* fix(proxy): evict the cached user on every proxy for every /user/update and /user/bulk_update
Previously the cached user row was only evicted and broadcast when the
update touched max_budget, model_max_budget, tpm_limit, rpm_limit or
metadata, so a models or user_role change kept being served by peer
proxies until the cache TTL expired
* test(proxy): cover user cache eviction past the first bulk invalidation batch
* fix(config): treat blank numeric env values as unset instead of crashing at import
A numeric knob exported as an empty string, which is what a Helm value: "" or an
emptied .env line produces, raised ValueError inside litellm/constants.py and the
proxy crash-looped before serving traffic. Every int(os.getenv(...)) and
float(os.getenv(...)) in constants.py now goes through get_env_int / get_env_float,
which treat blank as unset and fall back to the default with a warning on garbage.
* fix(guardrails): treat blank litellm_params values as unset so the guardrail still loads
A guardrail whose litellm_params read os.environ/X resolves X to "" when the
variable is exported but empty. Pydantic rejected "" for bool and typed fields
such as default_on and fail_on_error, the boot log reported the guardrail as
skipped and the proxy came up without it. A before-validator on BaseLitellmParams
now drops blank string values so each field takes its default, and the
default_on shortcut in LitellmParams treats a blank value as False.
* fix(config): warn once about an unparsable numeric env value so logging cannot recurse
The LiteLLM loggers' filter reads MAX_STRING_LENGTH_STDOUT_LOG and MAX_BASE64_LENGTH_STDOUT_LOG through get_env_int on every record. Warning about a garbage value from inside that read re-entered the filter, so every log call raised RecursionError. The value is now marked as reported before the warning is logged, which also stops per-record readers from repeating it. get_env_int_or_none and the new get_env_float_or_none share the same parser
* fix(config): treat blank values as unset for the remaining import-time numeric knobs
LITELLM_MAX_STREAMING_DURATION_SECONDS, REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES, a whitespace CLI_JWT_EXPIRATION_HOURS, LITELLM_CONFIG_PARAM_CACHE_TTL_SECONDS, the agent health check timeouts and the Prisma reconnect knobs still crashed the proxy at boot when blank. A blank or unparsable REQUEST_TIMEOUT no longer counts as explicitly set, so chat completion() keeps its 600s fallback
* fix(guardrails): map blank litellm_params to the field default so PATCH can still clear a value
Dropping blank keys before validation made PATCH /guardrails/{id} with api_base "" a silent no-op. Blank values of optional fields now resolve to the field default and stay in the set fields, so the PATCH clears the stored value. Required fields and unknown keys keep the value as given
* test: cover proxy numeric knobs in-process and expect no warning for a blank Straiker api_version
The three proxy import-time numeric knobs move into litellm/constants.py so the existing in-process blank-env test covers them, replacing the subprocess test. A blank api_version now means unset, so the Straiker test asserts the key-prefix version with no warning.
* fix(guardrails): keep LitellmParams kwargs typed without adding argument-type errors
---------
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
* fix(vllm): write spend log rows for /vllm passthrough calls to router models
The vLLM passthrough config never built a logged response for relayed calls, so stream and non-stream calls through /vllm with a router model wrote no spend row and never charged the key. vLLM now shares the OpenAI-shaped relay logging that Azure already had, moved into an openai_like helper both configs call.
* ci: run the vllm passthrough unit tests in the llms-providers shard
* fix(proxy): log /vllm text completions and ask router models for stream usage
A chat stream relayed to a router model without stream_options now gets
include_usage added on the way out, and the usage chunk vLLM appends is
removed before the client sees it, so the spend row carries vLLM's own
token counts instead of a local estimate. /vllm/completions calls now
write a row too, stream and non-stream.
* fix(vllm): keep usage, fingerprints, and disconnect rows correct on /vllm streams
Inject and strip stream usage on /vllm/completions too, hold every finish
frame for the fingerprint when n > 1, pass a forced-usage server's stream
through untouched, stop turning continuous_usage_stats alone into per-chunk
usage, close the upstream when the client hangs up so the row is written,
and assemble text choices with one sort instead of a scan per choice
* fix(vllm): keep n>1 stream order when holding finish chunks for the fingerprint
A held finish chunk now goes out as soon as any other chunk arrives, so a
choice that finishes while another still streams keeps its place. Only the
finish chunks right before the usage chunk get vLLM's system_fingerprint.
