LangChain and LangGraph development
Agent orchestration where control flow is explicit, inspectable and resumable — not an agent improvising its own loop.
LangChain is the integration layer: model providers, retrieval, tools and document handling behind one set of interfaces, so swapping a model or a vector store is a configuration change rather than a rewrite. LangGraph is the part that matters in production, because it makes control flow explicit.
The difference is operational. A free-running agent loop is hard to debug and impossible to resume — when it fails halfway through a twelve-step workflow you restart from the beginning and pay for it again. A LangGraph state machine checkpoints, so a run can be inspected, resumed after a restart, paused for human approval and replayed step by step when someone asks what it did.
LangChain & LangGraph, specifically.
Is a framework necessary at all?
Often not, and we say so. A single-step call needs nothing but the provider SDK. Frameworks pay off once you have tool calling, multi-step control flow, retries and evaluation to manage — at which point you would otherwise build a worse version of the same thing yourself.
How do you debug an agent that behaves oddly?
Tracing every step, tool call and token, plus deterministic replay of a recorded run. That is precisely why we favour explicit state graphs: you can point at the step that went wrong instead of re-reading a prompt and guessing.
Which models do you run behind it?
Usually Claude for reasoning-heavy steps, with smaller or open models routed onto high-volume simple paths for cost. The abstraction is the point — model choice stays a decision you can revisit against your evaluation set rather than a rewrite.
Tell us what you are building.
A short conversation is usually enough to tell whether we are the right firm for the problem. If we are not, we will say so and point you somewhere better.