LangGraph Development Services
When a team routes every decision through engineers because their agents forget the last step, work stalls out. Kodexo Labs's LangGraph development services fix that gap. The graphs remember each step, giving Extensiv's operations team 90%+ accuracy across 207 tables and four databases.
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TRUSTED BY ENTERPRISES
LangGraph is the framework we reach for when one agent isn't enough. It handles graphs of agents that make decisions, call outside tools, pause for human review, and resume exactly where they stopped.
Our Core Capabilities
Stateful multi-agent graphs that hold context across an entire session.
Conditional routing that sends each request down the right branch.
Checkpoint and resume, so long jobs survive a crash.
Human-in-the-loop pauses for approval before an agent acts.
Tool nodes that pull answers from your live databases.
Streaming responses and LangSmith tracing for production monitoring
IN THE NEWS
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on Clutch
Expert team
Client retention
LangGraph Development Services, Built To Last
LangGraph is the framework we reach for when one agent isn't enough. It handles graphs of agents that make decisions, call outside tools, pause for human review, and resume exactly where they stopped.
Graph Design
We define each node, map the edges between them, and set the conditional rules that route each incoming request down the correct path.
We break every task down into a focused node that does one clear job.
Each rule checks the last result and picks the next node to run now.
Ready to Put an Agent Behind Your Workflow?
Tell us which decisions still bottleneck through your team, and we'll map the graph, the state store, and the handoffs your workflow needs.
Agentic Systems in Production
Extensiv
Extensiv's operations team once queued behind engineering for every data question, and decisions routinely stalled for days. We built an agentic system on LangGraph that reads plain-English questions and answers them straight from the live operational database. The team now self-serves at 90%+ accuracy across 207 tables and 4 databases. An Inc. 5000 client.
90%+
SQL Accuracy
207
Tables
04
Databases
Diesel Laptops
Diesel Laptops' fleet technicians were burning more time hunting through diagnostic records than actually fixing trucks, and downtime added up fast. We built an AI search layer, hosted inside their own AWS VPC, that surfaces the right answer across 160,000+ technical records in seconds. Lookup time fell 85%. They are an Inc. 5000 company.
85%
Faster Lookup
160,000
Records Searched
AWS VPC
Self-Hosted
IFPG
IFPG runs the largest franchise network in North America. Yet its chatbot kept serving prospects broken answers across 1,000+ franchise listings, and warm leads died at the first click. We rebuilt the reasoning layer using chain-of-thought prompting. HTML errors fell to zero, a full 100% cleanup, and answer accuracy climbed 85% across the network.
1,000+
Listings
85%
Accuracy Lift
Zero
HTML Errors
What Clients Say About The Team
Fast-growing organisations do not applaud a consulting partner for polished slide presentations; they praise it for showing up when something actually breaks. The notes below come from founders who watched Kodexo Labs work the problem in real time.
Kodexo Labs has met all expectations; the team delivers on time and manages the project seamlessly. They respond promptly to needs and communicate effectively through virtual meetings, Google Chat, and WhatsApp. Overall, they're highly passionate about the project and excel in customer service.
Christopher Brigham
MD President, Brigham and Associates, Inc.
WATCH VIDEO
- HIPAA-compliant agent graphsAuditable care-session stateCheckpointed patient-record workflowsClinical intake routing agents
Where This Approach Fits Across Eight Distinct Industry Verticals
Every vertical routes state differently, so we shape each agent graph around the workflows that actually run there. These eight examples show how we apply these patterns to the daily operations your teams already know so well.
Ready To Put A Stateful Multi-Agent System Into Real Production?
Bring us the workflow that keeps stalling because your current agents forget context. We will map the state graph, size the architecture, and show you exactly what a production-ready build actually looks like.
Why Teams Choose Kodexo Labs To Build Stateful Agents That Reach Real Production
We have shipped 51 products across more than 25 industries since 2021, and 94% of our clients stay with us. That track record shapes how we approach every agent build we take on for you.
LangGraph Builds Under Load
Notebooks collapse once real traffic hits. IFPG proves that ours do not. Their reasoning layer runs live on more than 1,000 franchise listings at 85% higher accuracy with zero HTML errors, every day.
Graph State Mapped First
Agent-graph projects stall past milestone one because branching logic was never mapped up front. We map the full state graph before we write a single node, catching every branch, loop, and dead end.
Orchestration Sized To Scale
Some workflows need two agents; others need a wider pipeline. We size each architecture to the work. Diesel Laptops cut lookup time 85% across more than 160,000 records under real daily query volume.
Memory That Survives Restarts
A crash should not erase what an agent learned mid-task. We pair graphs with Redis and PostgreSQL state stores, so checkpoints save each state transition and the agent resumes right where it stopped.
Ready For An Agent System That Lasts?
Bring us your workflow and your constraints. We will map where agents fit, where humans stay, and what a production build actually requires.
Independently Verified Recognition
Third-party reviewers, not our own marketing, place us here. These award badges reflect verified client feedback and platform standing, giving your team outside signals before any engagement begins.
Overcoming LangGraph Development Challenges In Production
Most teams do not stall on the first agent. They stall on the second change. We have shipped these systems across production environments since 2021, so we design around the failure points that surface after launch, not before it lands.
The Stack Behind Your Agent Graph
We build on tools our engineers run in production every week, chosen for proven reliability, not novelty.
How We Build Your Production Agent Graph
Our process runs in weekly sprints with working software at every step, so progress stays visible instead of hidden until launch.
Discovery And Mapping
We sit with your team to map the current workflow. We identify where agents should take over, where humans must stay, and which handoffs carry real risk. You leave discovery with a documented, clear scope.
Graph State Architecture
Before any code, we design the graph structure, the state stores, and the checkpoint strategy. We decide how state persists, how the system recovers from failure, and where cycles belong. That blueprint prevents rework.
Agent Build Sprints
Now our engineers implement the nodes, the tools, and the routing logic. We work in weekly sprints, demoing software each Friday so you can steer early. Every agent behavior gets tested against your inputs.
Observability And Wiring
With the agents running, we connect tracing across every node and add human approval gates where decisions carry weight. You get full visibility into each run, plus the ability to pause, review, and override.
Deployment And Handover
We ship to production behind monitoring, then train your team to read the traces, extend the graph, and add new agents without us. You own the system, and keep the knowledge to grow it.
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Frequently Asked Questions
It orchestrates stateful, multi-step AI agents through controlled routing.