… on /v1/messages path (#30341)"

This reverts commit c9e8a17.

* fix(proxy): bump health-check max_tokens default to 16 for GPT-5 compatibility (#30708)

OpenAI GPT-5 models require max_completion_tokens >= 16.
Health checks were using 5 (proxy/health_check.py) and 10
(health_check_helpers.py), causing failures on GPT-5 models.

Fixes #23836

* fix: increase health check max_tokens from 5 to 16 (#23836) (#26610)

GPT-5 models enforce a minimum of 16 for max_output_tokens. The current
default of 5 still causes health checks to fail for these models. Bump
the non-wildcard default to 16 — the smallest value that satisfies all
known provider minimums while keeping health checks lightweight.

Also tightens the wildcard test assertion from a weak disjunctive check
to strict key-absence.

Co-authored-by: Sameer Kankute <[email protected]>

* fix: ensure checks show gemini-3-flash-preview supports responseJsonS… (#30696)

* fix: ensure checks show gemini-3-flash-preview supports responseJsonSchema.

* fix: remove async keyword from test.

* fix: make Bedrock Mantle Responses routing data-driven per model (#30700)

* Make Bedrock Mantle Responses routing data-driven per model

Route Bedrock Mantle models to the native Responses API based on each
model's price-map capability signal instead of a hardcoded model-name
heuristic, and derive the OpenAI-compatible base path segment per model.

Responses dispatch now selects the native config when the model advertises
responses support (/v1/responses in supported_endpoints, or mode=responses),
both overridable via register_model and proxy model_info. This enables
native Responses for gpt-oss-120b/20b and the gemma-4 family while keeping
chat-only models (gpt-oss safeguard, nvidia, mistral, ...) on the existing
chat-completions emulation. Capability is per-model, so gpt-oss-120b routes
natively while gpt-oss-safeguard-120b does not despite sharing the gpt-oss
substring.

The wire path is a separate concern, driven by the existing
use_openai_responses_path flag rather than a model-name match: gpt-5.x and
gemma-4-* on /openai/v1, everything else (incl. gpt-oss) on /v1. The chat
config now derives its base from the same flag, fixing gemma-4
chat-completions requests that previously went to /v1 instead of /openai/v1.

Cost maps: add supported_endpoints to the gpt-oss entries (responses for the
non-safeguard variants, chat-only for safeguard) and supported_endpoints +
use_openai_responses_path to all three gemma-4 entries.

Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>

* Address review: move capability helper into bedrock_mantle package

Move the Responses capability check out of utils.py into
litellm/llms/bedrock_mantle/common_utils.py as mantle_supports_responses,
alongside its companion wire-path helper mantle_base_segment. Both are now
pure functions of (model, model_cost): the price-map mode/supported_endpoints
read replaces the get_model_info call, so the rules are unit-testable without
patching global state and the Bedrock Mantle package is self-contained.

Use str | None instead of Optional[str] on the new signatures to satisfy the
ruff UP045 strict-rule gate. Add direct unit tests for both helpers.

Fix test_register_model_restore_undoes_existing_key_overwrite: gpt-oss-120b
now legitimately supports Responses, so it can no longer be the
"None after restore" vehicle; use the chat-only safeguard variant, which
isolates the register/restore effect from the model's own capability.

Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>

---------

Co-authored-by: Claude Opus 4.8 (1M context) <[email protected]>
Co-authored-by: Sameer Kankute <[email protected]>

* fix(proxy): fail fast on non-PostgreSQL DATABASE_URL instead of hanging on startup (#30366)

* fix(proxy): fail fast on non-PostgreSQL DATABASE_URL instead of hanging on startup

LiteLLM's Prisma datasource is pinned to provider = 'postgresql', so a sqlite:// or mysql:// DATABASE_URL can never connect.

Today that surfaces as an opaque startup stall where the port never binds, and a separate 'DB not connected' 500 on /key/generate when no DATABASE_URL is set at all leaves operators guessing what to configure.

Validate the DATABASE_URL / DIRECT_URL scheme in run_server before any Prisma call and exit with an actionable message naming the unsupported scheme.

Also reword CommonProxyErrors.db_not_connected_error to tell the operator to set DATABASE_URL to a postgresql:// connection string.

Add regression tests covering postgres acceptance and sqlite/mysql/mssql rejection.

* fix: resolve CI failures and proxy DB URL typing issue

* fix(dashscope): treat an explicit 0.0 tier cost as a real price, not missing (#30653)

The tiered cost calculator resolved a tier's per-token cost with
`tier.get(cost_key) or tier.get(fallback_cost_key, 0)`. Because `or`
short-circuits on any falsy value, a tier that legitimately prices a
component at 0.0 (e.g. a free-cache-read tier with
cache_read_input_token_cost: 0.0, or a free-reasoning tier) is treated
as missing and silently billed at the full fallback rate
(input_cost_per_token / output_cost_per_token).

The flat-pricing path in the same module already handles this correctly
with an `is None` guard. Resolve tier costs through a small helper that
mirrors it, so 0.0 is honored at both the in-range and overflow sites.

No shipped model currently has a 0.0 tier cost, so this is a latent
defect; the fix makes the tiered path consistent with the flat path and
prevents over-charging the first time such a tier appears. Adds unit
tests covering the in-range and overflow paths, and drops an unused
import flagged by ruff in the touched test file.

* feat(proxy): show session-aggregate cost and duration in request logs (#25708) (#30507)

* fix(anthropic): don't leak tool 'type' into OpenAI function parameters schema (#30618)

In the messages->chat/completions bridge, translate_anthropic_tools_to_openai
merged every non-mapped tool key into the function parameters dict. The
Anthropic tool 'type' (e.g. 'custom') thus overwrote parameters.type ('object'
-> 'custom'), and providers reject it ('custom' is not a valid JSON-Schema type).
Exclude 'type' from the passthrough. Fixes #30557.

* fix(proxy): stop IAM-refresh engine restart from cascading reconnects (#29176) (#30183)

An RDS IAM token refresh recreates the Prisma client, which SIGKILLs the
running query-engine and spawns a new one. That planned kill was
indistinguishable from a crash, and three reconnect paths used two
uncoordinated locks, so a single refresh triggered a cascade of engine
kill/respawn cycles:

  1. `_safe_refresh_token` (holds `_reconnection_lock`) -> recreate -> kill old
     engine, spawn new one.
  2. The engine-death watcher sees that kill, assumes a crash, and calls
     `attempt_db_reconnect(force=True)` (a different lock,
     `_db_reconnect_lock`) -> recreate again -> kills the fresh engine.
  3. In-flight queries failing during the swap are classified as transport
     errors and trigger their own `attempt_db_reconnect` -> recreate again.

Fix coordinates planned restarts across the wrapper and the watcher:

  - PrismaWrapper records the old engine PID in `_expected_engine_deaths`
    before killing it; all four watcher death-detectors (waitpid thread,
    pidfd, already-dead probe, os.kill poll) consume that PID and skip the
    reconnect instead of treating it as a crash.
  - `recreate_prisma_client` now serializes through `_reconnection_lock` and
    bumps a monotonic `_engine_generation`. Callers pass `expected_generation`
    as an optimistic-lock token, so racing/cascading recreates collapse into a
    single restart (losers no-op). This closes the two-lock gap.
  - The direct reconnect path probes the writer with SELECT 1 before
    recreating; a healthy connection (e.g. engine already replaced by a
    refresh) skips the recreate entirely.
  - `_safe_refresh_token` coalesces: it skips when the current token still has
    more than the refresh buffer of runway, so stacked triggers (proactive
    loop + __getattr__ fallback) don't each restart the engine. An
    `on_engine_replaced` hook re-arms the watcher on the new PID.

RoutingPrismaWrapper forwards `expected_generation` and skips recreating the
reader when the writer recreate was skipped.

