feat(contrib): merge SDLC agents into sdlc-workflow-suite - #2601
agolebiowska wants to merge 3 commits into
Conversation
- Consolidate sdlc-task-planner, sdlc-technical-designer, and sdlc-user-story-refiner into contrib/python/sdlc-workflow-suite - Implement an end-to-end SequentialAgent pipeline across the three SDLC stages - Deduplicate artifact_tools.py into a single shared tool - Update model references to gemini-3.5-flash with MODEL_NAME env var support - Fix task planner test to validate execution plan table output - Add test_runnability.py and unit test suite - Retire legacy sample directories in python/agents/
![github-actions[bot]](https://avatars.githubusercontent.com/in/15368?s=60&v=4){kind=small}
| """Configuration for the SDLC Workflow Suite agents.""" | ||
|
|
||
| model_name: str = Field( | ||
| default_factory=lambda: os.getenv("MODEL_NAME", "gemini-3.5-flash"), |
There was a problem hiding this comment.
Environment reads should not specify a hardcoded fallback default in the code; defaults belong in .env.example. Remove the fallback value from the os.getenv call.
| 2. If the user provides a very sparse or incomplete story draft, proactively search for prior stories to suggest standard acceptance criteria or to identify missing edge cases. | ||
| 3. Use semantic similarity search or standard queries to find related historical records. | ||
| 4. Always verify your assumptions by searching first before asking the user. | ||
| 5. When utilizing Spanner tools, you must use the following configuration: project_id: {config.spanner_project_id}, instance_id: {config.spanner_instance_id}, database_id: {config.spanner_database_id} |
There was a problem hiding this comment.
The prompt instruction detailing Spanner toolset configuration parameters is duplicated in near-verbatim form. Extract this configuration line to a shared string template or helper function.
| """ | ||
| ).strip() | ||
|
|
||
| runner = InMemoryRunner(agent=technical_designer_agent) |
There was a problem hiding this comment.
The logic to instantiate InMemoryRunner and establish a test session is duplicated across three separate test functions. Refactor this setup into a shared pytest fixture.
| """ | ||
| Provides a list containing the available SpannerToolset to be consumed by an agent | ||
| """ | ||
| """Provides a list containing the available SpannerToolset to be consumed by an agent.""" |
There was a problem hiding this comment.
The get_toolset method references config, but the config module is never imported in this file. Add an import for config at the top of the file to avoid a runtime NameError.
| ) -> dict[str, Any]: | ||
| """ | ||
| Saves text content as an ADK artifact. | ||
| """Saves text content as an ADK artifact. |
There was a problem hiding this comment.
The function signature references ToolContext as a type annotation, but ToolContext is not imported. This will cause a NameError on module import.
| " granular, dependency-linked task execution plan." | ||
| ), | ||
| instruction=get_task_planner_prompt(), | ||
| tools=[], |
There was a problem hiding this comment.
The task_planner_agent has tools=[], but the integration test test_task_planner_happy_path expects it to call save_artifact. Register save_artifact in the tools list so the test can pass successfully.
| planner=BuiltInPlanner( | ||
| thinking_config=types.ThinkingConfig( | ||
| include_thoughts=True, | ||
| thinking_budget=-1, |
There was a problem hiding this comment.
The Gemini thinking_budget must be a positive integer or 0. Setting thinking_budget=-1 is invalid and will cause an API or validation error at runtime on lines 55 and 71.
…ts, tools, and tests - Remove fallback model literal from os.getenv in config.py - Extract shared Spanner config instruction in prompt.py - Refactor InMemoryRunner session creation into pytest fixture - Guard SpannerQueryTools.get_toolset and import config - Explicitly import ToolContext in artifact_tools.py - Register save_artifact tool in task_planner_agent - Remove invalid thinking_budget=-1 from ThinkingConfig - Add conftest.py and unit tests for tool and prompt functions
There was a problem hiding this comment.
Automated Hygiene review — 0 finding(s).
Also, on lines this PR does not change:
contrib/python/sdlc-workflow-suite/sdlc_workflow_suite/tools/artifact_tools.py:19— The 'types' module from 'google.genai' is imported but never used in this file. It can be safely removed.
There was a problem hiding this comment.
Automated Correctness review — 1 finding(s).
