LangGraph_
>> LangGraph is the orchestration engine for agents that must not lose their place. We model typed state, conditional edges, durable checkpointers, and approval interrupts so long-running workflows resume cleanly after crashes and redeploys.
Calls/month
Production AI volume across live systems
PII detection
Compliance-ready detection accuracy in production AI
Projects
Shipped since 2012 with senior engineers only
Direct answer
Long-running agents fail when state dies on restart. We ship LangGraph StateGraph workflows with durable checkpoints, human-in-the-loop interrupts, and multi-agent control your ops team can resume.
Trusted by leaders
>> System map
Adjacent AI layers we ship with this expertise. Technology-first, not vendor theater.
>> When this system fits
Honest fit gates. We will tell you when another approach is better.
Strong fit
- Workflows with branching, loops, or parallel specialists
- Human approval gates mid-run with resume from checkpoint
- Long-running agents that must survive process restarts
- Teams outgrowing a linear create_agent loop
Weak fit
- Simple single-tool Q&A that does not need graph control
- Projects refusing persistent storage for checkpoints
Stack: LangGraph · StateGraph · Checkpointers · Python · TypeScript · Multi-agent
What we deliver
_> Capabilities on this stack
StateGraph design
Explicit nodes, edges, and typed state that engineers can reason about in review.
Durable execution
Postgres or cloud checkpointers so runs resume after failure instead of restarting from zero.
Human-in-the-loop
Interrupts at policy boundaries with clean resume and auditability.
Multi-agent topologies
Router and specialist patterns with clear ownership of tools and outputs.
LangGraph delivery
State and failure map
Define state schema, interrupt points, and what durability means for the business.
Graph vertical slice
One durable path with tracing and evals before expanding topology.
Ops readiness
Time-travel debugging, replay, alerts, and handover docs for your team.
Related projects
_> See how we've applied our expertise
>>Related guides
_> Cite-worthy depth behind this stack
Frameworks and scorecards buyers and answer engines can quote. Each guide links back to delivery proof.
LangChain vs LangGraph for Production Agents
2026 decision guide: LangChain create_agent on the LangGraph runtime vs explicit StateGraph for durable branching and HITL.
Agent Eval, Cost, and CI Gates
Build offline eval suites, online scorers, cost budgets, and merge-blocking CI gates for production agents.
Agentic AI: Ship Real Workflows With Guardrails That Hold Up
A practical guide to agentic AI in production: boundaries, tool permissions, approval gates, failure modes, rollout stages, KPIs, and templates to ship safely.
AI Observability for SaaS Leaders: LLM Quality, Latency, Cost
A practical guide to AI observability in SaaS: track LLM quality, latency, and cost in a boilerplate stack with concrete metrics, tests, and rollout steps.
FAQ
When you need explicit control over branching, durable checkpoints, or multi-agent coordination. Many teams use both: LangChain for assembly, LangGraph for the graph.
Not required. We instrument for the observability stack you approve. LangSmith is a strong default inside the LangChain ecosystem.
>> Where this goes next
Adjacent expertise and the engagement models we deliver it through.