LLM Agent Cookbook: From ReAct to Multi-Agent in 4 Weeks
A 4-week curriculum for LLM Agent development using ReAct, LangGraph, and CrewAI.
LLM Agent Cookbook: From ReAct to Multi-Agent in 4 Weeks
Not sure where to start with LLM Agent development? You've heard of the ReAct pattern but don't know how to implement it? Curious about LangGraph and building multi-agent systems with CrewAI? This Cookbook has the answers.
Why LLM Agents?
LLMs like ChatGPT and Claude are powerful but have limitations:
- No access to real-time information
- Cannot interact with external systems
- Cannot execute complex multi-step tasks
Agents overcome these limitations. Give an LLM tools, and let it reason and act autonomously.
Curriculum Overview
Week 1: Foundations
- Understanding and implementing the ReAct pattern
- How Tool Calling works
- Designing robust schemas with Pydantic
Week 2: Enhanced Reasoning
- RAG and Memory systems
- Building complex workflows with LangGraph
- Improving retrieval quality with Self-RAG
Week 3: Multi-Agent Systems
- Building teams with CrewAI
- MCP and Agent-to-Agent communication
- Orchestration with Supervisor patterns
Week 4: Production Deployment
- Building safety with Guardrails
- Implementing Human-in-the-Loop
- Deploying with FastAPI + Docker
- Prompt optimization with DSPy
Key Features
- Hands-on Jupyter Notebook exercises
- Weekly real-world projects
- Solution code available on GitHub
- Bilingual support (Korean/English)
- Integration with DrillCheck AI interview practice
Who Is This For?
- Those with basic Python knowledge
- Anyone with LLM API experience
- Developers wanting to build their own agents
- Engineers looking to deploy agents in production
Get Started
Start building production-ready LLM Agents in just 4 weeks with this free Cookbook.
Related Posts
MiniMax M2.5: Opus-Level Performance at $1 per Hour
MiniMax M2.5 achieves SWE-bench 80.2% using only 10B active parameters from a 230B MoE architecture. 1/20th the cost of Claude Opus with comparable coding performance. Forge RL framework, benchmark analysis, pricing comparison.
Diffusion LLM Part 4: LLaDA 2.0 -> 2.1 -- Breaking 100B with MoE + Token Editing
MoE scaling, Token Editing (T2T+M2T), S-Mode/Q-Mode, RL Framework -- how LLaDA 2.X makes diffusion LLMs practical.
Diffusion LLM Part 3: LLaDA -- Building an 8B LLM with Masked Diffusion
Variable Masking, Fisher Consistency, In-Context Learning, Reversal Curse -- how LLaDA built a real LLM with diffusion.