The ai_agents_az Project is a library of resources created specifically for AI agents, featuring multiple playbacks and tutorials covering everything from creating drug prescription agents to creating social media content using tools like n8n. The project is maintained by David Gyori and Kais and is primarily developed using the Python language.
Project Brief – “AI Agents A-Z”
This repository is mainly a collection of n8n workflow templates for the “AI Agents A-Z” series (videos/tutorials/hands-on projects).
In other words, it is not a single AI model or framework, but rather a different “Agent” application process implemented in n8n (a low-code/visual workflow automation tool) for learners to refer to or reuse.
n8n is a popular automation/process orchestration platform that combines triggers, API calls, condition judgments, script nodes, and more into a visual workflow. The template in this repository is to break down various actions of the “intelligent agent” (e.g., search, writing, scheduling, social media posting, etc.) into process nodes and use n8n to connect them.
In each episode of “AI Agents A-Z”, a specific intelligent agent use case is usually proposed (e.g., automatic blogging, automatic research, automatic posting, etc.), and then there is an n8n workflow template corresponding to that episode in this repository.
From the README, you can see that the repository structure is divided into episodes, such as episode_1, episode_2, … episode_24 etc. The repository also contains the “servers” folder, which contains some backend or service support templates related to agent execution (e.g., short video generation service, narrative generation service, etc.).
Feature & use case examples
By looking at its directory structure and description, here are some typical “Episode/Agent” types, as well as the functional scenarios supported by this repository:
| Episode / Agent Type | Description / Functional direction |
|---|---|
| Episode 1: Creating a prescription agent | Build a “prescription” agent (which may be automatically generated for medical or prescription-related) |
| Episode 2: Daily digest agent | Daily summary generation agent (organize and summarize the information of the day) |
| Episode 3: LinkedIn posts with human-in-loop | Publish content on LinkedIn and join the process of “human moderation” |
| Episode 4: Deep research agent using Google | An agency process that uses Google for in-depth research |
| Episode 5: Blog writing with deep research | An agency process that automatically generates blog posts based on research |
| Episode 6: Lead generation with X-Ray search & LinkedIn | Use X-Ray Search + LinkedIn for lead generation flow |
| Episode 7,8,… up to Episode 24 | It involves various agent types and combination processes such as short video production, social account automation operation, image generation, sleep video, UGC video, scheduling system, model integration, etc., especially in the direction of content/media/social platform automation. |
In addition, the project comes with a “Server/Backend” support template:
- AI Agents No-Code Tools: Service support for integration with no-code tools (assists n8n in integration with other platforms)
- Short video maker MCP/REST server and Narrated story creator REST/MCP server: Backend service templates for short videos, narrated story video generation, and more.
In its promotional/community materials, it can also be seen that this project is closely related to video production and media content automation, and is a routine system for “using AI to produce video/content/social media”.
Technical Features & Core Design Ideas
From the perspective of the positioning and content of the project, the following are its more prominent technical or design features:
- Low-code/visual workflows
Leverage n8n’s visual interface to build AI agent flows. This eliminates the need for coding from scratch, and complex processes can be modularized and debugged. - Modular + Templated
Each proxy use case is split into multiple nodes (triggers, API calls, judgments, data processing, sending results, etc.) and saved with templates for easy reuse and combination. - “Human-in-the-loop” design
Some processes may include manual review and confirmation steps to prevent AI from making fully automated errors or behaving inappropriately. - Multi-service/module collaboration
Workflows may call multiple external tools or services (search APIs, social platform APIs, video processing services, Text-to-Speech / Image generation APIs, etc.) at the same time.
Back-end service templates (such as short video generation services, narrative services, etc.) are also part of its link. - Scalability and customization
Although it is a template, users can customize on top of these templates, insert new nodes, rewrite logic, replace models, etc. - Content/media/social direction is prioritized
Many of the proxy examples revolve around content-driven scenarios such as writing (blogs, social posts, summaries), visual/video/image generation, video scripting, social account management, and more. - Education/practice is the main focus
This project is essentially a teaching/case library project, designed to help learners understand how to build AI agents through practical cases.
Project value & use cases
The value of this project may vary depending on the type of user:
- Learner / AI Developer
If you want to understand how “AI agents” are designed and implemented, these templates and examples are very valuable for reference. You can deepen your understanding by running these n8n processes, observing their data flow, modifying logic, and replacing models. - Rapid prototyping/validation projects
When you want to quickly validate an agent-based idea (such as asking AI to help you write blogs, do research, auto-post, etc.), you can directly use these templates as a basis and write less plumbing code. - Content/media/self-media direction automation
There are many demands in this direction: auto-generating videos, making content plans, generating social media posts, summarizing articles, etc. This project already has multiple use cases in these directions and is a good place to start. - Team/Internal Automation
If your company itself needs some agency-based services (such as automated copywriting, automatic report generation, scheduling system, content management automation, etc.), you can learn from these processes and adapt them to your company’s internal systems.
However, it should also be noted that these templates are educational/example in nature, and if they are used in production-level systems, they also need to do a good job of error handling, model stability, permission control, security review, etc.
Github：https://github.com/gyoridavid/ai_agents_az
Tubing: