If you search for AI agent frameworks in 2026, you'll find LangChain and LangGraph mentioned together constantly — often interchangeably, which is wrong. They're related (LangGraph is built on top of LangChain) but they solve fundamentally different problems. Using the wrong one for a given task is like using React Router to manage local component state. Technically possible, unnecessarily complicated.
This is the honest comparison. What each one does, when to use which, code examples of both, and a direct recommendation based on what you're building.
What LangChain Actually Is
LangChain started as a library for composing LLM calls into chains — sequences of operations where the output of one step feeds into the next. Its core abstraction is the LCEL (LangChain Expression Language), which uses a pipe operator to connect components:
from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
llm = ChatAnthropic(model="claude-sonnet-4-6")
chain = (
ChatPromptTemplate.from_template("Summarize this document: {document}")
| llm
| StrOutputParser()
)
result = chain.invoke({"document": "..."})
That's a LangChain chain. A prompt template pipes into an LLM which pipes into an output parser. Linear, composable, readable.
LangChain also provides:
- Retrievers — abstractions over vector stores (Pinecone, Chroma, pgvector) for RAG pipelines
- Document loaders — load PDFs, web pages, Notion pages, GitHub repos
- Output parsers — parse LLM responses into structured objects
- Memory — conversation history management
- Tools — standardized interface for functions the LLM can call
LangChain's strength is the breadth of integrations. There are connectors for hundreds of data sources, vector stores, and LLM providers. If you're building a RAG pipeline, LangChain gives you everything you need without reinventing integrations.
What LangGraph Actually Is
LangGraph is a library for building stateful, multi-step workflows where the execution path is not fixed. It models your agent as a directed graph — nodes are actions (LLM calls, tool executions, human checkpoints), and edges define what happens next based on the current state.
The critical difference: LangGraph supports cycles. Your workflow can loop. An agent can call a tool, evaluate the result, decide it needs more information, call another tool, and loop again — until it decides it's done.
from langgraph.graph import StateGraph, END
from typing import TypedDict, List
class AgentState(TypedDict):
messages: List[dict]
tool_calls: int
def agent_node(state: AgentState) -> AgentState:
# LLM decides what to do next
response = llm_with_tools.invoke(state["messages"])
return {"messages": state["messages"] + [response]}
def should_continue(state: AgentState) -> str:
last_message = state["messages"][-1]
if last_message.tool_calls:
return "tools"
return END
workflow = StateGraph(AgentState)
workflow.add_node("agent", agent_node)
workflow.add_node("tools", tool_executor_node)
workflow.set_entry_point("agent")
workflow.add_conditional_edges("agent", should_continue)
workflow.add_edge("tools", "agent") # Loop back
app = workflow.compile()
This graph loops: agent calls tools, tools execute, result goes back to agent, agent decides whether to call more tools or finish. That cycle is impossible to express cleanly with LangChain's linear chains.
LangGraph also provides:
- Persistence — checkpointing agent state to databases (Postgres, SQLite)
- Human-in-the-loop — pausing workflows for human approval before continuing
- Streaming — streaming partial results from any node in the graph
- LangGraph Studio — a visual debugger that shows your graph execution step by step
- Multi-agent coordination — supervisor agents routing work to specialized sub-agents
The Core Difference
LangChain = pipeline orchestration. Fixed sequence of steps. Excellent for RAG, document processing, simple Q&A systems with retrieval.
LangGraph = agent orchestration. Dynamic, stateful, cyclic workflows. Required for anything where the agent needs to reason about what to do next, loop, or branch based on intermediate results.
If your workflow looks like input → retrieve → generate → output, use LangChain.
If your workflow looks like input → think → act → observe → maybe think again → act differently → ...eventually output, use LangGraph.
