llm-app-stack

https://github.com/a16z-infra/llm-app-stack

aka Emerging Architectures for LLM Applications

This is a list of available tools, projects, and vendors at each layer of the LLM app stack.

 

LlamaIndex vs LangChain

https://www.datacamp.com/blog/langchain-vs-llamaindex

• Choose LlamaIndex if your primary need is data retrieval and search capabilities for applications that handle large volumes of data that require quick access.
• Choose LangChain if you need a flexible framework to support complex workflows where intricate interaction and context retention are highly prioritized.

Here's a comparative table to summarize the key differences:

Feature	LlamaIndex	LangChain
Primary Focus	Search and retrieval	Flexible LLM-powered application development
Data Indexing	Highly efficient	Modular and customizable
Retrieval Algorithms	Advanced and optimized	Integrated with LLMs for context-aware outputs
User Interface	Simple and user-friendly	Comprehensive and adaptable
Integration	Multiple data sources, seamless platform integration	Supports diverse AI technologies and services
Customization	Limited, focused on indexing and retrieval	Extensive, supports complex workflows
Context Retention	Basic	Advanced, crucial for chatbots and long interactions
Use Cases	Internal search, knowledge management, enterprise solutions	Customer support, content generation, code documentation
Performance	Optimized for speed and accuracy	Efficient in handling complex data structures
Lifecycle Management	Integrates with debugging and monitoring tools	Comprehensive evaluation suite (LangSmith)

 

https://datasciencedojo.com/blog/llamaindex-vs-langchain/#Comparing_LLamaIndex_and_LangChain

Which Framework Should I Choose? LlamaIndex vs LangChain

Understanding these unique aspects empowers developers to choose the right framework for their specific project needs:

• Opt for LlamaIndex if you are building an application with a keen focus on search and retrieval efficiency and simplicity, where high throughput and processing of large datasets are essential.
• Choose LangChain if you aim to construct more complex, flexible LLM applications that might include custom query processing pipelines, multimodal integration, and a need for highly adaptable performance tuning.

example

for llamaindex

https://github.com/machinelearningzuu/awesome-llm-projects

https://github.com/run-llama/llamacloud-demo/tree/main

for langchain

https://github.com/langchain-ai/langchain/tree/master/templates

Fullstack tools

https://github.com/tensorchord/Awesome-LLMOps

https://github.com/Hannibal046/Awesome-LLM