# MARKTECHPOST-AI-MEDIA-INC/AI-Agents-Projects-Tutorials

Multi-agent systems, memory, planning, reasoning loops

Repository: https://github.com/MARKTECHPOST-AI-MEDIA-INC/AI-Agents-Projects-Tutorials
Canonical: https://ross.abutalabs.com/products/ai-agents-projects-tutorials
Homepage: https://marktechpost.com/
Language: Jupyter Notebook
License Family: other
Topics: aiagents, agent-skills, agentic-ai, agentic-ai-development, agentic-coding, agentic-engineering, agentic-framework, agentic-rag, agentic-workflow, ai, aiagent
Last push: 2026-08-24T20:18:54+00:00

## Health v2 (maintenance only)
Score: 63/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 33
- inputs: {"age_days": 475, "days_push": 9, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2881, forks 619 (observed 2026-08-28T04:07:27.623312+00:00)

## What it is
A curated collection of Jupyter notebook tutorials and implementations covering agentic AI, multi-agent systems, memory, planning, and reasoning loops. Each notebook pairs runnable code with a companion tutorial article from Marktechpost.

## Use cases
- learn how to build multi-agent AI workflows
- tutorials on agentic coding with CLI agents
- build agents with MCP connectors and skills
- examples of agentic RAG and reasoning loops
- learn to build autonomous data science agents
- benchmark and evaluate AI agents

## When to choose
- you want hands-on, notebook-driven examples of agentic AI patterns
- you want to follow along with written tutorials while running code
- you're exploring multiple agent frameworks like Claude, Kimi CLI, and Omnigent

## When to avoid
- you need a production-ready library or framework to import
- you require a permissively licensed codebase - no license is specified
- you need stable, versioned APIs rather than tutorial code

## Facets
- artifact type: learning-resource
- maturity: active
- function: agent-framework, rag, mcp, llm-inference, machine-learning
- domain: artificial-intelligence, large-language-models, tutorials, developer-tools
- platform: python, cross-platform
- tags: jupyter-notebooks, agentic-ai, multi-agent-systems, hands-on-tutorials, no-license, ai-agents

## Member repositories
- MARKTECHPOST-AI-MEDIA-INC/AI-Agents-Projects-Tutorials (main) score 63

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:27.623312+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:35:27.951975+00:00, confidence not recorded.
  - readme: https://github.com/MARKTECHPOST-AI-MEDIA-INC/AI-Agents-Projects-Tutorials (fetched 2026-08-28T04:07:27.623312+00:00, sha 6272a9b94179)
  - homepage: https://marktechpost.com/ (fetched 2026-08-29T09:50:56.557912+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.