REPOGEO REPORT · LITE

metauto-ai/agent-as-a-judge

Default branch main · commit 43c2fa7b · scanned 6/4/2026, 4:47:52 AM

GitHub: 773 stars · 105 forks

AI VISIBILITY SCORE

27 /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

1 / 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 metauto-ai/agent-as-a-judge, 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 to clearly state the project's purpose

Why:

CURRENT

The README currently starts with a `div` and then a `NOTE` about 'Agent-as-a-Judge is not only limited to the content of this repo, but a philosophy...'.

COPY-PASTE FIX

Place this sentence at the very top of your README:
`Agent-as-a-Judge is an open-source framework for automated, agent-based evaluation of AI agent performance, providing continuous feedback and reward signals for agentic training.`

mediumtopics#2

Expand repository topics for better categorization

Why:

CURRENT

agent-as-a-judge, llm-as-a-judge, llms

COPY-PASTE FIX

agent-as-a-judge, llm-as-a-judge, llms, ai-agent-evaluation, agentic-ai, automated-evaluation, llm-evaluation-framework, deep-reinforcement-learning-feedback

lowreadme#3

Add a comparison section to the README

Why:

COPY-PASTE FIX

Add a new section to the README, for example:
`## 🆚 Agent-as-a-Judge vs. Other Evaluation Tools

Unlike general LLM frameworks (e.g., LangChain, LlamaIndex) or MLOps platforms (e.g., MLflow, Weights & Biases), Agent-as-a-Judge specializes in automated, agent-based evaluation of AI agent performance, providing continuous, step-by-step feedback and reward signals specifically for agentic training and improvement.`

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 metauto-ai/agent-as-a-judge

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

langchain-ai/langchain

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

langchain-ai/langchain · recommended 2×
run-llama/llama_index · recommended 2×
mlflow/mlflow · recommended 1×
Arize AI · recommended 1×
Weights & Biases · recommended 1×

CATEGORY QUERY
How can I automate evaluation of AI agent performance and provide continuous feedback?
you: not recommended
AI recommended (in order):
1. MLflow (mlflow/mlflow)
2. Arize AI
3. LangChain (langchain-ai/langchain)
4. LlamaIndex (run-llama/llama_index)
5. Weights & Biases
6. Prometheus (prometheus/prometheus)
7. Grafana (grafana/grafana)
8. Scale AI
9. Labelbox
10. Surge AI
11. PostgreSQL
12. MongoDB
13. Pandas (pandas-dev/pandas)
14. Matplotlib (matplotlib/matplotlib)
AI recommended 14 alternatives but never named metauto-ai/agent-as-a-judge. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help use large language models for judging open-ended agentic system outputs?
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. LlamaIndex (run-llama/llama_index)
3. OpenAI Evals (openai/evals)
4. Weights & Biases (W&B) Prompts
5. Humanloop
6. Ragas (explodinggradients/ragas)
7. OpenAI APIs
8. Anthropic APIs
9. Google APIs
AI recommended 9 alternatives but never named metauto-ai/agent-as-a-judge. 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 metauto-ai/agent-as-a-judge?
pass
AI did not name metauto-ai/agent-as-a-judge — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts metauto-ai/agent-as-a-judge in production, what risks or prerequisites should they evaluate first?
pass
AI named metauto-ai/agent-as-a-judge 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 metauto-ai/agent-as-a-judge solve, and who is the primary audience?
pass
AI did not name metauto-ai/agent-as-a-judge — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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metauto-ai/agent-as-a-judge — 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