LangGraph
LangGraph is the framework that takes agents seriously as long-running, stateful programs: graphs with checkpoints, interrupts, and time travel instead of a while-loop and a prayer. Used well, it's how you build agent workflows that pause for human approval on Tuesday and resume on Thursday without losing their minds.
Used badly, it's a new place to hide the same old problems. We use LangGraph where its state machine model genuinely fits — approval workflows, multi-step document processing, anything durable — and we deploy it like the distributed system it is: persistent checkpointers on real databases, horizontal scaling on Kubernetes, and traces on every node transition.
Stateful agent graphs
Workflows modeled as explicit graphs with typed state — reviewable, testable, debuggable.
Durable execution
Postgres-backed checkpointing so runs survive restarts, deploys, and week-long human pauses.
Interrupt-driven approvals
Human gates as first-class graph nodes, not bolted-on webhooks.
Kubernetes deployment
LangGraph services scaled, monitored, and rolled out like any production workload.
Migration from prototype loops
Your hand-rolled agent loop rebuilt as a graph you can actually maintain.
LangGraph vs. rolling our own orchestration?
If your workflow needs durable state, human interrupts, or resumability, LangGraph saves you months of reinventing checkpointing. If it's a single-shot pipeline, a plain function chain is simpler. We've built both and will recommend the one that fits, not the one that's fashionable.
Can LangGraph run in our environment?
Yes — it's Python or TypeScript you host yourself. We deploy it on your Kubernetes cluster with your databases, your observability stack, and your security controls. No mandatory SaaS dependency.
We have a LangGraph prototype that works in a notebook. Now what?
That's the classic deployment gap. We harden it: real checkpointer, error boundaries, evals, load behavior, tracing — then ship it. Prototype-to-production is usually three to six weeks.
Put LangGraph to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
Book a deployment →