LLM IO Intelligence Plugin

A plugin for LLM that provides access to IO Intelligence models with full tool calling support.

Features

• 31 IO Intelligence models including Llama, Qwen, DeepSeek, Gemma, and more
• Complete tool calling support - works with all LLM tools
• Text-based tool call parsing - innovative approach for models that simulate tool calls
• Streaming and non-streaming support
• Vision model support for image analysis
• Embedding models for text embeddings

Installation

llm install llm-io-intelligence

Setup

Set your IO Intelligence API key:

llm keys set ionet
# Paste your API key when prompted

Or set it as an environment variable:

export IONET="your-api-key-here"

Tool Calling Support

This plugin provides complete tool calling support for IO Intelligence models. All LLM tools work seamlessly:

Basic Tools

# Get LLM version
llm --tool llm_version "What version?" --td

# Get current time
llm --tool llm_time "What time is it?" --td

Mathematical Tools

# Install math tools
llm install llm-tools-simpleeval

# Use mathematical calculations
llm --tool simple_eval "Calculate 15 * 23 + 7" --td

Database Tools

# Install SQLite tools
llm install llm-tools-sqlite

# Query a database
llm -T 'SQLite("database.db")' "Show me all users" --td

JavaScript Tools

# Install JavaScript tools
llm install llm-tools-quickjs

# Execute JavaScript code
llm --tool quickjs "Calculate factorial of 5" --td

Custom Functions (Inline)

# Powerful math calculator (one-liner)
llm -m llama-3.3-70b --functions 'def calc(expression): import math; return eval(expression, {"math": math, "sqrt": math.sqrt, "sin": math.sin, "pi": math.pi})' --td 'Calculate sqrt(144) + sin(pi/2) * 10'

# Simple operations
llm --functions 'def add(a, b): return a + b' --td 'Add 15 and 27'

# String manipulation
llm --functions 'def reverse(text): return text[::-1]' --td 'Reverse the word "hello"'

File-based Functions

# Create a functions file
echo 'def calc(expression): import math; return eval(expression, {"math": math, "sqrt": math.sqrt, "sin": math.sin, "pi": math.pi})' > my_functions.py

# Use the functions file
llm -m qwen3-235b --functions my_functions.py --td 'What is the area of a circle with radius 5?'

Available Models

Chat Models

Model ID	Full Name	Context Length	Tool Support
llama-3.3-70b	meta-llama/Llama-3.3-70B-Instruct	128K	✅ Full
qwen3-235b	Qwen/Qwen3-235B-A22B-FP8	32K	✅ Full
llama-3.2-90b-vision	meta-llama/Llama-3.2-90B-Vision-Instruct	16K	✅ Full
llama-4-maverick-17b	meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8	430K	✅ Full
llama-3.1-nemotron-70b	neuralmagic/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic	128K	✅ Full
deepseek-r1	deepseek-ai/DeepSeek-R1	128K	❌ Server config
phi-4	microsoft/phi-4	16K	❌ Server config

And 24 more models - see full list with llm models list

Vision Models

• llama-3.2-90b-vision - Image analysis and understanding
• qwen2-vl-7b - Vision-language model

Embedding Models

• bge-multilingual-gemma2 - Multilingual embeddings
• mxbai-embed-large-v1 - Large context embeddings

Usage Examples

Basic Chat

llm -m llama-3.3-70b "Explain quantum computing"

With Tools

# Mathematical calculation
llm -m llama-3.3-70b --tool simple_eval "What's the square root of 12345?" --td

# Database query
llm -m llama-3.3-70b -T 'SQLite("data.db")' "Show top 5 customers by revenue" --td

# Custom function calculation
llm -m qwen3-235b --functions 'def calc(expression): import math; return eval(expression, {"math": math, "sqrt": math.sqrt, "sin": math.sin, "pi": math.pi})' --td 'Calculate sqrt(144) + sin(pi/2) * 10'

Vision Analysis

llm -m llama-3.2-90b-vision "Describe this image" -a image.jpg

Python API

import llm
from llm_tools_sqlite import SQLite

# Get model
model = llm.get_model("llama-3.3-70b")

# Use with tools
response = model.prompt(
    "Show me all users with age > 25",
    tools=[SQLite("database.db")]
)

print(response.text())

# Check tool calls
for tool_call in response.tool_calls:
    print(f"Tool: {tool_call.name}, Args: {tool_call.arguments}")

# Using custom functions
import math

def calc(expression):
    """Powerful calculator with math functions"""
    safe_dict = {"math": math, "sqrt": math.sqrt, "sin": math.sin, "pi": math.pi}
    return str(eval(expression, safe_dict))

# Register as tool and use
response = model.prompt(
    "Calculate the circumference of a circle with radius 10",
    tools=[calc]  # Functions can be used directly as tools
)

How Tool Calling Works

This plugin implements an innovative text-based tool call parsing approach:

1. Tool definitions sent - Proper OpenAI-compatible tool schemas sent to API
2. Model outputs JSON - Models output tool calls as JSON text like {"name": "tool_name", "arguments": {}}
3. Text parsing - Plugin detects and parses JSON patterns from model output
4. Tool execution - Converts text to actual ToolCall objects and executes them
5. Results returned - Tool results fed back to model for final response

This approach bridges the gap between IO Intelligence's text-based responses and LLM's tool calling framework.

Tool Compatibility

✅ Working Tools:

• llm_version, llm_time - Built-in LLM tools
• simple_eval - Mathematical expressions
• SQLite - Database queries and schema inspection
• quickjs - JavaScript code execution
• Datasette - Remote database queries
• Custom functions - Inline Python functions via --functions

✅ All tool types supported:

• Simple tools (no parameters)
• Parameterized tools (with arguments)
• Complex toolbox-style tools (multiple methods)
• Async and sync tools
• Inline functions (one-liners and file-based)
• Custom eval functions with math libraries

✅ Tested Models:

• llama-3.3-70b - Full tool support, multiple verification calls
• qwen3-235b - Full tool support, clean execution with thinking process
• llama-3.2-90b-vision - Vision + tool calling combined
• llama-4-maverick-17b - Advanced tool calling capabilities

Configuration

Model Options

# Set temperature
llm -m llama-3.3-70b -o temperature 0.8 "Creative writing task"

# Set max tokens
llm -m llama-3.3-70b -o max_tokens 1000 "Long explanation needed"

# Enable reasoning content (for compatible models)
llm -m deepseek-r1 -o reasoning_content true "Complex problem"

Default Model

# Set default model
llm models default llama-3.3-70b

# Now you can omit -m
llm "Hello world"

Development

Running Tests

# Test basic functionality
python debug_tool_execution.py

# Test SQLite integration
python test_sqlite_real.py

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

Troubleshooting

Tool Calling Issues

If tools aren't working:

1. Check model support - Use models like llama-3.3-70b or qwen3-235b that support tool calling
2. Verify API key - Ensure IOINTELLIGENCE_API_KEY is set correctly
3. Use debug mode - Add --td flag to see tool call details
4. Check tool installation - Ensure tool plugins are installed
5. Parameter names - For custom functions, use parameter names the model expects (e.g., expression not expr)

Common Errors

• "does not support tools" - Use a tool-compatible model (see table above)
• "API key not found" - Set the IOINTELLIGENCE_API_KEY environment variable
• "Chain limit exceeded" - Model made too many tool calls (safety limit)
• "unexpected keyword argument" - Check function parameter names match model expectations
• "missing required argument" - Ensure function parameters are properly defined

License

Apache License 2.0

Links

• LLM Documentation
• IO Intelligence API
• Tool Plugins Directory
• Simon Willison's Tool Guide