* fix(vllm): keep caller stream_options and log both success handlers
Forward a stream_options the proxy cannot read, or a non-boolean
include_usage, to vLLM as the caller sent it. Only a false
always_include_stream_usage changes the /vllm relay, so clients get the
same stream bytes under every setting.
Record the passthrough origin when logging starts so the sync and async
success handlers both parse the relayed body, hand callbacks a parsed
text completion, and read relayed messages and prompt separately so a
stray field no longer drops the local count.
Add the audit's integration tests for the relay, the settings, and the
Azure spend row.
* test(vllm): expect the caller's stream under always_include_stream_usage true
* fix(vllm): relay long SSE frames in linear time
* test(vllm): time the relay's usage and finish_reason prefilter on one large frame
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(streaming): close sync streams so the connection goes back to the pool
CustomStreamWrapper gets a sync close() (plus context manager support) that
closes the wrapped provider stream, and both the sync and async wrappers close
the provider stream when the stream reaches its end. The Anthropic stream
iterator keeps the httpx response it reads so close()/aclose() can release the
socket after message_stop instead of leaving it checked out of the pool
Co-authored-by: Blair Jordan <blair.j.jordan@gmail.com>
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(streaming): keep the basedpyright ceiling flat in CustomStreamWrapper.close()
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(streaming): close the response on the shared sync stream path too
BaseModelResponseIterator gets a sync close() next to its aclose(), and the
sync branch of BaseLLMHTTPHandler hands it the httpx response the way the
async branch already does, so providers built on it release the socket on
close() instead of waiting for garbage collection
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(streaming): unit tests for CustomStreamWrapper.close() and the shared iterator close()
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(streaming): close the provider stream before re-raising a mid-stream error
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Blair Jordan <blair.j.jordan@gmail.com>
* fix(s3_v2): bound each upload attempt, add jitter, retry on 429 and 503
Each S3 PUT now carries a per-attempt httpx timeout read from
S3_UPLOAD_ATTEMPT_TIMEOUT_SECONDS (default 30s, clamped to 1..3600) instead
of inheriting the handler's unbounded default. Retries back off with jitter
instead of a fixed 1s then 2s, 429 joins 403, 500 and 503 in the in-call
retry set, and transport errors or litellm.Timeout on an attempt are retried
in the same call instead of ending it. Each flush logs one warning per batch
of dropped uploads that names how many were terminal and how many aged out.
* fix(s3_v2): retry uploads in a bounded loop and align tests with the new retry contract
The async and sync retry helpers called themselves, which the recursive-function check rejects. They are now a bounded loop over the attempt count with the same outcome.
The 429 integration test now expects in-call jittered backoff, and the aged-out test matches the new drop warning text. The sync transport test drives the real HTTPHandler so a dropped per-attempt timeout or a missed litellm.Timeout fails it, and the drop-reason test uses unequal counts so swapped labels fail.
---------
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
* fix(vertex_ai): unwrap tool-emulated json_schema envelopes and send output_config.format for Claude
Vertex AI Claude deployments that emulate response_format through a forced
tool sometimes nest the schema object under an extra key such as
`parameters` or `input`, and that envelope reached message.content with a
200. The converter now unwraps exactly one nested object that matches the
schema, leaving a payload that is itself schema-shaped alone.
Native structured-output rows sent the deprecated top-level `output_format`,
which Vertex rejects with a 400. The Vertex request now carries it as
`output_config.format`, with an explicit caller value winning.
