pico-ioc: A Robust, Async-Native IoC Container for Python

pico-ioc is a lightweight, async-ready, decorator-driven IoC container built for clarity, testability, and performance. It brings Inversion of Control and dependency injection to Python in a deterministic, modern, and framework-agnostic way.

Requires Python 3.11+

The pico ecosystem is built for the AI era: machine-readable conventions in every repo, installable AI coding skills, and scaffolds that generate AI-maintainable projects from the first commit.

Core Principles

• Single Purpose – Do one thing: dependency management.
• Declarative – Use simple decorators (@component, @factory, @provides, @configured) instead of complex config files.
• Deterministic – No hidden scanning or side-effects; everything flows from an explicit init().
• Async-Native – Fully supports async providers, async lifecycle hooks (__ainit__), and async interceptors.
• Fail-Fast – Detects missing bindings and circular dependencies at bootstrap (init()).
• Testable by Design – Use overrides and profiles to swap components instantly.
• Zero Core Dependencies – Built entirely on the Python standard library. Optional features may require external packages (see Installation).

Why pico-ioc?

As Python systems evolve, wiring dependencies by hand becomes fragile and unmaintainable. pico-ioc eliminates that friction by letting you declare how components relate — not how they’re created.

Highlights (v2.2+)

• Typed resolution (v2.4): container.get(UserService) is inferred as UserService, not Any — IDE autocomplete and type-checkers resolve the component.
• Public introspection (v2.4): container.keys() and container.metadata_for(key) enumerate the registry without reaching into the container internals.
• Unified Configuration: Use @configured to bind both flat (ENV-like) and tree (YAML/JSON) sources via the configuration(...) builder (ADR-0010).
• Hot config refresh: container.refresh_config() re-reads tree sources and publishes a ConfigChanged event with the changed prefixes.
• Extensible Scanning: Use CustomScanner to hook into the discovery phase and register functions or custom decorators (ADR-0011).
• Async-aware AOP: Method interceptors via @intercepted_by.
• Scoped resolution: singleton, prototype, request, session, transaction, and custom scopes.
• Tree-based configuration: Advanced mapping with reusable adapters (Annotated[Union[...], Discriminator(...)]).
• Observable context: Built-in stats, health checks (@health), observer hooks (ContainerObserver), and dependency graph export.

Installation

pip install pico-ioc

Optional extras:

• YAML configuration support (requires PyYAML)

  pip install pico-ioc[yaml]
  

• Dependency graph export as DOT/SVG (requires Graphviz)

  pip install pico-ioc[graphviz]
  

Important Note

Breaking Behavior in Scope Management (v2.1.3+): Scope LRU Eviction has been removed to guarantee data integrity.

• Frameworks (pico-fastapi): Handled automatically.
• Manual usage (recommended): open the scope with with container.scope("scope_name", scope_id, cleanup=True): — on block exit the cached instances are evicted and their @cleanup hooks run automatically. (Added in v2.2.6.)
• Manual usage (low-level): alternatively, call container._caches.cleanup_scope("scope_name", scope_id) yourself when a context ends to prevent memory leaks.

Quick Example (Unified Configuration)

import os
from dataclasses import dataclass
from pico_ioc import component, configured, configuration, init, EnvSource

# 1. Define configuration with @configured
@configured(prefix="APP_", mapping="auto")  # Auto-detects flat mapping
@dataclass
class Config:
    db_url: str = "sqlite:///demo.db"

# 2. Define components
@component
class Repo:
    def __init__(self, cfg: Config):  # Inject config
        self.cfg = cfg
    def fetch(self):
        return f"fetching from {self.cfg.db_url}"

@component
class Service:
    def __init__(self, repo: Repo):  # Inject Repo
        self.repo = repo
    def run(self):
        return self.repo.fetch()

# --- Example Setup ---
os.environ['APP_DB_URL'] = 'postgresql://user:pass@host/db'

# 3. Build configuration context
config_ctx = configuration(
    EnvSource(prefix="")  # Read APP_DB_URL from environment
)

# 4. Initialize container
container = init(modules=[__name__], config=config_ctx)  # Pass context via 'config'

# 5. Get and use the service
svc = container.get(Service)
print(svc.run())

# --- Cleanup ---
del os.environ['APP_DB_URL']

Output:

fetching from postgresql://user:pass@host/db

Testing with Overrides

class FakeRepo:
    def fetch(self): return "fake-data"

# Build configuration context (might be empty or specific for test)
test_config_ctx = configuration()

# Use overrides during init
container = init(
    modules=[__name__],
    config=test_config_ctx,
    overrides={Repo: FakeRepo()}  # Replace Repo with FakeRepo
)

svc = container.get(Service)
assert svc.run() == "fake-data"

Profiles

Use profiles to enable/disable components or configuration branches conditionally.

# Enable "test" profile when bootstrapping the container
container = init(
    modules=[__name__],
    profiles=["test"]
)

Profiles are typically referenced in decorators or configuration mappings to include/exclude components and bindings.

