rio
Rio is a fully autonomous, one-shot coding agent. It has no built-in interactive TUI, no conversation history, no human steer in the middle and no follow-up turns. Start a task and Rio runs until the task completes or aborts.
Getting Started
Install via uv:
uv tool install rio
Usage
Login to your preferred providers:
rio login anthropicrio login github-copilotrio login codex- ...
Run a adhoc task:
rio run "Fix gh issue 123."
Rio works in a Jupyter notebook: it acts by adding Python, shell, and %%edit
cells, which run in one live kernel in the project directory.
Run a predefined workflow:
rio run task.md
The workflow is to write the task in a Markdown file, then run Rio against it.
You can add a short instruction when invoking it:
uv run rio run "Follow feature.md, run the tests, and finish the implementation"
Use --provider NAME --model MODEL to choose a provider and a model.
Rio prints a compact human transcript by default. Use --output json for one JSON
event per line. Each human-mode run prints a session id; continue it with
rio run --resume SESSION_ID "Next task".
Documentation
See the documentation for tutorials, guides, command-line reference, and an explanation of Rio's architecture.
Development
uv run pytest
uv run ruff check .
To run the Lean 4 verification, install elan and build the pinned toolchain from the repository root:
cd tests/lean && lake build
See the Lean behavior models for their scope and limits.
rio.aiis a multi-provider LLM streaming SDK.rio.agentis the Context Language Model runtime: the context is a Jupyter notebook the model patches, and the runtime runs the changed cells.rio.codingsupplies the autonomous coding skill.rio.cliis the public one-shot command-line entry point.
Metadata
Release files for rio 0.7.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| rio-0.7.1.tar.gz | 540.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| rio-0.7.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 957.1 kB
Release files / rio-0.7.1.tar.gz
| Download URL | rio-0.7.1.tar.gz |
|---|---|
| Size | 540.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
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Transparency logRelease files / rio-0.7.1-py3-none-any.whl
| Download URL | rio-0.7.1-py3-none-any.whl |
|---|---|
| Size | 416.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
9a77acfdcdea842a14e2209ff48f290ac2c76b30cecbd80150688c88187e9973
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BLAKE2b-256 checksum How to use checksums |
475ac5a89823fcc358345aae944a9a1943cbb78e804bc363c35c60f6abeec97a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 4, 2026.
Transparency log