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מתחיל 4 June 2026 08:37

נגמר 4 June 2026

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Deploying AI Agents: LLMs, LangGraph, and Production APIs

Master deploying LLM-powered AI agents using LangGraph, FastAPI, and CrewAI—covering schema validation, persistent memory, orchestration, and benchmarking for enterprise-grade production systems.
Board Infinity via Coursera

Board Infinity

2868 קורסים


16 hours

שדרוג אופציונלי זמין

בינוני

התקדמות בקצב שלך

Paid Course

שדרוג אופציונלי זמין

סקירה כללית

"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"

סילבוס

  • Foundations of Multi-Agent Collaboration
  • This 4-hour module introduces learners to the transition from single-agent to collaborative multi-agent systems, emphasizing teamwork dynamics, communication strategies, and distributed reasoning.
  • Designing Role-Based Multi-Agent Workflows
  • This 4-hour module has learners design and simulate a functional, role-based workflow demonstrating structured collaboration between multiple agents using CrewAI's orchestration tools.
  • Shared Memory and Context Coordination
  • This 4-hour module explores shared memory integration in multi-agent systems, focusing on context continuity, communication efficiency, and memory optimization strategies using Mem0.
  • Orchestrating and Evaluating Multi-Agent Systems
  • In this final 4-hour module, learners orchestrate multi-agent collaboration using Agno, simulate a real-world Customer Support workflow, and conduct comparative evaluations of leading frameworks.

נלמד על ידי

Board Infinity


נושאים

Artificial Intelligence