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LangChain vs LangGraph vs Deep Agents: Which Framework Should You Use in 2026?

Cowrie
Cowrie
Dev @ Bswen

Problem

When I first started with LangChain in 2026, I was confused by three products with similar names: Deep Agents, LangChain, and LangGraph. Which one should I use? Do I need all three?

The official docs have renamed their entry point to “Build Overview” with a clear “Choose your starting point” card layout, but the question is still: how do I decide?

The decision tree

The official docs recommend this decision flow:

Choose your framework
Are you building a quick prototype or simple chatbot?
  -> Deep Agents (fastest setup, built-in planning and subagents)


Do you need a customizable agent loop with tool use?
  -> LangChain create_agent (configurable harness, middleware support)


Do you need custom state management, branching, or human-in-the-loop?
  -> LangGraph directly (full control over graph and state)


Is it a dead-simple prompt to output pipeline?
  -> LCEL chain with | pipe syntax (still fine for linear chains)

What happened?

I tried building a simple Q and A agent with LangGraph first because I heard it was “the future.” But after reading the docs, I realized I was overcomplicating things. LangGraph is low-level orchestration — it is the runtime that powers everything else.

The three products are NOT competing frameworks. They are layers of the same stack:

LangChain ecosystem layers
+------------------------------------------+
|           Deep Agents                     |
|  Batteries-included: planning, filesystem,|
|  subagents, memory. Fastest to start.     |
+------------------------------------------+
|           LangChain                       |
|  Configurable agent harness: models,      |
|  tools, prompts, middleware.              |
+------------------------------------------+
|           LangGraph                       |
|  Low-level graph runtime: durable         |
|  execution, streaming, interrupts.        |
+------------------------------------------+
|      Models, Tools, Integrations          |
|      (shared across all layers)           |
+------------------------------------------+
|      LangSmith (observability)            |
|      (wraps all layers for tracing)       |
+------------------------------------------+

LangChain create_agent is built on LangGraph. Deep Agents is built on LangChain. They share models, tools, integrations, and the LangSmith observability platform. Moving between them is composition, not migration.

How to choose

Deep Agents

Use this when you want the fastest path to a working agent. Deep Agents includes automatic context compression, a virtual filesystem, subagent spawning, and long-term memory out of the box.

I would use Deep Agents for:

  • Quick prototypes and demos
  • Simple chatbots
  • When I do not want to configure tools or prompts manually

LangChain create_agent

Use this when you need a customizable agent loop. create_agent is the current standard agent API.

langchain_agent.py
from langchain.agents import create_agent


agent = create_agent(
    model="openai:gpt-5.5",
    tools=[search_tool, calculator_tool],
    system_prompt="You are a helpful assistant with tools",
)


result = agent.invoke({"messages": [{"role": "user", "content": "Calculate 42 * 15"}]})

I would use create_agent for:

  • Standard tool-use agents
  • When I need middleware composition
  • Production services with moderate complexity

LangGraph directly

Use this when you need full control. LangGraph works at the graph level where you define nodes, edges, state, and branching logic.

langgraph_agent.py
from langgraph.graph import StateGraph, MessagesState, START, END


def my_agent_node(state: MessagesState):
    # Custom logic with branching, loops, human-in-the-loop
    return {"messages": [{"role": "assistant", "content": "Processed"}]}


def should_continue(state):
    return "continue" if state.get("needs_more") else "end"


graph = StateGraph(MessagesState)
graph.add_node("agent", my_agent_node)
graph.add_conditional_edges("agent", should_continue, {
    "continue": "agent",
    "end": END,
})
graph.add_edge(START, "agent")
app = graph.compile()

I would use LangGraph directly for:

  • Stateful multi-step workflows
  • Human-in-the-loop approvals
  • Branching execution paths
  • Long-running durable agents

Common mistakes

I have seen three common mistakes:

  1. Using LangGraph for a simple Q and A agent — overkill. Use create_agent.
  2. Using LCEL for multi-step tool-use agents — LCEL is fine for linear chains but cannot handle branching. Use create_agent or LangGraph.
  3. Thinking you must choose one framework — they interoperate. You can start with Deep Agents and drop to LangGraph when you need fine-grained control.

Summary

In this post, I compared LangChain’s three agent frameworks and gave a decision tree for choosing between them. The key point is that Deep Agents, LangChain, and LangGraph are layers of the same stack, not competing products. Start with Deep Agents or create_agent, and drop to LangGraph only when you need explicit control over state, branching, or durable execution. Bookmark the build-overview page at docs.langchain.com for the official guidance.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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