RRepoGEO

REPOGEO REPORT · LITE

neosigmaai/auto-harness

Default branch main · commit de6b3ed5 · scanned 6/6/2026, 11:37:38 AM

GitHub: 510 stars · 59 forks

AI VISIBILITY SCORE
35 /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
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 neosigmaai/auto-harness, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumreadme#1
    Refine the README's H1 and tagline for clearer positioning

    Why:

    CURRENT
    # auto-harness

> Give a coding agent a benchmark and an agent file. Let it iterate overnight. It reads failures, improves the system prompt and tools, gates every change against a self-maintained eval suite, and repeats.
    COPY-PASTE FIX
    # auto-harness: A Framework for Self-Improving AI Agent Systems

> Build a self-improving AI agent system with automated evaluations. Give a coding agent a benchmark and an agent file. Let it iterate overnight. It reads failures, improves the system prompt and tools, gates every change against a self-maintained eval suite, and repeats.
  • lowreadme#2
    Add a 'Why auto-harness?' section to the README

    Why:

    COPY-PASTE FIX
    ## Why auto-harness?

While many tools offer general LLM evaluation or agent frameworks, auto-harness focuses specifically on building *self-improving agentic systems*. It provides a streamlined and flexible framework for automated LLM evaluation using custom datasets and logic, emphasizing ease of setup and adaptability for specific, user-defined tasks. Our system automatically mines failures, optimizes agent prompts and tools, and gates against regressions, allowing your agent to iterate and improve autonomously.

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 neosigmaai/auto-harness
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. Haystack · recommended 2×
  3. AutoGPT · recommended 2×
  4. LlamaIndex · recommended 2×
  5. MLflow · recommended 1×
  • CATEGORY QUERY
    How can I build a self-improving AI agent system with automated evaluations?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. MLflow
    3. Weights & Biases
    4. OpenAI Evals
    5. Haystack
    6. Deepset's Evaluation Framework
    7. AutoGPT
    8. Rasa
    9. LlamaIndex

    AI recommended 9 alternatives but never named neosigmaai/auto-harness. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework to automatically optimize agent prompts and tools based on task failures.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. AutoGPT
    5. DSPy

    AI recommended 5 alternatives but never named neosigmaai/auto-harness. 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 neosigmaai/auto-harness?
    pass
    AI named neosigmaai/auto-harness explicitly

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

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

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

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neosigmaai/auto-harness — 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