* 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(memory): add observation memory lifecycle
Add file-backed observation memory with deterministic markdown records,
structured record_observation tooling, startup indexing, and
profile/observation prompt guidance.
Launch post-turn and post-subagent EvoMemory workers through LangGraph
dev so completed runs can update profile memory, save durable
observations, and write subagent execution summaries without blocking
the active agent.
Wire memory middleware into the main agent, subagents, async graphs, TUI
status reporting, worker activity accounting, and observation-aware
research prompts, with regression coverage for storage, lifecycle
scheduling, graph registration, status display, and stream reset
behavior.
* fix(cli): sync background agent server on resume
Resume flows now need to keep the LangGraph dev background server
aligned with the active workspace even when async subagents are
disabled. EvoMemory workers use that server too, so gating resume-time
sync on enable_async_subagents could leave workers pinned to the launch
workspace after resuming a thread from another workspace.
Run workspace sync unconditionally for Rich CLI and Textual resume
paths, while preserving WorkspaceMismatchError handling so failed sync
aborts the resume before mutating the active thread or workspace.
Propagate aborted resume callbacks through the command UI so
channel-issued /resume commands do not send false success or history
output. Channel slash dispatch now treats CommandManager-caught command
errors as command errors and skips completion hooks for those failed
commands.
Add regression coverage for disabled async subagents, callback aborts,
and channel command error reporting.
* fix(cli): prepare serve resume workspace before adopting
Load the resumed workspace agent and sync the background server as a
single pre-adoption step. Restore the previous active workspace if
preparation fails so serve mode keeps using the old session
consistently.
* fix(memory): untrack abandoned worker status watches
Stop treating watcher shutdown as confirmed worker completion. Terminal
worker statuses still count memory deltas, while poll failures or
watcher setup failures now remove the active run without crediting
partial outputs.
* fix(cli): report channel command failures accurately
Treat command_error as a None sentinel so empty error strings still
fail, and let TUI resumes continue only on non-mismatch
background-server sync failures while reporting degraded mode.
* fix(stream): clear memory counters for resume streams
Reset completed-memory counters for every new agent stream, including
Command-based HITL and resume streams, so saved-memory indicators do not
leak across turns.
* docs(tools): make observation recording guidance conditional
Clarify that agents should call record_observation only when the
observation tool is available, preserving the existing durability and
usefulness criteria.
* feat(config): add controls for profile and observation memory
Add config flags for profile memory, observation memory, observation
writer placement, and background memory workers.
Wire the controls through main agents, subagents, EvoMemory middleware,
and memory lifecycle workers so observation writes can be assigned to
the live agent, subagent worker, both, or neither. Keep turn memory
workers profile-only and make prompts reflect the available observation
read/write paths. Skip langgraph dev startup when neither async
subagents nor memory workers need the background server.
Add coverage for config parsing, prompt gating, middleware wiring, and
worker tool availability.
* test(cli): include memory defaults in serve config stubs
* fix(memory): offload async worker launch blocking calls
Run the langgraph-dev health check and memory-output snapshot in worker
threads from the async EvoMemory launcher so it does not block the event
loop.
* chore(memory): harden turn worker subagent guardrail
* chore(memory): refresh profile context per request
* fix(memory): offload async profile file reads
* fix(memory): offload async worker completion accounting
* Enhance multimodal handling in LLM model
- Updated `_flatten_message_content` to preserve media blocks (images, files) while flattening text content.
- Introduced `_sanitize_messages` to manage media hoisting for tool messages, ensuring compatibility with OpenAI APIs.
- Modified `_patch_openai_compat_content` to accommodate new media handling logic, including retry mechanisms for media errors.
- Added comprehensive tests for media preservation, including various scenarios with images, files, and unsupported media types.
