REPOGEO REPORT · LITE
opendilab/DI-engine
Default branch main · commit d0b21d06 · scanned 5/20/2026, 1:21:18 AM
GitHub: 3,617 stars · 433 forks
Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.
2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface opendilab/DI-engine, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.
Action plan — copy-paste fixes
3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.
- highreadme#1Reposition README introduction to highlight industrial-grade, distributed RL
Why:
CURRENTDI-engine is a generalized decision intelligence engine for PyTorch and JAX.
COPY-PASTE FIXDI-engine is an industrial-grade, high-performance, and distributed reinforcement learning framework for PyTorch and JAX, designed for complex real-world decision intelligence applications.
- mediumtopics#2Add topics emphasizing industrial and real-world application
Why:
CURRENTatari, distributed-reinforcement-learning, distributed-system, drl, exploration-exploitation, imitation-learning, impala, inverse-reinforcement-learning, minigrid, model-based-reinforcement-learning, mujoco, multiagent-reinforcement-learning, offline-rl, python, pytorch-rl, r2d2, reinforcement-learning, reinforcement-learning-algorithms, self-play, smac
COPY-PASTE FIXatari, distributed-reinforcement-learning, distributed-system, drl, exploration-exploitation, imitation-learning, impala, inverse-reinforcement-learning, minigrid, model-based-reinforcement-learning, mujoco, multiagent-reinforcement-learning, offline-rl, python, production-ready, pytorch-rl, r2d2, real-world-applications, reinforcement-learning, reinforcement-learning-algorithms, self-play, smac
- lowabout#3Update repository description to reflect core differentiator
Why:
CURRENTOpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
COPY-PASTE FIXOpenDILab's DI-engine: an industrial-grade, high-performance, and distributed reinforcement learning framework for complex real-world decision intelligence.
Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash
Category visibility — the real GEO test
Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?
Same questions for every model — switch tabs to compare answers and rankings.
- Ray RLlib · recommended 1×
- Acme · recommended 1×
- Stable Baselines3 · recommended 1×
- OpenSpiel · recommended 1×
- TorchRL · recommended 1×
- CATEGORY QUERYWhat are the best Python frameworks for distributed reinforcement learning research and development?you: not recommendedAI recommended (in order):
- Ray RLlib
- Acme
- Stable Baselines3
- OpenSpiel
- TorchRL
- Tianshou
AI recommended 6 alternatives but never named opendilab/DI-engine. This is the gap to close.
Show full AI answer
- CATEGORY QUERYSeeking a robust deep reinforcement learning library supporting multi-agent and offline learning.you: not recommendedAI recommended (in order):
- RLlib (ray-project/ray)
- Acme (deepmind/acme)
- CleanRL (vwxyzjn/cleanrl)
- Tianshou (thu-ml/tianshou)
- Stable Baselines3 (DLR-RM/stable-baselines3)
AI recommended 5 alternatives but never named opendilab/DI-engine. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesspass
- README presencepass
Self-mention check
Does AI even know your repo exists when asked about it directly?
- Compared to common alternatives in this category, what is the core differentiator of opendilab/DI-engine?passAI named opendilab/DI-engine explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts opendilab/DI-engine in production, what risks or prerequisites should they evaluate first?passAI named opendilab/DI-engine explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- In one sentence, what problem does the repo opendilab/DI-engine solve, and who is the primary audience?passAI named opendilab/DI-engine explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
Drop this badge into the README of opendilab/DI-engine. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
Live preview[](https://repogeo.com/en/r/opendilab/DI-engine)<a href="https://repogeo.com/en/r/opendilab/DI-engine"><img src="https://repogeo.com/badge/opendilab/DI-engine.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
opendilab/DI-engine — Lite scans stay free; this card itemizes Pro deep limits vs Lite.
- Deep reports10 / month
- Brand-free category queries5 vs 2 in Lite
- Prioritized action items8 vs 3 in Lite