Getting Started with LangChain & LangGraph: Core Concepts of AI Agents

Introduction

AI Agent is the hottest concept for 2025 large‑model applications. It combines large models, prompts, and tools into automated workflows such as RAG knowledge bases or translation systems. Mastering AI Agent development, especially with LangChain, is essential for developers.

What is LangChain?

Basic concepts

LangChain is an open‑source framework launched by Harrison Chase in October 2022 to simplify LLM‑based application development. It supports Python and JavaScript and is used for chatbots, intelligent search, Q&A, summarisation, and RPA agents.

It integrates model, prompt, and tool components into executable pipelines, likened to building with LEGO bricks. An example shown builds a Chinese‑English translation system.

Core features

Model interface abstraction : unified calls to OpenAI, Claude, Cohere, Qwen, etc.

Structured output : automatic parsing of JSON, schemas, function signatures, documents.

Memory management : buffer, summary, entity, conversation memories.

Tool integration : web search, SQL databases, Python executor, API proxies.

Agent architecture : ReAct, Self‑Ask, OpenAI Function Agent scheduling.

RAG integration : various retrievers, vector stores, document‑splitting strategies.

Server/API deployment : quick publishing of chains as web services or A2A agents.

Debug & Callback : token usage statistics, LangSmith visual tracing.

Core architecture

Bottom layer – Large‑model API abstraction: provides a uniform interface for different models, with dedicated libraries for stability.

Middle layer – Workflow API abstraction: defines the LangChain Expression Language (LCEL) that lets developers compose prompts, models, and tools into workflows, similar to n8n.

Top layer – Agent API abstraction: builds on the chain layer to create dynamic agents that can instantiate chains on‑the‑fly, supporting multi‑tool parallel and sequential calls (e.g., DeepSeek‑R1‑0528, Qwen3).

What is LangGraph?

LangGraph shares the same underlying architecture and APIs as LangChain but focuses on graph‑structured workflows with explicit state tracking, extending LCEL’s linear chain syntax.

It is essentially a higher‑level orchestration tool built on LangChain; each graph node still executes a linear chain.

Comparison with other AI‑Agent frameworks

Google ADK, OpenAI Agent SDK, and Qwen‑Agent adopt a minimal‑code approach suitable for quick research prototypes. LangChain, by contrast, offers a richer set of fine‑grained functionalities that handle many LLM execution scenarios, making it indispensable for production‑grade agents.

Even with new frameworks from OpenAI, Google, and Alibaba, LangChain remains dominant, while AutoGen, CrewAI, and Dify are positioned as lightweight alternatives.

Popular LangChain projects

Notable open‑source projects built on LangChain & LangGraph include ByteDance’s “Deep Research” (deer‑flow) and Google’s “Gemini Fullstack LangGraph Quickstart”.

Conclusion

The article covered LangChain & LangGraph’s core concepts, features, architecture, comparisons, and example projects, laying a theoretical foundation for the upcoming hands‑on tutorials.