* refactor(onboard): shared flow for ccproxy providers
* feat(onboard): support oauth configuration for auxiliary models
* fix(onboard): reuse main model auth for same-provider auxiliary
* fix(onboard): reconcile oauth providers
* feat(middleware): reposition code interpreter middleware in the stack
* feat(models): add qwen3.7-plus model entry and update context window comment
* feat(models): add qwen3.7-max and qwen3.7-plus model entries for DashScope
* feat(auxiliary): implement auxiliary model support for background tasks and tool selection
- Added auxiliary model configuration to EvoScientistConfig.
- Introduced _ensure_auxiliary_chat_model function to manage auxiliary model instances.
- Updated onboarding steps to include auxiliary model selection.
- Modified middleware to route tool selection to the auxiliary model when applicable.
- Enhanced tests to cover auxiliary model functionality and configuration.
* feat(steps): update UI backend selection options and descriptions
* Refactor code structure for improved readability and maintainability
* feat(patches): implement OpenRouter response reasoning item stripping to prevent multi-turn errors
* feat: update version to v0.1.4 in badges, README, and pyproject.toml; adjust skill counts in steps.py
* feat(config): add auxiliary model and provider environment variables to test setup
* feat: add WebUI mode support with related configuration and onboarding steps
* feat: enhance WebUI port configuration to prevent conflicts with backend port
* feat: add support for fresh interactive session detection in WebUI
* Add onboarding wizard with style and validation components
- Introduced `style.py` for shared visual elements used in the onboarding wizard.
- Created `validators.py` for input validation, including integer and choice validators, and API key validation functions for various providers.
- Implemented `wizard.py` as the entry point for the onboarding process, managing user prompts and configuration steps.
- Added progress rendering and autosave functionality to enhance user experience during the onboarding process.
* feat(onboarding): enhance validation and configuration for onboarding wizard
- Added validation for UI backends, workspace modes, and providers in the onboarding command.
- Updated channel definitions to include secret field handling for sensitive tokens.
- Improved user prompts for required fields, ensuring sensitive data is masked.
- Introduced constants for valid providers, UI backends, and workspace modes to maintain consistency.
- Implemented tests to ensure alignment between constants and interactive choices in onboarding steps.
* feat(onboarding): improve WeChat account ID prompt and validation for newly enabled channels
* feat(onboarding): enhance WeChat backend credential prompts and validation
* feat(onboarding): refine WeChat backend credential prompts for wecom and wechatmp
* Refactor onboarding package for improved structure and clarity
- Simplified the onboarding package by removing unnecessary re-exports and consolidating public API to only include `run_onboard`.
- Updated `install_back_keys` to `install_navigation_keys` for clarity and consistency in the prompter module.
- Enhanced the `NonInteractivePrompter` class to support strict mode, allowing for better handling of non-interactive prompts.
- Adjusted the onboarding steps to utilize the new navigation keys installation method.
- Improved the `run_onboard` function to handle section implications based on user flags, enhancing the onboarding experience.
- Updated tests to reflect changes in imports and ensure compatibility with the new structure.
* feat(onboarding): enhance validation logic for non-interactive prompts
* refactor(onboarding): streamline onboarding module structure and enhance validation error handling
* refactor(onboarding): enhance config revert logic to preserve original file state
* refactor(onboarding): enhance tavily key validation and error handling in onboarding process
* Refactor sub-agent architecture and introduce async support
- Removed the legacy subagent.yaml file and replaced it with individual YAML files for each sub-agent in the subagents directory.
- Updated the load_subagents function to support both directory and single file layouts for loading sub-agent configurations.
- Added new langgraph_dev module for managing async sub-agent lifecycle and deployment.
- Created graphs for async sub-agents (writing-agent, data-analysis-agent) and updated langgraph.json for deployment.
- Introduced new sub-agent definitions for planner, research, debug, code, and writing agents with appropriate system prompts and configurations.
- Enhanced package data inclusion in pyproject.toml to accommodate new sub-agent YAML files.
