Development Multi Agent And Ai Agent In Crypto

AI Agent and Crypto

What you'll learn

Create a crypto token for an AI agent in Virtuals Protocol.

Run DeepSeek, Qwen, and Llama models locally.

Create a Multi Agent based on the G.A.M.E. framework from Virtuals Protocol.

Build Multi-Agent systems with RAG for freelance projects.

Develop an X-bot for analysing tweets and posting tweets with LLM based on the G.A.M.E. framework from Virtuals Protocol.

Create a Telegram bot for analysing graphs with VLM based on the AutoGen framework.

Build AI systems from freelance projects based on the AutoGen framework.

Create a knowledge base using Qdrant to implement RAG architecture.

Requirements

No programming skills required, you will learn everything you need to know.

Need to be in a good mood :)

Need enthusiasm, engagement, and active participation in comments and reviews :)

Description

Dive into LLM app development Create tokens for Crypto AI Agent.Master tools: Virtual Protocol, GAME, AutoGen, Ollama, MCP Work with DeepSeek, Llama, Qwen models Build AI Agents, Multi-Agent, RAG, VLM, and integrate with Virtual Protocol, Telegram and X.Practice-focused, innovate from automation to analyticsA friendly international community with support for any questions.  Be sure to join this community.Course ObjectivesEquip you with the skills to build innovative applications using LLMs, leveraging tools like AutoGen, GAME, Virtual Protocol, MCP, Ollama, and models such as DeepSeek, Llama, Qwen, plus AI Agent, Multi-Agent, VLM, and RAG. You’ll master the development of intelligent agents and learn to apply various integrations.  Why Choose This Course?Helps you create relevant Pet Projects (AI Agents / RAG).  Strong focus on practical application.  Covers the full journey—from core concepts to advanced solutions.  Modular structure suitable for all skill levels.  Built on best practices for effective learning.  Taught by a practitioner with experience in major projects and a teaching background.  Memes   SupportWhat You’ll Gain After Completing the Course Pet Projects with AI in your portfolio.  Skills in working with AutoGen, RAG, VLM, and LLM optimization.  Ability to design multi-agent systems.  Expertise in data integration  Experience building AI-driven applications.  Course HighlightsPrepares you for the latest industry challenges.  Goes beyond basic courses with cutting-edge IT knowledge.  Real-world examples from practice.  Challenges, metaphors, and humor for engaging learning.  What You’ll Be DoingStudying theory paired with hands-on tasks.  Analyzing real-world scenarios.  Exploring programmatic implementations of AI Agents and applying your knowledge.  Course Topics and Tasks Building Telegram Bots and bots for X  Creating a portfolio with AI-driven projects.  Fundamentals of Multi-Agent Systems and RAG.  Optimizing and fine-tuning LLMs.  Working with VLM. Integrating with modern solutions.  And much more!Who This Course Is For:DevelopersYou build apps and integrate AI, but struggle with quickly implementing AI agents and Multi-Agent systems. Learn to use AutoGen, GAME, Virtual Protocol, MCP, and Ollama for innovative solutions.Situation: "I need to speed up chatbot creation, but traditional approaches slow me down."ML EngineersYou work with DeepSeek, Llama, Qwen models but want to enhance RAG and VLM for complex tasks. This course optimizes data and integrates with AI agents.Situation: "My models need more context, but data processing takes hours."Career UpgradesYou want to refresh your skills and master modern AI tools. The course covers Multi-Agent, RAG, and LLM tuning, preparing you for 2025 industry challenges.Situation: "My current skills are outdated; I need to learn 2025 trends."Trend ExplorersYou follow tech advancements and want to dive into cutting-edge AI Agent and VLM approaches. Gain practical skills with the latest frameworks.Situation: "I want to understand how Multi-Agent systems are reshaping the market, but don’t know where to start."Crypto AI Agent SpecialistsYou work in blockchain and aim to automate trading or market analysis with AI. Learn to build predictive agents.Situation: "I need to forecast Bitcoin trends, but current tools lack accuracy."What You’ll Gain:In-demand skills for LLM tasks (AI Agents/RAG).Knowledge employers seek in AI.Hands-on practice with theory.Diverse examples—from basic to advanced—for your portfolio.Access to a community forum for solutions and discussions.

Overview

Section 1: Welcome :)

Lecture 1 Intro | Step #1

Lecture 2 Intro | Step #2

Lecture 3 Intro | Step #3

Lecture 4 Intro | Step #4

Section 2: Disclamer

Lecture 5 Disclamer | Step #1

Lecture 6 Disclamer | Step #2

Lecture 7 Disclamer | Step #3

Lecture 8 Disclamer | Step #4

Lecture 9 Disclamer | Step #5

Lecture 10 Disclamer | Step #6

Section 3: Base | 1956-2017

Lecture 11 Base | 1956-2017 | Step #1

Lecture 12 Base | 1956-2017 | Step #2

Section 4: Base | 2017-present

Lecture 13 Base | 2017-present | Step #1

Lecture 14 Base | 2017-present | Step #2

Lecture 15 Base | 2017-present | Step #3

Lecture 16 Base | 2017-present | Step #4

Lecture 17 Base | 2017-present | Step #5

Lecture 18 Base | 2017-present | Step #6

Section 5: Preparing for development | GPU

Lecture 19 Preparing for development | GPU | Theory

Lecture 20 Preparing for development | GPU | Customisation

Lecture 21 Preparing for development | GPU | Video

Section 6: Preparing for development | Ollama

Lecture 22 Preparing for development | Ollama | Theory

Lecture 23 Preparing for development | Ollama | Installation

Lecture 24 Preparing for development | Ollama | Practice

Lecture 25 Preparing for development | Ollama | Video

Section 7: Preparing for development | Python

Lecture 26 Preparing for development | Python | Theory

Lecture 27 Preparing for development | Python | Installation

Lecture 28 Preparing for development | Python | Practice

Section 8: Preparing for development | uv

Lecture 29 Preparing for development | uv | Theory

Lecture 30 Preparing for development | uv | Installation

Lecture 31 Preparing for development | uv | Practice

Section 9: Preparing for development | PyCharm

Lecture 32 Preparing for development | PyCharm | Theory

Lecture 33 Preparing for development | PyCharm | Installation

Lecture 34 Preparing for development | PyCharm | Practice

Section 10: Preparing for development | Docker

Lecture 35 Preparing for development | Docker | Theory

Lecture 36 Preparing for development | Docker | Installation | Part #1

Lecture 37 Preparing for development | Docker | Installation | Part #2

Section 11: Preparing for development | Qdrant

Lecture 38 Preparing for development | Qdrant | Theory

Lecture 39 Preparing for development | Qdrant | Step #3

Lecture 40 Preparing for development | Qdrant | Practice

Section 12: Preparing for development | Wallet for Virtuals Protocol

Lecture 41 Preparing for development | Wallet for Virtuals Protocol | Theory

Lecture 42 Preparing for development | Wallet for Virtuals Protocol | Instalation

Lecture 43 Preparing for development | Wallet for Virtuals Protocol | Challenge

Section 13: Prompt Engineer

Lecture 44 Prompt Engineer | What Is a Prompt and How LLMs Interpret Instructions

Lecture 45 Prompt Engineer | Mechanisms of Prompt Interpretation in Language Models

Lecture 46 Prompt Engineer | Foundational Principles of Prompt Engineering | Part #1

Lecture 47 Prompt Engineer | Foundational Principles of Prompt Engineering | Part #2

Lecture 48 Prompt Engineer | Fundamental Prompting Techniques

Lecture 49 Prompt Engineer | Advanced Prompting Techniques | Part #1

Lecture 50 Prompt Engineer | Prompt Frameworks

Lecture 51 Prompt Engineer | Advanced Prompting Techniques | Part #2

Lecture 52 Prompt Engineer | Prompt Security

Lecture 53 Prompt Engineer | LLM Aplication

Section 14: VIRTUALS Protocol

Lecture 54 VIRTUALS Protocol | Tokens

Lecture 55 VIRTUALS Protocol | Theory

Lecture 56 VIRTUALS Protocol | Customisation

Section 15: G.A.M.E.

Lecture 57 G.A.M.E. | Theory

Lecture 58 G.A.M.E. | Customisation | Step #1

Lecture 59 G.A.M.E. | Customisation | Step #2

Section 16: G.A.M.E. | Worker Agent

Lecture 60 G.A.M.E. | Worker Agent | Step #1

Lecture 61 G.A.M.E. | Worker Agent | Step #2

Lecture 62 G.A.M.E. | Worker Agent | Step #3

Section 17: G.A.M.E. | Agent

Lecture 63 G.A.M.E. | Agent | Step #1

Lecture 64 G.A.M.E. | Agent | Step #2

Lecture 65 G.A.M.E. | Agent | Step #3

Section 18: G.A.M.E. | Integration with X (Twitter)