Real Code Comparison
RAG pipeline — LangChain is the right choice:
from langchain_anthropic import ChatAnthropic
from langchain_chroma import Chroma
from langchain_core.prompts import ChatPromptTemplate
from langchain_anthropic import AnthropicEmbeddings
# Setup
embeddings = AnthropicEmbeddings()
vectorstore = Chroma(embedding_function=embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
llm = ChatAnthropic(model="claude-sonnet-4-6")
# Chain: retrieve relevant docs, then answer
rag_chain = (
{"context": retriever, "question": lambda x: x}
| ChatPromptTemplate.from_template(
"Answer based on context:\n\n{context}\n\nQuestion: {question}"
)
| llm
| StrOutputParser()
)
answer = rag_chain.invoke("What is our refund policy?")
Clean, readable, correct for this use case. LangGraph would add unnecessary complexity here.
Research agent — LangGraph is the right choice:
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode
from langchain_anthropic import ChatAnthropic
tools = [web_search, read_url, extract_data]
llm = ChatAnthropic(model="claude-sonnet-4-6").bind_tools(tools)
class ResearchState(TypedDict):
query: str
findings: List[str]
messages: List[dict]
def researcher(state: ResearchState) -> ResearchState:
response = llm.invoke(state["messages"])
return {"messages": state["messages"] + [response]}
def route(state: ResearchState) -> str:
if state["messages"][-1].tool_calls:
return "tools"
return END
graph = StateGraph(ResearchState)
graph.add_node("researcher", researcher)
graph.add_node("tools", ToolNode(tools))
graph.set_entry_point("researcher")
graph.add_conditional_edges("researcher", route)
graph.add_edge("tools", "researcher")
agent = graph.compile()
# The agent searches, reads pages, extracts data, loops as needed
result = agent.invoke({
"query": "What are the main AI agent frameworks in 2026?",
"findings": [],
"messages": [{"role": "user", "content": "Research AI agent frameworks"}]
})
Try to express this with LangChain chains. You can't — not cleanly. The loop, the conditional branching, the accumulated state — that's LangGraph's entire purpose.
When Each One Makes Sense
Use LangChain when:
- Building a RAG pipeline (document Q&A, knowledge base search)
- You need integrations — loading PDFs, connecting to vector stores, calling APIs
- Your workflow is linear with a fixed number of steps
- You're building a simple chatbot with memory
- Processing pipelines: classify → extract → summarize
Use LangGraph when:
- Building an agent that uses tools and loops until done
- You need conditional branching ("if the tool failed, try a different approach")
- You need state that persists across steps
- You need human-in-the-loop checkpoints
- Building multi-agent systems with a supervisor routing work
- You need to debug complex agent behavior step by step
Use both when:
- Your agent uses LangGraph for orchestration AND LangChain retrievers for RAG within one of its nodes. This is the most common production pattern.
What the Job Market Is Asking For
Looking at AI engineering job postings in 2026:
- "LangChain" appears in roughly 60% of postings requiring specific frameworks
- "LangGraph" is growing fast and appears in ~40% — particularly in roles requiring "agentic AI" or "multi-step agents"
- The highest-paying roles consistently list both, which makes sense: real AI systems use LangGraph for agent logic and LangChain for retrieval integrations
The practical conclusion: learn both, but learn them in order. LangChain first — its concepts (chains, retrievers, output parsers) are foundational and LangGraph builds on them. Then LangGraph to handle stateful agents.
The Honest Recommendation
Start with LangChain. Build a RAG pipeline. Understand LCEL. Understand how retrievers and output parsers work. This gives you the vocabulary and the building blocks.
Move to LangGraph as soon as you want to build an agent. Which should be within a few weeks. LangGraph's graph model is the right abstraction for agents, and the shift from chain thinking to graph thinking is the most important conceptual upgrade in going from intermediate to advanced AI engineering.
Don't learn AutoGen, CrewAI, or the other frameworks first. They're higher-level and opaque. LangGraph makes you understand what's happening inside. Once you understand LangGraph, the other frameworks become thin layers over familiar concepts.
Phase 5 of MindloomHQ's Agentic AI course covers LangGraph in depth — building real agents with tool use, state management, and multi-agent coordination. Explore Phase 5 →