REPOGEO REPORT · LITE

majacinka/crewai-experiments

Default branch main · commit 2636efde · scanned 6/18/2026, 7:43:04 PM

GitHub: 1,016 stars · 251 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

17 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 0 warn · 1 fail

Objective metadata checks

AI knows your name

1 / 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 majacinka/crewai-experiments, 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

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

crewai, llm, ai-agents, local-llm, ollama, agent-orchestration, llm-experiments, multi-agent-systems

highlicense#2

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Create a LICENSE file in the repository root with the text of a common open-source license like MIT or Apache-2.0.

mediumreadme#3

Clarify the README's opening to highlight LLM comparison

Why:

CURRENT

For my experiments with CrewAI, I decided to try 3 different projects, starting from the easiest to the most complex. The aim of the experiments was to have a team of AI agents do following work for me:

COPY-PASTE FIX

This repository documents my experiments with CrewAI, focusing on evaluating and comparing various LLMs—both API-based and local (via Ollama)—for multi-agent AI workflows. The aim is to assess how different models perform across several agent-driven tasks:

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 majacinka/crewai-experiments

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

langchain-ai/langchain

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

langchain-ai/langchain · recommended 1×
run-llama/llama_index · recommended 1×
joaomdmoura/crewAI · recommended 1×
microsoft/autogen · recommended 1×
OpenAI API · recommended 1×

CATEGORY QUERY
What are the best practices for orchestrating AI agent teams across different LLMs?
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. LlamaIndex (run-llama/llama_index)
3. CrewAI (joaomdmoura/crewAI)
4. AutoGen (microsoft/autogen)
5. OpenAI API
6. Chroma (chroma-core/chroma)
7. Pinecone
8. Weaviate (weaviate/weaviate)
9. LangSmith
10. Weights & Biases (wandb/wandb)
AI recommended 10 alternatives but never named majacinka/crewai-experiments. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I integrate local open-source LLMs with external tools for agent workflows?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Haystack
4. AutoGPT
5. CrewAI
6. Ollama
7. llama-cpp-python
AI recommended 7 alternatives but never named majacinka/crewai-experiments. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
fail
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 majacinka/crewai-experiments?
pass
AI did not name majacinka/crewai-experiments — 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 majacinka/crewai-experiments in production, what risks or prerequisites should they evaluate first?
pass
AI named majacinka/crewai-experiments 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 majacinka/crewai-experiments solve, and who is the primary audience?
pass
AI did not name majacinka/crewai-experiments — 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?

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MARKDOWN (README)

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majacinka/crewai-experiments — 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