langchain4j and LangChain

langchain4j
72
Verified
LangChain
23
Experimental
Maintenance 22/25
Adoption 10/25
Maturity 16/25
Community 24/25
Maintenance 0/25
Adoption 6/25
Maturity 8/25
Community 9/25
Stars: 11,081
Forks: 2,029
Downloads:
Commits (30d): 141
Language: Java
License: Apache-2.0
Stars: 16
Forks: 2
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License:
No Package No Dependents
No License Stale 6m No Package No Dependents

About langchain4j

langchain4j/langchain4j

LangChain4j is an open-source Java library that simplifies the integration of LLMs into Java applications through a unified API, providing access to popular LLMs and vector databases. It makes implementing RAG, tool calling (including support for MCP), and agents easy. LangChain4j integrates seamlessly with various enterprise Java frameworks.

This library helps Java developers integrate powerful AI language models into their applications. It takes various large language models (LLMs) and vector databases as input, allowing developers to build features like advanced chatbots or intelligent data retrieval systems. The output is a Java application supercharged with AI capabilities, used by software engineers to enhance their products.

Java development AI application development LLM integration Enterprise software API integration

About LangChain

sienlonglim/LangChain

This project implements RAG using OpenAI's embedding models and LangChain's Python library

This project helps you quickly get answers from your own collection of documents, videos, and web pages without needing to manually search through them. You provide various file types like PDFs, Word documents, text files, YouTube links, or Wikipedia pages, and it allows you to ask questions to get concise, relevant answers. This is ideal for researchers, analysts, or anyone who needs to extract specific information from a large, diverse set of content.

information-retrieval document-analysis knowledge-discovery content-qa research-assistance

Scores updated daily from GitHub, PyPI, and npm data. How scores work