* Refactor code for improved readability by consolidating conditional statements and formatting
* feat: enhance async sub-agent support with workspace synchronization and user feedback
- Added console status messages during async sub-agent server startup and workspace synchronization to improve user experience.
- Implemented a new WorkspaceSyncWidget for live feedback during workspace sync operations.
- Updated onboarding to reject occupied ports and ensure proper workspace handling for async sub-agents.
- Introduced locking mechanisms to manage concurrent access to langgraph dev processes and workspace states.
* feat: add async sub-agent configuration and server management functions
* feat: improve port occupation handling and log file management in start_langgraph_dev
* feat: enhance async sub-agent handling and introduce comprehensive tests
- Updated `_maybe_swap_async_subagents` to improve async sub-agent management, ensuring internal flags are stripped before handoff.
- Enhanced port management in `onboard.py` to allow reuse of occupied ports if already running by the same service.
- Introduced file locking in `manager.py` to prevent race conditions during concurrent CLI invocations.
- Added new tests for async sub-agent swapping and langgraph manager functionalities to ensure reliability and correctness.
- Updated dependencies in `pyproject.toml` to include `psutil` and `filelock`.
* fix(docs): clarify sub-agent configuration in README
* test(manager): isolate _PID_DIR + tighten reuse-path assertion
Addresses CodeRabbit review on tests/test_langgraph_manager.py:
- Patch _PID_DIR to tmp_path so the FileLock setup in
ensure_langgraph_dev doesn't mkdir the user's real
~/.config/evoscientist/ dir as a test side-effect.
- Tighten "result is None or hasattr(result, 'poll')" to a strict
"result is None" — the reuse path returns None unconditionally,
so the OR clause was hiding potential regressions.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(manager): clean up stale PID file when unrelated process reuses PID
* feat(tests): add validation tests for async flag in load_subagents
* fix(load_subagents): restrict to .yaml files and clarify configuration handling
* fix(load_subagents): improve error handling for non-dict specifications in YAML
* feat(onboard): add "LangGraph Port" step to onboarding process
* feat(langgraph): add concurrency configuration for langgraph dev workers
* feat(async-subagents): enhance MCP tool routing for async sub-agents
* fix(manager): update exception handling for connection errors and prevent zombie processes
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(onboard): detect installed skill packs via install manifest
Onboarding's _step_skills only inspected USER_SKILLS_DIR and matched
recommended entries by directory-name hint, so a pack like
EvoScientist/EvoSkills@skills (which explodes into paper-writing/,
evo-memory/, etc. under GLOBAL_SKILLS_DIR) was never detected and kept
appearing as not-yet-installed.
skills_manager now writes a per-tier .installed.yaml mapping skill
directory name -> original install source on every install, removes the
entry on uninstall, and exposes installed_sources(). _step_skills checks
both tiers and treats a recommended source as installed when present in
any manifest -- so packs are recognized regardless of how their child
dirs are named.
* fix(onboard): write install manifest atomically
Stage to a sibling temp file, fsync, then os.replace into place. A crash
mid-write can no longer leave a half-written .installed.yaml behind,
which would otherwise wipe out pack detection until the next reinstall.
* style: fmt
* fix(onboard): catch decode errors when loading install manifest
read_text() can raise UnicodeDecodeError on a hand-edited or corrupt .installed.yaml; pin encoding="utf-8" and add UnicodeError to the except clause so the function honors its "returns {} on any error" contract.
* fix: update OpenRouter API key validation to use /auth/key endpoint and httpx
* Refactor code structure for improved readability and maintainability
* feat: enhance welcome banner to include file commands indication
* feat: update LaTeX setup prompt to use selection UI for better user experience
* feat: update version to v0.0.5 in badges and project configuration
* refactor(mcp): extract MCP server registry from onboard into shared module
Move _RECOMMENDED_MCP_SERVERS, _install_pip_package, and
_pip_install_hint from config/onboard.py into mcp/registry.py as a
shared MCPServerEntry dataclass and registry functions. This enables
reuse by the new /install-mcp command and marketplace integration.
