RRepoGEO

REPOGEO REPORT · LITE

microsoft/TypeChat

Default branch main · commit d4933767 · scanned 6/27/2026, 7:32:28 AM

GitHub: 8,670 stars · 414 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
59 /100
Needs work
Category recall
1 / 2
Avg rank #8.0 when recommended
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 microsoft/TypeChat, 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 to emphasize 'schema engineering' as an alternative to prompt engineering

    Why:

    CURRENT
    TypeChat is a library that makes it easy to build natural language interfaces using types.
    COPY-PASTE FIX
    TypeChat is a library that replaces complex prompt engineering with schema engineering, making it easy to build robust natural language interfaces using types.
  • mediumreadme#2
    Add TypeChat's unique iterative repair mechanism to the repository description

    Why:

    CURRENT
    TypeChat is a library that makes it easy to build natural language interfaces using types.
    COPY-PASTE FIX
    TypeChat is a library for building natural language interfaces using types, featuring a robust, iterative repair mechanism to ensure LLM responses conform to your defined schemas.
  • lowtopics#3
    Add more specific topics related to schema engineering and structured LLM output

    Why:

    CURRENT
    ai, llm, natural-language, types
    COPY-PASTE FIX
    ai, llm, natural-language, types, schema-engineering, structured-output, llm-validation, type-safety

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
1 / 2
50% of queries surface microsoft/TypeChat
Avg rank
#8.0
Lower is better. #1 = top recommendation.
Share of voice
6%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. Pydantic · recommended 1×
  3. Instructor · recommended 1×
  4. Guidance · recommended 1×
  5. JSON Schema · recommended 1×
  • CATEGORY QUERY
    How to reliably structure large language model responses using defined types for application logic?
    you: #8
    AI recommended (in order):
    1. Pydantic
    2. Instructor
    3. Guidance
    4. JSON Schema
    5. Marvin
    6. LangChain
    7. Zod
    8. TypeChat ← you
    9. LMQL
    Show full AI answer
  • CATEGORY QUERY
    What are alternatives to complex prompt engineering for building robust natural language interfaces?
    you: not recommended
    AI recommended (in order):
    1. Rasa
    2. Dialogflow
    3. Microsoft Bot Framework Composer
    4. Amazon Lex
    5. OpenAI Function Calling
    6. Haystack
    7. LangChain

    AI recommended 7 alternatives but never named microsoft/TypeChat. 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 microsoft/TypeChat?
    pass
    AI named microsoft/TypeChat explicitly

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

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

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

Embed your GEO score

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MARKDOWN (README)
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HTML
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microsoft/TypeChat — 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