RRepoGEO

REPOGEO REPORT · LITE

stanford-iris-lab/meta-harness

Default branch main · commit 95175f70 · scanned 6/1/2026, 4:03:00 PM

GitHub: 1,011 stars · 94 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 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 README opening to clarify LLM agent optimization focus

    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 over task-specific model harnesses, specifically designed to *optimize* the surrounding code for LLM agents. It focuses on end-to-end optimization of agent components like memory, retrieval, and interaction logic, helping you get the most out of your LLM applications.
  • mediumtopics#2
    Add more specific LLM optimization topics

    Why:

    CURRENT
    harness-engineering, llm-agents
    COPY-PASTE FIX
    harness-engineering, llm-agents, llm-optimization, agent-optimization, generative-ai-optimization
  • lowreadme#3
    Add a 'Comparison to other LLM frameworks' section

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## Comparison to other LLM frameworks' with content like: 'Unlike general LLM agent frameworks (e.g., LangChain, LlamaIndex) that help you *build* agents, Meta-Harness focuses on *optimizing* the components (memory, retrieval, interaction logic) *within* or *around* your existing LLM agents. It's a tool for end-to-end performance tuning, not for initial agent construction.'

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
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LangSmith · recommended 1×
  3. OpenAI Evals · recommended 1×
  4. Weights & Biases · recommended 1×
  5. Prometheus · recommended 1×
  • CATEGORY QUERY
    How can I automatically optimize the surrounding code for my LLM agents?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LangSmith
    3. OpenAI Evals
    4. Weights & Biases
    5. Prometheus
    6. Grafana
    7. Snowflake
    8. BigQuery
    9. Tableau
    10. Looker
    11. GPT-4
    12. Claude 3 Opus

    AI recommended 12 alternatives but never named stanford-iris-lab/meta-harness. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a framework to optimize LLM agent memory, retrieval, and 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. AutoGen (microsoft/autogen)
    5. DSPy (stanfordnlp/dspy)
    6. MemGPT (cpacker/MemGPT)

    AI recommended 6 alternatives 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?

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stanford-iris-lab/meta-harness — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite