Unset (0) now resolves at call time (2048 / 4096 / 24576 with sandbox) so the editor can offer Default vs Custom without silently rewriting a saved value.
Generated PPT/report scripts overflowed the shell_exec command cap, and
session agents kept mutating the frozen skill tree or failing SkillHub
installs on display titles. Add write/edit sandbox tools, attach the
skill venv to /workspace scripts, and accept slug-based install names.
Introduce the hidden skill-installer agent and TenantSkillService so a
sandbox config can install or remove skills into snapshot images under a
per-config lock, without yet exposing HTTP or UI entry points.
Co-authored-by: Cursor <cursoragent@cursor.com>
Move citation protocol injection to the system layer so custom prompts no
longer own citation syntax. Agents can disable inline citations while source
aliases still encode and decode tool arguments correctly.
Replace raw chunk/document/KB IDs in LLM context with short cN/dN/bN/wN
aliases, decode tool arguments on the way in, and expand compact <ref/>
citations to the public <kb>/<web> format before user-facing output.
Update the classification logic for follow-up questions to include reasoning about previously described images or documents. Ensure that follow-up queries referencing earlier attachments are correctly identified and processed without triggering unnecessary knowledge base searches. This change improves the model's ability to maintain context and respond accurately to user inquiries related to past interactions.
When retrieved context or tool results contain Markdown images, dynamically
append output requirements to system prompts and final-answer instructions.
Also normalize fullwidth image parentheses in the frontend renderer so
localized model output still renders correctly.
Chat and agent tool context now emits answer-ready Markdown images instead
of internal <image> XML so copied content renders safely. The frontend also
normalizes legacy XML blocks in persisted conversations during streaming.
Tighten per-turn @MCP/@Skill/@tag handling after review: enforce agent
MCP/Skill whitelist, ignore mentions when selection mode is none, tag-scope
grep_chunks, and simplified English must_use lines without tool dumps.
Skip persisting scope envelopes to history rendered_content.
Wire per-turn @mentions for skills, MCP services, tags, and files through
QA handlers into agent config, runtime_context/must_use user blocks, and
tool registration. Fix tag-only scopes dropping KB tools, refresh suggested
questions on mention changes, and strengthen MCP must_use so @selected
services are invoked before local KB search.
- Updated the agent system prompt to clarify mandatory deep read requirements for FAQ entries, ensuring accurate retrieval and response generation.
- Added new localization strings for FAQ-related labels in English and Chinese, improving user experience and consistency across the application.
- Refactored the document and grep results components to support distinct FAQ identification, enhancing clarity in the output.
- Introduced new tests for FAQ metadata handling, ensuring robust functionality and correctness in the FAQ processing logic.
- Eliminated all instances of the final_answer tool from the agent's configuration and logic, transitioning to a model where the agent concludes responses with plain text instead.
- Updated event handling and response analysis to reflect this change, ensuring that the agent's output is now directly delivered as text without relying on a dedicated final answer tool.
- Adjusted localization strings across multiple languages to remove references to final_answer, enhancing clarity and consistency in user-facing messages.
- Improved the overall structure of the agent's response handling to streamline the process and reduce potential confusion regarding tool usage.
refactor(agent): streamline answer handling and preamble logic
- Updated the agent's response handling to treat any text streamed before a tool call as a preamble, ensuring it is not included in the final answer.
- Removed the superseded marker logic from the event emission process, simplifying the flow of answer content and enhancing clarity in the UI.
- Adjusted the event handling to ensure that preamble segments are correctly retracted and relocated into the steps tree, improving the overall user experience.
- Enhanced the agent's internal logic to better manage answer segments and their states during streaming, leading to a more coherent response structure.
Allow built-in models to be declared in config/builtin_models.yaml
instead of inserting rows via SQL. On every startup the file is read
and each entry is UPSERT-ed into the models table (is_builtin=true)
by stable id.
Any string field may reference an environment variable with ${NAME}.
Unset variables are left as the literal placeholder so
misconfiguration surfaces clearly in provider calls rather than
failing silently with an empty token.
The file is optional: missing file, parse errors, and per-entry
upsert failures all log a warning without aborting startup.
docker-compose.yml adds env_file (.env, required:false) so
deployment-specific variables are passed through automatically.
When a custom agent has `MultiTurnEnabled=false`, `applyAgentOverridesToChatManage`
sets `chatManage.MaxRounds = 0` to signal "no history". Two pipeline plugins
mistreated this zero value as "use the global default" and silently re-loaded
session history into the LLM context:
- `PluginLoadHistory` fell back to `Conversation.MaxRounds` when
`chatManage.MaxRounds == 0`.
