* fix(agent): stop todo reminders after run hard limits
Respect loop_capped and token_capped before scheduling completion reminders, and include the behavior in the assembly policy fingerprint.
Cover sync and async agent graphs, blocked post-stop tools, preserved Todo state, and continuation from the same thread checkpoint. Validation: 316 targeted tests passed.
* docs(agent): explain todo behavior after hard stops
Document that loop and token hard stops suppress Todo completion reminders while retaining unfinished tasks for later runs.
* docs(changelog): record todo hard-stop fix
Add English and Chinese Unreleased entries referencing issue #7953.
* fix(agent): mark safety stops with partial responses
* fix(agent): honor provider stops before todo retries
* docs(agent): clarify todo behavior after provider stops
* test(agent): isolate todo safety-chain runtime dependencies
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: Totoro <msy626836554@gmail.com>
* fix(channels): keep Feishu cards within the card size limit
Card text was unbounded and ASCII-escaped, so an answer of about 4,400 CJK
characters made the card request body exceed Feishu's 30 KB limit (230025).
The final patch and its reply fallback were both rejected, the answer was
lost and the running card stayed on its last streamed text.
Cards are now serialized without ASCII escapes and budgeted by the bytes
their text adds to the request body. A streamed update shows what fits; a
longer final answer continues in up to 10 cards replied to the same source
message, split at line breaks, and a retry resumes after the cards already
delivered.
* docs(changelog): reference #7798 for the Feishu card size fix
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat(skills): expose MiniMax resolution/duration in video-generation
Hailuo 2.3 accepts explicit resolution (768P/1080P) and duration (6/10s)
in the /v1/video_generation body, but the skill sent neither — and its
SKILL.md told the caller --aspect-ratio is ignored for MiniMax without
offering any alternative, leaving cost/quality/length uncontrollable.
Read MINIMAX_VIDEO_RESOLUTION and MINIMAX_VIDEO_DURATION from the
environment and pass them through when set (unset keeps the API defaults
byte-for-byte); values are passed through unvalidated beyond the integer
check so newer tiers (H3 per-second 4-15s) need no script change. Document
both knobs in SKILL.md with a draft/final example. Fixes#7911.
* fix(skills): address MiniMax video override review
---------
Co-authored-by: vicfei <4058491+vicfei@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
compact_thread_context built the summarization middleware inline on the
Gateway event loop, and with it the run-profile model and any separately
configured summary model. When the provider reads local credential files
(the Codex provider reads the Codex CLI auth.json while it is constructed),
each POST /api/threads/{thread_id}/compact did synchronous file IO on the
shared loop, once for every model it built, so other requests and streams
waited for it. The route passes no app_config, so the call also read the
live config on the loop first, which stats and hashes config.yaml on every
call. #7767 listed this site in the utils guidance as not covered.
Build the middleware through run_assembly, as #7484 and #7767 do for the
one-shot helpers, skill moderation and the goal evaluator, and read the live
config in that pool too when no app_config is passed. The lead agent already
builds the same middleware there through run_agent's agent factory call.
Model resolution, the memory-flush policy, the disabled, failed and
pending-work outcomes and the written checkpoint are unchanged, and
cancellation during construction neither starts the summary call nor writes
a checkpoint.
tests/blocking_io/test_compaction_model_offloop.py builds real
CodexChatModels under the strict Blockbuster gate. On main, compaction
reported itself disabled because every model build hit BlockingError
(os.stat), and the /compact handler answered 500 because the gate rejected
its config read first.
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* fix(memory): restore facts from older backups
Use the current snapshot revision for existing facts during backup import so saved contents can be restored after later edits. Preserve manifest and fact preconditions, scope isolation, and caller-owned input data.
* refactor(memory): share import revision preconditions
* docs(memory): fit import guidance within chain budget
* fix(memory): validate backup revisions before restoration
---------
Co-authored-by: liyinlong <173714821+lilong555@users.noreply.github.com>
* fix(community): initialize InfoQuest TLS clients off the event loop
InfoQuest's fetch, web search and image search constructed
httpx.AsyncClient inline in their coroutines, so the default transport
loaded the TLS trust store synchronously on the agent event loop
(~125 ms per call in a local probe). RAGFlow, LightRAG, You.com,
GroundRoute and Webz already construct their clients with
asyncio.to_thread; InfoQuest was the missed sibling.
Move the three constructions to asyncio.to_thread and cover all three
methods in the blocking-I/O TLS test. Requests, cleanup, timeouts and
responses stay on the event loop.
Co-Authored-By: Claude Code <noreply@anthropic.com>
* docs: qualify the off-loop HTTPX provider list
The list is not exhaustive; several community clients still construct
their client inline. Use non-exhaustive wording until the follow-up
sweep lands.
