Langchain and Llamaindex are two widely used mainstream LLM application development frameworks. What is the difference between them? How should we use them? Below I have made a preliminary selection based on various sources and related documents.
I. Langchain
1. Applicable scenarios
(1) The need to build flexible and scalable general-purpose applications.
(2) Requires complex workflow support.
(3) Complex interactions and context retention features are required.
(4) Extensive integration of functions and tools is required.
2. Strengths
(1) A more generalized framework for a variety of applications.
(2) Provides rich tools for loading, processing and indexing data and interacting with the LLM.
(3) Highly flexible, allowing users to customize the behavior of the application.
3. Characteristics
(1) LangSmith is a platform for building production-grade LLM applications. It allows you to closely monitor and evaluate your applications so you can deliver quickly and confidently.
(2) LangServe helps developers deploy runnable LangChain objects and chains as REST APIs.
II. LlamaIndex
1. Applicable scenarios
(1) Need to build efficient and simple search and retrieval applications.
(2) Applications that need to handle large amounts of data.
(3) The need for quick access and retrieval of data.
2. Strengths
(1) Designed for search and retrieval applications.
(2) Provide a simple interface to query LLMs and retrieve related documents.
(3) Efficiently handle large amounts of data, making it better at handling big data.
III. Overall conclusions
(1) LangChain is suitable for general-purpose applications that require flexibility and complexity.
(2) LlamaIndex is suitable for applications that require efficient data retrieval and search functions.
IV. Individual perspectives
(1) LangChain is an essential framework for LLM application development.
(2) If you only want to get started with RAG-related development, you can first consider using LlamaIndex for a quick and efficient implementation, and then if you want to make RAG the center of Agent and other implementations, you are advised to introduce LangChain.
V. References
1. Relevant information
(1)Comparing LangChain and LlamaIndex with 4 tasks
(2)Differences between Langchain & LlamaIndex [closed]
(3)What is the difference between LlamaIndex and LangChain
2. Official documents
(1)LlamaIndex
(2)LangChain
(3)LangSmith