LangChain
Explained

The Problem

Raw API calls don't scale

Building a simple chatbot is easy. Building one with memory, tools, document retrieval, guardrails, and structured output? That's where manual wiring becomes a liability.

The Solution

Composable abstractions

LangChain provides standardised interfaces for models, prompts, tools, memory, and retrieval — all designed to work together through a consistent API.

The Result

Weeks, not months

Teams move from prototype to production faster, swap models without rewriting code, and get observability built in from day one.

Concept 01

Chains

A sequence of operations — take input, process it through steps, return output. Chains are LangChain's fundamental building block. Input flows through prompt templates, model calls, and output parsers in a composable pipeline.

from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

chain = prompt | model | parser
result = chain.invoke({"topic": "AI agents"})

Concept 02

Retrieval (RAG)

Connect LLMs to your own data. Retrieval-Augmented Generation loads documents, chunks them, embeds them into a vector store, and retrieves relevant context at query time. This is how you build a chatbot that knows your company's docs.

# Load → Split → Embed → Store → Retrieve
docs = loader.load()
splits = splitter.split_documents(docs)
vectorstore.add_documents(splits)
retriever = vectorstore.as_retriever()

Concept 03

Tools & Function Calling

Give your LLM the ability to act. Tools let the model call external functions — search the web, query a database, execute code, or hit an API. The model decides which tool to use and LangChain handles the execution loop.

@tool
def search(query: str) -> str:
    """Search the web for information."""
    return tavily.search(query)

agent = create_agent(model, tools=[search])

Concept 04

Memory & State

Conversations need context. LangChain's memory system maintains chat history, summarises long conversations to stay within context windows, and persists state across sessions. Agents can remember what happened 50 messages ago.

Concept 05

Agents

The culmination of everything above. Agents combine an LLM with tools and reasoning to iteratively work toward solutions. They decide which tools to use, analyse intermediate results, and adapt their approach. With LangChain v1.0+, creating one is a single function call:

from langchain.agents import create_agent

agent = create_agent(
    "anthropic:claude-sonnet-4-20250514",
    tools=[search, calculator, code_exec],
    system_prompt="You are a research assistant..."
)

LangChain

The high-level framework. Composable chains, prompts, tools, memory, and output parsers. Supports 20+ LLM providers. Great for straightforward agent builds and prototyping.

Python · TypeScript · v1.0 stable

LangGraph

Low-level agent orchestration. Stateful, graph-based workflows for complex multi-step agents, human-in-the-loop flows, and branching logic. The engine under LangChain's agent layer.

Cycles · Persistence · Controllability

LangSmith

Observe, evaluate, and deploy. Tracing, experiment comparison, automated evals, and production monitoring. The DevOps layer for AI agents. Now includes Fleet for no-code agent building.

Traces · Evals · Deployment · Fleet

Knowledge

Document Q&A

Internal docs, manuals, legal contracts — employees ask questions in natural language instead of hunting through PDFs. RAG-powered, always grounded in your data.

Customer

Support Agents

Agents that resolve tickets, look up order status, escalate complex issues, and learn from past resolutions. Tool-using agents, not keyword bots.

Research

Data Analysis

Agents that read spreadsheets, query databases, run calculations, and produce reports. Particularly powerful with code execution tools and Deep Agents.

Engineering

Coding Assistants

Code review, bug detection, documentation generation, and automated testing. Open SWE provides the architecture for internal coding agents.

Workflow

Process Automation

Agents that monitor inboxes, classify documents, update CRMs, and trigger downstream actions. Multi-agent systems where each agent owns a step.

Content

Generation Pipelines

Marketing teams using chains to generate, review, and refine content — with brand guidelines baked into the prompt template and structured output enforced.

✓ Use LangChain when

You're building agents with tools and reasoning loops.

You need RAG over your own documents.

You want to swap models without rewriting code.

You need observability and evaluation from day one.

You're orchestrating multi-step workflows with LangGraph.

Your team wants a standard, well-documented framework.

✗ Skip LangChain when

A single API call solves your problem.

You're building a simple chatbot with no tools.

You need maximum control over every HTTP request.

Your use case is pure text generation with no orchestration.

You're deeply invested in another framework (CrewAI, AutoGen, etc.).

The abstraction layer adds complexity you don't need.

Enterprise Use Cases

Governed agent stacks

LangSmith tracing + LangGraph workflows for regulated industries. Full audit trail, human-in-the-loop approvals, structured outputs.

Prototyping

Rapid AI sprints

LangChain's high-level abstractions make fast prototyping practical. Swap models, test approaches, and ship in weeks rather than months.

Learning

Hands-on fundamentals

LangChain is a solid framework for learning AI application development — chains, RAG, agents, and tool use. The best way to understand what AI can do is to build with it.

Official

LangChain Resources

langchain.com — Official site & platform ↗
docs.langchain.com — Documentation & changelog ↗
github.com/langchain-ai — Source code ↗
changelog.langchain.com — Product updates ↗
LangChain Academy — Free courses ↗

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