Analysis updated 2026-07-03
Build a multi-agent pipeline that researches, writes, and edits Instagram posts using separate AI agents each with a defined role.
Create an advanced flow that writes a full book by running chapter-generation agents in parallel and merging their output.
Process meeting notes with AI agents that extract action items and post them automatically to Trello and Slack.
Connect CrewAI to Azure OpenAI or NVIDIA-hosted models using the ready-made integration examples.
| crewaiinc/crewai-examples | ed-donner/llm_engineering | snakers4/silero-models | |
|---|---|---|---|
| Stars | 5,944 | 5,943 | 5,917 |
| Language | Jupyter Notebook | Jupyter Notebook | Jupyter Notebook |
| Setup difficulty | moderate | easy | easy |
| Complexity | 3/5 | 2/5 | 2/5 |
| Audience | developer | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires the UV package manager and API keys for whichever AI provider each example uses.
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.
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.
Mainly Jupyter Notebook. The stack also includes Python, Jupyter Notebook, CrewAI.
License not specified in the explanation.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.