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returns 402 with payment requirements until paid."}},{"slug":"open-spiel","name":"open_spiel","author":"google-deepmind","domain":"gaming","integrations":["Chess","Poker","Go"],"language":"C++","license":"Apache-2.0","stars":5275,"traits":["oss"],"summary":"Collection of environments and algorithms for reinforcement learning and search in over 70 board, card, and strategy games.","repo":"https://github.com/google-deepmind/open_spiel","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/open-spiel","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"arcade-learning-environment","name":"Arcade-Learning-Environment","author":"Farama-Foundation","domain":"gaming","integrations":["Atari"],"language":"C++","license":"Other","stars":2426,"traits":["oss"],"summary":"The ALE platform that lets agents play hundreds of Atari 2600 games for reinforcement-learning research.","repo":"https://github.com/Farama-Foundation/Arcade-Learning-Environment","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/arcade-learning-environment","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"vizdoom","name":"ViZDoom","author":"Farama-Foundation","domain":"gaming","integrations":["Doom"],"language":"C++","license":"Other","stars":2034,"traits":["oss"],"summary":"Reinforcement-learning environment built on the 1993 game Doom for training agents from raw visual input.","repo":"https://github.com/Farama-Foundation/ViZDoom","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/vizdoom","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"nle","name":"nle","author":"facebookresearch","domain":"gaming","integrations":["NetHack"],"language":"C","license":"Other","stars":984,"traits":["oss"],"summary":"The NetHack Learning Environment, a fast procedurally generated roguelike sandbox for reinforcement-learning agents.","repo":"https://github.com/facebookresearch/nle","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/nle","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"crafter","name":"crafter","author":"danijar","domain":"gaming","integrations":["Crafter"],"language":"Python","license":"MIT","stars":561,"traits":["oss"],"summary":"Open-world survival-game benchmark that evaluates a broad spectrum of agent capabilities in a single procedurally generated environment.","repo":"https://github.com/danijar/crafter","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/crafter","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"unity-mcp","name":"unity-mcp","author":"CoplayDev","domain":"gaming","integrations":["Unity"],"language":"C#","license":"MIT","stars":10763,"traits":["mcp","oss"],"summary":"MCP bridge between AI assistants and the Unity Editor for managing assets, controlling scenes, and editing scripts.","repo":"https://github.com/CoplayDev/unity-mcp","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/unity-mcp","note":"GET with an x402 client to tip the author; 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returns 402 with payment requirements until paid."}},{"slug":"minerl","name":"minerl","author":"minerllabs","domain":"gaming","integrations":["Minecraft"],"language":"Java","license":"Other","stars":957,"traits":["oss"],"summary":"Minecraft-based reinforcement-learning environment and dataset for sample-efficient agent research.","repo":"https://github.com/minerllabs/minerl","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/minerl","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"craftax","name":"Craftax","author":"MichaelTMatthews","domain":"gaming","integrations":["JAX"],"language":"Python","license":"MIT","stars":414,"traits":["oss"],"summary":"JAX reimplementation of Crafter and NetHack as a fast open-ended benchmark for reinforcement-learning agents.","repo":"https://github.com/MichaelTMatthews/Craftax","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/craftax","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"procgen","name":"procgen","author":"openai","domain":"gaming","integrations":["Gym"],"language":"C++","license":"MIT","stars":1167,"traits":["oss"],"summary":"Suite of procedurally generated game-like Gym environments for benchmarking generalization in RL agents.","repo":"https://github.com/openai/procgen","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/procgen","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"stable-baselines3","name":"Stable-Baselines3","author":"DLR-RM","domain":"gaming","integrations":["Gymnasium","PyTorch","ALE"],"language":"Python","license":"MIT","stars":13448,"traits":["oss"],"summary":"The standard library of reliable PyTorch RL algorithm implementations (PPO, SAC, DQN) used to train game-playing agents.","repo":"https://github.com/DLR-RM/stable-baselines3","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/stable-baselines3","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"meltingpot","name":"Melting Pot","author":"google-deepmind","domain":"gaming","integrations":["dmlab2d","PettingZoo","TensorFlow"],"language":"Python","license":"Apache-2.0","stars":846,"traits":["oss"],"summary":"DeepMind suite of multi-agent RL scenarios for evaluating cooperation, competition, and social behavior across