Agentic AI with LangGraph & OpenAI Tools
Build autonomous AI agents using LangGraph, LangChain, and OpenAI Tools. Design multi-step, goal-seeking AI that reasons, acts, and adapts.
Modules
- Agents vs Prompts
- Planning, Acting, Reflecting
- LLM-as-a-Service Architecture
- OpenAI Functions & Tool Use
- Toolkits, Memory, Multi-Agent
- Conversational Agents
- Agent Executor, Agent Types
- Adding Tools: Search, Calculator, API
- LangGraph Setup and Concepts
- Node & Edge Graph Modeling
- Control Flow, Loops, Retry, Error States
- Multi-Agent Workflow Construction
- Autonomous Research Assistant
- Multi-Step Data Analysis Bot
- AI Workflow Orchestrator
- Autonomous Coding Assistant
Industry Insights
92%
Industry Relevance
High
Market Demand
14 LPA+
Avg. Salary
Ready to start learning?
Your Learning Roadmap
Follow this path to mastery. Our AI guide leads the way.
Agentic AI Fundamentals
6 hrsAgents vs prompts, architecture & planning
LangChain Agents
10 hrsToolkits, memory, multi-agent orchestration
LangGraph & State Machines
12 hrsWorkflow modeling, retries & loops
End-to-End Agentic Projects
8 hrsResearch, data & coding assistants
Why Agentic AI?
- Agentic workflows are the future of automation, from AI assistants to research bots.
- LangGraph enables memory, tools, routing, retries, and persistent state.
- Agents can read, write, search, browse, and decide, like autonomous co-workers.
- Prepares you to build AutoGPT-style and research-assistant tools.
- Real-world project deployment: code agents, data agents, research agents.