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Agent routing for Blender, BioRender, Unity, Unreal, and future creative tools. AppAutoAction gives Codex, AgInTiFlow, Claude, local LLMs, and other MCP-aware agents one practical control plane for app automation.
Quick Start · Paper Figures · 3D Design · Targets · Research · Languages
Live studio demo: chat asks for a V-SPICE experiment setup, OpenSCAD exports the CAD proxy, and Blender renders the selected right-side canvas artifact.
Creative tools are gaining agent bridges, but each bridge has a different install path, port, protocol, and safety model. AppAutoAction keeps those targets in one registry, validates them, emits MCP client config, and dispatches dry-run or live JSON envelopes to the right adapter.
It is intentionally small: Python standard library, explicit config, no hidden editor automation.
Install from npm:
npm install -g @lazyingart/app-auto-action
app-auto-action --version
app-auto-action webapp start --port 19473Or run from a source checkout:
PYTHONPATH=src python -m agenticapp list
PYTHONPATH=src python -m agenticapp doctor
PYTHONPATH=src python -m agenticapp dispatch blender "Create a red cube at the origin" --dry-run
PYTHONPATH=src python -m agenticapp mcp-config
PYTHONPATH=src python -m agenticapp studio status
PYTHONPATH=src python -m unittest discover -s testsAfter installation, the console command is also available as:
app-auto-action list
app-auto-action dispatch unity "Create a test scene with three labeled cubes" --dry-run
app-auto-action studio figure-grid "optical device icons 2x3" --rows 2 --cols 3
app-auto-action webapp start --port 19473app-auto-action web --port 8787 --openThe web app now has a bright-by-default theme, chat panel, artifact canvas, scene editor, and backend settings. It can:
- Switch the visible studio UI across the same 11 languages as the localized READMEs.
- Treat generated overview images as concepts, then decompose them into editable atomic parts.
- Generate exact
NxMSVG paper-figure grids with black panel boundaries. - Prepare AgInTi image-generation dry-run payloads for scientific icon concepts.
- Store BioRender MCP settings without storing secrets.
- Export the current scene to OpenSCAD for mechanical layout planning.
- Render the scene through Blender and preview PNG,
.blend,.scad, JSON, and text artifacts. - Toggle Blender, OpenSCAD, AgInTi image generation, BioRender MCP, and target-registry routing settings.
- Dry-run any configured target from the studio and save the dispatch envelope as a canvas artifact.
Artifacts are tracked under output/webapp/artifacts.json and served in the canvas rail. The intended figure architecture is documented in docs/EDITABLE_FIGURE_PIPELINE.md. See also docs/PAPER_FIGURE_STUDIO.md, docs/STUDIO_CLI.md, docs/WEBAPP.md, and docs/NPM.md.
AppAutoAction now includes a systematic Blender workflow for paper setup figures, optical benches, device concepts, and experiment design:
app-auto-action web --port 8787 --open
app-auto-action scene-template experiment-setup --output my-setup.scene.json
app-auto-action render-scene my-setup.scene.json --dry-run
app-auto-action render-scene my-setup.scene.json --output-dir output/scenesThe web app provides chat, JSON scene editing, dry-run planning, and render preview. The source of truth is a JSON scene spec. Blender runs headless and produces a .png preview plus a .blend scene. Start from examples/paper-optics-setup.scene.json, inspect the generated example render, or read docs/WEBAPP.md and docs/SCENE_SPEC.md.
For a no-sudo local Blender install and a real headless scene generation test:
scripts/install_blender_portable.sh
app-auto-action --config configs/blender-local-command.example.json doctor
app-auto-action --config configs/blender-local-command.example.json dispatch blender "Draw a welcoming modern building with a tower"The command bridge is bridges/codex_exec_blender.sh. It reads the AppAutoAction JSON envelope from stdin, runs Blender in background mode, stores Blender logs under output/blender/, and returns clean JSON with .blend and .png artifact paths.
| Target | Current adapter | Best bridge shape | Notes |
|---|---|---|---|
| Blender | http_json |
Blender MCP add-on, local HTTP, or command bridge | Good for scene generation, materials, rendering, export. |
| AgInTi | local_command via web settings |
aginti image --json |
Dry-run image payloads for figure concepts; live calls require provider keys. |
| BioRender | browser plus MCP metadata |
Official remote MCP connector | Use OAuth/API-supported flows; avoid scraping. |
| Unity | http_json |
Unity package, WebSocket proxy, or C# editor bridge | Good for scenes, assets, scripts, tests, play mode. |
| Unreal | http_json |
Unreal MCP plugin or Python remote execution proxy | Treat as privileged editor access. |
Copy configs/targets.example.json to agenticapp.targets.json for local ports, commands, and tokens. This override file is ignored by git.
The design follows the MCP split between tools, resources, and prompts, then adapts it to live editor bridges. The research brief is in docs/RESEARCH.md, covering:
- Blender MCP projects with headless and live-GUI modes.
- Unity MCP packages with scene, asset, script, and play-mode control.
- Unreal MCP servers using plugins or Python Remote Execution.
- BioRender's documented MCP connector endpoint.
- Security tradeoffs for agents with editor write access.
Agent or MCP client
|
| command / dry-run / MCP config
v
AppAutoAction CLI
|
| target registry
v
Transport adapter: http_json | local_command | browser | noop
|
v
Blender / BioRender / Unity / Unreal bridge
Every dispatch receives the same envelope:
{
"target": "blender",
"kind": "blender",
"instruction": "Create a red cube at the origin",
"payload": {},
"metadata": {
"source": "agenticapp"
}
}Localized READMEs live under i18n/ and use the same profile-style language switcher as this root README:
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PYTHONPATH=src python -m unittest discover -s tests
PYTHONPATH=src python -m agenticapp doctorKeep transport behavior covered by tests before adding live editor features. See AGENTS.md for contributor guidance and SECURITY.md for the editor-automation security model.

