AIOpStack

Build your AIOps MCP Agent within 5 minutes.

AIOpStack is a collection of AI Operational Agents built with Langchain/LangGraph and Streamlit‑based GUI for operational interaction and visualization.

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🎯 Motivation

1. Fast‑boot – AIOpStack drastically reduces study and setup time for DevOps, Sysadmins and Developers.
2. Lightweight – IDEs like Cursor and VSCode with Cline are resource‑heavy. AIOpStack stays minimal and nimble.
3. Local Deployment – Deploy locally to access private environments.
4. Free & Open – Fully open‑source, no vendor lock‑in or licensing fees.

🚀 Features

• OpenAI‑compatible LLM API Integration – Connect to any OpenAI‑compatible LLM endpoint.
• MCP Integration – Seamless bridge between LLMs and popular MCP tools (e.g., Kubernetes, Ansible).
• Human‑in‑the‑loop Feedback – Pause for confirmation or iterative refinement at key steps.
• Pure Python & GUI‑free – Fully Python‑powered: no frontend skills required for reuse or extension.

Video Demo

Agents Repository

📖 Quick Start

Requirements

• Python 3.8+
• OpenAI‑compatible LLM API URL & Key

Installation

pip install aiopstack

Usage Examples

run aiops with no parameter

aiops

the project can be listening at localhost:8501 by default.

⚙️ MCP Settings

Note:

The settings must be written in JSON format.

• For stdio mode:
  • The args parameter must use an absolute path.
  • Windows uses \\ for paths, Linux uses /.
  • Ensure all required Python dependencies are pre-installed.
• For sse and streamable_http modes:
  • It is recommended to use a fully qualified domain name (FQDN) or a valid IP address to avoid connectivity issues.

MCP Configuration Example

The following configurations correspond to test examples located in the test_mcp_servers/ directory.

{
  "math": {
    "command": "python",
    "args": ["C:\\Users\\yfxue\\PycharmProjects\\aiopstack\\test_mcp_server\\math_server.py"],
    "transport": "stdio"
  },
  "weather": {
    "url": "http://192.168.2.103:8000/sse/",
    "transport": "sse"
  }
}

Strongly recommended: follow up the official document langchain-mcp-adapters

🤝 Contributing

We welcome contributions! If you have suggestions or feature requests, feel free to open an issue or submit a pull request.

Development Setup

1. Clone the repository:

git clone https://github.com/ohtaman/streamlit-desktop-app.git

2. Install dependencies:

cd src/aiopstack
pip install -r requirements.txt

3. Run Streamlit

streamlit run app.py