Catalog Systems Memory benchmarks & evaluation MineDojo

MineDojo

https://minedojo.org/

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).

Active · Verified 2026-10-28 · fresh as of 2026-10-28 · 29 cited facts, 0 with a third-party source · Suggest a correction

At a glance

Type
Open-ended Minecraft agent benchmark + knowledge base vendor fix
Tier
T3
Section
Memory benchmarks & evaluation
Created
2022-06 (Fan et al. NeurIPS 2022) vendor fix
Latest release
not applicable — not OSS fix
License
MIT (env wrapper); Mojang EULA on Minecraft vendor fix
GitHub
2.4k★ Python vendor fix
Pricing
not applicable — open benchmark; free to use vendor fix
Funding
not applicable — not commercial fix
Freshness
2026-10-28 fix
Layer
agent-infra repo fix
Licensing
open-source vendor fix

Memory 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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  • GAIA T3

    Benchmark for general AI assistants made of real-world questions that require reasoning, multimodality, web browsing and tool use, with a public leaderboard on Hugging Face. Traction: ~917 likes on the Hugging Face dataset

  • LongBench T3

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  • LongMemEval T3

    5 core memory abilities — info extraction, multi-session reasoning, temporal reasoning, knowledge updates, abstention — via 500 curated questions in synthetic chat histories. Two scales: LongMemEval-S (115K tokens), LongMemEval-M (up to 1.5M).

  • RULER T3

    NVIDIA. Extends NIAH with multi-needle retrieval, multi-hop entity tracing, aggregation, QA — configurable for arbitrary length / difficulty. Evaluated 17 long-context models on 13 tasks.

  • InfiniteBench (∞Bench) T3

    Tsinghua / OpenBMB. First benchmark with average input >100K tokens. 12 tasks across English + Chinese: long novel QA, code debugging, math in long context, summarisation, retrieval. Realistic documents (novels, code) rather than synthetic filler.

Sources (28 cited facts, 0 third-party)

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