games.","repo":"https://github.com/google-deepmind/meltingpot","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/meltingpot","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"textworld","name":"TextWorld","author":"microsoft","domain":"gaming","integrations":["OpenAI Gym","Inform7","PyTorch"],"language":"Python","license":"MIT","stars":1419,"traits":["oss"],"summary":"Microsoft sandbox for training and evaluating agents on text-based games, generating procedural interactive fiction worlds.","repo":"https://github.com/microsoft/TextWorld","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/textworld","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"godot-rl","name":"Godot RL Agents","author":"edbeeching","domain":"gaming","integrations":["Godot","Gymnasium","Stable-Baselines3"],"language":"Python","license":"MIT","stars":1507,"traits":["oss"],"summary":"Bridge that turns Godot Engine games into RL environments for training NPC and character behaviors; the Godot analog to ML-Agents.","repo":"https://github.com/edbeeching/godot_rl_agents","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/godot-rl","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"stable-retro","name":"Stable-Retro","author":"Farama-Foundation","domain":"gaming","integrations":["Gymnasium","libretro"],"language":"C++","license":"MIT","stars":379,"traits":["oss"],"summary":"Maintained Farama fork of Gym Retro turning classic console games (Genesis, SNES, NES) into Gym environments for RL agents.","repo":"https://github.com/Farama-Foundation/Stable-Retro","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/stable-retro","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"deepmind-lab","name":"DeepMind Lab","author":"google-deepmind","domain":"gaming","integrations":["Python","Bazel"],"language":"C","license":"Other","stars":7365,"traits":["oss"],"summary":"Customisable 3D first-person platform for agent research, providing navigation, memory, and puzzle tasks for RL agents.","repo":"https://github.com/google-deepmind/lab","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/deepmind-lab","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"minigrid","name":"Minigrid","author":"Farama-Foundation","domain":"gaming","integrations":["Gymnasium","PettingZoo","BabyAI"],"language":"Python","license":"MIT","stars":2463,"traits":["oss"],"summary":"Lightweight, configurable gridworld environments (including BabyAI) for benchmarking exploration and instruction-following agents.","repo":"https://github.com/Farama-Foundation/Minigrid","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/minigrid","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"google-research-football","name":"Google Research Football","author":"google-research","domain":"gaming","integrations":["OpenAI Gym","TensorFlow","PettingZoo"],"language":"Python","license":"Apache-2.0","stars":3620,"traits":["oss"],"summary":"Physics-based 3D football environment for training RL agents in single- and multi-agent modes with a scenario academy.","repo":"https://github.com/google-research/football","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/google-research-football","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"gaming-agent","name":"GamingAgent (lmgame-Bench)","author":"lmgame-org","domain":"gaming","integrations":["OpenAI","Anthropic","Gemini"],"language":"Python","license":"MIT","stars":940,"traits":["oss","keys"],"summary":"Framework of LLM/VLM gaming agents plus lmgame-Bench that evaluates models by having them actually play games like Sokoban and Mario.","repo":"https://github.com/lmgame-org/GamingAgent","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/gaming-agent","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"factorio-learning-environment","name":"Factorio Learning Environment","author":"JackHopkins","domain":"gaming","integrations":["Factorio","MCP","OpenAI"],"language":"Python","license":"MIT","stars":1004,"traits":["oss","mcp","keys"],"summary":"Open-ended environment for evaluating LLM agents in Factorio, testing long-horizon planning, program synthesis, and optimization.","repo":"https://github.com/JackHopkins/factorio-learning-environment","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/factorio-learning-environment","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"balrog","name":"BALROG","author":"balrog-ai","domain":"gaming","integrations":["NLE","Crafter","Gymnasium"],"language":"Python","license":"MIT","stars":255,"traits":["oss","keys"],"summary":"Benchmark for agentic LLM/VLM reasoning across challenging games including NetHack, MiniHack, Crafter, BabyAI, and Baba Is You.","repo":"https://github.com/balrog-ai/BALROG","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/balrog","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}},{"slug":"llm-colosseum","name":"llm-colosseum","author":"OpenGenerativeAI","domain":"gaming","integrations":["DIAMBRA","OpenAI","Anthropic"],"language":"Python","license":"MIT","stars":1483,"traits":["oss","keys"],"summary":"Benchmarks LLMs by having them fight each other in real-time Street Fighter III, a head-to-head game-agent evaluation harness.","repo":"https://github.com/OpenGenerativeAI/llm-colosseum","community":false,"tip":{"method":"x402","network":"eip155:8453","endpoint":"/api/tip/llm-colosseum","note":"GET with an x402 client to tip the author; returns 402 with payment requirements until paid."}}]}