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AgInTi LabCanvas

Editable scientific figure and experiment-design studio for agent workflows.
Chat, preview, decompose, route, and rebuild paper figures through Blender, OpenSCAD, BioRender, AgInTi, KiCad, Unity, Unreal, and MCP-style tool bridges.

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What It Does

AgInTi LabCanvas is a small local control plane for agent-assisted scientific visuals and app automation. It keeps generated figures editable: an overview image can start an idea, but final outputs are rebuilt from atomic parts, scene specs, CAD files, manifests, and tool-specific artifacts.

Current Highlights

Quick Start

Model Policy

Automatic LabCanvas turns use the shared configs/model-policy.json:
auto-code-review with low reasoning for ordinary chat and medium reasoning for
ordinary durable tasks. Complex implementation may use gpt-5.6-sol high;
demanding autonomous research, design, and presentation synthesis may use
gpt-5.6-sol xhigh. Matching-effort fallbacks are retained, and explicit
--model and --effort selections still win.

Run from a source checkout:

PYTHONPATH=src python -m agenticapp list
PYTHONPATH=src python -m agenticapp doctor
PYTHONPATH=src python -m agenticapp web --port 8787 --open
PYTHONPATH=src python -m agenticapp agent capabilities
PYTHONPATH=src python -m agenticapp agent chat "Design and render a C-mount sensor holder"
PYTHONPATH=src python -m agenticapp grant run "Draft specific aims with verified evidence and an editable figure" --title "Research Grant" --dry-run
PYTHONPATH=src python -m agenticapp studio biorender-figure "Three-panel mechanism figure" --panel "A: mechanism" --panel "B: intervention" --panel "C: validation" --json
PYTHONPATH=src python -m agenticapp protein start --json
PYTHONPATH=src python -m agenticapp protein status --json
PYTHONPATH=src python -m agenticapp protein submit references/target.fasta --dry-run
PYTHONPATH=src python -m agenticapp studio figure-grid "optical device icons 2x3" --rows 2 --cols 3
PYTHONPATH=src python -m agenticapp studio lab-task "prepare Lumileds no-resistor PCB and C-mount reflector CAD"
PYTHONPATH=src python -m agenticapp wechat worker reprocess TASK_ID "recover completed report" --artifact-recovery-only --send --queue agentic_tools/wecom_agent/.private/wecom_task_queue.jsonl
PYTHONPATH=src python -m unittest discover -s tests

The npm package has been renamed in this repository to @lazyingart/labcanvas, with labcanvas as the primary CLI and app-auto-action / agenticapp kept as compatibility aliases. The new npm package still needs a fresh authenticated publish; until then, use the source checkout or the previously published package name.

# After the renamed npm package is published:
npm install -g @lazyingart/labcanvas
labcanvas --version
labcanvas webapp start --port 19473

Studio Workflow

1. Start with chat or a saved JSON scene spec.
2. Generate overview concepts through AgInTi image payloads or another image backend.
3. Split the figure into editable atoms: panels, icons, labels, CAD parts, renders, and TeX assembly layers.
4. Use BioRender for academic assets, OpenSCAD for mechanical layout, Blender for 3D setup renders, and KiCad for PCB artifacts.
5. Keep every artifact in the canvas manifest so later chat edits can target one part instead of flattening the whole figure.

For visible end-to-end Studio control, start the isolated LabCanvas browser and use the reusable chat controller:

scripts/launch_labcanvas_studio_novnc.sh start
scripts/labcanvas_studio_browser.py chat --message "Design and validate a C-mount holder" --effort medium

This submits through the real composer, monitors the exact durable task, and saves browser evidence without sharing the WeChat or Xiaoyunque profiles. See LabCanvas Studio Browser Control.

