RRepoGEO

REPOGEO REPORT · LITE

deepseek-ai/DeepEP

Default branch main · commit a422b5a3 · scanned 5/21/2026, 9:52:05 AM

GitHub: 9,646 stars · 1,251 forks

Scan history for this repo

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.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

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 deepseek-ai/DeepEP, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumabout#1
    Refine the 'About' description for clearer positioning

    Why:

    CURRENT
    DeepEP: an efficient expert-parallel communication library
    COPY-PASTE FIX
    DeepEP: a high-performance communication library for expert parallelism (MoE) in large-scale deep learning training and inference.
  • mediumreadme#2
    Add a 'Target Audience' section to the README

    Why:

    COPY-PASTE FIX
    ## Target Audience

DeepEP is designed for machine learning engineers, researchers, and practitioners working on large-scale distributed deep learning models, particularly those utilizing expert parallelism (MoE) for training and inference. It is ideal for optimizing communication in GPU clusters for high-throughput and low-latency operations.

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.

Recall
0 / 2
0% of queries surface deepseek-ai/DeepEP
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSpeed
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSpeed · recommended 1×
  2. FairScale · recommended 1×
  3. Megatron-LM · recommended 1×
  4. Colossal-AI · recommended 1×
  5. JAX · recommended 1×
  • CATEGORY QUERY
    How to efficiently manage communication for expert parallelism in large-scale machine learning models?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed
    2. FairScale
    3. Megatron-LM
    4. Colossal-AI
    5. JAX
    6. PyTorch FSDP

    AI recommended 6 alternatives but never named deepseek-ai/DeepEP. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a high-performance, low-latency GPU communication library for MoE dispatch and combine operations.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA NCCL
    2. NVIDIA cuBLAS
    3. cuDNN
    4. Open MPI
    5. Intel oneCCL
    6. PyTorch Distributed
    7. TensorFlow Distributed
    8. Gloo

    AI recommended 8 alternatives but never named deepseek-ai/DeepEP. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

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 deepseek-ai/DeepEP?
    pass
    AI named deepseek-ai/DeepEP explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts deepseek-ai/DeepEP in production, what risks or prerequisites should they evaluate first?
    pass
    AI named deepseek-ai/DeepEP 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 deepseek-ai/DeepEP solve, and who is the primary audience?
    pass
    AI named deepseek-ai/DeepEP explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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deepseek-ai/DeepEP — 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
deepseek-ai/DeepEP — RepoGEO report