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Google ADK Module - Prompts, Instructions, I/O Schema & Model Config Full Tutorial !!
This tutorial covers Module 02 of the Google Agent Development Kit (ADK), focusing on prompts, instructions, I/O schema, and model configuration. The content provides a comprehensive guide to structuring agent prompts, defining input/output schemas, and configuring language models for optimal agent performance. Key concepts include prompt engineering best practices, schema validation, and model parameter tuning for AI agent development.
- Understand the role of prompts and instructions in defining agent behavior and capabilities
- Learn how to structure effective prompts that guide agent decision-making and responses
- Define and validate I/O schemas to ensure consistent data flow between agent components
- +5 more key points...
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Agent Teams
See all 4 →AutoGen vs CrewAI | Which Multi-Agent AI Framework Should You Choose? | Uplatz
AutoGen and CrewAI are two leading frameworks for building multi-agent AI systems, each with distinct approaches to agent orchestration and collaboration. AutoGen uses a conversational pattern where agents communicate through message exchanges, while CrewAI employs a hierarchical task-based structure with defined roles and responsibilities. The choice between them depends on your use case, team structure, and preference for either flexible conversation-driven workflows or structured task-oriented pipelines.
Uplatz
LangGraph vs AutoGen | Which AI Agent Framework Should You Choose? | Uplatz
LangGraph and AutoGen are both frameworks for building agentic AI systems, but they differ significantly in their approach to workflows, state management, and agent interactions. LangGraph provides a graph-based architecture with explicit state control and is ideal for complex, multi-step workflows. AutoGen focuses on conversational agent orchestration with built-in multi-agent communication patterns. The choice between them depends on your use case: LangGraph for deterministic, state-driven applications and AutoGen for collaborative, conversation-based agent systems.
Uplatz
Meta CEO Mark Zuckerberg Introduces Muse: Meta’s AI Agent That Can Act, Plan & Work For You 24/7
Meta CEO Mark Zuckerberg introduces Muse, an AI agent capable of autonomous action, planning, and task execution 24/7. Muse represents Meta's advancement toward superintelligent AI systems that can work independently on behalf of users. The agent demonstrates Meta's commitment to developing practical AI solutions that extend beyond conversational capabilities to real-world task automation and decision-making.
Business Today
Integrations
Biến Zalo Thành Trợ Lý AI OpenClaw Trên Ubuntu | Ngố Tech
This video demonstrates how to transform Zalo (a Vietnamese messaging app) into an AI assistant using OpenClaw on Ubuntu. The tutorial shows setup and integration steps to automate Zalo message handling with AI capabilities, helping users manage high message volumes efficiently. It's a practical guide for Vietnamese developers looking to build AI-powered chatbot solutions on Linux systems.
Ngố Tech
Dự án AI openclaw trên GitHub: 389k sao và 81k fork
OpenClaw is a popular open-source AI project on GitHub with over 389,000 stars and 81,867 forks. Built with TypeScript, it provides a framework for AI agent development. The project demonstrates significant community adoption and serves as a reference implementation for building scalable AI systems.
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