Claude Plays Pokemon (Anthropic)
https://www.twitch.tv/claudeplayspokemon
Anthropic's public Twitch livestream of Claude (3.7 Sonnet → Claude 4 Opus) playing Pokemon Red. Agent uses screenshot vision + Game Boy controller actions + a scratchpad memory file the model can edit. Demonstrates long-horizon agentic capability publicly; ran continuously from Feb 2025; viewed by hundreds of thousands cumulatively; key data point for Anthropic's marketing around Claude as a long-horizon agent.
At a glance
- Type
- Anthropic agent demo — Claude 3.7/4 Sonnet plays Pokemon Red on Twitch
- Tier
- T2
- Section
- Memory benchmarks & evaluation
- Created
- 2025-02 (Anthropic livestream launch)
- Latest release
- Twitch livestream ongoing; runs distinct per Claude model version
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- not applicable — research demo / community project
- Funding
- not applicable — not commercial
Taxonomy
- storage
- kv
- retrieval
- extraction-pull
- persistence
- session-only
- update
- agent-controlled
- unit
- episode
- governance
- opaque
- conflict
- agent
When to use
Optimised for: benchmarking long-horizon agentic capability via game play
Anti-fit: not applicable - eval dataset, not a system
Pros & cons
Pros
Public, observable, real-time evidence of long-horizon LLM-agent capability; high mindshare; live discussion of failure modes (loops, forgetting, hallucinating items).
Cons
Closed agent harness (Anthropic-internal); not reproducible by third parties; benchmark protocol informal — no standardized scoring.
Claims & capabilities
Anthropic-run Twitch stream Feb 2025 onwards; Claude 3.7 Sonnet reached Mt. Moon / 2 badges over ~80hr; later runs with Claude 4 Opus reached deeper progress; one of the most-watched public LLM-agent demos of 2025
Technical surface
- API surface
- not applicable — eval dataset, not a system
- Backend storage
- not applicable — eval dataset, not a system
- Deployment
- not applicable — research demo
- 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.
- LoCoMo T3
Conversations spanning up to 35 sessions / 300 turns / ~9K tokens, built from LLM-agent personas grounded on temporal event graphs and verified by human annotators. Tests QA (single-hop, multi-hop, temporal, commonsense, adversarial), event-graph summarisation, multimodal dialogue.
- LongBench T3
Tsinghua THUDM. 21 datasets across 6 categories (single-doc QA, multi-doc QA, summarisation, few-shot ICL, synthetic, code completion) in English + Chinese. ~6.7K words English / 13.4K chars Chinese average. v2 at ACL 2025 with harder, longer instances.
- 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.
- 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.