A New Way to Build Data Apps: Orchestrate analytics, logic, and visualization as code.

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Warning

We are in alpha. Full release coming Q1/Q2 2026

LunivCore Framework: Business Intelligence as Code

Build dashboards, analytical reports, and entire BI workflows using Lua or the interpreted lcore language without the overhead of traditional enterprise tools.

We provide a foundation for individuals, teams, and enterprises seeking to standardize analytics, accelerate experimentation, and integrate BI logic directly into their stack.

This monorepo houses the full LunivCore ecosystem:

• a compact lcore interpreter written in C/Lua
• structured documentation website
• development utilities like a Lua unit testing framework, designed to deliver a fast, extensible, and highly maintainable BI platform.
• Permissable MIT LICENSE*
• Examples

*Dependencies and outside libraries subject to different licensing terms

How It Works

See It In Action

LunivCore is an open-source, engineer focused alternative to PowerBI, Jupyter Notebook, evidence.dev, Tableau, Streamlit, Shiny, and other business intelligence/reporting tool.

Instantly start answering the most important questions for your business.

This .lcore file:

# Sales Report Q1 (examples/helloworld.lcore)
text "Regional sales are strong this year in the West."

dataset Sales "Q1 Regional Sales" {
    North: 340
    South: 120
    East: 210
    West: 450
}

plot Sales as bar

view MarketShare "Market Share by Segment (%)" {
    SegmentA: 90
    SegmentB: 8
    SegmentC: 2
    text "Analysis Notes: North dominates the market."
    text "Segments B and C are negligible."
}
plot MarketShare as bar

Outputs:

╔════════════════════════════════════════════════╗
║Regional sales are strong this year in the West.║
╚════════════════════════════════════════════════╝
--- Q1 Regional Sales ---
North      | ██████████████████████████████████ (340)
South      | ████████████ (120)
East       | █████████████████████ (210)
West       | █████████████████████████████████████████████ (450)
----------------
╔════════════════════════════════════════════════╗
║          Market Share by Segment (%)           ║
╠════════════════════════════════════════════════╣
║ SegmentA : 90 │█████████████████████████████ │ ║
║ SegmentB :  8 │███░░░░░░░░░░░░░░░░░░░░░░░░░░ │ ║
║ SegmentC :  2 │█░░░░░░░░░░░░░░░░░░░░░░░░░░░░ │ ║
║  Analysis Notes: North dominates the market.   ║
║        Segments B and C are negligible.        ║
╚════════════════════════════════════════════════╝

Or run it on the Lua VM through C functions:

-- Create datasets dynamically and aggregate values
local regions = {"North", "South", "East", "West"}
local q1_values = {340, 210, 210, 450}
local q2_values = {400, 190, 220, 470}

local q1_ds = BI.new("Q1 Aggregation KPIs")
local q2_ds = BI.new("Q2 Aggregation KPIs")

-- Populate datasets using loops
for i, region in ipairs(regions) do
    q1_ds:add(region, q1_values[i])
    q2_ds:add(region, q2_values[i])
end

-- Perform basic analytics
print("Q1 Total:", q1_ds:sum())
print("Q2 Total:", q2_ds:sum())
print("Difference Q2-Q1:", q2_ds:sum() - q1_ds:sum())

-- Advanced analytics: conditional highlights
for i, region in ipairs(regions) do
    local q1_val = q1_values[i]
    local q2_val = q2_values[i]
    if q2_val > q1_val then
        BI.text(region .. " exceeded projections by " .. (q2_val - q1_val))
    else
        BI.text(region .. " lagged projections by " .. (q1_val - q2_val))
    end
end

-- Combine datasets into a single view
local market_view = {
    title = "Quarterly Market Share Comparison",
    rows = {},
    children = {}
}

for i, region in ipairs(regions) do
    table.insert(market_view.rows, {
        name = region,
        value = q2_values[i]
    })
    table.insert(market_view.children, {
        type = "text",
        value = string.format("%s Q1: %d, Q2: %d", region, q1_values[i], q2_values[i])
    })
end

BI.render_view(market_view)

-- Final summary
BI.text("All datasets processed successfully.")

Q1 Total: 1210
Q2 Total: 1280
Difference Q2-Q1: 70
╔════════════════════════════════════════════════╗
║        North exceeded projections by 60        ║
╚════════════════════════════════════════════════╝
╔════════════════════════════════════════════════╗
║         South lagged projections by 20         ║
╚════════════════════════════════════════════════╝
╔════════════════════════════════════════════════╗
║        East exceeded projections by 10         ║
╚════════════════════════════════════════════════╝
╔════════════════════════════════════════════════╗
║        West exceeded projections by 20         ║
╚════════════════════════════════════════════════╝
╔════════════════════════════════════════════════╗
║       Quarterly Market Share Comparison        ║
╠════════════════════════════════════════════════╣
║ North : 400 │██████████████████████████░░░░░ │ ║
║ South : 190 │█████████████░░░░░░░░░░░░░░░░░░ │ ║
║ East  : 220 │███████████████░░░░░░░░░░░░░░░░ │ ║
║ West  : 470 │███████████████████████████████ │ ║
║             North Q1: 340, Q2: 400             ║
║             South Q1: 210, Q2: 190             ║
║             East Q1: 210, Q2: 220              ║
║             West Q1: 450, Q2: 470              ║
╚════════════════════════════════════════════════╝
╔════════════════════════════════════════════════╗
║      All datasets processed successfully.      ║
╚════════════════════════════════════════════════╝

LunivCore generates a terminal-dashboard or website from markdown-inspired .lcore or Lua files:

• Low-level framework engineered for integration anywhere that supports C, including microcontrollers, websites, servers, and more.

• Built-in Interpreter: View the powerful lcore interpreter.

• SQL-like statements inside markdown-inspired files to run queries against your data sources
• Charts and components are rendered using these query results
• Templated pages generate many pages from a single markdown template
• Loops and If / Else statements allow control of what is displayed to users
• Coded for Performance: LunivCore is made with C, enabling high-performance.
• Production-Grade: Ships with a Lua unit testing framework.
• Portable: Built using standard C (C99) and the embeddable Lua library.
• Support: On-going, lifetime support from the passionate dev team at Luniv Technology

Benchmark Results

The following table summarizes the performance as the dataset size scaled by a factor of nearly 3,000x (from 5.5K to 11.9M rows).

Test	Dataset Count (Rows)	Executed Time (ms)	User Time (ms)	System Time (ms)	Throughput
1	5,508	1.64	0.23	1.47	approx 3.3 million rows per second
2	396,647	13.46	11.05	2.41	approx 29.5 million rows per second
3	3,173,183	88.27	78.76	9.14	approx 35.9 million rows per second
4	6,346,367	169.73	138.69	30.48	approx 37.4 million rows per second
5	11,908,037	367.70	304.11	60.99	approx 32.4 million rows per second*

Read the full report.

Why Now?

I built LunivCore after getting tired of the constraints of traditional enterprise BI platforms.

Instead of relying on a Markdown-driven system like evidence, I wanted a lower-level foundation with less external dependencies.

Something lean, predictable, and fully under my control. Using C and Lua gives me that flexibility.

This gives me the freedom to layer features on top such as Markdown rendering, web output, and anything else in a deliberate manner.

-Nick

License

LunivCore is licensed under the MIT license. See the LICENSE file for licensing information.