FormuLab

Local-first AI research workbench with chemical formulation discovery and cost optimization.

Built with Tauri, MCP, and agent skills — for macOS, Windows & Linux.

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What it is

FormuLab is a desktop workbench that pairs a general AI research environment (agents, notebooks, files, figures, runs, provenance) with a purpose-built chemical formulation toolkit:

• Formulation Optimizer — a linear program (PuLP + CBC) that finds the lowest-cost raw-material mix meeting an active-content target within stock and usage limits. Available as a UI tab and an agent skill.
• Formulation Discovery — give a target product ("an anti-dandruff, soothing shampoo") and the agent retrieves open-access literature (OpenAlex), extracts the ingredients/functions/concentrations reported there, synthesizes an evidence-based candidate formula with citations, and hands it to the optimizer.

Everything runs locally by default; your data, runs, and provenance stay on your machine.

Features

• Chat + Agents — local/API models, tools, MCP, files, shell, skills, memory.
• Formulation Optimizer — cost-minimal blending under active-content, stock, and max-usage constraints.
• Formulation Discovery — literature-driven candidate formulas with citations.
• Notebooks — real .ipynb, local Python/R kernels, managed Jupyter via uv.
• Runs & Provenance — append-only run logs and artifact lineage.
• Deep Research — multi-step web research with source reading and reports.

Formulation quick start

Optimize a blend from a materials CSV or JSON:

# CSV: name,unit_price,stock,active_matter_pct,max_usage_pct
python runtime/skills/core/formulation-optimizer/optimize.py \
  --materials materials.csv --batch 1000 --min-active 40

Discover a formula from the literature (open access only):

python runtime/skills/core/formulation-discovery/discover.py \
  "antidandruff shampoo formulation" --max 40 --pdfs

Candidates are evidence-based proposals, not validated recipes. Bench validation and regional regulatory review are required before any use. The discovery skill refuses hazardous/illicit targets by design.

Build from source

Prerequisites: Node.js >= 20, pnpm 9, Rust toolchain, and the Tauri system dependencies for your OS.

git clone https://github.com/Sekiph82/FormuLab
cd FormuLab
pnpm install

# Fetch pinned sidecars and bundled skills (git-ignored).
bash scripts/dev/fetch-opencode.sh
bash scripts/dev/fetch-uv.sh
bash scripts/dev/fetch-skills.sh
bash scripts/dev/fetch-goal-plugin.sh

# Run in development or build installers.
pnpm --filter @ai4s/desktop tauri dev
pnpm --filter @ai4s/desktop tauri build

Checks: pnpm test - pnpm typecheck - pnpm lint.

Safety and privacy

• Workspace files, raw data, session history, provenance, and runs stay local by default.
• Command execution, file deletion, dependency installation, and remote connections are human-approved flows in the app.
• Provider credentials are written to app-private runtime config, never to the workspace, provenance, git, or exports.

License

MIT — see LICENSE. FormuLab builds on an open-source, MIT-licensed research-workbench foundation; that copyright notice is retained in LICENSE.