* feat(bedrock): support file content retrieval for batch output files (#30595)

Implements transform_file_content_request and transform_file_content_response
in BedrockFilesConfig so GET /v1/files/{id}/content works for Bedrock batch
files. The request transform resolves the file id (direct s3:// URI or base64
unified id) to its S3 object, validates bucket and key prefix against the
server-configured bucket, and SigV4-signs an S3 GetObject using the same
credential and region resolution as the existing upload path. The credential
and region params are validated into a typed model at the boundary, so the only
untyped values left are the botocore signing primitives.

Also fixes the proxy managed-files path: CredentialLiteLLMParams now carries
s3_bucket_name (previously dropped when building deployment credentials) and
the managed-files hook passes the deployment credential snapshot when routing
afile_content, so unified-id content retrieval works with per-model bucket
config instead of only the AWS_S3_BUCKET_NAME env var.

Preserves managed-file access control: the proxy file-content endpoint now
rejects raw cloud-storage ids (s3://, gs://), which would otherwise skip the
owner/team check that only runs for unified ids and let a caller read another
tenant's batch output by its object key. Managed outputs are reachable only
through their unified file id. The afile_content "not found" error now reports
the caller's unified id rather than the resolved internal S3 URI.

Fixes #16186, #15563

* fix(oci): make Cohere {{trace}} judges work (tool param types + agentic tool-calling continuation) (#30646)

* fix(oci): map Cohere tool array/object params to lowercase builtins

OCI's Cohere backend returns HTTP 500 on a tool parameter typed as a bare
"List", which is what OCI_JSON_TO_PYTHON_TYPES produced for JSON-schema
arrays. MLflow {{trace}} judges trip this: their tools (get_root_span,
get_span) take an attributes_to_fetch array. The lowercase builtins list/dict
are accepted; only the bare "List" 500s ("Dict" happens to be tolerated, but
both are lowercased for consistency).

Verified live against us-chicago-1 (cohere.command-a-03-2025 and
command-latest). Adds a unit regression on the transformed parameterDefinitions
plus a gated integration test exercising an array-param tool end to end.

* fix(oci): make Cohere agentic tool-calling continuation work

Two bugs broke the OCI Cohere tool-calling loop that MLflow {{trace}} judges
drive once a tool has been executed and its result is fed back.

Request side: litellm pulled the last user message into the top-level `message`
and emitted the tool result as a TOOL entry in chatHistory. OCI rejects that
("cannot specify message if the last entry in chat history contains tool
results"), and an empty message alone is rejected too ("message must be at least
1 token long or tool results must be specified"). OCI carries the current turn's
results in a dedicated top-level `toolResults` field. The Cohere transform now
sends an empty message, keeps the user turn in chatHistory, and puts the results
in `toolResults`, matching the langchain-oracle reference. Tool results are no
longer represented as chatHistory entries.

Response side: tool-grounded answers come back with citations carrying
`documentIds` (camelCase) and no `document_ids`, which made the required
`CohereCitation.document_ids` field fail validation and sink the whole response
parse. Those citations are never surfaced, so the field (and CohereSearchQuery's
generation_id) is now optional.

Verified live against us-chicago-1 (cohere.command-a-03-2025 and command-latest),
single and multi-round tool loops. Adds unit regressions on the transformed
request shape and on citation parsing, plus gated integration tests for the
continuation.

* feat: integrate Repelloai Argus guardrail (#30673)

* feat(guardrails): add RepelloAI Argus guardrail integration (#1)

* feat(guardrails): add RepelloAI Argus guardrail integration

Add a new guardrail hook backed by RepelloAI Argus, with dashboard-managed
asset policies enforced via an asset_id and X-API-Key auth.

* fix(guardrails): harden RepelloAI Argus guardrail

- scan streaming responses on output (was bypassing the guardrail)
- log blocked verdicts as guardrail_intervened instead of success
- treat auth/config errors (401/403/404/422) as misconfiguration that
  always blocks, not a fail-open-able unreachable error
- default unreachable_fallback to fail_closed and read it directly;
  block on unknown/malformed verdicts so an API change can't silently
  disable enforcement
- type unreachable_fallback as a Literal, drop the duplicate config model,
  expose unreachable_fallback in the config schema, and stop leaking the
  raw provider response / exception strings to the client

* fix(guardrails): address RepelloAI Argus review feedback

- support ARGUS_API_KEY (with REPELLOAI_API_KEY fallback)
- make asset_id required in the config model
- normalize unreachable_fallback so only fail_open opens; block on 400 misconfig
- correct the shared unreachable_fallback field description

* docs(guardrails): add RepelloAI Argus docs page and dashboard listing

- add docs page covering config, env vars, modes, verdicts, failure semantics
- list RepelloAI Argus in the Guardrail Garden with provider/logo mappings
- add a regression test for the provider logo and display-name resolution

* fix(guardrails): keep RepelloAI asset_id optional in config model

A required asset_id leaked onto the shared LitellmParams (which inherits
RepelloAIGuardrailConfigModel), breaking validation for every other
guardrail. Keep it optional like sibling models; the guardrail __init__
still raises when asset_id is missing, which is the real enforcement.

* Add comment for last user turn scanning

* feat(guardrails): harden repelloai scanning

* feat(guardrails): expand repelloai scanning to include tool definitions

Add extraction of tool definitions and tool call arguments to the RepelloAI
guardrail scanning. Improves detection coverage by including function schemas
and parameters in the prompt sent to the guardrail service. Also captures
detailed error responses in logs and adds guardrail header to streaming responses.

* refactor(guardrails): fix and harden repelloai schema text extraction

- Fix duplicate text in _iter_schema_text: previously all dict values were
  re-queued onto the stack even after scalar/list keys were already extracted
  explicitly, causing names/descriptions to appear twice in the scanned prompt
- Extract schema key frozensets to module-level constants so they are not
  reconstructed on every call
- Change _iter_schema_text from @classmethod to @staticmethod (cls unused)
- Narrow _call_analyze stage param from str to Literal["prompt", "response"]
- Add HttpxResponse type annotation to _raise_for_config_error
- Add LLMResponseTypes annotation to async_post_call_success_hook response param

* fix(guardrails): resolve pyright type errors in repelloai guardrail

- Narrow async_handler.post return from Response|None to Response with
  explicit None guard before calling raise_for_status/json
- Fix list comprehension returning str|None by switching to explicit loop
  with isinstance guard so pyright tracks the narrowing
- Cast model_dump() result to Dict since hasattr does not narrow object
  type in pyright

* fix(guardrails/repello): include Responses API instructions field in prompt scan

The /v1/responses top-level `instructions` field was not included in
_extract_prompt_text, allowing a caller to bypass guardrail policy checks
by putting blocked content in `instructions` while keeping `input` benign.

* feat: add api_key to config model and read prompt from data dict

* fix(guardrails/repello): plug input_text and tool-call response bypass gaps

Responses API input content parts with type 'input_text' were silently
dropped by build_inspection_messages (which only handles type='text'),
allowing callers to send blocked content via that path without triggering
the pre-call scan. Fix: add _extract_input_text_parts to RepelloAIGuardrail
and call it when walking the Responses API input messages.

Post-call scanning skipped responses whose choices contained only tool_calls
or function_call (message.content=None), letting models put blocked output in
function arguments undetected. Fix: _extract_chat_completion_text now calls
_extract_tool_call_args_from_message on each choice message.

Also replace typing.Dict/List with builtin dict/list to clear TID251 strict
ruff violations introduced by this file.

* fix(guardrails/repello): scan Responses API function_call output arguments

Output items with type 'function_call' in a /v1/responses response were
skipped by _extract_responses_api_text; only 'message' items were walked.
A model could return blocked content in function_call.arguments undetected.
Now extract arguments from function_call output items before scanning.

