OSRS Bench (Old School RuneScape agent benchmark)
https://github.com/grahamannett/osrs-bench
Community benchmark suite for LLM agents playing Old School RuneScape, a sandbox MMORPG with deep partial-observability, skill-grinding loops, and economy interaction. Tests long-horizon planning (hour-to-day quests), tool inventory management, and visual UI comprehension. Community-built; not affiliated with Jagex.
At a glance
- Type
- Long-horizon MMO agent benchmark — Old School RuneScape
- Tier
- T4
- Section
- Memory benchmarks & evaluation
- Created
- 2024 (community project)
- Latest release
- not applicable — not OSS
- License
- MIT (assumed — community repo, no explicit LICENSE file checked)
- 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
One of the few long-horizon (multi-hour to multi-day) agentic benchmarks built around a real, deeply-engineered game world; high partial-observability and economic loops.
Cons
Niche community benchmark with no standardized leaderboard; reproducibility hampered by Jagex anti-bot policies (RuneScape EULA forbids automation); not peer-reviewed.
Claims & capabilities
Used by community as exemplar long-horizon agent task — quest completion times measured in hours; full account progression measured in months of agent runtime
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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- LongBench T3
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- LongMemEval T3
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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.