explaingit

crewaiinc/crewai-examples

Analysis updated 2026-07-03

5,944Jupyter NotebookAudience · developerComplexity · 3/5Setup · moderate

TLDR

Official collection of runnable applications built on CrewAI, a Python framework for coordinating multiple AI agents. Covers simple crew patterns like content generation and stock analysis, plus advanced flows with state management, branching, and human-in-the-loop steps.

Mindmap

mindmap
  root((repo))
    Crew examples
      Content creation
      Stock analysis
      Trip planning
    Flow examples
      Book writing
      Meeting notes
      Lead scoring
    Integrations
      Azure OpenAI
      NVIDIA models
      LangGraph
    Getting started
      Starter template
      Learning path
      UV setup
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Code map

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What do people build with it?

USE CASE 1

Build a multi-agent pipeline that researches, writes, and edits Instagram posts using separate AI agents each with a defined role.

USE CASE 2

Create an advanced flow that writes a full book by running chapter-generation agents in parallel and merging their output.

USE CASE 3

Process meeting notes with AI agents that extract action items and post them automatically to Trello and Slack.

USE CASE 4

Connect CrewAI to Azure OpenAI or NVIDIA-hosted models using the ready-made integration examples.

What is it built with?

PythonJupyter NotebookCrewAIUV

How does it compare?

crewaiinc/crewai-examplesed-donner/llm_engineeringsnakers4/silero-models
Stars5,9445,9435,917
LanguageJupyter NotebookJupyter NotebookJupyter Notebook
Setup difficultymoderateeasyeasy
Complexity3/52/52/5
Audiencedeveloperdeveloperdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires the UV package manager and API keys for whichever AI provider each example uses.

License not specified in the explanation.

In plain English

CrewAI-examples is the official collection of complete, runnable applications built on top of the CrewAI framework. CrewAI is a Python library for coordinating multiple AI agents to work together on a task, where each agent has a defined role and can use tools, call APIs, or read files. This repository shows what that looks like in practice, across a range of real-world use cases. The examples are organized into three main categories. The Crews folder contains the simpler pattern: a fixed group of AI agents collaborating on a single job. Examples include generating Instagram posts, writing job descriptions, analyzing stocks using SEC data, planning trips, matching resumes to job openings, and building Python games. The Flows folder contains more advanced examples where multiple crews are chained together with state management, branching, and loops. Examples here include writing an entire book with parallel chapter generation, processing meeting notes and posting results to Trello and Slack, and a lead scoring system where a human reviews results before the workflow continues. The Integrations folder shows how to connect CrewAI to Azure OpenAI, NVIDIA models, and the LangGraph framework. Every example is self-contained with its own folder and README. The repository includes a suggested learning path: beginners start with the starter template and basic content generation examples, then move to multi-agent collaboration, then to advanced flow orchestration. All examples use CrewAI version 0.152.0 and are set up using the UV package manager. The companion repository CrewAI Cookbook covers individual features and short tutorials, while this repository focuses on end-to-end applications. Both are linked from the main CrewAI framework documentation at docs.crewai.com.

Copy-paste prompts

Prompt 1
Using the crewai-examples Crews folder as a reference, write a Python crew with a researcher agent and a writer agent that together produce a one-page summary of a company given its name.
Prompt 2
Based on the meeting-notes flow in crewai-examples, create a CrewAI Flow that takes a transcript, extracts action items, assigns owners, and posts results to a Slack channel using the SlackTool.
Prompt 3
Using the lead-scoring flow from crewai-examples as a template, build a flow where a human must approve each lead before the next agent continues processing.
Prompt 4
Set up the stock-analysis crew from crewai-examples and modify it to analyze five tickers in parallel, then combine results into a single comparison report.

Frequently asked questions

What is crewai-examples?

Official collection of runnable applications built on CrewAI, a Python framework for coordinating multiple AI agents. Covers simple crew patterns like content generation and stock analysis, plus advanced flows with state management, branching, and human-in-the-loop steps.

What language is crewai-examples written in?

Mainly Jupyter Notebook. The stack also includes Python, Jupyter Notebook, CrewAI.

What license does crewai-examples use?

License not specified in the explanation.

How hard is crewai-examples to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is crewai-examples for?

Mainly developer.

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