All Technologies
AI & ML·expert

LangChain

Building applications with LLMs

LangChain is my framework for building LLM applications. I create sophisticated chains, agents, and RAG systems that integrate seamlessly with production infrastructure.

2+years in production
30+projects shipped
expertproficiency

My take

Why I use LangChain

LangChain (especially LangGraph for agents and LangSmith for observability) gives me a structured way to compose LLM calls, retrievers, and tools. I treat it as scaffolding - useful early, often replaced piecewise as needs sharpen.

Want the broader stack philosophy? Read about how Sri picks tools or browse engineering insights.

Honest assessment

Strengths & tradeoffs

No tool is perfect. Here's what shines and what to watch for.

Strengths

  • Wide integration coverage (every LLM, every store)
  • LangGraph for stateful agent workflows
  • LangSmith for tracing and evals
  • Both Python and JavaScript versions
  • Active community and frequent releases

Tradeoffs (honestly)

  • Abstractions can hide important details
  • API churn between major versions
  • Easy to over-abstract simple flows
  • Python and JS feature parity lags

Fit assessment

When to reach for LangChain

Pick the right tool for the job.

Best fits

RAG pipelines with multiple retrievers

Multi-step agents with tool use

Document processing pipelines

Multi-LLM router patterns

Not ideal for

Single-call LLM use - call the SDK directly

Teams wanting a stable, mature framework

Workflows where direct provider SDKs are clearer

Common use cases

RAG systemsAI agentsDocument processingChatbots

Resources

Learn more

Curated official docs, tutorials, and writing on LangChain.

Need help with LangChain?

Whether you're starting fresh or optimizing an existing implementation, I can help you get the most out of this technology. Read more in insights or get in touch.

Command Palette

Search for a command to run...