* refactor(guardrails/repello): clean up typing and remove lint-any workarounds

- Replace Optional[X]/Union[X,Y] with X|None/X|Y union syntax throughout
- Use dict[str, object] instead of bare dict in all signatures
- Remove **kwargs from __init__; declare guardrail_name, event_hook, default_on explicitly
- Replace getattr(litellm_params, ...) with direct attribute access now that LitellmParams inherits RepelloAIGuardrailConfigModel
- Add _event_hook_from_mode() to convert str|list[str]|Mode to typed GuardrailEventHooks
- Use TypeAdapter.validate_json() instead of response.json() + manual dict construction
- Add _is_object_dict/_is_object_list TypeGuard helpers to narrow object types without Any
- Remove cast() workarounds and typed intermediate variables that existed only for the now-removed lint-any CI check
- Drop _AddLiteLLMCallback Protocol; budget has sufficient slack for the one reportUnknownMemberType
- Fix GuardrailConfigModel missing type arg: GuardrailConfigModel[BaseModel]

* fix(guardrails/repello): suppress LIT007 on TypeGuard helpers and add streaming scan-skip warning

- Add guard-ok suppressions to _is_object_dict and _is_object_list to satisfy the LIT007 hard-zero budget gate
- Emit verbose_proxy_logger.warning when the streaming hook finds no inspectable text after assembly, matching observability of pre/post hooks

* refactor: modifications for lint check

* feat: add Pinstripes as an OpenAI-compatible provider (#30567)

* feat: add Pinstripes as an OpenAI-compatible provider

Pinstripes (https://pinstripes.io) is an OpenAI-compatible inference
provider serving open-source models (GLM-4.5-Air, Qwen3, DeepSeek, etc.)
with per-token pricing and no subscriptions.

Changes:
- `litellm/llms/openai_like/providers.json`: register pinstripes with
  base_url, api_key_env, and max_completion_tokens→max_tokens mapping
- `litellm/types/utils.py`: add `PINSTRIPES = "pinstripes"` to LlmProviders
- `litellm/constants.py`: add to openai_compatible_providers and
  openai_compatible_endpoints lists
- `litellm/litellm_core_utils/get_llm_provider_logic.py`: auto-detect
  provider when api_base is "https://pinstripes.io/v1"
- `provider_endpoints_support.json`: document supported endpoints
- `tests/`: 7 unit tests covering provider registration, resolution,
  URL auto-detection, api_base override, and Router config

Usage:
    import litellm
    response = litellm.completion(
        model="pinstripes/ps/glm-4.5-air",
        messages=[{"role": "user", "content": "Hello"}],
        api_key=os.environ["PINSTRIPES_API_KEY"],
    )

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

* fix(pinstripes): resolve Greptile P1 review comments

- Add api_base_env: PINSTRIPES_API_BASE to providers.json so env var override works
- Set responses: false in provider_endpoints_support.json — not actually wired up
- Remove docs/my-website/docs/providers/pinstripes.md — belongs in litellm-docs repo

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

* fix(pinstripes): add api_base_env and correct responses capability

- Add api_base_env: PINSTRIPES_API_BASE to providers.json
- Set responses: false in provider_endpoints_support.json

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

* fix(pinstripes): wire up Responses API — add supported_endpoints

Adds supported_endpoints: ["/v1/chat/completions", "/v1/responses"] so
JSONProviderRegistry.supports_responses_api returns true correctly,
matching what provider_endpoints_support.json advertises.

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

* feat(pinstripes): enable embeddings endpoint

Pinstripes serves nomic-embed-text-v1.5 and bge-m3 via /v1/embeddings.
Add /v1/embeddings to supported_endpoints and set embeddings: true.

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

* fix(pinstripes): use 4-space indentation in model_prices_and_context_window.json

Matches the file's existing convention. Flagged by Greptile review.

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

* fix(pinstripes): set a2a: false — A2A protocol not implemented

All comparable JSON-configured providers (tensormesh, parasail, empiriolabs,
libertai, neosantara) have a2a: false. Pinstripes does not implement the
Google A2A protocol, so this should be false to match.

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

---------

Co-authored-by: inference_provider <[email protected]>
Co-authored-by: Claude Sonnet 4.6 <[email protected]>

* fix(rag): attach existing OpenAI file ids (#30628)

* fix(rag): attach existing OpenAI file ids

* chore: use modern typing in rag ingest fix

* chore: retrigger ci

* fix(anthropic-messages): apply cache_control_injection_points on /v1/messages path (#30341)

cache_control_injection_points was only consumed by the chat/completions
prompt-management hook; on the native Anthropic /v1/messages path it was
forwarded unused, so deployment-level cache injection was silently dropped
(cache_creation_input_tokens stayed 0 for Anthropic-native clients).

Add AnthropicCacheControlHook.apply_to_anthropic_messages_request to inject
cache_control at block level for system / tools / message locations (the only
forms /v1/messages accepts), wire it into the native anthropic_messages
handler, and pop the param so it does not leak upstream as an unknown field.
A {location: message, role: system} config is redirected to the top-level
system prompt so the same YAML works on both endpoints.

Injection respects Anthropic's 4-block cache_control limit shared across
system, tools, and messages: client-supplied markers count toward the cap and
are never overwritten, a slot is reserved per Bedrock tool_config point, and
injection stops once the budget is exhausted. Locations this path cannot
represent (tool_config) are forwarded downstream instead of being silently
consumed, mirroring get_chat_completion_prompt's remaining_points pass-through.

Built on litellm_internal_staging. Refs #30293

* fix(proxy): release budget reservation when a request is cancelled mid-flight (#30522)

* fix(proxy): release budget reservation on cancel when no chunk was delivered

The pre-call budget reservation increments the cross-pod spend counter by a
request's worst-case cost, then reconciles it on success (cost callback) or
error (failure hook). A client disconnect or timeout cancels the request and
surfaces as CancelledError / GeneratorExit, which neither path catches, so the
reservation leaks. Under a retry storm the leaked holds accumulate, pin the
counter above real spend, and return spurious 429 "Budget has been exceeded" to
keys whose spend is far below budget; the counter only recovers when its TTL
lapses, so the failure is intermittent and self-healing.

Release the reservation in async_streaming_data_generator (which the Anthropic
and Google SSE generators delegate to) on the (CancelledError, GeneratorExit)
path, alongside the existing max_parallel_requests release. release_budget_
reservation_on_cancel runs under asyncio.shield so it completes despite the
in-progress cancellation, is guarded by the reservation's finalized flag, and
swallows a failing release so it cannot replace the in-flight cancellation.

The refund is gated on whether a chunk reached the client. The flag is set
immediately before the yield, after the slow-path hook await: an async generator
suspends at the yield, so a GeneratorExit on disconnect after a delivered chunk
sees it True (keep the hold), while a cancellation during the slow-path await
leaves it False (refund, nothing sent). A non-streaming cancellation delivers
nothing and a completed non-streaming response is reconciled by the success
callback, so neither needs a release here.

Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>

* fix(proxy): reconcile a cancelled reservation to input cost, not zero

A streaming request cancelled before the first chunk previously reconciled its
reservation to zero and finalized it. But by the time the generator is
consuming the response the provider call was already dispatched, so the input
tokens were billed even though no chunk reached the client, and the
success/failure cost callbacks are skipped on cancellation. Refunding to zero
let a caller send an expensive request and abort pre-token to dodge the input
charge.

Compute the request's input-token cost at reservation time and reconcile the
cancelled reservation to it instead of zero. The worst-case output portion of
the reservation is still released (so a legitimate mid-flight cancellation no
longer pins the counter and 429s the key), while the input the provider already
processed is charged.

---------

Co-authored-by: Bytechoreographer <[email protected]>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>

* fix(caching): encode object name in GCS cache GET path (#30378)

GCS cache reads always missed when gcs_path was set. The GET methods
interpolated the object name directly into the URL path, while the GCS
JSON API requires it to be URL-encoded (a "/" must be sent as %2F).

