A single chatbot chain breaks down fast once a workflow needs branching, retries, or more than one agent working together. LangGraph models that as a graph — explicit state, explicit transitions — instead of a chain hoping the next step goes well. We build multi-agent systems on LangGraph with the reliability and error recovery a stateful workflow actually needs in production.
A linear chain works until a task needs branching logic, a retry loop, or more than one specialized agent. LangGraph is built for that; a chain isn't.
Every workflow gets explicit state and explicit transitions, so you can reason about what the system will do next instead of debugging emergent behavior.
Specialized agents (research, execution, review) handed off deliberately, with clear boundaries on what each one is allowed to do.
Retries, fallbacks, and human-in-the-loop checkpoints on the steps where a silent failure would actually cost you something.
LangGraph for orchestration, LangChain's tooling for models, retrieval, and integrations — used together deliberately, not bolted on.
Documented graphs, tracked state transitions, and clean code so your team can extend the workflow without reverse-engineering a black box.
Scoped to the workflow you're actually automating — not a generic agent demo.
Defining agent roles, handoff points, and shared state so specialized agents collaborate predictably on a task.
Graphs with branching, loops, and conditional edges for processes that don't fit a linear chain.
Retry logic, fallback paths, and checkpoints so a failed step degrades gracefully instead of taking down the whole run.
Approval gates on high-stakes steps, so an agent proposes and a person confirms before anything irreversible happens.
Taking an existing LangGraph proof of concept and making it observable, testable, and reliable under real traffic.
LangChain gives you the building blocks — model calls, retrieval, tools — usually composed into a linear chain. LangGraph adds a graph layer on top: explicit state, branching, loops, and multi-agent coordination for workflows that don't fit a straight line. Most production systems we build use both — LangChain's components inside a LangGraph orchestration layer.
Yes. That's the core use case for LangGraph — specialized agents (research, drafting, review, execution) with defined handoff points and shared state, rather than one general-purpose agent trying to do everything in a single pass.
We design retry logic and fallback paths for the steps that are likely to fail, add human-in-the-loop checkpoints before high-stakes actions, and keep state observable so a failed run can be inspected and resumed rather than silently dropped.
It depends on how many agents are involved, how much state the workflow needs to track, and how much reliability the use case demands. A focused internal workflow costs far less than a customer-facing multi-agent system with strict accuracy requirements. We give a fixed estimate after a short scoping call rather than a generic range.
Tell us about the workflow or multi-agent system you're building — we'll scope an approach and a fixed estimate.