01
Cyclic graph orchestration
Modeling complex multi-step reasoning with loops, conditional branching and fallback strategies.
Technologies — Cloud, DevOps & AI
LangChain & LangGraph stateful multi-agent engineering
We build reliable, stateful multi-agent AI systems using LangGraph with cyclic decision graphs and human-in-the-loop checkpoints.
Core capabilities
Why we build with LangChain & LangGraph
02
State persistence & memory
Checkpointer backends (Postgres or Redis) preserving conversation and tool execution history.
03
Human-in-the-loop checkpoints
Pausing agent execution before high-risk actions to require human sign-off in a web UI.
Use cases
Where LangChain & LangGraph fits
Research & triage agents
Agents that search internal knowledge bases, draft responses and verify claims with citations.
Multi-agent workflow automation
Multi-agent teams where specialized agents draft, critique and execute code changes.
How we staff it
LangChain & LangGraph engineers you interview first
Seniority and experience are agreed in the proposal, and you interview every engineer before they start.
Working-hours overlap is agreed for each engagement and written into the statement of work — the shared window, who shifts hours, and how handoffs work outside it.
Technical FAQs
Frequently asked engineering questions
LangGraph introduces cyclic state graphs and persistence, allowing agents to loop, correct errors, and pause for human review without losing state.
We run automated evaluation suites using DeepEval and LangSmith to measure task completion rates and catch regressions.
Ecosystem
Related technologies
Planning a LangChain & LangGraph project?
Tell us about your architecture, backlog and team. We'll reply within one business day with an honest read on whether we can help.