* fix(anthropic): unwrap tool-emulated json_schema envelopes on streams and tool-name wrappers
* test(anthropic): expect the json tool description through a shared constant
* fix(anthropic): drop the json tool description that makes sonnet-5 omit required keys
* fix(anthropic): keep schema-accepted answers whole and decide held streams at the second key
An answer the json_schema accepts is returned as is before any envelope rule runs, the tool-name
wrapper is unwrapped only when the schema rejects it, a malformed schema turns the unwrap off, a
streamed answer whose first key is a schema property named name or tool_name waits for its second
key before streaming, the held fragments go through a StringIO, and a prefix that stays undecided
past 4 KB is held to the end
* test(anthropic): keep the stream family cells to providers whose stream iterator gets the schema
* fix(vertex_ai): unwrap claude json_schema answers nested under up to three wrapper keys
A tool-emulated json_schema answer was unwrapped only when the real object sat one key down. In live QA, 1 of 58 answers (/v1/responses, claude-sonnet-5) came back as {"parameter": {"value": {...}}} and reached the client still wrapped
The unwrap now follows single-key wrappers up to three levels down and stops at the first object that has a key the schema declares. That object is returned only when it has every required key. A payload wrapped deeper than three levels, a level with more than one key, and a level whose key is one of the schema's own property names all come back exactly as they arrived
The streamed path needs no change because the buffered answer goes through the same unwrap when the tool block closes, and a test now pins that both paths agree
* fix(bedrock): unwrap streamed claude json_schema answers on claude platform and mantle
A streamed json_schema answer on bedrock/claude_platform and Bedrock Mantle Claude reached the client as tool_calls deltas holding the json_tool_call wrapper, with finish_reason tool_calls and no content. The shared HTTP handler built the async stream iterator without json_mode, and neither stream path passed the response_format schema
The handler now asks the provider config for the iterator through get_model_response_iterator_for_request, which carries json_mode and the request's optional params on both the sync and async paths. Claude Platform and Mantle override it to hand the json tool schema to the Anthropic stream iterator, so the streamed answer is the bare schema object with finish_reason stop, the same as the non-streamed one
Databricks is the only other provider on this handler whose iterator reads json_mode, so its async Claude json mode stream now returns the answer as content like its sync stream already did
The stream family integration test covers claude_platform_unmapped and bedrock_mantle again
* fix(anthropic): unwrap json tool envelopes by structure instead of schema validation
The unwrap now reads only the schema's top-level property names. A lone
undeclared key holding an object, alone or beside a name equal to the json
tool's own name, is the envelope, and the unwrap repeats while the inner
object shares no key with the schema. jsonschema and referencing are no
longer imported on the request path. The stream holds only an answer that
opens with the tool's name or with an undeclared key holding an object, and a
schema without top-level properties streams as it arrives.
* fix(llms): keep other providers' async streams as they were
The request-aware iterator hook passed json_mode on async streams to every provider on the shared HTTP handler, which changed Databricks and any other provider whose iterator reads it. The default hook now makes the same two calls the handler made before, so only Claude Platform and Bedrock Mantle change, and the Databricks test that pinned the wider change is removed
* fix(anthropic): walk json tool envelopes iteratively
* test(llms): pin which stream iterators get json_mode from the shared handler
A provider config that does not override the request-aware iterator hook still gets json_mode on a sync stream and still does not get it on an async stream, the same as before the hook existed
* fix(anthropic): keep json tool arguments too deep to decode instead of raising
* fix(anthropic): unwrap json tool envelopes beside undeclared string labels
* fix(anthropic): keep json answers whole on schemas that allow extra keys and stream held text as sent
A schema that allows undeclared keys and requires none now keeps an undeclared wrapper key on both the stream and non-stream paths. When no envelope is found, the held stream text is now sent exactly as the model wrote it. On Vertex /v1/messages, output_format now wins over output_config.format when both are set, which matches the Anthropic adapter.
* fix(anthropic): join a json_schema answer the model splits into one tool block per field
With tool emulation the model sometimes answers with one json_tool_call block per schema field, each naming the field and holding its value. The non-stream path returned only the first block and the stream sent every block's text back to back, so the client got one field or invalid JSON. Both paths now build one schema object from those blocks, and any other set of blocks keeps today's output
* refactor(anthropic): set the streamed finish reason in one expression
* test(anthropic): cover label envelopes, dynamic-key schemas, as-written held streams and output_format precedence
* fix(anthropic): unwrap a streamed values wrapper like the non-stream answer
* fix(anthropic): join streamed split answers on schemas without required keys, keep client tool calls and length on json_schema streams
* fix(anthropic): join streamed split answers that carry text labels on schemas without required keys
* test(bedrock): script the wrapped json_schema stream with the wrapper Bedrock Mantle sends
* test(bedrock): script the wrapped json_schema stream with the wrapper Bedrock Mantle sent raw
* fix(anthropic): join a split json_schema answer whose fields all hold the same string
* fix(anthropic): join a split json_schema answer sent as one partial object per field
* fix(anthropic): join field blocks after an empty streamed answer without a leading comma
* fix(proxy): check previous_response_id ownership on /v1/responses/compact
* test(proxy): cover the unmanaged response id escape hatches on every response id route
* test(proxy): audit compact previous_response_id ownership
Add integration cells for the native /v1/responses/compact routes covering owner,
admin, cross-tenant, unmanaged, malformed, duplicated and retained ids, provider
errors, the response cache, id-security settings, an upstream outage, a held
upstream, a worker kill and a proxy restart. Unskip the provider wire cell whose
bug this branch fixes.
