LangGraph
LangChainFor building multi-step AI you can follow, step by step, and control at every point.
Score breakdown
Our verdict
LangGraph is what we reach for once an agent's logic outgrows a simple loop — when you need explicit branches, retries, and approval gates that you can actually point to and debug, not an opaque chain.
It's overkill for a single-purpose chatbot, but for the multi-step agents we build for clients — the ones with real business logic — the explicit graph model has repeatedly saved us debugging time.
Pros & cons
Pros
- ✓Explicit control flow makes complex agent logic debuggable, not a black box
- ✓Fine-grained control over retries, branching, and human-in-the-loop steps
- ✓Open-source core, with an optional managed platform for hosting
- ✓Strong fit for agents with real business-logic complexity
Cons
- –Steeper learning curve than a simple prompt-chaining library
- –More boilerplate for simple use cases than they're worth
- –Best documentation examples skew toward Python
Ideal for
- Multi-step work where the path changes based on the situation
- Jobs where a person has to approve certain decisions
- Teams that need to see exactly why the AI did what it did
- Long-running work that has to remember where it left off
Pricing
Open-source framework, free · LangGraph Platform managed hosting priced separately
Questions, answered.
No, there's a JavaScript/TypeScript version too, though the Python ecosystem and examples are more mature.
You can use LangGraph standalone, though it composes well with the broader LangChain ecosystem.
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