With gcs_path configured the object name is "<prefix>/<sha256>", so the
raw slash produced a malformed object path and GCS returned 404. httpx
does not raise on 4xx, so the status_code == 200 check fell through and
get/async_get returned None, silently missing on every read. Without
gcs_path the key has no slash, which is why this went unnoticed.

Wrap the object name with urllib.parse.quote(..., safe="") in get_cache
and async_get_cache. Apply the same encoding to the name= query
parameter in set_cache and async_set_cache so the key written matches
the key read back.

Adds regression tests asserting the GET path and SET query are encoded
(%2F) when gcs_path is set, for both sync and async paths; these fail on
the unpatched code.

Fixes #30377

* chore: add soniox stt-async-v5 model (#30672)

* fix(proxy): include model group aliases in v1 model info (#30626)

* Include model group aliases in v1 model info

* Fix model info alias implementation

* removed extra blank line

* chore: rerun CI

* fix(lint): remove redundant noqa directive in proxy_cli.py

* fix: address greptile review - restore bedrock_mantle auth symbols, guard OCI empty message list, validate DIRECT_URL scheme

* Revert "fix: address greptile review - restore bedrock_mantle auth symbols, guard OCI empty message list, validate DIRECT_URL scheme"

This reverts commit 52c7a07.

* Revert "fix(anthropic-messages): apply cache_control_injection_points on /v1/messages path (#30341)"

This reverts commit c9e8a17.

* Revert "fix(proxy): stop IAM-refresh engine restart from cascading reconnects (#29176) (#30183)"

This reverts commit 85828da.

* fix(proxy): stop IAM-refresh engine restart from cascading reconnects (#29176) (#30183)

An RDS IAM token refresh recreates the Prisma client, which SIGKILLs the
running query-engine and spawns a new one. That planned kill was
indistinguishable from a crash, and three reconnect paths used two
uncoordinated locks, so a single refresh triggered a cascade of engine
kill/respawn cycles:

  1. `_safe_refresh_token` (holds `_reconnection_lock`) -> recreate -> kill old
     engine, spawn new one.
  2. The engine-death watcher sees that kill, assumes a crash, and calls
     `attempt_db_reconnect(force=True)` (a different lock,
     `_db_reconnect_lock`) -> recreate again -> kills the fresh engine.
  3. In-flight queries failing during the swap are classified as transport
     errors and trigger their own `attempt_db_reconnect` -> recreate again.

Fix coordinates planned restarts across the wrapper and the watcher:

  - PrismaWrapper records the old engine PID in `_expected_engine_deaths`
    before killing it; all four watcher death-detectors (waitpid thread,
    pidfd, already-dead probe, os.kill poll) consume that PID and skip the
    reconnect instead of treating it as a crash.
  - `recreate_prisma_client` now serializes through `_reconnection_lock` and
    bumps a monotonic `_engine_generation`. Callers pass `expected_generation`
    as an optimistic-lock token, so racing/cascading recreates collapse into a
    single restart (losers no-op). This closes the two-lock gap.
  - The direct reconnect path probes the writer with SELECT 1 before
    recreating; a healthy connection (e.g. engine already replaced by a
    refresh) skips the recreate entirely.
  - `_safe_refresh_token` coalesces: it skips when the current token still has
    more than the refresh buffer of runway, so stacked triggers (proactive
    loop + __getattr__ fallback) don't each restart the engine. An
    `on_engine_replaced` hook re-arms the watcher on the new PID.

RoutingPrismaWrapper forwards `expected_generation` and skips recreating the
reader when the writer recreate was skipped.

* fix(lint): modernize type annotations in IAM-refresh prisma client files (UP006/UP045)

* Revert "feat(proxy): show session-aggregate cost and duration in request logs (#25708) (#30507)"

This reverts commit f530b22.

* Revert "fix(dashscope): treat an explicit 0.0 tier cost as a real price, not missing (#30653)"

This reverts commit 4f58bd0.

* Revert "fix(oci): make Cohere {{trace}} judges work (tool param types + agentic tool-calling continuation) (#30646)"

This reverts commit 50f34e0.

* Revert "fix(proxy): fail fast on non-PostgreSQL DATABASE_URL instead of hanging on startup (#30366)"

This reverts commit 0544eed.

* fix(bedrock_mantle): restore BedrockMantleAuthMixin and constants removed by routing rewrite

* fix(key management): restore exact /key/list user_id & key_alias matching by default (#30593)

Before substring search was added (commit 33bd570), /key/list matched user_id
and key_alias exactly. That change made admin-authenticated calls substring-match
by default, breaking the prior contract: a caller passing an exact user_id as an
access filter (e.g. an integration scoping to one user with an admin key) then
received other users' keys -- user_id="alice" also returned "alice2",
"alice-test", etc. This is a cross-user key disclosure.

Make substring matching opt-in via a new admin-only substring_matching=true query
param; default to exact, restoring the prior behavior. The dashboard search box
(keyListCall) passes the flag so partial search still works. Non-admins remain
exact and scoped to their own keys.

Updates the proxy-behavior key_alias test to opt in and adds an exact-by-default
guard; adds list_keys unit coverage for the opt-in gate.

---------

Co-authored-by: perseus <[email protected]>
Co-authored-by: Hannah Smith <[email protected]>
Co-authored-by: Charlie Patterson <[email protected]>
Co-authored-by: Matthew Lapointe <[email protected]>
Co-authored-by: Claude Opus 4.8 (1M context) <[email protected]>
Co-authored-by: KRISH SONI <[email protected]>
Co-authored-by: Yash Raj Pandey <[email protected]>
Co-authored-by: Nitish Agarwal <[email protected]>
Co-authored-by: hcl <[email protected]>
Co-authored-by: tushar8408 <[email protected]>
Co-authored-by: AD Mohanraj <[email protected]>
Co-authored-by: Fede Kamelhar <[email protected]>
Co-authored-by: Lavish Bansal <[email protected]>
Co-authored-by: max-amos <[email protected]>
Co-authored-by: inference_provider <[email protected]>
Co-authored-by: NK <[email protected]>
Co-authored-by: 安妮的心动录 <[email protected]>
Co-authored-by: Rick <[email protected]>
Co-authored-by: Bytechoreographer <[email protected]>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: Burak Ömür <[email protected]>
Co-authored-by: Dan Lemon <[email protected]>
Co-authored-by: Vanika Dangi <[email protected]>
Co-authored-by: Jay Gowdy <[email protected]>

…messages path (#30341)

cache_control_injection_points was only consumed by the chat/completions
prompt-management hook; on the native Anthropic /v1/messages path it was
forwarded unused, so deployment-level cache injection was silently dropped
(cache_creation_input_tokens stayed 0 for Anthropic-native clients).

Add AnthropicCacheControlHook.apply_to_anthropic_messages_request to inject
cache_control at block level for system / tools / message locations (the only
forms /v1/messages accepts), wire it into the native anthropic_messages
handler, and pop the param so it does not leak upstream as an unknown field.
A {location: message, role: system} config is redirected to the top-level
system prompt so the same YAML works on both endpoints.

Injection respects Anthropic's 4-block cache_control limit shared across
system, tools, and messages: client-supplied markers count toward the cap and
are never overwritten, a slot is reserved per Bedrock tool_config point, and
injection stops once the budget is exhausted. Locations this path cannot
represent (tool_config) are forwarded downstream instead of being silently
consumed, mirroring get_chat_completion_prompt's remaining_points pass-through.

Built on litellm_internal_staging. Refs #30293

… on /v1/messages path (#30341)"

This reverts commit c9e8a17.