* test(proxy): cover the no signing key path on every response id route
* feat(proxy): add deployment warning endpoints with instance-wide ignore and snooze
GET /health/deployment_warnings returns the active deployment warnings
(env credential login, license expiry, no Redis on multiple workers,
detailed debug logging) with a severity and ignore state, for proxy admins
and proxy admin viewers. PATCH /health/deployment_warnings/{warning_id}
lets a proxy admin ignore a warning forever or snooze it for up to 30 days,
or restore it. Ignores live in one LiteLLM_UISettings row, apply to every
admin, and are written to the config audit log. Expired snoozes drop out
on read, and LITELLM_DISABLE_NO_REDIS_WARNING is reported as an env var
ignore. /health/readiness/details is unchanged
* fix(proxy): keep readiness Redis short-circuit ahead of the env lookup and harden ignore storage
Readiness details again answers from Redis before reading
LITELLM_DISABLE_NO_REDIS_WARNING, so proxies with Redis and a secret
manager do not fetch it on every poll. Stored ignores require timezone
aware datetimes and are read from the writer before a rewrite
* fix(proxy): keep deployment warning ignores readable in the config audit log
The audit payload keyed ignores by warning id, so the secret matcher
redacted the env_credential_login entry and the audit row lost who
snoozed it and until when. Log a list of id and ignore entries instead
* fix(anthropic): forward service_tier on chat completions to Anthropic and Azure
Chat completions calls to Claude with service_tier were rejected client-side
(or dropped with drop_params), so the tier never reached Anthropic. Map auto
and standard_only through, send OpenAI's default as standard_only, and raise
a 400 with a drop_params hint for values Anthropic has no equivalent for.
Bedrock Invoke and Vertex reject the field upstream, so they keep rejecting it.
* fix(anthropic): keep the caller's service_tier value out of the drop warning log
* feat(dashscope): add Wan 3.0, Wan 2.7 and HappyHorse video generation via /v1/videos
DashScopeVideoConfig maps litellm's OpenAI Videos API onto Alibaba Model
Studio's async task API: create hits
/api/v1/services/aigc/video-generation/video-synthesis and status and content
poll /api/v1/tasks/{task_id}. It is registered for dashscope and for the
qwencloud and qwen_ai_platform brand aliases, following the same
get_dashscope_family_*_config pattern image generation uses. Unlike the
Sora-shaped providers the request body is nested, so prompt and media go under
input and everything else goes under parameters. X-DashScope-Async is always
sent, because DashScope rejects the call outright without it
OpenAI params translate to DashScope's contract. seconds becomes duration, and
size yields both a ratio and a named resolution tier (1280x720 gives 16:9 plus
720P), since sending only the ratio leaves the request on the default 1080P and
silently bills at that rate. input_reference becomes a single media entry,
base64 encoded into a data URI for file inputs. Which media type a model takes
(first_frame or, on the reference-to-video models, reference_image) is read
from provider_specific_entry.dashscope_video_reference_input via
get_model_info rather than hardcoded; models without it predate the media
array and get the flat img_url field. A caller-supplied media array always
wins, as that is the only way to express multi-role combinations
The create call is the only billed call, so it bills what DashScope will
deliver: the requested duration and tier, or the documented 5 second and 1080P
defaults when they are omitted. Smart duration (duration -1) lets the service
pick the length after that call, so it is rejected rather than recorded at
zero, with a 400. Task creation and task lookup both report failures as a 200 carrying
top-level code and message with no output, so that shape is raised instead of
surfacing as a queued video with an empty id. Status responses re-encode the
deployment model from the polled id, so follow-up polls and downloads route to
the same deployment. Timestamps parse as UTC+8. DashScope publishes no list,
delete or remix endpoint, so those raise rather than guess at a URL
The end-to-end tests drive the real avideo_generation() entrypoint through an
httpx MockTransport and assert on the JSON body httpx actually encoded. That is
what catches a mapper that drops input_reference before the request is built,
or a body fragment json cannot serialize, neither of which a test calling the
transform directly would see
Both model_prices maps gain wan3.0-video, wan3.0-video-prime, wan2.7-t2v,
wan2.7-i2v, wan2.7-r2v and the HappyHorse 1.0 and 1.1 t2v, i2v and r2v models,
for dashscope and the qwencloud and qwen_ai_platform aliases, priced per output
second by resolution tier from Model Studio's USD rate cards. dashscope and
qwen_ai_platform default to the Beijing host and use its rate card; qwencloud
defaults to the international host and uses Singapore's. The three
*_API_BASE_VIDEO overrides are documented in BerriAI/litellm-docs#1920
Co-authored-by: AaronHowell <237895480@qq.com>
* fix(dashscope): fail loudly on video responses that cannot be read
Addresses the review on the video generation PR. A body that is not the
documented task shape, an output block missing task_id or task_status, an
undocumented task_status and a missing output all raise DashScopeVideoError
now, instead of defaulting into a queued video with an empty id that callers
poll forever. A JSONDecodeError from a gateway response no longer escapes as
a generic connection error.
