This release is a pre-release and may not be stable for production use.
Corio
Corio is a practical Python toolkit for building small services, automation, data pipelines, and AI/ML support code without re-solving the same project plumbing every time.
It bundles the pieces that tend to sit between "standard library" and "application code": typed paths with serialization helpers, pydantic-friendly data models, CLI settings, structured logging spans, iterator progress, FastAPI/MCP endpoint scaffolding, release metadata generation, test environment generation, and opt-in integrations for docs, Docker, search, tabular data, PDFs, OpenAI, Home Assistant, DNS, caching, encryption, and more.
Corio is organized as one package with optional extras. Install the small core by default, then opt into the heavier tools a project actually needs.
Install
pip install corio
Examples:
pip install "corio[api]"
pip install "corio[tabular]"
pip install "corio[db.search]"
pip install "corio[infra]"
Why Use It
- Use paths as real application objects. Read and write JSON, YAML, TOML, text, and derived paths directly from
corio.Path. - Make CLIs from typed models. Corio builds on pydantic settings patterns so small tools can stay declarative.
- Wrap iteration with observability.
corio.iterator.Iteratortracks count, percentage, elapsed time, ETA, completion, and cleanup. - Expose tools as HTTP and MCP endpoints. Define endpoint classes once and mount them into FastAPI and MCP surfaces.
- Keep release metadata generated.
corio pyprojectturns Corio dependency groups and project metadata into the generatedpyproject.tomlsections. - Install integrations only when needed. Extras keep heavy dependencies like API servers, vector search, ML, PDFs, and tabular tooling out of the base install.
Examples
Typed Paths And Data Files
from corio import Path
path = Path("runs/latest/config.yaml")
path.parent.mkdirf()
path.write_data({
"model": "embedder-v2",
"batch_size": 64,
})
config = path.read_data()
json_path = path.get_conversion_path("json")
json_path.parent.mkdirf()
json_path.write_data(config)
Observable Iteration
from corio.iterator import Iterator
documents = [{"id": "a"}, {"id": "b"}, {"id": "c"}]
for document in Iterator(documents):
# Work happens inside per-item logging spans with progress stats.
process(document)
The iterator tracks count, total, percentage, elapsed time, rate, ETA, completion logs, and closes the wrapped iterator when possible.
FastAPI And MCP Tools
from functools import cached_property
from corio import api
class Search(api.endpoint.API):
"""Search indexed documents."""
PATH = "/search"
TAGS = "documents"
async def run(self, query: str):
return {"query": query, "results": []}
class Reindex(api.endpoint.Tool):
"""Rebuild the document index."""
async def run(self):
return {"status": "queued"}
class Service(api.Base):
TITLE = "Document Service"
PORT = 8080
@cached_property
def ENDPOINTS(self):
return [Search, Reindex]
Service().launch()
The same endpoint model can feed HTTP routes and MCP tools, so internal automation and external APIs stay aligned.
Project Metadata
Corio projects keep dependency intent in [tool.corio.dependencies]; generated package metadata lives in [project.optional-dependencies].
[tool.corio.dependencies]
semantic = ["sentence_transformers", "metrics"]
metrics = ["tabular", "ranx"]
tabular = ["pandas", "tabulate", "openpyxl", "odfpy", "deepdiff"]
Then regenerate:
corio pyproject
That keeps extras flattened for packaging while preserving a maintainable source-of-truth dependency graph.
Useful Areas
corio.path: typed path helpers, app paths, file type guessing, serialization helpers.corio.dmandcorio.sets: pydantic-oriented models and settings/CLI support.corio.api: FastAPI and MCP endpoint composition.corio.iterator: progress-aware iterable wrapper and small collection helpers.corio.infra: release, docs, tox, and pyproject generation tooling.corio.db.search: vector search helpers around qdrant and embeddings.corio.tabular,corio.semantic,corio.metric: data and retrieval metrics helpers.corio.encrypt,corio.sec: project secret and encryption workflows.corio.ha,corio.dns,corio.mqtt,corio.webhook: operations and home automation integrations.
Documentation
- Published docs: https://fmtr.github.io/corio
- Local docs source: docs
License
This project is licensed under Apache 2.0. See LICENSE.
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file corio-2.6.4a10.tar.gz.
File metadata
- Download URL: corio-2.6.4a10.tar.gz
- Upload date:
- Size: 134.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.14.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
6428d7c1b914b4b66883fa2c22dcca5b531c18f93e3b49de3c31dd35e4d92ae1
|
|
| MD5 |
ce9fdbeb31b07915d0c9bdbc4004a9db
|
|
| BLAKE2b-256 |
725a181d56a6eab06509376db1a52f04d4a2835b3565af6464967565c85c0315
|
File details
Details for the file corio-2.6.4a10-py3-none-any.whl.
File metadata
- Download URL: corio-2.6.4a10-py3-none-any.whl
- Upload date:
- Size: 168.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.14.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
3c934f9ab83e5cb669bfbd8e20fc60a06bf9cc829b115716535eb2c782715a3b
|
|
| MD5 |
6d7e4b5d65020caf010c8a569578463d
|
|
| BLAKE2b-256 |
f161fae82b87e81254badd47f8f2e196195022c439f187f991bd5945aba8d876
|
