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Agentic Architectures 2026: LangGraph, CrewAI, AutoGen — Comparative Guide
A practical comparative course for developers building production multi-agent systems. You will implement the same document processing pipeline in LangGraph (state machine), CrewAI (role-based teams), and AutoGen (conversational agents), then apply 3 structured decision trees to determine which framework fits your next project. Covers memory strategies, tool calling reliability, token budgets, and resilience patterns for each framework.
Duration
2 days
Level
Advanced
Price
9.99 EUR/month (all courses included)
Max group
12 participants
What you will learn
+Understand the fundamental paradigm differences between LangGraph, CrewAI, and AutoGen
+Build the same multi-step pipeline in all three frameworks
+Apply structured decision trees to choose the right framework for any use case
+Implement production-grade memory and persistence in each framework
+Measure and compare token costs across frameworks for the same task
+Identify failure modes and resilience patterns for production deployments
Course program
Module 1: The Agentic Framework Landscape in 2026
2h- Three paradigms: state machine vs. team vs. conversation
- Ecosystem snapshot: stable APIs vs. moving targets
- Framework selection matrix and common mismatch patterns
- Real-world cost of wrong framework choice
Module 2: LangGraph — State Machine Precision
3h30- TypedDict state schemas and Annotated reducers
- Graph construction: nodes, edges, conditional routing
- SQLite and Redis checkpointing for fault tolerance
- Human-in-the-loop: pause, review, and resume patterns
Module 3: CrewAI — Role-Based Team Coordination
3h- Agent roles, goals, and backstories as system prompts
- Sequential vs. hierarchical process models
- CrewAI Flow API: structured state on top of crew semantics
- Long-term memory with ChromaDB embeddings
Module 4: AutoGen — Conversational Multi-Agent Patterns
3h- AutoGen 0.4 AgentChat API — migration from 0.2
- RoundRobinGroupChat vs. SelectorGroupChat
- Termination conditions: TextMentionTermination + MaxMessageTermination
- Where AutoGen genuinely wins: iterative refinement tasks
Module 5: Decision Framework — Three Trees
2h- Decision Tree 1: Control vs. Expressiveness
- Decision Tree 2: Token Budget Constraint
- Decision Tree 3: Team Size and Failure Modes
- Real-world vignettes: fintech, healthcare, legal, dev tools
Module 6: Memory, Persistence & Tool Calling
2h30- Short-term, long-term, and episodic memory across frameworks
- Redis checkpointing and thread isolation for parallel processing
- Tool calling reliability patterns and production error handling
- Token cost benchmarks and framework selection scoring
Ready to get started?
9.99 EUR/month — All courses included, cancel anytime