vishipayyallore/practical-ai-agents

# Practical AI Agents      A hands-on repository for learning, building, and experimenting with AI Agents, Agentic AI, MCP, RAG, orchestration, multi-agent systems, and production-grade AI architectures. This repository focuses on: * practical implementations, * architecture patterns, * orchestration workflows, * observability, * evaluation, * security, * scalable AI engineering practices. ## Vision The goal of this repository is to evolve from: * foundational AI agent experiments to: * production-grade AI systems engineered with software architecture principles. This repository is designed as: * a learning workspace, * an experimentation sandbox, * a portfolio, * an AI architecture reference repository. ## Repository Structure practical-ai-agents/ │ ├── README.md ├── LICENSE ├── .gitignore │ ├── docs/ │ ├── adr/ │ ├── diagrams/ │ ├── learnings/ │ └── references/ │ ├── src/ │ ├── fundamentals/ │ │ ├── prompts/ │ │ ├── embeddings/ │ │ ├── rag/ │ │ ├── vector-stores/ │ │ └── tools/ │ │ │ ├── agents/ │ │ ├── single-agent/ │ │ ├── multi-agent/ │ │ ├── planner-executor/ │ │ ├── reflection/ │ │ ├── react-pattern/ │ │ ├── tool-calling/ │ │ └── memory/ │ │ │ ├── frameworks/ │ │ ├── langchain/ │ │ ├── langgraph/ │ │ ├── semantic-kernel/ │ │ ├── autogen/ │ │ ├── crewai/ │ │ └── openai-agents-sdk/ │ │ │ ├── protocols/ │ │ ├── mcp/ │ │ ├── a2a/ │ │ └── function-calling/ │ │ │ ├── orchestration/ │ │ ├── workflows/ │ │ ├── state-machines/ │ │ ├── event-driven/ │ │ └── durable-execution/ │ │ │ ├── observability/ │ │ ├── tracing/ │ │ ├── evaluations/ │ │ ├── telemetry/ │ │ └── prompt-testing/ │ │ │ ├── security/ │ │ ├── prompt-injection/ │ │ ├── guardrails/ │ │ ├── sandboxing/ │ │ └── secrets-management/ │ │ │ ├── architecture/ │ │ ├── patterns/ │ │ ├── anti-patterns/ │ │ ├── scalability/ │ │ └── distributed-agents/ │ │ │ └── projects/ │ ├── customer-support-agent/ │ ├── code-review-agent/ │ ├── research-agent/ │ ├── data-analysis-agent/ │ └── autonomous-workflows/ │ ├── notebooks/ ├── assets/ ├── scripts/ ├── tests/ └── references/ ## Core Focus Areas ### AI Foundations * Prompt Engineering * Embeddings * Vector Databases * Retrieval Augmented Generation (RAG) * Function Calling * Tool Usage ### Agentic AI * ReAct Pattern * Planner-Executor Architecture * Reflection & Self-Correction * Autonomous Agents * Memory Systems * Tool-Orchestrated Agents ### Multi-Agent Systems * Agent Coordination * Task Delegation * Routing & Scheduling * Shared Memory * Negotiation Patterns ### Protocols & Standards * Model Context Protocol (MCP) * Agent-to-Agent Communication (A2A) * OpenAI Function Calling * Tool Interoperability ### AI System Architecture * Event-Driven Architectures * Durable Execution * Workflow Orchestration * Distributed Agents * Scalability Patterns * Fault Tolerance ### Production AI Engineering * Observability & Tracing * Evaluations & Benchmarks * Prompt Testing * Governance * Security & Guardrails * Cost Optimization ## Technologies & Frameworks ### Languages * Python * C# ### AI Frameworks * LangChain * LangGraph * Semantic Kernel * AutoGen * CrewAI * OpenAI Agents SDK ### Vector Databases * ChromaDB * Pinecone * FAISS * Weaviate ### Infrastructure * Docker * Redis * PostgreSQL * Kafka ## Learning Roadmap ### Phase 1 — Foundations * Prompts * Embeddings * RAG * Tool Calling * Memory ### Phase 2 — AI Agents * ReAct * Reflection * Planning * Tool Orchestration * Autonomous Loops ### Phase 3 — Multi-Agent Systems * Delegation * Coordination * Routing * Shared Context * Negotiation ### Phase 4 — Production Systems * Observability * Evaluations * Security * Governance * Reliability ### Phase 5 — Advanced Architectures * Distributed Agents * MCP Ecosystems * Durable Execution * Event-Driven AI Systems * Human-in-the-Loop Architectures ## Repository Principles This repository prioritizes: * understanding concepts deeply over framework hopping, * architecture thinking over toy demos, * production readiness over shortcuts, * engineering discipline over copy-paste tutorials. Every module should ideally contain: * implementation, * README, * architecture notes, * limitations, * future improvements, * learnings. ## Architecture Decision Records (ADR) Architecture decisions are documented under: docs/adr/ Examples: * Why LangGraph? * Why MCP? * Why Python for orchestration? * Why Redis for memory? * Why event-driven workflows? ## Diagrams Architecture and workflow diagrams are stored under: docs/diagrams/ Tools: * Mermaid * Draw.io * Excalidraw ## References Learning materials, books, papers, videos, and external references are organized under: references/ ## Long-Term Goal Build AI Agents engineered like production systems. Not: * isolated tutorials, * fragile demos, * framework-only examples. But: * scalable, * observable, * maintainable, * architecture-driven AI systems. ## License This repository is licensed under the MIT License.