RRepoGEO

REPOGEO REPORT · LITE

lightseekorg/tokenspeed

Default branch main · commit 4bc8d833 · scanned 5/29/2026, 2:57:23 AM

GitHub: 1,272 stars · 131 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 lightseekorg/tokenspeed, 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.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening sentence to clarify core identity

    Why:

    CURRENT
    TokenSpeed is a speed-of-light LLM inference engine designed for **agentic workloads**, with TensorRT-LLM-level performance and vLLM-level usability. Our goal is to be the most performant inference engine for production agentic workloads.
    COPY-PASTE FIX
    TokenSpeed is a speed-of-light LLM inference engine and serving framework, purpose-built for **agentic workloads**. It delivers TensorRT-LLM-level performance and vLLM-level usability for deploying and running large language models, and is not a tokenization library or rate limiter.
  • hightopics#2
    Add specific category topics for LLM inference and serving

    Why:

    CURRENT
    blackwell, deepseek, gpt-oss, kimi, lightseek, llm, minimax, nemotron, qwen, speed-of-light, tokenspeed
    COPY-PASTE FIX
    blackwell, deepseek, gpt-oss, kimi, lightseek, llm, minimax, nemotron, qwen, speed-of-light, tokenspeed, llm-inference, llm-serving, gpu-inference, tensorrt-llm-alternative, vllm-alternative, agentic-ai
  • mediumreadme#3
    Add a text-based performance comparison to key competitors

    Why:

    COPY-PASTE FIX
    Under the 'Performance Comparison' section, add a concise text table or bullet points that highlight key metrics (e.g., TPS, latency) and directly compare TokenSpeed to vLLM and TensorRT-LLM for agentic workloads. For example:
    
    **TokenSpeed vs. Competitors (Agentic Workloads):**
    - **Throughput (TPS):** TokenSpeed (580) > vLLM (X) > TensorRT-LLM (Y)
    - **Latency (ms):** TokenSpeed (A) < vLLM (B) < TensorRT-LLM (C)
    
    (Replace X, Y, A, B, C with actual benchmark numbers.)

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 lightseekorg/tokenspeed
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
vLLM
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. vLLM · recommended 2×
  2. OpenVINO · recommended 2×
  3. ONNX Runtime · recommended 2×
  4. NVIDIA TensorRT-LLM · recommended 1×
  5. Text Generation Inference (TGI) · recommended 1×
  • CATEGORY QUERY
    Need a high-performance LLM inference engine optimized for production agentic AI workloads.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT-LLM
    2. vLLM
    3. Text Generation Inference (TGI)
    4. OpenVINO
    5. DeepSpeed-MII
    6. ONNX Runtime

    AI recommended 6 alternatives but never named lightseekorg/tokenspeed. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best low-latency LLM serving frameworks for agent applications?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. TGI (Text Generation Inference) by Hugging Face
    3. TensorRT-LLM
    4. DeepSpeed-MII (Model Inference Interface)
    5. Ray Serve
    6. OpenVINO
    7. ONNX Runtime

    AI recommended 7 alternatives but never named lightseekorg/tokenspeed. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 lightseekorg/tokenspeed?
    pass
    AI named lightseekorg/tokenspeed explicitly

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

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

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

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lightseekorg/tokenspeed — 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