DSPy vs LlamaIndex
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
A Python framework for building and optimizing language model programs instead of hand-writing prompts.
Open source · MIT
Python framework for building LLM apps that query your own documents and data.
The short answer
Choose DSPy if you need…
- Python developers building LM-based pipelines such as RAG or multi-step agents
- Teams that want to improve prompts with measurable optimization instead of manual tweaking
- Researchers experimenting with prompt and weight optimization methods
- People who already have a way to measure output quality and want to tune against it
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
Side-by-side
| DSPy | LlamaIndex | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 38.9k | 52.8k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Platforms | API, Linux, macOS, Windows | — |
| Works with | OpenAI, Anthropic | — |
| Main language | Python | Python |
| Last code update | 4 days ago | 10 days ago |
What each one is
DSPy
DSPy is a Python framework for building AI systems out of modular code rather than long hand-written prompts. You describe what each step should do in Python, and DSPy provides algorithms to optimize the prompts and model weights behind those steps. The README says it is used for simple classifiers, RAG pipelines, and agent loops.
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.
Best for
DSPy
- Python developers building LM-based pipelines such as RAG or multi-step agents
- Teams that want to improve prompts with measurable optimization instead of manual tweaking
- Researchers experimenting with prompt and weight optimization methods
- People who already have a way to measure output quality and want to tune against it
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
Strengths
DSPy
- Declarative Python code instead of brittle, hand-written prompt strings
- Includes optimization algorithms for prompts and weights, backed by published research
- Permissive MIT license with a large community (about 38.8k GitHub stars)
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
Not ideal for
DSPy
- People who only need a one-off chat prompt; the framework adds structure that is unnecessary for simple use
- Non-Python users, since the README only shows Python code and pip installation
- Anyone wanting a no-code tool; the README describes a programming framework
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
Trade-offs
DSPy
- The README points to external docs for most details, so the top-level README gives limited hands-on guidance
- The README notes a recent LM integration change with breaking changes to provider options, so check the migration guide before upgrading
- Optimization benefits depend on having a sensible evaluation metric, which the README does not explain in detail
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
Getting started
LlamaIndex
Install core and selected integrations
pip install llama-index-coreAdd an LLM integration
pip install llama-index-llms-openaiFrequently asked questions
What is the difference between DSPy and LlamaIndex?
DSPy: A Python framework for building and optimizing language model programs instead of hand-writing prompts. LlamaIndex: Python framework for building LLM apps that query your own documents and data. DSPy suits python developers building lm-based pipelines such as rag or multi-step agents, while LlamaIndex suits python developers building retrieval-augmented generation (rag) apps over private documents.
Are DSPy and LlamaIndex free?
DSPy: Free and open source. LlamaIndex: Free and open source.
Is DSPy or LlamaIndex more popular?
LlamaIndex has more GitHub stars (52.8k) than DSPy (38.9k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to DSPy and LlamaIndex?
Other options people compare them with include LangChain, guidance, PromptPerfect, promptfoo, Braintrust.
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.