* fix: preserve order of text and media blocks in message flattening
* test: add tests for _strip_media_types to ensure position preservation and deduplication
* feat(memory): migrate to profile memory files
* chore(stream): read profile headings from templates
* fix(display): keep assistant responses if response_text has started
* fix(memory): do not treat failed bootstraps as profile creation
* chore(memory): unlink blank legacy memory
* fix(memory): resolve project_id once
* fix(memory): preserve unreadable profile files
* chore(tui): render streamed narration inline with tool timeline
Update the TUI streaming timeline so assistant text emitted before or
between tool calls is rendered inline where it occurs, rather than being
kept as a single answer bubble above or below the tools.
If the model begins an assistant response and then emits another tool
call, the provisional response is converted into inline narration before
that tool. The final assistant message then renders only the remaining
response suffix, avoiding duplicate text in the completed transcript.
Stop/cancel handling now preserves any active inline narration, appends
the visible stopped marker only to the remaining displayed segment, and
still returns the full normalized stopped response for channel callers.
Completed tools continue to collapse while long runs are active, but
expand again when the turn reaches a final state so the completed
transcript shows the full tool timeline.
* fix(stream): preserve narration around tool timelines
Keep assistant narration attached to the tool call that follows it
instead of folding all streamed text into the final answer block.
Track narrated response segments in stream state, render them before
their corresponding regular or task tool entries, and keep final answers
limited to the response suffix that has not already been shown inline.
Preserve narration across normal completion, stop/error final frames,
sub-agent task calls, and collapsed live tool summaries.
Add regression coverage for pending tools, completed tools, sub-agent
task delegations, collapsed completed/running tool summaries, and final
stop frames.
* fix(tui): finalize inline narration transitions
* test(memory): use canonical project id helper
* 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
* fix: update deepagents and langchain versions; enhance _reduce_messages_delta handling for None state
* fix: update langchain version constraint to >=1.3 in pyproject.toml and uv.lock
* feat: implement configurable sandbox execute timeout and enhance recovery instructions
* feat: add background process management tools and middleware for sandbox execution
* feat: enhance background process management with completion notifications and deduplication
* feat: enhance sandbox execution timeout validation and update related messages
* feat: enhance background process management with thread-specific completion notifications and HITL approval handling
* test: assert completion notification waits for process finish timestamp
* fix(cli): forward async-notifier replies back to originating channel
When PR #214's auto-notifier fires a synthetic agent turn after a
channel-originated conversation, the synthesized response only rendered
to the local CLI/TUI — the channel user (iMessage etc.) saw nothing
and had to manually re-prompt to find out what happened.
Adds a per-thread channel-origin registry in cli/channel.py and wires
the three notifier paths (Rich CLI / TUI / serve) to publish the final
response back via bus.publish_outbound when the originating thread was
started by a channel turn. Publish is fire-and-forget (scheduled on the
bus loop + done-callback for failure logging) so the notifier turn
doesn't block on the asyncio / textual event loop.
The registry is cleared on /new and /resume rotation so stale entries
don't accumulate.
* fix(cli): address review feedback on channel-origin forwarding
Follow-up to the review on #244 (din0s, X-iZhang):
- Guard the /resume origin cleanup on a real thread change in Rich CLI
and TUI (serve mode already did via thread_changed). Resuming the
already-active thread no longer wipes its still-live origin, which
would otherwise silently drop a later async-notifier forward — the
exact gap this PR closes.
- Re-bind the now-current thread to its channel after a channel-issued
/new or /resume slash command (which rotates the thread inside the
dispatch), so notifier turns on the rotated thread still forward.
- Guard the publish done-callback against a cancelled future, whose
.exception() raises CancelledError (rather than returning it) on
bus-loop teardown, so the intended warning still logs.
- Mirror the normal reply path's manager.record_message(channel, "sent")
for forwarded notifications so per-channel stats stay accurate.
- Print the closing "[channel: Replied to ...]" line in all three
notifier paths (Rich CLI / TUI / serve) when a forward actually
happened, so the forwarded block reads as terminated on screen.
Adds test_publish_records_sent_metric. ruff clean; notification-origin
suite (10) + related channel/CLI/serve suites (728) pass.