Co-Authored-By: Claude Code <noreply@anthropic.com>
* docs: correct the off-loop provider list after merging main
---------
Co-authored-by: Claude Code <noreply@anthropic.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat(subagents): continue native batches and honor item dependencies
* docs: link native batch follow-up changelog entries
* test(migrations): pin isolated history-seed upgrade to 0050
* fix(subagents): preserve follow-up scope, blockers and fair scheduling
* test(memory): freeze capacity evaluation time
* fix(subagents): guard follow-up admission and retain run controls
* fix(subagents): enforce runs:cancel on tool cancellation of follow-up batches
* style(tests): format test_batch_followup_host with ruff
* fix(subagents): drop the admission checkpoint check and keep SDK cancel compatible
The checkpoint check inside run admission waited on the checkpoint saver's
lock while holding the SQLite write lock, which could make another chat's
checkpoint write fail. Remove it; dispatch still checks the conversation
before admission.
A follow-up waiting for an unanswered question now keeps its claim until the
lease expires instead of rereading the conversation every poll. Cancel passes
the caller's run authority only to submitters that take it, so existing SDK
submitters keep the two-argument call. Document that retrying a prerequisite
by hand does not requeue its blocked items, and list 0052 as the migration head.
---------
Co-authored-by: Totoro-qaq <279883115+Totoro-qaq@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* fix(agents): pair null call IDs with empty result IDs
* fix(agents): treat producer sentinel IDs as absent when pairing
Middleware producers spell a missing provider ID three ways: "" is canonical,
str(tool_call.get("id", "")) yields "None" once the key is present with a
null value, and the artifact/deferred/knowledge middlewares fall back to
"missing_tool_call_id". Only "" reached the pairing pass, so a result carrying
either sentinel was dropped as an orphan and the middleware's guidance was
replaced by the interrupted placeholder — the exact outcome this PR set out to
avoid, just through a different spelling.
Route every absent spelling through one canonicalisation used by the pairing
gate, the candidate comparison and the turn-completeness count.
---------
Co-authored-by: huyengiang101086-spec <270684046+huyengiang101086-spec@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* fix(channels): ignore non-finite WeChat server longpoll timeouts
The getupdates response's longpolling_timeout_ms was passed through
float() with only TypeError/ValueError handled. A huge JSON integer
raised OverflowError before the message loop and cursor write, so the
same batch was re-polled forever, while Infinity/NaN were stored and
turned the next request timeout into inf/nan.
Apply the same positive-finite rule the channel already uses for
configured timing values: catch OverflowError and reject non-finite
results, keeping the previous valid timeout.
Co-Authored-By: Claude Code <noreply@anthropic.com>
* fix(channels): bound WeChat server long-poll hints
---------
Co-authored-by: Claude Code <noreply@anthropic.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* fix(todos): keep an unusable stored todo list from failing every model call
`todos` is a reducer channel that `POST /threads/{id}/state` and run input can
Overwrite directly, but neither boundary validated its shape. The todo
middleware rendered it with `entry.get(...)` in both `before_model` and
`after_model`, so a non-object entry — or a value that is not a list — raised
AttributeError on every model call, permanently for that thread.
Validate caller-written todos beside `goal` in `_normalize_external_todos`: a
list of objects with string content and a known status, or HTTP 400. The
middleware now reads the channel through `_usable_todos` and ignores entries of
any other stored shape, so checkpoints and direct integrations written before
this boundary still run.
* fix(todos): normalize stored todos in token-usage attribution
TokenUsageMiddleware reads the same stored `todos` channel through
`_build_attribution` whenever the last AIMessage calls write_todos, and
`token_usage.enabled` defaults on, so a value the todo middleware now tolerates
still aborted the step here. Route the stored list through `_normalize_todos`
before reading entries.
Document that a caller-written todo without a status is accepted as pending and
cover that shape at both external boundaries.
* docs: keep backend agent guidance within size budgets
* fix(todos): keep the channel metadata strip on caller-written todos
The dedicated todos branch in strip_server_owned_state_metadata replaced the
generic list branch, which stripped server-owned metadata from every entry. A
caller could therefore persist additional_kwargs.original_user_content,
untrusted_input, or artifact.knowledge_sources on a todo entry. Re-apply the
per-entry strip after normalization, keeping None and empty lists untouched.
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Thread title generation runs inside the agent graph (aafter_model), and it
built its configured model inline. create_chat_model can read local CLI
credentials while it is constructed (Codex auth.json, Claude Code credential
files), so the first turn of every new thread read them on the shared loop
and other requests waited for it.
Build the provider through run_assembly, the dedicated assembly pool the
one-shot helpers, skill moderation, the goal evaluator and start_run already
use. Inference still awaits on the loop, and the sync path is unchanged.
A Blockbuster regression builds the real Codex provider: on main the gate
rejects the credential read, the broad fallback swallows it, and the local
fallback title replaces the model's title; with the change the provider is
constructed on a worker thread and the model's title is kept.
