Deploying AI Agents: LLMs, LangGraph, and Production APIs
About this course
"Take your AI agent skills into production with this hands-on course on building, validating, and deploying LLM-powered agents using LangGraph, LangChain, Pydantic-AI, Mem0, CrewAI, Agno, and FastAPI. You’ll learn to turn prototypes into reliable, enterprise-grade agent systems. Module 1 covers integrating LLMs (OpenAI, Anthropic) into LangGraph reasoning pipelines, designing nodes, control flow, token management, and iterative workflow testing. Module 2 focuses on schema enforcement with Pydantic-AI, structured outputs, and building a Business Workflow Assistant with validated, reliable I/O. Module 3 guides you through full deployment — FastAPI backends, persistent memory with Mem0 and vector stores, and orchestration with Agno and CrewAI in production. Module 4 teaches evaluation: metrics, logging, load testing, benchmarking, and comparing LangGraph, CrewAI, and Agno for enterprise-scale deployment. By the end of this course, you will: - Integrate LLMs into modular LangGraph reasoning pipelines - Validate agent I/O using Pydantic-AI schemas for reliable outputs - Deploy agents via FastAPI with Mem0 and vector-store persistence - Evaluate and benchmark frameworks to justify production choices"
68/100
CourseAsk score
- What the provider tells you
- 32/45
- Who stands behind it
- 20/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- Integrate LLMs into modular LangGraph reasoning pipelines
- Validate agent I/O using Pydantic-AI schemas for reliable outputs
- Deploy agents via FastAPI with Mem0 and vector-store persistence
- Evaluate and benchmark frameworks for production choices
Price shown by Coursera — confirm on their site.
Enroll on CourseraYou'll be redirected to Coursera to complete enrollment.
- Listed & compared by CourseAsk
- English · 0
Udemy
Coursera
Compared on these lists
Where this course ranks against the alternatives.