* fix(cli): store sender information separately from chat_id in channel origin
---------
Co-authored-by: X-iZhang <zacharyzhang2022@gmail.com>
* 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
* feat(feishu): scan-to-create QR onboarding flow
Add a device-code flow against accounts.feishu.cn/oauth/v1/app/registration
that lets users scan a terminal QR code with Feishu / Lark mobile to
auto-create a PersonalAgent bot app with the required IM permissions
pre-attached. The poll endpoint returns app_id + app_secret, which the
onboarding wizard then writes into the channel config — no manual app
creation on open.feishu.cn required.
- channels/feishu/onboard.py: qr_register() public entry, init/begin/poll
helpers, QR rendering via the soft qrcode dep, automatic feishu↔lark
domain switch based on the scanning user's tenant_brand, and a
best-effort bot probe to surface the bot name in the wizard
- channels/feishu/__init__.py: re-export qr_register (mirrors qq)
- config/onboard.py: offer "Scan QR code (recommended) / Enter manually"
in the Feishu branch, ask for region (feishu vs lark), then call
qr_register and populate feishu_app_id / feishu_app_secret /
feishu_domain; add qrcode>=7.4 to the feishu pip extras
* fix(feishu): silently absorb unsubscribed WebSocket events
Feishu auto-subscribes PersonalAgent apps to many event types
(im.message.reaction.created_v1, message.read_v1, message.recalled_v1,
chat.member.*, ...) that EvoScientist doesn't register handlers for.
Without intervention, lark-oapi's dispatcher raises EventException
("processor not found, type: ..."), the WS client logs it at ERROR and
replies HTTP 500 on the frame, and Feishu marks the event as failed
and retries it.
The problem is amplified by _send_ack_reaction: every inbound message
triggers our own reaction, which Feishu echoes back as
reaction.created_v1, creating a continuous ERROR-log feedback loop and
pointless retries.
Wrap EventDispatcherHandler._do_without_validation after build() to
swallow "processor not found" EventExceptions (debug log + return None)
while letting all other errors propagate. Failure-safe: if lark-oapi's
internal API changes the wrapper degrades to the prior behavior rather
than breaking the channel.
---------
Co-authored-by: Xi Zhang <106144707+X-iZhang@users.noreply.github.com>
* feat(deploy): implement standalone LangGraph server and CLI command for deployment
* feat(deploy): enhance port validation and environment variable management for deployment
* Refactor langgraph dev deployment and introduce workspace sidecar protocol
- Updated the deployment mode handling in `server.py` to use a single environment variable `EVOSCIENTIST_DEPLOY_MODE` with values `full` and `stripped`.
- Enhanced the `manager.py` to implement a workspace fingerprint sidecar, allowing cross-process reuse of langgraph dev instances while ensuring workspace consistency.
- Introduced functions to write and read the workspace sidecar, with error handling for missing or corrupt data.
- Added tests for the workspace sidecar functionality, including validation of the JSON schema and ensuring proper error handling for workspace mismatches.
- Updated existing tests to reflect changes in deployment mode handling and added new tests for signal handling during shutdown.
- Ensured that cleanup routines remove the workspace sidecar alongside the PID file during shutdown.
* fix(langgraph): improve workspace sidecar checks for process ownership and stale handles
* feat(middleware): add CodeInterpreterMiddleware with project-specific configuration
chore(config): increase checkpoint retention limit for runaway conversations
fix(tests): update database schema references from 'blob' to 'value'
chore(deps): update deepagents dependency to include quickjs support
* feat(deepagents): update to version 0.6.1 and add optional dependencies for quickjs
* feat(sessions): improve error handling for message deltas and update Overwrite type check
* Enhance PruningCheckpointer with DeltaChannel Awareness
- Introduced a new pruning strategy in `_prune_after_put` to preserve the `_DeltaSnapshot` chain during checkpoint pruning.
- Implemented methods to fetch recent checkpoint IDs and walk to snapshot ancestors, ensuring that necessary checkpoints are retained.
