LlamaIndex vs Prompt flow
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
Open-source toolkit for building, testing, evaluating and deploying LLM apps, from prototype to production.
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 Prompt flow if you need…
- Teams with existing Prompt flow applications that need to keep running until migration
- Developers who want to trace and evaluate LLM app behavior on datasets
- Engineers who want to run flow tests in CI/CD pipelines
- Developers who prefer a VS Code extension for visual flow editing
Side-by-side
| LlamaIndex | Prompt flow | |
|---|---|---|
| Type | Library | Dev tool |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 52.8k | 11.3k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 10 days ago | 6 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.
Prompt flow
Prompt flow is a set of development tools for building applications that use large language models (LLMs). You connect LLMs, prompts and Python code into a flow, then debug it, run it against larger datasets to evaluate quality, and deploy it. The README notes that Prompt flow is being retired by Microsoft on April 20, 2027, and is no longer recommended for new development.
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
Prompt flow
- Teams with existing Prompt flow applications that need to keep running until migration
- Developers who want to trace and evaluate LLM app behavior on datasets
- Engineers who want to run flow tests in CI/CD pipelines
- Developers who prefer a VS Code extension for visual flow editing
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
Prompt flow
- Covers the full cycle from prototyping through testing, evaluation and deployment in one toolkit
- Includes a VS Code extension and a command-line interface for flow development
- Released under the MIT license with an active public repository
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
Prompt flow
- Anyone starting a new project: the README says Prompt flow is no longer recommended for new development and suggests Microsoft Agent Framework instead
- Teams relying on Prompt flow runtime container images, which no longer receive security or package updates
- Non-developers, since setup uses the command line and a Python environment
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
Prompt flow
- The README states retirement on April 20, 2027, and no new-development recommendation, so long-term support is limited
- Container images are no longer updated, which is a security concern for deployed workloads
- Setup and tutorials assume familiarity with Python and LLM API keys
Getting started
LlamaIndex
Install core and selected integrations
pip install llama-index-coreAdd an LLM integration
pip install llama-index-llms-openaiPrompt flow
Install the packages
pip install promptflow promptflow-toolsFrequently asked questions
What is the difference between LlamaIndex and Prompt flow?
LlamaIndex: Python framework for building LLM apps that query your own documents and data. Prompt flow: Open-source toolkit for building, testing, evaluating and deploying LLM apps, from prototype to production. LlamaIndex suits python developers building retrieval-augmented generation (rag) apps over private documents, while Prompt flow suits teams with existing prompt flow applications that need to keep running until migration.
Are LlamaIndex and Prompt flow free?
LlamaIndex: Free and open source. Prompt flow: Free and open source.
Is LlamaIndex or Prompt flow more popular?
LlamaIndex has more GitHub stars (52.8k) than Prompt flow (11.3k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to LlamaIndex and Prompt flow?
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.