AI-Agents-Projects-Tutorials
Multi-agent systems, memory, planning, reasoning loops
AI-Agents-Projects-Tutorials is a comprehensive educational repository by MarkTechPost containing 100+ Jupyter notebooks, Python scripts, and step-by-step tutorials for implementing multi-agent systems, agentic workflows, and autonomous AI architectures. The repository covers practical implementations of memory systems, planning loops, reasoning frameworks, tool-calling patterns, and multi-agent coordination using frameworks like LangGraph, AutoGen, CrewAI, and various LLM APIs (OpenAI, Gemini, Claude, Mistral). Each tutorial pairs executable code with detailed written guides published on MarkTechPost, demonstrating production-ready patterns for financial analysis, research automation, data science, healthcare, and enterprise AI workflows.
Key Features
Use Cases
- 01Learning to build autonomous coding agents with non-interactive workflows and session memory
- 02Implementing policy-governed financial research agents with multi-agent collaboration
- 03Creating self-evolving agents with skill learning, lineage tracking, and low-cost reuse
- 04Designing enterprise AI governance systems with approval workflows and audit logging
- 05Building research assistants with agentic RAG, hybrid retrieval, and provenance-first citations
- 06Developing browser-use agents, web scraping pipelines, and computer-control automation
Related Agents
View morehermes-agent
The agent that grows with you
agency-agents
A complete AI agency at your fingertips - From frontend wizards to Reddit community ninjas, from whimsy injectors to reality checkers. Each agent is a specialized expert with personality, processes, and proven deliverables.
openinterpreter
A coding agent for open models like Kimi K3
cline
Autonomous coding agent as an SDK, IDE extension, or CLI assistant.
AI-Agents-Projects-Tutorials — FAQ
What is AI-Agents-Projects-Tutorials?+
It is an educational repository containing 100+ tutorials, notebooks, and code implementations demonstrating how to build multi-agent systems, agentic workflows, memory systems, and autonomous AI architectures using modern frameworks and LLM APIs.
How do I use these tutorials?+
Clone the repository, navigate to the specific tutorial folder, and open the Jupyter notebook (.ipynb) or Python script (.py). Each tutorial includes executable code and links to detailed written guides on MarkTechPost explaining the concepts and implementation steps.
Which AI frameworks and models are covered?+
The tutorials cover LangGraph, LangChain, AutoGen, CrewAI, CAMEL, PydanticAI, SmolAgents, and integration with OpenAI, Gemini, Claude, Mistral, Qwen, and Hugging Face models. Many examples work with both commercial and open-source models.
Do I need API keys?+
Most tutorials require API keys for OpenAI, Google Gemini, Anthropic Claude, or Mistral. Some tutorials demonstrate fully local implementations using Hugging Face models or Ollama that don't require external API keys.
Is this free to use?+
Yes, the repository and all code examples are freely available on GitHub. However, using commercial LLM APIs (OpenAI, Gemini, Claude) requires paid API access; local model examples are completely free.
Can I run these in Google Colab?+
Yes, many tutorials include Colab badges and are designed to run in Google Colab with GPU support (like T4) for resource-intensive agent workflows, making them accessible without local hardware requirements.
How do I install AI-Agents-Projects-Tutorials?+
Open the source repository on GitHub and follow its README. AI-Agents-Projects-Tutorials is a agent — MCP Agents Market links you directly to the official repo.
Is AI-Agents-Projects-Tutorials free?+
AI-Agents-Projects-Tutorials is an open-source project hosted on GitHub. Check the repository for its license and any usage requirements.