WebCortex
Status: pre-release. Not yet published; build from source. Once released it installs as
web-cortex-frameworkand imports aswebcortex— the same split asdjangorestframework→import rest_framework.
📖 Documentation · Tutorial · Use cases · Security
A Python web framework with a Rust core, built on one idea:
If you declared it, Rust can run it — and an agent can call it.
Django and Rails were designed when the only client was a browser. Today the client is just as likely to be a model. WebCortex treats that as the primary case rather than something you bolt on with a second, hand-maintained tool server.
# api.py
from webcortex import WebCortex
app = WebCortex("bookstore", database="sqlite://./app.db")
app.api_key("WEBCORTEX_API_KEY", id="service", scopes=["read", "write"])
app.rate_limit(per_second=50)
app.anonymous_scopes("read")
app.resource(
"books",
fields={"id": int, "title": str, "author": str, "year": int},
tools=True,
read_scopes=["read"],
write_scopes=["write"],
)
@app.get("/books/{id}/blurb", tool=True, scopes=["read"])
def blurb(id: int) -> str:
"""One-line pitch for a book."""
return f"Book {id} — highly recommended."
$ webcortex dev
You now have a REST API, an OpenAPI 3.1 document, a live MCP server exposing all six endpoints as tools, authentication, rate limiting, and security headers. No second file, no schema written twice, no drift.
Start here
# Not yet on PyPI — see Status below. For now, build from source:
git clone https://github.com/slimboi34/web_cortex_framework && cd webcortex
uv venv --python 3.14 && uv pip install maturin && maturin develop --uv
webcortex new myapp # --template api | fullstack | agent | behaviour
cd myapp
export WEBCORTEX_API_KEY=$(webcortex keygen)
webcortex dev
Why a Rust core, specifically
Most Rust-accelerated Python servers put Rust at the socket and call Python for every request. You get faster parsing; your handler is still interpreted.
WebCortex puts the boundary somewhere more useful. Python is a declaration language that compiles to a plan the Rust runtime executes. A route whose work is expressible as data — a query, a proxy, a rendered page, a static file, an agent invocation — runs entirely in Rust and never enters the interpreter at request time. In practice that is most of a CRUD API.
$ webcortex check
12 routes, 9 served without touching Python
When a route genuinely needs Python, it crosses onto a pool of free-threaded interpreter workers (CPython 3.13+/3.14, GIL disabled), each running its own event loop. Handlers run in real parallel — measured at 4.82× vs 1.38× under the GIL (DESIGN.md has the numbers and their caveats).
WebCortex runs correctly on a GIL build too, and tells you which mode it is in.
Behaviours
A "skill" written as a prompt is a suggestion. The model reads it and may ignore it, and "if X then Y" fails silently when it does.
A Behaviour inverts that. The control flow is real Python — a for loop is
a loop, an if is a branch, and both execute whether or not a model would have
chosen to. Only the leaves are probabilistic:
@app.behaviour("triage", tools=["list_tickets", "update_tickets"],
max_steps=100, token_budget=100_000)
def triage(ctx, input):
"""Classify every open ticket and escalate the urgent ones."""
tickets = ctx.call("list_tickets", limit=50)
escalated = []
for ticket in tickets: # a real loop
verdict = ctx.ask( # a model call
f"Grade this ticket:\n{ticket['body']}",
schema={
"type": "object",
"properties": {
"urgency": {"type": "integer"},
"category": {"enum": ["bug", "billing", "other"]},
},
"required": ["urgency", "category"],
},
)
if verdict["urgency"] >= input.get("threshold", 7): # a real branch
escalated.append(ticket["id"])
ctx.call("update_tickets", id=ticket["id"], body=ticket["body"],
urgency=verdict["urgency"],
state="escalated" if ticket["id"] in escalated else "triaged")
return {"escalated": escalated, "usage": ctx.usage}
You get a procedure with deterministic structure and probabilistic steps, rather than a probabilistic procedure.
