LlamaIndex vs arXiv Paper Curator
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
A hands-on course project that builds a research assistant for arXiv papers using production RAG methods.
The short answer
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 arXiv Paper Curator if you need…
- Engineers learning how to build RAG systems step by step
- Students who want a complete project tied to weekly blog posts
- Developers who already know Python and Docker and want a hands-on search-plus-LLM example
- Anyone who wants to follow the course path week by week using tagged releases
Side-by-side
| LlamaIndex | arXiv Paper Curator | |
|---|---|---|
| Type | Library | Learning resource |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 52.8k | 10k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Clone and build from source |
| Main language | Python | Python |
| Last code update | 10 days ago | 5 months 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.
arXiv Paper Curator
This is a learner-focused course project that walks you through building a research assistant. The system fetches academic papers from arXiv, parses and indexes them, and answers questions about them with retrieval-augmented generation (RAG). It is built in weekly stages, from Docker and search infrastructure up to an agentic version with LangGraph and a Telegram bot.
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
arXiv Paper Curator
- Engineers learning how to build RAG systems step by step
- Students who want a complete project tied to weekly blog posts
- Developers who already know Python and Docker and want a hands-on search-plus-LLM example
- Anyone who wants to follow the course path week by week using tagged releases
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
arXiv Paper Curator
- Organized as a clear weekly progression with blog posts and tagged releases for each stage
- Starts from search fundamentals before adding vector retrieval, which the README presents as a deliberate teaching choice
- Covers a full stack of components (FastAPI, PostgreSQL, OpenSearch, Airflow, Ollama) in one runnable project
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
arXiv Paper Curator
- People who want a ready-made research tool without setup, since it requires Docker, Python 3.12+ and a sizeable machine (8GB+ RAM, 20GB+ disk)
- Readers new to Python or containers, because the README assumes familiarity with Docker Compose and uv
- Teams looking for a general production library, since this is structured as a course rather than a reusable package
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
arXiv Paper Curator
- Setup is heavy: Docker Desktop, Python 3.12+, uv, and a Jina API key plus Langfuse keys are needed before the system runs fully
- Several parts rely on external blog posts for explanation, so the README alone gives limited detail
- The README is truncated here, so later weeks and some configuration specifics could not be verified
Getting started
LlamaIndex
Install core and selected integrations
pip install llama-index-coreAdd an LLM integration
pip install llama-index-llms-openaiarXiv Paper Curator
Clone the repository
git clone <repository-url>Copy the environment template
cp .env.example .envFrequently asked questions
What is the difference between LlamaIndex and arXiv Paper Curator?
LlamaIndex: Python framework for building LLM apps that query your own documents and data. arXiv Paper Curator: A hands-on course project that builds a research assistant for arXiv papers using production RAG methods. LlamaIndex suits python developers building retrieval-augmented generation (rag) apps over private documents, while arXiv Paper Curator suits engineers learning how to build rag systems step by step.
Are LlamaIndex and arXiv Paper Curator free?
LlamaIndex: Free and open source. arXiv Paper Curator: Free and open source.
Is LlamaIndex or arXiv Paper Curator more popular?
LlamaIndex has more GitHub stars (52.8k) than arXiv Paper Curator (10k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to LlamaIndex and arXiv Paper Curator?
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.