LANGGRAPH · MULTI-AGENT · STATEFUL AI

LangGraph Development India — AI Agents That Think in Graphs

We build complex stateful AI agent workflows using LangGraph — multi-agent collaboration, human-in-the-loop approvals, conditional branching, and autonomous task execution.

Build Your Agent Workflow LangChain Development

LangChain vs LangGraph

When to Use LangGraph

Capability	LangChain	LangGraph
Linear pipelines (A→B→C)	Yes	Yes
Stateful workflows with memory	Limited	Native
Conditional branching	Hard	Built-in
Multi-agent collaboration	No	Yes
Human-in-the-loop approvals	No	Yes
Retry loops & error recovery	No	Yes

What We Build

LangGraph Use Cases

🕸️

Multi-Agent Orchestration

Multiple specialized AI agents (researcher, writer, reviewer, executor) working in parallel — each with its own tools and memory, coordinated by a supervisor agent.

🔄

Cyclical Reasoning Agents

Agents that loop — plan, execute, evaluate, and retry until they achieve the goal. Ideal for research automation, code generation, and complex problem solving.

👤

Human-in-the-Loop Workflows

AI workflows that pause at critical decision points for human approval — procurement approvals, content sign-off, legal review — before continuing autonomously.

🗂️

Stateful Document Processing

Multi-step document workflows where state is maintained across phases — extract, validate, enrich, verify, output — with rollback on any step failure.

🔍

Research & Analysis Agents

Autonomous research agents that search the web, read documents, synthesize information, and produce structured reports — without human hand-holding at each step.

💼

Enterprise Workflow Automation

Replace complex RPA workflows with AI agents that understand context, handle exceptions intelligently, and improve over time — built on LangGraph's state machine model.

FAQ

LangGraph Questions

What is LangGraph? +

LangGraph is a framework built on top of LangChain for building stateful, multi-agent AI systems. It models agent workflows as directed graphs where nodes are AI actions and edges are conditional transitions — enabling complex, cyclical agent behaviors that simple chains cannot handle.

When do I need LangGraph instead of LangChain? +

Use LangGraph when your AI application needs: (1) stateful workflows that persist across multiple steps, (2) conditional branching based on LLM decisions, (3) multiple AI agents collaborating, (4) human-in-the-loop approval steps, or (5) retry and error recovery loops. For simple linear chains, LangChain alone is sufficient.

What industries use LangGraph? +

LangGraph is used in financial services (autonomous research agents), healthcare (clinical decision support), legal (multi-step document analysis), software development (automated code review agents), and customer service (complex escalation workflows requiring approval steps).

Can LangGraph agents run for hours or days? +

Yes. LangGraph supports long-running agents with checkpointing — if an agent is running a 6-hour research task and the system restarts, it resumes from the last checkpoint. We implement LangGraph with PostgreSQL or Redis checkpointers for production resilience.