REPOGEO REPORT · LITE

regent-vcs/re_gent

Default branch main · commit 1cde53e3 · scanned 6/15/2026, 8:46:44 PM

GitHub: 728 stars · 51 forks

AI VISIBILITY SCORE

33 /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

2 / 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 regent-vcs/re_gent, 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 unique value for AI agents.

Why:

CURRENT

Track what your agent did, which prompt wrote each line, and inspect any step.

COPY-PASTE FIX

Traditional version control systems like Git struggle with the unique challenges of AI agent-generated code. Regent provides purpose-built version control to track exactly what your AI agent did, which prompt wrote each line, and allows you to inspect any step in its code generation process, making AI development auditable and debuggable.

mediumtopics#2

Add more specific topics to improve categorization.

Why:

CURRENT

ai-agent, claude-code, developer-tools, devtools, golang, version-control

COPY-PASTE FIX

ai-agent, claude-code, developer-tools, devtools, golang, version-control, ai-code-auditing, agent-development, llm-ops, code-generation-tracking

lowreadme#3

Add a dedicated section explaining how `re_gent` differs from generic VCS for AI code.

Why:

COPY-PASTE FIX

## Why Regent for AI Agents?
Traditional Git-based workflows are ill-suited for the iterative, non-linear, and often opaque nature of AI agent code generation. Regent is designed from the ground up to:
- **Automatically track every agent turn:** No manual commits needed.
- **Attribute code to prompts:** See which prompt generated each line.
- **Inspect and revert any step:** Easily debug and understand agent behavior.
- **Focus on code provenance:** Understand the 'why' behind agent changes, not just the 'what'.

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 regent-vcs/re_gent

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Git

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Git · recommended 1×
GitHub · recommended 1×
GitLab · recommended 1×
Bitbucket · recommended 1×
Gerrit · recommended 1×

CATEGORY QUERY
How do I version control and review code changes made by an AI programming agent?
you: not recommended
AI recommended (in order):
1. Git
2. GitHub
3. GitLab
4. Bitbucket
5. Gerrit
6. Phabricator
7. Azure DevOps
8. Azure Repos
9. Perforce Helix Core
10. Perforce Swarm
11. Jenkins
12. GitHub Actions
13. GitLab CI/CD
14. Azure Pipelines
15. Azure Boards
AI recommended 15 alternatives but never named regent-vcs/re_gent. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a system to track and audit the development history of AI-generated code.
you: not recommended
AI recommended (in order):
1. Git with Git LFS (git-lfs/git-lfs)
2. DVC (iterative/dvc)
3. MLflow (mlflow/mlflow)
4. Weights & Biases (W&B) (wandb/wandb)
5. Neptune.ai (neptune-ai/neptune-client)
6. Pachyderm (pachyderm/pachyderm)
AI recommended 6 alternatives but never named regent-vcs/re_gent. 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 regent-vcs/re_gent?
pass
AI did not name regent-vcs/re_gent — 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 regent-vcs/re_gent in production, what risks or prerequisites should they evaluate first?
pass
AI named regent-vcs/re_gent 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 regent-vcs/re_gent solve, and who is the primary audience?
pass
AI named regent-vcs/re_gent explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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HTML

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite