Add course documentation for AI Agents #414
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- Created a comprehensive course on AI Agents covering 20 lessons - Included documentation for course topics like agent architecture, tools, memory, multi-agent systems, and deployment - Added structured MDX files for each lesson with code examples, explanations, and best practices - Updated `mint.json` to include new course navigation and pages - Provided a step-by-step learning path for understanding and building AI agents
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Summary of Changes
This pull request introduces a comprehensive course on AI Agents, comprising 20 lessons. It includes documentation for various course topics such as agent architecture, tools, memory, multi-agent systems, and deployment. The lessons are structured using MDX files, incorporating code examples, explanations, and best practices. The mint.json file has been updated to include the new course navigation and pages, providing a step-by-step learning path for understanding and building AI agents.
Highlights
- Course Creation: A new course on AI Agents has been created, covering 20 lessons with detailed documentation.
- MDX Lesson Files: Structured MDX files have been added for each lesson, including code examples, explanations, and best practices.
- Navigation Update: The
mint.jsonfile has been updated to include new course navigation and pages, ensuring easy access to the course content. - Comprehensive Learning Path: The pull request provides a step-by-step learning path for understanding and building AI agents.
Changelog
Click here to see the changelog
- docs/course/agents/01-introduction.mdx
- Added an introductory lesson to the AI Agents course, covering basic concepts and examples.
- Included a Mermaid diagram illustrating the AI agent's perceive-decide-act loop.
- Added a card group to encourage users to continue to the next lesson.
- docs/course/agents/02-types-of-agents.mdx
- Created a lesson on different types of AI agents, including simple reflex, model-based, goal-based, utility-based, and learning agents.
- Included card groups to highlight key characteristics of each agent type.
- Added Mermaid diagrams to illustrate the structure of simple reflex agents.
- Provided a tip on understanding agent complexity and a guide on choosing the right agent type.
- docs/course/agents/03-agent-architecture.mdx
- Added a lesson on agent architecture, covering basic components such as sensors/input, processing unit, decision-making, and actions/output.
- Included a Mermaid diagram illustrating the basic components of an AI agent.
- Explained the agent loop and the PraisonAI agent architecture.
- Provided a Python code example for a simple agent structure using the PraisonAI framework.
- docs/course/agents/04-agent-instructions.mdx
- Created a lesson on creating effective agent instructions, emphasizing the importance of clarity, focus, boundaries, and personality.
- Provided examples of good and weak instructions for role definition, purpose, process guidelines, and response format.
- Included an instructions template and examples in PraisonAI.
- Highlighted common instruction mistakes and a cycle for refining instructions.
- docs/course/agents/05-agent-tools.mdx
- Added a lesson on agent tools, explaining how tools extend agent capabilities.
- Defined agent tools and their importance, including accessing information, performing calculations, creating content, and controlling systems.
- Described common types of tools such as information retrieval, computation, content creation, and external API tools.
- Provided a Python code example for creating and using tools in PraisonAI.
- docs/course/agents/06-agent-memory.mdx
- Created a lesson on agent memory, explaining how agents maintain context and learn.
- Described types of agent memory, including short-term, long-term, and working memory.
- Explained how memory works in PraisonAI and its limitations.
- Provided best practices for memory implementation and usage in multi-agent systems.
- Added a note that the feature is yet to be developed.
- docs/course/agents/07-multi-agent-systems.mdx
- Added a lesson on multi-agent systems, explaining how multiple agents can work together.
- Described the benefits of using multiple agents, such as specialization, handling complex tasks, scalability, and diverse perspectives.
- Explained basic multi-agent architecture and patterns, including pipeline, expert panel, and hierarchical patterns.
- Provided a Python code example for creating a multi-agent system in PraisonAI.
- docs/course/agents/08-agent-process.mdx
- Added a lesson on agent processes, explaining agent workflows and processes.
- Defined agent processes and their flow, including sequential, parallel, and conditional processes.
- Provided a Python code example for creating a process in PraisonAI.
- Included best practices for process design and monitoring process execution.
- docs/course/agents/09-knowledge-bases.mdx
- Added a lesson on knowledge bases, explaining how to enhance agents with specialized knowledge.
- Defined knowledge bases and their importance, including domain expertise, current information, custom guidelines, and consistent responses.
- Described types of knowledge bases, such as document collections, structured data, and vector databases.
- Provided a Python code example for using a knowledge base with an agent in PraisonAI.
- docs/course/agents/10-agent-tasks.mdx
- Added a lesson on agent tasks, explaining how agents handle and manage tasks.
- Defined agent tasks and their anatomy, including objective, inputs, process, and outputs.
- Described task types, such as information, creation, analysis, and interaction tasks.
- Provided a Python code example for creating tasks in PraisonAI and best practices for task management.