…messages path (BerriAI#30341)

cache_control_injection_points was only consumed by the chat/completions
prompt-management hook; on the native Anthropic /v1/messages path it was
forwarded unused, so deployment-level cache injection was silently dropped
(cache_creation_input_tokens stayed 0 for Anthropic-native clients).

Add AnthropicCacheControlHook.apply_to_anthropic_messages_request to inject
cache_control at block level for system / tools / message locations (the only
forms /v1/messages accepts), wire it into the native anthropic_messages
handler, and pop the param so it does not leak upstream as an unknown field.
A {location: message, role: system} config is redirected to the top-level
system prompt so the same YAML works on both endpoints.

Injection respects Anthropic's 4-block cache_control limit shared across
system, tools, and messages: client-supplied markers count toward the cap and
are never overwritten, a slot is reserved per Bedrock tool_config point, and
injection stops once the budget is exhausted. Locations this path cannot
represent (tool_config) are forwarded downstream instead of being silently
consumed, mirroring get_chat_completion_prompt's remaining_points pass-through.

Built on litellm_internal_staging. Refs BerriAI#30293

… on /v1/messages path (BerriAI#30341)"

This reverts commit c9e8a17.

* fix(proxy): bump health-check max_tokens default to 16 for GPT-5 compatibility (BerriAI#30708)

OpenAI GPT-5 models require max_completion_tokens >= 16.
Health checks were using 5 (proxy/health_check.py) and 10
(health_check_helpers.py), causing failures on GPT-5 models.

Fixes BerriAI#23836

* fix: increase health check max_tokens from 5 to 16 (BerriAI#23836) (BerriAI#26610)

GPT-5 models enforce a minimum of 16 for max_output_tokens. The current
default of 5 still causes health checks to fail for these models. Bump
the non-wildcard default to 16 — the smallest value that satisfies all
known provider minimums while keeping health checks lightweight.

Also tightens the wildcard test assertion from a weak disjunctive check
to strict key-absence.

Co-authored-by: Sameer Kankute <[email protected]>

* fix: ensure checks show gemini-3-flash-preview supports responseJsonS… (BerriAI#30696)

* fix: ensure checks show gemini-3-flash-preview supports responseJsonSchema.

* fix: remove async keyword from test.

* fix: make Bedrock Mantle Responses routing data-driven per model (BerriAI#30700)

* Make Bedrock Mantle Responses routing data-driven per model

Route Bedrock Mantle models to the native Responses API based on each
model's price-map capability signal instead of a hardcoded model-name
heuristic, and derive the OpenAI-compatible base path segment per model.

Responses dispatch now selects the native config when the model advertises
responses support (/v1/responses in supported_endpoints, or mode=responses),
both overridable via register_model and proxy model_info. This enables
native Responses for gpt-oss-120b/20b and the gemma-4 family while keeping
chat-only models (gpt-oss safeguard, nvidia, mistral, ...) on the existing
chat-completions emulation. Capability is per-model, so gpt-oss-120b routes
natively while gpt-oss-safeguard-120b does not despite sharing the gpt-oss
substring.

The wire path is a separate concern, driven by the existing
use_openai_responses_path flag rather than a model-name match: gpt-5.x and
gemma-4-* on /openai/v1, everything else (incl. gpt-oss) on /v1. The chat
config now derives its base from the same flag, fixing gemma-4
chat-completions requests that previously went to /v1 instead of /openai/v1.

Cost maps: add supported_endpoints to the gpt-oss entries (responses for the
non-safeguard variants, chat-only for safeguard) and supported_endpoints +
use_openai_responses_path to all three gemma-4 entries.


* Address review: move capability helper into bedrock_mantle package

Move the Responses capability check out of utils.py into
litellm/llms/bedrock_mantle/common_utils.py as mantle_supports_responses,
alongside its companion wire-path helper mantle_base_segment. Both are now
pure functions of (model, model_cost): the price-map mode/supported_endpoints
read replaces the get_model_info call, so the rules are unit-testable without
patching global state and the Bedrock Mantle package is self-contained.

Use str | None instead of Optional[str] on the new signatures to satisfy the
ruff UP045 strict-rule gate. Add direct unit tests for both helpers.

Fix test_register_model_restore_undoes_existing_key_overwrite: gpt-oss-120b
now legitimately supports Responses, so it can no longer be the
"None after restore" vehicle; use the chat-only safeguard variant, which
isolates the register/restore effect from the model's own capability.


---------

Co-authored-by: Sameer Kankute <[email protected]>

* fix(proxy): fail fast on non-PostgreSQL DATABASE_URL instead of hanging on startup (BerriAI#30366)

* fix(proxy): fail fast on non-PostgreSQL DATABASE_URL instead of hanging on startup

LiteLLM's Prisma datasource is pinned to provider = 'postgresql', so a sqlite:// or mysql:// DATABASE_URL can never connect.

Today that surfaces as an opaque startup stall where the port never binds, and a separate 'DB not connected' 500 on /key/generate when no DATABASE_URL is set at all leaves operators guessing what to configure.

Validate the DATABASE_URL / DIRECT_URL scheme in run_server before any Prisma call and exit with an actionable message naming the unsupported scheme.

Also reword CommonProxyErrors.db_not_connected_error to tell the operator to set DATABASE_URL to a postgresql:// connection string.

Add regression tests covering postgres acceptance and sqlite/mysql/mssql rejection.

* fix: resolve CI failures and proxy DB URL typing issue

* fix(dashscope): treat an explicit 0.0 tier cost as a real price, not missing (BerriAI#30653)

The tiered cost calculator resolved a tier's per-token cost with
`tier.get(cost_key) or tier.get(fallback_cost_key, 0)`. Because `or`
short-circuits on any falsy value, a tier that legitimately prices a
component at 0.0 (e.g. a free-cache-read tier with
cache_read_input_token_cost: 0.0, or a free-reasoning tier) is treated
as missing and silently billed at the full fallback rate
(input_cost_per_token / output_cost_per_token).

The flat-pricing path in the same module already handles this correctly
with an `is None` guard. Resolve tier costs through a small helper that
mirrors it, so 0.0 is honored at both the in-range and overflow sites.

No shipped model currently has a 0.0 tier cost, so this is a latent
defect; the fix makes the tiered path consistent with the flat path and
prevents over-charging the first time such a tier appears. Adds unit
tests covering the in-range and overflow paths, and drops an unused
import flagged by ruff in the touched test file.

* feat(proxy): show session-aggregate cost and duration in request logs (BerriAI#25708) (BerriAI#30507)

* fix(anthropic): don't leak tool 'type' into OpenAI function parameters schema (BerriAI#30618)

In the messages->chat/completions bridge, translate_anthropic_tools_to_openai
merged every non-mapped tool key into the function parameters dict. The
Anthropic tool 'type' (e.g. 'custom') thus overwrote parameters.type ('object'
-> 'custom'), and providers reject it ('custom' is not a valid JSON-Schema type).
Exclude 'type' from the passthrough. Fixes BerriAI#30557.

* fix(proxy): stop IAM-refresh engine restart from cascading reconnects (BerriAI#29176) (BerriAI#30183)

An RDS IAM token refresh recreates the Prisma client, which SIGKILLs the
running query-engine and spawns a new one. That planned kill was
indistinguishable from a crash, and three reconnect paths used two
uncoordinated locks, so a single refresh triggered a cascade of engine
kill/respawn cycles:

  1. `_safe_refresh_token` (holds `_reconnection_lock`) -> recreate -> kill old
     engine, spawn new one.
  2. The engine-death watcher sees that kill, assumes a crash, and calls
     `attempt_db_reconnect(force=True)` (a different lock,
     `_db_reconnect_lock`) -> recreate again -> kills the fresh engine.
  3. In-flight queries failing during the swap are classified as transport
     errors and trigger their own `attempt_db_reconnect` -> recreate again.

