REPOGEO REPORT · LITE

tensorlakeai/tensorlake

Default branch main · commit 09efbd94 · scanned 6/12/2026, 9:02:07 AM

GitHub: 939 stars · 139 forks

AI VISIBILITY SCORE

35 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 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 tensorlakeai/tensorlake, 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

Clarify README H1 and opening statement

Why:

CURRENT

<h1 align="center"></h1> <p align="center">Build agents with sandboxes and serverless orchestration runtime</p>

COPY-PASTE FIX

<h1 align="center">Tensorlake: Serverless Runtime for AI Agents & Isolated Sandboxes</h1> <p align="center">Tensorlake is a compute infrastructure platform for building agentic applications with sandboxes and serverless orchestration runtime. It provides secure, stateful micro-VMs for running agents and executing LLM-generated code.</p>

hightopics#2

Add specific topics to improve categorization

Why:

CURRENT

(none)

COPY-PASTE FIX

ai-agents, serverless, sandboxes, micro-vms, llm-execution, agentic-ai, firecracker, orchestration, runtime

mediumreadme#3

Add explicit comparison to generic serverless and VM platforms

Why:

CURRENT

The README currently compares performance against Vercel, E2B, Modal, and Daytona, but doesn't explicitly differentiate from generic serverless or raw VM technologies.

COPY-PASTE FIX

Add a new section titled "Why Tensorlake for AI Agents?" or integrate a paragraph into the introduction: "While generic serverless functions (e.g., AWS Lambda, Cloudflare Workers) offer stateless compute, and raw VM technologies (e.g., Firecracker, Kata Containers) provide isolation, Tensorlake uniquely combines these for AI agents. It offers stateful, instantly clonable micro-VM sandboxes with near-SSD I/O, specifically designed for the dynamic and secure execution needs of agentic applications and LLM-generated code."

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 tensorlakeai/tensorlake

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Cloudflare Workers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Cloudflare Workers · recommended 2×
AWS Lambda · recommended 1×
AWS Step Functions · recommended 1×
Google Cloud Functions · recommended 1×
Google Cloud Workflows · recommended 1×

CATEGORY QUERY
How to deploy and orchestrate background AI agents using isolated serverless sandboxes?
you: not recommended
AI recommended (in order):
1. AWS Lambda
2. AWS Step Functions
3. Google Cloud Functions
4. Google Cloud Workflows
5. Azure Functions
6. Azure Durable Functions
7. Kubernetes
8. Knative
9. OpenFaaS
10. Kubeless
11. Argo Workflows
12. Apache Airflow
13. Vercel Serverless Functions
14. Redis
15. Cloudflare Workers
16. Cloudflare Durable Objects
AI recommended 16 alternatives but never named tensorlakeai/tensorlake. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best platforms for securely executing LLM-generated code in isolated micro-VMs?
you: not recommended
AI recommended (in order):
1. Firecracker
2. Kata Containers
3. gVisor
4. Cloudflare Workers
5. KubeVirt
AI recommended 5 alternatives but never named tensorlakeai/tensorlake. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
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 tensorlakeai/tensorlake?
pass
AI named tensorlakeai/tensorlake explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts tensorlakeai/tensorlake in production, what risks or prerequisites should they evaluate first?
pass
AI named tensorlakeai/tensorlake 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 tensorlakeai/tensorlake solve, and who is the primary audience?
pass
AI named tensorlakeai/tensorlake 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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Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite