The 12 old adapters that now have an EBA replacement are tagged
`spec.legacy: true` in their source manifests. Principle: don't delete,
de-emphasize.
- sources/*.yaml (aiocqhttp, dingtalk, discord, kook, lark,
officialaccount, qqofficial, slack, telegram, wecom, wecombot,
wecomcs): add spec.legacy: true
- Adapter / IChooseAdapterEntity types: add optional legacy flag
- BotForm adapter Select: split legacy adapters into a collapsed,
grayscale group at the bottom with an explanatory hint; auto-expand
when the bot already uses a legacy adapter
- Wizard platform picker: same collapsed legacy section
- i18n: legacyAdapters / legacyAdaptersHint (zh-Hans, en-US)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
groupByCategory pushed multi-category adapters (lark, wecom, discord,
slack) into every matching bucket, so the adapter Select rendered
duplicate SelectItem values — triggering React duplicate-key warnings
and corrupting Radix item tracking. Assign each item to its highest
-priority matching category only. Also de-dupes the wizard card grid.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.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
Replace the three-way transport choice (stdio / sse / httpstream) for
connecting LangBot to external MCP servers with two modes: local (stdio)
and remote. Remote servers only require a URL; the runtime auto-detects
the transport (tries Streamable HTTP, falls back to SSE).
- provider/tools/loaders/mcp.py: add _init_remote_server() with
Streamable-HTTP-then-SSE probing; dispatch 'remote' lifecycle, keep
legacy sse/http branches for back-compat
- plugin/connector.py: normalize legacy http/sse marketplace modes to
'remote' on Space install, preserving connection params
- entity/persistence/mcp.py: document mode as stdio, remote (legacy: sse, http)
- alembic 0006: idempotent data migration mapping existing sse/http rows
to remote (downgrade maps back to http)
- api/http/service/mcp.py: stash runtime_info (status + tool list) into
test task metadata before tearing down the temp session
- web: collapse mode dropdown to local/remote, remote renders URL+timeout
only, edit auto-maps legacy sse/http to remote; show tools after test in
create mode from task metadata; remove dead plugins/mcp-server/ tree
- i18n: local/remote labels + mode/url hints across 8 locales
* feat(box): bidirectional attachment transfer for sandbox
Materialize inbound attachments into the sandbox workspace so agents can
process user-sent files, and collect agent-produced files from the outbox
to attach them back to the reply.
- box(service): add materialize_inbound_attachments / collect_outbound
attachments. Prefer direct host-filesystem read/write on the bind-mounted
workspace (no size limit), falling back to chunked exec only for
non-shared backends (e2b/remote). Clear per-query inbox/outbox dirs at
turn start to avoid query_id-reuse collisions.
- provider(localagent): inject inbound attachment descriptors into the
sandbox and append a system note telling the agent the inbox/outbox paths.
- pipeline(wrapper): collect outbox files on the final stream chunk and
append them as attachment components to the response chain.
- web(debug-dialog): render File components with a download link when
base64/url is present; add base64/path fields to the File entity.
- tests: cover inbound/outbound, large-file transfer without truncation,
and stale-dir clearing (86 passing).
* feat(box): support voice/file attachment round-trip end-to-end
Extends the bidirectional attachment transfer to audio and arbitrary files
through the real webchat UI, and fixes the model-payload errors that
non-image attachments triggered.
- platform(websocket_adapter): resolve Voice/File component storage keys to
base64 (previously only Image), so audio/documents reach the sandbox inbox.
- web(debug-dialog): accept audio/* and any file in the uploader (was
image-only), classify by mimetype, upload Voice/File via the documents
endpoint, and render non-image staged attachments as a chip.
- provider(litellmchat): drop non-image file parts (file_base64 / file_url)
when building the OpenAI/LiteLLM payload. These come from Voice/File
attachments — including ones replayed from conversation history — and the
agent reads their bytes from the sandbox, not the model. Without this the
provider rejects the request: 'invalid content type=file_base64'.
- provider(localagent): also strip those parts from the current user message
alongside the sandbox-path note (model-facing clarity; the requester is the
real safety net for history).
- tests: cover the requester strip/keep behavior (file dropped, image kept and
reshaped to image_url, mixed history, plain-string content).
* test(box): cover inbound/outbound attachment helpers; fix ruff format
- ruff format localagent.py (CI ruff format --check was failing)
- add unit tests for ResponseWrapper outbound-attachment helpers (wrapper.py 78%->98%)
- add unit tests for LocalAgentRunner._inject_inbound_attachments
- add unit tests for WebSocketAdapter._process_image_components (0%->covered)
Lifts PR patch coverage from 68.97% to ~88% (>75% target).
- Add PanelToolbar/PanelBody primitives so all four settings tabs share
the same top-toolbar + scrollable-body rhythm under the unified header.
- API panel: drop the heavy gray shadowed TabsList; move the create
action into the toolbar next to the tabs, lighten per-tab hints.
- Storage panel: reuse PanelToolbar for the generated-at/refresh bar.
- Account panel: wrap content in PanelBody for consistent padding.
- Models panel: keep the pinned LangBot Models (Space) card at the very
top, above the add-custom-provider row (intentional pin), using
PanelBody instead of a top toolbar.
Add a Logs tab beside Documentation on the plugin detail page, showing
the output a plugin prints through the standard Python logger (per the
wiki style guide). Logs are captured from the plugin's stderr by the
plugin runtime and fetched on demand.
- Bump langbot-plugin pin to 0.4.4 (adds GET_PLUGIN_LOGS action)
- plugin_connector/handler: get_plugin_logs RPC client
- HTTP route GET /api/v1/plugins/<author>/<name>/logs (limit + level)
- Frontend: wrap detail right panel in Docs/Logs Tabs; PluginLogs
component with level filter, manual + 3s auto refresh, bottom-follow
- i18n: 7 new keys across all 8 locales
* refactor(provider): use LiteLLM as unified LLM requester backend
- Replace 23+ individual requester implementations with unified litellmchat.py
- Add litellm_provider field to 27 YAML manifests for provider routing
- Delete redundant requester subclasses
- Add unit tests for LiteLLMRequester (29 tests)
- Fix num_retries parameter name (was max_retries)
- Fix exception handling order for subclass exceptions
LiteLLM provides unified API for 100+ providers, eliminating need for
provider-specific requesters.
* fix: ruff format provider.py
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* refactor(provider): simplify LiteLLM requester usage handling
- Remove unused Anthropic-specific tool schema generation
- Share completion argument construction between normal and streaming calls
- Use LiteLLM/OpenAI native usage fields for monitoring
- Collect stream token usage from LiteLLM stream_options
- Update LiteLLM requester tests for unified usage fields
* restore: restore deleted provider requester files
Restore individual provider requester implementations that were
removed in de61b5d3. These files coexist with the unified
litellmchat.py backend.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* feat: update requesters and improve provider selection UI
- Added `litellm_provider` field to various requesters' YAML configurations.
- Removed obsolete Python requester files for OpenRouter, PPIO, QHAIGC, ShengSuanYun, SiliconFlow, Space, TokenPony, VolcArk, and Xai.
- Introduced new requesters for Tencent and Together AI with corresponding YAML configurations and SVG icons.
- Enhanced the ProviderForm component to include a searchable dropdown for selecting providers, improving user experience.
- Updated localization files to include search provider text for both English and Chinese.
* fix(provider): align litellm rebase with master
* fix(provider): capture streaming token usage; add token observability
The LiteLLM streaming requester only captured usage when a chunk had an
empty `choices` list. Many OpenAI-compatible gateways (e.g. new-api) and
providers send the final usage payload in a chunk that still carries an
empty-delta choice, so streamed calls always recorded 0 tokens in the
monitoring logs/dashboard (non-streaming worked).
- Capture stream usage whenever a chunk carries it, regardless of choices
- Add robust _normalize_usage (dict/obj shapes, derive missing total_tokens)
- Register litellm in bootutils/deps.py (was in pyproject only)
- Add MonitoringService.get_token_statistics + /monitoring/token-statistics
endpoint: summary, per-model breakdown, token timeseries, and a
zero-token-success data-quality signal
- Add TokenMonitoring dashboard tab (summary tiles, stacked token chart,
per-model table) + i18n (en/zh)
- Regression tests for stream usage capture and usage normalization
Verified end-to-end against a real OpenAI-compatible endpoint with
gpt-5.5 and claude-opus-4-8: tokens now recorded non-zero for both
streaming and non-streaming paths.
