Catalog Systems Memory benchmarks & evaluation AndroidWorld

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.

Active · Verified 2026-10-28 · fresh as of 2026-10-28 · 31 cited facts, 1 with a third-party source · Suggest a correction

At a glance

Type
Mobile-OS agent benchmark — 116 real Android app tasks repo fix
Tier
T3
Section
Memory benchmarks & evaluation
Created
2024-05 (Rawles et al. arXiv 2405.14573) repo fix
Latest release
not applicable — not OSS fix
License
Apache-2.0 repo fix
GitHub
1.2k★ Python repo fix
Pricing
not applicable — open benchmark; free to use repo fix
Funding
not applicable — not commercial fix
Freshness
2026-10-28 fix
Layer
agent-infra repo fix
Licensing
open-source repo fix
Developer activity
OpenRank 0.24 (12-mo avg 2.38) · 6 participants — 2026-09 third-party fix
Maintainer health
5 issues in 90 days, 60% answered or closed · 0 releases in 12 months · bus factor 3 — as of 2026-11-01 repo

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

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

Similar systems

Other memory benchmarks & evaluation in the catalog, ranked by inbound references.

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Sources (30 cited facts, 1 third-party)

Each fact links to the page it came from; the badge says who published it (how to read sources).