Enterprise-grade Multi-Agent AI Automation Framework built using Azure OpenAI, LangGraph, LangChain and Python.
This project demonstrates an enterprise-ready Agentic AI system where multiple specialized AI agents collaborate to automate complex workflows, make decisions, retrieve information and execute business processes.
Inspired by real-world enterprise automation and AI orchestration patterns.
- Multi-Agent Architecture
- LangGraph Workflow Orchestration
- Memory Management
- Tool Calling Framework
- Autonomous Task Execution
- Agent Collaboration
- Human-in-the-Loop Support
- FastAPI Service Layer
- Enterprise Workflow Automation
User Request │ ▼ Orchestrator Agent │ ┌───┼─────────┐ ▼ ▼ ▼ Research Analysis Execution Agent Agent Agent │ ▼ Memory Layer │ ▼ Final Response
src/ ├── agents/ ├── orchestrator/ ├── memory/ ├── tools/ ├── workflows/ └── api/
tests/
- User submits task
- Orchestrator receives request
- Research agent gathers context
- Analysis agent evaluates findings
- Execution agent performs actions
- Memory stores workflow state
- Final response generated
- Python
- Azure OpenAI
- LangGraph
- LangChain
- FastAPI
- Pydantic
- Enterprise Process Automation
- AI Decision Support Systems
- Network Operations Automation
- Incident Management Workflows
- Data Validation Pipelines
- Multi-Step AI Task Execution
Ansh Yadav
Automation Engineer | Agentic AI Solutions | Azure OpenAI | Python