Regular team members could not see service accounts belonging to their
team. Additionally, former team members could still see service accounts
they created via the created_by filter after leaving the team.
- Add get_member_team_ids() to retrieve all teams a user belongs to
- Regular members now see team service accounts (user_id=NULL) but not
other members' personal keys
- Scope created_by filter to the user's current team memberships
- Add 7 unit tests covering the new visibility rules
Replace find_many + Python-side aggregation with a single SQL GROUP BY
query via query_raw in get_daily_activity_aggregated. This collapses
rows across entities (users/teams/orgs) in the database, reducing ~150k
rows to ~2-3k grouped rows before transfer to Python.
Also adds composite indexes (entity_id, date) to all 6 daily spend
tables for faster filtered queries.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The admin bulk-delete path (prisma_client.delete_data) was never comparing
the DB return value against the requested tokens, so failed_tokens was
always empty for admins even when the DB silently skipped some tokens.
Adds the same mismatch check as the non-admin path, and a new test that
exercises admin bulk-delete returning fewer tokens than requested.
Fixes gap identified by Greptile review on #21609.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Instead of raising an exception when some requested tokens are not found
in the DB, populate `failed_tokens` in the return dict so callers can
inspect which keys were not deleted without catching a generic Exception.
Supersedes #12577.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Avoid constructing a default Usage() object that gets immediately
overwritten by convert_to_model_response_object. Set usage=None
instead; the real Usage is assigned via setattr later.
Also fix Bedrock Qwen2/Qwen3 transform_response to assign a new
Usage object instead of mutating a potentially missing one.
When extended thinking is enabled, the websearch interception agentic loop
builds a follow-up assistant message with only tool_use blocks. Anthropic's
API requires assistant messages to start with thinking/redacted_thinking
blocks when thinking is enabled, causing a 400 Bad Request.
Extract thinking blocks from the model's initial response, thread them
through the agentic loop, and prepend them to the follow-up assistant
message — matching the pattern used by anthropic_messages_pt in factory.py.
Fixes the error: "Expected 'thinking' or 'redacted_thinking', but found
'tool_use'"
* fix aviation safety topic filter: remove overly broad exceptions, add cockpit access block words
* fix airline brand protection filter: add identifier words, competitor/ops block words, tighten exceptions
* add example_sentences to all policy templates + topic-filtering and prompt-injection templates
* add policy_endpoints package with AI policy suggester
* update test patch targets for policy_endpoints package move
* add unit tests for AI policy suggester
* add suggestPolicyTemplates networking function
* add AI suggestion modal component
* add Use AI button and template loading callback to PolicyTemplates
* wire up AI suggestion modal in policies page
* fix policy_templates_backup.json path after package move
* add estimated_latency field to all policy templates
* use llm_router and accept model parameter in ai_policy_suggester
* add model param to suggest templates endpoint
* pass model param in suggestPolicyTemplates
* polish ai suggestion modal: model selector, auto-growing textareas, latency badges
* add template queue for processing multiple AI-suggested templates
* show template progress badge in guardrail selection modal
Moonshot's _transform_messages unconditionally flattened content arrays
to plain text, dropping image_url blocks. Vision models like kimi-k2.5
accept the standard OpenAI content array format.
Now checks for image_url blocks before flattening — if any message
contains images the content array is preserved intact.
Fixes#20862
AgentCore MCP server endpoints require the Accept header to contain
both application/json and text/event-stream per the MCP specification
(Streamable HTTP transport). Without this header, requests are rejected
with a 406 Not Acceptable error (JSON-RPC code -32011).
Sets the Accept header at the top of sign_request() so both JWT/Bearer
and SigV4 authentication paths include it.
* fix(scim): handle deprovisioning operations without path field
When SCIM providers send deprovisioning requests without a path field
(e.g., {"op": "replace", "value": {"active": false}}), the code was
storing the value under an empty string key in metadata.
This fix:
- Detects operations with no path where value is a dict
- Extracts and handles known fields like 'active' correctly
- Sets metadata["scim_active"] = false instead of metadata[""] = {"active": false}
Fixes: SCIM deprovisioning creating empty string keys in user metadata
* fix(scim): handle all known fields in operations without path
Extended the fix to handle all SCIM fields (not just active) when
operations have no path field:
- active -> scim_active
- displayName -> user_alias
- externalId -> user_id
- name.givenName/familyName -> scim_metadata
Added comprehensive test for multiple fields without path.
