Throw it at the wall and see what sticks.

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What is this?

Spaghetti is a structured experiment framework for research that needs to move fast without losing rigor. Claim experiments, run them, record findings, kill what doesn't work — all tracked in a queryable database.

Works for any domain: ML research, due diligence, SEO testing, marketing experiments, or anything where you need to track hypotheses, evidence, and outcomes.

How it works

1. Hypothesize — every experiment starts from a cited source or prior finding
2. Run — execute the experiment, collect measurements
3. Record — findings are tagged as conclusive, supported, provisional, or killed
4. Query — full-text search across experiments, evidence, and findings

Structure

packages/cli/       # experiment CLI (oclif + Bun)
packages/db/        # database schema and client (Drizzle ORM + Turso)
micro/              # small-scale experiments
macro/              # full-scale validation
references/         # source material and notes
scripts/            # tooling and automation

Setup from scratch

Prerequisites

• Bun — JS runtime and package manager
• A Turso account (free tier works fine)

1. Create a Turso database

# Install the Turso CLI
brew install tursodatabase/tap/turso    # or: curl -sSfL https://get.tur.so/install.sh | bash

# Sign up / log in
turso auth login

# Create a database (pick any name)
turso db create spaghetti

# Get your credentials
turso db show spaghetti --url            # → libsql://spaghetti-yourname.turso.io
turso db tokens create spaghetti         # → your auth token

2. Configure environment

cp .env.example .env

Fill in your .env:

TURSO_DATABASE_URL=libsql://spaghetti-yourname.turso.io
TURSO_AUTH_TOKEN=your-token-here

3. Install dependencies and push schema

bun install

# Push the Drizzle schema to your new database
cd packages/db
bunx drizzle-kit push

This creates all tables (experiments, findings, evidence, kill criteria, methods, references, tags) directly from the TypeScript schema — no migration files needed.

4. Initialize full-text search

FTS tables and triggers are created automatically the first time you run any experiment command. They're idempotent, so re-running is safe.

5. Verify it works

experiment stats                          # should show all zeroes
experiment add my-first-experiment \
  --title "Does X improve Y?" \
  --notes "Based on [source]" \
  --scale micro
experiment list                           # should show your experiment

Usage

# Workflow
experiment add <id> --title "..." --notes "..." --scale micro
experiment claim <worker-id>              # grab the next open experiment
experiment complete <id> --status supported --evidence "..."

# Findings
experiment finding-add --title "..." --status supported --result "..."
experiment finding-list
experiment finding-get <id>

# Search everything
experiment query "search term"

# Methods bank (reusable techniques)
experiment method-add --name "..." --description "..." --solves "..."
experiment method-list

Adapting to your domain

The schema is general-purpose. The core concepts map to any research context:

Concept	ML research	Marketing	Due diligence
Experiment	"Does LoRA rank 8 beat rank 4?"	"Does CTA color affect conversion?"	"Is vendor X compliant with SOC2?"
Kill criteria	"Loss > 2.0 after 100 steps"	"CTR below baseline after 1k impressions"	"No audit report available"
Evidence	"Loss=1.3 at step 100, pass"	"CTR +12% after 2k impressions, pass"	"SOC2 Type II report dated 2025-11"
Finding	"Rank 8 outperforms rank 4 on GSM8K"	"Green CTA converts 15% better"	"Vendor X meets all compliance requirements"
Method	"Cosine annealing schedule"	"A/B test with 95% confidence"	"Evidence triangulation framework"

To customize the schema (add columns, tables), edit packages/db/src/schema.ts and run bunx drizzle-kit push again.

Tech stack

• Runtime: Bun
• CLI framework: oclif
• Database: Turso (remote SQLite via libsql)
• ORM: Drizzle
• Search: SQLite FTS5 (full-text search with triggers)