Lecture 66 G.A.M.E. | Integration with X (Twitter) | Step #1

Lecture 67 G.A.M.E. | Integration with X (Twitter) | Step #2

Lecture 68 G.A.M.E. | Integration with X (Twitter) | Step #3

Section 19: G.A.M.E. | Demo

Lecture 69 G.A.M.E. | Demo #1

Section 20: Ollama

Lecture 70 Ollama | Theory

Lecture 71 Ollama | Theory | Telegram Bot

Lecture 72 Ollama | Theory | X Bot

Lecture 73 Ollama | Theory | RAG

Lecture 74 Ollama | Customisation

Section 21: Ollama | Chat Bot 2 Telegram

Lecture 75 Ollama | Chat Bot 2 Telegram | Step #1

Lecture 76 Ollama | Chat Bot 2 Telegram | Step #2

Lecture 77 Ollama | Chat Bot 2 Telegram | Step #3

Section 22: Ollama | Chat Bot 2 X (Twitter)

Lecture 78 Ollama | Chat Bot 2 X (Twitter) | Step #1

Lecture 79 Ollama | Chat Bot 2 X (Twitter) | Step #2

Lecture 80 Ollama | Chat Bot 2 X (Twitter) | Step #3

Section 23: Ollama | RAG

Lecture 81 Ollama | RAG | Step #1

Lecture 82 Ollama | RAG | Step #2

Lecture 83 Ollama | RAG | Step #3

Lecture 84 Ollama | RAG | Step #4

Lecture 85 Ollama | RAG | Step #5

Lecture 86 Ollama | RAG | Step #6

Section 24: Ollama | Web UI for RAG

Lecture 87 Ollama | Web UI for RAG

Section 25: Ollama | Demo

Lecture 88 Ollama | Demo #1

Lecture 89 Ollama | Demo #2

Lecture 90 Ollama | Demo #3

Section 26: AutoGen

Lecture 91 AutoGen | Theory

Lecture 92 AutoGen | Customisation

Section 27: AutoGen | AI Agent 4 translation

Lecture 93 AutoGen | AI Agent 4 translation | Step #1

Lecture 94 AutoGen | AI Agent 4 translation | Step #2

Section 28: AutoGen | AI Agent 4 QA

Lecture 95 AutoGen | AI Agent 4 QA | Step #1

Lecture 96 AutoGen | AI Agent 4 QA | Step #2

Lecture 97 AutoGen | AI Agent 4 QA | Step #3

Section 29: AutoGen | AI Agent 4 Freelance 1.0

Lecture 98 AutoGen | AI Agent 4 Freelance 1.0 | Step #1

Lecture 99 AutoGen | AI Agent 4 Freelance 1.0 | Step #2

Lecture 100 AutoGen | AI Agent 4 Freelance 1.0 | Step #3

Section 30: AutoGen | AI Agent 4 Freelance 2.0

Lecture 101 AutoGen | AI Agent 4 Freelance 2.0 | Step #1

Lecture 102 AutoGen | AI Agent 4 Freelance 2.0 | Step #2

Lecture 103 AutoGen | AI Agent 4 Freelance 2.0 | Step #3

Section 31: AutoGen | AI Agent 4 Freelance 3.0

Lecture 104 AutoGen | AI Agent 4 Freelance 3.0 | Step #1

Lecture 105 AutoGen | AI Agent 4 Freelance 3.0 | Step #2

Lecture 106 AutoGen | AI Agent 4 Freelance 3.0 | Step #3

Section 32: AutoGen | MCP

Lecture 107 AutoGen | MCP | Step #1

Lecture 108 AutoGen | MCP | Step #2

Lecture 109 AutoGen | MCP | Step #3

Section 33: AutoGen | Demo

Lecture 110 AutoGen | Demo #1

Section 34: See you soon :)

Lecture 111 Outro

A course for Python developers who want to create a smart agent with LLM, a browser, custom tools, and AutoGen.,For AI enthusiasts who want to use MCP, Ollama, and AutoGen to create interactive and flexible assistants.,For AI enthusiasts who want to use large language models on their own hardware or in closed systems. Local launch (self-host / on-premises) DeepSeek / Qwen / Llama.,Suitable for developers familiar with Python who want to learn how to integrate LLM and tools into a single agent.,A course for those who want to dive deeper into AutoGen and build their own agent with web search and code execution capabilities.,Ideal for developers learning DeepSeek / Qwen / Llama and AutoGen and wanting to build a next-generation agent.,A course for professionals interested in creating LLM agents that can combine web tools and custom functions.,For advanced users interested in using local LLMs, such as LLaMA, in conjunction with external APIs.,Suitable for Python developers who want to implement custom functions and web tools in their AI assistants.,The course will be useful for developers who are already familiar with the basics of LLM and want to build an agent with complex behaviour.,For engineers interested in AutoGen, async architecture, and the practical implementation of agents with MCP tools.