task_id and task_status are required on _TaskOutput, and a missing output
raises even when the body carries no code or message.
Download failures move into the 4xx range, since a 5xx has the proxy retry
them: 409 while the task is still running, 400 for a task that failed, 404
for an id that expired. The status a provider reported is kept when it
reported one, so an in-body failure that arrives on a 200 is billed as the
400 it is rather than as a retryable connection error.
characters and image are only dropped when drop_params is set; the params
that are rebuilt into the body or consumed by the caller (model, prompt,
user, extra_*) stay silently skipped, since the proxy stamps user from the
caller's end-user header.
Co-authored-by: AaronHowell <237895480@qq.com>
* fix(dashscope): clear the lint gates after merging main
* fix(dashscope): accept file tuples and paths as video input_reference
video_generation() types input_reference as FileTypes, which includes
(filename, content[, content_type[, headers]]) tuples and pathlib paths,
but the DashScope encoder only read bytes and file objects, so those
image-to-video calls raised before reaching DashScope. Bare strings stay
URLs and are never opened as paths.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(dashscope): snap size to a supported ratio and read file tuples and paths
* test(dashscope): assert whole mapped values and bill against hand-written rates
The tier pricing test read its expected rate from the same map the cost
calculator uses, so a wrong rate passed. Billing is now checked against
fixed rates driven through the status response usage, while the map test
only asserts every supported tier has its own rate. Size mapping tests
compare the full mapped dict, and the single-test wiring class is a
plain function.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(videos): download content through the caller's client and stop retrying dead tasks
Two Cursor Bugbot findings on the DashScope video PR.
The DashScope artifact lives on OSS, so transform_video_content_response makes a
second request. It built its own cached client, discarding an injected client and
its ssl_verify. Thread the client through the content response the way
transform_video_status_retrieve_response already does, and use it in the three
providers that fetch from a second URL (DashScope, RunwayML, fal.ai). The
remaining providers accept it and read the response body, matching how they
already accept an unused client on status retrieve.
A FAILED or CANCELED task reported without a code or message fell past the
in-flight check into the 502 branch, so the proxy retried a task that can never
produce a video. Raise 400 for a terminal status instead.
