auto-maple
Python AI for playing MapleStory using machine learning and computer vision
GraphCanon updated 2w · GitHub synced 2w
Decision brief
Auto Maple employs TensorFlow for machine learning and OpenCV for computer vision to navigate and automate gameplay in MapleStory.
Good fit when
- When seeking to automate gameplay actions in MapleStory with specific support for command books tailored to the game's mechanics
- For users looking to integrate a quadtree-based Layout object for improved navigation through map routines
Avoid when
- When the need is for general-purpose game automation not specific to MapleStory's unique requirements and content
- For users who do not require or prefer not to use TensorFlow for machine learning aspects, focusing instead on more straightforward scripting methods
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Slowing (217d since push)
- As of 2w
- Provenance
- Not a fork · Personal account
- As of 2w
- Security (OSV)
- No criticals
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install auto-mapleSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Auto Maple uses TensorFlow for machine learning and OpenCV for computer vision to play MapleStory by simulating key presses.
Capability facts
- Languages
- python
Source: github.language · Jul 31, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 31, 2026)
Auto Maple is an intelligent Python AI that plays MapleStory, a 2D side-scrolling MMORPG, using simulated key presseSource link
Tags
README
Auto Maple
Auto 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.
Community-created resources, such as command books for each class and routines for each map, can be found in the resources repository.
Minimap
Auto 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.
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Command Books
The above video shows Auto Maple consistently performing a mechanically advanced ability combination.
| Designed 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. |
Routines
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A 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.
Below is a summary of the most commonly used routine components:
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For agents
This page has a .md twin and JSON over the API.