A GitHub repository by Muhannad Khaled presents all 21 agentic AI design patterns from Antonio Gulli's book, explained in Egyptian Arabic with runnable Python examples. Each pattern uses only the OpenAI SDK, avoiding frameworks like LangChain or CrewAI. The repo addresses a stated lack of serious Arabic technical content on agentic AI.
A curated repository of 48 functional AI agent implementations covering common patterns like research, code review, SQL, data analysis, and web scraping. Each example is designed to be cloned and run immediately, solving the problem of broken or incomplete agent tutorials for developers building AI systems.
A curated repository of 48 functional AI agent implementations covering common patterns like research, code review, SQL, data analysis, and web scraping. Each example is designed to be cloned and run immediately, solving the problem of broken or incomplete agent tutorials for developers building AI systems.
AI-Agent-Skills is an open-source, structured knowledge base that compiles notes on building AI agents into 20 categories, covering reasoning strategies, tool use, RAG, MCP, multi-agent patterns, safety, and deployment. It serves as a comprehensive handbook for developers, combining the organization of Awesome Lists with the depth of Papers with Code, aiming to replace scattered blog posts and research rabbit holes.
AI-Agent-Skills is an open-source, structured knowledge base that compiles notes on building AI agents into 20 categories, covering reasoning strategies, tool use, RAG, MCP, multi-agent patterns, safety, and deployment. It serves as a comprehensive handbook for developers, combining the organization of Awesome Lists with the depth of Papers with Code, aiming to replace scattered blog posts and research rabbit holes.
A developer with AI/ML and Python background asks the community for a structured learning path into AI agents, wanting to build agents from scratch rather than only learning frameworks like LangChain or LangGraph. The question reflects practitioner demand for foundational understanding of agent internals over tool-specific tutorials.
A developer with AI/ML and Python background asks the community for a structured learning path into AI agents, wanting to build agents from scratch rather than only learning frameworks like LangChain or LangGraph. The question reflects practitioner demand for foundational understanding of agent internals over tool-specific tutorials.
A GitHub repository that provides practical, runnable examples and demos for experimenting with various AI agent frameworks. It helps developers quickly set up and compare different agent frameworks in their latest versions, making Agentic AI less abstract and more accessible.
A GitHub repository that provides practical, runnable examples and demos for experimenting with various AI agent frameworks. It helps developers quickly set up and compare different agent frameworks in their latest versions, making Agentic AI less abstract and more accessible.
A developer open-sourced callm, a Python library that decorates existing OpenAI, Anthropic, and Google GenAI SDK call sites with retries, caching, fallbacks, cost budgets, and guardrails. Unlike LiteLLM or a gateway, it keeps native SDK calls intact and runs safeguards in-process. It is early-stage (v0.1.0) and the author is soliciting production feedback.
A developer open-sourced callm, a Python library that decorates existing OpenAI, Anthropic, and Google GenAI SDK call sites with retries, caching, fallbacks, cost budgets, and guardrails. Unlike LiteLLM or a gateway, it keeps native SDK calls intact and runs safeguards in-process. It is early-stage (v0.1.0) and the author is soliciting production feedback.