* feat: report independent instance and workspace identities
* test: include workspace in OAuth callback fixture
* ci: pin production cloud adapter to Space release
* fix: preserve authenticated Workspace telemetry attribution
* ci: pin production cloud adapter to final Space release
---------
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
* fix(cloud): show owner model balance and enforce single owner
* fix(migrations): create owner index idempotently
---------
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
Telegram file.file_path is a full URL of the form
https://api.telegram.org/file/bot<TOKEN>/<path> that embeds the bot
token. Since #2362 this URL was copied into Image.url, so the token was
serialized into the message chain and thereby persisted to the
monitoring database, shown in the dashboard, and forwarded to every
installed plugin via event dispatch. Anyone with dashboard or plugin
access could recover the token and take full control of the bot.
Unlike the public CDN URLs used by the other adapters changed in #2362,
Telegram file URLs are only usable with the embedded token, so there is
no safe URL to expose. Store base64 only (as before #2362); the vision
path already relies solely on base64, so nothing downstream changes.
Add a regression test asserting the token never appears in the
converted Image or the serialized message chain.
Co-authored-by: Constantine1916 <Constantine1916@users.noreply.github.com>
Preserve the platform CDN URL in Image.url alongside base64 data,
enabling plugins to use ContentElement.from_image_url() for direct
vision API access without redundant local download.
- aiocqhttp: use msg_data["data"]["url"] and msg.data["url"]
- discord: use attachment.url
- telegram: use file.file_path
- slack: use pic_url
- wecom: use picurl
- qqofficial: use pic_url
Satori adapter already follows this pattern (satori.py:168).
The change is purely additive — base64 is preserved for backward
compatibility, and get_bytes() priority (url → base64 → path)
ensures plugins can choose the optimal path.
Closes#2355
Co-authored-by: douxt <8429023+douxt@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
Previously, MCP tool calls via call_tool() had no timeout, so a hung MCP
server would block the entire session indefinitely (exacerbated by
concurrency.session=1). This wraps the call in asyncio.timeout(30) and
raises an Exception on expiry, letting the LLM recover gracefully.
Closes#2339
When the LLM (e.g. MiniMax-M3) returns multiple rounds after tool calls, the _StreamAccumulator was initialized with initial_content=first_content, causing every subsequent round to repeat the entire first message. Remove the re-seeding so each round starts with a clean accumulator.
Add regression test verifying multi-round tool call content is not duplicated.
Move the Lark SDK synchronous connection URL lookup off the main asyncio loop, serialize reconnects, and cover the incident with a non-blocking regression test.
* fix(provider): strip think tags for MiniMax-M3 and other OpenAI-compatible models
MiniMax-M3 (and other OpenAI-compatible providers) emit chain-of-thought
reasoning directly in the content field wrapped in tags, instead
of using a separate reasoning_content field or the legacy CRETIRE_REASONING
markers. The existing remove_think logic only handled CRETIRE_* tags, so
think blocks leaked into user-visible output even when remove_think was enabled.
- Add _ThinkStripState: a stateful filter that correctly handles tags
split across streaming chunk boundaries.
- Add _strip_think classmethod with regex patterns for both and
CRETIRE_* tags.
- Wire think_state into invoke_llm_stream so deltas are filtered before
reaching the accumulator.
- Add remove_think safety net in _StreamAccumulator so the final message
from tool-call rounds also gets stripped.
- Fix remove_think resolution to use defensive nested .get() so
pipelines missing output.misc don't raise AttributeError.
* fix(litellmchat): add missing _CLOSE_TAG class attribute on _ThinkStripState
* fix(provider): handle think stripping across LiteLLM paths
---------
Co-authored-by: WangCham <651122857@qq.com>
* feat: Implement workflow form handling for paused workflows
- Added module-level storage for pending forms to manage state across sessions.
- Introduced functions to set, get, and clear pending forms with expiration handling.
- Enhanced DifyServiceAPIRunner to support resuming paused workflows via form actions.
- Implemented logic to yield human input requests and display appropriate messages.
- Updated workflow submission methods to handle paused states and resume actions.
- Ensured proper merging of pending form actions with user inputs for seamless interaction.
* feat: Add '_routed_by_rule' variable to form action in Lark and Telegram adapters
* feat: Enhance Lark and Telegram adapters with new form handling for paused workflows
* feat: Enhance TelegramAdapter to handle form action buttons and message threading
* feat: Improve TelegramAdapter message handling with enhanced error management and draft message support
* feat: Add the function for formatting human input text to support adapters without rich UI.
* feat(dingtalk): implement human input card support and card action handling
- Add a new module `card_callback.py` to handle card action button clicks from DingTalk.
- Introduce `DingTalkCardActionHandler` to process card action callbacks and extract parameters.
- Update `DingTalkAdapter` to manage card state and handle form input through a single card template.
- Add configuration for `human_input_card_template_id` in `dingtalk.yaml` to specify the template for human input.
- Create a new card template `dingtalk_human_input_card.json` for rendering human input prompts and buttons.
* feat(dingtalk): enhance human input card functionality with streaming support and active turn management
- Updated the DingTalk card template to enable streaming mode and multi-update configuration.
- Removed the obsolete delete_card method from DingTalkClient to streamline card management.
- Enhanced DingTalkAdapter to manage active turn cards and accumulated streaming text, ensuring a seamless user experience during human input prompts.
- Modified the create_message_card method to utilize existing active cards for resumed workflows, preventing duplication.
- Improved the _paint_form_on_card method to update existing cards with human input prompts and buttons dynamically.
- Updated the dingtalk_human_input_card.json template to reflect the new streaming capabilities and configuration options.
* feat(wecom): implement Dify human input pause handling with button interaction support
* feat(qqofficial): implement Dify human input button interaction handling and markdown keyboard support
* feat(qqofficial): implement one-click QR binding and enhance localization support
* feat(discord): implement Discord form view with button interactions for Dify actions
* fix(telegram): correct group chat type check and handle oversized callback data for Telegram actions
fix(difysvapi): ensure safe access to remove-think configuration in pipeline settings
* feat(dify): add support for chatflow app type and enhance human input handling
* feat(telegram): add action title feedback for user selections in Telegram messages
* feat(lark): enhance LarkAdapter to store form content for resume notices
* feat(dingtalk): update display formatting for card content with HTML line breaks
* feat(dingtalk): add feedback functionality to cards with 👍/👎 buttons
- Implemented feedback state management for cards, allowing users to provide feedback via thumbs up/down buttons.
- Enhanced card rendering to include feedback buttons when appropriate.
- Registered feedback listeners to handle feedback events and update card states accordingly.
- Updated the card template to support dynamic button rendering for feedback actions.
- Improved error handling and logging for feedback actions and card updates.
* fix: add Avatar component to dingtalk_human_input_card.json for enhanced user interaction
* feat(wecom): add optional source block to interactive template cards for enhanced branding
* feat(wecom): add functions for template card action extraction and update, enhance button interaction handling
* feat(qqofficial): synchronize passive-reply counter with inbound message sequence
* feat(qqofficial): add method to identify invisible form placeholder chunks in messages
* feat(dingtalk): add download link for human input card template and enhance dynamic form configuration
* feat(telegram): enhance message handling with group stream deletion and form placeholder detection
* Add unit tests for DingTalk, Lark, WeComBot, and Dify service API runners
- Implement tests for DingTalk adapter helper functions including form content cleaning, input extraction, and completed input lines.
- Create unit tests for Lark adapter helper functions focusing on input extraction and completed input lines.
- Add tests for WeComBot template card functionalities, including event extraction and payload building for human input.
- Enhance Dify service API runner tests to cover human input forms, including input collection, action handling, and form snapshot extraction.
* feat: Enhance Telegram and QQ Official adapters with select field handling and form action processing
- Added support for select fields in Telegram adapter, including option extraction and callback handling.
- Implemented form action processing for Telegram callbacks, improving user interaction feedback.
- Introduced new helper functions for building keyboards and resolving select button actions in QQ Official adapter.
- Enhanced DifyServiceAPIRunner to handle cumulative streaming responses and improve error handling during workflow resumes.
- Added unit tests for new functionalities in Telegram and QQ Official adapters, ensuring robust behavior for select fields and form actions.
