LangChain has become the most popular framework for building applications with large language models. It provides the building blocks for connecting LLMs to data, tools, and workflows. Here’s a practical guide to getting started.
What LangChain Is
LangChain is an open-source framework that simplifies building LLM-powered applications. Instead of writing everything from scratch, LangChain provides pre-built components for common patterns:
Chains. Sequences of operations — take user input, process it, call an LLM, format the output. Chains let you build multi-step workflows.
Agents. LLMs that can decide which tools to use and in what order. An agent might search the web, query a database, and calculate results to answer a question.
Retrieval. Components for building RAG systems — document loaders, text splitters, embedding models, vector stores, and retrievers.
Memory. Conversation history management. Different memory types for different use cases — buffer memory, summary memory, and entity memory.
When to Use LangChain
Good for:
– RAG applications (chatbots over your documents)
– AI agents that use tools
– Multi-step LLM workflows
– Prototyping LLM applications quickly
Not ideal for:
– Simple, single-call LLM applications (just use the API directly)
– Production systems where you need full control over every detail
– Applications where LangChain’s abstractions add unnecessary complexity
Getting Started
Installation. pip install langchain langchain-openai (or langchain-anthropic, langchain-google-genai for other providers).
Basic chain. Create a prompt template, connect it to an LLM, and run it. LangChain’s expression language (LCEL) makes this declarative and composable.
RAG in 5 steps:
1. Load documents (PDF, web pages, databases) using document loaders
2. Split documents into chunks using text splitters
3. Create embeddings and store in a vector database
4. Create a retriever that searches the vector database
5. Build a chain that retrieves context and generates responses
Agents. Define tools (functions the agent can call), create an agent with an LLM and tools, and let it decide how to answer questions.
LangChain vs. Alternatives
vs. LlamaIndex. LlamaIndex is more focused on data indexing and retrieval. If your primary use case is RAG, LlamaIndex might be simpler. LangChain is more general-purpose.
vs. Semantic Kernel. Microsoft’s framework for LLM applications. Better integration with Azure and .NET. LangChain has a larger community and more examples.
vs. Haystack. Deepset’s framework for NLP pipelines. Strong for search and question-answering. LangChain is more flexible for diverse use cases.
vs. Direct API calls. For simple applications, calling the OpenAI or Anthropic API directly is simpler and gives you more control. LangChain adds value when you need complex workflows, RAG, or agents.
LangSmith
LangSmith is LangChain’s observability platform:
Tracing. See every step of your chain or agent execution — inputs, outputs, latency, and token usage.
Evaluation. Build evaluation datasets and measure your application’s performance over time.
Debugging. When something goes wrong, LangSmith shows you exactly where and why.
LangSmith is essential for production LangChain applications. Without observability, debugging LLM applications is nearly impossible.
Common Patterns
Conversational RAG. A chatbot that answers questions from your documents while maintaining conversation history. The most common LangChain application.
SQL agent. An agent that converts natural language questions into SQL queries, executes them, and returns results in natural language.
Document summarization. Chains that summarize long documents using map-reduce or refine strategies.
Multi-agent systems. Multiple agents collaborating on complex tasks, each with different tools and expertise.
My Take
LangChain is valuable for prototyping and building complex LLM applications quickly. Its abstractions save significant development time for common patterns like RAG and agents.
However, for production systems, consider whether LangChain’s abstractions help or hinder. Some teams find that direct API calls with custom code give them more control and better performance. Start with LangChain for speed, and refactor to custom code if you hit limitations.
The LangChain ecosystem (LangSmith, LangGraph, LangServe) is maturing rapidly, making it increasingly viable for production use.
🕒 Last updated: · Originally published: March 14, 2026