Async Components

pico-ioc supports async lifecycle and resolution.

import asyncio
from pico_ioc import component, init

@component
class AsyncRepo:
    async def __ainit__(self):
        # e.g., open async connections
        self.ready = True

    async def fetch(self):
        return "async-data"

async def main():
    container = init(modules=[__name__])
    repo = await container.aget(AsyncRepo)   # Async resolution
    print(await repo.fetch())
    
    # Graceful async shutdown (calls @cleanup async methods)
    await container.ashutdown()

asyncio.run(main())

• __ainit__ runs after construction if defined.
• Use container.aget(Type) to resolve components that require async initialization.
• Use await container.ashutdown() to close resources cleanly.

Lifecycle & AOP

import time
from pico_ioc import component, init, intercepted_by, MethodInterceptor, MethodCtx

# Define an interceptor component
@component
class LogInterceptor(MethodInterceptor):
    def invoke(self, ctx: MethodCtx, call_next):
        print(f" calling {ctx.cls.__name__}.{ctx.name}")
        start = time.perf_counter()
        try:
            res = call_next(ctx)
            duration = (time.perf_counter() - start) * 1000
            print(f"← {ctx.cls.__name__}.{ctx.name} done ({duration:.2f}ms)")
            return res
        except Exception as e:
            duration = (time.perf_counter() - start) * 1000
            print(f"← {ctx.cls.__name__}.{ctx.name} failed ({duration:.2f}ms): {e}")
            raise

@component
class Demo:
    @intercepted_by(LogInterceptor)  # Apply the interceptor
    def work(self):
        print("   Working...")
        time.sleep(0.01)
        return "ok"

# Initialize container (must scan module containing interceptor too)
c = init(modules=[__name__])
result = c.get(Demo).work()
print(f"Result: {result}")

Observability & Cleanup

• Export a dependency graph in DOT format:

  c = init(modules=[...])
  c.export_graph("dependencies.dot")  # Writes directly to file
  

• Health checks:

  • Annotate health probes inside components with @health for container-level reporting.
  • The container exposes health information that can be queried in observability tooling.

• Container cleanup:

  • For sync apps: container.shutdown()
  • For async apps: await container.ashutdown()

Use cleanup in application shutdown hooks to release resources deterministically.

Documentation

The full documentation is available within the docs/ directory of the project repository. Start with docs/README.md for navigation.

• Getting Started: docs/getting-started.md
• User Guide: docs/user-guide/README.md
• Advanced Features: docs/advanced-features/README.md
• Observability: docs/observability/README.md
• Cookbook (Patterns): docs/cookbook/README.md
• Architecture: docs/architecture/README.md
• API Reference: docs/api-reference/README.md
• ADR Index: docs/adr/README.md

Development

pip install tox
tox

Changelog

See CHANGELOG.md — Significant redesigns and features in v2.0+.

Latest: v2.3.2 (2026-07-10) — fixes lazy components with async @configure resolved via aget(), and defers lazy materialization to first use singleton identity when resolving by base class or component name (#20): the cache was written under the requested key instead of the canonical one, so cold-cache resolutions could create a second singleton.

Built for AI-assisted development

pico-ioc is designed for a workflow where humans and coding agents build software together. Architecture, conventions and integration patterns are explicit enough that an agent can extend an application without introducing a parallel, incompatible style — and can verify its own changes before proposing them.

The verification loop comes first:

• pico-testing gives any agent (or human) a three-line feedback loop: containers are isolated from the environment by default, the module under test is declared once, and make_container/make_client boot exactly what the test names. A change is not done until this loop is green.
• pico-initializer scaffolds runnable projects with the canonical layout, so every project starts on the same conventions instead of inventing them.
• pico-examples are reference applications with hermetic test suites plus real-infrastructure smoke tests (Docker Compose, Kubernetes) - each failure path shown is asserted by a test.
• pico-learn turns the patterns into executable lessons; every lab runs green in CI against the pinned published wheels.
• pico-skills gives coding agents task-specific instructions (/add-component, /add-tests, controllers, repositories, integrations).

Every package in the ecosystem ships the artifacts an agent needs to stay on-architecture: AGENTS.md with the working conventions, llms.txt indexing the docs for machine consumption, architecture decisions recorded in docs/, and documented behaviour pinned by regression tests - with coverage tracked per module on Codecov and 0.0% duplication across the fleet on SonarCloud.

Releases are gated the same way: nothing is published without the full ecosystem booting together and exercising a complete application flow against real infrastructure (PostgreSQL, Redis, RabbitMQ, Kafka). Versioning is strict SemVer with per-release compatibility notes in every changelog.

Install the agent skills for Claude Code or OpenAI Codex:

curl -sL https://raw.githubusercontent.com/dperezcabrera/pico-skills/main/install.sh | bash -s -- ioc

All skills: curl -sL https://raw.githubusercontent.com/dperezcabrera/pico-skills/main/install.sh | bash - see pico-skills.

License

MIT — LICENSE