* feat(lark): add functions for current input definitions and visible form content handling
feat(qqofficial): update fallback text handling for non-streaming scenarios
feat(difysvapi): enhance form content processing for interactive fields and actions
test: add unit tests for Lark and QQ Official adapter functionalities
* Add tests for DingTalk adapter content processing and markdown formatting
- Updated the assertion in `test_dingtalk_completed_input_lines_include_text_and_select_values` to remove unnecessary markdown formatting.
- Added new tests to verify that `_dingtalk_clean_form_content` maintains the order of prompts and completed values in various scenarios.
- Introduced `test_dingtalk_card_markdown_preserves_internal_line_breaks` to ensure internal line breaks are correctly converted to HTML line breaks.
* feat: Refactor input handling and feedback messages across multiple adapters
* feat: Update the human-computer interaction template cards, and optimize the prompt information and content display.
* feat: Refactor pending form handling to isolate by bot and pipeline
* feat: Enhance error handling and caching for Dify and WeCom interactions
* feat: Enhance select input handling and validation in Dify API runner and Telegram adapter
* feat: Add missing completed input lines handling in DingTalk adapter
* feat: Add pipeline_uuid handling across multiple adapters and update related tests
Skip the unsupported valkey-glide dependency on Windows while preserving automatic installation on supported platforms. Keep missing-client runtime and test paths safe, and update the Valkey integration documentation.
* feat(vector): add Valkey Search vector database backend
Add a new opt-in VectorDatabase backend backed by the Valkey Search module
(valkey/valkey-bundle), accessed via the official valkey-glide client's native
ft command namespace.
- Implements the full VectorDatabase ABC: VECTOR, FULL_TEXT and HYBRID search,
all 8 metadata filter operators, and pagination with exact totals.
- HYBRID uses filter-then-KNN (no app-side weighted fusion); vector_weight is
accepted for interface parity but NOT honored (docstring + one-time warning +
docs caveat).
- Lazy connect so a down Valkey never blocks boot; mandatory
client_name=langbot_vector_client; optional auth + TLS (never logged).
- Registered via a single elif branch in vector/mgr.py; disabled by default
(vdb.use stays chroma) for toC compatibility.
- Adds valkey-glide>=2.4.1,<3.0.0; no protobuf/pydantic downgrade; no ORM
change so no Alembic migration.
- Unit tests (fast lane, no server) + slow-gated integration tests
(TEST_VALKEY_URL, valkey/valkey-bundle:9.1.0) + integration doc.
* fix(vector): paginate Valkey Search deletes and guard delete_by_filter
Address self-review follow-ups for the Valkey Search VDB backend:
- _search_keys now paginates through the full result set in batches of
_DELETE_SCAN_BATCH instead of capping at a single hard-coded 10000-key
page, so delete_by_file_id / delete_by_filter fully remove files and
filters that match more than one page of chunks (no orphaned vectors).
- Add unit regression tests for the delete_by_filter mass-deletion guard:
a filter referencing only non-indexed fields must skip and return 0
(never fall back to match-all), and a supported filter still deletes
matching keys.
* refactor(vector): harden Valkey Search backend and add adversarial tests
Address the self-review NICE-TO-HAVE items for the Valkey Search VDB backend:
- Guard the username-without-password credential edge (skip auth + warn
instead of building ServerCredentials(password=None, ...), which glide
rejects).
- Add an async close() teardown that closes the glide client and resets
cached state (re-init is safe via the existing None guard).
- Hoist 'import json' to module top (was imported inside three methods).
- Document the FT TAG literal-brace limitation in _escape_tag (fails closed,
never widens).
Tests:
- Add an adversarial-input integration test proving crafted file_id /
query_text cannot break out of or widen a query (fail-closed on braces).
- Add unit tests for close() and the credential-build guard.
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
* fix(vector): make Valkey Search file_id TAG support arbitrary characters
Valkey Search's FT TAG query parser cannot handle '{', '}' or '*' even when
backslash-escaped, so a file_id containing those characters previously
produced an unparseable query (it failed closed / raised). Percent-encode
exactly those FT-unsafe characters (plus '%' for reversibility) in the
file_id TAG value, applied identically at write time and query time, so an
arbitrary file_id round-trips. For normal UUID/hash ids this is a no-op and
the stored value is unchanged; the original file_id is always preserved
verbatim in metadata_json.