Also, on lines this PR does not change:
contrib/python/sdlc-workflow-suite/sdlc_workflow_suite/tools/artifact_tools.py:19— Importing types from google.genai is incorrect here because google.genai.types does not contain an Artifact class, which will cause an AttributeError when save_artifact is executed. Change the import to use the correct module defining Artifact.contrib/python/sdlc-workflow-suite/deployment/deploy.py:51— The python-dotenv package is missing from the list of requirements. This will cause deployment or startup to fail on Vertex Reasoning Engine with a ModuleNotFoundError when trying to import sdlc_workflow_suite.
| and event.content.parts | ||
| and event.content.parts[0].text | ||
| ): | ||
| response = event.content.parts[0].text |
There was a problem hiding this comment.
The response variable is overwritten with the text of the latest chunk rather than being accumulated. Use response += event.content.parts[0].text to correctly capture the full streaming response.
There was a problem hiding this comment.
Automated Maintainability review — 2 finding(s).
Also, on lines this PR does not change:
contrib/python/sdlc-workflow-suite/deployment/deploy.py:51— The hardcoded dependency list duplicates the requirements specified in 'pyproject.toml'. Consider reading them dynamically from 'pyproject.toml' (e.g., using Python's built-in 'tomllib') to avoid maintenance overhead and out-of-sync dependencies.
|
|
||
| if tools_enabled: | ||
| logger.info("Initializing SDLC agents with Spanner tools enabled.") | ||
| spanner_tools = list(SpannerQueryTools.get_toolset()) |
There was a problem hiding this comment.
Redundant 'list()' conversion. Since 'SpannerQueryTools.get_toolset()' already returns a list, wrapping it in 'list()' is unnecessary and can be removed.
|
|
||
| def test_spanner_query_tools_unconfigured(): | ||
| """Verify get_toolset returns an empty list when Spanner config is unset.""" | ||
| assert SpannerQueryTools.get_toolset() == [] |
There was a problem hiding this comment.
This assertion, as well as 'test_agent_config_defaults', relies on Spanner environment variables being unset. Consider using mock patching or resetting environment variables in these tests to ensure they remain deterministic regardless of the host environment.
|
|
||
| user_story_refiner_agent = LlmAgent( | ||
| name="user_story_refiner", | ||
| model=config.model_name or "", |
There was a problem hiding this comment.
config.model_name defaults to None when MODEL_NAME is unset, so this or "" hands the agent an empty model id rather than failing. Would it be better to let it raise here?
| """Test that the agent config provides standard defaults and aliases.""" | ||
| cfg = AgentConfig() | ||
| assert cfg.model_name is not None | ||
| assert cfg.default_llm == cfg.model_name |
There was a problem hiding this comment.
default_llm is a property that returns self.model_name, so this one can't fail
|
|
||
| ### Deploying to Vertex AI Agent Engine | ||
|
|
||
| You can deploy the agent suite to Google Cloud Vertex AI Reasoning Engine using the included deployment script: |
There was a problem hiding this comment.
the heading two lines up says Agent Engine
| You can deploy the agent suite to Google Cloud Vertex AI Reasoning Engine using the included deployment script: | ||
|
|
||
| ```bash | ||
| uv run deployment/deploy.py --create |
There was a problem hiding this comment.
deploy.py imports absl, but absl-py is in the deployment group and the install step above only runs uv sync --dev. Can you please mention --group deployment there?
| To launch the interactive ADK web interface: | ||
|
|
||
| ```bash | ||
| uv run adk web sdlc_workflow_suite |
There was a problem hiding this comment.
Should this be uv run adk web .? Passing sdlc_workflow_suite makes the package itself the agents directory rather than its parent.
| 2. **Technical Designer (`technical_designer`)**: Evaluates the refined user story against codebase architecture (optionally querying a Spanner Code Knowledge Graph) to generate an RFC Technical Design document, Mermaid architecture diagram, and Architecture Decision Records (ADRs). | ||
| 3. **Task Planner (`task_planner`)**: Translates the technical design into granular, testable development tasks and Pull Request merge chains formatted in a comprehensive execution table. | ||
|
|
||
|  |
There was a problem hiding this comment.
Only sdlc_agents_workflow.webp is referenced here, and it's the sole reference in the recipe — agent_pattern.webp sits in the recipe root at 104 KB with nothing pointing at it. Please drop it if it's a leftover from the merge.
|
|
||
| logger = logging.getLogger(__name__) | ||
|
|
||
| __all__ = ["ToolContext", "save_artifact"] |
There was a problem hiding this comment.
