LangChain¶

What it is¶

LangChain is a framework for developing applications powered by large language models. It provides a set of tools and abstractions for working with LLMs, including chain of thought, retrieval augmented generation, and agentic workflows.

What problem it solves¶

Provides reusable building blocks and standardized abstractions for common LLM application patterns, so developers do not have to implement prompt chaining, RAG, or agent loops from scratch.

Where it fits in the stack¶

AI & Knowledge — serves as a foundational framework that other tools in the stack (such as Flowise) build upon for LLM application development.

Typical use cases¶

Building retrieval-augmented generation pipelines over private data
Creating multi-step agent workflows with tool use
Exploring LangGraph for complex multi-agent orchestration

Strengths¶

Large and active open-source community with extensive documentation
Wide range of integrations with LLM providers, vector stores, and tools
Supports both Python and JavaScript/TypeScript

Limitations¶

Abstractions can add complexity and make debugging harder
Rapid pace of change can lead to breaking changes between versions
Can be overkill for simple LLM interactions

When to use it¶

When building complex LLM applications that require chaining, RAG, or agent patterns
When you need integrations with many different LLM providers and data sources

When not to use it¶

When the use case is a simple single-prompt LLM call
When you prefer a data-centric framework like LlamaIndex for pure RAG workloads

Getting started¶

Installation¶

Install the core LangChain package and the OpenAI integration:

pip install langchain langchain-openai

Minimal Python Example¶

Minimal example to call an LLM:

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o")
response = llm.invoke("Hello, how are you?")
print(response.content)

API examples¶

Simple Chain with Prompt Template, LLM, and Output Parser¶

This example demonstrates the recommended way to compose components using LangChain Expression Language (LCEL).

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

# 1. Define the Prompt Template
prompt = ChatPromptTemplate.from_template("Tell me a short joke about {topic}")

# 2. Initialize the Model
model = ChatOpenAI(model="gpt-4o")

# 3. Initialize the Output Parser
output_parser = StrOutputParser()

# 4. Compose the Chain using LCEL
chain = prompt | model | output_parser

# 5. Invoke the Chain
response = chain.invoke({"topic": "bears"})
print(response)

Sources / references¶

Contribution Metadata¶

Last reviewed: 2026-03-03
Confidence: medium