REPOGEO REPORT · LITE

genkit-ai/genkit

Default branch main · commit 2c105d21 · scanned 6/30/2026, 11:16:47 PM

GitHub: 6,158 stars · 776 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 genkit-ai/genkit, 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

Strengthen README opening with core differentiators

Why:

CURRENT

Genkit is an open-source framework for building full-stack AI-powered applications, built and used in production by Google's Firebase.

COPY-PASTE FIX

Genkit is an open-source, developer-centric framework for building full-stack AI-powered applications with a local-first development experience and built-in observability, used in production by Google's Firebase.

mediumcomparison#2

Add a 'Why Genkit?' or 'Comparison' section to README

Why:

COPY-PASTE FIX

## Why Genkit? Genkit differentiates itself with a developer-centric, local-first approach to building AI applications, providing integrated tooling for local development and testing, and built-in observability for inspecting flows, traces, and model interactions. While other frameworks focus on orchestration, Genkit emphasizes a complete development lifecycle from local iteration to production deployment.

lowtopics#3

Expand repository topics with more specific keywords

Why:

CURRENT

agents, ai, embedders, genkit, llm, multimodal, rag, vector-database

COPY-PASTE FIX

agents, ai, embedders, genkit, llm, multimodal, rag, vector-database, orchestration, observability, full-stack, development-framework

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 genkit-ai/genkit

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenAI Assistants API

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

OpenAI Assistants API · recommended 2×
LangChain · recommended 1×
LlamaIndex · recommended 1×
Haystack · recommended 1×
Microsoft Semantic Kernel · recommended 1×

CATEGORY QUERY
What framework helps build full-stack AI applications with agents across multiple languages?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Haystack
4. Microsoft Semantic Kernel
5. AutoGPT
6. BabyAGI
7. OpenAI Assistants API
8. CrewAI
AI recommended 8 alternatives but never named genkit-ai/genkit. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I easily integrate diverse large language models and RAG into my application?
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. LlamaIndex (run-llama/llama_index)
3. Haystack (deepset-ai/haystack)
4. Microsoft Semantic Kernel (microsoft/semantic-kernel)
5. OpenAI Assistants API
6. Hugging Face Transformers (huggingface/transformers)
7. Hugging Face Datasets (huggingface/datasets)
8. Hugging Face Accelerate (huggingface/accelerate)
9. LiteLLM (BerriAI/litellm)
AI recommended 9 alternatives but never named genkit-ai/genkit. 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 genkit-ai/genkit?
pass
AI named genkit-ai/genkit explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts genkit-ai/genkit in production, what risks or prerequisites should they evaluate first?
pass
AI named genkit-ai/genkit 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 genkit-ai/genkit solve, and who is the primary audience?
pass
AI named genkit-ai/genkit 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 genkit-ai/genkit. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/genkit-ai/genkit.svg)](https://repogeo.com/en/r/genkit-ai/genkit)

HTML

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

Pro

Subscribe to Pro for deep diagnoses

genkit-ai/genkit — 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