- `PluginQueryUnderstand.loadHistory` had the same fallback, and even when
`LOAD_HISTORY` was skipped it would re-populate `chatManage.History`,
leaking previous turns into rewrite, image analysis, and the final answer.
The RAG branch in `session_knowledge_qa` also added `LOAD_HISTORY`
unconditionally, unlike the pure-chat branch which guarded it with `hasHistory`.
This change:
- Treats `chatManage.MaxRounds <= 0` as an explicit disable in both plugins;
no fallback to global config.
- Makes the RAG pipeline consistent with the pure-chat path by gating
`LOAD_HISTORY` on `hasHistory`.
- Removes the duplicated `Current Time: {{current_time}}` line from
`agent_system_prompt.yaml`. The agent already receives a fresh
`<runtime_context><current_time>` block with each turn from
`observe.buildRuntimeContextBlock`, so the static placeholder was
redundant.
The ReAct agent path (`session_agent_qa`) already checked `MultiTurnEnabled`
directly and is not affected.
Closes#1479
Documents whose only payload is an embedded image (e.g. a docx with a
single picture) intermittently produced the refusal line "No textual
content was extractable from this document." even though the vision
model had successfully extracted a caption.
Three coordinated fixes:
- Clarify the summary prompt that text inside `<image_caption>` and
`<image_ocr>` is first-class extracted content, not an image
reference, so the model only triggers the empty-content branch when
the body is genuinely textless.
- For image-dominated documents (real text < 200 runes after stripping
image markup) include OCR alongside captions so screenshots and
scanned figures contribute their actual content; text-heavy
documents continue to use caption-only enrichment to avoid OCR
noise from incidental figures.
- Add `EnrichContentCaptionAndOCR` which embeds caption + OCR text
inline next to the original Markdown image link, deliberately
omitting the `<image url=...>` and `<image_original>` wrapper
blocks. Those wrappers carry only opaque export hashes that consume
tokens and have been observed to retrigger the LLM's "image
reference with no extracted text" heuristic.
The grep_chunks tool previously accepted an array of regex queries (1-5)
and an optional knowledge_base_ids filter and limit. In practice the LLM
either fired multiple near-duplicate calls or split synonyms across
entries instead of using POSIX alternation, and KB scoping plus result
limit are server-side concerns the model should not control.
Reshape the contract to match `grep -E -i` semantics:
- Schema accepts a single required `query` string. Combine concepts with
`|` alternation in one regex instead of multiple calls.
- Drop `knowledge_base_ids` and `limit` from the schema; the tool now
always searches the full agent scope and uses a fixed internal cap.
- Legacy `pattern`, `queries`, `patterns`, `max_results` keys are still
accepted and joined into a single alternation regex so older callers
and in-flight model outputs keep working.
- Update the agent system prompt template to document the new single
`query` field.
- Frontend tool title now reads `query`/`queries`/`pattern`/`patterns`
in that order so the search text is shown again under the new schema.
- Add a dedicated `grepSearch` / `grepSearchFailed` tool status (zh-CN,
en-US, ko-KR, ru-RU) and rename the zh-CN tool label to "搜索关键词"
so the UI no longer prefixes the call with a generic "调用 ..." label.
Scanned PDFs without a successful OCR pass (e.g. when no VLM is configured
on the KB, or when the VLM returned a "no text content" reply that the
sanitizer discarded) reach the summary and wiki-ingest pipelines with
nothing but markdown image references for content. The downstream LLM
prompts used to receive the filename, file type, and title alongside that
empty content, and the generate_summary template explicitly forbade the
model from refusing — so the model would invent a topic from the filename
alone, producing e.g. a "Canon scanner manual" summary for an MX5280.pdf
that is actually a scanned legal letter.
Two changes shut this down:
1. Drop filename / title / file-type from every LLM prompt input. The
wiki summary, knowledge extract, candidate slug, and chunk citation
prompts now see only the document content. The knowledge summary
path no longer prepends the "Document Type: X / File Name: Y" intro.
2. Add a content-sufficiency guard in front of the LLM call. A new
helper strips markdown image references and HTML image tags and
refuses to invoke the model when fewer than 50 real-text runes
remain. getSummary returns a sentinel error in that case and the
caller marks the knowledge as SummaryStatusFailed instead of
surfacing the first chunk's bare image reference. Wiki ingest skips
LLM extraction with a clear log message.
The generate_summary.yaml prompt is also updated: the "NEVER output
refusal phrases" clause is removed and replaced with an explicit
empty-content rule that returns a deterministic placeholder line.