ctx is how a behaviour reaches the world:
ctx.call(tool, **kwargs) |
invoke one of the app's tools, in-process |
ctx.ask(prompt, schema=...) |
a model call; a schema forces the shape, so branches switch on real values |
ctx.log(msg) / ctx.halt(reason) |
narrate or stop deliberately |
ctx.usage / ctx.trace |
budget consumed and every leaf executed, readable mid-run |
ctx.user / ctx.tools |
the delegated principal and what it may call |
A behaviour is a tool. It registers as a route, so it is automatically an MCP tool, an OpenAPI operation, and something an agent — or another behaviour — can call. Composition is just a tool call, so budgets and scopes still apply.
The runtime enforces the limits, not your diligence:
max_stepscaps total leaf operations. A loop that would run 100 times withmax_steps=3stops at 3 and returns{"halted": true, "reason": ...}.token_budgetcaps spend.- A behaviour cannot call a tool it did not declare.
- An approval-gated tool cannot be laundered through a behaviour — the gate halts the run, exactly as it halts an agent.
- Scopes are delegated by intersection, never unioned.
webcortex new myapp --template behaviour
The seven kinds of route
| Kind | Declared with | Runs in |
|---|---|---|
| Static | app.static(...) |
Rust |
| Query | app.query(...), app.resource(...) |
Rust |
| Page | app.page(...) |
Rust (minijinja) |
| Files | app.static_files(...) |
Rust |
| Proxy | app.proxy(...) |
Rust |
| Agent | app.agent(..., expose_at=...) |
Rust |
| Behaviour | @app.behaviour(...) |
Python worker pool |
| Python | @app.get(...) |
Python worker pool |
Every route is a tool
Mark a route tool=True and it appears in MCP tools/list, with an input
schema derived from the handler's own type hints. Agents declared in the same
app call those tools in-process — a function call through the same
dispatcher the HTTP server uses, not a loopback request.
app.agent(
"librarian",
model="claude-opus-5",
tools=["list_books", "get_books"],
scopes=["read"], # what a run may do
expose_scopes=["read"], # who may start one
max_steps=8,
token_budget=50_000,
expose_at="/ask",
)
A typo in tools fails at boot with a "did you mean" suggestion.
What makes agents safe to deploy
These are enforced by the runtime, not by your diligence:
Delegated authority. An agent run executes as
caller.delegate_to_agent(...), whose scopes are intersected with the
caller's — never unioned. An anonymous caller cannot launch a privileged agent.
This is the confused-deputy defence, and it is a property of the type, not a
convention.
Human approval gates. Mark a route approval="required" and an agent asking
for it does not get it — the run suspends and records an approval request. The
gate also applies to direct MCP calls, so it cannot be stepped around.
@app.delete("/books/all", tool=True, scopes=["write"], approval="required")
def clear_catalogue(confirm: bool = False) -> dict: ...
Runtime-enforced budgets. max_steps and token_budget are checked before
each provider call. A looping model costs a bounded amount.
Scope-filtered tool lists. tools/list shows only what that caller can
invoke. A reader sees three tools where an admin sees six.
A full audit trail, including refused calls, at GET /_webcortex/audit.
Security defaults
Deny-by-default throughout; relaxing something costs a line, tightening it costs
nothing. API keys are referenced by environment variable, hashed with SHA-256,
and compared in constant time. JWT (HS/RS) with mandatory expiry validation.
Per-principal token-bucket rate limiting. Security headers on every response.
