MineDojo
NVIDIA + Caltech open-ended embodied-agent benchmark on Minecraft. 3000+ programmatic + creative tasks across survival / harvest / tech-tree / combat / creative. Includes an internet-scale knowledge base (730K YouTube videos, 7K wiki pages, 340K Reddit posts) for grounding. Released at NeurIPS 2022 (outstanding paper award).
At a glance
- Type
- Open-ended Minecraft agent benchmark + knowledge base
- Tier
- T3
- Section
- Memory benchmarks & evaluation
- Created
- 2022-06 (Fan et al. NeurIPS 2022)
- Latest release
- not applicable — not OSS
- License
- MIT (env wrapper); Mojang EULA on Minecraft
- GitHub
- 2.4k★ Python
- Pricing
- not applicable — open benchmark; free to use
- Funding
- not applicable — not commercial
Taxonomy
- storage
- n/a
- retrieval
- n/a
- persistence
- n/a
- update
- read-only
- unit
- n/a
- governance
- n/a
- conflict
- n/a
When to use
Optimised for: not applicable - eval dataset, not a system
Anti-fit: not applicable - eval dataset, not a system
Pros & cons
Pros
Open-ended task space (3000+ tasks) rather than single objective; bundled internet-scale knowledge base; outstanding-paper recognition at NeurIPS 2022.
Cons
Built on Malmo (older Minecraft interface) — limits transfer to current MC versions; knowledge base quality is web-scrape level.
Claims & capabilities
NeurIPS 2022 outstanding paper; 3000+ tasks; 730K YouTube videos in knowledge base; Voyager (2023) built on MineDojo and Reflexion ideas
Technical surface
- API surface
- not applicable — eval dataset, not a system
- Backend storage
- not applicable — eval dataset, not a system
- Deployment
- not applicable — run locally
- Embedding model
- not applicable — eval dataset, not a system
- Multi-tenancy
- not applicable — eval dataset, not a system
- MCP
- not applicable — benchmark / evaluation harness
- A2A
- not applicable — benchmark / evaluation harness
- OpenTelemetry
- not applicable — benchmark / evaluation harness
Similar systems
Other memory benchmarks & evaluation in the catalog, ranked by inbound references.
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- RULER T3
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- InfiniteBench (∞Bench) T3
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- Atari 100k T3
Sample-efficiency benchmark protocol on Arcade Learning Environment (ALE) — agents limited to 100k environment steps (~2 hours of game-play) before evaluation. Introduced by SimPLe (Kaiser et al. 2019) and adopted by EfficientZero, IRIS, BBF, DreamerV3, Storm. Tests world-model / model-based methods where memory-as-imagination matters more than huge replay buffers.