* fix(dashscope): answer 404, not 409, when a Wan video is downloaded before it finishes
A download that lands while the task is still PENDING or RUNNING answered 409. That
reads as a client error, which was the intent, but litellm's retry predicate counts 409
as retryable, so the proxy polled DashScope twice more before giving up and then counted
the caller's own early download against the model's error rate
404 is what the OpenAI videos API documents for content that is not ready, and it is the
one status in that family the retry predicate leaves alone. The rest of the mapping is
unchanged: 400 for a task the provider reported as failed, 404 for an expired task id,
502 when a succeeded task carries no artifact
The download tests now drive avideo_content and assert the caller sees NotFoundError
rather than an internal server error
* fix(dashscope): route a video poll back to the model group that created the task
The create response encoded the upstream model name into the returned id. Both the
dashscope and qwen_ai_platform prefixes serve the same model names from their own hosts,
so a proxy with a deployment per region had deployments that only the model group told
apart. Encoding the bare model name resolved a poll to whichever deployment the router
lists first, which sent it to the other region's host with that region's key and a task
id that region never issued. That is the reroute the earlier proof records as the
dashscope and qwen_ai_platform poll ids coming back tagged qwencloud
The id now carries the model group the create was routed to, which is what the proxy
resolves a poll back to, and falls back to the model name for a direct SDK call that has
no router
The regression test drives a real router with one deployment per region, under a
different prefix and under the same one, and asserts the poll carries the key of the
region that created the task
* fix(dashscope): read the routed model group from litellm_metadata
The router stamps the model group it picked into litellm_metadata. metadata only
carries a copy when the caller sent no metadata of its own, and every proxy request
sends some, so a proxied create still encoded the bare model name and the poll
resolved to whichever region's deployment the router lists first
The regression test now also creates the video with caller metadata, which fails
without this change
* fix(dashscope): reject unbillable video requests and keep fal and runway on their own clients
* fix(dashscope): show the caller why a video task is gone or not ready instead of the bare upstream body
* fix(dashscope): check video request values before the billed create and honor per-call drop_params
Type check documented DashScope parameters so a bad seed answers 400 instead of a billed failed task,
pass the call's and deployment's drop_params into the video mapping, answer 400 for an expired task id so
the deployment is not cooled down, read DASHSCOPE_API_BASE for video, keep polling SUSPENDED tasks, and
refuse to map size onto models litellm has no price for.
* fix(dashscope): carry the requested model name in video ids so alias and wildcard keys can poll
A key scoped to an alias or to a name a wildcard serves could create a
video and then got a 403 on the poll and the download, because the id
carried the group that served the call (wan3, qwencloud/*) and the key
was never given that name. The id now carries the name the router was
asked for on that hop, and the router resolves an alias, routing group
or wildcard name in an id back to itself when no deployment name or
provider model matches it first
* fix(dashscope): keep a routing group's video polls on the member that created the task
A routing group spans several model groups, so an id carrying the group's name
load-balanced its polls, and some left for another region's host and key with a
task id that region never issued. The id now carries the member that served the
task when the request named a routing group, and keeps the requested name for a
model group, an alias, or a wildcard match.
* fix(dashscope): carry the requested and served model in video ids so group-scoped keys poll in the creating region
* fix(proxy): pin a video's served group only after key and team aliases resolve
* test(integration): audit DashScope video create, poll, download, routing and chaos paths
* fix(proxy): keep catch-all video polls on their provider and count served polls against the requested group
* test(routing): expect a forged served group to be dropped from the polled video id
* test(integration): cover rate-limited routing group polls and catch-all polls of DashScope videos
---------
Co-authored-by: AaronHowell <237895480@qq.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
* fix(proxy): build configured logging callbacks at boot so a broken callback fails startup instead of the first request
Startup already built every configured callbacks, success_callback and
failure_callback entry, but through the per-request helper that swallows
every construction error. A callback whose SDK could not be imported (for
example langfuse outside the supported version range) stayed a string, so
the first request on each worker rebuilt it inside function_setup and
returned a 500.
Startup now builds them with a variant that re-raises ImportError naming
the callback, which fails boot. Other construction errors keep the old
non-blocking behavior, and callbacks added at runtime keep the lenient path.
* refactor(proxy): build the configured callback replacements in one pass
Collect the startup-built callbacks with a generator and a frozen mapping
instead of a mutable dict filled in a loop, and drop a docstring that
restated the function body
---------
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
* fix(anthropic): check Vertex and Bedrock forced-tool support on the real model
The Vertex and Bedrock invoke Claude transforms swapped the model for a
claude-3-sonnet stand-in before calling the shared Anthropic mapping so
response_format would take the tool-based path. Every model-keyed check
in that mapping then ran against the stand-in, so the synthetic json
tool was forced on models whose map entry says forced tool use is
unsupported and the request 400ed upstream. Bedrock patched the one
symptom with a backstop, Vertex had nothing.
The shared mapping now asks a uses_native_structured_output hook for the
structured-output decision and the two overrides answer it with their
provider-scoped lookup, so the real model reaches every check and the
stand-in, the restore dance and the Bedrock backstop go away.
