Adds first-class support for configuring more than one custom
OpenAI-compatible provider (e.g. SiliconFlow, DeepSeek, local vLLM)
and switching the active one from the web console, addressing #2838.
Backend:
- config: new `custom_providers` (list) and `custom_active_provider`
fields, fully backward compatible with the legacy single
`open_ai_api_base`/`model` fields (used as fallback).
- models/custom_provider.py: centralized resolver
`resolve_custom_credentials()` returning (api_key, api_base, model),
with active-provider selection and graceful fallback.
- chat_gpt_bot.py wired to use the resolver.
- web_channel.py: `_provider_overview` expands `custom_providers` into
one card per provider (id `custom:<name>`, active flag, masked key);
new POST actions `set_custom_provider`, `delete_custom_provider`,
`set_active_custom_provider` with hermetic persistence + bridge reset.
Frontend:
- console.js: dedicated "Custom providers" section with add / edit /
delete / set-active actions, masked-key keep-existing handling, and
~20 new zh/en i18n strings.
- chat.html: custom provider modal.
Tests:
- tests/test_custom_provider.py (11) - resolver/config behavior.
- tests/test_custom_provider_handlers.py (18) - write-side handlers and
overview expansion, including duplicate-name rejection.
All 29 unit tests pass.
Add a self-evolution subsystem that reviews idle conversations in an
isolated agent and durably learns from them — patching/creating skills,
finishing unfinished tasks, and backfilling missed memory.
- Trigger: background idle scan, fires when a session is idle >= N min AND
(>= N turns OR context usage > 80%). In-memory cursor reviews only new
messages so a session never re-learns old content.
- Isolated review agent: same model, restricted toolset, hard write-guard
confining edits to the workspace (built-in skills are protected).
- Safety: file-level backup before edits + evolution_undo tool; notify the
user ONLY when a workspace file actually changed (no-nag rule); capped
concurrency.
- Records to memory/evolution/<date>.md, surfaced in the memory UI's
renamed "Self-Evolution" tab (merged with dream diaries).
- Hide internal [SCHEDULED]/[EVOLUTION]/backup_id markers from chat history
display (also fixes scheduler marker leakage) while keeping them in stored
content for undo.
- Flat config: self_evolution_enabled (default off until release),
self_evolution_idle_minutes (15), self_evolution_min_turns (6).
- Tests: tests/test_evolution.py (stub + real model modes, 7 scenarios).
- Add language labels and copy buttons to code blocks
- Enhance drag-and-drop to full chat view
- Fix data consistency bugs in message operations
- Use RLock to prevent deadlock in conversation store"
- Add MINIMAX_M3 = "MiniMax-M3" constant and put it first in MODEL_LIST
- Default MinimaxBot model: MiniMax-M2.7 -> MiniMax-M3
- Keep MiniMax-M2.7 and MiniMax-M2.7-highspeed as legacy options
- Drop MINIMAX_M2_5 / MINIMAX_M2_1 / MINIMAX_M2_1_LIGHTNING / MINIMAX_M2
- Update web console recommended/provider model lists
- Update README capability table and docs/models index (en/zh/ja)
- Update docs/models/minimax.mdx and coding-plan.mdx MiniMax section
- Update run.sh / run.ps1 installer default and menu hint
- Update zh CLI status sample output
- Update unit tests to assert new M3 default and constant
TTS (speech-2.*) and API base URL remain unchanged.
Agent-generated images are sent as IMAGE_URL with a file:// path, but the wechatmp channel always used requests.get, which fails on file:// with InvalidSchema. Now read local files directly (file:// or local path) and fall back to HTTP download for remote URLs, in both passive and active reply modes.
Co-authored-by: Cursor <cursoragent@cursor.com>
Previously the passive reply only drained the cache after the agent task fully finished, so for long multi-turn tasks the user could not retrieve already-cached intermediate segments. Now return cached segments as soon as they are available, even while the task is still running; the next user message fetches the rest.
