* feat(cli): add strix login for managed platform sign-in (device flow)
* feat(cli): add --scopes flag to strix login
* docs: document strix login and managed billing in README, AGENTS, docs, and managed skill
* fix(cli): handle malformed login responses and credential file failures
* fix(cli): reject sign-in responses without an API token
* feat(login): interactive workspace and scope selection with presets
* fix(login): reject malformed API token values in sign-in responses
* fix(login): skip the scope prompt when stdin is not a terminal
* fix(login): tolerate malformed selection containers and remove unreadable credential files on logout
* fix(login): treat overflowing timing values as invalid
* fix(login): show the configured platform host in the sign-in banner
* fix(login): bound device flow timing values and clean up unreplaced secret temp files
* feat(cli): add the strix cloud command surface for the managed platform
* feat(cli): manage workspaces and hosted onboarding links from strix cloud
* fix(cli): report a leftover temporary secret file instead of hiding it
* feat(cli): pass a Stripe payment method to the top-up wallet client
* docs(cloud): recommend the Stripe agent wallet as the default payment path
* fix(cloud): preserve API auth during MPP payment
* fix(cloud): drop knowledge query and settings commands removed from the API
* fix(cloud): align agent commands with API contracts
* fix(cloud): send required PR review integration fields
* fix(cloud): preserve scopes when switching workspaces
* fix(cloud): make session command help non-destructive
* feat(cloud): improve human navigation and output
* feat(cli): add native shell completions
* feat(cloud): tailor human list and detail views
* feat(cloud): upload local source for managed scans
* fix(cloud): infer scan type from local targets
* Add agent-friendly managed cloud CLI
* Harden cloud CLI type boundaries
* Clarify cloud test user MFA options
* Correct cloud vulnerability status guidance
* Clarify chat file path handling
* Allow signed storage upload URLs
* Fix provider token request handling
* Improve cloud CLI human list views
* Make cloud CLI workflows actionable and safe
* Make cloud workspace switching session-safe
* Preserve CLI session metadata in JSON output
* Remove preview protection bypass plumbing from cloud CLI
* add a generic MCP client and a config for connecting MCP servers
* Add MCP docs and CLI polish: docs page, startup connect summary, --mcp-config flag, compact tool output
* Add MCP connection notes and per-run selection; clean up on cancel and dedupe names
* Show errored MCP tool calls as failed in the TUI
* Sanitize namespaced tool names so model APIs accept them
* Show MCP tool calls distinctly in the terminal and the run viewer
* Say what MCP servers are worth connecting for
* Correct the notes docstring to match how notes reach the agent
* keep the mcp tests from reading your shell's STRIX_MCP_* vars
* fix(llm): cap the tool calls one assistant response may queue
* fix(llm): cap the subscription backend's responses too
---------
Co-authored-by: Ahmed Allam <ahmed39652003@gmail.com>
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.
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.
Co-authored-by: Jonathan Singer <jonathansinger@Jonathans-MacBook-Pro.local>
Co-authored-by: Jonathan Singer <jonathansinger@Mac-3004.lan>
Co-authored-by: Ahmed Allam <ahmed39652003@gmail.com>
cryptography 49.x ships arm64-only macOS wheels (no universal2), forcing the
Intel macOS (macos-x86_64) release runner to build from sdist under
`uv sync --frozen`. Pin to 48.0.1, which still clears GHSA-537c-gmf6-5ccf
(fixed in 48.0.1) and provides a macosx_10_9_universal2 wheel.
Resolves GHSA-537c-gmf6-5ccf (vulnerable OpenSSL in cryptography wheels,
fixed in 48.0.1) and CVE-2026-59885 / CVE-2026-59886 (pyasn1 DoS via
OBJECT IDENTIFIER / REAL decoding, fixed in 0.6.4).
* fix(deps): cap openai<2.45 and add litellm[proxy] so fresh installs can run
* chore(deps): sync uv.lock with openai cap and litellm[proxy]
Regenerate the lockfile so locked/frozen installs pick up the openai<2.45 cap and litellm[proxy] extras (fastapi, orjson, ...); remove inline dependency comments.
