RRepoGEO

REPOGEO REPORT · LITE

PrefectHQ/marvin

Default branch main · commit 7c3e20a5 · scanned 6/29/2026, 10:27:00 AM

GitHub: 6,176 stars · 404 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 PrefectHQ/marvin, 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
  • highabout#1
    Update repository description to be more specific

    Why:

    CURRENT
    an ambient intelligence library
    COPY-PASTE FIX
    A Python framework for reliable structured data extraction and building robust, agentic LLM workflows.
  • highreadme#2
    Strengthen the README's opening sentence to emphasize core use cases

    Why:

    CURRENT
    Marvin is a Python framework for producing structured outputs and building agentic AI workflows.
    COPY-PASTE FIX
    Marvin is a Python framework for reliably extracting structured data from unstructured text and building robust, agentic AI workflows with LLMs.
  • mediumcomparison#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison to LangChain and LlamaIndex

[Add a section here explaining Marvin's unique approach, e.g., its focus on native Python types, Pydantic integration, and declarative API for structured outputs and agents, in contrast to other frameworks.]

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 PrefectHQ/marvin
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. OpenAI GPT-4 · recommended 1×
  4. GPT-3.5 Turbo · recommended 1×
  5. Anthropic Claude 3 · recommended 1×
  • CATEGORY QUERY
    How to reliably extract structured data from unstructured text using an LLM?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4
    2. GPT-3.5 Turbo
    3. Anthropic Claude 3
    4. LlamaIndex
    5. LangChain
    6. Google Gemini
    7. Mistral Large
    8. Mixtral 8x7B

    AI recommended 8 alternatives but never named PrefectHQ/marvin. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Python library for building multi-agent AI systems and orchestrating complex LLM tasks?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. CrewAI
    3. AutoGen
    4. LlamaIndex
    5. Haystack

    AI recommended 5 alternatives but never named PrefectHQ/marvin. 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 PrefectHQ/marvin?
    pass
    AI named PrefectHQ/marvin explicitly

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

  • If a team adopts PrefectHQ/marvin in production, what risks or prerequisites should they evaluate first?
    pass
    AI named PrefectHQ/marvin 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 PrefectHQ/marvin solve, and who is the primary audience?
    pass
    AI named PrefectHQ/marvin 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 PrefectHQ/marvin. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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PrefectHQ/marvin — 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