AI-Agents-Projects-Tutorials
A collection of tutorial notebooks from Marktechpost that walk through building agentic AI systems, covering multi-agent orchestration, memory, tool calling, benchmarking, and fine-tuning.
AI-Agents-Projects-Tutorials is a collection of tutorial notebooks and accompanying articles on building agentic AI systems. Each entry is a runnable, hands-on implementation rather than a purely conceptual explainer, covering topics such as multi-agent coordination, memory, planning, and reasoning loops.
Individual tutorials build a policy-governed multi-agent financial research workflow, skill-driven financial analysis agents using MCP connectors, a data-science agent that runs sandboxed code on modest GPU hardware, browser-use agents, and agent benchmarking and leaderboard analysis, among many others, most implemented in notebooks that pair explanatory text with working code.
It is distributed as Jupyter notebooks.