RRepoGEO

REPOGEO REPORT · LITE

plastic-labs/honcho

Default branch main · commit a2adeb9f · scanned 6/20/2026, 12:42:41 AM

GitHub: 5,295 stars · 643 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 plastic-labs/honcho, 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
    Add explicit disambiguation in README's opening

    Why:

    CURRENT
    The current README starts with: "**Honcho is memory infrastructure for building stateful agents that understand changing people, agents, groups, projects, and ideas over time.**"
    COPY-PASTE FIX
    Add the following sentence immediately after the initial bold statement in the README: "Please note: This project is a memory system for AI agents, not a process manager or CLI tool also named 'Honcho'."
  • highreadme#2
    Refine README's core positioning statement

    Why:

    CURRENT
    **Honcho is memory infrastructure for building stateful agents that understand changing people, agents, groups, projects, and ideas over time.**
    COPY-PASTE FIX
    **Honcho is a dedicated memory infrastructure for building stateful AI agents, enabling long-term context and understanding of evolving interactions over time.** It provides a robust system for storing messages and events, allowing agents to reason in the background and query peer representations, session context, search results, or natural-language insights from any model or framework.
  • mediumabout#3
    Refine GitHub 'About' description for clarity and uniqueness

    Why:

    CURRENT
    Memory library for building stateful agents
    COPY-PASTE FIX
    Dedicated memory infrastructure for stateful AI agents, enabling long-term context and understanding of evolving interactions. Not a process manager.

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 plastic-labs/honcho
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. Weaviate · recommended 2×
  4. Pinecone · recommended 2×
  5. Faiss · recommended 2×
  • CATEGORY QUERY
    Tools for giving AI agents memory and understanding evolving interactions?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. MemGPT
    4. Weaviate
    5. Pinecone
    6. Redis
    7. Faiss

    AI recommended 7 alternatives but never named plastic-labs/honcho. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Python libraries for managing long-term context in stateful AI agents?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Faiss
    5. Chroma
    6. Weaviate
    7. Pinecone

    AI recommended 7 alternatives but never named plastic-labs/honcho. 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 plastic-labs/honcho?
    pass
    AI named plastic-labs/honcho explicitly

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

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

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

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plastic-labs/honcho — 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