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Choose LangChain for a conventional model–tools–response agent loop; choose LangGraph when you need direct control over a stateful workflow’s steps and transitions; consider Deep Agents when you want a ready-made harness with planning, subagents, and context management. These are different layers, not mutually exclusive alternatives: LangChain’s create_agent runs on LangGraph, so you can start with the higher-level framework and use LangGraph for workflows that need more explicit orchestration.
What do LangChain, LangGraph, and Deep Agents each do?
LangChain’s current terminology separates the stack into three layers. In a 2026 article, LangChain describes LangGraph as an agent runtime, LangChain as an agent framework, and Deep Agents as an agent harness. “Harness” is the company’s label for its own offering, not a universally standardized software category. LangChain’s explanation of the three layers was published August 6, 2026.
LangChain: a framework for the common agent loop
LangChain provides create_agent, model and tool abstractions, integrations, and middleware. Its default agent pattern calls a model, executes any requested tools, adds their results, and repeats until the model returns a final answer. Middleware lets you adapt that loop, including by inserting deterministic logic.
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LangGraph: explicit workflow orchestration
LangGraph provides lower-level primitives for workflows with explicit steps, transitions, and state. It can coordinate deterministic operations and LLM-driven decisions in the same workflow. The official documentation says LangGraph can be used without LangChain; LangChain’s abstractions are conveniences for common components and loops, not a required dependency. See the LangGraph overview.
Deep Agents: a more opinionated harness
Deep Agents packages capabilities such as planning, subagents, file and context handling, and memory into a more complete agent setup, according to LangChain. It is worth considering when those built-in conventions match your needs; for more specialized control, LangChain’s guidance is to work at a lower layer.
How do the options compare?
| Decision point | LangChain | LangGraph | Deep Agents |
|---|---|---|---|
| Abstraction | Higher-level framework for a standard agent loop and integrations | Lower-level runtime with explicit workflow control | Higher-level, more opinionated harness with ready-made capabilities |
| Typical flow | Model, tools, response; customize with middleware | Custom graph combining explicit steps and transitions | Ready-made agent harness, adaptable as needed |
| State and duration | Standard loop may be enough for common interactions | Designed for stateful and long-running workflows, including persistence and resumption, according to LangChain | Integrated memory and context management, according to LangChain |
| Human oversight | Can be added through middleware | Human control is presented as a first-class capability | Use a lower layer when more specialized control is required |
| Good starting point | A conventional agent or a customized version of the usual loop | A workflow that needs fine-grained orchestration beyond the usual loop | An agent that benefits from built-in planning, context management, or subagents |
This comparison summarizes LangChain’s recommendations; it is not an independent benchmark. The official materials do not establish that one option is faster, cheaper, or more reliable across projects. The relevant sources are LangChain’s layer comparison, its open-source overview, and the LangGraph documentation.
When should you choose LangChain or LangGraph?
Start with LangChain when the normal agent loop fits
If your application mainly needs a model to choose among tools, receive their results, and produce an answer, try LangChain’s create_agent. Middleware may cover additional predictable behavior without requiring you to define the entire orchestration graph.
Move to LangGraph when transitions need to be explicit
LangGraph is the stronger fit when the workflow must coordinate more than the standard loop—for example, deterministic validation alongside LLM decisions, conditional routing, cycles, retries, pauses, recovery, or approval steps. It makes the graph and its state directly controllable rather than treating the agent loop as the primary structure.
Consider persistence for long-running work
LangChain describes LangGraph as supporting persistence and the ability to resume interrupted workflows, including approvals that span sessions. Those capabilities are useful design considerations for long-running or paused jobs, not a guarantee of performance or reliability in every configuration. Review the current documentation for the setup and behavior your application requires.
Use Deep Agents when its built-in conventions are useful
If planning, subagents, and context management are immediate requirements, assess whether Deep Agents’ prepackaged approach saves you from composing those features yourself. If its conventions do not fit, the LangChain stack allows work at lower levels.
Rank #3
Can LangChain and LangGraph be used together?
Yes. LangChain’s create_agent is built on the LangGraph runtime, and the agent can also be used inside LangGraph workflows. You do not have to treat choosing LangChain as a permanent commitment to its highest-level abstraction. LangChain’s October 22, 2025 release announcement put it this way: “LangChain agents are built on LangGraph, so you’re not locked in.” This is the company’s description of its architecture, not an independent assessment. See the v1.0 announcement.
What changed with the 1.0 releases, and what should you check?
LangChain announced LangChain 1.0 and LangGraph 1.0 on October 22, 2025. The announcement said the 1.0 line would remain stable without breaking changes until 2.0; that is the vendor’s commitment as stated at launch, rather than an independent guarantee about future releases.
The same announcement said legacy features had moved out of the main LangChain package into langchain-classic, and that Python 1.0 required Python 3.10 or later after Python 3.9 reached end of support in October 2025. These package and compatibility details can change, so confirm the current release notes before planning a migration.
Installation examples in the official materials
The commands below are examples presented in LangChain’s release announcement and documentation; check the current package instructions before using them.
| Package | Python example | JavaScript example |
|---|---|---|
| LangChain | uv pip install --upgrade langchain |
npm install @langchain/langchain@latest |
| LangGraph | pip install -U langgraph |
npm install @langchain/langgraph @langchain/core |
The LangGraph documentation also shows uv add langgraph for Python and lists package-manager alternatives. For up-to-date commands and migration guidance, consult the release announcement and current LangGraph overview.
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What can the official claims tell you—and what can’t they?
LangChain’s materials attribute durable execution, streaming, human intervention, persistence, memory, and coordination of deterministic code with LLM decisions to LangGraph. The 1.0 announcement describes persistence as a way to save and resume interrupted workflows. These are vendor-described capabilities; they do not establish comparative speed, cost, reliability, or universal superiority.
Best Value
LangChain’s open-source overview, consulted October 7, 2026, advertises “200M+ Monthly Downloads” and “63% Of Fortune 500 Using LangChain OSS.” The page does not provide a visible publication date or, in the cited material, a methodology for interpreting those figures. The 2025 launch announcement separately cited 90 million monthly downloads; its basis is not established as comparable to the 2026 page’s figure, so the numbers should not be read as a measured growth series. See the LangChain open-source overview and 2025 launch announcement.
Where can you learn the basics?
LangChain’s open-source overview lists free LangChain Academy courses, including an introduction to Deep Agents, LangGraph fundamentals in Python, and an introduction to LangChain. They are learning resources for readers who want guided instruction before building.
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