IBM i MCP Agents: LangChain

AI agents for IBM i system administration and monitoring built with LangChain/LangGraph framework and Model Context Protocol (MCP) tools. This project provides intelligent agents that can analyze IBM i system performance, manage security, and assist with administrative tasks.

What is this project?

The IBM i LangChain Agents project provides Python-based intelligent agents that leverage MCP tools to perform system administration tasks on IBM i systems. Built on LangGraph, these agents feature advanced capabilities like human-in-the-loop approval for security operations and persistent conversation memory.

Key Features

Five Specialized Agents: Purpose-built agents for different IBM i tasks
Multi-Model Support: Works with OpenAI, Anthropic Claude, and local Ollama models
MCP Integration: Connects to the IBM i MCP Server for system operations
Human-in-the-Loop: Security operations require manual approval for write operations
Persistent Memory: Agents maintain context across sessions using in-memory checkpointing
Rich Logging: Detailed tool call and response logging for debugging
Interactive & Batch Modes: CLI for interactive chat or automated testing

Available Agents

Performance Agent - Monitor and analyze system performance metrics (CPU, memory, I/O) - 12 tools
Discovery Agent - High-level system discovery, inventory, and service summaries - 5 tools
Browse Agent - Detailed exploration of system services by category or schema - 4 tools
Search Agent - Find specific services, programs, or system resources - 5 tools
Security Agent - Security vulnerability assessment and remediation - 25 tools with human-in-the-loop

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, or Ollama)

Setup Guide

Follow these step-by-step instructions to set up and run the IBM i LangChain 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:

npm run dev

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

2.3 Verify the server is running:

curl http://127.0.0.1:3010/mcp

Expected response:

{"status":"ok","server":{"name":"ibmi-mcp-server","version":"1.9.1",...}}

Step 3: Install Agent Dependencies

3.1 Install the ibmi-agent-sdk package:

cd agents/packages/ibmi-agent-sdk
uv pip install -e .

3.2 Install the LangChain agents package:

cd ../../frameworks/langchain
uv pip install -e .

Step 4: Configure Environment Variables

Create a .env file in the agents/frameworks/langchain directory:

cd agents/frameworks/langchain
cp .env.example .env

4.1 Edit the .env file with your configuration:

# Choose your default model (Ollama recommended for local development)
DEFAULT_MODEL=ollama:llama3.2

# Add API keys only for providers you plan to use:

# OpenAI (for GPT-4, GPT-4o models)
OPENAI_API_KEY=

# Anthropic (for Claude models)
ANTHROPIC_API_KEY=

# MCP Server Configuration (usually defaults are fine)
MCP_URL=http://127.0.0.1:3010/mcp
MCP_TRANSPORT=streamable_http

# Logging (set to false for minimal output)
VERBOSE_LOGGING=true

# Security (enable human-in-the-loop for write operations)
ENABLE_HUMAN_IN_LOOP=true

4.2 For Ollama (local models - no API key needed):

# Install Ollama from: https://ollama.ai
# Pull a model:
ollama pull llama3.2

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

Step 5: Verify Setup

Run a quick test to verify all agents can be created:

uv run src/ibmi_agents/agents/test_agents.py --quick

Expected output:

================================================================================
Quick Agent Creation Test
================================================================================
Model: ollama:llama3.2

Creating performance agent... ✅ IBM i Performance Monitor
Creating discovery agent... ✅ IBM i SysAdmin Discovery
Creating browse agent... ✅ IBM i SysAdmin Browser
Creating search agent... ✅ IBM i SysAdmin Search
Creating security agent... ✅ IBM i Security Operations
🔒 Human-in-the-loop enabled for 2 non-readonly tools:
   - repopulate_special_authority_detail
   - execute_impersonation_lockdown

────────────────────────────────────────────────────────────────────────────────
Result: 5/5 agents created successfully
================================================================================

Usage Examples

Quick Test (Verify Setup)

uv run src/ibmi_agents/agents/test_agents.py --quick

Test Specific Agent

# Test performance agent with sample query
uv run src/ibmi_agents/agents/test_agents.py --agent performance

# Test with quiet mode (minimal output)
uv run src/ibmi_agents/agents/test_agents.py --agent performance --quiet

Interactive Mode

# Chat interactively with an agent
uv run src/ibmi_agents/agents/test_agents.py --agent performance --interactive

Example session:

👤 You: What is my system status?
🤖 IBM i Performance Monitor: 

### Quick‑look snapshot

| Metric | Value | Why it matters |
|--------|-------|----------------|
| **Configured CPUs** | 3 | The number of logical CPUs the system is allowed to use. |
| **Average CPU utilization** | **0 %** | Indicates the system is essentially idle. |
| **Main storage size** | **133 MB** | Total RAM allocated to the system. |
| **System ASP used** | 56 % | How much of the ASP's storage is in use. |

> **Bottom line:** CPU is not a bottleneck right now, and memory usage is comfortably below capacity.