Fix coordinates planned restarts across the wrapper and the watcher:

  - PrismaWrapper records the old engine PID in `_expected_engine_deaths`
    before killing it; all four watcher death-detectors (waitpid thread,
    pidfd, already-dead probe, os.kill poll) consume that PID and skip the
    reconnect instead of treating it as a crash.
  - `recreate_prisma_client` now serializes through `_reconnection_lock` and
    bumps a monotonic `_engine_generation`. Callers pass `expected_generation`
    as an optimistic-lock token, so racing/cascading recreates collapse into a
    single restart (losers no-op). This closes the two-lock gap.
  - The direct reconnect path probes the writer with SELECT 1 before
    recreating; a healthy connection (e.g. engine already replaced by a
    refresh) skips the recreate entirely.
  - `_safe_refresh_token` coalesces: it skips when the current token still has
    more than the refresh buffer of runway, so stacked triggers (proactive
    loop + __getattr__ fallback) don't each restart the engine. An
    `on_engine_replaced` hook re-arms the watcher on the new PID.

RoutingPrismaWrapper forwards `expected_generation` and skips recreating the
reader when the writer recreate was skipped.

* feat(bedrock): support file content retrieval for batch output files (BerriAI#30595)

Implements transform_file_content_request and transform_file_content_response
in BedrockFilesConfig so GET /v1/files/{id}/content works for Bedrock batch
files. The request transform resolves the file id (direct s3:// URI or base64
unified id) to its S3 object, validates bucket and key prefix against the
server-configured bucket, and SigV4-signs an S3 GetObject using the same
credential and region resolution as the existing upload path. The credential
and region params are validated into a typed model at the boundary, so the only
untyped values left are the botocore signing primitives.

Also fixes the proxy managed-files path: CredentialLiteLLMParams now carries
s3_bucket_name (previously dropped when building deployment credentials) and
the managed-files hook passes the deployment credential snapshot when routing
afile_content, so unified-id content retrieval works with per-model bucket
config instead of only the AWS_S3_BUCKET_NAME env var.

Preserves managed-file access control: the proxy file-content endpoint now
rejects raw cloud-storage ids (s3://, gs://), which would otherwise skip the
owner/team check that only runs for unified ids and let a caller read another
tenant's batch output by its object key. Managed outputs are reachable only
through their unified file id. The afile_content "not found" error now reports
the caller's unified id rather than the resolved internal S3 URI.

Fixes BerriAI#16186, BerriAI#15563

* fix(oci): make Cohere {{trace}} judges work (tool param types + agentic tool-calling continuation) (BerriAI#30646)

* fix(oci): map Cohere tool array/object params to lowercase builtins

OCI's Cohere backend returns HTTP 500 on a tool parameter typed as a bare
"List", which is what OCI_JSON_TO_PYTHON_TYPES produced for JSON-schema
arrays. MLflow {{trace}} judges trip this: their tools (get_root_span,
get_span) take an attributes_to_fetch array. The lowercase builtins list/dict
are accepted; only the bare "List" 500s ("Dict" happens to be tolerated, but
both are lowercased for consistency).

Verified live against us-chicago-1 (cohere.command-a-03-2025 and
command-latest). Adds a unit regression on the transformed parameterDefinitions
plus a gated integration test exercising an array-param tool end to end.

* fix(oci): make Cohere agentic tool-calling continuation work

Two bugs broke the OCI Cohere tool-calling loop that MLflow {{trace}} judges
drive once a tool has been executed and its result is fed back.

Request side: litellm pulled the last user message into the top-level `message`
and emitted the tool result as a TOOL entry in chatHistory. OCI rejects that
("cannot specify message if the last entry in chat history contains tool
results"), and an empty message alone is rejected too ("message must be at least
1 token long or tool results must be specified"). OCI carries the current turn's
results in a dedicated top-level `toolResults` field. The Cohere transform now
sends an empty message, keeps the user turn in chatHistory, and puts the results
in `toolResults`, matching the langchain-oracle reference. Tool results are no
longer represented as chatHistory entries.

Response side: tool-grounded answers come back with citations carrying
`documentIds` (camelCase) and no `document_ids`, which made the required
`CohereCitation.document_ids` field fail validation and sink the whole response
parse. Those citations are never surfaced, so the field (and CohereSearchQuery's
generation_id) is now optional.

Verified live against us-chicago-1 (cohere.command-a-03-2025 and command-latest),
single and multi-round tool loops. Adds unit regressions on the transformed
request shape and on citation parsing, plus gated integration tests for the
continuation.

* feat: integrate Repelloai Argus guardrail (BerriAI#30673)

* feat(guardrails): add RepelloAI Argus guardrail integration (#1)

* feat(guardrails): add RepelloAI Argus guardrail integration

Add a new guardrail hook backed by RepelloAI Argus, with dashboard-managed
asset policies enforced via an asset_id and X-API-Key auth.

* fix(guardrails): harden RepelloAI Argus guardrail

- scan streaming responses on output (was bypassing the guardrail)
- log blocked verdicts as guardrail_intervened instead of success
- treat auth/config errors (401/403/404/422) as misconfiguration that
  always blocks, not a fail-open-able unreachable error
- default unreachable_fallback to fail_closed and read it directly;
  block on unknown/malformed verdicts so an API change can't silently
  disable enforcement
- type unreachable_fallback as a Literal, drop the duplicate config model,
  expose unreachable_fallback in the config schema, and stop leaking the
  raw provider response / exception strings to the client

* fix(guardrails): address RepelloAI Argus review feedback

- support ARGUS_API_KEY (with REPELLOAI_API_KEY fallback)
- make asset_id required in the config model
- normalize unreachable_fallback so only fail_open opens; block on 400 misconfig
- correct the shared unreachable_fallback field description

* docs(guardrails): add RepelloAI Argus docs page and dashboard listing

- add docs page covering config, env vars, modes, verdicts, failure semantics
- list RepelloAI Argus in the Guardrail Garden with provider/logo mappings
- add a regression test for the provider logo and display-name resolution

* fix(guardrails): keep RepelloAI asset_id optional in config model

A required asset_id leaked onto the shared LitellmParams (which inherits
RepelloAIGuardrailConfigModel), breaking validation for every other
guardrail. Keep it optional like sibling models; the guardrail __init__
still raises when asset_id is missing, which is the real enforcement.

* Add comment for last user turn scanning

* feat(guardrails): harden repelloai scanning

* feat(guardrails): expand repelloai scanning to include tool definitions

Add extraction of tool definitions and tool call arguments to the RepelloAI
guardrail scanning. Improves detection coverage by including function schemas
and parameters in the prompt sent to the guardrail service. Also captures
detailed error responses in logs and adds guardrail header to streaming responses.

* refactor(guardrails): fix and harden repelloai schema text extraction

- Fix duplicate text in _iter_schema_text: previously all dict values were
  re-queued onto the stack even after scalar/list keys were already extracted
  explicitly, causing names/descriptions to appear twice in the scanned prompt
- Extract schema key frozensets to module-level constants so they are not
  reconstructed on every call
- Change _iter_schema_text from @classmethod to @staticmethod (cls unused)
- Narrow _call_analyze stage param from str to Literal["prompt", "response"]
- Add HttpxResponse type annotation to _raise_for_config_error
- Add LLMResponseTypes annotation to async_post_call_success_hook response param

* fix(guardrails): resolve pyright type errors in repelloai guardrail

- Narrow async_handler.post return from Response|None to Response with
  explicit None guard before calling raise_for_status/json
- Fix list comprehension returning str|None by switching to explicit loop
  with isinstance guard so pyright tracks the narrowing
- Cast model_dump() result to Dict since hasattr does not narrow object
  type in pyright

* fix(guardrails/repello): include Responses API instructions field in prompt scan

The /v1/responses top-level `instructions` field was not included in
_extract_prompt_text, allowing a caller to bypass guardrail policy checks
by putting blocked content in `instructions` while keeping `input` benign.

