agno

IBM i MCP Agents: Agno

AI agents for IBM i system administration and monitoring built with Agno's AgentOS framework and Model Context Protocol (MCP) tools. This project provides intelligent agents that can analyze IBM i system performance, manage resources, and assist with administrative tasks.

What is this project?

The IBM i MCP Agents project provides Python-based intelligent agents that leverage MCP tools to perform system administration tasks on IBM i systems.

Key Features

• Multiple Specialized Agents: Six purpose-built agents for different IBM i tasks
• Multi-Model Support: Works with OpenAI, Anthropic Claude, IBM WatsonX, and local Ollama models
• MCP Integration: Connects to the IBM i MCP Server for system operations
• Persistent Memory: Agents maintain context across sessions using SQLite
• Interactive CLI: Simple command-line interface for agent interaction

Available Agents

1. Performance Agent - Monitor and analyze system performance metrics (CPU, memory, I/O)
2. Discovery Agent - High-level system discovery, inventory, and service summaries
3. Browse Agent - Detailed exploration of system services by category or schema
4. Search Agent - Find specific services, programs, or system resources
5. Web Agent - General web search using DuckDuckGo (no MCP required)
6. Agno Assist - Learn about the Agno framework and agent development

Requirements

• Python 3.13+ - The project requires Python 3.13 or newer
• uv - Python package manager for installing dependencies and managing virtual environments (Install uv)
• IBM i MCP Server - Must be installed and running on your system
• API Keys - For your chosen LLM provider (OpenAI, Anthropic, WatsonX, or Ollama)

Setup Guide

Follow these step-by-step instructions to set up and run the IBM i Agno MCP Agents.

Step 1: Install Prerequisites

1.1 Install Python 3.13+

# Check your Python version
python --version  # or python3 --version

# If you need to install Python 3.13+, visit:
# https://www.python.org/downloads/

1.2 Install uv (Python package manager)

# On macOS and Linux:
curl -LsSf https://astral.sh/uv/install.sh | sh

# On Windows (PowerShell):
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# Alternative: Install via pip
pip install uv

Step 2: Set Up the IBM i MCP Server

Ensure you have the IBM i MCP Server installed and running.

Note

Follow the MCP Server installation guide → Quickstart Guide

Configure the server → Server Configuration Guide

2.1 Install dependencies and build the server:

cd ibmi-mcp-server
npm install
npm run build

2.2 Start the MCP server:

npx ibmi-mcp-server --transport http --tools ./tools

The server will start on http://127.0.0.1:3010/mcp by default.

Step 3: Configure Environment Variables

Create a .env file in the agents/frameworks/agno directory with your API keys:

cd agents/frameworks/agno
touch .env

3.1 Add API keys for your chosen provider(s):

# OpenAI (for GPT-4, GPT-4o models)
OPENAI_API_KEY=sk-your-openai-api-key

# Anthropic (for Claude models)
ANTHROPIC_API_KEY=sk-ant-your-anthropic-api-key

# Ollama (local models - no API key needed)
# Ensure Ollama is installed and running: https://ollama.ai
# Start with: ollama serve

Note: You only need API keys for the providers you plan to use.

Step 4: Run an Agent

4.1 List available agents:

cd agents/frameworks/agno
uv run ibmi_agentos.py --list

4.2 Run an agent with your chosen model:

# OpenAI GPT-4o
uv run ibmi_agentos.py --agent performance --model openai:gpt-4o

# Anthropic Claude Sonnet
uv run ibmi_agentos.py --agent discovery --model anthropic:claude-sonnet-4-5

# Local Ollama model
uv run ibmi_agentos.py --agent search --model ollama:gpt-oss:20b

4.3 Interact with the agent:

• Type your questions or requests at the prompt
• The agent will use IBM i MCP tools to fulfill your requests
• Type exit or quit to end the session

Usage Examples

Performance Monitoring

uv run ibmi_agentos.py --agent performance --model openai:gpt-4o

Example questions:

• "What is the current CPU utilization?"
• "Show me memory usage trends"
• "Are there any performance bottlenecks?"

System Discovery

uv run ibmi_agentos.py --agent discovery --model openai:gpt-4o

Example questions:

• "Give me an overview of the system services"
• "What databases are available?"
• "List all active subsystems"

Detailed Browsing

uv run ibmi_agentos.py --agent browse --model openai:gpt-4o

Example questions:

• "Show me details about the QSYS library"
• "Explore the database schemas"
• "What's in the QTEMP library?"

System Search

uv run ibmi_agentos.py --agent search --model openai:gpt-4o

Example questions:

• "Find all programs named CUST*"
• "Search for services containing 'SQL'"
• "Locate file CUSTOMER in any library"

Advanced Options

Debug Mode

Enable debug output to troubleshoot issues:

uv run ibmi_agentos.py --agent performance --model openai:gpt-4o --debug

Custom MCP Server URL

If your MCP server runs on a different host or port:

uv run ibmi_agentos.py --agent performance --model openai:gpt-4o --mcp-url http://localhost:8080/mcp

Architecture Overview

How It Works

1. Agent Selection: You choose an agent specialized for a specific task (performance, discovery, etc.)
2. MCP Connection: The agent connects to the IBM i MCP Server via HTTP
3. Tool Filtering: Each agent only has access to relevant tools (e.g., performance agent gets performance tools)
4. Model Execution: Your chosen LLM model processes requests and generates tool calls
5. Persistent Memory: Agent sessions and memory are stored in SQLite (tmp/ibmi_agents.db)

Supported Models

Provider	Model Examples	Usage
OpenAI	gpt-4o, gpt-4o-mini, gpt-4-turbo	openai:gpt-4o
Anthropic	claude-sonnet-4-5, claude-opus-4	anthropic:claude-sonnet-4-5
WatsonX	llama-3-3-70b, granite-3-3-8b	watsonx:llama-3-3-70b-instruct
Ollama	llama3.2, gpt-oss, mistral	ollama:llama3.2