# Voyager
Voyager is an open-ended embodied agent for [Minecraft](https://www.edgepedia.org/minecraft), built on GPT-4, that continuously explores the game world, acquires skills, and makes discoveries without human intervention; it was introduced in May 2023 by Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi "Jim" Fan, and [Anima Anandkumar](https://www.edgepedia.org/anima-anandkumar), affiliated with UT Austin, UW Madison, NVIDIA, Caltech, and Stanford.[1](https://arxiv.org/html/2305.16291v2) The authors describe it as the first LLM-powered embodied lifelong learning agent in Minecraft.[1](https://arxiv.org/html/2305.16291v2) Its central design choice is that GPT-4 is queried only as a black box: the model's world knowledge enters through text and generated code, with no parameter fine-tuning and no gradient updates of any kind.[1](https://arxiv.org/html/2305.16291v2) A retrospective account summarizes the result as an agent that learned to chop wood, then smelt iron, then mine diamond, on its own, without a single gradient update.[2](https://agentmuseum.org/exhibit/voyager-open-ended-agent)
| Key fact | Detail |
|---|---|
| What it is | LLM-powered embodied lifelong learning agent in Minecraft, no fine-tuning or gradient updates[1](https://arxiv.org/html/2305.16291v2) |
| Introduced | May 2023, arXiv:2305.16291, by Wang et al. (UT Austin, UW Madison, NVIDIA, Caltech, Stanford)[1](https://arxiv.org/html/2305.16291v2) |
| Three mechanisms | Automatic curriculum, ever-growing skill library of executable code, iterative prompting with environment feedback[1](https://arxiv.org/html/2305.16291v2) |
| Self-reported results | 63 unique items in 160 prompting iterations (preprint)[1](https://arxiv.org/html/2305.16291v2); 3.1x more unique items, 2.3x longer travel, up to 15.3x faster tech-tree milestones than prior SOTA (TMLR version)[3](https://mlanthology.org/tmlr/2024/wang2024tmlr-voyager/) |
| Zero-shot generalization | Solved unseen tasks in a new world; AutoGPT solved none within 50 iterations[1](https://arxiv.org/html/2305.16291v2) |
| Perception | Text only; no visual perception, since the GPT-4 API was text-only at the time[1](https://arxiv.org/html/2305.16291v2) |
| Code | Open source under the MineDojo GitHub organization; research use only, not an official NVIDIA product[4](https://github.com/MineDojo/Voyager) |
| Publication | NeurIPS 2023 presentation; peer-reviewed in Transactions on Machine Learning Research in 2024[3](https://mlanthology.org/tmlr/2024/wang2024tmlr-voyager/) |
## How it works
Voyager runs on three components that feed each other in a loop.[1](https://arxiv.org/html/2305.16291v2)
**Automatic curriculum.** Rather than following a fixed objective, the agent proposes its own next tasks to maximize exploration. This is what makes the agent open-ended: the curriculum grows out of what the agent has already learned and what it observes in the world, without human intervention.[1](https://arxiv.org/html/2305.16291v2)
Skill library. Every new ability Voyager acquires is written as executable code and stored in an ever-growing library. Skills are stored in a vector database keyed by the embedding of a program description generated by GPT-3.5, with the program itself as the value; when a new task arrives, the top-5 most relevant skills are retrieved and injected into the prompt.[1](https://arxiv.org/html/2305.16291v2) Complex skills are synthesized by composing simpler programs, which the authors say compounds capabilities and alleviates the catastrophic forgetting that affects other continual-learning methods.[1](https://arxiv.org/html/2305.16291v2) This differs from fine-tuning or reinforcement-learning policies in that learning is stored as inspectable, reusable code rather than as changes to model weights; a learned skill library can even be passed to a fresh agent, which the official repository supports by loading a previously learned skill library directory and running task decomposition followed by inference with learned skills, for example "Craft a diamond pickaxe".[4](https://github.com/MineDojo/Voyager)
**Iterative prompting.** Generated programs are executed in Minecraft, and the environment feedback, execution errors, and a self-verification check are fed back into GPT-4's prompt for refinement; the loop repeats until the self-verification module confirms task completion, at which point the program is committed to the skill library.[1](https://arxiv.org/html/2305.16291v2) A later analysis describes this as storing winning code on task success, with up to four refinement rounds per task.[5](https://beancount.io/bean-labs/research-logs/2026/05/08/voyager-open-ended-embodied-agent-lifelong-learning)
The pieces fit together as a cycle: the curriculum proposes a task, retrieval pulls relevant skills, GPT-4 writes or refines code through the iterative prompting loop, and verified code joins the library, expanding what future curricula and retrievals can build on.[1](https://arxiv.org/html/2305.16291v2) With human feedback, either as critic or as curriculum provider, Voyager can also construct complex 3D structures such as a Nether Portal and a house.[1](https://arxiv.org/html/2305.16291v2)
## Origin, release and code
The paper appeared on arXiv in May 2023 (arXiv:2305.16291).[1](https://arxiv.org/html/2305.16291v2) It was presented at NeurIPS 2023 and subsequently passed peer review, appearing in Transactions on Machine Learning Research in 2024.[3](https://mlanthology.org/tmlr/2024/wang2024tmlr-voyager/) Although NVIDIA researchers are among the authors, the official code lives under the MineDojo GitHub organization and carries a disclaimer that the project is strictly for research purposes and not an official product from NVIDIA.[4](https://github.com/MineDojo/Voyager) Running it requires an [OpenAI API](https://www.edgepedia.org/openai-api) key and Azure (Minecraft) credentials.[4](https://github.com/MineDojo/Voyager)
## By the numbers
All performance figures below are the authors' own evaluations in the [MineDojo](https://www.edgepedia.org/minedojo) environment; no independent replication or third-party evaluation is in evidence, so they should be read as self-reported results.
