RRepoGEO

REPOGEO REPORT · LITE

chopratejas/headroom

Default branch main · commit bcf55172 · scanned 5/14/2026, 11:11:23 AM

GitHub: 1,744 stars · 158 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 chopratejas/headroom, 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
    Add a concise, direct problem statement at the very top of the README

    Why:

    CURRENT
    The README currently starts with ASCII art and then a tagline.
    COPY-PASTE FIX
    Headroom is a context compression layer for AI agents that reduces LLM token usage by 60-95% for tool outputs, logs, files, and RAG chunks, without sacrificing answer quality. It provides a library, proxy, and MCP server.
  • mediumtopics#2
    Remove potentially misleading 'cursor' topic

    Why:

    CURRENT
    agent, ai, anthropic, claude-code, compression, context-engineering, context-window, cursor, fastapi, langchain, llm, mcp, openai, prompt-engineering, proxy, python, rag, token-optimization, tokens, typescript
    COPY-PASTE FIX
    agent, ai, anthropic, claude-code, compression, context-engineering, context-window, fastapi, langchain, llm, mcp, openai, prompt-engineering, proxy, python, rag, token-optimization, tokens, typescript
  • lowabout#3
    Slightly rephrase the description to emphasize AI agent context

    Why:

    CURRENT
    Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 60-95% fewer tokens, same answers. Library, proxy, MCP server.
    COPY-PASTE FIX
    Headroom is a context compression layer for AI agents, reducing LLM token usage by 60-95% for tool outputs, logs, files, and RAG chunks while maintaining answer quality. It functions as a library, proxy, or MCP server.

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 chopratejas/headroom
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 3×
  2. huggingface/transformers · recommended 2×
  3. run-llama/llama_index · recommended 2×
  4. GPT-3.5 Turbo · recommended 1×
  5. Llama 3 8B Instruct · recommended 1×
  • CATEGORY QUERY
    How can I reduce token usage and costs for my LLM-powered AI agent?
    you: not recommended
    AI recommended (in order):
    1. GPT-3.5 Turbo
    2. Llama 3 8B Instruct
    3. Together.ai
    4. Anyscale Endpoints
    5. Mistral 7B Instruct
    6. Mistral AI API
    7. Code Llama
    8. NLLB-200
    9. Hugging Face Transformers (huggingface/transformers)
    10. Pinecone
    11. Weaviate (weaviate/weaviate)
    12. Qdrant (qdrant/qdrant)
    13. Chroma (chroma-core/chroma)
    14. Faiss (facebookresearch/faiss)
    15. Sentence Transformers (UKPLab/sentence-transformers)
    16. Redis (redis/redis)
    17. Memcached
    18. PostgreSQL
    19. MySQL (mysql/mysql-server)
    20. LangChain's Semantic Cache (langchain-ai/langchain)
    21. LlamaIndex's Cache (run-llama/llama_index)
    22. OpenAI Function Calling
    23. LangChain Agents (langchain-ai/langchain)
    24. LlamaIndex Agents (run-llama/llama_index)

    AI recommended 24 alternatives but never named chopratejas/headroom. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a library or service to compress RAG chunks and agent tool outputs for LLMs.
    you: not recommended
    AI recommended (in order):
    1. Cohere Rerank
    2. Voyage AI Rerank
    3. LangChain (langchain-ai/langchain)
    4. OpenAI's GPT-3.5 Turbo / GPT-4
    5. NLTK / spaCy
    6. Hugging Face Transformers (huggingface/transformers)

    AI recommended 6 alternatives but never named chopratejas/headroom. 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 chopratejas/headroom?
    pass
    AI named chopratejas/headroom explicitly

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

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

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

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MARKDOWN (README)
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  • Brand-free category queries5 vs 2 in Lite
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chopratejas/headroom — RepoGEO report