RRepoGEO

REPOGEO REPORT · LITE

mozilla-ai/any-llm

Default branch main · commit 0f249923 · scanned 5/17/2026, 11:51:51 PM

GitHub: 1,991 stars · 167 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
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 mozilla-ai/any-llm, 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 statement to clarify its role as a unified LLM API

    Why:

    CURRENT
    **Communicate with any LLM provider using a single, unified interface.** Switch between OpenAI, Anthropic, Mistral, Ollama, and more without changing your code.
    COPY-PASTE FIX
    **any-llm provides a lightweight, unified Python API to seamlessly integrate and switch between various LLM providers like OpenAI, Anthropic, Mistral, and Ollama.** Designed for developers familiar with tools like LiteLLM, it offers a consistent interface without requiring code changes when switching models or providers.
  • hightopics#2
    Add more specific topics to improve category matching

    Why:

    CURRENT
    ai, anthropic, developer-tools, inference, llm, openai, python, text-completion
    COPY-PASTE FIX
    ai, anthropic, developer-tools, inference, llm, openai, python, text-completion, unified-api, llm-abstraction, multi-llm
  • mediumreadme#3
    Create a dedicated comparison section in the README

    Why:

    COPY-PASTE FIX
    ## Why any-llm? (Compared to X, Y, Z)

[Add a brief section here comparing `any-llm` to competitors like LiteLLM, LangChain, and LlamaIndex, highlighting its specific advantages or use cases.]

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 mozilla-ai/any-llm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. LiteLLM · recommended 1×
  4. Haystack · recommended 1×
  5. OpenAI Python Library · recommended 1×
  • CATEGORY QUERY
    How can I integrate multiple large language models into my Python application seamlessly?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. LiteLLM
    4. Haystack
    5. OpenAI Python Library
    6. Instructor

    AI recommended 6 alternatives but never named mozilla-ai/any-llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a standardized Python interface to interact with various text generation AI providers.
    you: not recommended
    AI recommended (in order):
    1. LiteLLM (BerriAI/litellm)
    2. LangChain (langchain-ai/langchain)
    3. OpenAI Python Library (openai/openai-python)
    4. Instructor (jxnl/instructor)
    5. Hugging Face `transformers` library (huggingface/transformers)

    AI recommended 5 alternatives but never named mozilla-ai/any-llm. 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 mozilla-ai/any-llm?
    pass
    AI named mozilla-ai/any-llm explicitly

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

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

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

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mozilla-ai/any-llm — 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