REPOGEO REPORT · LITE

Yuan-lab-LLM/ClawManager

Default branch main · commit 134e1ca2 · scanned 6/22/2026, 8:37:04 PM

GitHub: 1,822 stars · 148 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)

2 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 Yuan-lab-LLM/ClawManager, 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 H1 to specify category
Why:
CURRENT
```
# ClawManager
```
COPY-PASTE FIX
```
# ClawManager: Kubernetes-Native Control Plane for AI Agent Instance Management
```

hightopics#2

Add more specific topics for AI agent management

Why:

CURRENT

hermes, kubernetes, openclaw, webtop

COPY-PASTE FIX

kubernetes, ai-agents, agent-orchestration, control-plane, llm-ops, ai-gateway, runtime-management, openclaw

mediumreadme#3

Add a section differentiating ClawManager from general ML orchestration tools

Why:

COPY-PASTE FIX

## Why ClawManager? Differentiating from General ML Orchestration

While tools like Kubeflow and MLflow offer broad machine learning orchestration, ClawManager is purpose-built as a Kubernetes-native control plane specifically for *AI agent instance management*. It provides governed AI access, runtime orchestration, and reusable resources tailored for autonomous agents, rather than general model training or serving. Our focus is on the lifecycle and governance of AI agents within a Kubernetes environment, offering a specialized solution where generic ML platforms provide broader, less focused capabilities.

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 Yuan-lab-LLM/ClawManager

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Kubeflow

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Kubeflow · recommended 1×
Kubeflow Pipelines · recommended 1×
KFServing (now KServe) · recommended 1×
Jupyter Notebooks · recommended 1×
Katib · recommended 1×

CATEGORY QUERY
How to manage and orchestrate multiple AI agent instances on a Kubernetes cluster?
you: not recommended
AI recommended (in order):
1. Kubeflow
2. Kubeflow Pipelines
3. KFServing (now KServe)
4. Jupyter Notebooks
5. Katib
6. Argo Workflows
7. Argo Events
8. Ray
9. Ray RLlib
10. Ray Tune
11. Ray on Kubernetes Operator
12. Knative
13. Knative Serving
14. Knative Eventing
15. OpenFaaS
16. KEDA
17. Cortex
AI recommended 17 alternatives but never named Yuan-lab-LLM/ClawManager. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Need a control plane for managing AI agent access and shared resources across diverse runtimes.
you: not recommended
AI recommended (in order):
1. Kubernetes
2. KubeFlow (kubeflow/kubeflow)
3. OpenShift AI
4. Rancher Prime
5. MLflow (mlflow/mlflow)
6. Ray (ray-project/ray)
7. Metaflow (Netflix/metaflow)
8. Domino Data Lab
AI recommended 8 alternatives but never named Yuan-lab-LLM/ClawManager. 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 Yuan-lab-LLM/ClawManager?
pass
AI named Yuan-lab-LLM/ClawManager explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts Yuan-lab-LLM/ClawManager in production, what risks or prerequisites should they evaluate first?
pass
AI named Yuan-lab-LLM/ClawManager 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 Yuan-lab-LLM/ClawManager solve, and who is the primary audience?
pass
AI named Yuan-lab-LLM/ClawManager explicitly
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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HTML

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Yuan-lab-LLM/ClawManager — 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