--- title: "Voyager" type: "tool" slug: "minedojo-voyager" canonical_url: "https://www.graphcanon.com/tools/minedojo-voyager" github_url: "https://github.com/MineDojo/Voyager" homepage_url: "https://voyager.minedojo.org/" stars: 7033 forks: 686 primary_language: "JavaScript" license: "MIT" categories: ["llm-frameworks", "ai-agents"] tags: ["minecraft", "embodied-learning", "open-ended-learning", "large-language-models"] updated_at: "2026-07-07T19:50:46.945903+00:00" --- # Voyager > LLM-powered embodied lifelong learning agent in Minecraft An open-ended embodied agent that uses large language models, specifically GPT-4, to explore the Minecraft world autonomously, acquire diverse skills, and generate novel discoveries. It includes a curriculum for maximizing exploration, an expandable skill library of executable code, and an iterative prompting mechanism for program improvement. ## Facts - Repository: https://github.com/MineDojo/Voyager - Homepage: https://voyager.minedojo.org/ - Stars: 7,033 · Forks: 686 · Open issues: 8 · Watchers: 73 - Primary language: JavaScript - License: MIT - Last pushed: 2024-04-03T18:51:36+00:00 ## Categories - [LLM Frameworks](/categories/llm-frameworks.md) - [AI Agents](/categories/ai-agents.md) ## Tags minecraft, embodied-learning, open-ended-learning, large-language-models ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,991) - [hermes-agent](/tools/nousresearch-hermes-agent.md) - The self-improving AI agent built by Nous Research (★ 210,911) - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,420) - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. (★ 175,664) - [prompts.chat](/tools/f-prompts-chat.md) - The world's largest open-source prompt library for AI (★ 165,025) - [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,350) - [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful platform for building and deploying AI-powered agents and workflows. (★ 151,311) - [dify](/tools/langgenius-dify.md) - Production-ready platform for agentic workflow development (★ 148,074) ## README (excerpt) ```text # Voyager: An Open-Ended Embodied Agent with Large Language Models
[[Website]](https://voyager.minedojo.org/) [[Arxiv]](https://arxiv.org/abs/2305.16291) [[PDF]](https://voyager.minedojo.org/assets/documents/voyager.pdf) [[Tweet]](https://twitter.com/DrJimFan/status/1662115266933972993?s=20) ______________________________________________________________________ https://github.com/MineDojo/Voyager/assets/25460983/ce29f45b-43a5-4399-8fd8-5dd105fd64f2
We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-growing skill library of executable code for storing and retrieving complex behaviors, and 3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement. Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning. The skills developed by Voyager are temporally extended, interpretable, and compositional, which compounds the agent’s abilities rapidly and alleviates catastrophic forgetting. Empirically, Voyager shows strong in-context lifelong learning capability and exhibits exceptional proficiency in playing Minecraft. It obtains 3.3× more unique items, travels 2.3× longer distances, and unlocks key tech tree milestones up to 15.3× faster than prior SOTA. Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize. In this repo, we provide Voyager code. This codebase is under [MIT License](LICENSE). # Installation Voyager requires Python ≥ 3.9 and Node.js ≥ 16.13.0. We have tested on Ubuntu 20.04, Windows 11, and macOS. You need to follow the instructions below to install Voyager. ## Python Install ``` git clone https://github.com/MineDojo/Voyager cd Voyager pip install -e . ``` ## Node.js Install In addition to the Python dependencies, you need to install the following Node.js packages: ``` cd voyager/env/mineflayer npm install -g npx npm install cd mineflayer-collectblock npx tsc cd .. npm install ``` ## Minecraft Instance Install Voyager depends on Minecraft game. You need to install Minecraft game and set up a Minecraft instance. Follow the instructions in [Minecraft Login Tutorial](installation/minecraft_instance_install.md) to set up your Minecraft Instance. ## Fabric Mods Install You need to install fabric mods to support all the features in Voyager. Remember to use the correct Fabric version of all the mods. Follow the instructions in [Fabric Mods Install](installation/fabric_mods_install.md) to install the mods. # Getting Started Voyager uses OpenAI's GPT-4 as the language model. You need to have an OpenAI API key to use Voyager. You can get one from [here](https://platform.openai.com/account/api-keys). After the installation process, you can run Voyager by: ```python from voyager import Voyager # You can also use mc_port instead of azure_login, but azure_login is highly recommended azure_login = { "client_id": "YOUR_CLIENT_ID", "redirect_url": "https://127.0.0.1/auth-response", "secret_value": "[OPTIONAL] YOUR_SECRET_VALUE", "version": "fabric-loader-0.14.18-1.19", # the version Voyager is tested on } openai_api_key = "YOUR_API_KEY" voyager = Voyager( azure_login=azure_login, openai_api_key=openai_api_key, ) # start lifelong learning voyager.learn() ``` * If you are running with `Azure Login` for the first time, it will ask you to follow the command line instruction to generate a config file. * For `Azure Login`, you also need to select the world and open the world to LAN by yourself. After you run `voyager.learn()` the game will pop up soon, you need to: 1. ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/minedojo-voyager`](/api/graphcanon/tools/minedojo-voyager) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_