LangChain
The framework for building LLM-powered applications
LangChain is a framework that makes it easier to build applications on top of LLMs. Think of it as the plumbing that connects your AI model to your data, your tools, and your users. It's become the most popular framework for building AI apps, for better or worse (it's gotten some criticism for being over-engineered).
langchain.comUse Cases
RAG applications
Connecting LLMs to your company's documents so the AI can answer questions with actual facts.
AI agents
Building systems where the AI can use tools, search the web, query databases, and take actions.
Chatbots with memory
Chat applications that remember previous conversations and maintain context.
Document processing
Chunking, embedding, and querying large document collections.
Key Features
- →LangGraph — for building complex, stateful AI agents
- →LangSmith — for debugging and monitoring LLM applications
- →150+ integrations with models, databases, and tools
- →Supports Python and JavaScript/TypeScript
- →Open source with a commercial platform
Why This Matters for PMs
If your team is building AI features, they're probably using LangChain (or considering it). As a PM, you should know that it speeds up development but can add complexity. The key question to ask your team: are we using LangChain because it genuinely helps, or because it's the default? For simple use cases, calling the API directly might be simpler.