RRepoGEO

REPOGEO REPORT · LITE

unbody-io/unbody

Default branch main · commit 5111b537 · scanned 6/4/2026, 3:18:12 PM

GitHub: 528 stars · 48 forks

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 unbody-io/unbody, 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 README to clarify project status and successor

    Why:

    CURRENT
    # Unbody (archived)

> **This repository is archived and no longer actively maintained.**

Unbody started as an open-source project to build the Supabase of the AI era. That vision has evolved.

We now build under **Unbody Labs** — shipping focused tools and products for the AI world. Our most active and direct continuation of Unbody's mission is **Adapt**.
    COPY-PASTE FIX
    # Unbody: The Foundational AI-Native Backend (Archived)

> **This repository represents the foundational open-source work for Unbody, a modular backend designed for knowledge-centric AI applications. While this specific repository is no longer actively maintained, its vision and core mission continue through [Adapt](https://github.com/unbody-io/adapt), our lightweight AI memory and learning framework.**
  • mediumcomparison#2
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why Unbody?

Unbody's core differentiator lies in its integrated, end-to-end approach to automatically transforming unstructured data into a structured knowledge graph using AI. This makes data directly queryable via natural language, setting it apart from traditional vector databases or general-purpose LLM frameworks by offering a complete solution for AI-native knowledge management.
  • lowabout#3
    Update the repository description

    Why:

    CURRENT
    The Supabase of AI era. A modular, open-source backend for building AI-native software — designed for knowledge, not static data.
    COPY-PASTE FIX
    The foundational open-source backend for building AI-native software, designed for knowledge. This project is archived; its vision continues with Adapt.

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 unbody-io/unbody
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. Weaviate · recommended 2×
  4. Chroma · recommended 1×
  5. PostgreSQL with pgvector · recommended 1×
  • CATEGORY QUERY
    What open-source backend solution helps build AI-native applications with knowledge management features?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Weaviate
    4. Chroma
    5. PostgreSQL with pgvector
    6. Milvus

    AI recommended 6 alternatives but never named unbody-io/unbody. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a modular open-source framework for managing AI knowledge and RAG capabilities.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Rasa
    5. OpenSearch
    6. Weaviate

    AI recommended 6 alternatives but never named unbody-io/unbody. 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 unbody-io/unbody?
    pass
    AI named unbody-io/unbody explicitly

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

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

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

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unbody-io/unbody — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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