RRepoGEO

REPOGEO REPORT · LITE

stanford-iris-lab/meta-harness

Default branch main · commit 95175f70 · scanned 7/1/2026, 4:08:15 PM

GitHub: 1,178 stars · 113 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
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 stanford-iris-lab/meta-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

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 paragraph to clarify its focus on LLM agent optimization

    Why:

    CURRENT
    Meta-Harness is a framework for automated search over task-specific model harnesses: the code around a fixed base model that decides what to store, retrieve, and show while the model works.
    COPY-PASTE FIX
    Meta-Harness is a framework for automated search and end-to-end optimization of LLM agent harnesses. These harnesses are the surrounding code that dictates an agent's memory, retrieval, and interaction strategies, allowing for performance improvements beyond the base LLM.
  • mediumtopics#2
    Add more specific topics related to LLM agent optimization and search

    Why:

    CURRENT
    harness-engineering, llm-agents
    COPY-PASTE FIX
    harness-engineering, llm-agents, llm-optimization, agent-optimization, automated-search, memory-systems, retrieval-augmented-generation
  • lowcomparison#3
    Add a 'Comparison to LLM Frameworks' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison to LLM Frameworks (e.g., LangChain, LlamaIndex, DSPy)

Meta-Harness is not a general-purpose LLM application framework. Instead, it focuses specifically on the *automated search and optimization* of the 'harness' components (memory, retrieval, interaction logic) that surround a fixed base LLM or agent. While frameworks like LangChain provide tools to *build* LLM applications and agents, Meta-Harness provides the methodology and framework to *optimize* the performance of those agent components through systematic search.

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 stanford-iris-lab/meta-harness
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 1×
  2. run-llama/llama_index · recommended 1×
  3. deepset-ai/haystack · recommended 1×
  4. microsoft/guidance · recommended 1×
  5. stanfordnlp/dspy · recommended 1×
  • CATEGORY QUERY
    How can I automatically improve the performance of my LLM agent's interaction logic?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Haystack (deepset-ai/haystack)
    4. Microsoft Guidance (microsoft/guidance)
    5. DSPy (stanfordnlp/dspy)
    6. OpenAI Evals (openai/evals)
    7. Weights & Biases Prompts (wandb/wandb)

    AI recommended 7 alternatives but never named stanford-iris-lab/meta-harness. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for automated search over LLM agent memory and retrieval strategies.
    you: not recommended
    AI recommended (in order):
    1. LangChain

    AI recommended 1 alternative but never named stanford-iris-lab/meta-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
    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 stanford-iris-lab/meta-harness?
    pass
    AI named stanford-iris-lab/meta-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 stanford-iris-lab/meta-harness in production, what risks or prerequisites should they evaluate first?
    pass
    AI named stanford-iris-lab/meta-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 stanford-iris-lab/meta-harness solve, and who is the primary audience?
    pass
    AI named stanford-iris-lab/meta-harness explicitly

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

Embed your GEO score

Drop this badge into the README of stanford-iris-lab/meta-harness. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/stanford-iris-lab/meta-harness.svg)](https://repogeo.com/en/r/stanford-iris-lab/meta-harness)
HTML
<a href="https://repogeo.com/en/r/stanford-iris-lab/meta-harness"><img src="https://repogeo.com/badge/stanford-iris-lab/meta-harness.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

stanford-iris-lab/meta-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