- Updated SQL queries to handle checkpoint and write deletions more efficiently.
- Added comprehensive tests for DeltaChannel-aware pruning, ensuring that the pruning logic correctly handles various checkpoint scenarios, including those with and without snapshot seeds.
- Refactored `_load_checkpoint_messages` to utilize the new saver interface, improving message reconstruction from checkpoints.
* feat(tests): add migration sweep test to preserve snapshot ancestor
* feat(sessions): enhance checkpoint retrieval to prevent transcript leakage in multi-agent scenarios
* feat(middleware): enhance CodeInterpreterMiddleware with configurable timeout and result character limit
feat(config): add CodeInterpreterMiddleware tuning parameters to EvoScientistConfig
feat(sessions): implement inline message delta reducer for improved message handling
* feat(dependencies): update deepagents version to 0.6.2 in pyproject.toml and uv.lock
* feat(backends): implement tier-aware virtual mount resolution for skills and memories
* test: add end-to-end test for workspace tier shadowing global tier in CustomSandboxBackend
* feat(backends): enhance virtual mount resolution for skills and memories with tier paths and quoting
* fix(tests): update Python command in virtual mount resolution tests to use python3
* fix(cli): handle spaces in @file mentions
The @file parser truncated at the first space, so dragging or pasting a
filename like `@PREPING_ Building Agent.pdf` only matched `@PREPING_`
and warned "file not found". Now supports `@"..."` / `@'...'` quoted
form for explicit paths, plus a greedy expansion fallback that walks
across whitespace until an existing file resolves (bounded by newlines,
the next `@`, and a 20-token cap). Autocomplete also returns quoted
mentions for any candidate containing a space.
* style: apply ruff format to file_mentions
* feat(llm): add dashscope-code provider for Alibaba Coding Plan keys
Alibaba Cloud Bailian "Coding Plan" subscription keys (sk-sp-*) route
through a separate endpoint (coding.dashscope.aliyuncs.com/v1) that the
standard `dashscope` provider can't reach. Add a sibling provider entry
matching the zhipu/zhipu-code and moonshot/kimi-coding precedents, with
its own validator (the coding endpoint returns 404 on /models, so probe
via chat.completions instead).
Closes#224
* fix(llm): keep dashscope as default provider for qwen3-coder shortcut
The MODELS dict is built from _MODEL_ENTRIES via a last-write-wins dict
comprehension. The initial commit listed dashscope-code AFTER dashscope,
which silently flipped the bare `get_chat_model("qwen3-coder")` shortcut
to the coding endpoint — breaking standard sk-* keys.
Reorder to match the zhipu-code / zhipu precedent: coding endpoint first,
general endpoint last so the general endpoint wins the collision and
remains the default for the shared "qwen3-coder" short name.
* feat(qq): add inline keyboard buttons for C2C HITL approval
QQ Bot supports inline buttons via `markdown + keyboard` payloads. Clicks
arrive as `interaction_create` events through the existing botpy
WebSocket gateway — no extra subscription needed beyond enabling the
`interaction` intent. Group-scope clicks are out of scope here (DM only).
Send path
- `_build_qq_keyboard(buttons)` mirrors the Feishu helper, mapping the
generic `{text, value, type}` shape to QQ's `{render_data, action}`
with action.type=1 (callback). One button per row for mobile clarity.
- `_send_chunk` extracts `metadata["buttons"]` and threads a `keyboard`
payload into `_post_markdown_message` for C2C only.
- Markdown→plain fallback can't carry a keyboard, so when buttons were
attached the fallback content gets a textual `Reply: 1=Approve, …`
hint built from the button list. `_parse_approval_reply` accepts
the same values typed manually, so the user is never stuck.
Receive path
- `on_interaction_create` is registered on the bot class.
- `_on_interaction` extracts `data.resolved.button_data`, builds an
InboundMessage, runs it through inbound middleware (Dedup suppresses
retry callbacks), and publishes directly to the bus — bypassing the
per-sender debounce buffer so the click value isn't merged with any
text typed in the same window.