Co-authored-by: Cursor <cursoragent@cursor.com>
In subscription account passive reply mode, WeChat allows only one reply per request. Multi-turn agent output was cached as separate entries, forcing the user to send an extra message to fetch each one. Now drain and merge all consecutive cached text segments into a single reply; media still returns one at a time.
Co-authored-by: Cursor <cursoragent@cursor.com>
Adopt the same channel-level pattern as weixin/wecom_bot/feishu so
the agent actually sees attachments the user sent:
- IMAGE: agent mode never reads memory.USER_IMAGE_CACHE, so a photo
sent before a question (e.g. "image" then 30s later "what's this?")
used to be lost. Now lone images go into channel.file_cache and
the next TEXT turn appends "[图片: <path>]" to the query before
producing the context. Cross-batch image+text combinations now
work as users expect.
- FILE: previously dropped at the sync_msg filter and unsupported
by WechatKfMessage. Add msgtype="file" parsing, download via the
WeCom media API, preserve the original filename from
Content-Disposition (RFC 5987 + plain forms), and route through
the same file_cache pipeline as images, surfacing as
"[文件: <path>]" in the next text turn.
Move the sync_msg cursor file from the project-local tmp/ dir to ~/.wechat_kf_cursors.json so it survives tmp/ cleanups and cwd changes across restarts. Aligns with the weixin channel's credentials file convention.
- add wechat_kf_cursor_path config (default ~/.wechat_kf_cursors.json)
- expand ~ via os.path.expanduser in the channel init
- chmod the cursor file to 0o600 after each flush (no-op on Windows)
_dedup_image_text_pair previously fell back to returning only the last message whenever the batch was not exactly an image+text pair, which silently dropped multiple texts/images sent in quick succession.
Cursor freshness is already guaranteed by sync_msg, so no extra stale-history protection is needed. Now we return all messages by default and only collapse a batch when it is exactly a 2-message image+text pair within a 5s window (order-insensitive, normalized to [image, text]).
WeCom requires the callback HTTP response within ~5s, otherwise it retries the same notification. The previous code ran sync_msg pulling synchronously inside Query.POST, so a backlog could exceed the deadline and trigger retries that race on the same cursor and end up replying to the same user multiple times.
- Dispatch consume_callback to a background ThreadPoolExecutor and return 'success' immediately from the HTTP handler.
- Serialize work per open_kfid with a lock so retried/concurrent callbacks queue up instead of racing the cursor window.
- Shutdown the executor on channel stop().
Rename the WeCom customer-service channel and give it its own corp_id
field so users no longer have to share `wechatcom_corp_id` with the
self-built WeCom app channel.
Renames (channel-side):
- channel type / const: wechatcom_kf -> wechat_kf
- package dir: channel/wechatcom_kf/ -> channel/wechat_kf/
- python files / classes: WechatComKf* -> WechatKf*
- config keys: wechatcom_kf_{secret,token,aes_key,port} ->
wechat_kf_{secret,token,aes_key,port}; new wechat_kf_corp_id
- env vars: WECHATCOM_KF_* -> WECHAT_KF_*; new WECHAT_KF_CORP_ID
- log prefix / cursor file: [wechatcom_kf] -> [wechat_kf]
- web console CHANNEL_DEFS key + startup log line
Renames (docs):
- docs/channels/wecom-kf.mdx -> docs/channels/wechat-kf.mdx (zh/en/ja)
- update docs.json sidebar entries and all field names inside the docs
In addition, the Web Console "微信客服" entry now exposes its own
Corp ID field instead of reusing the wechatcom_app one, and includes
the screenshot of the visual config in the channel guide.
Web Console onboarding section is added (Tabs: Web Console / config
file) and the local URL `http://127.0.0.1:9899/` parenthetical is
dropped for consistency with other channel docs.
Co-authored-by: Cursor <cursoragent@cursor.com>
