.com/u/69165598?v=4","primary_language":"Python","stars":671,"forks":321,"topics":["ai","computer-vision","machine-learning","maplestory"],"archived":false,"github_pushed_at":"2025-12-26T03:55:23+00:00","maintenance_label":"Slowing","url":"https://www.graphcanon.com/tools/tanjeffreyz-auto-maple","markdown_url":"https://www.graphcanon.com/tools/tanjeffreyz-auto-maple.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/tanjeffreyz-auto-maple","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=tanjeffreyz-auto-maple","description":"Artificial intelligence for MapleStory that uses machine learning and computer vision to navigate challenging in-game environments","homepage_url":null,"license":null,"open_issues":60,"watchers":33,"ai_summary":null,"readme_excerpt":"

\n Auto Maple\n

\n\nAuto Maple is an intelligent Python AI that plays MapleStory, a 2D side-scrolling MMORPG, using simulated key presses, TensorFlow machine learning, OpenCV template matching, and other computer vision techniques.\n\nCommunity-created resources, such as **command books** for each class and **routines** for each map, can be found in the **[resources repository](https://github.com/tanjeffreyz/auto-maple-resources)**.\n\n
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\n Minimap\n

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\nAuto Maple uses OpenCV template matching to determine the bounds of the minimap as well as the various elements within it, allowing it to accurately track the player's in-game position. If record_layout is set to True, Auto Maple will record the player's previous positions in a quadtree-based Layout object, which is periodically saved to a file in the \"layouts\" directory. Every time a new routine is loaded, its corresponding layout file, if it exists, will also be loaded. This Layout object uses the A* search algorithm on its stored points to calculate the shortest path from the player to any target location, which can dramatically improve the accuracy and speed at which routines are executed.\n \n \n
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\n Command Books\n

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\n \n The above video shows Auto Maple consistently performing a mechanically advanced ability combination.\n \n

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\nDesigned with modularity in mind, Auto Maple can operate any character in the game as long as it is provided with a list of in-game actions, or a \"command book\". A command book is a Python file that contains multiple classes, one for each in-game ability, that tells the program what keys it should press and when to press them. Once a command book is imported, its classes are automatically compiled into a dictionary that Auto Maple can then use to interpret commands within routines. Commands have access to all of Auto Maple's global variables, which can allow them to actively change their behavior based on the player's position and the state of the game.\n
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\n Routines\n

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\n \n Click here to view the entire routine.\n \n

\n 		\nA routine is a user-created CSV file that tells Auto Maple where to move and what commands to use at each location. A custom compiler within Auto Maple parses through the selected routine and converts it into a list of Component objects that can then be executed by the program. An error message is printed for every line that contains invalid parameters, and those lines are ignored during the conversion. \n

\nBelow is a summary of the most commonly used routine components:\n
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  • \n Point stores the commands directly below it and will execute them in that order once the character is within move_tolerance of the specified location. There are also a couple optional keyword arguments:\n
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    • \n adjust fine-tunes the character's position to be within adjust_tolerance of the tar","github_created_at":"2021-04-14T06:57:42+00:00","created_at":"2026-07-11T12:28:22.192669+00:00","updated_at":"2026-07-11T12:28:26.962451+00:00","categories":[{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"},{"slug":"developer-tools","name":"Developer Tools","url":"https://www.graphcanon.com/categories/developer-tools","markdown_url":"https://www.graphcanon.com/categories/developer-tools.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/developer-tools"},{"slug":"computer-vision","name":"Computer Vision","url":"https://www.graphcanon.com/categories/computer-vision","markdown_url":"https://www.graphcanon.com/categories/computer-vision.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/computer-vision"}],"tags":[{"slug":"ai","name":"ai"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"python","name":"python"},{"slug":"maplestory","name":"maplestory"},{"slug":"computer-vision","name":"computer-vision"}],"trust":{"provenance":{"is_fork":false,"github_id":357805407,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T12:28:22.896Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":197,"last_release_at":"2023-03-15T10:47:59Z"},"security_summary":{"status":"ok","scanner":"osv@v1","low_count":0,"high_count":0,"last_scan_at":"2026-07-11T12:28:24.152Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-11T12:28:23.732Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-07-11T12:28:23.732Z"}}}}