Install the FERS Python package
One pip command gets you the Python modelling layer and the compiled Rust solver. Local analysis needs no account, no key and no network — credentials are only for cloud storage, the agent channel and Pro limits.
1. Install the package
The package is published on PyPI as FERS. It ships the Python model-building layer plus the compiled solver wheel, so there is nothing else to build.
pip install FERSRequires Python 3.11 or newer. Wheels are published for Windows, macOS and Linux — no Rust toolchain is needed to install.
2. Verify the installation
Import the core objects and construct an empty model. If this runs without raising, the compiled solver loaded correctly.
from fers_core import FERS, Node, Member, Section, Material, NodalSupport, NodalLoad
# Quick smoke-test
model = FERS()
print("FERS installed successfully")3. Authenticate with the cloud (optional)
You only need this for saving models to your account, the MCP agent channel, and Pro limits. There are two ways to authenticate, depending on how you signed up.
- Option A — email and password account — Exchange your credentials for a 1-hour Bearer token via
POST /api/sdk/token. Only works for accounts created with an email and password. - Option B — Google or GitHub account (recommended for everyone) — Create a permanent API key from your Profile page → API Keys. No password required, it works for any sign-in method, and you can revoke it at any time.
Option A: short-lived Bearer token
The token lasts one hour. Fetch a fresh one when it expires rather than storing it.
import requests
resp = requests.post("https://ferscloud.com/api/sdk/token", json={
"email": "you@example.com",
"password": "your_password",
})
token = resp.json()["token"]Option B: persistent API key
An API key has the shape <keyId>.<secret> and is shown once, at creation. Store it in an environment variable and keep it server-side — never in browser code or a committed file.
import os
os.environ["FERS_API_KEY"] = "<keyId>.<secret>"Cloud authentication is only needed for saving models, MCP and Pro features. Local analysis with run_analysis() works without any credentials.
What you get without paying
The free tier solves models up to 100 members, with no account and no key. Pro raises that to 10,000 members and adds cloud model storage.
The agent and REST channels are metered separately: 100 free solves per rolling week, then €0.01 per solve from prepaid credit. Pro is €19.95/month.
| Free | Pro | |
|---|---|---|
| Members per model | 100 | 10,000 |
| Local solving | Unlimited | Unlimited |
| Account required | No | Yes |
| Cloud model storage | No | Yes |
| Solves per week via API/MCP | 100 | Unlimited |
Next steps
- Build and solve a model — the core objects and a first analysis.
- Worked examples — cantilever, simply supported beam, portal frame and an EN 1993-1-1 check, each with its closed-form hand check.
- Why script a calculation at all — the case for the Python route.
Related pages
See also
Frequently asked questions
What is the pip package called?
pip install FERS. The import name is fers_core — for example from fers_core import FERS, Node, Member.Do I need an API key to run an analysis?
run_analysis() solves locally with no credentials and no network access. Keys are only for cloud model storage, the MCP/REST channels and Pro limits.