Skip to main content

Schema-driven module development framework for AI-perceivable interfaces

Project description

apcore logo

apcore

Schema-driven module development framework for AI-perceivable interfaces.

apcore provides a unified task orchestration framework with strict type safety, access control, middleware pipelines, and built-in observability. It enables you to define modules with structured input/output schemas that are easily consumed by LLMs and other automated systems.

Features

  • Schema-driven modules -- Define input/output contracts using Pydantic models with automatic validation
  • 10-step execution pipeline -- Context creation, safety checks, ACL enforcement, validation, middleware chains, and execution with timeout support
  • @module decorator -- Turn plain functions into fully schema-aware modules with zero boilerplate
  • YAML bindings -- Register modules declaratively without modifying source code
  • Access control (ACL) -- Pattern-based, first-match-wins rules with wildcard support
  • Middleware system -- Composable before/after hooks with error recovery
  • Observability -- Tracing (spans), metrics collection, and structured context logging
  • Async support -- Seamless sync and async module execution
  • Safety guards -- Call depth limits, circular call detection, frequency throttling
  • Extension points -- Unified extension management for discoverers, middleware, ACL, span exporters, and module validators
  • Async task management -- Background module execution with status tracking, cancellation, and concurrency limiting
  • W3C Trace Context -- traceparent header injection/extraction for distributed tracing interop

Requirements

  • Python >= 3.11

Installation

pip install -e .

For development:

pip install -e ".[dev]"

Quick Start

Define a module with the decorator

from apcore import module

@module(description="Add two integers", tags=["math"])
def add(a: int, b: int) -> int:
    return a + b

Define a module with a class

from pydantic import BaseModel
from apcore import Context

class GreetInput(BaseModel):
    name: str

class GreetOutput(BaseModel):
    message: str

class GreetModule:
    input_schema = GreetInput
    output_schema = GreetOutput
    description = "Greet a user"

    def execute(self, inputs: dict, context: Context) -> dict:
        return {"message": f"Hello, {inputs['name']}!"}

Register and execute

from apcore import Registry, Executor

registry = Registry()
registry.register("greet", GreetModule())

executor = Executor(registry=registry)
result = executor.call("greet", {"name": "Alice"})
# {"message": "Hello, Alice!"}

Add middleware

from apcore import LoggingMiddleware, TracingMiddleware

executor.use(LoggingMiddleware())
executor.use(TracingMiddleware())

Access control

from apcore import ACL, ACLRule

acl = ACL(rules=[
    ACLRule(callers=["admin.*"], targets=["*"], effect="allow", description="Admins can call anything"),
    ACLRule(callers=["*"], targets=["admin.*"], effect="deny", description="Others cannot call admin modules"),
])
executor = Executor(registry=registry, acl=acl)

Project Structure

src/apcore/
    __init__.py          # Public API
    async_task.py        # Background task manager
    cancel.py            # Cooperative cancellation primitives
    context.py           # Execution context & identity
    executor.py          # Core execution engine
    decorator.py         # @module decorator
    bindings.py          # YAML binding loader
    config.py            # Configuration
    acl.py               # Access control
    extensions.py        # Extension point manager
    errors.py            # Error hierarchy
    module.py            # Module annotations & metadata
    trace_context.py     # W3C trace context helpers
    middleware/          # Middleware system
    observability/       # Tracing, metrics, logging
    registry/            # Module discovery & registration
    schema/              # Schema loading, validation, export
    utils/               # Utilities

Development

Run tests

pytest

Run tests with coverage

pytest --cov=src/apcore --cov-report=html

Lint and format

ruff check --fix src/ tests/
ruff format src/ tests/

Type check

mypy src/ tests/

📄 License

Apache-2.0

🔗 Links

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

apcore-0.6.0.tar.gz (97.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

apcore-0.6.0-py3-none-any.whl (73.6 kB view details)

Uploaded Python 3

File details

Details for the file apcore-0.6.0.tar.gz.

File metadata

  • Download URL: apcore-0.6.0.tar.gz
  • Upload date:
  • Size: 97.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for apcore-0.6.0.tar.gz
Algorithm Hash digest
SHA256 2626a98c6ea6e808aa9341589359c8523206b7d05392e8cf58c3e3e93f703466
MD5 bdd62d473ba67798ef45f26c3bb94f28
BLAKE2b-256 fd03c04d506234f28a2bd5383ab493dc1fac5ccefa0a4b77e232a45607195fda

See more details on using hashes here.

File details

Details for the file apcore-0.6.0-py3-none-any.whl.

File metadata

  • Download URL: apcore-0.6.0-py3-none-any.whl
  • Upload date:
  • Size: 73.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for apcore-0.6.0-py3-none-any.whl
Algorithm Hash digest
SHA256 f3e85493745ad87a3ce353b6995a52a1148e4287c7430690c9cdc110eca23704
MD5 218dc5110a22305fdabea0f775223f3d
BLAKE2b-256 84b2fce8ac0a2be997645841f2f97931c6fe8c6905a8cedc34f0f8f8f32a52ab

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page