- Always ACKs via `api.on_interaction_result(id, 0)` in `finally` so
QQ doesn't show the button as "expired", even if middleware drops
the click or something throws downstream.
`QQ.inline_buttons=True`; `_approval_prompt_metadata` now auto-attaches
the Approve/Reject/Approve-all button row for QQ HITL prompts.
* fix(qq): button-value coercion, ACK timing, HITL consumer wiring
Fixes 6 bugs found in the inline-keyboard commit and consolidates the
button helpers so the keyboard builder, plain-text fallback hint, and
interaction handler share one coercion path.
- Plain-text fallback no longer crashes on non-string `value` (e.g.
`{"text": "OK", "value": 42}`). Extracted `_normalize_button` is now
the single place that resolves `(label, value)` and coerces non-strings.
- `metadata["button_value"]` is the coerced string instead of the raw
payload, matching `content` and downstream string comparisons.
- `_on_interaction` ACKs first, before publishing to the bus, so the
QQ button UI never shows "expired" if middleware is slow.
- Wire `_approval_prompt_metadata` + `_format_approval_prompt(with_buttons=)`
into `InboundConsumer._stream_with_hitl` and `cli.channel.channel_hitl_prompt`
so the QQ `inline_buttons=True` capability is actually used end-to-end
(HITL prompts auto-attach Approve/Reject/Approve-all buttons when the
channel advertises the capability).
- Trim contradictory `_QQ_DEFAULT_PERMISSION` comment.
- Fix `test_group_interaction_ignored` docstring (ACK runs first now,
not in `finally` after a `return`).
Tests: `_normalize_button` covered indirectly via existing keyboard tests;
new regressions for non-string fallback hint, ACK-on-handler-throw, and
string-coerced `button_value` metadata.
* refactor(qq): slim button helpers and explicit has_buttons flag
Inline single-use _button_hint and the _QQ_BUTTON_STYLE/_QQ_DEFAULT_PERMISSION
constants in qq/channel.py; tighten _on_interaction (drop unreachable
"[button click]" sentinel and unused triggering_message_id metadata; collapse
"if resolved else" ternaries via `or ""`).
Replace the metadata round-trip ("buttons" in metadata) used to detect button
support in consumer.py and cli/channel.py with an explicit has_buttons bool
threaded through both the prompt formatter and metadata builder.
Apply ruff format to the previously unformatted blocks introduced earlier on
this branch so CI lint passes.
* feat(qq): send post-decision confirmation after HITL approval
Send a visible confirmation message ("✅ 已批准" / "❌ 已拒绝") right after
the user resolves a HITL approval — QQ Bot has no message-recall or edit API
for C2C, so a follow-up message is the only way to give the click/reply
strong feedback.
Bus consumer (consumer.py): only sends the confirmation when the user
actually responded (event was set), to avoid pretending the user approved
when the request really timed out and auto-approved.
CLI HITL prompt (cli/channel.py): mirrors the same set of confirmation
strings. Timeout and unrecognized-reply paths keep their existing English
text since their semantics differ (auto-reject vs auto-approve, plus a
hint about the unparsed input).
* feat(middleware): add ConfigurableModelMiddleware for dynamic model resolution
- Introduced ConfigurableModelMiddleware to resolve chat models from RunnableConfig.configurable on each call.
- Updated middleware initialization to include ConfigurableModelMiddleware.
- Enhanced context editing middleware tests to verify presence of ConfigurableModelMiddleware.
- Implemented tests for ConfigurableModelMiddleware to ensure correct model overriding and caching behavior.
- Added tests for deepagents model-passthrough patch to verify configuration injection in async tasks.