Real OCR (Tesseract / PaddleOCR) and filename-pattern sanitisation are
intentionally left for separate PRs.
When users ask broad queries like "请整理知识库中的数据" in RAG mode,
vector/keyword search returns nothing because the query has no specific
content to match. The user needs to see what documents exist, not search
results.
- Add buildKBDocumentListing() to fetch document titles/filenames from
the knowledge base and inject them into the fallback prompt via
{{kb_documents}} placeholder
- Update fallback prompt templates (model_fallback, default_fallback_prompt)
to include document listing context so the LLM can guide users
- Improve rewrite prompt: tighten summarize vs kb_search classification,
expand kb_search to cover browse/organize/list operations, add examples
Closes#959
Revised the agent system prompt to clarify the synthesis process and the handling of factual errors. The instructions now emphasize the mandatory use of the `final_answer` tool for submitting responses and the need to flag issues before calling `final_answer`. This enhances the clarity and consistency of the agent's operational guidelines.
- Introduced a unified capability requirements system for agent tools, ensuring consistent filtering of knowledge bases based on their capabilities.
- Implemented derived `kb_filter` logic to streamline compatibility checks between agent tools and knowledge bases, reducing redundancy in configuration.
- Updated frontend components to reflect new filtering logic, including improved handling of empty states and user feedback when no compatible knowledge bases are available.
- Enhanced internationalization support by adding relevant messages for tool compatibility issues across multiple languages.
- Changed `kb_selection_mode` in agent type presets from "selected" to "all" to allow agents to access all knowledge bases by default.
- Revised prompt templates to clarify the handling of bound knowledge bases, replacing the `{{knowledge_bases}}` placeholder with a reference to the `<bound_knowledge_bases>` block in the user message's `<runtime_context>`.
- Enhanced internal logic to ensure that only searchable knowledge bases are considered during retrieval, improving the efficiency of knowledge searches.
- Added new functions for better formatting and handling of knowledge base metadata in the runtime context.
- Updated documentation and comments for clarity on the changes made to knowledge base interactions.
- Introduced extraction granularity settings in the wiki configuration, allowing users to control the level of detail in entity and concept extraction (focused, standard, exhaustive).
- Updated the UI to reflect these new options, including tooltips for better user guidance.
- Refactored the knowledge base editor to support the new extraction granularity feature, ensuring a seamless user experience.
- Improved the backend logic for wiki ingest tasks to utilize the new granularity settings, enhancing the accuracy of extracted content.
- Added tests to validate the new extraction granularity functionality and ensure robust performance across different configurations.
- Eliminated the "thinking" and "todo_write" tools from the allowed tools list in multiple sections of the `builtin_agents.yaml` file, streamlining the agent capabilities.
- This change enhances the clarity and relevance of the tools available to built-in agents, ensuring a more focused and efficient configuration.
- Changed the agent type from "custom" to "rag-qa" in the `builtin_agents.yaml` file, aligning with the new agent type presets introduced previously.
- This update enhances the configuration of built-in agents, ensuring they utilize the appropriate agent type for improved functionality and performance.
These changes contribute to a more coherent and effective agent system by standardizing agent types across configurations.
- Enhanced the agent system prompt to clarify citation formats for wiki and chunk sources, ensuring accurate inline citations.
- Introduced a new citation format section in the prompt, detailing the required syntax for both wiki pages and knowledge chunks.
- Updated the frontend to visually align wiki citations with knowledge base citations, improving user experience and consistency in citation presentation.
- Implemented truncation for long wiki labels in citations to maintain visual consistency in chat bubbles.
These changes improve the clarity and usability of citations in the agent system, enhancing the overall information retrieval process.
- Updated the agent system prompt to emphasize the use of regex for chunk searches, improving retrieval accuracy and efficiency.
- Introduced a new `Capabilities` field in the `KnowledgeBaseInfo` struct to define the retrieval surfaces available for each knowledge base, guiding the agent's strategy selection.
- Enhanced the `grep_chunks` tool to support regex queries, allowing for more flexible and powerful text pattern matching in knowledge base chunks.
- Improved JSON handling in the `RepairJSON` function to address invalid escape sequences, particularly for regex patterns, ensuring robust parsing and error handling.
- Updated documentation and comments across various files to clarify usage and expectations for regex and knowledge base capabilities.
These changes significantly enhance the agent's ability to retrieve relevant information while ensuring the integrity of input data handling.
- Added a new configuration file `agent_type_presets.yaml` to define various agent type presets, including RAG Q&A, Wiki Q&A, Hybrid (Wiki + RAG), and Data Analysis.
