# MineDojo/MineDojo

Building Open-Ended Embodied Agents with Internet-Scale Knowledge

Repository: https://github.com/MineDojo/MineDojo
Canonical: https://ross.abutalabs.com/products/minedojo
Language: Java
License: MIT
License Family: permissive
Last push: 2024-03-18T18:10:28+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 1537, "days_push": 898, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2251, forks 197 (observed 2026-08-28T04:06:31.007281+00:00)

## What it is
MineDojo is an AI research framework built on Minecraft for training open-ended, generally capable embodied agents. It provides a simulation suite with thousands of diverse tasks plus an internet-scale knowledge base of YouTube videos, wiki pages, and Reddit posts.

## Use cases
- train embodied agents in minecraft
- benchmark reinforcement learning agents on diverse 3d tasks
- learn agent policies from youtube videos
- research open-ended generally capable ai agents
- use internet-scale knowledge base for agent training
- evaluate multimodal agents with mineclip rewards

## When to choose
- you need a rich 3d simulation environment for embodied AI research
- you want to train agents with internet-scale human knowledge
- you need a standardized benchmark suite for open-ended agent learning

## When to avoid
- you need a lightweight or fast-turnaround RL environment
- you don't want a Minecraft/Java dependency
- you need production agent deployment rather than research

## Facets
- artifact type: framework
- maturity: maintenance
- function: simulation, machine-learning, reinforcement-learning, agent-framework, benchmarking, rag
- domain: artificial-intelligence, reinforcement-learning, simulation
- platform: python
- tags: minecraft, embodied-agents, open-ended-learning, benchmark-suite, youtube-dataset, mineclip, neurips-award, game-development, research, linux, macos, gpu

## Member repositories
- MineDojo/MineDojo (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:31.007281+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:43:50.036338+00:00, confidence not recorded.
  - readme: https://github.com/MineDojo/MineDojo (fetched 2026-08-28T04:06:31.007281+00:00, sha 80d27d236fa7)
- Data as of 2026-08-30T08:39:29.467469+00:00.