LangChain - davidmarsoni/llog GitHub Wiki

LangChain

LangChain is a framework designed to simplify the development of applications powered by LLMs. It provides tools and abstractions to streamline the integration of language models into your projects.

:sparkles: Features

LangChain offers a variety of features to enhance your LLM applications. For our alternative analysis, we will focus on the following features that are the most relevant for our use case:

LangChain Features

  • Integration: LangChain integrates with various LLMs, including OpenAI, Hugging Face, and others.
  • LangChain : Provides a framework for building applications powered by LLMs.
  • LangGraph : A tool for building agents that can interact with various APIs and data sources.

:building_construction: Architecture of the libraries

  • langchain-core: Base abstractions for chat models and other components.
  • Integration packages (e.g. langchain-openai, langchain-anthropic, etc.): Important integrations have been split into lightweight packages that are co-maintained by the LangChain team and the integration developers.
  • langchain: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
  • langchain-community: Third-party integrations that are community maintained.
  • langgraph: Orchestration framework for combining LangChain components into production-ready applications with persistence, streaming, and other key features.

:link: Integration

LangChain integrates with various LLMs, including OpenAI, Claude, Google, Meta and more. It also supports various data sources and APIs, allowing you to build applications that can interact with different systems.

:chains: LangChain

LangChain provides tools to create simple agents that can be used to perform specific tasks. The following notebook will show you how to create a simple agent using LangChain.

LangChain Agent Example Notebook

LangChain also provides a rudimentary way to create multi-agent applications. The following notebook will show you how to create a simple multi-agent application using LangChain.

LangChain Multi-Agent Example Notebook

:bar_chart: LangGraph

LangGraph is a tool for building agents that can interact with various APIs and data sources. It provides a framework for creating agents that can perform tasks autonomously, making it easier to build complex applications.

To help you get started with LangGraph, we have created a simple example notebook that demonstrates how to build an agentic application using LangGraph. This notebook will guide you through the process of creating a simple multi-agent application that can perform tasks autonomously.

LangGraph Example Notebook

:left_right_arrow: Comparison with LlamaIndex

Both LangChain and LlamaIndex are frameworks designed to work with LLMs, and in general, have similar features. However, there are some differences between the two frameworks.

LangChain is more focused on providing a comprehensive framework for building applications powered by LLMs, as it provides a wide range of features and integrations.

LlamaIndex, on the other hand, is more focused on providing a framework for contextualizing LLMs with data sources, allowing you to build applications that can interact with different systems.

LlamaIndex is better at indexing and querying data, while LangChain is better at building applications.

To have a deeper understanding of the differences between the two frameworks, you can refer to the following links:

:checkered_flag: Conclusion

LangChain is a powerful framework for building applications powered by LLMs. However for our project, LlamaIndex is a better fit as it is more powerful for indexing and querying data, while LangChain is better at building applications.

Our main goal is really to provide an application that can interact with different data sources, and LlamaIndex is better suited for this task.

While LangChain would have been a possible alternative, its relative weakness in indexing and querying is noticeable.

:rocket: Going even further

As all LLMs, LangChain can be used on its own, but you can add other frameworks to make your workflows even more performant to complete complex tasks.

The official LangChain documentation proposes the following for their environment:

:books: Resources