Strengthen the adversarial integration test to assert a brace/star-bearing
file_id matches and deletes exactly its own row (no widening, no raise), and
add unit tests for _encode_file_id and the filter encoding.
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
* refactor(vector): address Valkey Search review feedback
- Add configurable request_timeout (default 5000ms; glide default 250ms is
too low for KNN); expose in config.yaml + docs table
- Validate embedding dimension consistency in add_embeddings (fail fast on
mixed lengths to avoid silent KNN corruption)
- Use ft.info (O(1)) instead of ft.list (O(n)) for index existence checks in
the query hot path; also closes the check-then-create TOCTOU window
- Pipeline HSETs via a non-atomic Batch instead of N sequential awaits
- Extract shared _iter_reply_docs to deduplicate reply parsing between
_reply_to_chroma and list_by_filter
- Parenthesize multi-condition pre-filters before the => KNN clause
- Fail closed when a username is configured without a password
- Catch only RequestError on ft.dropindex (let connection/auth errors surface)
- Bound the delete_collection SCAN loop with a safety cap
- Add VectorDatabase.close() (no-op default) + VectorDBManager.shutdown()
- Simplify _MATCH_ALL literal; normalize typing to builtin generics
* fix(vector/valkey_search): address round-2 review feedback
- Serialize lazy client creation with an asyncio.Lock (double-checked) so
concurrent first-use callers don't construct and leak duplicate clients.
- Make the filter operator chain exhaustive: raise on an unhandled op rather
than silently dropping the condition (which could widen delete_by_filter).
- Cast numeric range (///) values to float, failing closed on
non-numeric input and pre-empting a future NUMERIC-field injection surface.
* refactor(vector): remove shutdown/close from base ABC per maintainer feedback Per maintainer request, interface changes to VectorDatabase ABC and VectorDBManager should be in a separate PR with implementation across all backends. The ValkeySearchVectorDatabase.close() method remains but does not override an ABC method.
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
* docs(test): list valkey_search in vdb coverage exclusions Add valkey_search to the documented vector/vdbs/ coverage-exclusion list, matching the existing chroma/milvus/pgvector/qdrant/seekdb entries. These adapters require a live database instance and are covered by env-gated integration tests instead of unit tests.
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
---------
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
Operators can now set a global default memory limit for all stdio MCP
servers in config.yaml or via environment variable:
config.yaml:
box:
default_memory_mb: 2048 # default: 1536
env:
BOX__DEFAULT_MEMORY_MB=2048
The default is raised from 1024 to 1536 MB — a safer floor for
Node.js V8 + WASM (undici llhttp) under nsjail cgroup limits.
Individual MCP servers can still override via their own box.memory_mb.
Previously the fallback was hardcoded to 1024 MB, causing OOM kills
(return_code=137) on node/npx MCP servers that need more RAM.
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
All MCPs share one Box session (mcp-shared). When session memory_mb differed
by command type (512 for python, 1024 for node), the second MCP to call
create_session raised BoxSessionConflictError. Fix: always use 1024 MB for
the shared session so python and node MCPs coexist without conflict.
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
Node.js MCP servers (npx/bunx) were being OOM-killed (return_code=137) by the
default 512MB nsjail cgroup_mem_max. Node V8 reserves large virtual address
space and instantiates WebAssembly modules (undici llhttp) on startup, easily
exceeding 512MB resident. This caused every node-based MCP (memory,
sequential-thinking, filesystem, weather, docker, excel) to crash-loop.
Fix: when the stdio command is npx/bunx/pnpm, default memory_mb to 1024 unless
the operator explicitly set a value. Python/uvx servers keep the 512MB default.
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
_ColdStartRetry was caught in _lifecycle_loop_with_retry which set
_preserve_managed_process = True — but by then the finally block inside
_lifecycle_loop had already run and called _cleanup_box_stdio_session(),
stopping the live managed process (return_code=143 SIGTERM). The cold-start
retry then restarted a fresh process, eliminating the warm-up advantage.
Fix: add an explicit except _ColdStartRetry in _lifecycle_loop that sets
_preserve_managed_process = True before re-raising. The finally block then
sees the flag and skips stop_managed_process, leaving the live process
untouched for the next handshake attempt.
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
Hyu