LangGraphLangGraph
Agent workflows as explicit state machines
LangGraph models an agent as a graph of nodes and edges with a typed shared state, so every transition is something you wrote rather than something the model improvised. It is the most controllable way to build a production agent and the least forgiving of teams who wanted a weekend project.
[ 01 ] The verdict
The right default when an agent has to be correct rather than impressive. You pay for that determinism in ramp-up time, and teams without a dedicated platform engineer consistently underestimate the maintenance surface.
Best for
Engineering teams building long-running, stateful agents where a wrong branch has real consequences.
Watch out
The learning curve is genuinely steep and the abstractions move faster than the documentation. Budget for a rewrite of your first implementation.
Strengths
- Explicit control over state, branching and retries
- First-class durable execution and human-in-the-loop interrupts
- Deep tracing integration makes failures reproducible
- Self-hostable, so regulated workloads stay in your tenancy
Trade-offs
- Requires real engineering ownership; not an ops-team tool
- API surface has churned between major versions
- Hosted pricing is hard to forecast before you know node counts
[ 02 ] What it actually does
What LangGraph actually ships.
Typed graph state
A shared state object flows through nodes with reducers controlling merges, so concurrent branches converge predictably.
Durable checkpointing
Runs persist between steps, which lets an agent pause for a human approval and resume days later without losing context.
Time-travel debugging
Any checkpoint can be forked, letting you replay a failed run from the step before it went wrong.
Streaming primitives
Token, node and state-level streams make it practical to build responsive UIs over slow multi-step work.
[ 03 ] Head-to-head
LangGraph against the tools it usually loses or wins deals to.
[ 04 ] LangGraph alternatives