────────────────────────────────────────────────────────────────────────────────

👤 You: Show me memory pool information
🤖 IBM i Performance Monitor: [analyzes memory pools]

👤 You: quit
👋 Goodbye!

Test Security Agent with Category Filter

# Test only vulnerability assessment tools
uv run src/ibmi_agents/agents/test_agents.py --agent security --category vulnerability-assessment

# Available categories:
# - vulnerability-assessment: Identify security vulnerabilities
# - audit: Audit security configurations
# - remediation: Generate and execute security fixes
# - user-management: Manage user capabilities and permissions

Test with Different Models

# Use OpenAI GPT-4o
uv run src/ibmi_agents/agents/test_agents.py --agent performance --model openai:gpt-4o

# Use Anthropic Claude
uv run src/ibmi_agents/agents/test_agents.py --agent discovery --model anthropic:claude-3-7-sonnet-20250219

# Use local Ollama model
uv run src/ibmi_agents/agents/test_agents.py --agent search --model ollama:llama3.2

Test All Agents

# Run comprehensive test suite
uv run src/ibmi_agents/agents/test_agents.py

Agent Details

1. Performance Agent

Purpose: Monitor and analyze IBM i system performance

Tools Available: 12 performance monitoring tools

Example Questions:

"What is my system status?"
"Show me memory pool utilization"
"Which jobs are consuming the most CPU?"
"What is the current system activity level?"

Sample Output:

### Quick‑look snapshot

| Metric | Value | Why it matters |
|--------|-------|----------------|
| **Configured CPUs** | 3 | The number of logical CPUs the system is allowed to use. |
| **Current CPU capacity** | 3 | How many CPUs are actually available right now. |
| **Average CPU utilization** | **0 %** | Indicates the system is essentially idle. |
| **Main storage size** | **133 MB** | Total RAM allocated to the system. |
| **System ASP used** | 56 % | How much of the ASP's storage is in use. |

> **Bottom line:** CPU is not a bottleneck right now, and memory usage is comfortably below capacity.

2. Discovery Agent

Purpose: High-level system discovery and summarization

Tools Available: 5 discovery tools

Example Questions:

"Give me an overview of available system services"
"How many services are in each schema?"
"What types of SQL objects are available?"

3. Browse Agent

Purpose: Detailed system browsing and exploration

Tools Available: 4 browse tools

Example Questions:

"Show me services in the QSYS2 schema"
"List all views related to system monitoring"
"What procedures are available for job management?"

4. Search Agent

Purpose: Find specific services and usage information

Tools Available: 5 search tools

Example Questions:

"Search for services related to system status"
"Find examples of using ACTIVE_JOB_INFO"
"Where is the SYSTEM_STATUS service located?"

5. Security Agent

Purpose: Security vulnerability assessment and remediation

Tools Available: 25 security tools

Special Feature: 🔒 Human-in-the-Loop Approval

The security agent automatically enables human-in-the-loop approval for non-readonly operations:

Read-only tools (vulnerability assessment, auditing) execute immediately
Write operations (remediation, configuration changes) require manual approval

How it works:

The agent identifies tools marked with readOnly: false in their metadata
Before executing these tools, the agent pauses and requests approval
You can approve or reject the operation
This prevents accidental system changes during security assessments

Tools requiring approval:

repopulate_special_authority_detail - Refreshes security audit data
execute_impersonation_lockdown - Applies security remediation commands

Example Questions:

"Check for user profiles vulnerable to impersonation attacks"
"List database files with public write access"
"Analyze library list security vulnerabilities"
"Generate commands to lock down impersonation vulnerabilities"

Security Categories (use with --category flag):

vulnerability-assessment: Identify security vulnerabilities
audit: Audit security configurations
remediation: Generate and execute security fixes
user-management: Manage user capabilities and permissions

Advanced Options

Using Agents in Python Code

import asyncio
from ibmi_agents import create_ibmi_agent, chat_with_agent

async def main():
    # Create a performance agent
    ctx = await create_ibmi_agent("performance", model_id="ollama:llama3.2")
    
