LangGraph
LangGraph is a low-level orchestration framework from the LangChain team for building stateful, multi-step agent workflows. It provides fine-grained control over execution flow with persistent state, human-in-the-loop checkpoints, and cycle support — ideal for complex agents that need more than simple chain sequencing.
Overview
"LangGraph" is a "Open Source" resource curated by AI Resource Hub, filed under the AI Agents category and suited to Advanced-level learners. It is provided by LangChain, was last updated on 2026-06-25, and holds an editorial score of 4.6/5 from our team. Click "Visit Resource" on the right to open the original page.
Our Verdict
LangGraph is the framework to reach for when your agent needs memory, loops, and real control — the point where simple chains and free-form loops start letting you down. Explicit state graphs make complex, stateful workflows debuggable, and first-class persistence, streaming, and human-in-the-loop support are genuinely best in class. It runs standalone — nodes are plain functions — but the graph abstraction is real conceptual overhead, and simple prompts don't need it. For complex agents, start here; for a single linear call, stay with a plain chain.
Tags
Key Features
- ▹Model agents as explicit state graphs
- ▹Control, loops, and persistence
- ▹Built on the LangChain ecosystem
Pros
- +Reliable, controllable agent workflows
- +Good for complex, stateful agents
- +First-class persistence, streaming, and human-in-the-loop
Cons
- −More concepts to learn
- −Graph abstraction is overkill for simple prompts
- −Fast-moving API; docs can lag behind
FAQ
Details
- Pricing
- Free and open source
- Author
- LangChain
- Editorial score
- ★ 4.6 / 5
- Last updated
- Jun 25, 2026