AI-assisted structural engineering workspace for AEC workflows.

• Conversational engineering workflow from natural language to analysis artifacts
• Unified orchestration loop: draft -> validate -> analyze -> code-check -> report
• Web UI, API backend, and backend-hosted Python analysis runtime in one monorepo
• Regression and contract scripts for repeatable engineering validation

frontend (Next.js)
	-> backend (Fastify + Prisma + Agent orchestration + analysis runtime host)
	-> backend/src/agent-skills/analysis-execution/python
	-> reports / metrics / artifacts

Main directories:

• frontend/: Next.js 14 application
• backend/: Fastify API, agent/chat flows, Prisma integration, and analysis execution host
• scripts/: startup helpers and the sclaw / sclaw_cn CLI implementation
• tests/: regression runner (node tests/runner.mjs ...), install smoke, and CI-covered frontend checks (type-check, Vitest, lint) after native smoke
• docs/: user handbook and protocol references

If Node.js is not installed yet, use the helper installer script first:

bash ./scripts/install-node-linux.sh

Windows PowerShell (run as Administrator for first-time package install):

powershell -ExecutionPolicy Bypass -File ./scripts/install-node-windows.ps1

Recommended local flow:

./sclaw doctor
./sclaw start
./sclaw status

China mirror flow (same subcommands, mirror defaults enabled):

./sclaw_cn doctor
./sclaw_cn start
./sclaw_cn status

Notes:

• SQLite is now the default local database. ./sclaw start uses .runtime/data/structureclaw.start.db, and ./sclaw doctor uses .runtime/data/structureclaw.doctor.db so preflight checks do not touch the active local runtime database.
• ./sclaw doctor no longer requires a preinstalled system Python 3.12. It will ensure uv and prepare backend/.venv with Python 3.12 automatically when needed. On Windows, this automatic setup currently requires winget; if winget is unavailable, install uv manually before running ./sclaw doctor.
• If your old local .env still points DATABASE_URL at a local PostgreSQL instance, ./sclaw doctor and ./sclaw start will auto-migrate that data into SQLite, rewrite .env to the SQLite default, and keep the original PostgreSQL URL in POSTGRES_SOURCE_DATABASE_URL.
• That first auto-migration also creates a local backup file like .env.pre-sqlite-migration.<timestamp>.bak.
• sclaw_cn defaults to China mirror settings when unset: PIP_INDEX_URL=https://pypi.tuna.tsinghua.edu.cn/simple, NPM_CONFIG_REGISTRY=https://registry.npmmirror.com, and Docker mirror prefix via DOCKER_REGISTRY_MIRROR.
• You can override mirror values in .env or shell environment (PIP_INDEX_URL, NPM_CONFIG_REGISTRY, DOCKER_REGISTRY_MIRROR, APT_MIRROR).

Useful follow-up commands:

./sclaw logs
./sclaw stop
node tests/runner.mjs backend-regression
node tests/runner.mjs analysis-regression

Use the built-in CLI batch convert command to transform structure model JSON files and write a summary report:

./sclaw convert-batch --input-dir tmp/input --output-dir tmp/output --report tmp/report.json --target-format compact-1

Windows PowerShell:

node .\sclaw doctor
node .\sclaw start
node .\sclaw status
node .\sclaw logs all --follow
node .\sclaw stop

Windows users can now start the full stack directly with Docker, which is the easiest path for beginners who do not want to install local Node.js and Python first.

Recommended steps:

1. Install and start Docker Desktop.
2. If Docker Desktop asks you to enable WSL 2 or required container features on first launch, follow the setup wizard and restart Docker Desktop.
3. Run the interactive Docker bootstrap command from the project root:

node .\sclaw docker-install

For CI or scripted setup, use the non-interactive variant:

node .\sclaw docker-install --non-interactive --llm-base-url https://api.openai.com/v1 --llm-api-key <your-key> --llm-model gpt-4.1

Once the stack is ready, the main entrypoints are:

• Frontend: http://localhost:30000
• Backend health check: http://localhost:30010/health
• Analysis routes: http://localhost:30010/analyze
• Database status page: http://localhost:30000/console/database

To stop the containers:

node .\sclaw docker-stop

Or:

docker compose down

Copy and adjust environment variables from .env.example.

Key variables include:

• PORT, FRONTEND_PORT
• DATABASE_URL, POSTGRES_SOURCE_DATABASE_URL
• LLM_API_KEY, LLM_MODEL, LLM_BASE_URL (OpenAI-compatible)
• ANALYSIS_PYTHON_BIN, ANALYSIS_PYTHON_TIMEOUT_MS, ANALYSIS_ENGINE_MANIFEST_PATH

Backend:

• POST /api/v1/agent/run
• POST /api/v1/chat/message
• POST /api/v1/chat/stream
• POST /api/v1/chat/execute

Backend-hosted analysis:

• POST /validate
• POST /convert
• POST /analyze
• POST /code-check

• Skills are enhancement layers, not the only execution path.
• Unmatched selected skills fall back to generic no-skill modeling.
• User-visible content must support both English and Chinese.
• Keep module boundaries explicit across frontend/backend/analysis skills.

• English handbook: docs/handbook.md
• Chinese handbook: docs/handbook_CN.md
• English reference: docs/reference.md
• Chinese reference: docs/reference_CN.md
• Chinese overview: README_CN.md
• Contribution guide: CONTRIBUTING.md

Please read CONTRIBUTING.md before opening a PR.

MIT. See LICENSE.