AndroidWorld
https://github.com/google-research/android_world
Google DeepMind's AndroidWorld — 116 hand-crafted tasks across 20 real Android apps (Calendar, Files, Markor, Tasks, etc.). Evaluates LLM agents controlling a real Android device via accessibility APIs. Distinct from AndroidEnv (RL-environment) — AndroidWorld is the benchmark suite. Released 2024; SOTA agent (M3A with GPT-4) at ~30% task success.
At a glance
- Type
- Mobile-OS agent benchmark — 116 real Android app tasks
- Tier
- T3
- Section
- Memory benchmarks & evaluation
- Created
- 2024-05 (Rawles et al. arXiv 2405.14573)
- Latest release
- not applicable — not OSS
- License
- Apache-2.0
- GitHub
- 1.2k★ 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
Real Android apps (not simulated) with reliable evaluation harness; broad app coverage; reproducible auto-grading.
Cons
Android-only (no iOS); SOTA still ~30% — long way from human-comparable; setup heavier than synthetic benchmarks.
Claims & capabilities
Google DeepMind 2024; 116 tasks across 20 real apps; SOTA ~30% success on multi-app compositional tasks; successor to AndroidEnv (RL environment)
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
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