* refactor(provider): simplify litellm capabilities
* style: simplify wrapped expressions
* feat(models): persist context metadata
* fix(provider): handle dict embeddings and openai-compatible rerank in LiteLLMRequester
- invoke_embedding: support both object- and dict-shaped response.data
entries (OpenAI-compatible gateways like new-api return dicts)
- invoke_rerank: litellm.arerank rejects the 'openai' provider, so for
openai-compatible (or unspecified) providers call the standard
Jina/Cohere-style POST /v1/rerank endpoint directly over HTTP
- accept both 'relevance_score' and 'score' fields in rerank results
- add unit tests for the openai-compatible HTTP rerank path
* feat(provider): enforce requester support_type when adding models
- frontend: AddModelPopover only shows model-type tabs (llm/embedding/
rerank) that the provider's requester declares in its manifest
support_type; ModelsDialog fetches requester manifests and maps
requester -> support_type, passed down through ProviderCard
- backend: add _validate_provider_supports guard in create_llm_model /
create_embedding_model / create_rerank_model so a model cannot be
attached to a provider whose requester does not support that type,
even if the frontend restriction is bypassed (manifests without
support_type are allowed for backward compatibility)
- manifests: correct support_type for providers that do not offer all
three model types:
- llm only: anthropic, deepseek, groq, moonshot, openrouter, xai
- llm + text-embedding: openai, gemini, mistral
- add rerank to new-api (verified working via /v1/rerank)
- set llm + text-embedding + rerank for aggregator/unknown gateways
* feat(provider): add searchable alias to requester manifests
- add a free-text 'alias' field to every requester manifest spec,
containing the vendor's English/Chinese names, pinyin, common
nicknames and flagship model-series names (e.g. moonshot -> kimi,
月之暗面; zhipu -> glm, 智谱清言)
- frontend: ProviderForm requester search now also matches against
alias (substring/contains), so searching 'kimi' surfaces Moonshot,
'硅基' surfaces SiliconFlow, etc.
- also fix support_type: openrouter (relay) supports embedding+rerank;
LangBot Space gains rerank (coming soon)
* fix(provider): make support_type guard defensive against incomplete model_mgr
- _validate_provider_supports now uses getattr to gracefully skip when
model_mgr / provider_dict / manifest lookup is unavailable, instead of
raising AttributeError (fixes unit tests that mock ap.model_mgr as a
bare SimpleNamespace)
- add TestValidateProviderSupports covering: allow supported type,
reject unsupported type, allow when support_type missing, allow when
provider unknown, degrade safely when model_mgr is incomplete
* fix(persistence): guard 0004 migration against missing llm_models table
The 0004_add_llm_model_context_length migration called
inspector.get_columns('llm_models') unconditionally, raising
NoSuchTableError when the table does not exist (e.g. migrating a
fresh/empty DB, as exercised by the integration tests where
create_all() registers no tables because the ORM models are not
imported). Every other migration guards with a table-existence check
first; add the same guard here for both upgrade and downgrade.
Also restore the test head assertion to 0004 (it had been lowered to
0003 to mask this failure).
* Merge branch 'master' into feat/litellm
Resolve conflicts:
- uv.lock: regenerated via 'uv lock' to reconcile litellm/fastuuid
(ours) with openai bump (master).
- Alembic migrations: master added 0004_add_mcp_readme while this
branch added 0004_add_llm_model_context_length, both as children of
0003 (would create multiple heads). Re-chain the litellm migration as
0005_add_llm_model_context_length with down_revision=0004_add_mcp_readme
for a single linear head. Update test head assertion accordingly.
* fix(persistence): shorten migration revision id to fit varchar(32)
PostgreSQL stores alembic_version.version_num as varchar(32).
'0005_add_llm_model_context_length' (33 chars) overflowed it, raising
StringDataRightTruncationError in the PG migration tests. Rename the
revision (and file) to '0005_add_llm_context_length' (27 chars) and
update the head assertions in both SQLite and PostgreSQL migration
tests.
---------
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Co-authored-by: fdc310 <2213070223@qq.com>
Co-authored-by: RockChinQ <rockchinq@gmail.com>
RockChinQ