A lightweight CLI for explicit, reproducible AI coding workflows.
Project description
Aethr
A tiny CLI for running explicit AI coding workflows from YAML.
Core Idea
Coding with LLMs is not one-shot generation.
Real development is:
plan -> implement -> review -> iterate
Aethr makes those workflows programmable. A run is just:
task + workflow + explicit context + model routing
Aethr is stateless. The only project file it creates is .aethr.yaml.
Install
pip install aethr
For local development:
pip install -e ".[dev]"
Quickstart
For reviewing the current repo:
aethr init review-existing-diff
aethr run "review my current changes before I commit"
For a multi-model implementation workflow:
aethr init plan-implement-review --force
aethr run "add support for loading .env files"
Aethr copies a YAML preset into .aethr.yaml. Edit it like any other project
file. The default plan-implement-review preset uses OpenCode for the
implementation step, so install the opencode CLI if you want that step to
edit the working tree.
In that preset, the reviewer reads latest_diff, meaning the most recent
implementation diff rather than the whole repository diff.
How Aethr Works
- Task: the instruction passed on the command line.
- Workflow: the YAML file that defines ordered steps.
- Steps: sequential units of work. Aethr runs them in order.
- Roles: named responsibilities such as
planner,reviewer, orwriter. - Context: explicit repo input declared per step.
- Artifacts: structured implementation output such as changed files and diffs, passed forward in memory to later steps.
- Model routing: each role can point at a different LiteLLM model.
Each step receives the task, prior step outputs, and its declared context. The step result stays in memory, streams to the terminal as it is generated, and is printed in a Rich panel when complete.
Example Workflow Config
workflow: review-existing-diff
roles:
reviewer: Review the provided task context as if it were an existing diff.
models:
reviewer: openai:gpt-4o-mini
steps:
- id: review
role: reviewer
context:
- git_diff
For real code changes, Aethr can hand an implementation step to OpenCode:
- id: implement
role: implementer
backend: opencode
unsafe_permissions: true
That keeps the workflow explicit while letting a real coding agent edit the
working tree. Leave unsafe_permissions off if you want OpenCode to keep its
normal permission checks.
Built-In Workflows
plan-implement-review: plan a task, then hand implementation to OpenCode before reviewing the resultinglatest_diff.review-existing-diff: review the current working tree diff.debug-failing-test: diagnose a failing test, propose a fix, review it.add-tests: plan, draft, and review focused test coverage.docs-sync: update docs from the current diff and README context.custom: a minimal one-step workflow to edit freely.
List presets:
aethr init --list
Initialize another preset:
aethr init docs-sync --force
Examples
The examples/ directory contains small workflow files you can copy from:
examples/review-existing-diff.yamlexamples/add-tests.yamlexamples/docs-sync.yaml
These examples intentionally show different providers across roles so you can see routing in practice, not just the default presets.
Explicit Context
Aethr uses explicit context instead of automatic retrieval. That keeps runs easy to understand: the YAML shows exactly what each step can see.
Supported context sources:
git_diff: runsgit diff --no-ext-diff.latest_diff: the most recent implementation diff from the prior step.file:<path>: reads one UTF-8 file relative to the project root.glob:<pattern>: reads matching UTF-8 files relative to the project root, with a small content cap.
Use git_diff when a step should inspect the whole working tree. Use
latest_diff when a later step should inspect only the most recent
implementation output from the workflow itself.
Example:
steps:
- id: review-docs
role: reviewer
context:
- git_diff
- file:README.md
- glob:docs/**/*.md
Missing files, empty diffs, non-git directories, and unreadable files appear as clear placeholder notes in the prompt.
Loops
A step can repeat an earlier contiguous slice of the workflow until a condition is met. This stays explicit in YAML and keeps the workflow sequential.
Example:
steps:
- id: implement
role: implementer
backend: opencode
- id: review
role: reviewer
repeat:
back_to: implement
until_review_pass: true
max_iterations: 3
Use this for bounded review/fix cycles. The controller step should emit
Review status: pass when there are no high or medium findings, and
Review status: revise when another pass is needed.
For loop-heavy workflows, you can also narrow step history visibility:
steps:
- id: implement
role: implementer
backend: opencode
history_visibility: latest
Use latest when the next step only needs the most recent result, summary
when you want a compressed history, and none when the step should only see
its explicit context.
Prompt Previewing
Use --show-prompt to see exactly what Aethr would send to each model:
aethr run "review my current changes before I commit" --show-prompt
Aethr does not call models in prompt preview mode. For later steps, it uses a clear placeholder where real previous step output would appear.
Mock Mode
Aethr works without API keys by returning deterministic mock responses.
Aethr also loads a project-level .env automatically before model calls, so
credentials can live alongside the workflow file without extra flags.
Use the models configured in .aethr.yaml:
AETHR_LIVE=1 aethr run "review my current changes"
Override every configured model with one LiteLLM model:
AETHR_MODEL=openai:gpt-4o-mini aethr run "review my current changes"
Auth
Use aethr auth login to write a provider key into the project .env file.
Aethr loads that file automatically on the next run.
aethr auth login openai
aethr auth status
Supported providers in the helper are:
openaianthropicgoogle/geminiopenrouterxai
Philosophy
Aethr should feel like:
gitpytestrgcargo
It should not feel like:
- an agent framework
- an autonomous coding platform
- an AI operating system
Aethr intentionally avoids persistence, replay systems, caches, plugins, DAGs, async runtimes, vector search, automatic retrieval, memory systems, and agent abstractions.
If a workflow fails, Aethr writes a temporary checkpoint file and prints a
compact resume command. Pass that checkpoint back with --resume-checkpoint
to continue from the next step without rerunning the earlier ones. Use
--verbose if you want the raw checkpoint JSON.
Future Work
One likely future UX is workflow promotion: take a one-off run that worked and
turn it into an editable .aethr.yaml workflow. The idea is to help users go
from ad hoc sessions to repeatable workflows without introducing session
storage, replay systems, or hidden history.
Architecture
aethr/
cli.py
config.py
context.py
executor.py
llm.py
prompts.py
workflow.py
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