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Basic Information

CrewAI Quickstart is a developer-focused cookbook that provides code templates, example notebooks, and guides to help developers build with CrewAI. It collects sequential and hierarchical example notebooks, Python script templates, Streamlit GUI examples, and local LLM setups so practitioners can experiment with agentic workflows and tool usage. The repo is organized around copy-pastable snippets and ready-to-run notebooks demonstrating retrieval-augmented generation and tool integration. It assumes familiarity with CrewAI and requires an API key from major language model providers mentioned in the README. Contributors can adapt the templates to their own projects, extend tools, or follow example tasks such as event planning. The project aims to accelerate prototyping by offering concrete, reusable patterns and configurations for orchestrating agents and tools within the CrewAI ecosystem.

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Features
The repository provides a broad set of ready-made artifacts and examples. It includes basic sequential and hierarchical Google Colab notebooks, many notebooks demonstrating tool use for RAG and search across filetypes, a custom tool template, example event planning task, and Python script folders for sequential and hierarchical patterns. There is a catalog of tool notebooks for TXT, CSV, DOCX, PDF, JSON, MDX, XML, PostgreSQL, GitHub, website and YouTube search, browser-based and Selenium scraping, code documentation search, a code interpreter tool, and Composio integration. GUI examples using Streamlit are supplied for both sequential and hierarchical flows. It also contains quickstarts for running local LLMs with Ollama using llama2 and llama3. The project is MIT licensed and welcomes contributions.
Use Cases
This quickstart helps developers learn CrewAI concepts faster by providing structured examples that demonstrate common agent patterns, tool integrations, and RAG workflows. The notebooks and scripts reduce setup time by offering copyable code and concrete demonstrations of how to search and interact with various data sources, interpret code, scrape websites, and run local models. Streamlit GUIs let users prototype interactive interfaces quickly, while the local LLM examples show how to experiment without remote services. The included custom tool template and example tasks make it easier to extend functionality for specific use cases. Community contribution guidance encourages incremental improvement and sharing of additional recipes, making the repo a practical learning and prototyping resource.

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