LangChain and LangGraph: Building Reliable Agentic AI Workflows
Modern AI applications are no longer simple chatbot wrappers around an LLM.
Real enterprise AI systems need to:
- understand user intent
- retrieve relevant context
- call tools and APIs
- maintain state
- follow business rules
- validate outputs
- retry failed steps
- escalate risky decisions
- produce auditable results
This is where LangChain and LangGraph are useful.
LangChain provides building blocks for connecting LLMs with tools, prompts, retrievers, vector databases, APIs, and external systems.
LangGraph provides a graph-based orchestration layer for building stateful, multi-step, controllable AI workflows.
In simple terms:
LangChain connects the AI to capabilities.
LangGraph controls how those capabilities are used.
1. What Is LangChain?
LangChain is a framework for building applications powered by large language models.
It helps developers connect LLMs with external components such as:
- prompt templates
- tools
- APIs
- retrievers
- vector stores
- document loaders
- output parsers
- memory
- agents
A typical LangChain-based application may look like this:
text
User Query
|
v
Prompt Template
|
v
Retriever / Tool
|
v
LLM
|
v
Output Parser
|
v
Final Response
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