ToolContext is a third-party class this module only imports for the annotation
happyhuman
left a comment
happyhuman
left a comment
There was a problem hiding this comment.
Just left a few comments. PTAL.
| display_name="sdlc-workflow-suite", | ||
| requirements=[ | ||
| "google-adk (>=1.31.0)", | ||
| "google-cloud-aiplatform[adk,agent_engines] (>=1.93.0)", |
There was a problem hiding this comment.
pyproject.toml spells this one google-cloud-aiplatform[adk,agent-engines] with a hyphen. Which extra name is right?
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # https://www.apache.org/licenses/LICENSE-2.0 |
There was a problem hiding this comment.
https:// here, http:// in every other header in the recipe
| from google.adk.runners import InMemoryRunner | ||
| from google.genai.types import Part, UserContent | ||
|
|
||
| from sdlc_workflow_suite.agent import ( |
There was a problem hiding this comment.
user_story_refiner_agent isn't imported alongside the other two. Is the first stage of the pipeline covered anywhere?
| tools=[save_artifact], | ||
| ) | ||
|
|
||
| root_agent = SequentialAgent( |
There was a problem hiding this comment.
Nice, this reads much better as one pipeline than three separate recipes did. :-)
|
Three more, on lines the diff view won't let me anchor to. None of these blocks CI:
|
|
|
||
| import os | ||
|
|
||
| os.environ.setdefault("MODEL_NAME", "gemini-3.5-flash") |
There was a problem hiding this comment.
Avoid using os.environ.setdefault to set default environment variables. Environment defaults should be defined in .env.example rather than hardcoded in Python code.
|
|
||
| logger = logging.getLogger(__name__) | ||
|
|
||
| tools_enabled = bool( |
There was a problem hiding this comment.
The configuration check to determine if Spanner is enabled is duplicated between this module and spanner_query_tools.py. Consider extracting this check as a helper property like is_spanner_configured on AgentConfig to centralize the logic.
2 participants
Summary
Merges the three SDLC agents (
sdlc-task-planner,sdlc-technical-designer, andsdlc-user-story-refiner) into a unified recipe undercontrib/python/sdlc-workflow-suitein preparation for the adk-samples → adk-recipes restructure.Changes
sdlc-technical-designerand implemented a real ADKSequentialAgentinsdlc_workflow_suite/agent.pyconnecting:user_story_refiner: Refines rough requirements into Jira/GitLab formatted stories with BDD acceptance criteria.technical_designer: Analyzes stories and produces RFC technical design documents with Mermaid diagrams and ADRs.task_planner: Deconstructs technical designs into merge request plans with dependency-linked task breakdown tables.artifact_tools.pyinto a single canonical tool undersdlc_workflow_suite/tools/.gemini-3.5-flashper repo deprecation policy.MODEL_NAME,GOOGLE_CLOUD_PROJECT,GOOGLE_CLOUD_LOCATION,GOOGLE_CLOUD_STORAGE_BUCKET,SPANNER_PROJECT_ID,SPANNER_INSTANCE_ID,SPANNER_DATABASE_ID).load_dotenv()insdlc_workflow_suite/__init__.py.tests/test_runnability.pyandtests/test_suite.pyunit tests.manifest.yamland alignedpyproject.toml(hatchling build system, public PyPI default index, >=3.11).ruff.uv.lockwith Python 3.11.README.md.python/agents/sdlc-task-planner,python/agents/sdlc-user-story-refiner, andpython/agents/sdlc-technical-designer.Validation
uv run validate contrib/python/sdlc-workflow-suite-> PASS (manifest, structure, readme, placement)uv run --no-project python3 .github/scripts/check_env_vars.py contrib/python/sdlc-workflow-suite-> PASSuv run --no-project --with pyyaml --with packaging python .github/scripts/check_recipe_pyproject.py contrib/python/sdlc-workflow-suite-> PASSgit diff --diff-filter=AM --name-only | uv run python tools/check_frozen_paths.py-> PASSuv run ruff check contrib/python/sdlc-workflow-suite-> PASSuv run pytest tests/test_runnability.py tests/test_suite.py-> 6 passed