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Building with AutoGen and CrewAI: Design Role-Based, Multi-Agent Systems with Python - Master Orchestration, Tool Use, Planning, Memory, and Self-Evaluating AI Agents
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AI Agent Memory & Context Engineering: A Practical Guide to Building Smarter, Personalized, and Scalable AI Agents with LangChain, RAG, and Vector Databases
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The Context Engineering Lab: Hands-On Projects in Prompt Design, Memory Systems, RAG, MCP, and Multi-Agent Workflows to Build Real-World AI ... to ... to Multi-Agent Workflows, MCP, and Reliable)
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Context Engineering for Multi-Agent Systems: Optimizing Memory, Communication, and Workflows in AI Agents: 2 (Context Engineering for Multi AI systems Guide)
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AI Agent’s Memory & Context Engineering: Empower Your AI Agents to Deliver Accurate, Consistent, and Personalized Interactions
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AI Agent Memory Architecture: Build Smarter, Scalable, and Reliable Agents with Long-Term Knowledge and Retrieval Systems
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Engineering with Manus AI: Architecting Autonomous Agents, Memory Systems, and Multi-Agent Workflows for Next-Gen LLM Applications
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Scalable AI Agent Engineering: Extend Context Windows and Implement Reliable Memory Systems with Semantic Kernel and Modern Vector Stores (Agentic AI Systems & Workflows)
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Context Engineering for Modern AI: Strategies for Precision Prompting, Memory Management, and Reliable Agent Workflows
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AI Agent Memory Architecture: he Cognitive Science Blueprint for Building LLMs That Remember
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Context Engineering for Multi-Agent Systems: Optimizing Memory, Communication, and Workflows in AI Agents (Context Engineering for Multi AI systems Guide)
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Introduction to Context Engineering for LLMs: Master Prompt Design, Context Memory, and Agent-Based AI Systems with LangChain, RAG, and the Model Context Protocol
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Context Engineering Fundamentals: Learn Prompt Design, Context Memory, and Agent-Based AI with LangChain, RAG, and MCP for Next-Gen LLM Applications
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LangChain Deep Agents in Action: Design Smarter, Context-Aware AI Systems with Planning, Sub-Agent Delegation, and Virtual Memory
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Context Engineering for Multi-Agent AI: Designing Transparent, Memory-Driven, and Orchestrated Intelligence Beyond Prompting
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Introduction to Context Engineering for LLMs: Master Prompt Design, Context Memory, and Agent-Based AI Systems with LangChain, RAG, and the Model Context Protocol
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Context Engineering for AI Agents and LLMs: Design Reliable AI Agents and LLMs with Smarter Prompts, Structured Memory, RAG, and Multi-Agent Workflows for Scalable Real-World Systems
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LangChain Deep Agents in Action: Design Smarter, Context-Aware AI Systems with Planning, Sub-Agent Delegation, and Virtual Memory
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