- Updated the `builtin_agents.yaml` to associate built-in agents with the new agent type presets, enhancing their configuration options.
- Enhanced the agent system prompt templates to reflect the new agent types and their functionalities.
- Implemented API endpoints to retrieve agent type presets, allowing for dynamic loading in the frontend.
- Updated the frontend to support agent type selection in the agent editor modal, improving user experience by auto-filling relevant configurations based on selected presets.
These changes significantly enhance the flexibility and usability of the agent system, allowing users to easily configure agents based on predefined types.
- Updated the agent system prompt to include new auxiliary tools and improved synthesis and issue flagging steps, enhancing the clarity of the workflow.
- Revised constraints to emphasize the importance of retrieving information from the wiki first and using skills when applicable.
- Expanded localization files for English, Korean, Russian, and Chinese to include new wiki tool descriptions and statuses, ensuring consistency across languages.
- Improved the agent editor modal to provide a clearer overview of tool statuses and retrieval preferences, enhancing user experience.
These changes significantly improve the functionality and usability of the agent system, particularly in relation to wiki interactions and tool management.
- Renamed the "Wiki Researcher" to "Wiki Questioner" and adjusted its description for better alignment with its functionality.
- Updated the "Wiki Fixer" to "Wiki Revising" with a revised description to reflect its focus on revising Wiki pages based on inspection issues.
- Made corresponding changes to localized names and descriptions in Chinese, Japanese, and Korean to ensure consistency across languages.
These updates enhance the clarity and usability of the built-in agents in the system.
- Updated the agent system prompt to require wiki-links in citations, improving user navigation to source pages.
- Implemented logic in AgentStreamDisplay to handle wiki link clicks and keyboard interactions, facilitating seamless navigation to knowledge base wiki pages.
- Enhanced WikiBrowser to auto-select pages based on query parameters, improving user experience when accessing specific wiki content.
- Added localization support for new error messages related to knowledge base identification, ensuring clarity in user interactions.
These changes significantly improve the usability and functionality of the wiki navigation system, providing users with a more intuitive experience when accessing and citing wiki content.
- Updated the agent system prompt to refine the workflow for fixing issues, emphasizing the need to verify if issues still exist before making edits.
- Introduced a new `embeddedMode` prop in the frontend components to manage UI behavior based on the context of use.
- Simplified issue fix prompts in multiple languages for clarity, ensuring users receive concise instructions for resolving issues.
- Enhanced the WikiBrowser component to improve the display of issues and actions, including updated icons and streamlined interaction elements.
These changes significantly improve the user experience and functionality of the Wiki Fixer agent, fostering more efficient issue resolution and content management.
- Added a new built-in agent, "Wiki Fixer," designed to repair and optimize Wiki pages based on linter issues, with multilingual support for enhanced accessibility.
- Implemented a corresponding system prompt detailing the agent's role, mission, and workflow for effective issue resolution.
- Introduced new API functions for listing and updating wiki issues, allowing for better management of content conflicts and errors.
- Enhanced the frontend with new UI elements to display pending issues and facilitate auto-fixing, improving user interaction with the Wiki content.
These changes significantly enhance the agent's capabilities for maintaining the accuracy and quality of Wiki pages, fostering a more reliable knowledge base.
- Added a new tool, `wiki_flag_issue`, enabling users to report factual errors, mixed entities, or outdated information on wiki pages.
- Updated the agent system prompt and tool definitions to include the new tool, enhancing the agent's capabilities for maintaining wiki accuracy.
- Implemented backend functionality for creating, listing, and updating the status of flagged issues, ensuring effective tracking and resolution.
These changes significantly improve the agent's ability to manage and report issues within the wiki, fostering a more reliable knowledge base.
- Added a new tool, `wiki_read_source_doc`, allowing agents to access specific source documents for detailed information retrieval when wiki page content is insufficient.
- Updated the agent system prompt and tool definitions to incorporate the new tool, enhancing the agent's capabilities for in-depth knowledge extraction.
- Modified existing tools to support the new functionality, ensuring seamless integration within the agent's workflow.
These changes significantly improve the agent's ability to provide accurate and detailed responses by leveraging source documents alongside wiki content.
- Updated the agent system prompt to clarify the workflow for knowledge retrieval, emphasizing the use of `wiki_search` and `wiki_read_page` tools for specific queries and general overviews.
- Modified the `wikiReadPageTool` to support reading multiple wiki pages simultaneously, improving efficiency in fetching content.