CORS that refuses * with credentials at boot. Path traversal, symlink
escapes, and dotfiles refused by the static server.
webcortex security prints exactly what is reachable without a credential:
$ webcortex security
{"auth_configured": true, "public_routes": ["GET /"], "gated_tools": ["DELETE /books/all"]}
The frontend, without the mess
Two clean paths sharing one data layer, chosen per route:
Server-rendered pages, executed in Rust:
app.page("/", "index.html", sql="SELECT * FROM books LIMIT 20", bind="books")
app.static_files("/assets", "static")
The rule that keeps this clean: a template receives a data object and nothing else. It has no database handle and cannot call Python, so it cannot grow logic. Data is resolved before rendering, from a constant, a query, or a Python handler. Autoescaping is on and derived from the file extension.
A typed TypeScript client for SPA frontends, from the same route table:
$ webcortex typegen # writes client/api.ts
Zero dependencies, fetch-based, with auth built in. Pages and static mounts
are excluded — they are not part of the JSON API surface.
Commands
webcortex new <name> scaffold a project (api | fullstack | agent | behaviour)
webcortex dev run with a startup report
webcortex check routes, tools, and the public attack surface
webcortex security what is reachable without a credential
webcortex tools the agent tool manifest
webcortex typegen generate a typed TypeScript client
webcortex openapi the OpenAPI 3.1 document
webcortex sql DDL for declared resources
webcortex keygen mint an API key
Security
WebCortex has been through an adversarial review of its own controls — auth,
authorization, injection, traversal, SSRF, exhaustion, disclosure — plus stress
and soak testing. Six issues were found and fixed, each with a regression
test in tests/test_pentest.py:
| Severity | |
|---|---|
| Remote DoS + total auth failure via a JWT library panic | Critical |
| No panic boundary on the request path | High |
| Proxy path traversal usable as an SSRF primitive | High |
| Unbounded behaviour recursion exhausting the worker pool | High |
| Python tracebacks returned to clients | Medium |
| Client input faults reported as 500s | Low |
Soak: 1,786,805 requests, 0 errors, 0 panics, memory at steady state.
SECURITY.md has the full report — including what was not
tested and the known limits.
Status
v0.3. Working and tested: the manifest IR, router, native ops (static / query / proxy / page / files), the free-threaded Python bridge, authentication and scopes, rate limiting, CORS, security headers, graceful shutdown, Behaviours, the agent runtime with approval gates and budgets, the audit trail, OpenAPI, the MCP server, and TypeScript generation. 251 tests (66 Rust, 185 Python, including a 54-test adversarial suite), clippy clean.
Not yet: Postgres, SSE streaming, durable agent runs, local model supervision. See DESIGN.md for the roadmap, honest risk grading, and — just as importantly — what is deliberately not being built.
Known issue: CPython 3.14.7 with the GIL enabled
On CPython 3.14.7 specifically, with the GIL enabled, the compiled extension fails to import:
ValueError: module functions cannot set METH_CLASS or METH_STATIC
3.12, 3.13, 3.14.6 and the free-threaded 3.14.7 build (3.14t) are all
unaffected. 3.14.7 was released on 5 August 2026; the failure began the same day.
This is not a version-resolution accident on your machine — uv resolves 3.14
to the newest patch build available when it runs, so an environment that worked
yesterday can stop working today without anything in your project changing. Pin
to 3.13, or use the free-threaded 3.14 build, until this is resolved.
The investigation so far, including what has been ruled out, is in AGENTS.md §11.
For AI coding tools
AGENTS.md is the machine-facing reference: the complete API surface with exact signatures and defaults, the binding and scope rules, the constraints the runtime enforces, and the specific mistakes that are cheap to make and expensive to debug. Claude Code, Cursor, Codex, Aider and Copilot Workspace all read it by convention.
It is written to be correct rather than welcoming. Humans should start with the docs site instead.
Building from source
# 3.13 is the recommended toolchain today — see "Known issue" above.
uv venv --python 3.13
uv pip install maturin pytest
.venv/bin/maturin develop --uv
.venv/bin/python -m pytest tests/
The free-threaded build is also supported and is the configuration the bridge was designed around:
uv venv --python 3.14t
License
Apache-2.0
Metadata
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