Langx101
langX101
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langX101
A comprehensive repository for testing and exploring GenAI Observability, AI Agent Frameworks, Model Context Protocol (MCP), and Agent-to-Agent (A2A) Protocol implementations.
π― 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.
π Repository Structure
π€ AI Agents & Multi-Agent Systems
| Directory | Description |
|---|---|
a2a/ | Agent-to-Agent (A2A) protocol implementations |
a2a_langgraph_mcp/ | LangGraph with MCP integration for multi-agent systems |
adk/ | Agent Development Kit with MCP server implementations |
swarm/ | OpenAI Swarm agent implementations |
oai-agent/ | OpenAI agent examples with function calling |
crew/ | CrewAI multi-agent orchestration examples |
langgraph/ | LangGraph stateful agent workflows |
agent-semantic-convention/ | OpenTelemetry semantic conventions for AI agents |
π§ Model Context Protocol (MCP)
| Directory | Description |
|---|---|
mcp/ | Core MCP implementations (weather, email, tutorial servers) |
mcp-client/ | MCP client implementations |
mcp-go/ | Go-based MCP server and client examples |
oai-mcp/ | OpenAI integration with MCP (filesystem, SSE) |
langchain-mcp/ | LangChain integration with MCP servers |
fastmcp/ | FastMCP server implementations |
π¦ LangChain Ecosystem
| Directory | Description |
|---|---|
langchain/ | Comprehensive LangChain examples (callbacks, RAG, AutoGPT) |
langchainjs/ | LangChain JavaScript/TypeScript examples |
langgraph/ | LangGraph stateful workflows and agents |
langserve/ | LangServe server implementations |
langflow/ | LangFlow visual workflows and custom components |
langsmith/ | LangSmith evaluation and monitoring |
langfuse/ | LangFuse observability integration |
π Observability & Monitoring
| Directory | Description |
|---|---|
otel/ | OpenTelemetry instrumentation for AI frameworks |
traceloop/ | Traceloop SDK integration examples |
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 |
OpenTelemetry (otel/) Includes:
- OpenAI auto-instrumentation
- LangChain instrumentation
- ChromaDB instrumentation
- WatsonX instrumentation
- Custom span and trace examples
βοΈ Cloud Providers & Models
| Directory | Description |
|---|---|
aws/ | AWS Bedrock examples and model implementations |
watsonx/ | IBM WatsonX examples with RAG |
openai/ | OpenAI API examples and assistants |
deepseek/ | DeepSeek model implementations |
litellm/ | LiteLLM proxy examples |
llamastack/ | Meta Llama Stack implementations |
ποΈ Vector Databases & Memory
| Directory | Description |
|---|---|
milvus/ | Milvus vector database examples |
embedchain/ | Embedchain implementations |
mem0/ | Mem0 memory layer for AI agents |
postgres/ | PostgreSQL with pgvector examples |
π‘οΈ Security & Evaluation
| Directory | Description |
|---|---|
llmguard/ | LLM security and guardrails |
eval/ | Model evaluation frameworks |
π Web & API Development
| Directory | Description |
|---|---|
graphql_instana/ | GraphQL with Instana monitoring |
graphql-example/ | GraphQL examples |
my_flask_graphql_app/ | Flask GraphQL application |
streamlit-test/ | Streamlit application examples |
drizzle-demo/ | Drizzle ORM demo |
π§ Utilities & Tools
| Directory | Description |
|---|---|
python/ | Python utilities and decorators |
mylib/ | Custom library implementations |
autowrapt/ | Auto-wrapping utilities |
oauth/ | OAuth authentication examples |
cline/ | Cline AI coding assistant examples |
scira/ | Scira search integration |
π Getting Started
Prerequisites
- Python 3.8+
- Node.js (for TypeScript/JavaScript examples)
- Go (for Go examples)
- Docker (for some services)
- uv (recommended for Python dependency management)
Installation
- Clone the repository:
git clone https://github.com/gyliu513/langX101.git
cd langX101
- Set up Python environment (using uv):
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
uv sync
Or using pip:
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt # if available in specific directory
- Configure environment variables:
cp dot-env.txt .env
# Edit .env with your API keys
π 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
- Multi-tool scenarios
Agent-to-Agent (A2A) Communication
Explore the /a2a/ and /a2a_langgraph_mcp/ directories for:
- Agent orchestration patterns
- Multi-agent coordination
- MCP + A2A integration
OpenTelemetry GenAI Observability
The /otel/ directory contains comprehensive examples for:
- Auto-instrumentation of LLM calls
- Custom span attributes for AI operations
- Integration with various observability backends
RAG Implementations
Find various RAG patterns:
- WatsonX RAG in
/watsonx/ - LangChain RAG examples in
/langchain/ - Llama Stack RAG in
/llamastack/ - Vector database integrations
π Related Resources
- Model Context Protocol (MCP) Documentation
- OpenTelemetry GenAI Semantic Conventions
- LangChain Documentation
- LangGraph Documentation
- Traceloop OpenLLMetry
π€ Author
Guangya Liu - Senior Technical Staff Member at IBM
- OpenTelemetry GenAI Semantic Convention Maintainer
- Apache Mesos PMC Member & Committer
- IBM Master Inventor with 30+ patents
- LinkedIn | GitHub | Medium
π€ Contributing
Contributions are welcome! Feel free to:
- Add new tool integrations
- Improve existing examples
- Share interesting use cases
- Report issues or suggestions
π License
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.
π·οΈ Tags
genai observability mcp a2a langchain langgraph openai agents rag monitoring opentelemetry ai-agents multi-agent traceloop
