Release Process
This project follows a lightweight release process appropriate for a small library.
Pre-release Checklist
uv sync --extra dev --extra openai
uv run ruff check .
uv run mypy
uv run pytest
uv run mkdocs build --strict
uv build
If python-rag-framework is accessible locally or through Git credentials,
also run:
uv sync --extra dev --extra openai
uv pip install "python-rag-framework @ git+https://github.com/lelouchzr/python-rag-framework.git@v0.1.0"
uv run pytest tests/test_rag_integration.py
Review:
README.mddocs/CHANGELOG.mdpyproject.toml- examples in
examples/
v0.1.0 Release Notes
v0.1.0 establishes the complete initial scope:
- Lightweight
AgentandRuntime. - Prompt building, history and optional memory.
- Tool registration and execution.
- Callback lifecycle events.
- Streaming support.
- Optional
python-rag-frameworkretrieval integration. - Callable and OpenAI LLM adapters.
- Examples, documentation, CI and package metadata.
Private RAG Dependency
python-rag-framework is a private portfolio dependency. The main CI workflow
does not install the rag extra, so it can pass without access to that private
repository. The RAG integration test is still present and is skipped when the
package is unavailable.
To verify the integration in GitHub Actions, configure a repository secret named
RAG_FRAMEWORK_TOKEN. Use a fine-grained GitHub token with read-only
Contents access to the private python-rag-framework repository, then run the
manual RAG Integration workflow.
Publishing
Build artifacts locally:
uv build
Publish only after CI passes and the changelog reflects the release.
Documentation Site
The documentation site is deployed with the manual or push-triggered Pages
workflow. Configure GitHub Pages with Source: GitHub Actions, then use:
https://lelouchzr.github.io/python-agent-runtime/