* fix(middlewares): prevent todo retries after text-only output caps
* docs(middlewares): clarify text-only output cap markers
* test(subagents): isolate cleanup test from shared assembly pool
* docs(middlewares): keep cap guidance within chain budget
---------
Co-authored-by: linkangjun <331227811+linkangjun@users.noreply.github.com>
* feat(knowledge): add optional ingestion and status center
* docs(knowledge): record upstream CI budget fix and regression results
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
scan_skill_content and the goal evaluator built their chat model inline on
the Gateway event loop. When the provider reads local credential files (the
Codex provider reads the Codex CLI auth.json while it is constructed), each
scanned skill edit, rollback, install or agent-managed skill write, and the
evaluator construction in each goal run, did synchronous file IO on the
shared loop, so other requests and streams waited for it. #7484 removed the
same stall for the one-shot helpers and listed these two constructors in the
utils guidance as not covered.
Build both providers through run_assembly, as the one-shot helpers do: the
moderation model in scan_skill_content, the evaluator in
evaluate_goal_completion when no model is injected, and the run worker's
cached evaluator factory. A scan without app_config (the in-graph
skill_manage call) also reads the live config in that pool, and its
fail-closed fallback reuses that config instead of reading config.yaml again
on the loop; an unreadable config still fails closed. Verdicts, the
fail-open/fail-closed policy, error propagation and tracing are unchanged,
and cancellation during construction never starts inference.
tests/blocking_io/test_system_model_offloop.py builds a real CodexChatModel
under the strict Blockbuster gate. On main the scan fell back to its
fail-closed "unavailable" block, the evaluator raised BlockingError
(os.stat), and the worker stood the goal down as evaluator_failed.
Loop detection, clarification and dangling-tool-call repair parse model-provided
tool arguments and options. A JSON string, Python structure or native option
label nested past the interpreter recursion limit raised RecursionError and
aborted the run; these paths now treat it like any other unusable payload
(opaque args, an empty replay object, or a skipped option), and the dict-option
flatten and _stable_tool_key use guarded serialization.
Rebased onto current main; the stale-base docs commit is superseded by main and
the AGENTS note is trimmed to stay inside the guidance chain budget.
* fix(memory): tolerate unbounded stored fact confidence
A stored memory fact can carry a JSON integer such as 10**400. float() on it
raises OverflowError in _coerce_source_confidence and in the dedup merge
comparison, aborting the whole update cycle even though both sites already
handle every other unusable confidence. Treat the overflow like a missing or
non-finite value: 0.5 for ranking, 0.0 for the merge comparison.
* fix(memory): harden the remaining fact-confidence coercions
The import normalizer, prompt builder and relevance scorer each coerce a
stored fact confidence with float() and caught only TypeError/ValueError, so
an unbounded JSON integer such as 10**400 raised OverflowError: import failed
before the updater fallbacks ran, and prompt/relevance ranking could abort.
Catch OverflowError and take the existing defaults; document the full set of
coercions.
* fix(citations): keep copied markdown references valid
formatCitationMarkdownReference pasted the source title and URL into
"[title](url)" verbatim. A title with a stray "[" or "]" ends the link text
early, and a URL with an unbalanced parenthesis ends the destination, so a
copied reference no longer rendered as a link. Escape brackets in the title and
wrap a parenthesized URL in the angle-bracket form.
* fix(citations): only angle-bracket a parenthesized URL
normalizeUrl already percent-encodes "<", ">" and whitespace, so including
them in the trigger could wrap such a URL in an angle-bracket destination that
CommonMark forbids, which is worse than the bare form. Parentheses are the only
reachable delimiter that breaks the bare destination.
* fix(persistence): normalize non-finite floats before every JSON column write
The shared ORM engine serialized JSON columns with the lenient json.dumps
default (allow_nan=True), and the sync agent-storage engine set no
json_serializer at all. A non-finite float reaching a JSON column — run
metadata merges caller-supplied values, goal_verdict and
token_usage_by_model carry model/provider numbers, and json.loads('1e400')
yields inf — was emitted as the bare NaN/Infinity token, which PostgreSQL
jsonb rejects as invalid JSON, failing the whole write with DataError.
SQLite silently accepts and round-trips the invalid token, so the failure
was postgres-only and environment-dependent.
Adds dumps_json_safe/json_safe_value to persistence/json_compat.py (same
None-normalization the canonical display serializer applies since #7670,
which explicitly does not cover persisted state) and wires it into both
engines' json_serializer. Regressions pin: nested non-finite floats
normalize everywhere, finite values/bools pass through unchanged, the
async engine kwargs and the sync agent engine (postgres only; SQLite takes
no json_serializer) both route through it.
* fix(persistence): normalize JSON across synchronous engines
* docs: keep frontend guidance within the CI size budget
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat(runtime): add Queue and Steer for active Web runs
* style(frontend): format Queue and Steer controls
* fix(runtime): retain user Steer controls and order active inputs
* docs: add Chinese changelog entry for Queue and Steer
---------
Co-authored-by: Totoro-qaq <279883115+Totoro-qaq@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