- docs/course/agents/11-creating-your-first-agent.mdx
- Added a lesson providing a step-by-step guide to building a first AI agent.
- Covered installation, environment setup, agent definition, and execution.
- Provided examples of modifying agent behavior and examining how it works.
- Highlighted common challenges and next steps for further experimentation.
- docs/course/agents/12-adding-tools-to-agents.mdx
- Added a lesson on enhancing agents with specialized capabilities through tools.
- Explained what agent tools are and why they're useful, including code and no-code examples.
- Provided instructions on creating custom tools and assigning them to agents.
- Discussed when agents use tools and best practices for tool design.
- docs/course/agents/13-building-multi-agent-system.mdx
- Added a lesson on building a multi-agent system with multiple cooperative agents.
- Covered creating specialized agents, setting up the workflow, and customizing agent interactions.
- Provided alternative agent configurations, such as parallel processing and expert panel.
- Highlighted best practices for designing effective multi-agent systems.
- docs/course/agents/14-conversational-agents.mdx
- Added a lesson on building conversational agents that maintain context in conversations.
- Explained what makes a good conversational agent and how context works.
- Discussed conversation settings and handling different types of conversations.
- Provided examples of customer support and multi-turn educational conversations.
- docs/course/agents/15-research-agents.mdx
- Added a lesson on building research agents that gather and analyze information.
- Covered creating basic, web-search-capable, and structured-output research agents.
- Provided examples of specialized research agents, such as comparison and literature review agents.
- Highlighted best practices for the research process.
- docs/course/agents/16-content-creation-agents.mdx
- Added a lesson on building content creation agents that generate various types of content.
- Covered creating basic and specialized content creation agents, such as blog post, social media, and email campaign agents.
- Provided an example of content creation with templates.
- Highlighted best practices for content creation.
- docs/course/agents/17-data-analysis-agents.mdx
- Added a lesson on building data analysis agents that analyze and interpret data.
- Covered creating basic data analysis agents and adding data processing tools.
- Provided examples of specialized data analysis agents, such as market trend and customer data analysis agents.
- Included recommendations for data visualization and best practices for data analysis.
- docs/course/agents/18-customer-support-agents.mdx
- Added a lesson on building customer support agents that handle customer inquiries and issues.
- Covered creating basic customer support agents and adding a knowledge base.
- Provided examples of troubleshooting, conversational, and support ticket creation agents.
- Highlighted best practices for customer support.
- docs/course/agents/19-personal-assistant-agents.mdx
- Added a lesson on building personal assistant agents that help with personal tasks and productivity.
- Covered creating basic personal assistant agents and specialized agents for task management, information organization, daily planning, decision support, and recommendations.
- Highlighted best practices for personal assistants.
- docs/course/agents/20-deploying-agents.mdx
- Added a lesson on deploying agents, explaining how to deploy and share AI agents with others.
- Covered deployment options, such as local, web application, and serverless function deployment.
- Discussed environment variables, security, and creating a simple chat interface.
- Highlighted scaling considerations and best practices for deployment, including testing, documentation, and version control.
- docs/mint.json
- Updated the
mint.jsonfile to include the new course navigation and pages for the AI Agents course. - Added a new group for the course and listed all the lesson files.
- Updated the
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In realms of code, where agents roam,
A course is built, a digital home.
From architecture to tasks defined,
A learning journey for every mind.
With tools and memory, they start to blend,
AI agents, man's digital friend.
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Code Review
The pull request introduces a comprehensive course on AI Agents, covering various topics and providing a step-by-step learning path. The addition of structured MDX files for each lesson with code examples and explanations is a great way to help users learn about AI Agents. The update to mint.json to include the new course navigation and pages is also well done.
Merge Readiness
The pull request appears to be well-structured and provides valuable content for users interested in learning about AI Agents. I am unable to directly approve the pull request, and users should have others review and approve this code before merging. There are no critical or high severity issues, so it seems like it's in good shape to be merged after the author addresses the medium severity issues.
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| # The conversation history is maintained automatically | ||
| agent.start("My name is Alex") | ||
| agent.continue("What's my name?")# Note: TODO: This Feature yet to be developed # Agent will remember "Alex" |
There was a problem hiding this comment.
It's great that you've included a TODO note here to indicate that this feature is still under development. To make this even more helpful, consider adding a brief comment explaining the intended functionality and any potential challenges you foresee in implementing it.
| For more advanced applications, you can implement custom memory systems: | ||
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| ```python | ||
| # Note: TODO: This Feature yet to be tested |
There was a problem hiding this comment.
It's great that you've included a TODO note here to indicate that this feature is still under development. To make this even more helpful, consider adding a brief comment explaining the intended functionality and any potential challenges you foresee in implementing it.
mint.jsonto include new course navigation and pages