Langflow 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
Visual builder for creating and deploying AI agents and workflows, with API and MCP server output.
Open source · MIT
Python framework for building LLM apps that query your own documents and data.
Our verdict
Langflow is a visual platform for building AI agents and workflows, which you can run as a desktop app and then expose as an API or MCP server. LlamaIndex is a Python library for connecting an LLM to your own documents and data, and it requires writing code. They overlap in building LLM apps, but Langflow suits people who want a visual builder and deployable tools, while LlamaIndex suits Python developers focused on document question-answering.
Pick Langflow if you want to prototype LLM workflows visually and publish them as an API or MCP server for other apps to use.
Pick LlamaIndex if you are a Python developer building retrieval apps over your own documents and want to swap components in code.
| Key difference | Langflow | LlamaIndex |
|---|---|---|
| Type | App: visual builder for agents and workflows | Library: Python framework for coding LLM apps |
| Best at | Visual flows deployed as APIs or MCP servers | Retrieval over private documents and data sources |
| Coding needed | Visual editor, with Python for custom components | Requires writing Python code for every app |
| Setup | Intermediate; desktop installer exists, admin password needed | Intermediate; install via package manager, pick integrations |
| Project status | Actively updated; last code update within the last day | Company focus shifted to commercial LlamaParse; last update 3 days ago |
Written with AI from both products' catalogue entries · updated 2026-10-29
Who each one is for
Choose Langflow if you need…
- Developers who want a visual way to prototype LLM workflows and then move them into code
- Teams building multi-agent or retrieval-based assistants
- People who want to expose a workflow as an API or MCP tool for other apps
- Users who prefer a desktop installer, which the README says exists for Windows and macOS
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
| Langflow | LlamaIndex | |
|---|---|---|
| Type | App | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 156k | 52.8k |
| 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 | 4 days ago | 10 days ago |
What each one is
Langflow
Langflow is a platform for building AI-powered agents and workflows. It offers a visual editor for assembling flows, and each finished workflow can be exposed as an API or as an MCP server so other applications and tools can use it. The README says it supports major LLMs, vector databases, and a range of AI tools.
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
Langflow
- Developers who want a visual way to prototype LLM workflows and then move them into code
- Teams building multi-agent or retrieval-based assistants
- People who want to expose a workflow as an API or MCP tool for other apps
- Users who prefer a desktop installer, which the README says exists for Windows and macOS
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
Langflow
- Visual builder combined with source code access to customize components in Python
- Flows can be deployed as APIs or MCP servers, not just used in the editor
- Open source under the MIT license with a large community around it
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
Langflow
- People who need a simple chat app with no setup, since it is a broader platform with many components to configure
- Anyone deploying to a public server without planning authentication, because the README says auto-login is disabled in official releases and a password must be set
- Users on Python versions outside 3.10–3.14, as the README lists that range as required
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
Langflow
- The README is truncated and gives limited detail on configuration, so setup for production use requires reading the linked docs
- Running it safely requires setting an administrator password, which adds a step for newcomers
- Its scope is broad, so there is a learning curve to understand components, flows, and deployment options
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
Langflow
Install Langflow
uv pip install langflow -UStart Langflow (set LANGFLOW_SUPERUSER_PASSWORD first)
uv run langflow runLlamaIndex
Install core and selected integrations
pip install llama-index-coreAdd an LLM integration
pip install llama-index-llms-openaiFrequently asked questions
Should I use Langflow or LlamaIndex?
Langflow is a visual platform for building AI agents and workflows, which you can run as a desktop app and then expose as an API or MCP server. LlamaIndex is a Python library for connecting an LLM to your own documents and data, and it requires writing code. They overlap in building LLM apps, but Langflow suits people who want a visual builder and deployable tools, while LlamaIndex suits Python developers focused on document question-answering. Pick Langflow if you want to prototype LLM workflows visually and publish them as an API or MCP server for other apps to use. Pick LlamaIndex if you are a Python developer building retrieval apps over your own documents and want to swap components in code.
What is the difference between Langflow and LlamaIndex?
Langflow: Visual builder for creating and deploying AI agents and workflows, with API and MCP server output. LlamaIndex: Python framework for building LLM apps that query your own documents and data. Langflow suits developers who want a visual way to prototype llm workflows and then move them into code, while LlamaIndex suits python developers building retrieval-augmented generation (rag) apps over private documents.
Are Langflow and LlamaIndex free?
Langflow: Free and open source. LlamaIndex: Free and open source.
Is Langflow or LlamaIndex more popular?
Langflow has more GitHub stars (156k) than LlamaIndex (52.8k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Langflow and LlamaIndex?
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.