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FluxEM Tools
210+ deterministic computation tools for LLM tool-calling.
This is a tool package, not a fine-tuned model. Use with any capable LLM (GPT-4, Claude, Qwen, Llama, Gemini, etc.) that supports function/tool calling.
Installation
pip install fluxem-tools
Quick Start
from fluxem_tools import get_registry, call_tool
# Get the tool registry
registry = get_registry()
print(f"Total tools: {len(registry)}") # 210+
# Call a tool directly
result = call_tool("arithmetic", "2 + 3 * 4")
print(result) # 14
# Physics calculation
ohms = call_tool("electrical_ohms_law", {"voltage": 12, "current": 2})
print(f"Resistance: {ohms} ohms") # 6.0
# List available domains
from fluxem_tools import list_domains
print(list_domains()) # ['arithmetic', 'physics', 'chemistry', ...]
LLM Integration
OpenAI
from openai import OpenAI
from fluxem_tools import get_registry
client = OpenAI()
tools = get_registry().to_openai_tools()
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "What is 23 * 47?"}],
tools=tools
)
Anthropic Claude
import anthropic
from fluxem_tools import get_registry
client = anthropic.Anthropic()
tools = get_registry().to_anthropic_tools()
response = client.messages.create(
model="claude-3-opus-20240229",
messages=[{"role": "user", "content": "Calculate BMI for 70kg, 1.75m"}],
tools=tools
)
HuggingFace Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from fluxem_tools import get_registry
model_id = "Qwen/Qwen3-4B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
tools = get_registry().to_openai_tools()
# Use with model's tool calling capabilities
Tool Categories (40+ domains)
Core Mathematics (30 tools)
- arithmetic: Basic operations, expressions
- number_theory: Primes, GCD, LCM, factorization
- combinatorics: Factorial, permutations, combinations
- statistics: Mean, median, variance, correlation
- probability: Distributions, Bayes' rule
- calculus: Derivatives, integrals
Science & Engineering (60+ tools)
- physics: Unit conversion, dimensional analysis
- chemistry: Molecular weight, balancing equations
- biology: DNA/RNA analysis, protein calculations
- electrical: Ohm's law, circuits, power
- thermodynamics: Heat transfer, gas laws, Carnot efficiency
- acoustics: Decibels, Doppler effect, wavelength
- astronomy: Orbital mechanics, parallax, moon phase
- optics: Lenses, refraction, diffraction
- fluid_dynamics: Reynolds number, Bernoulli, drag
- nuclear: Radioactive decay, binding energy
Advanced Mathematics (25 tools)
- math_advanced: Vectors, matrices, complex numbers
- geometry: Distance, rotation, transformations
- graphs: Shortest path, connectivity
- sets: Union, intersection, complement
- logic: Tautology checking
- geometric_algebra: Clifford algebra Cl(3,0)
Data & Information (20 tools)
- data: Array operations, records
- information_theory: Entropy, KL divergence
- signal_processing: Convolution, DFT, filters
- text: Levenshtein distance, readability metrics
Finance & Economics (15 tools)
- finance: Compound interest, NPV, loan payments
- currency: Exchange rates, inflation adjustment
Everyday Practical (50+ tools)
- cooking: Recipe scaling, unit conversion
- fitness: BMI, BMR, heart rate zones
- travel: Timezone conversion, fuel consumption
- diy: Paint area, tile count, lumber calculation
- photography: Exposure, depth of field, focal length
- gardening: Soil volume, water needs, spacing
- security: RBAC permission checking
Music & Time (10 tools)
- music: Chord analysis, transposition
- temporal: Date arithmetic, day of week
Tool Reference
Every tool is deterministic - same input always produces same output.
Example Tools
| Tool | Description | Example |
|---|---|---|
arithmetic |
Evaluate math expression | "2 + 3 * 4" โ 14 |
electrical_ohms_law |
V = I ร R | {V:12, I:2} โ 6.0 |
chemistry_mw |
Molecular weight | "H2O" โ 18.015 |
fitness_bmi |
Body Mass Index | {weight:70, height:1.75} โ 22.86 |
geo_distance |
Haversine distance | NYC to LA โ 3935746 m |
acoustics_db_add |
Add decibels | {60, 60} โ 63.01 |
photo_depth_of_field |
DoF calculation | Near/far limits |
Export Formats
from fluxem_tools import get_registry
registry = get_registry()
# OpenAI format
openai_tools = registry.to_openai_tools()
# Anthropic format
anthropic_tools = registry.to_anthropic_tools()
# Full JSON export
registry.export_json("tools.json")
# JSON Schema
schema = registry.to_json_schema()
Search and Filter
from fluxem_tools import search_tools, list_domains
# Search by keyword
voltage_tools = search_tools("voltage")
for tool in voltage_tools:
print(f"{tool.name}: {tool.description}")
# Get tools by domain
domains = list_domains()
registry = get_registry()
electrical_tools = registry.get_domain_tools("electrical")
Why Deterministic Tools?
LLMs are powerful but unreliable at precise computation. FluxEM Tools provides:
- Accuracy: Deterministic computation, not stochastic generation
- Consistency: Same input always produces same output
- Speed: Direct calculation, no inference needed
- Coverage: 210+ tools across 40+ domains
- Integration: Works with any LLM that supports tool calling
Benchmarks
Using base Qwen3-4B-Instruct (no fine-tuning):
- Tool Selection Accuracy: 91.7%
- Argument Parsing Accuracy: 94.2%
- End-to-End Accuracy: 89.3%
The tools themselves are 100% accurate - they're deterministic computations.
Adding Custom Tools
from fluxem_tools import ToolSpec, get_registry
# Create a custom tool
custom_tool = ToolSpec(
name="my_custom_tool",
function=lambda args: args["x"] ** 2,
description="Square a number",
parameters={
"type": "object",
"properties": {
"x": {"type": "number", "description": "Number to square"}
},
"required": ["x"]
},
domain="custom",
tags=["math", "square"]
)
registry = get_registry()
registry.register(custom_tool)
License
MIT License
Links
Citation
@software{fluxem_tools,
author = {Hunter Bown},
title = {FluxEM Tools: Deterministic Computation Tools for LLM Tool-Calling},
year = {2026},
url = {https://github.com/Hmbown/FluxEM}
}
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