steltic 0.1.4

Free, local AISC 360/341 steel-design agent: your LLM + OpenSees + stamped-style reports

Steltic

A free, open-source AISC 360/341 steel-design agent that runs locally. Paste a design brief and watch the agent build an OpenSees model + a stamped-style HTML report + an interactive 3D viewer — bringing your own LLM (your API base-url + key, held in memory only, never written to disk).

browser ──▶ FastAPI app (localhost) ──▶ your LLM (key in app memory, never stored)
                 │  parses tool calls
                 ▼
           sandbox executor (run_python only) — Docker when available
                 │
           steel engine + OpenSees ─▶ report.html + viewer_3d.html

Not for construction. Every output is produced by your own AI model, may be incomplete or incorrect, and must be independently checked and sealed by a licensed professional engineer before any use for design, construction, or permitting. See DISCLAIMER.md.

Try the hosted version of Steltic for free using our servers stelticai.com

Install & run

With uv (recommended — no Python setup needed):

uv tool install --python 3.12 steltic
steltic                      # starts on http://127.0.0.1:8000 and opens your browser

The --python 3.12 matters: openseespy (the analysis engine) ships compiled binaries for Python 3.10–3.12 only, and uv ignores the package's upper Python bound — without the flag it may install onto a newer interpreter where the engine can't load (Steltic will tell you and refuse to start). uv downloads 3.12 automatically if you don't have it. Or with pipx (which respects the bound): pipx install --python 3.12 steltic. The install includes the full engine (openseespy/numpy/scipy/matplotlib, ~400MB) so designs run out of the box without Docker. Or from a checkout:

git clone <this repo> && cd steltic
python -m venv .venv && . .venv/bin/activate
pip install -e .
./run_local.sh               # http://localhost:8000

First run: open Settings, enter your provider's API base URL (any OpenAI-compatible endpoint — OpenRouter, vLLM, Together, Fireworks, OpenAI, Anthropic…), your API key, and a model id. Then paste a brief (stories, bays & spacings, loads, seismic, system) — or pick one of the 36 built-in example briefs — and click Design building.

Offline smoke test: set Model to MOCK — it drives the whole pipeline (sandbox, engine, OpenSees, report) with no LLM.

Sandbox

The agent's Python only ever executes in a sandbox. EXECUTOR (env / .env) picks the mode:

value	isolation	notes
auto	Docker if available, else subprocess	default
docker	container per run: no network, read-only fs, non-root, cpu/mem caps	build once: ./sandbox_image/build.sh
subprocess	none (child process with rlimits)	needs pip install openseespy numpy scipy matplotlib; fine for local single-user use

Steltic binds to 127.0.0.1 and has no authentication — don't expose the port. Designs are saved under your OS user-data dir (e.g. %LOCALAPPDATA%\Steltic, ~/.local/share/Steltic); override with DATA_DIR.

Engineering-standards, OpenSees & design-examples RAG (highly recommended)

The hosted version of Steltic at stelticai.com grounds the agent with RAG vector databases of AISC 360, 341 and 358 (2022), the AISC Steel Construction Manual Design Examples (V16.0, 168 worked examples), an examples database of validated OpenSees models, and two databases of the OpenSees documentation. The specifications and design examples ground the LLM in the current procedures; the OpenSees databases help it build the model — and when OpenSees throws errors, resolve them. Steltic's performance has been validated with these databases and will likely reduce without them.

For copyright reasons the vector databases of AISC 360, 341 and 358 are not open-sourced in this project. The rest are freely downloadable from this repo's Releases page as portable .jsonl.gz dumps (pre-embedded; see rag_v2/README.md for loading them into your own Qdrant): steel_design_examples (168 original worked Q&A covering the AISC Design Examples scope), opensees_buildings_3d (40 validated 3D building models), opensees_building_templates, and the two OpenSees documentation sets.

Two options:

1. None (default — not recommended). Leave RAG_API_URL empty — the agent relies on its own cited AISC knowledge. Good models do respectably; grounded runs are better.
2. Bring your own server. Run any server exposing the small API described in rag_v2/README.md: load the downloadable OpenSees databases, and build the standards collections from your own licensed copies with the rag_v2/ chunking + ingestion starter kit. Then put the connection in the environment (or a .env next to where you launch): RAG_API_URL=http://your-server:8080/query and, if your server enforces one, RAG_API_TOKEN=....

Repo map

steltic/ FastAPI app + agent loop + sandbox executors · steel_engine/ OpenSees modelling, design pipeline, consistency checks, report + 3D viewer · contract/ the agent's working contract and references · frontend/ vanilla JS UI · sandbox_image/ Docker sandbox image · test_buildings/ 50+ example briefs (steel + CFS) with assessment rubrics · rag_v2/, rag_update/ build-your-own-RAG starter kit.

License

MIT — see LICENSE, NOTICE (third-party data notes) and DISCLAIMER.md (engineering disclaimer; also shown in-app at /terms).