* feat(mcp): add /install-mcp command for browsing and installing MCP servers
Interactive browser for MCP servers (built-in registry + EvoSkills
marketplace), supporting three modes:
- /install-mcp — interactive tag filter + checkbox selection
- /install-mcp <name> — direct install by name or tag pre-filter
- /install-mcp file.yaml — import servers from arbitrary YAML file
Also available as /mcp install and EvoSci mcp install. Includes TUI
browser widget (MCPBrowserWidget) mirroring the skill browser UX.
* chore(tavily): conditionally pass tavily_search when TAVILY_API_KEY is set
* fix(tui): Enter key detection for /install-mcp
- Fix message handler names: Textual converts MCPBrowserWidget to
mcpbrowser_widget (not mcp_browser_widget), so Confirmed/Cancelled
messages were never received by the app
- Distinguish empty selection from cancel in result handling
* chore(widgets): stop auto-advancing cursor on Space toggle in browser widgets
* style: linter
* refactor(mcp): simplify MCP registry to marketplace-only
Remove built-in server list and arbitrary YAML import — all server
definitions now come from the EvoSkills marketplace (mcp/*.yaml).
Onboarding filters by the `onboarding` tag instead of a hardcoded list.
* refactor(mcp): consolidate /install-mcp into /mcp install
Remove standalone /install-mcp command — use /mcp install as the
single entry point. CLI adapter now delegates logic to the shared
InstallMCPCommand class, keeping only the questionary UI layer.
* chore(cli): rm reference to yaml import
- Added blank lines for better separation of test cases in multiple test files.
- Reformatted event handling in tests for clarity and consistency.
- Ensured consistent use of multi-line formatting for dictionary arguments in event handling.
- Improved assertions and test descriptions for better understanding.
- Updated test cases across various modules including test_stream_state, test_stream_utils, test_summarization, test_thread_selector, test_tool_error_handler, test_tui_widgets, test_ui_runtime, and test_wechat_channel.
feat(prompts): update system prompt to eliminate numeric limits for sub-agents and delegation rounds
test(tests): adjust tests to reflect changes in configuration and onboarding logic
- Introduced UserMessage widget for displaying user input with a styled prompt.
- Updated onboarding steps to include UI backend selection (Rich CLI or Textual TUI).
- Modified EvoScientistConfig to store selected UI backend.
- Enhanced configuration handling to support UI backend environment variable.
- Updated README with new UI backend options and commands.
- Added tests for new UI backend functionality and UserMessage widget.
- Removed obsolete test files and ensured existing tests are updated accordingly.
Introduce the new channel unification architecture:
- Core framework: base channel class, bus, consumer, channel manager,
middleware, mixins, capabilities, formatter, retry, plugin system
- Telegram channel implementation with bot token validation
- Discord channel implementation with bot token validation
- Refactored iMessage to use new base channel architecture
- Updated CLI: /channel commands, serve mode, channel setup wizard
- Updated onboard wizard to support multi-channel selection
- Config settings for all channel types
- Stream events: media attachment support, done event content field
- Comprehensive test coverage for channels, bus, and manager
- Updated README.md to clarify tool allowlist supports glob wildcards.
- Enhanced docstrings in prompts.py, utils.py, and formatter.py for better understanding of function parameters and return values.
- Refactored test cases in test_llm.py and test_onboard.py to use consistent mocking style with unittest.mock.patch.
- Improved test coverage and clarity in test_skills_manager.py and test_stream_state.py by adding descriptive comments and organizing sections.
- Adjusted tool result formatting logic in formatter.py to streamline success checks.
- Ensured backward compatibility in various modules while enhancing functionality.
- Updated iMessage channel to read send_thinking preference from config.
- Modified _auto_start_channel to accept send_thinking parameter.
- Removed view_image tool and adjusted related functionality.
- Enhanced README to reflect changes in media handling.
- Updated tests to cover new behavior and removed tests for view_image.
nb213