Choose LangChain when a conventional model–tool–response loop is enough. Choose LangGraph when you need to design and control the workflow itself—its state, branches, pauses, and recovery. Consider Deep Agents when you want a more opinionated harness with planning, subagents, and context management already assembled. These are different layers, not mutually exclusive products: LangChain’s create_agent runs on the LangGraph runtime, so you can start with the higher-level interface and use lower-level orchestration when the workflow calls for it.
What do LangChain, LangGraph, and Deep Agents mean?
LangChain’s current terminology describes three layers: LangChain as an agent framework, LangGraph as an agent runtime, and Deep Agents as an agent harness. That is the company’s taxonomy, not a universal industry standard. It is useful because it focuses the choice on how much of the agent’s behavior you want to assemble and control.
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LangChain: a framework for the standard agent loop
LangChain provides create_agent, model and tool abstractions, integrations, and middleware. A typical agent asks a model what to do, executes a requested tool, returns the tool result to the model, and repeats until the model produces a final response. Middleware lets you adapt that loop—for example, by adding deterministic logic around it.
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LangGraph provides lower-level primitives for building stateful workflows as graphs, with explicit steps and transitions. A graph can combine ordinary code with model-driven decisions and represent branching, cycles, pauses, and recovery. The official documentation says LangGraph can be used without LangChain; LangChain’s abstractions are conveniences for common components and loops, not a prerequisite.
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Deep Agents: a more complete agent harness
Deep Agents groups ready-made approaches to planning, subagents, context management, and memory. It is the more opinionated option in this stack: useful when those capabilities are immediate requirements and you would rather begin with a bundled harness than compose each part yourself. Check its current documentation for the behavior and limits of individual features.
How do the three options compare?
| Dimension | LangChain | LangGraph | Deep Agents |
|---|---|---|---|
| Abstraction | Higher-level framework for common agent loops and integrations | Lower-level runtime with explicit orchestration | Higher-level, more opinionated harness |
| Workflow shape | Model, tools, and response, adaptable with middleware | Custom graph of deterministic and agentic steps | Ready-made agent approach that can be extended |
| State and duration | The standard loop may be sufficient for common interactions | Designed for stateful workflows, including persistence and resuming execution | Includes memory and context management, according to LangChain |
| Human oversight | Can be added through middleware | Human control is presented as a first-class capability | For more specific control, LangChain recommends moving to lower layers |
| Good starting point | A conventional agent or a customized standard loop | A workflow requiring fine-grained orchestration | A complex agent that benefits from built-in planning, subagents, or context management |
This comparison reflects recommendations and capability descriptions from LangChain’s official materials, not an independent benchmark. The company’s documentation describes LangGraph features including persistence, durable execution, streaming, human intervention, memory, and mixing deterministic code with model decisions. Those descriptions do not establish that a particular implementation will be faster, cheaper, or more reliable.
Rank #2
Which should you choose for your workflow?
Choose LangChain if the flow is mostly model → tools → response
Start with create_agent if the agent’s core job is to decide whether to call a tool, receive its result, and continue toward an answer. Middleware may cover modest custom behavior without requiring you to define the entire orchestration graph.
Choose LangGraph if transitions need to be explicit
Reach for LangGraph when the workflow needs predictable validation, conditional routing, repeated steps, explicit pauses, or recovery paths that are awkward to express as middleware around the standard loop. It is also the more direct fit when you need to define how state moves between steps rather than leave the sequence primarily to the agent loop.
Consider LangGraph for work that must pause and resume
LangChain identifies persistence and resuming interrupted workflows as relevant LangGraph capabilities for long-running work. For example, an approval may pause execution and continue after a person responds, potentially across sessions. Treat this as a capability claim, not a performance guarantee: behavior depends on the workflow and its configuration.
Consider Deep Agents when you want a bundled harness
If planning, subagents, and context management are core requirements from the outset, Deep Agents may be a more direct starting point than assembling those practices yourself. If the application instead needs unusually specific orchestration or oversight, the lower-level LangGraph runtime offers more direct control.
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 a LangGraph workflow. That makes the choice less like selecting one replacement for the other and more like selecting where to work in the stack. You can use a ready-made agent loop for one part of an application and graph-level orchestration around it when the larger workflow needs explicit control.
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LangChain announced LangChain 1.0 and LangGraph 1.0 on October 22, 2025. The announcement presented create_agent as LangChain’s agent abstraction over the LangGraph runtime and recommended LangChain for conventional patterns and LangGraph for complex, controllable workflows. The company also stated at launch that the 1.0 line would remain stable without breaking changes until 2.0; that was the vendor’s commitment at the time, not an independent forecast.
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The same launch announcement said legacy functionality had moved out of the main LangChain package into langchain-classic, and that LangChain 1.0 for Python required Python 3.10 or later because Python 3.9 support had ended in October 2025. These are version-sensitive details. Verify current release notes and package requirements before upgrading or planning a migration.
What are the installation commands?
The commands below are examples shown in the official pages consulted for this article. Package names and recommended commands can change, so check the current installation documentation for your language and package manager before using them.
| Package | Language | Example command |
|---|---|---|
| LangChain | Python | uv pip install --upgrade langchain |
| LangChain | JavaScript | npm install @langchain/langchain@latest |
| LangGraph | Python | pip install -U langgraph |
| LangGraph | JavaScript | npm install @langchain/langgraph @langchain/core |
What does the evidence establish—and what does it not?
The cited capability and product descriptions come from LangChain’s own documentation and announcements. They can explain how the vendor positions the tools, but they are not independent comparative tests. The materials cited here do not establish that LangGraph is faster, less expensive, more reliable, or best for every project.
LangChain’s overview page, consulted in 2026, also promotes “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, enough methodology to interpret these as independently verified adoption measures. They are vendor-published figures, not evidence that one framework is technically preferable.
For official learning resources, LangChain lists free LangChain Academy courses, including introductions to Deep Agents and LangChain and a Python course on LangGraph fundamentals. The course catalog is the appropriate place to check current course availability.
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