* feat(async-subagent): update middleware handling to prevent deadlocks in async sub-agents
* style: Refactor code formatting for improved readability in patches and test files
* refactor: streamline middleware construction and improve async handling in ConfigurableModelMiddleware
* fix: remove unused request parameter from _read_model_override function
* refactor: improve async handling in _ClientProxy and enhance logging in ConfigurableModelMiddleware
test: add behavior test to ensure AskUserMiddleware is excluded in async subagent mode
* feat(qq): add QR-code scan-to-configure onboarding for QQ Bot
Adds a `qr_register()` flow that drives q.qq.com's create_bind_task /
poll_bind_result APIs so the wizard can auto-fill `qq_app_id` and
`qq_app_secret` after the developer scans a QR code with a bound QQ
account, falling back to manual entry on failure or cancel.
- channels/qq/crypto.py: AES-256-GCM helpers for decrypting the bot's
client_secret returned by poll_bind_result.
- channels/qq/onboard.py: portal API client + polling loop.
- channels/qq/__init__.py: re-export `qr_register`.
- config/onboard.py: QQ branch in `_step_channels` that offers
"Scan QR code" vs "Enter manually", and skips the manual prompt
loop when a scan succeeded.
* style(qq): fix ruff lint errors in onboard.py
Move `import os` to the top-level import block (E402), drop the legacy
`typing.Optional`/`typing.Tuple` imports (UP035), and use the PEP 585/604
builtin generics (`tuple[...]`, `X | None`) for the few annotations that
still referenced them (UP006/UP045). No behavior change.
* fix(qq): harden QR onboard error paths and declare scan deps
Address review feedback on PR #213:
- Declare cryptography>=41.0 and qrcode>=7.4 in [qq]/[all-channels]
extras and in _CHANNEL_PIP_DEPS so the scan flow no longer fails
with an opaque ImportError on a fresh `evoscientist[qq]` install.
- Polling loop logs each _poll_bind_result failure and aborts after
5 consecutive errors instead of silently spinning until the 600s
timeout, restoring the documented Raises: RuntimeError contract.
- Wrap decrypt_secret in try/except so failures honor the
None-on-failure contract instead of letting exceptions escape.
- Preflight `import cryptography` in the scan branch and offer
install or fall back to manual entry.
* style: ruff format collapse two over-wrapped log/console lines
* fix: Improve watcher logic to prevent false-positive notifications on clean stream exits
* fix: Update watcher logic to drop notifications on persistent runs.get failures
* fix: Refactor test for watcher persistent failure notification handling
* fix: Enhance watcher test to validate all notification queues are empty after reconnect budget exhaustion
* feat: Implement async sub-agent auto-notification system
- Added async notifier functionality to handle notifications for sub-agents reaching terminal states.
- Introduced `AsyncTaskNotification` dataclass for structured notification data.
- Implemented `watch_run_and_notify` to monitor agent runs and enqueue notifications.
- Created `spawn_watcher` to manage watcher tasks and ensure proper cancellation of previous watchers.
- Developed `consume_notifications` to process notifications, deduplicate them, and format messages for LLM.
- Added tests for notification handling, including draining, deduplication, and formatting.
- Patched deepagents to integrate the new watcher functionality into start and update tools.
* Enhance async notifier with per-thread notification routing and error handling
- Introduced `origin_cli_thread_id` to `AsyncTaskNotification` for routing notifications back to the originating CLI session.
- Implemented per-thread notification queues to handle notifications based on the originating thread.
- Updated `has_pending_notifications` and `drain_notifications` to respect thread-specific queues.
- Enhanced `watch_run_and_notify` to detect in-band error events from the SSE stream and handle clean exits.
- Modified tests to verify the new notification routing behavior and ensure proper handling of notifications across threads.
- Added a fixture to restore the async watcher patch state in tests to prevent state leakage.
- Updated deepagents patching to capture the main agent's CLI thread ID for notification routing.
* feat: Enhance async notifier with thread-specific watcher management and notification filtering
* test: Enhance notification draining logic for cleaner test setup
* refactor: Remove summary field from AsyncTaskNotification and update related tests
* feat: Enhance async notification handling with target thread ID support
* Refactor async notifier and middleware for improved task management
- Removed the no-op shutdown watcher loop from async_notifier.py as it is no longer needed.
