REPOGEO REPORT · LITE

aiming-lab/MetaClaw

Default branch main · commit 922caf3a · scanned 6/24/2026, 11:46:53 PM

GitHub: 3,441 stars · 442 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 aiming-lab/MetaClaw, 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's opening paragraph to clarify project type

Why:

CURRENT

Inspired by how brains learn. Meta-learn and evolve your 🦞 from every conversation in the wild. No GPU required.

COPY-PASTE FIX

MetaClaw is an open-source framework for building conversational AI agents that continually learn and evolve from every interaction. Inspired by how brains learn, it enables meta-learning without requiring a GPU.

mediumtopics#2

Add more specific topics to improve categorization

Why:

CURRENT

agent, ai-agent, continual-learning, fine-tuning, llm, lora, meta-learning, metaclaw, online-learning, openclaw, reinforcement-learning, skill-learning, tinker

COPY-PASTE FIX

agent, ai-agent, continual-learning, fine-tuning, llm, lora, meta-learning, metaclaw, online-learning, openclaw, reinforcement-learning, skill-learning, tinker, conversational-ai, ai-framework, agent-framework

lowreadme#3

Add a 'What MetaClaw is NOT' section to prevent misinterpretations

Why:

COPY-PASTE FIX

### What MetaClaw is NOT
*   **Not a web crawler:** MetaClaw is focused on AI agent learning, not data extraction from the web.
*   **Not a game editor:** While it involves agents, MetaClaw is not a Unity-based or graphical editor for games or simulations.

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 aiming-lab/MetaClaw

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Rasa Open Source

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Rasa Open Source · recommended 1×
OpenAI Gym · recommended 1×
Stable Baselines3 · recommended 1×
Hugging Face Transformers · recommended 1×
GPT-2 · recommended 1×

CATEGORY QUERY
How can I build an AI agent that continually learns and evolves from user interactions?
you: not recommended
AI recommended (in order):
1. Rasa Open Source
2. OpenAI Gym
3. Stable Baselines3
4. Hugging Face Transformers
5. GPT-2
6. GPT-3.5
7. Llama 2
8. BERT
9. TensorFlow
10. PyTorch
11. LangChain
12. LlamaIndex
13. Apache Kafka
14. Apache Flink
15. Apache Spark Streaming
AI recommended 15 alternatives but never named aiming-lab/MetaClaw. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a framework for online meta-learning with conversational AI agents, without a GPU.
you: not recommended
AI recommended (in order):
1. ParlAI (facebookresearch/ParlAI)
2. OpenAI Gym (openai/gym)
3. Ray (ray-project/ray)
4. PyTorch (pytorch/pytorch)
5. learn2learn (learn2learn/learn2learn)
6. TensorFlow (tensorflow/tensorflow)
7. JAX (google/jax)
AI recommended 7 alternatives but never named aiming-lab/MetaClaw. 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 aiming-lab/MetaClaw?
pass
AI named aiming-lab/MetaClaw explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts aiming-lab/MetaClaw in production, what risks or prerequisites should they evaluate first?
pass
AI named aiming-lab/MetaClaw 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 aiming-lab/MetaClaw solve, and who is the primary audience?
pass
AI named aiming-lab/MetaClaw 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 aiming-lab/MetaClaw. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/aiming-lab/MetaClaw.svg)](https://repogeo.com/en/r/aiming-lab/MetaClaw)

HTML

<a href="https://repogeo.com/en/r/aiming-lab/MetaClaw"><img src="https://repogeo.com/badge/aiming-lab/MetaClaw.svg" alt="RepoGEO" /></a>

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aiming-lab/MetaClaw — 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