* feat: add api_key to config model and read prompt from data dict

* fix(guardrails/repello): plug input_text and tool-call response bypass gaps

Responses API input content parts with type 'input_text' were silently
dropped by build_inspection_messages (which only handles type='text'),
allowing callers to send blocked content via that path without triggering
the pre-call scan. Fix: add _extract_input_text_parts to RepelloAIGuardrail
and call it when walking the Responses API input messages.

Post-call scanning skipped responses whose choices contained only tool_calls
or function_call (message.content=None), letting models put blocked output in
function arguments undetected. Fix: _extract_chat_completion_text now calls
_extract_tool_call_args_from_message on each choice message.

Also replace typing.Dict/List with builtin dict/list to clear TID251 strict
ruff violations introduced by this file.

* fix(guardrails/repello): scan Responses API function_call output arguments

Output items with type 'function_call' in a /v1/responses response were
skipped by _extract_responses_api_text; only 'message' items were walked.
A model could return blocked content in function_call.arguments undetected.
Now extract arguments from function_call output items before scanning.

* refactor(guardrails/repello): clean up typing and remove lint-any workarounds

- Replace Optional[X]/Union[X,Y] with X|None/X|Y union syntax throughout
- Use dict[str, object] instead of bare dict in all signatures
- Remove **kwargs from __init__; declare guardrail_name, event_hook, default_on explicitly
- Replace getattr(litellm_params, ...) with direct attribute access now that LitellmParams inherits RepelloAIGuardrailConfigModel
- Add _event_hook_from_mode() to convert str|list[str]|Mode to typed GuardrailEventHooks
- Use TypeAdapter.validate_json() instead of response.json() + manual dict construction
- Add _is_object_dict/_is_object_list TypeGuard helpers to narrow object types without Any
- Remove cast() workarounds and typed intermediate variables that existed only for the now-removed lint-any CI check
- Drop _AddLiteLLMCallback Protocol; budget has sufficient slack for the one reportUnknownMemberType
- Fix GuardrailConfigModel missing type arg: GuardrailConfigModel[BaseModel]

* fix(guardrails/repello): suppress LIT007 on TypeGuard helpers and add streaming scan-skip warning

- Add guard-ok suppressions to _is_object_dict and _is_object_list to satisfy the LIT007 hard-zero budget gate
- Emit verbose_proxy_logger.warning when the streaming hook finds no inspectable text after assembly, matching observability of pre/post hooks

* refactor: modifications for lint check

* feat: add Pinstripes as an OpenAI-compatible provider (BerriAI#30567)

* feat: add Pinstripes as an OpenAI-compatible provider

Pinstripes (https://pinstripes.io) is an OpenAI-compatible inference
provider serving open-source models (GLM-4.5-Air, Qwen3, DeepSeek, etc.)
with per-token pricing and no subscriptions.

Changes:
- `litellm/llms/openai_like/providers.json`: register pinstripes with
  base_url, api_key_env, and max_completion_tokens→max_tokens mapping
- `litellm/types/utils.py`: add `PINSTRIPES = "pinstripes"` to LlmProviders
- `litellm/constants.py`: add to openai_compatible_providers and
  openai_compatible_endpoints lists
- `litellm/litellm_core_utils/get_llm_provider_logic.py`: auto-detect
  provider when api_base is "https://pinstripes.io/v1"
- `provider_endpoints_support.json`: document supported endpoints
- `tests/`: 7 unit tests covering provider registration, resolution,
  URL auto-detection, api_base override, and Router config

Usage:
    import litellm
    response = litellm.completion(
        model="pinstripes/ps/glm-4.5-air",
        messages=[{"role": "user", "content": "Hello"}],
        api_key=os.environ["PINSTRIPES_API_KEY"],
    )


* fix(pinstripes): resolve Greptile P1 review comments

- Add api_base_env: PINSTRIPES_API_BASE to providers.json so env var override works
- Set responses: false in provider_endpoints_support.json — not actually wired up
- Remove docs/my-website/docs/providers/pinstripes.md — belongs in litellm-docs repo


* fix(pinstripes): add api_base_env and correct responses capability

- Add api_base_env: PINSTRIPES_API_BASE to providers.json
- Set responses: false in provider_endpoints_support.json


* fix(pinstripes): wire up Responses API — add supported_endpoints

Adds supported_endpoints: ["/v1/chat/completions", "/v1/responses"] so
JSONProviderRegistry.supports_responses_api returns true correctly,
matching what provider_endpoints_support.json advertises.


* feat(pinstripes): enable embeddings endpoint

Pinstripes serves nomic-embed-text-v1.5 and bge-m3 via /v1/embeddings.
Add /v1/embeddings to supported_endpoints and set embeddings: true.


* fix(pinstripes): use 4-space indentation in model_prices_and_context_window.json

Matches the file's existing convention. Flagged by Greptile review.


* fix(pinstripes): set a2a: false — A2A protocol not implemented

All comparable JSON-configured providers (tensormesh, parasail, empiriolabs,
libertai, neosantara) have a2a: false. Pinstripes does not implement the
Google A2A protocol, so this should be false to match.


---------

Co-authored-by: inference_provider <[email protected]>

* fix(rag): attach existing OpenAI file ids (BerriAI#30628)

* fix(rag): attach existing OpenAI file ids

* chore: use modern typing in rag ingest fix

* chore: retrigger ci

* fix(anthropic-messages): apply cache_control_injection_points on /v1/messages path (BerriAI#30341)

cache_control_injection_points was only consumed by the chat/completions
prompt-management hook; on the native Anthropic /v1/messages path it was
forwarded unused, so deployment-level cache injection was silently dropped
(cache_creation_input_tokens stayed 0 for Anthropic-native clients).

Add AnthropicCacheControlHook.apply_to_anthropic_messages_request to inject
cache_control at block level for system / tools / message locations (the only
forms /v1/messages accepts), wire it into the native anthropic_messages
handler, and pop the param so it does not leak upstream as an unknown field.
A {location: message, role: system} config is redirected to the top-level
system prompt so the same YAML works on both endpoints.

Injection respects Anthropic's 4-block cache_control limit shared across
system, tools, and messages: client-supplied markers count toward the cap and
are never overwritten, a slot is reserved per Bedrock tool_config point, and
injection stops once the budget is exhausted. Locations this path cannot
represent (tool_config) are forwarded downstream instead of being silently
consumed, mirroring get_chat_completion_prompt's remaining_points pass-through.

Built on litellm_internal_staging. Refs BerriAI#30293

* fix(proxy): release budget reservation when a request is cancelled mid-flight (BerriAI#30522)

* fix(proxy): release budget reservation on cancel when no chunk was delivered

The pre-call budget reservation increments the cross-pod spend counter by a
request's worst-case cost, then reconciles it on success (cost callback) or
error (failure hook). A client disconnect or timeout cancels the request and
surfaces as CancelledError / GeneratorExit, which neither path catches, so the
reservation leaks. Under a retry storm the leaked holds accumulate, pin the
counter above real spend, and return spurious 429 "Budget has been exceeded" to
keys whose spend is far below budget; the counter only recovers when its TTL
lapses, so the failure is intermittent and self-healing.

Release the reservation in async_streaming_data_generator (which the Anthropic
and Google SSE generators delegate to) on the (CancelledError, GeneratorExit)
path, alongside the existing max_parallel_requests release. release_budget_
reservation_on_cancel runs under asyncio.shield so it completes despite the
in-progress cancellation, is guarded by the reservation's finalized flag, and
swallows a failing release so it cannot replace the in-flight cancellation.

