LangGraph vs LangChain
LangGraph vs LangChain compared for 2026 — features, license, ease of use, performance and which one to choose. Stateful, controllable agent graphs vs Compose chains, tools and agents.
Updated regularly · curated by olud.ai
LangGraph vs LangChain at a glance
| Spec | LangGraph | LangChain |
|---|---|---|
| Category | AI agent framework | AI agent framework |
| Type | Agent orchestration (graphs) | LLM app framework |
| License | MIT | MIT |
| Runs locally | Cloud-optional | Cloud-optional |
| Primary language | Python / JS | Python / JS |
| Ease of use | Advanced | Intermediate |
| Best for | developers needing controllable agent workflows | developers building tool-using LLM apps |
| GitHub stars | 37.8k | 142.3k |
How LangGraph and LangChain score
| Criterion | LangGraph | LangChain |
|---|---|---|
| Popularity | 4.0 | 5.0 |
| Maintenance | 5.0 | 5.0 |
| Ease of use | 2.5 | 3.5 |
| Privacy | 3.5 | 3.5 |
| License freedom | 5.0 | 5.0 |
Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.
What each one is
LangGraph
LangGraph is a library for building stateful, controllable agents as graphs, giving you fine-grained control over loops, branching and persistence.
- Explicit, controllable agent state machines
- Persistence and human-in-the-loop built in
- Integrates with the LangChain ecosystem
LangChain
LangChain is a framework for building LLM applications by composing prompts, models, tools, memory and agents, with a vast ecosystem of integrations.
- Huge ecosystem of integrations
- Building blocks for chains, tools and agents
- Python and JavaScript support
Key differences
LangGraph is agent orchestration (graphs), while LangChain is lLM app framework. LangGraph leans more advanced-friendly, whereas LangChain is more suited to intermediate users. In short, LangGraph fits developers needing controllable agent workflows, and LangChain fits developers building tool-using LLM apps.
Which should you choose?
Choose LangGraph for developers needing controllable agent workflows. Choose LangChain for developers building tool-using LLM apps.
There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.
Frequently asked questions
Is LangGraph or LangChain easier to use?
LangChain is generally the easier of the two to get started with, while LangGraph rewards more setup with more control.
Are LangGraph and LangChain free?
LangGraph is free and open source (MIT), and LangChain is free and open source (MIT). Neither charges for the core software.
Can I run LangGraph and LangChain locally?
LangGraph: cloud-optional · LangChain: cloud-optional. Both can be used without sending your data to a third-party cloud where their setup allows.
LangGraph vs LangChain — which should I pick in 2026?
Choose LangGraph for developers needing controllable agent workflows. Choose LangChain for developers building tool-using LLM apps.
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