* refactor(ltm): redesign long-term memory with context compaction
- Add raw_records / contexts / summaries data model per group
- Add LLM summary compaction strategy alongside truncation
- Add turn-based (_split_into_rounds) granularity
- Add image caption integration into LTM history
- Add tool_call / tool_result persistence into raw_records
- Add active reply support driven by LTM state
- Improve summary injection prefix with system note and delimiters
- Add info-level logging for summary compaction lifecycle
- Clarify default summary prompt with explicit preserve/drop rules
- Add context_guard for history overflow protection in agent runner
- Add internal agent history compaction in agent_sub_stages
- Add comprehensive LTM unit tests and compaction test suites
* fix(ltm): handle malformed JSON in tool args and clean up lock on session removal
* fix(ltm): guard against duplicate system prompt note injection
* fix(ltm): fall back to user message when internal marker parsing fails
- Treat lines starting with <T:CALL>, <T:RES, or <BOT/ as regular user
messages when their respective parsers return None, instead of silently
dropping them. Defensive guard against malformed internal markers.
* fix(ltm): release session lock during LLM summary generation
* fix(ltm): trim raw_records in handle_message to prevent unbounded growth
* perf(ltm): use len(s) instead of len(s.encode()) in trim loop
Avoid allocating a new bytes object for every string when calculating
buffer size in _trim_raw_records. Character count is sufficient for
the approximate memory cap.
* feat(ltm): make user segment truncation limits configurable
* feat(ltm): pre-fill default LTM summary prompt in config and i18n
* refactor(ltm): hardcode internal segment/trim constants
* refactor(ltm): unify compaction strategy with main agent runner
* feat(ltm): add @mention weight marker for group chat messages
* test: fix test failures from LTM compaction unification
* chore(dashboard): remove obsolete LTM compaction i18n metadata
* chore: shrink codebase
* feat(group-chat): implement group chat context management and related functionality
---------
Co-authored-by: Tsukumi <112180165+Tsukumi233@users.noreply.github.com>
Co-authored-by: zenfun <zenfun510@gmail.com>
Co-authored-by: Soulter <905617992@qq.com>
Only overwrite tool-call-related fields from the re-query response, preserving the original completion_text and reasoning_content that were already sent to the user.
* fix: handle delta=None chunks in streaming to prevent SDK to_dict() error
When certain OpenAI-compatible providers (Gemini, DeepSeek, some proxies)
return chunks with choice.delta=None (e.g. ContentBlockDeltaEvent),
ChatCompletionStreamState._convert_initial_chunk_into_snapshot internally
calls choice.delta.to_dict() at line 747, causing:
'NoneType' object has no attribute 'to_dict'
Fix:
1. Skip handle_chunk when delta is None (delta=None chunks have no
content contribution anyway)
2. Wrap get_final_completion in try/except to gracefully fall back to
empty ChatCompletion if SDK state is corrupted
Refs: openai-python#5069, openai-python#5047
* fix: resolve bugs found by Sourcery and gemini-code-assist review
- Remove orphan logger.error that caused NameError on every chunk
- Replace broken empty ChatCompletion fallback with clean return;
streamed content already yielded, no data loss
Co-authored-by: sourcery-ai[bot] <sourcery-ai[bot]@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <gemini-code-assist[bot]@users.noreply.github.com>
* fix: properly replace get_final_completion fallback in _query_stream
Previous fix_pr_v3 wrongly injected code into terminate() instead.
Now correctly:
1. Replace empty ChatCompletion fallback with clean return in _query_stream
2. Revert terminate() to original (await self.client.close() only)
* fix: revert corrupted terminate() to original
Previous fix_pr_v3 injected wrong-indentation code into terminate().
---------
Co-authored-by: sourcery-ai[bot] <sourcery-ai[bot]@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <gemini-code-assist[bot]@users.noreply.github.com>
* feat(lark): implement app registration and bot info retrieval
- Add app registration functionality for Lark and Feishu platforms, including endpoints and request handling.
- Introduce polling mechanism for app registration status.
- Create bot info retrieval functionality to fetch bot details after successful registration.
- Enhance dashboard with new UI components for one-click QR setup and manual setup options.
- Update internationalization files to support new features and actions.
- Add unit tests for app registration endpoint resolution and data handling.
* feat(weixin_oc): add WeChat login registration and QR code handling
* fix: handle None tool arguments returned by Claude API for no-parameter tools
* fix: handle None tool arguments from Claude API for no-parameter tools
* fix: generalize None tool args comment
* fix: generalize None tool args comment
* 去除空格，以保证格式正确
- Added advanced settings option in update dialog for better user control.
- Implemented detailed progress tracking for update stages including download size and speed.
- Updated localization files for English, Russian, and Chinese to include new strings for update progress and advanced settings.
- Improved UI for update dialog with better layout and responsiveness.
- Enhanced test coverage for update process including progress tracking.
* fix(webui): enforce 12-char dashboard password policy with backend+frontend validation
* fix(i18n): update password policy hints and validation rules for improved security
* test: adapt dashboard auth fixtures for hashed default password
* fix(security): increase PBKDF2 iterations
* feat(auth): implement secure login challenge and proof verification
* chore: ruff format
* fix(auth): update md5 import syntax for consistency
* feat(dashboard): implement random password generation for empty dashboard password
* feat(auth): enforce plaintext password requirement for legacy MD5 hashes
* fix(i18n): update password hint texts to reflect auto-generated initial passwords
* feat(dashboard): implement password change requirement and reset logic
* feat(auth): implement account setup flow and password change requirements
* feat: Implement legacy password support and upgrade mechanism
- Added `hash_legacy_dashboard_password` function for MD5 hashing of passwords.
- Updated configuration handling to store both PBKDF2 and legacy password hashes.
- Introduced logic to check if password storage has been upgraded and if a password change is required.
- Modified dashboard authentication routes to handle legacy password checks and prompts for upgrades.
- Updated frontend to display warnings for legacy password storage and required upgrades.
- Enhanced tests to cover scenarios for legacy password handling and migration to new storage format.
* fix(logo): update text color styles to use CSS variables for consistency
* feat(dashboard): upgrade password storage and enforce change requirement
* fix(dashboard): update minimum password length from 12 to 10 characters
* fix(dashboard): update minimum password length from 10 to 8 characters
---------
Co-authored-by: RC-CHN <1051989940@qq.com>
When both aiohttp and httpx are installed, google-genai prefers aiohttp
as the async HTTP backend. In error response paths, the aiohttp backend
returns raw aiohttp.ClientResponse objects that google-genai cannot handle,
masking real API errors with:
Unsupported response type: <class 'aiohttp.client_reqrep.ClientResponse'>
This fix explicitly creates an httpx.AsyncClient and passes it via
HttpOptions.httpx_async_client, ensuring the chat provider always uses
the httpx backend. The managed client is closed in terminate().
- Preserve HTTP_PROXY/HTTPS_PROXY support via trust_env=True.
- Preserve provider-level proxy via httpx.AsyncClient(proxy=...).
- Avoid logging full proxy URLs for security.
Fixes#7564
