1. Conduid
  2. Developer Tools
  3. Langx101
MCP server · Developer Tools

Langx101

by · github.com/gyliu513/langX101

langX101

Unclaimed Apache-2.0 devtools
InstallView source
64Good

Scored 5 days ago · breakdown

About Langx101

Langx101 is an MCP server published by gyliu513 in the Developer Tools category: langX101.

The repository has 16 stars and 13 forks, with the last commit a month ago. Recent commit activity is the strongest signal that reported issues will get attention.

Install

From source
git clone https://github.com/gyliu513/langX101

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

Ask AI

✦

Ask AI about Langx101

Powered by Claude · Grounded in docs

I know everything about Langx101. Ask me about installation, configuration, usage, or troubleshooting.

Security checks

Static checks derived from the repository 4 days ago. Heuristics on the source, not an audit.

  • ✓README presentThe repository ships documentation.
  • ✓License declaredUsable under a stated license.
  • ✓Tests presentA test suite exists in the repository.
  • ✓Dependencies pinnedA lockfile pins dependency versions.
  • !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.

1 check has not been run yet.

README

Ask DeepWiki GitHub stars License

langX101

A comprehensive repository for testing and exploring GenAI Observability, AI Agent Frameworks, Model Context Protocol (MCP), and Agent-to-Agent (A2A) Protocol 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 & 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

🔧 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

🦜 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

📊 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

☁️ 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

🗄️ 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

🛡️ Security & Evaluation

Directory Description
llmguard/ LLM security and guardrails
eval/ Model evaluation frameworks

🌐 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

🔧 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

🚀 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

  1. Clone the repository:
git clone https://github.com/gyliu513/langX101.git
cd langX101
  1. 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
  1. Configure environment variables:
cp dot-env.txt .env
# Edit .env with your API keys

📚 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

🔗 Related Resources

👤 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

🤝 Contributing

Contributions are welcome! Feel free to:

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

📄 License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

🏷️ Tags

genai observability mcp a2a langchain langgraph openai agents rag monitoring opentelemetry ai-agents multi-agent traceloop

README fetched from the repository 4 days ago. The original is authoritative.

Questions

About Langx101

How do I install Langx101?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is Langx101 safe to use with an AI agent?

Its trust score is 64 out of 100 (good). It passes 4 of the 5 static security checks run so far; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Langx101 still maintained?

Yes — the last commit was a month ago. The repository has 16 stars and 13 forks and 6 open issues.