- Updated watch_run_and_notify to clarify notification handling and race conditions.
- Cleaned up shutdown handling in commands.py, interactive.py, and tui_interactive.py by removing obsolete shutdown watcher calls.
- Deleted the deepagents async watcher patch from patches.py, transitioning to a new middleware approach.
- Introduced AsyncWatcherMiddleware to handle async task notifications directly during tool calls.
- Updated tests to validate the new middleware functionality and ensure proper watcher spawning and cancellation.
- Enhanced test coverage for async watcher middleware, including edge cases and error handling.
* feat(tests): add fixture to reset notifier state before each test
* feat(wechat): add personal-WeChat (iLink) backend with QR-code login
Adds a third WeChat backend alongside WeCom and Official Account:
``personal`` rides Tencent's iLink Bot long-poll gateway so a personal
WeChat account can act as a bot. Credentials are obtained via QR-code
scan and persisted under ``DATA_DIR/wechat_personal/accounts/``.
- channels/wechat/personal.py: WeixinPersonalChannel + qr_login.
- channels/wechat/crypto.py: aes128_ecb_decrypt + parse_ilink_aes_key
for the iLink CDN media protocol.
- channels/wechat/probe.py: validate_wechat_personal credential probe.
- channels/wechat/serve.py: --backend personal CLI + --qr-login flow.
- channels/wechat/__init__.py: factory dispatch on wechat_backend; pull
in the new dependencies in the docstring.
- config/settings.py: wechat_personal_* fields.
- config/onboard.py: WeChat-backend picker + QR-scan flow in the wizard
+ personal-backend probe in _probe_channel.
- pyproject.toml / uv.lock: add qrcode + certifi to wechat & all-channels
extras (aiohttp was already pulled in transitively).
* fix(wechat): address ruff failures and CodeRabbit review on personal-WeChat PR
- personal.py: drop unused imports (`field`, `PollingMixin`); replace
`asyncio.TimeoutError` with builtin; hold references to background
`asyncio.create_task` results so they aren't GC'd; wire `dm_policy`
through `_process_message` (disabled/allowlist) so `wechat_personal_dm_policy`
actually takes effect for DMs.
- onboard.py: import-check gate now validates the full WeChat dependency
set (aiohttp, qrcode, Crypto, certifi) instead of only aiohttp; mask
`WeCom Secret` and `MP App Secret` prompts via `questionary.password`;
derive the QR-login hint path from `_account_dir()` instead of the
hard-coded `~/.evoscientist/...`; stop copying the QR-login token into
the main config (already persisted per-account on disk — copying broadens
secret exposure and risks staleness).
- pyproject.toml: allow Chinese full-width punctuation in `allowed-confusables`
for user-facing CN messages.
* style(wechat): apply ruff format
`ruff format --check` was failing CI on three files (one pre-existing in
`__init__.py` plus formatter-driven line-merges in the files touched by
the previous fix commit). Ran `ruff format` to bring them in line; both
`ruff check` and `ruff format --check` now pass.
* refactor(channels): remove dead MessageBus dispatcher
Outbound routing has two implementations: ``MessageBus.dispatch_outbound``
(subscriber-based) and ``ChannelManager._dispatch_outbound`` (registry
lookup). Only the latter is ever started in production — the former
is reachable solely from tests, yet both consume from the same
``bus.outbound`` queue. If anyone followed the bus's own API surface
they would silently steal messages from the real dispatcher.
Drop the unused machinery to leave a single, obvious outbound path:
- ``MessageBus.subscribe_outbound`` / ``dispatch_outbound`` / ``stop``
- ``_running`` flag and ``_outbound_subscribers`` map
- ``OutboundCallback`` type alias
- The lone ``bus.stop()`` call in ``cli/channel.py`` (was no-op)
- Four tests covering the removed code paths
* test(channels): drop empty MessageBus stubs after dispatcher removal
---------
Co-authored-by: Xi Zhang <106144707+X-iZhang@users.noreply.github.com>
* 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>
dinos