- Voyager obtained 63 unique items within 160 prompting iterations, about 3.3x more novel items than counterparts in the original preprint.[1](https://arxiv.org/html/2305.16291v2)
- The peer-reviewed TMLR version reports 3.1x more unique items, a revision from the preprint's 3.3x, along with 2.3x longer travel, and tech-tree milestones up to 15.3x faster than prior SOTA.[3](https://mlanthology.org/tmlr/2024/wang2024tmlr-voyager/)
- In zero-shot generalization tests in a new world with a cleared inventory, Voyager solved all unseen tasks while [AutoGPT](https://www.edgepedia.org/autogpt) could not solve any within 50 prompting iterations.[1](https://arxiv.org/html/2305.16291v2)
## How it compares with other agents
The paper's baselines are ReAct, Reflexion, and AutoGPT, all LLM-based agent frameworks rather than trained policies. In the MineDojo evaluations, AutoGPT lagged considerably while ReAct and [Reflexion](https://www.edgepedia.org/reflexion) struggled to make significant progress.[1](https://arxiv.org/html/2305.16291v2) The skill library also worked as a plug-and-play asset: adding it boosted AutoGPT's performance.[1](https://arxiv.org/html/2305.16291v2) The peer-reviewed version retains the claim that Voyager uses its learned skill library in a new Minecraft world to solve novel tasks from scratch while other methods fail to generalize.[3](https://mlanthology.org/tmlr/2024/wang2024tmlr-voyager/) Comparisons with DreamerV3's reinforcement-learning approach or STEVE-1 are not covered by the sources in evidence and cannot be made here.
## Limits and criticisms
**GPT-4 dependence and cost.** The GPT-4 API is 15x more expensive than GPT-3.5, and the authors found that GPT-3.5 and open-source LLMs could not provide the required code-generation quality.[1](https://arxiv.org/html/2305.16291v2)
**No visual perception.** Voyager does not support visual perception, because the available GPT-4 API was text-only at the time of writing; the authors note it could be augmented by multimodal perception models.[1](https://arxiv.org/html/2305.16291v2) The agent therefore perceives the world through text-structured state information, not pixels.
**Brittleness.** The paper acknowledges that despite the iterative prompting mechanism, there are still cases where the agent gets stuck and fails to generate the correct skill, and that the self-verification module can fail, for example not recognizing spider string as a success signal.[1](https://arxiv.org/html/2305.16291v2) The repository's usage notes acknowledge that task decomposition may occasionally be illogical and advise rerunning the decomposition when printed sub-goals look flawed.[4](https://github.com/MineDojo/Voyager)
**Simulator-bound lifelong learning.** A May 2026 analysis argues that Minecraft's simulator properties, catching errors instantly, resetting cleanly, and having no side effects outside the game, underpin Voyager's self-verification, and that this would not transfer directly to real-world domains where failed attempts cannot be rolled back; in Minecraft a failed skill attempt resets, whereas in a live ledger it does not.[5](https://beancount.io/bean-labs/research-logs/2026/05/08/voyager-open-ended-embodied-agent-lifelong-learning)
## References
1. Voyager: An Open-Ended Embodied Agent with Large Language Models (arXiv, Wang et al., May 2023), https://arxiv.org/html/2305.16291v2
2. Voyager, an agent that taught itself Minecraft, The Agent Museum, https://agentmuseum.org/exhibit/voyager-open-ended-agent
3. Voyager: An Open-Ended Embodied Agent with Large Language Models (TMLR 2024, peer-reviewed version), https://mlanthology.org/tmlr/2024/wang2024tmlr-voyager/
4. MineDojo/Voyager (official GitHub repository), https://github.com/MineDojo/Voyager
5. Voyager: Skill Libraries as the Foundation for Lifelong AI Agent Learning (Beancount research log, May 2026), https://beancount.io/bean-labs/research-logs/2026/05/08/voyager-open-ended-embodied-agent-lifelong-learning
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*Topic: Encyclopedia › Technology and the built world › Computing and digital systems › Modern AI: foundation models, generative AI, and the AI industry › Foundation-model methods and training › Multimodal, embodied, and world-model methods*
*Initially written Sep 17, 2026 · Edited: Oct 11, 2026; Oct 15, 2026*
*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*
License: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license