agentic_design_patterns
Agentic Design Patterns for LLM Workflows — A curated collection of reusable, modular patterns for building robust and autonomous LLM-powered agents, covering multi-agent orchestration, memory, prompt flows, and tool integrations
Welcome to the Agentic Design Patterns project! This repository demonstrates how to build, use, and test different types of LLM-powered agents using Python. It is designed for beginners and educators interested in agentic workflows, tool use, and collaborative AI systems.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/bassem-elsodany-agenticdesignpatterns — read its card at https://meshkore.com/agent/bassem-elsodany-agenticdesignpatterns/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/bassem-elsodany-agenticdesignpatternsFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/bassem-elsodany-agenticdesignpatterns/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own agentic_design_patterns?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.