    # Use the agent within its context
    async with ctx as (agent, session):
        # Send a query
        response = await chat_with_agent(
            agent,
            "What is my system status? Give me CPU and memory metrics.",
            thread_id="my-session-1"
        )
        print(response)

if __name__ == "__main__":
    asyncio.run(main())

Available Python Functions

from ibmi_agents import (
    create_ibmi_agent,                    # Create any agent by type
    create_performance_agent,             # Create performance agent
    create_sysadmin_discovery_agent,      # Create discovery agent
    create_sysadmin_browse_agent,         # Create browse agent
    create_sysadmin_search_agent,         # Create search agent
    create_security_ops_agent,            # Create security agent
    chat_with_agent,                      # Send message to agent
    list_available_agents,                # Get agent information
    set_verbose_logging,                  # Control logging verbosity
    get_verbose_logging,                  # Check logging status
)

Debug Mode

Enable verbose logging to see detailed tool calls and responses:

# Via command line
uv run src/ibmi_agents/agents/test_agents.py --agent performance --verbose

# Or in .env
VERBOSE_LOGGING=true

LangSmith Tracing

For advanced debugging, enable LangSmith tracing:

# In .env
LANGCHAIN_TRACING_V2=true
LANGCHAIN_API_KEY=your_langsmith_key_here
LANGCHAIN_PROJECT=ibmi-agents

Then view traces at: https://smith.langchain.com/

Architecture Overview

How It Works

Agent Selection: You choose an agent specialized for a specific task (performance, discovery, security, etc.)
MCP Connection: The agent connects to the IBM i MCP Server via HTTP
Tool Filtering: Each agent only has access to relevant tools (e.g., performance agent gets 12 performance tools)
Model Execution: Your chosen LLM model processes requests and generates tool calls
Human-in-the-Loop: Security operations pause for approval before executing write operations
Persistent Memory: Agent sessions maintain context using in-memory checkpointing

Troubleshooting

MCP Server Not Running

Error: Connection refused to http://127.0.0.1:3010/mcp

Solution: Start the IBM i MCP Server

cd /path/to/ibmi-mcp-server
npm run dev

Ollama Model Not Found

Error: model 'llama3.2' not found

Solution: Pull the model

ollama pull llama3.2

API Key Not Set

Error: OpenAI API key not found

Solution: Set the API key in .env

OPENAI_API_KEY=sk-...

Import Errors

ModuleNotFoundError: No module named 'ibmi_agent_sdk'

Solution: Install the SDK package

cd ../../packages/ibmi-agent-sdk
uv pip install -e .

Additional Resources

IBM i MCP Server Documentation: See main project README
LangChain Documentation: https://python.langchain.com/
LangGraph Documentation: https://langchain-ai.github.io/langgraph/
Ollama Documentation: https://ollama.ai/docs

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IBM i MCP Agents: LangChain

What is this project?

Key Features

Available Agents

Requirements

Setup Guide

Step 1: Install Prerequisites

Step 2: Set Up the IBM i MCP Server

Step 3: Install Agent Dependencies

Step 4: Configure Environment Variables

Step 5: Verify Setup

Usage Examples

Quick Test (Verify Setup)

Test Specific Agent

Interactive Mode

Test Security Agent with Category Filter

Test with Different Models

Test All Agents

Agent Details

1. Performance Agent

2. Discovery Agent

3. Browse Agent

4. Search Agent

5. Security Agent

Advanced Options

Using Agents in Python Code

Available Python Functions

Debug Mode

LangSmith Tracing

Architecture Overview

How It Works

Troubleshooting

MCP Server Not Running

Ollama Model Not Found

API Key Not Set

Import Errors

Additional Resources

FilesExpand file tree

README.md

Latest commit

History

README.md

File metadata and controls

IBM i MCP Agents: LangChain

What is this project?

Key Features

Available Agents

Requirements

Setup Guide

Step 1: Install Prerequisites

Step 2: Set Up the IBM i MCP Server

Step 3: Install Agent Dependencies

Step 4: Configure Environment Variables

Step 5: Verify Setup

Usage Examples

Quick Test (Verify Setup)

Test Specific Agent

Interactive Mode

Test Security Agent with Category Filter

Test with Different Models

Test All Agents

Agent Details

1. Performance Agent

2. Discovery Agent

3. Browse Agent

4. Search Agent

5. Security Agent

Advanced Options

Using Agents in Python Code

Available Python Functions

Debug Mode

LangSmith Tracing

Architecture Overview

How It Works

Troubleshooting

MCP Server Not Running

Ollama Model Not Found

API Key Not Set

Import Errors

Additional Resources