LlamaIndex vs OpenAI Cookbook
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Python framework for building LLM apps that query your own documents and data.
Open source · MIT
Example code and guides for common tasks with the OpenAI API.
Our verdict
LlamaIndex is a Python library you install to build apps that answer questions over your own documents and data, so it suits developers building a working product. OpenAI Cookbook is not a product at all: it is a collection of official, runnable example notebooks for using the OpenAI API, so it suits anyone learning how to call that API. They are different kinds of resource, and many developers would use the Cookbook to learn and LlamaIndex to build.
Pick LlamaIndex if you are a Python developer who wants to build a document question-answering or RAG app over your own files or databases.
Pick OpenAI Cookbook if you are new to the OpenAI API and want working code examples to read, run and adapt.
| Key difference | LlamaIndex | OpenAI Cookbook |
|---|---|---|
| What it is | Python library you install to build document-querying LLM apps | Collection of example code and guides for the OpenAI API |
| Setup | Intermediate; install with a package manager | Beginner; clone the repository and build from source |
| Model providers | Swappable LLM, embedding and vector store providers | Specific to the OpenAI API; needs an OpenAI key |
| Main language | Python | Jupyter Notebook, mostly Python examples |
| Current focus | README says the company's main focus has moved to its commercial LlamaParse platform | Official examples maintained under the OpenAI organization |
Written with AI from both products' catalogue entries · updated 2026-10-29
Who each one is for
Choose LlamaIndex if you need…
- Python developers building retrieval-augmented generation (RAG) apps over private documents
- Teams wanting a modular toolkit where they can swap LLM, embedding and vector store providers
- Developers building agent workflows that query their own data
- Beginners who want a short path to a working document question-answering prototype
Choose OpenAI Cookbook if you need…
- Developers who are new to the OpenAI API and want working examples
- Python programmers who want to see API usage patterns in notebook form
- Students or self-learners exploring how to build with language models
- Teams looking for reference code to adapt for their own projects
Side-by-side
| LlamaIndex | OpenAI Cookbook | |
|---|---|---|
| Type | Library | Learning resource |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 52.8k | 76.9k |
| Setup difficulty | Intermediate | Beginner |
| How you get it | Install with a package manager | Clone and build from source |
| Main language | Python | Jupyter Notebook |
| Last code update | 10 days ago | 5 days ago |
What each one is
LlamaIndex
LlamaIndex is an open-source Python framework for connecting large language models to your own data, such as PDFs, documents, APIs and SQL databases. It provides connectors to load data, ways to structure it into indexes, and retrieval tools that feed relevant context to an LLM. The README also notes that the company's main focus has shifted toward its commercial LlamaParse document platform, with the open-source framework still available.
OpenAI Cookbook
The OpenAI Cookbook is a collection of example code and written guides showing how to accomplish common tasks with the OpenAI API. Most examples are Jupyter notebooks written in Python, though the README notes the ideas can be applied in other languages. It is mainly useful as a set of working references you can read, run, and adapt.
Best for
LlamaIndex
- Python developers building retrieval-augmented generation (RAG) apps over private documents
- Teams wanting a modular toolkit where they can swap LLM, embedding and vector store providers
- Developers building agent workflows that query their own data
- Beginners who want a short path to a working document question-answering prototype
OpenAI Cookbook
- Developers who are new to the OpenAI API and want working examples
- Python programmers who want to see API usage patterns in notebook form
- Students or self-learners exploring how to build with language models
- Teams looking for reference code to adapt for their own projects
Strengths
LlamaIndex
- Large ecosystem of over 300 integration packages, per the README
- Modular design lets you customize or replace components like retrievers and query engines
- Offers both a high-level API for quick starts and lower-level APIs for advanced control
OpenAI Cookbook
- Official examples maintained under the OpenAI organization
- Organized as runnable notebooks that show code and explanation together
- Released under the permissive MIT license
Not ideal for
LlamaIndex
- Non-programmers, since it is a Python library that requires writing code
- Projects that need a fully managed hosted service, because the README points to LlamaParse for the hosted offering and the framework is a toolkit you run yourself
- Users who only need simple one-off document parsing, where a standalone parser may be simpler
OpenAI Cookbook
- People without an OpenAI account or API key, since the examples require one (the README says a free account can be created)
- Non-programmers looking for a ready-made app, because this is a set of code examples rather than a finished product
- Developers who need a non-OpenAI model provider, since the material is specific to the OpenAI API
Trade-offs
LlamaIndex
- The README says the company's primary focus has shifted to LlamaParse, so the open-source framework is secondary to its commercial product
- The README is not updated as often as the documentation, so you should check the docs for current details
- Many separate integration packages means you must choose and install the ones you need
OpenAI Cookbook
- Examples depend on an OpenAI account and API key, which may incur usage costs
- Most code is in Python, so other-language users must translate the concepts
- The README is short and does not describe a version schedule or release history
Getting started
LlamaIndex
Install core and selected integrations
pip install llama-index-coreAdd an LLM integration
pip install llama-index-llms-openaiOpenAI Cookbook
Clone the repository
git clone https://github.com/openai/openai-cookbook.gitFrequently asked questions
Should I use LlamaIndex or OpenAI Cookbook?
LlamaIndex is a Python library you install to build apps that answer questions over your own documents and data, so it suits developers building a working product. OpenAI Cookbook is not a product at all: it is a collection of official, runnable example notebooks for using the OpenAI API, so it suits anyone learning how to call that API. They are different kinds of resource, and many developers would use the Cookbook to learn and LlamaIndex to build. Pick LlamaIndex if you are a Python developer who wants to build a document question-answering or RAG app over your own files or databases. Pick OpenAI Cookbook if you are new to the OpenAI API and want working code examples to read, run and adapt.
What is the difference between LlamaIndex and OpenAI Cookbook?
LlamaIndex: Python framework for building LLM apps that query your own documents and data. OpenAI Cookbook: Example code and guides for common tasks with the OpenAI API. LlamaIndex suits python developers building retrieval-augmented generation (rag) apps over private documents, while OpenAI Cookbook suits developers who are new to the openai api and want working examples.
Are LlamaIndex and OpenAI Cookbook free?
LlamaIndex: Free and open source. OpenAI Cookbook: Free and open source.
Which is easier to set up, LlamaIndex or OpenAI Cookbook?
OpenAI Cookbook is easier to get started with (rated beginner); LlamaIndex is rated intermediate and usually needs more setup.
Is LlamaIndex or OpenAI Cookbook more popular?
OpenAI Cookbook has more GitHub stars (76.9k) than LlamaIndex (52.8k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to LlamaIndex and OpenAI Cookbook?
Other options people compare them with include LangChain, LangGraph, PageIndex, OpenAI Agents SDK, LightRAG.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.