LangChain vs. LangGraph vs. Deep Agents

Three tools, one ecosystem. LangChain is the framework, LangGraph is the runtime, Deep Agents is the batteries-included harness. Here is when to use each.

Quick Reference

→LangChain = agent framework — unified interface, composable chains, middleware
→LangGraph = agent runtime — stateful graphs, persistence, human-in-the-loop
→Deep Agents = agent harness — batteries-included, auto compression, subagent spawning
→Deep Agents is built on LangChain, which runs on LangGraph under the hood
→Start with Deep Agents, drop to LangChain for customization, use LangGraph for low-level control

How They Relate

These are not competing tools — they are layers of the same stack. LangGraph is the lowest level: a runtime for building stateful, graph-based workflows. LangChain sits on top: a framework that gives you a unified interface over LLMs, tools, memory, and chains. Deep Agents sits on top of that: a fully pre-configured agent harness you can run without writing any infrastructure code. Every Deep Agent is a LangChain agent. Every LangChain agent runs on LangGraph under the hood.

LangGraph — the runtime

Stateful graph execution engine. Nodes, edges, checkpointing, streaming, human-in-the-loop. The foundation everything else runs on. You define exactly what happens, when, and in what order.

LangChain — the framework

Unified interface over LLMs, tools, memory, and chains. create_agent() with middleware, LCEL composition, provider integrations. You build the agent loop yourself using standardized building blocks.

Deep Agents — the harness

Pre-configured autonomous agent system built on LangChain. Comes with auto compression, a virtual filesystem, subagent spawning, and smart prompts out of the box. You provide tools and a goal — it figures out the rest.

LangChain — Build Your Own Loop

LangChain is the right choice when you want control over the agent loop but do not want to build everything from scratch. It gives you a unified interface for any LLM, composable LCEL chains, a middleware system, and a growing library of integrations. You decide the prompt, the tools, the memory strategy, and the middleware stack.

LangGraph — Low-Level Control

LangGraph is the right choice when you need deterministic control over workflow structure — parallel branches, conditional routing, multi-agent coordination, or resumable long-running jobs. You define a graph: nodes are functions, edges are the routing logic. The runtime handles checkpointing, streaming, and interrupts.

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