RRepoGEO

REPOGEO REPORT · LITE

jackmpcollins/magentic

Default branch main · commit 58b72cf5 · scanned 5/22/2026, 2:31:46 PM

GitHub: 2,408 stars · 126 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 jackmpcollins/magentic, 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
    Clarify magentic's unique positioning in the README's opening

    Why:

    CURRENT
    Seamlessly integrate Large Language Models into Python code. Use the `@prompt` and `@chatprompt` decorators to create functions that return structured output from an LLM. Combine LLM queries and tool use with traditional Python code to build complex agentic systems.
    COPY-PASTE FIX
    Seamlessly integrate Large Language Models into Python code. Magentic offers a highly Pythonic, decorator-based approach to define LLM calls as regular functions, making structured output and complex agentic systems feel like native Python. Use the `@prompt` and `@chatprompt` decorators to create functions that return structured output from an LLM. Combine LLM queries and tool use with traditional Python code to build complex agentic systems.
  • mediumreadme#2
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

Magentic differentiates itself from broader LLM frameworks like LangChain and LlamaIndex by focusing on a lightweight, Pythonic integration of LLMs directly into functions using decorators and type hints. While tools like Instructor and Pydantic-LLM also emphasize structured output, Magentic aims for a more seamless, native Python function experience for both simple prompts and complex agentic workflows, minimizing boilerplate and maximizing developer ergonomics.
  • lowtopics#3
    Add more specific keywords to the repository topics

    Why:

    CURRENT
    agent, agentic, ai, chatbot, chatgpt, gpt, llm, magenta, magentic, magnetic, openai, openai-api, prompt, pydantic
    COPY-PASTE FIX
    agent, agentic, ai, chatbot, chatgpt, gpt, llm, magenta, magentic, magnetic, openai, openai-api, prompt, pydantic, type-hints, structured-output, function-calling-llm, python-llm-library

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 jackmpcollins/magentic
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Instructor · recommended 1×
  4. Pydantic-LLM · recommended 1×
  5. LiteLLM · recommended 1×
  • CATEGORY QUERY
    How to easily call large language models from Python functions with type hints?
    you: not recommended
    AI recommended (in order):
    1. Instructor
    2. Pydantic-LLM
    3. LangChain
    4. LlamaIndex
    5. LiteLLM
    6. OpenAI Python Client
    7. Guidance

    AI recommended 7 alternatives but never named jackmpcollins/magentic. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python library helps build complex LLM agentic systems with function calling?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. CrewAI
    4. AutoGen
    5. Marvin

    AI recommended 5 alternatives but never named jackmpcollins/magentic. 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 jackmpcollins/magentic?
    pass
    AI named jackmpcollins/magentic explicitly

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

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

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

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jackmpcollins/magentic — 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