LangGraph vs LangChain comparison
LangGraph is a stateful, graph-based AI agent framework focused on explicit state management and flow control, while LangChain is a versatile, chain-based framework emphasizing modular LLM pipelines and integrations. Use LangGraph for complex stateful workflows and LangChain for flexible chaining and tool integrations.VERDICT
LangChain for general-purpose LLM chaining and tool integration; choose LangGraph when you need explicit stateful graph-based control over AI agent workflows.| Tool | Key strength | Pricing | API access | Best for |
|---|---|---|---|---|
| LangGraph | Stateful graph-based AI agent orchestration | Free (open source) | Python SDK | Complex stateful workflows |
| LangChain | Modular LLM chains and tool integrations | Free (open source) | Python SDK | Flexible LLM pipelines and integrations |
| LangGraph | Explicit state and edge management | Free | No hosted API | Multi-step decision making |
| LangChain | Wide ecosystem and community support | Free | No hosted API | Rapid prototyping and production |
Key differences
LangGraph uses a stateful graph model where nodes represent processing steps and edges define transitions, enabling explicit control over AI agent workflows. LangChain uses a chain-of-responsibility pattern to compose LLM calls and tools in a linear or branching manner but without explicit state graphs.
LangGraph focuses on state management and deterministic flow control, ideal for complex multi-turn interactions. LangChain excels at chaining LLM calls, prompt templates, and tool use with a rich ecosystem.
LangGraph compiles graphs into callable apps with explicit entry and exit points, while LangChain provides composable chains and agents for flexible pipelines.
Side-by-side example
Both frameworks can implement a simple conversational agent that appends a response to messages.
from langgraph.graph import StateGraph, END
from typing import TypedDict
class State(TypedDict):
messages: list
def my_node(state: State) -> State:
return {"messages": state["messages"] + ["Hello from LangGraph!"]}
graph = StateGraph(State)
graph.add_node("my_node", my_node)
graph.set_entry_point("my_node")
graph.add_edge("my_node", END)
app = graph.compile()
result = app.invoke({"messages": ["Hi"]})
print(result["messages"])['Hi', 'Hello from LangGraph!']
LangChain equivalent
Using LangChain, the same task is done by chaining a simple function with an LLM call or prompt template.
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
import os
llm = ChatOpenAI(model="gpt-4o-mini", api_key=os.environ["OPENAI_API_KEY"])
prompt = ChatPromptTemplate.from_template("{messages}")
messages = ["Hi"]
response = llm.invoke([{"role": "user", "content": ' '.join(messages) + ' Respond with a greeting.'}])
print(response.content)Hello! How can I assist you today?
When to use each
Use LangGraph when your AI application requires explicit state tracking, complex branching, and deterministic control flows, such as multi-turn agents with conditional logic.
Use LangChain for rapid development of LLM pipelines, tool integrations, and prompt chaining where explicit state graphs are unnecessary.
| Scenario | Recommended tool |
|---|---|
| Multi-step AI agent with explicit state and branching | LangGraph |
| Flexible prompt chaining and tool integration | LangChain |
| Rapid prototyping with large community support | LangChain |
| Deterministic workflow orchestration | LangGraph |
Pricing and access
Both LangGraph and LangChain are open-source Python libraries with no direct cost. They require external LLM API keys (e.g., OpenAI) for model calls.
| Option | Free | Paid | API access |
|---|---|---|---|
| LangGraph | Yes (open source) | No | Python SDK only |
| LangChain | Yes (open source) | No | Python SDK only |
| LLM APIs (OpenAI, Anthropic, etc.) | Limited free tier | Paid plans | REST API / SDKs |
Key Takeaways
-
LangGraphexcels at explicit stateful AI workflows with graph control. -
LangChainoffers flexible, modular chaining for LLMs and tools with broad ecosystem support. - Choose
LangGraphfor complex multi-turn agents requiring deterministic flow. - Choose
LangChainfor rapid prototyping and integrating multiple AI tools. - Both are free open-source libraries requiring external LLM API keys for model inference.