Addresses Greptile review feedback on RFC 7644 compliance.
* trigger PR update
When providers like OpenRouter send a usage chunk after the finish_reason
chunk, _hidden_params["usage"] was already calculated (with zeros) before
the usage data arrived. The StopIteration handler now recalculates usage
from stream_chunk_builder and updates the shared _hidden_params dict so
the user's copy reflects the real provider-reported token counts.
Fixes#20760
Move the fix to the OpenRouter level: define native OpenRouter models
(openrouter/auto, openrouter/free, openrouter/bodybuilder) and check
them in get_llm_provider() before the provider_list stripping logic.
This prevents the second strip across all bridges without modifying
each adapter/handler individually.
Fixes#16353
aspectRatio and imageSize were silently dropped because they weren't
listed in get_supported_openai_params(), so the validation layer filtered
them out before they could reach transform_image_generation_request().
Fixes#21070
logprobs, top_p, top_logprobs are only accepted by OpenAI when
reasoning_effort="none". Add validation matching the existing
temperature logic: raise UnsupportedParamsError or drop when
reasoning_effort is set to other values.
Gemini returns finishReason="STOP" even when tool calls are present,
and sends tool_calls and finishReason in separate streaming chunks.
The ModelResponseIterator now tracks tool_calls across chunks and
correctly maps finish_reason to "tool_calls" per the OpenAI spec.
Fixes#21041
Remove logit_bias, modalities, prediction, audio, web_search_options
from supported params for all GPT-5 reasoning models (OpenAI rejects
them). Add logprobs, top_p, top_logprobs for gpt-5.1/5.2 which support
them when reasoning_effort="none".
Related to #21572
gpt-5-search-api models were routed through OpenAIGPT5Config which
listed params like n, temperature, tools, reasoning_effort as supported,
but OpenAI rejects all of these for search models.
Fixes#21572
Add test to verify that get_deployment_credentials_with_provider correctly
resolves litellm_credential_name to actual credential values and removes
the credential name from the returned dictionary.
For multi-turn conversations, convert thinking_blocks on assistant
messages into content blocks prepended before the rest of the content,
so reasoning context is passed back to the hosted_vllm API.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Virtual keys only track created_at and updated_at, which don't indicate
when a key was last used. This adds a last_active field that gets updated
during the async batch spend update, giving admins visibility into which
keys are actively being used.
Changes:
- Add last_active DateTime? to VerificationToken and
DeletedVerificationToken in all 3 schema files and Python types
- Set last_active in the batch key spend update alongside spend increment
- Add Last Active column to virtual keys UI table with info popover
and hover tooltip showing full date/time with timezone
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix aviation safety topic filter: remove overly broad exceptions, add cockpit access block words
* fix airline brand protection filter: add identifier words, competitor/ops block words, tighten exceptions
* update policy templates with competitor pre/post guardrails and streaming enrichment
* sync policy_templates_backup.json with policy_templates.json
* add streaming enrichment endpoint, competitor variations, and model selection
* add streaming enrichPolicyTemplate networking function
* rewrite template parameter modal with streaming tags, AI/manual toggle, model selector
* update policies panel to pass enrichment options and show discovered competitors
* show discovered competitors as tags in guardrail selection modal
* use llm_router instead of litellm.acompletion, extract helpers, move constants
* validate competitors list size, cap variation prompt input
* add refinement instruction support for competitor discovery
* add instruction and existingCompetitors params to streaming enrichment
* add refinement input for iterating on competitor list with AI
* emit status events during variation generation so UI shows progress
* add onStatus callback to streaming enrichment
* show status spinner during variation generation, widen modal to 700px
* add tests for competitor enrichment helper functions
Add a new team member permission `/team/daily/activity` that allows
non-admin team members to see all team usage data, not just their own.
## Changes
- Add `TEAM_DAILY_ACTIVITY` to `KeyManagementRoutes` enum and available
team member permissions
- Add `_team_member_has_permission` helper in `common_utils.py`
- Modify `/team/daily/activity` endpoint to skip API key filtering when
the member has this permission
- Add permission description and method detection in the UI
- Add backend and frontend tests
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
