⚠️ Warning! This project is in its very early stages of development. Expect frequent changes and potential breaking updates as we iterate on features and architecture.

AI Assistant for Rancher

Architecture

The system is composed of a UI Extension for user interaction, a ReAct Agent for intelligent decision-making, and an MCP Server that acts as the intermediary between the agent and the Rancher API.

ReAct Agent

The ReAct Agent is the orchestrator of the system’s intelligence. It leverages a Large Language Model (LLM) as its reasoning engine, while the agent itself provides the structure, coordination, and secure execution of actions.

🧠 LLM (Reasoning Engine)

• Interprets user inputs expressed in natural language.
• Performs the reasoning: breaks down complex requests into smaller steps.
• Decides what should happen next (keep reasoning vs. take action).
• Synthesizes outputs from tools into clear, human-readable responses.

⚙️ Agent (Orchestrator)

• Wraps the LLM with the ReAct (Reason + Act)
• Decides when to let the LLM keep reasoning or when to act on its instructions.
• Calls external tools (via the MCP Server) as directed by the LLM.
• Ensures secure interactions by passing the user’s Rancher token to the MCP server for authentication and authorization.

MCP Server

The MCP Server acts as a secure, controlled gateway between the ReAct Agent and the Rancher and Kubernetes APIs. Its functions include:

• Exposing Tools: It provides a set of well-defined, safe tools (API endpoints) that the ReAct Agent can call. These tools abstract away the complexity of direct Rancher/Kubernetes API interactions.
• Interaction with Rancher: It translates tool calls from the agent into the appropriate API requests to the Rancher management server, retrieving or modifying resources as needed.

UI Extension

The UI Extension provides the user-facing chat interface within the Rancher dashboard. It is designed to be a seamless part of the Rancher experience and is responsible for:

• User Input: It captures user queries and sends them to the ReAct Agent.
• Displaying Responses: It receives responses from the ReAct Agent and presents them in a chat-like format.

How It Works (Flow)

1. User Request → LLM
   The user submits a natural language query through the UI Extension.
2. LLM Reasoning
   The LLM interprets the request, reasons about the problem, and proposes a plan.
3. Agent Acting
   If the plan requires external operations, the agent calls the appropriate MCP Server tools.
4. LLM Response Formulation
   The LLM takes the tool outputs and crafts a coherent, human-readable response.
5. Response → User
   The answer is returned to the user through the UI Extension.

Installation

• No Helm chart package is currently available. You'll need to clone the repository and use the chart directory to install the application.
• Configure your LLM provider. You must set one of the following in values.yaml: ollamaUrl, googleApiKey, or openaiApiKey. The llmModel variable must also be specified.
• Run the Helm command: Execute helm install ai-agent chart --namespace cattle-ai-agent-system --create-namespace. The application must be installed in the cattle-ai-agent-system namespace.
• The temporary UI is available at https://yourRancherURl/api/v1/namespaces/cattle-ai-agent-system/services/http:rancher-ai-agent:80/proxy/agent. This will change once the UI extension is available.