Co-Authored-By: Ahmed Allam <ahmed39652003@gmail.com>
---------
Co-authored-by: Ahmed Allam <ahmed39652003@gmail.com>
* feat: add bedrock + vertex optional extras with install docs and import hints (#574)
Declare [project.optional-dependencies] with vertex (google-auth) and
bedrock (boto3) extras so "strix-agent[vertex]" / "strix-agent[bedrock]"
install the provider SDKs. Add an Installation section to the Bedrock docs
mirroring Vertex, and a _provider_import_hint helper in warm_up_llm that
surfaces a pip-install hint when a provider dependency is missing.
Fixes#574, #573
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* fix(providers): use pipx in install hint to match docs
A pipx-installed strix can't add an extra with 'pip install' (wrong env);
mirror the documented 'pipx install "strix-agent[...]"' command. Addresses
Greptile review.
* fix: resolve pre-commit check failures
- Change RuntimeError to TypeError for type validation in report/writer.py
- Update pyupgrade to v3.21.2 for Python 3.14 compatibility
* feat(cli): add --max-budget-usd flag
Raises BudgetExceededError in ReportUsageHooks after each LLM call when
accumulated cost reaches the limit, with clean "stopped" status and
child-agent cancellation in non-interactive mode.
* test: add budget enforcement unit tests
7 tests covering no-budget, under-budget, at-limit, over-limit, error
message content, None report state, and exception hierarchy.
Also adds pytest/pytest-asyncio to dev deps and a mypy override for tests.
* fix(budget): validate positive budget and check the live cost ledger
Two hardening fixes for --max-budget-usd enforcement:
- Reject non-positive budgets. ReportUsageHooks now raises ValueError for
max_budget_usd <= 0, and the CLI validates the flag via a custom argparse
type so '--max-budget-usd 0' fails fast with a friendly message instead of
silently killing the scan on the first model response.
- Read the live cost. The budget check now reads ReportState.get_total_llm_cost()
(the live ledger) instead of the persisted run-record snapshot, so it stays
accurate even when a usage save fails after a model call.
* fix(budget): stop the entire scan deterministically when the limit is hit
Previously a BudgetExceededError was handled per-agent: it was swallowed in
interactive mode (the loop kept waiting), a child's error escaped its detached
task as an unretrieved-exception warning, the parent was never released from
wait_for_message, and the stop was logged at ERROR with a traceback as if the
agent had failed.
Replace that with a single scan-wide signal on the coordinator:
- AgentCoordinator.trigger_budget_stop() sets a flag and wakes every parked
agent; wait_for_message returns as soon as the flag is set.
- The run loops check coordinator.budget_stopped and raise to exit cleanly,
marking themselves 'stopped'. The root's exception reaches run_strix_scan's
handler, which cancels descendants and tears the scan down once; child
exceptions are swallowed in their detached task.
- The budget stop is logged at INFO, not as a failure.
This is deterministic regardless of tree depth or which agent first sees the
limit, fixing the interactive/TUI hang where a deep agent's stop never reached
a parked root. Also re-raises BudgetExceededError explicitly in the stream
handler so it can't be mistaken for the LiteLLM 'after shutdown' race.
* fix(budget): treat a budget stop as a clean stop in the TUI
Add an explicit BudgetExceededError handler in the TUI scan thread so that, if
the error ever reaches it, the budget stop is logged as a graceful stop rather
than surfaced as a red scan error by the broad 'except Exception'. The runner
normally absorbs the error and returns cleanly, so this is defensive depth for
a money-spending feature.
* docs(cli): document --max-budget-usd behavior and limitations
Clarify that the budget is cumulative across all agents, checked after each
model response, that the scan stops cleanly (not as a failure), that the value
must be > 0, and that spend can slightly overshoot due to in-flight calls and
best-effort cost estimation.
* Apply suggestions from code review
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---------
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Register a litellm.success_callback that captures kwargs['response_cost']
into a new observed-cost bucket on LLMUsageLedger. record() skips the
tokens-times-registry estimate for LiteLLM-routed models so we do not
double-count with the callback; OpenAI direct routes keep estimating
since LiteLLM is not invoked for them. Per-agent attribution for
LiteLLM-routed calls is apportioned by token share at to_record() time.
