Agent Template

Build production-ready AI agents using LangGraph with enterprise security, observability, and Kubernetes deployment configurations.

The AI Agent Template provides a production-ready foundation for building LangGraph-based AI agents that can orchestrate complex workflows, integrate with MCP servers, and deploy to Kubernetes.

Overview

This template gives you everything you need to build sophisticated AI agents:

LangGraph Workflows - State machines for complex agent behaviors
MCP Integration - Connect to MCP servers for extended capabilities
Enterprise Security - Authentication, authorization, and audit trails
Observability - Logging, metrics, and tracing built-in
Kubernetes-Ready - Production deployment configurations included

Key Features

Workflow Orchestration Build complex multi-step agent workflows with LangGraph’s state machine patterns. Handle branching logic, error recovery, and human-in-the-loop interactions.

MCP Client Integration Seamlessly connect to any MCP server to extend your agent’s capabilities. Use tools for database queries, API calls, file operations, and more.

Production Deployment Includes complete Kubernetes manifests, container configurations, and CI/CD pipeline templates for deploying to any Kubernetes cluster.

Developer Experience Modern Python tooling with UV package manager, comprehensive testing, pre-commit hooks, and extensive documentation.

Architecture

graph TD A[User Request] --> B[Agent Workflow] B --> C{Task Planning} C --> D[Tool Selection] D --> E[MCP Client] E --> F[MCP Server Tools] F --> G[External Systems] G --> E E --> H[Response Formatting] H --> I[User Response] style B fill:#e3f2fd,stroke:#2196f3,stroke-width:2px style E fill:#f3e5f5,stroke:#9c27b0,stroke-width:2px style F fill:#fff3e0,stroke:#ff9800,stroke-width:2px

Getting Started

Info Info

Repository: template-agent on GitHub

Clone the repository and follow the README for complete setup instructions.

Quick Start

# Clone the template
git clone https://github.com/redhat-data-and-ai/template-agent.git
cd template-agent

# Set up environment
uv venv --python 3.12
source .venv/bin/activate

# Install dependencies
uv pip install -e ".[dev]"

# Run the agent
python -m agent_template.src.main

Use Cases

Data Analysis Agents Build agents that can query databases, analyze data, and generate insights using MCP servers for data access.

DevOps Automation Create agents that automate deployment workflows, monitor systems, and respond to incidents.

Document Processing Build agents that can read, summarize, and extract information from documents using specialized MCP tools.

Multi-Step Workflows Orchestrate complex workflows that require multiple tools, decision points, and error handling.

Technology Stack

LangGraph - Workflow orchestration and state management
Python 3.12+ - Modern Python with type hints
MCP Client - Integration with Model Context Protocol servers
FastAPI - Optional REST API for agent interactions
Pydantic - Configuration and data validation
pytest - Comprehensive testing framework

Next Steps

Explore the repository - View on GitHub
Build an MCP server first - MCP Server Template
Join the community - GitHub Discussions

Tip Tip

Prerequisites: Before building agents, we recommend setting up at least one MCP server to provide tools and capabilities for your agent to use.

Documentation Status

This template is under active development. Complete documentation will be added as the template matures. For now, refer to the GitHub repository for the latest information.

Quick Start

Get your first AI agent running in minutes using LangGraph and MCP integration.

Deployment Guide

Deploy your AI agent to Kubernetes with enterprise security, monitoring, and scaling.