* 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>
* feat(subagents): add thinking_enabled and reasoning_effort config for sub-agents (#4875)
(cherry picked from commit b1083728b23504cb0f1d1a463f8db70c5ec1c5a7)
* fix: resolve CI failures for subagent thinking config PR
- Fix UnboundLocalError in task_tool.py: initialize parent_thinking_enabled
and parent_reasoning_effort before the if runtime block
- Remove unused imports from test_subagent_thinking_config.py
(SimpleNamespace, pytest, resolve_subagent_model_name)
- Fix import sorting in subagents_config.py and test file
- Add reasoning_effort: None to executor test assertion
(cherry picked from commit 187851e307b871937e48bb67ae1b34b8b69bf92d)
* fix: address review comments - remove parent inheritance, supports_thinking clamp, Literal type
- Remove parent_thinking_enabled/parent_reasoning_effort from metadata
extraction in task_tool.py (backward compatible: subagents only think
when explicitly configured)
- Add supports_thinking clamp in _create_agent to prevent cross-model
mismatch errors
- Change SubagentConfig.reasoning_effort to Literal type
- Fix sandbox_state assignment that was accidentally removed
- Fix syntax errors in subagents_config.py (missing closing parens)
- Update all docstrings/comments from "inherit" to "no override"
- Simplify test helper and remove parent inheritance tests
(cherry picked from commit 74f9c659ef4a98bd1756109929ab0748fab82317)
* feat(subagents): adapt reasoning settings to current native workers
* fix(subagents): retain model context on reasoning rejection
* fix(subagents): preserve model-factory reasoning diagnostics
---------
Co-authored-by: Neon549 <neon549@users.noreply.github.com>
Co-authored-by: Totoro-qaq <279883115+Totoro-qaq@users.noreply.github.com>
* fix(scripts): read the checkout .env as data in serve.sh
serve.sh sourced the root .env under set -a, so bash ran it as a script:
$$ became the shell's PID, backticks and $(...) ran commands, and an
unquoted value with a space ran its second word as a command (exit 127
under set -e, stopping make dev). The exports also beat the Gateway's
own load_dotenv(override=False), so e.g. a $$ in AUTH_JWT_SECRET changed
the signing secret on every launch.
Parse .env with a stdlib-only scripts/dotenv_exports.py on the host
Python and eval its shell-quoted export lines. It tokenizes like
python-dotenv, so values match what the Gateway reads, and keeps the
launcher's precedence over inherited variables and its $NAME /
${NAME:-x} / leading-~ expansion that support_bundle.py mirrors. $$ is
now one token in both. Unparseable lines and invalid names are skipped
with warnings that never echo values; a non-UTF-8 file stops the
launcher.
Tests run serve.sh's real .env block: no execution, parity with
python-dotenv, precedence, warnings, the non-UTF-8 stop and a stdlib-only
run; the support-bundle launcher cases now exercise the parser.
* docs(changelog): reference #7606 for serve.sh reading .env as data
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Declare required thinking, native reasoning effort and image support for both
K3.1 Pro IDs. Normalize fixed parameters at the final Moonshot request boundary
and cover real SDK serialization with offline regression tests.
Fixes#7425
* fix(runtime): present outputs before a loop wrap-up and explain step-limit stops
Loop-detection warnings now ask the agent to present the files it already
created in /mnt/user-data/outputs before its final answer, when
present_files is available.
A run that exhausts recursion_limit now ends with stop_reason
recursion_capped and a plain message that lists outputs it created but
did not present. An active goal stands down without an evaluator call.
The /wait error envelope keeps the GraphRecursionError name, and IM
channels reply with the step-limit message instead of a generic failure.
* docs(changelog): note run-end delivery and step-limit stops (#7588)
* test(runtime): stop pinning LangGraph's step count in the step-limit test
The produced paths and the message already follow the observed model
calls, so the exact count only pinned LangGraph's per-step accounting.
Also note on the step-limit goal branch that its caller passes no
evaluator factory, so it has to stay ahead of the evaluator section.
---------
Co-authored-by: Totoro-qaq <279883115+Totoro-qaq@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
