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https://github.com/Monadical-SAS/cubbi.git
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Mathieu Virbel
bae951cf7c
* feat: migrate container configuration from env vars to YAML config files
- Replace environment variable-based configuration with structured YAML config files
- Add Pydantic models for type-safe configuration management in cubbi_init.py
- Update container.py to generate /cubbi/config.yaml and mount into containers
- Simplify goose plugin to extract provider from default model format
- Remove complex environment variable handling in favor of direct config access
- Maintain backward compatibility while enabling cleaner plugin architecture
* feat: optimize goose plugin to only pass required API key for selected model
- Update goose plugin to set only the API key for the provider of the selected model
- Add selective API key configuration for anthropic, openai, google, and openrouter
- Update README.md with comprehensive automated testing documentation
- Add litellm/gpt-oss:120b to test.sh model matrix (now 5 images × 4 models = 20 tests)
- Include single prompt command syntax for each tool in the documentation
* feat: add comprehensive integration tests with pytest parametrization
- Create tests/test_integration.py with parametrized tests for 5 images × 4 models (20 combinations)
- Add pytest configuration to exclude integration tests by default
- Add integration marker for selective test running
- Include help command tests and image availability tests
- Document test usage in tests/README_integration.md
Integration tests cover:
- goose, aider, claudecode, opencode, crush images
- anthropic/claude-sonnet-4-20250514, openai/gpt-4o, openrouter/openai/gpt-4o, litellm/gpt-oss:120b models
- Proper command syntax for each tool
- Success validation with exit codes and completion markers
Usage:
- pytest (regular tests only)
- pytest -m integration (integration tests only)
- pytest -m integration -k "goose" (specific image)
* feat: update OpenCode plugin with perfect multi-provider configuration
- Add global STANDARD_PROVIDERS constant for maintainability
- Support custom providers (with baseURL) vs standard providers
- Custom providers: include npm package, name, baseURL, apiKey, models
- Standard providers: include only apiKey and empty models
- Use direct API key values from cubbi config instead of env vars
- Only add default model to the provider that matches the default model
- Use @ai-sdk/openai-compatible for OpenAI-compatible providers
- Preserve model names without transformation
- All providers get required empty models{} section per OpenCode spec
This ensures OpenCode can properly recognize and use both native
providers (anthropic, openai, google, openrouter) and custom
providers (litellm, etc.) with correct configuration format.
* refactor: model is now a combination of provider/model
* feat: add separate integration test for Claude Code without model config
Claude Code is Anthropic-specific and doesn't require model selection like other tools.
Created dedicated test that verifies basic functionality without model preselection.
* feat: update Claude Code and Crush plugins to use new config system
- Claude Code plugin now uses cubbi_config.providers to get Anthropic API key
- Crush plugin updated to use cubbi_config.providers for provider configuration
- Both plugins maintain backwards compatibility with environment variables
- Consistent plugin structure across all cubbi images
* feat: add environments_to_forward support for images
- Add environments_to_forward field to ImageConfig and Image models
- Update container creation logic to forward specified environment variables from host
- Add environments_to_forward to claudecode cubbi_image.yaml to ensure Anthropic API key is always available
- Claude Code now gets required environment variables regardless of model selection
- This ensures Claude Code works properly even when other models are specified
Fixes the issue where Claude Code couldn't access Anthropic API key when using different model configurations.
* refactor: remove unused environment field from cubbi_image.yaml files
The 'environment' field was loaded but never processed at runtime.
Only 'environments_to_forward' is actually used to pass environment
variables from host to container.
Cleaned up configuration files by removing:
- 72 lines from aider/cubbi_image.yaml
- 42 lines from claudecode/cubbi_image.yaml
- 28 lines from crush/cubbi_image.yaml
- 16 lines from goose/cubbi_image.yaml
- Empty environment: [] from opencode/cubbi_image.yaml
This makes the configuration files cleaner and only contains
fields that are actually used by the system.
* feat: implement environment variable forwarding for aider
Updates aider to automatically receive all relevant environment variables
from the host, similar to how opencode works.
Changes:
- Added environments_to_forward field to aider/cubbi_image.yaml with
comprehensive list of API keys, configuration, and proxy variables
- Updated aider_plugin.py to use cubbi_config system for provider/model setup
- Environment variables now forwarded automatically during container creation
- Maintains backward compatibility with legacy environment variables
Environment variables forwarded:
- API Keys: OPENAI_API_KEY, ANTHROPIC_API_KEY, DEEPSEEK_API_KEY, etc.
- Configuration: AIDER_MODEL, GIT_* variables, HTTP_PROXY, etc.
- Timezone: TZ for proper log timestamps
Tested: All aider tests pass, environment variables confirmed forwarded.
