head to head · open source
llama_index vs langchain4j
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. langchain4j has 13,344 stars, 2,657 forks, 878 open issues and last shipped yesterday. llama_index leads on adoption by 296% (52,779 vs 13,344 stars). llama_index is written in Python under MIT; langchain4j is written in Java under Apache-2.0. llama_index has attracted 16% as many forks as stars, langchain4j 20%. langchain4j was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (llm), so they are genuine substitutes rather than adjacent tools.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
← all 20902 open source comparisons
Side by side
| llama_index | langchain4j | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 13K |
| License | MIT | Apache-2.0 |
| Written in | Python | Java |
| Last push | 2026-10-26 | 2026-11-02 |
| Forks | ⑂ 8.5K | ⑂ 2.7K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama_index if
- You weight community size — 53K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick langchain4j if
- You want the langchain4j feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Java
- You evaluated both and langchain4j fits your workflow better
About llama_index
🗂️ LlamaIndex (OSS Framework) 🦙 [!NOTE] The current focus of LlamaIndex is to build the best AI powered engine for document parsing and extraction. LlamaParse is our enterprise platform for agentic OCR, parsing, extraction, indexing and more. LiteParse represents our efforts to build the best free, fast, cheap text parser in the market. ParseBench and ExtractBench represent our commitment towards open benchmarking for parsing and extraction. The company itself has undergone an evolution since when this OSS framework first launched 3 years ago in 2023. Since the early days, the framework has consisted of a…
read the full llama_index overview →
About langchain4j
LangChain4j: idiomatic, open source Java library for building LLM powered applications on the JVM Introduction Welcome! The goal of LangChain4j is to simplify integrating LLMs into Java applications. Here's how: 1. Unified APIs: LLM providers (like OpenAI or Google Vertex AI) and embedding (vector) stores (such as Pinecone or Milvus) use proprietary APIs. LangChain4j offers a unified API to avoid the need for learning and implementing specific APIs for each of them. To experiment with different LLMs or embedding stores, you can easily switch between them without the need to rewrite your code. LangChain4j…
read the full langchain4j overview →
More in AI & Machine Learning
Related comparisons
More Machine Learning Infrastructure projects
Compare either of these against the rest of the Machine Learning Infrastructure field.
Frequently asked questions
Is llama_index or langchain4j more popular?
llama_index has 52,779 GitHub stars and langchain4j has 13,344. llama_index has the larger community by that measure.
Are llama_index and langchain4j free?
Both are open source. llama_index is licensed under MIT and langchain4j under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and langchain4j?
llama_index is written in Python and langchain4j in Java. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, llama_index or langchain4j?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose langchain4j if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.