Promote the coverage ledger from runtime state to a deliverable:
coverage.json beside vulnerabilities.json, a Coverage section rendered
into the report from the ledger rather than transcribed by an agent, and
SARIF pass / notApplicable / open results so a consumer can tell 'tested
and clean' from 'never tested'.
Ground it in what the runtime observed rather than only what agents
claimed: a risk class an agent carried a skill for and never accounted
for is published as a gap (and surfaced back to the root agent from
finish_scan while it can still act), and a run cut short is stamped
incomplete on both the artifact and the SARIF invocation.
Also: make the ledger's duplicate check and insertion one critical
section and persist under the lock; key a checkout and the URL it was
cloned from onto one threat-model identity; render the calibration
metadata (counterevidence, confidence, severity change conditions, fix
verification) that was being stored and then dropped.
* fix(agents): collapse repeated waits queued inside one model turn
* fix(agents): state that one wait is enough in every prompt variant
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Co-authored-by: Ahmed Allam <ahmed39652003@gmail.com>
* fix(llm): cap the tool calls one assistant response may queue
* fix(llm): cap the subscription backend's responses too
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Co-authored-by: Ahmed Allam <ahmed39652003@gmail.com>
An exception escaping a non-interactive cycle re-raised before the status
handling, so a dying child stayed 'running' and its parent waited out the
timeout on a completion report the child could no longer send. Set the
terminal status and wake the parent on the way out too.
A tool call for a name Strix does not register raised ModelBehaviorError
from the SDK turn resolver, which nothing retries: the root agent's raise
tore down the whole scan and a sub-agent died before its status was set.
Opt into the SDK's tool_not_found_behavior="return_error_to_model" so the
unknown call comes back as a tool result and the agent self-corrects.
The setting landed in openai-agents 0.19.0, which requires openai>=2.45,
so both pins move.
Adds the negative-path half of the analysis process: a mutable coverage ledger recording what was reviewed and cleared, a target-scoped threat model shared across agents and runs, a required counterevidence pass before filing, and a fix-verification gate on inline patches.
One tool was doing three jobs (wait on the user, wait on other agents, and
- wrongly - wait for a long-running command), so the driver had to guess which
one an agent meant and used parent_id as the proxy: the root waits for a human,
everyone else waits for agents. That proxy is wrong, since the user can message
any agent from the TUI's agent tree.
Tool identity now carries the intent, and the coordinator records it as a
wait_kind that survives snapshot/restore:
respond_to_user -> wait_kind="user", never auto-resumed (root or not)
wait_for_agents -> wait_kind="agents", auto-resumed on a 300s timer
recovery exhaust -> wait_kind="stalled"
respond_to_user fuses the message and the yield into one call, so there is no
way to answer and then forget to stop - the two-step that gpt-4o-mini skipped
2/2 in live testing. Plain text still renders as before.
Auto-resume is also bounded now: an agent that re-parks after every timeout
burned a model turn every 300s for the rest of the scan (and, since parked
children notify their parent, spammed the parent's inbox on the same cycle).
After _MAX_IDLE_AUTO_RESUMES it stays parked until a real message arrives.
The user can message any agent from the TUI, not only the root, so the
justification is that the parent is an agent with no other way to learn
the child parked - not that the child has no human resumer.
Parking is self-service only for the root, which the user is watching.
A parked child owes its parent a report it can no longer send, so the
parent would wait out its full timeout for nothing.
An exhausted agent parked in 'waiting' got a fresh nudge budget on every
600s auto-resume, so a wedged agent could nudge-park-nudge indefinitely.
Track the count on the coordinator, snapshot it, and reset it only on
real input or an explicit lifecycle tool.
Interactive turns ended by plain text left the agent parked in 'waiting'
forever. Require an explicit lifecycle tool in both modes and nudge a
text-only turn back into a tool call, bounded by a recovery limit.
LiteLLM treats provider-qualified metadata lookups as an auth path.
Use the underlying model slug so context sizing cannot block the scan
loop in a device-code poll.
Adds an integration test that drives Runner.run_streamed against a
non-streaming gateway through _NonStreamingModel: the synthetic terminal
event feeds the runner, which executes the tool call and continues to a
final answer over two non-streaming turns. Removes the README env-var note.
Some OpenAI-compatible gateways don't support Server-Sent Events (or
deliver them unreliably), but the SDK run loop Strix uses only issues
streamed requests, so such a gateway fails every turn. Add an opt-in
LLM_DISABLE_STREAMING setting that wraps the resolved model in
_NonStreamingModel: each turn makes one non-streaming get_response and
replays the completed result as a single terminal stream event, so tool
calls, usage, and the rest of the agent loop are unchanged. Subscription
(ChatGPT) models are always streamed and are not wrapped.
A configured tool_output_max_bytes smaller than the truncation notice
itself can't fit a bounded preview, so a persisted result could exceed the
ceiling. Enforce a config floor (ge=1024) so nonsensical values are
rejected at load time instead of being worked around at runtime.
Alex Schapiro