* refactor: remove unused volumes and init fields from cubbi_image.yaml files
Both 'volumes' and 'init' fields were loaded but never processed at runtime.
These were incomplete implementations that didn't affect container behavior.
Removed from all 5 images:
- volumes: List with mountPath: /app (incomplete, missing host paths)
- init: pre_command and command fields (unused during container creation)
The cubbi_image.yaml files now only contain fields that are actually used:
- Basic metadata (name, description, version, maintainer, image)
- persistent_configs (working functionality)
- environments_to_forward (working functionality where present)
This makes the configuration files cleaner and eliminates confusion
about what functionality is actually implemented.
* refactor: remove unused ImageInit and VolumeMount models
These models were only referenced in the Image model definition but
never used at runtime since we removed all init: and volumes: fields
from cubbi_image.yaml files.
Removed:
- VolumeMount class (mountPath, description fields)
- ImageInit class (pre_command, command fields)
- init: Optional[ImageInit] field from Image model
- volumes: List[VolumeMount] field from Image model
The Image model now only contains fields that are actually used:
- Basic metadata (name, description, version, maintainer, image)
- environment (loaded but unused - kept for future cleanup)
- persistent_configs (working functionality)
- environments_to_forward (working functionality)
This makes the data model cleaner and eliminates dead code.
* feat: add interactive configuration command
Adds `cubbi configure` command for interactive setup of LLM providers
and models through a user-friendly questionnaire interface.
New features:
- Interactive provider configuration (OpenAI, Anthropic, OpenRouter, etc.)
- API key management with environment variable references
- Model selection with provider/model format validation
- Default settings configuration (image, ports, volumes, etc.)
- Added questionary dependency for interactive prompts
Changes:
- Added cubbi/configure.py with full interactive configuration logic
- Added configure command to cubbi/cli.py
- Updated uv.lock with questionary and prompt-toolkit dependencies
Usage: `cubbi configure`
* refactor: update integration tests for current functionality
Updates integration tests to reflect current cubbi functionality:
test_integration.py:
- Simplified image list (removed crush temporarily)
- Updated model list with current supported models
- Removed outdated help command tests that were timing out
- Simplified claudecode test to basic functionality test
- Updated command templates for current tool versions
test_integration_docker.py:
- Cleaned up container management tests
- Fixed formatting and improved readability
- Updated assertion formatting for better error messages
These changes align the tests with the current state of the codebase
and remove tests that were causing timeouts or failures.
* fix: fix temporary file chmod
2.4 KiB
2.4 KiB
Integration Tests
This directory contains integration tests for cubbi images with different model combinations.
Test Matrix
The integration tests cover:
- 5 Images: goose, aider, claudecode, opencode, crush
- 4 Models: anthropic/claude-sonnet-4-20250514, openai/gpt-4o, openrouter/openai/gpt-4o, litellm/gpt-oss:120b
- Total: 20 image/model combinations + additional tests
Running Tests
Default (Skip Integration)
# Regular tests only (integration tests excluded by default)
uv run -m pytest
# Specific test file (excluding integration)
uv run -m pytest tests/test_cli.py
Integration Tests Only
# Run all integration tests (20 combinations + helpers)
uv run -m pytest -m integration
# Run specific image with all models
uv run -m pytest -m integration -k "goose"
# Run specific model with all images
uv run -m pytest -m integration -k "anthropic"
# Run single combination
uv run -m pytest -m integration -k "goose and anthropic"
# Verbose output with timing
uv run -m pytest -m integration -v -s
Combined Tests
# Run both regular and integration tests
uv run -m pytest -m "not slow" # or remove the default marker exclusion
Test Structure
test_image_model_combination
- Parametrized test with all image/model combinations
- Tests single prompt/response functionality
- Uses appropriate command syntax for each tool
- Verifies successful completion and basic output
test_image_help_command
- Tests help command for each image
- Ensures basic functionality works
test_all_images_available
- Verifies all required images are built and available
Command Templates
Each image uses its specific command syntax:
- goose:
goose run -t 'prompt' --no-session --quiet - aider:
aider --message 'prompt' --yes-always --no-fancy-input --no-check-update --no-auto-commits - claudecode:
claude -p 'prompt' - opencode:
opencode run -m MODEL 'prompt' - crush:
crush run 'prompt'
Expected Results
All tests should pass when:
- Images are built (
uv run -m cubbi.cli image build [IMAGE]) - API keys are configured (
uv run -m cubbi.cli configure) - Models are accessible and working
Debugging Failed Tests
If tests fail, check:
- Image availability:
uv run -m cubbi.cli image list - Configuration:
uv run -m cubbi.cli config list - Manual test:
uv run -m cubbi.cli session create -i IMAGE -m MODEL --run "COMMAND"