LangGraph Development

When the problem is actually simple. A single-turn Q&A bot, a linear enrich-then-write pipeline, or a two-step automation does not need a state graph — plain LLM calls, or a tool like n8n, will ship faster and cost less to maintain. LangGraph earns its complexity when runs are long-lived, when the agent must loop and branch based on intermediate results, or when checkpointing and human interrupts are requirements rather than nice-to-haves. There is also a team-skills consideration: graph architectures are easy to read but require discipline to change safely, so a team that just wants to tweak a prompt every week is better served by something thinner. During scoping we will tell you plainly which side your project falls on.

Loops are the point of a graph, so termination has to be engineered rather than hoped for. We set a recursion limit per run, encode explicit finish conditions in the routing logic, and add a budget guard that forces escalation when a run exceeds a step or token ceiling. Cyclic paths also get cycle detection in testing: if evals show the agent revisiting the same node with materially identical state, that is a design defect we fix before launch, not a quirk we tolerate. In production, per-run step counts go on the dashboard — a rising median is an early warning that a prompt or tool change has degraded the agent’s ability to conclude.

Checkpoints typically live in Postgres (or Redis for short-lived threads) inside your infrastructure, not ours. State is encrypted at rest, thread retention follows your data policy, and we minimise what goes into state in the first place — credentials, payment data and anything regulated are kept in tool-side systems and referenced by ID rather than copied into the graph. PII handling is decided in the state-design step, because retrofitting it into a persisted state object is painful. One subtlety we handle explicitly: because checkpoints capture full state for replay, deletion requests have to cascade through checkpoint history, not just the latest row — we build that purge path rather than discovering it during an audit.

Neither. LangGraph is model-agnostic — we routinely run graphs on OpenAI, Anthropic, Google and self-hosted open-weight models, sometimes mixing providers across nodes so expensive models only handle the hard steps. It also works fine without LangChain abstractions; nodes are ordinary functions, so you can call models directly. The only real commitment is to the graph model itself, which is a reasonable one given that the state, checkpointing and interrupt semantics are the parts that are genuinely hard to rebuild. If you later outgrow it, the nodes and tools port to another orchestrator far more easily than a framework whose logic lives inside a proprietary runtime.

Yes, and this is where the checkpointing model pays off. A failed tool call raises inside a node; the graph can route to a retry-with-backoff node, a fallback tool, or an interrupt that asks a human how to proceed — and because state was persisted before the failure, nothing already accomplished is lost. We design the failure edges deliberately per tool: which errors retry, which fall back, which escalate. Transient errors like rate limits and timeouts get automatic retry with jitter; semantic failures — the API rejected the request as invalid — go straight to a compensating path, because retrying a malformed call just burns tokens. That failure topology is part of the architecture review, not an afterthought.

A focused single-graph agent with a handful of tools typically runs four to ten weeks including evals and a shadow period; multi-graph systems with supervisor routing run longer. Cost is driven less by the framework than by the number of integrations, the quality bar (which sets eval effort), and how much human-in-the-loop surface the workflow needs. We scope fixed-price after a state-design workshop, so the estimate reflects your actual workflow rather than a generic template. Ongoing cost is inference plus the checkpointer database and workers, and it scales with completed runs — we instrument cost per successful run from week one so the economics are never a surprise.