v3.7.0 • 6,324 Functions + 7 Knowledge Packs

AI That Can't Get Math Wrong

A symbolic computation language for Python with 6,324 functions plus 7 knowledge packs (PubChem, Crossref, OpenAlex, GeoNames, World Bank, AlphaFold, JHTDB). Pattern matching, algebraic simplification, chemistry, physics, visualization, signal processing, and structured knowledge retrieval — all deterministic and verified.

Try Live Console → pip install mikoshilang Download Manual (PDF)

In[1]:= Diff[Sin[x^2], x]

Out[1]= 2*x*Cos[x^2]

In[2]:= BalanceEquation["H2 + O2 -> H2O"]

Out[2]= 2H2 + O2 -> 2H2O

In[3]:= UnitConvert[Quantity[100, "km/h"], "m/s"]

Out[3]= Quantity[27.7778, "m/s"]

Natural Language

Ask in English, Get Verified Math

Type a question in plain English. AI translates it to MikoshiLang syntax. MikoshiLang computes the verified result. No hallucination — guaranteed correct.

💬

Natural Language

"What's the derivative of sin x squared?"

→

🤖

AI Translation

Diff[Sin[x^2], x]

→

✅

Verified Result

2*x*Cos[x^2]

In[1]:= What's the derivative of sin x squared?

AI: Diff[Sin[x^2], x]

Out[1]= 2*x*Cos[x^2] ✅ verified

In[2]:= Balance the equation H2 + O2 -> H2O

AI: BalanceEquation["H2 + O2 -> H2O"]

Out[2]= 2H2 + O2 -> 2H2O ✅ verified

Free tier uses Ollama (local AI). Bring your own Claude or OpenAI key for premium models.

Features

Everything You Need

✅

Verified Computation

Every result is deterministic. No hallucination, no approximation errors. Symbolic computation that proves its answers.

⚗️

Chemistry

Balance equations, compute molar masses, look up elements. Full periodic table with electron configurations and properties.

📏

Physics Units

Convert between any units — velocity, mass, temperature, energy. Dimensional analysis built in.

📡

Signal Processing

Fourier transforms, convolution, filtering. Process signals symbolically before going numerical.

📓

Jupyter Integration

Rich display in Jupyter notebooks with LaTeX rendering. Interactive exploration of symbolic expressions.

🔀

Pattern Matching

Wolfram-style pattern matching and rule application. Define transformation rules that apply automatically.

📊

Advanced Visualization

2D/3D plots, interactive graphics (Plotly), vector fields, contours, parametric curves, animations. Publication-quality and exploratory plots.

🌐

5 Knowledge Packs

PubChem (100M+ compounds), Crossref (70M+ papers), OpenAlex (200M+ works), GeoNames (11M+ places), World Bank (1,400+ indicators). SPARQL graph queries, persistent cache, full license tracking.

🧮

6,324 Functions

Calculus, algebra, linear algebra, statistics, number theory, graph theory, physics, machine learning, hydrology, mining, manufacturing, renewable energy, and more — comprehensive coverage.

Visualization

Advanced Plotting & Graphics

Publication-quality static plots (matplotlib) and interactive 3D graphics (plotly). 2D/3D surfaces, vector fields, contours, parametric curves, polar plots, and animations.

// 2D plotting

Plot2D[Sin[x], {x, 0, 2*Pi}]

Plot2D[{Sin[x], Cos[x]}, {x, -Pi, Pi}]

// 3D surface (interactive)

Interactive3D[x^2 + y^2, {x, -3, 3}, {y, -3, 3}]

// Vector field with streamlines

VectorFieldPlot[{-y, x}, {x, -2, 2}, {y, -2, 2}, streamlines=True]

// Parametric curve (3D helix)

ParametricPlot[{Cos[t], Sin[t], t}, {t, 0, 4*Pi}]

// Animation

AnimatePlot[Sin[k*x], x, {k, 1, 10, 50}, save_as="wave.gif"]

📈

2D & Polar

Line plots, parametric, polar coordinates

🌐

3D Interactive

Surface plots with rotation, zoom, pan

🔶

Contours & Fields

Contour plots, vector field streamlines

🎬

Animations

Parameter sweeps, export to GIF

Install with: pip install mikoshilang[visualization]

Knowledge Layer (v3.5.0)

Structured Knowledge Retrieval

7 domain-specific knowledge packs with SPARQL graph queries, persistent caching (200x speedup), and full license compliance. Wikidata entity framework plus Wikipedia summaries.

// Chemistry: PubChem (100M+ compounds)

PackSearch["pubchem", "caffeine"]

# → [{"id": "CID2519", "license": "Public Domain"}]

PackValue["pubchem", "2519", "MolecularFormula"]

# → {"value": "C8H10N4O2"}

// Papers: Crossref (70M+ papers)

PackSearch["crossref", "machine learning", limit=5]

# → [{"id": "10.1145/...", "license": "Metadata: CC0"}]

// Scholarly graph: OpenAlex (200M+ works)

PackSearch["openalex", "deep learning"]

PackValue["openalex", "W2741809807", "CitationCount"]

// Geography: GeoNames (11M+ places)

PackSearch["geonames", "London"]

PackValue["geonames", "2643743", "Population"]

# → {"value": 8961989}

// Economics: World Bank (1,400+ indicators)

PackSearch["worldbank", "GDP"]

PackValue["worldbank", "NY.GDP.MKTP.CD", "Value", country="US"]

// Proteins: AlphaFold (200M+ structures)

PackSearch["alphafold", "hemoglobin"]

# → [{"id": "P69905", "license": "CC BY 4.0"}]

PackValue["alphafold", "P69905", "Confidence"]

# → {"value": 93.2}

// Turbulence: JHTDB (6 DNS datasets) — NEW in v3.5.0

PackSearch["jhtdb", "channel"]

# → [{"id": "channel5200", "source": "JHTDB"}]

PackValue["jhtdb", "channel5200", "ReynoldsNumber"]

# → {"value": 5200}

// Entity graph: Wikidata + SPARQL

EntitySearch["Douglas Adams"]

# → [{"id": "Q42", "license": "CC0"}]

EntityRelationships["Q42", "Occupation", depth=2]

# → Multi-hop graph traversal via SPARQL

All queries include provenance tracking (source URL + timestamp + license)

Comparison

How It Stacks Up

Feature	MikoshiLang	Wolfram	SymPy
Pattern Matching	✅ Native	✅ Native	❌ Limited
Open Source	✅ Apache 2.0	❌ Proprietary	✅ BSD
Chemistry	✅ Built-in	✅ Built-in	❌ Separate
Unit Conversion	✅ Built-in	✅ Built-in	✅ Add-on
Signal Processing	✅ Built-in	✅ Built-in	❌ No
3D Visualization	✅ Static + Interactive	✅ Native	✅ Matplotlib
Animations	✅ GIF Export	✅ Native	❌ Manual
Interactive Plots	✅ Plotly	✅ Manipulate	❌ No
Function Count	✅ 6,324	✅ 6,000+	✅ ~800
Knowledge Packs	✅ 7 sources	✅ Curated	❌ No
Entity Graph (SPARQL)	✅ Wikidata	✅ Native	❌ No
Python Native	✅ Pure Python	❌ Separate	✅ Pure Python
AI Integration	✅ Synapse/Turbo	❌ No	❌ No
Price	Free	$395+/yr	Free

Get Started

Install in Seconds

$ pip install mikoshilang

Links

Resources

📦 PyPI 🐙 GitHub 🧮 Console 🔮 Nexus

☁️

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