- Enhanced the `wikiSearchTool` to utilize PostgreSQL POSIX regular expressions for more effective search queries, allowing for complex pattern matching.
- Adjusted the `wikiPageRepository` to replace `ILIKE` with regex matching for search queries, increasing the precision of search results.
These changes significantly improve the agent's ability to retrieve and synthesize information from the wiki, enhancing user interactions and response accuracy.
- Added a new constraint to the agent system prompt emphasizing the need for fresh searches and reads for each question, ensuring up-to-date information retrieval.
- Updated the agent tool configuration to include `ToolWikiSearch` and `ToolWikiReadPage`, enhancing the agent's capabilities for accessing and utilizing wiki content effectively.
These changes improve the agent's performance in providing accurate and current responses based on wiki knowledge.
- Introduced a new built-in agent, "Wiki Researcher," designed for navigating and answering questions based on Wiki knowledge bases, complete with multilingual support.
- Added a corresponding system prompt that outlines the agent's role, mission, and workflow for effective knowledge graph traversal.
- Updated the agent configuration to include specific tools and parameters tailored for the Wiki Researcher, enhancing its functionality and user interaction.
- Removed deprecated wiki tools from the agent service to streamline the toolset and improve performance.
These changes significantly enhance the capabilities of the agent framework, providing users with a specialized tool for in-depth Wiki-based research.
- Introduced a new built-in agent, "Wiki Researcher," designed for navigating and answering questions based on Wiki knowledge bases, complete with multilingual support.
- Added a corresponding system prompt that outlines the agent's role, mission, and workflow for effective knowledge graph traversal.
- Updated the agent configuration to include specific tools and parameters tailored for the Wiki Researcher, enhancing its functionality and user interaction.
- Removed deprecated wiki tools from the agent service to streamline the toolset and improve performance.
These changes significantly enhance the capabilities of the agent system, providing users with a specialized tool for in-depth research and information retrieval from Wiki sources.
Enhanced the system prompts for the knowledge assistant to better guide users when web search is disabled. The updates clarify the assistant's role in answering questions based on the user's personal knowledge base and emphasize the importance of indicating when information may be outdated or uncertain. This aims to improve user experience and ensure accurate communication of capabilities.
- Updated the prompt template to clarify the task of understanding and rewriting user questions, improving the instructions for coreference resolution and ellipsis completion.
- Renamed sections for better clarity, changing "Rewriting Goals" to "Query Understanding."
- Added critical notes for intent classification regarding image and document attachments, ensuring accurate processing based on the presence of attachments.
- Improved examples to reflect the updated intent classification logic, enhancing the overall user experience in query handling.
- Introduced a new prompt template for document analysis, allowing the assistant to provide detailed responses based on attached documents.
- Updated the rewrite template descriptions to clarify the handling of image and document attachments, ensuring better user understanding of intent classification.
- Added a new intent type for document-only queries to improve response accuracy.
This update enhances the assistant's capabilities in processing and analyzing documents while refining the user experience in intent classification.
- Added max_input_chars configuration to limit input size for summary generation.
- Updated generate_summary.yaml to clarify summary generation steps and requirements.
- Introduced a new summary section in the document content view to display generated summaries or loading states.
- Refactored doc-content.vue to utilize computed properties for improved performance and readability.
- Enhanced knowledge base hooks to include description and summary status fields for better data handling.
- Updated internationalization files to include new summary-related labels in multiple languages.
- Updated the question generation template to clarify the role of surrounding context and main content.
- Enhanced quality rules for generated questions to better align with user search intent.
- Revised output format and added explicit instructions on what not to generate.
- Improved logging and output in the web parser for better visibility of parsed content and metadata.
- guard against empty content completion in analyzeResponse/executeLoop
with retry (max 2), nudge message, and fallback response
- propagate actual finish_reason through streaming pipeline instead of
hardcoding "stop" in streamThinkingToEventBus
- add FinishReason field to StreamResponse and streamLLMResult
- add finish marker chunk in processStreamDelta for empty content scenarios
- propagate lastFinishReason through EOF handler in processStream
- strengthen PureAgent system prompt with MCP tool guidelines and
graceful fallback instructions for unanswerable questions
- add unit tests for empty response bug and finish_reason propagation
- Updated context template to include runtime metadata such as current time and week, improving contextual awareness in user queries.
- Enhanced rewrite template with critical instructions for intent classification, ensuring that rewritten questions preserve essential entities and keywords.
- Refined intent classification logic to prioritize user intents more effectively, improving the accuracy of responses based on user queries.
- Added examples to clarify expected input and output formats for intent classification, enhancing usability for developers.
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