RRepoGEO

REPOGEO REPORT · LITE

WeaveMindAI/weft

Default branch main · commit 251323cd · scanned 5/11/2026, 9:51:58 PM

GitHub: 1,248 stars · 145 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 WeaveMindAI/weft, 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
  • hightopics#1
    Add specific topics for AI programming languages and orchestration

    Why:

    COPY-PASTE FIX
    ai-programming-language, agent-orchestration, llm-workflow, declarative-programming, graph-based-programming, ai-systems, durable-execution
  • highreadme#2
    Reposition the README's core identity and differentiate from frameworks

    Why:

    CURRENT
    # Weft

> **Building in public, two months in.** Weft is young. ...

**A programming language for AI systems.**

In 2026, real software calls LLMs, ...
    COPY-PASTE FIX
    # Weft: A Programming Language for AI Systems

Weft is a dedicated programming language where LLMs, humans, APIs, and infrastructure are base ingredients, designed to orchestrate complex AI agent workflows directly, rather than relying on general-purpose libraries or frameworks.

> **Building in public, two months in.** Weft is young. The language, the type system, and the durable executor are the stable parts. The node catalog is small and intentionally opinionated (a few dozen nodes across LLM, code, communication, flow, storage, and triggers). The long-term vision is to let projects define their own nodes fluently in the language itself, but that is still ahead. If you are evaluating it for production, treat it as a foundation to build on, not a finished product. Breaking changes are expected while the shape is still settling; they will be announced, and migration notes will come with them.
  • mediumreadme#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    Weft is licensed under [insert specific license name(s) here, e.g., 'a custom license detailed in the LICENSE file' or 'the Apache 2.0 License combined with a custom clause']. Please refer to the `LICENSE` file for full details.

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 WeaveMindAI/weft
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. run-llama/llama_index · recommended 2×
  3. deepset-ai/haystack · recommended 2×
  4. python/cpython · recommended 1×
  5. tiangolo/fastapi · recommended 1×
  • CATEGORY QUERY
    What programming language is best for building complex AI agent workflows and coordinating services?
    you: not recommended
    AI recommended (in order):
    1. Python (python/cpython)
    2. LangChain (langchain-ai/langchain)
    3. LlamaIndex (run-llama/llama_index)
    4. Haystack (deepset-ai/haystack)
    5. FastAPI (tiangolo/fastapi)
    6. Flask (pallets/flask)
    7. Celery (celery/celery)
    8. Apache Airflow (apache/airflow)
    9. Go (golang/go)
    10. Java (openjdk/jdk)
    11. Spring Boot (spring-projects/spring-boot)
    12. Apache Kafka (apache/kafka)
    13. Akka (akka/akka)
    14. TypeScript (microsoft/TypeScript)
    15. Node.js (nodejs/node)
    16. NestJS (nestjs/nest)
    17. Rust (rust-lang/rust)

    AI recommended 17 alternatives but never named WeaveMindAI/weft. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a robust programming environment to orchestrate LLM calls, databases, and human interactions.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. OpenAI Assistants API
    4. Microsoft Semantic Kernel (microsoft/semantic-kernel)
    5. Haystack (deepset-ai/haystack)
    6. FlowiseAI (FlowiseAI/Flowise)
    7. Dify (dify-ai/dify)

    AI recommended 7 alternatives but never named WeaveMindAI/weft. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • 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 WeaveMindAI/weft?
    pass
    AI did not name WeaveMindAI/weft — likely talking about a different project

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

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

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

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WeaveMindAI/weft — 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