langX101

A comprehensive repository for testing and exploring GenAI Observability Tools, AI Frameworks, and Model Context Protocol (MCP) implementations.

🎯 Overview

This repository serves as a hands-on laboratory for experimenting with various generative AI tools, observability platforms, and integration patterns. It contains practical examples, tutorials, and implementations across multiple AI frameworks and monitoring solutions.

📁 Repository Structure

🤖 AI Agents & Workflows

• a2a/ - Agent-to-agent communication examples
• a2a_langgraph_mcp/ - LangGraph with MCP integration for multi-agent systems
• adk/ - Agent Development Kit with MCP server implementations
• swarm/ - AI agent swarm implementations
• oai-agent/ - OpenAI agent examples with function calling

🔧 Model Context Protocol (MCP)

• mcp/ - Core MCP implementations including weather, email, and tutorial servers
• mcp-client/ - MCP client implementations
• mcp-go/ - Go-based MCP server and client examples
• oai-mcp/ - OpenAI integration with MCP (filesystem and SSE examples)
• langchain-mcp/ - LangChain integration with MCP servers
• fastmcp/ - Fast MCP server implementations

🦜 LangChain Ecosystem

• langchain/ - Comprehensive LangChain examples including callbacks, RAG, and AutoGPT
• langserve/ - LangServe server implementations
• langflow/ - LangFlow workflows and custom components
• langsmith/ - LangSmith evaluation and monitoring examples
• langfuse/ - LangFuse observability integration

📊 Observability & Monitoring

• otel/ - OpenTelemetry instrumentation for various AI frameworks
  • OpenAI instrumentation
  • LangChain instrumentation
  • ChromaDB instrumentation
  • WatsonX instrumentation
• arize/ - Arize AI monitoring integration
• helicone/ - Helicone observability examples
• langtrace/ - LangTrace monitoring implementation
• llmonitor/ - LLM monitoring examples
• newrelic/ - New Relic AI monitoring
• promptlayer/ - PromptLayer integration

☁️ Cloud Providers & Models

• aws/ - AWS Bedrock examples and model implementations
• watsonx/ - IBM WatsonX examples with RAG implementations
• openai/ - OpenAI API examples and assistants
• deepseek/ - DeepSeek model implementations
• litellm/ - LiteLLM proxy examples

🛡️ Security & Evaluation

• llmguard/ - LLM security and guardrails
• eval/ - Model evaluation frameworks and examples

🗄️ Vector Databases & Storage

• milvus/ - Milvus vector database examples
• embedchain/ - Embedchain implementations

🌐 Web & API Development

• graphql_instana/ - GraphQL with Instana monitoring
• my_flask_graphql_app/ - Flask GraphQL application
• streamlit-test/ - Streamlit application examples

🔄 AI Frameworks & Tools

• crew/ - CrewAI multi-agent examples
• haystack/ - Haystack RAG implementations
• react/ - ReAct pattern implementations
• python/ - Python utilities and decorators

🚀 Getting Started

Prerequisites

• Python 3.8+
• Node.js (for TypeScript/JavaScript examples)
• Go (for Go examples)
• Docker (for some services)

Installation

1. Clone the repository:

git clone https://github.com/gyliu513/langX101.git
cd langX101

2. Set up Python environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies for specific examples as needed (each directory may have its own requirements).

📚 Key Examples

MCP Server Setup

Check out the MCP examples in /mcp/ for setting up Model Context Protocol servers:

• Weather server implementation
• Email sending capabilities
• Tutorial and learning examples

Observability Integration

Explore comprehensive observability setups:

• OpenTelemetry auto-instrumentation in /otel/openai-auto/
• LangFuse integration in /langfuse/
• Multi-tool monitoring comparisons

Agent Workflows

See advanced agent implementations:

• Multi-agent systems in /a2a_langgraph_mcp/
• Agent orchestration patterns
• Function calling and tool usage

RAG Implementations

Find various RAG patterns:

• WatsonX RAG in /watsonx/
• LangChain RAG examples
• Vector database integrations

🔗 Related Resources

• Model Context Protocol Documentation
• LangChain Documentation
• OpenTelemetry Python

🤝 Contributing

This repository is primarily for testing and experimentation. Feel free to:

• Add new tool integrations
• Improve existing examples
• Share interesting use cases
• Report issues or suggestions

📄 License

See LICENSE file for details.

🏷️ Tags

genai observability mcp langchain openai agents rag monitoring opentelemetry ai-tools