A Spring AI library that brings Claude Code-inspired tools and agent skills to your AI applications.

🐔 Agentic systems explained with chickens. Workflows, agents & orchestration made simple. Mermaid diagrams included

Hands-on tutorials for building AI agents from scratch. Learn LLM APIs, prompt engineering, tool calling, and the agent loop through practical examples.

Build AI engineering skills from scratch with 260+ lessons, 20 phases, and hands-on tools for practical learning

A simple spring boot app demonstrating how to implement Evaluator Optimizer Workflow pattern using Spring AI

Reference implementation of Spring AI's Agentic Patterns — a hackable Java/Spring Boot AI agent that runs as a Telegram-native personal assistant with compounding file-based memory, or a one-shot task runner. Same JAR, both modes.

The CPTO's playbook for agentic AI: patterns, anti-patterns, decision trees, and procurement-grade checklists for shipping reliable agents in production. Vendor-neutral, with 5 runnable examples and an explicit EU AI Act lens.

You lead. Agents hunt. Cognitive + methodological harness for AI-assisted software — 139 skills across 7 companion packs + composition layer. Cross-tool (Claude Code, Cursor, Codex, Gemini CLI).

ReflectiveAgent

Tech stack: Python, A2A, ADK, MCP, CrewAI, FastAPI, Ollama, Pydantic, Uvicorn, etc.

Building Effective Agents explains the distinction between agents and workflows and introduces common patterns for both. In this project, I implement every workflow and agent pattern described in the LangChain and Anthropic blog from scratch using LangGraph with clean structure, clarity, and reproducible code

Generalizable Orchestrator → Planner → Executor → Reviewer multi-agent pattern using LangGraph. Plug-and-play tool nodes with YAML-configurable agent personas — designed as a reusable blueprint for enterprise ISV agentic AI deployments.

An implementation of a tool-using AI assistant for precise calculations. This project, part of a technical assessment, equips an LLM with a calculator to ensure mathematical accuracy.

Implementation of a tool-using agent in Python with LangChain. The agent is designed to dynamically select and use tools, such as API wrappers, to answer complex questions that require external data.

Implementation of a planning agent using LangGraph's state machines. The agent dynamically creates task lists and executes them sequentially, demonstrating a robust pattern for AI task decomposition.

Claude Code plugin marketplace with reusable agentic skill patterns for reasoning, decision-making, and AI workflows.

Blueprint: Multi-Agent Orchestration Patterns | Published on omnithium.ai

Workflow vs Agent architecture in LangGraph — 6 agentic patterns in one demo (Tool Use, Routing, Parallelization, Voting, Human-in-the-Loop, Orchestrator)

KVKK/GDPR-aware enterprise AI orchestration reference architecture — multi-domain, multi-channel, federated data; conductor pattern.

An implementation of the self-correction and reflection pattern using LangGraph. The agent generates a response, critiques it for errors or omissions, and then re-generates a refined answer.