Example Commands

labcanvas scene-template experiment-setup --output my-setup.scene.json
labcanvas agent chat "Create a clean Shapr3D-compatible optical holder" --model gpt-5.6-sol --effort medium
labcanvas agent chat "Inspect the current KiCad board and report problems" --mode plan
labcanvas agent tasks
labcanvas grant init "Draft a proposal grounded in the supplied call and primary literature" --title "Research Grant"
labcanvas grant run "Complete the proposal, editable figure, and checked PDF" --project-dir output/grants/PROJECT --effort medium
labcanvas grant validate --project-dir output/grants/PROJECT --json
labcanvas presentation init "Research roadmap" --objective "Explain the evidence and next experiment" --output-dir output/presentations/roadmap
labcanvas presentation build output/presentations/roadmap/presentation.json --render --json
labcanvas presentation validate output/presentations/roadmap/presentation.json --json
labcanvas render-scene my-setup.scene.json --dry-run
labcanvas render-scene my-setup.scene.json --output-dir output/scenes
labcanvas studio openscad examples/paper-optics-setup.scene.json
labcanvas studio lab-task "prepare Lumileds no-resistor PCB and C-mount reflector CAD"
labcanvas studio lab-task "prepare Lumileds no-resistor PCB" --mode pcb --execute
labcanvas studio dispatch blender "Prepare an editable paper figure setup"
labcanvas wechat worker --chat "懒人科研" enqueue "Use AgInTi image generation to make a 2x3 microscopy icon figure"
labcanvas wechat worker --chat "懒人科研" enqueue "Use LabCanvas to render the Lumileds PCB and C-mount CAD preview"
labcanvas wechat status
labcanvas wechat hold start
labcanvas wechat stack start --web-port 19474
labcanvas wechat career-agent start --send --attach-report --organize-report --organize-chat "写作 外语 挣钱"
labcanvas wechat career-agent organize --send --organize-chat "写作 外语 挣钱" --json
labcanvas wechat audio-intake --input <exact-local-media> --output-dir output/wechat-audio-read --json
labcanvas wechat shipinhao-transcribe --source-text-file <card.txt> --output-dir output/shipinhao-read --json
PYTHONPATH=src python agentic_tools/wecom_agent/scripts/wecom_daily_research.py run --json
PYTHONPATH=src python agentic_tools/wechat_gui_agent/scripts/echomind_language_scheduler.py --daily-pdf-now
labcanvas social project add --repo ../ZhJpBook --id pocketpolyglot
labcanvas social campaign create --project pocketpolyglot --name introduction --objective "Introduce the usable open-source Studio" --platform x --platform reddit:r/languagelearning --platform hackernews
labcanvas social draft generate CAMPAIGN_ID --dry-run --json
scripts/mix2s on --serial <MIX2S_SERIAL>
scripts/mix2s status --serial <MIX2S_SERIAL>
scripts/mix2s off --serial <MIX2S_SERIAL>
agentic_tools/android_device_agent/scripts/android_control.py status --serial <MIX2S_SERIAL>
agentic_tools/biorender_agent/scripts/start_biorender_stack.sh
python agentic_tools/biorender_agent/scripts/probe_biorender_mcp.py --json

For a local Blender bridge test:

scripts/install_blender_portable.sh
labcanvas --config configs/blender-local-command.example.json doctor
labcanvas --config configs/blender-local-command.example.json dispatch blender "Draw a welcoming modern building with a tower"

Architecture

Agent / MCP client / CLI / persistent web chat
        |
        | resumed session + dynamic policy + durable task
        v
AgInTi LabCanvas
        |
        | callable routines + target registry + artifact manifest
        v
Blender · OpenSCAD · BioRender · AgInTi · KiCad · LabVIEW · Unity · Unreal

Every target dispatch receives a reviewable JSON envelope:

{
  "target": "blender",
  "kind": "blender",
  "instruction": "Create a red cube at the origin",
  "payload": {},
  "metadata": {
    "source": "labcanvas"
  }
}

Copy configs/targets.example.json to labcanvas.targets.json for local ports, commands, and tokens. This override file is ignored by git.

Validation

npm test
npm run pack:dry-run
PYTHONPATH=src python -m agenticapp doctor

Keep transport behavior covered by tests before adding live editor features. Review SECURITY.md before enabling live dispatch to editor bridges or browser sessions.