The refund is gated on whether a chunk reached the client. The flag is set
immediately before the yield, after the slow-path hook await: an async generator
suspends at the yield, so a GeneratorExit on disconnect after a delivered chunk
sees it True (keep the hold), while a cancellation during the slow-path await
leaves it False (refund, nothing sent). A non-streaming cancellation delivers
nothing and a completed non-streaming response is reconciled by the success
callback, so neither needs a release here.

Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>

* fix(proxy): reconcile a cancelled reservation to input cost, not zero

A streaming request cancelled before the first chunk previously reconciled its
reservation to zero and finalized it. But by the time the generator is
consuming the response the provider call was already dispatched, so the input
tokens were billed even though no chunk reached the client, and the
success/failure cost callbacks are skipped on cancellation. Refunding to zero
let a caller send an expensive request and abort pre-token to dodge the input
charge.

Compute the request's input-token cost at reservation time and reconcile the
cancelled reservation to it instead of zero. The worst-case output portion of
the reservation is still released (so a legitimate mid-flight cancellation no
longer pins the counter and 429s the key), while the input the provider already
processed is charged.

---------

Co-authored-by: Bytechoreographer <[email protected]>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>

* fix(caching): encode object name in GCS cache GET path (BerriAI#30378)

GCS cache reads always missed when gcs_path was set. The GET methods
interpolated the object name directly into the URL path, while the GCS
JSON API requires it to be URL-encoded (a "/" must be sent as %2F).

With gcs_path configured the object name is "<prefix>/<sha256>", so the
raw slash produced a malformed object path and GCS returned 404. httpx
does not raise on 4xx, so the status_code == 200 check fell through and
get/async_get returned None, silently missing on every read. Without
gcs_path the key has no slash, which is why this went unnoticed.

Wrap the object name with urllib.parse.quote(..., safe="") in get_cache
and async_get_cache. Apply the same encoding to the name= query
parameter in set_cache and async_set_cache so the key written matches
the key read back.

Adds regression tests asserting the GET path and SET query are encoded
(%2F) when gcs_path is set, for both sync and async paths; these fail on
the unpatched code.

Fixes BerriAI#30377

* chore: add soniox stt-async-v5 model (BerriAI#30672)

* fix(proxy): include model group aliases in v1 model info (BerriAI#30626)

* Include model group aliases in v1 model info

* Fix model info alias implementation

* removed extra blank line

* chore: rerun CI

* fix(lint): remove redundant noqa directive in proxy_cli.py

* fix: address greptile review - restore bedrock_mantle auth symbols, guard OCI empty message list, validate DIRECT_URL scheme

* Revert "fix: address greptile review - restore bedrock_mantle auth symbols, guard OCI empty message list, validate DIRECT_URL scheme"

This reverts commit 52c7a07.

* Revert "fix(anthropic-messages): apply cache_control_injection_points on /v1/messages path (BerriAI#30341)"

This reverts commit c9e8a17.

* Revert "fix(proxy): stop IAM-refresh engine restart from cascading reconnects (BerriAI#29176) (BerriAI#30183)"

This reverts commit 85828da.

* fix(proxy): stop IAM-refresh engine restart from cascading reconnects (BerriAI#29176) (BerriAI#30183)

An RDS IAM token refresh recreates the Prisma client, which SIGKILLs the
running query-engine and spawns a new one. That planned kill was
indistinguishable from a crash, and three reconnect paths used two
uncoordinated locks, so a single refresh triggered a cascade of engine
kill/respawn cycles:

  1. `_safe_refresh_token` (holds `_reconnection_lock`) -> recreate -> kill old
     engine, spawn new one.
  2. The engine-death watcher sees that kill, assumes a crash, and calls
     `attempt_db_reconnect(force=True)` (a different lock,
     `_db_reconnect_lock`) -> recreate again -> kills the fresh engine.
  3. In-flight queries failing during the swap are classified as transport
     errors and trigger their own `attempt_db_reconnect` -> recreate again.

Fix coordinates planned restarts across the wrapper and the watcher:

  - PrismaWrapper records the old engine PID in `_expected_engine_deaths`
    before killing it; all four watcher death-detectors (waitpid thread,
    pidfd, already-dead probe, os.kill poll) consume that PID and skip the
    reconnect instead of treating it as a crash.
  - `recreate_prisma_client` now serializes through `_reconnection_lock` and
    bumps a monotonic `_engine_generation`. Callers pass `expected_generation`
    as an optimistic-lock token, so racing/cascading recreates collapse into a
    single restart (losers no-op). This closes the two-lock gap.
  - The direct reconnect path probes the writer with SELECT 1 before
    recreating; a healthy connection (e.g. engine already replaced by a
    refresh) skips the recreate entirely.
  - `_safe_refresh_token` coalesces: it skips when the current token still has
    more than the refresh buffer of runway, so stacked triggers (proactive
    loop + __getattr__ fallback) don't each restart the engine. An
    `on_engine_replaced` hook re-arms the watcher on the new PID.

RoutingPrismaWrapper forwards `expected_generation` and skips recreating the
reader when the writer recreate was skipped.

* fix(lint): modernize type annotations in IAM-refresh prisma client files (UP006/UP045)

* Revert "feat(proxy): show session-aggregate cost and duration in request logs (BerriAI#25708) (BerriAI#30507)"

This reverts commit f530b22.

* Revert "fix(dashscope): treat an explicit 0.0 tier cost as a real price, not missing (BerriAI#30653)"

This reverts commit 4f58bd0.

* Revert "fix(oci): make Cohere {{trace}} judges work (tool param types + agentic tool-calling continuation) (BerriAI#30646)"

This reverts commit 50f34e0.

* Revert "fix(proxy): fail fast on non-PostgreSQL DATABASE_URL instead of hanging on startup (BerriAI#30366)"

This reverts commit 0544eed.

* fix(bedrock_mantle): restore BedrockMantleAuthMixin and constants removed by routing rewrite

* fix(key management): restore exact /key/list user_id & key_alias matching by default (BerriAI#30593)

Before substring search was added (commit 33bd570), /key/list matched user_id
and key_alias exactly. That change made admin-authenticated calls substring-match
by default, breaking the prior contract: a caller passing an exact user_id as an
access filter (e.g. an integration scoping to one user with an admin key) then
received other users' keys -- user_id="alice" also returned "alice2",
"alice-test", etc. This is a cross-user key disclosure.

Make substring matching opt-in via a new admin-only substring_matching=true query
param; default to exact, restoring the prior behavior. The dashboard search box
(keyListCall) passes the flag so partial search still works. Non-admins remain
exact and scoped to their own keys.

Updates the proxy-behavior key_alias test to opt in and adds an exact-by-default
guard; adds list_keys unit coverage for the opt-in gate.

---------

Co-authored-by: perseus <[email protected]>
Co-authored-by: Hannah Smith <[email protected]>
Co-authored-by: Charlie Patterson <[email protected]>
Co-authored-by: Matthew Lapointe <[email protected]>
Co-authored-by: KRISH SONI <[email protected]>
Co-authored-by: Yash Raj Pandey <[email protected]>
Co-authored-by: Nitish Agarwal <[email protected]>
Co-authored-by: hcl <[email protected]>
Co-authored-by: tushar8408 <[email protected]>
Co-authored-by: AD Mohanraj <[email protected]>
Co-authored-by: Fede Kamelhar <[email protected]>
Co-authored-by: Lavish Bansal <[email protected]>
Co-authored-by: max-amos <[email protected]>
Co-authored-by: inference_provider <[email protected]>
Co-authored-by: NK <[email protected]>
Co-authored-by: 安妮的心动录 <[email protected]>
Co-authored-by: Rick <[email protected]>
Co-authored-by: Bytechoreographer <[email protected]>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: Burak Ömür <[email protected]>
Co-authored-by: Dan Lemon <[email protected]>
Co-authored-by: Vanika Dangi <[email protected]>
Co-authored-by: Jay Gowdy <[email protected]>