LangChain
The original LLM orchestration framework with a huge pre-built ecosystem of chains, agents, memory, and tool integrations. Very high adoption but community sentiment has shifted — frequent breaking changes are a known pain point.
Why LangChain?
You need the largest pre-built tool ecosystem for agents
You're building complex multi-step workflows
Your team already has LangChain experience
Signal Breakdown
What drives the Trust Score
Download Trend
Last 12 months
Tradeoffs & Caveats
Know before you commitYou want simple LLM calls — the direct SDK is cleaner
You need stable APIs — LangChain breaks between versions
You're new to AI development (try Vercel AI SDK instead)
Pricing
Free tier & paid plans
100% free, open-source (MIT)
Free & open-source
LangSmith tracing starts at $39/mo
Often Used Together
Complementary tools that pair well with LangChain
Learning Resources
Docs, videos, tutorials, and courses
Get Started
Repository and installation options
View on GitHub
github.com/langchain-ai/langchainjs
npm install langchainpip install langchainQuick Start
Copy and adapt to get going fast
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
llm = ChatOpenAI(model="gpt-4o")
prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful assistant."),
("user", "{input}"),
])
chain = prompt | llm
response = chain.invoke({"input": "Hello!"})
print(response.content)Code Examples
Common usage patterns
RAG chain with vector retriever
Retrieve relevant docs then answer with an LLM
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.runnables import RunnablePassthrough
from langchain_core.prompts import ChatPromptTemplate
docs = ["LangChain helps build LLM apps.", "It supports RAG pipelines."]
vectorstore = FAISS.from_texts(docs, OpenAIEmbeddings())
retriever = vectorstore.as_retriever()
prompt = ChatPromptTemplate.from_template(
"Context: {context}
Question: {question}"
)
llm = ChatOpenAI(model="gpt-4o")
chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| llm
)
print(chain.invoke("What does LangChain do?").content)Agent with tools
LangChain agent that can call custom tools
from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.tools import tool
@tool
def get_word_count(text: str) -> int:
"""Returns the number of words in a text."""
return len(text.split())
llm = ChatOpenAI(model="gpt-4o")
tools = [get_word_count]
agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)
result = executor.invoke({"input": "How many words in 'hello world'?"})Streaming LCEL chain
Stream tokens from a LangChain Expression Language chain
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
chain = ChatPromptTemplate.from_template("Tell me about {topic}") | ChatOpenAI(model="gpt-4o")
async for chunk in chain.astream({"topic": "LangChain"}):
print(chunk.content, end="", flush=True)Community Notes
Real experiences from developers who've used this tool