LangGraph: The Framework Behind Our Production AI Agents
AI agents that actually work in production need more than prompt engineering. They need stateful workflows, fault tolerance, and human oversight. That's why Laava builds every agentic application on LangGraph — the graph-based framework for reliable, controllable AI agents.
From PoC to production • Custom solutions
Why Most AI Agent Frameworks Fall Short in Production
The AI agent landscape is crowded with frameworks that demo well but break in production. Simple chain-based approaches can't handle the branching, looping, and error recovery that real business processes demand.
No-code tools lack version control and testability. And "autonomous" agent frameworks give you impressive demos but no control over what happens when things go wrong.
Enterprise AI agents need a different foundation — one built for complexity, reliability, and human oversight. That's the gap LangGraph fills, and why Laava standardized on it.
What LangGraph Enables for Your Business
Complex multi-step business processes automated with full auditability
AI agents that recover gracefully from failures — no lost work
Human approval gates where your business needs them (Shadow Mode)
Real-time streaming responses for interactive user experiences
Switch LLM providers (OpenAI, Anthropic, Azure) without rewriting agents
Full graph visualization for debugging, testing, and compliance
How We Build with LangGraph
LangGraph powers the Reasoning layer of our 3 Layer Architecture
Agentic Workflow Design
We map your business process into a LangGraph state machine — nodes for actions, edges for decisions. Every path is explicit, testable, and auditable. No black-box autonomy.
Tool & Integration Orchestration
Your agent needs to call APIs, query databases, process documents, and trigger actions. LangGraph's tool-calling architecture connects to your existing systems cleanly — CRM, ERP, document stores.
Multi-Agent Orchestration
Complex problems need multiple specialized agents working together. LangGraph enables supervisor patterns, collaborative workflows, and agent-to-agent communication — all with shared state.
Production Hardening
We deploy LangGraph agents with checkpointing, persistence, streaming, and monitoring. Your agent recovers from crashes, scales under load, and provides full observability.
Why LangGraph Over the Alternatives
We evaluated every major agent framework. Here's why LangGraph won.
vs. Simple LangChain Chains
Basic LLM chains work for linear tasks, but real business processes have loops, branches, and conditional logic. LangGraph adds cycles and state management to LangChain's ecosystem — giving us the full power of graph-based orchestration without leaving a battle-tested stack.
vs. AutoGen & CrewAI
These frameworks prioritize autonomous agent communication — impressive in demos, unpredictable in production. LangGraph gives us explicit control over every transition. We define exactly when agents act, when humans review, and how failures are handled. Less magic, more reliability.
vs. Custom-Built Frameworks
We could build our own orchestration layer. But LangGraph is maintained by a large team, used by thousands of companies, and evolving fast. We'd rather spend our engineering time on your business logic than reinventing state management and persistence.
vs. No-Code AI Tools
Tools like n8n and Make.com have AI features, but they lack version control, automated testing, and the flexibility to handle complex reasoning chains. LangGraph is code-first: every agent is testable, reviewable, and deployable through CI/CD — the way production software should be.
Our LangGraph Principles
Ready to Build AI Agents That Actually Work?
Let's discuss how LangGraph and our 3 Layer Architecture can automate your most complex business processes — reliably, transparently, and under your control.
Free 90-minute roadmap session • No commitment