* fix(anthropic): skip non-string metadata user ids instead of raising
_valid_user_id ran re.match straight on litellm_params metadata.user_id,
so an integer id raised TypeError and the proxy answered 500. The user
param path already skips non-strings, so the validator now does the
same for any caller: a non-string id is simply not forwarded.
* fix(utils): drop speed on every provider without fast mode
speed rides into get_optional_params as a provider-specific kwarg, so the
only gate for it lived in the Anthropic transform and Bedrock converse,
OpenAI, Azure and the extra_body legs forwarded it to a 400. The gate now
runs once in get_optional_params before provider-specific kwargs are
copied, so every provider drops it with drop_params or rejects it with a
clean 400, and a direct Anthropic fast-mode model keeps it.
* fix(utils): gate speed only under drop_params and keep it for litellm_proxy
Raising on speed when drop_params is off would turn requests that tolerant OpenAI-compatible backends accept into 400s, and dropping it for litellm_proxy would disable fast mode on a downstream proxy that supports it
* test(anthropic): derive the forced-tool regression model from the cost map and drop new docstrings
* test(utils): pin that speed is untouched without drop_params and annotate new test locals
* test(integration): lock the Vertex and Bedrock invoke json tool to the real model's forced-tool support
Vertex sends tool_choice only when the model accepts forced tools, reasoning_effort maps the same with and without response_format, and Bedrock invoke keeps sending the json tool unforced
* feat(ui): add decisions and classifier providers to the Add Model form
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(cost): use literal upstream decisions paths
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix: drop duplicate TYPESAFE provider fields entry superseded by main's TypeSafe
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(ui): drop stray blank line in provider_info_helpers test
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): type the OSS decisions provider fields test
* fix(decisions): keep Laya and Bespoke env keys on their configured server
Route laya and bespoke Decisions deployments through the same oss_connection
and validate_oss_model rules the pass-through and the classifier router use,
so LAYA_API_KEY and BESPOKE_API_KEY only go to LAYA_API_BASE and BESPOKE_API_BASE,
an api_base with credentials, a query, or a non-HTTP scheme answers 400, and
an unknown checkpoint name answers 400 before any upstream call
* fix(decisions): send the Laya and Bespoke env key to a form-added model on the env server
A deployment added through Add Model always carries an API base, so the
env key only reached deployments with no base. The env key now goes to a
deployment whose base matches LAYA_API_BASE or BESPOKE_API_BASE, an empty
deployment key counts as no key, and the Bespoke placeholder is the name
Ollama serves.
* test(decisions): cover the env-base classifier case in the complexity router test
* fix(decisions): match the env server with or without /v1 and point Ollama users at bespoke/nimble
* fix(decisions): call /v1/systemone once from classifiers and pass-through on a /v1 base
* fix(decisions): strip /v1 from the API base path only, so a host named v1 still works
---------
Co-authored-by: kerry <kerry@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* feat(mcp): separate ID-JAG leg-2 client identity, support public resource clients
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(mcp): format ID-JAG credential changes
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(mcp): satisfy lint gates and regenerate API snapshot for ID-JAG resource client
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(mcp): require a confidential ID-JAG resource client, drop none auth method
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(mcp): drop formatting churn from ID-JAG resource client tests
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci: retrigger after flaky test_mcp_tool_search timeout
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci: rerun against main after duplicate gemini-4-argon price keys were fixed
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(anthropic): generate auto mode safeguard_results with a classifier model on the /v1/messages chat bridge
* fix(anthropic): check caller limits for the safeguards classifier, mark truncated tool calls, and keep pre-compaction context
* fix(anthropic): log classifier spend under the caller's key hash and read list-shaped classifier replies
* feat(anthropic): generate safeguard_results on the Responses bridge and native non-Claude /v1/messages providers
* fix(messages): keep safeguards requests on Python under the Rust opt-in
* fix(anthropic): keep non-ASCII text unescaped in the safeguards classifier prompt
* test(anthropic): build the text-parts classifier reply without openai's construct path
Message.model_construct goes through openai's construct_type, which caches
discriminated unions in a weak-key map. On Python 3.12 a PEP 604 union cannot be
weak-referenced, so the fixture raised inside the fake classifier and the test
read an unavailable verdict. model_copy sets the list content without that path.
* fix(anthropic): scan only new bytes for SSE frame ends on safeguarded native streams
* fix(anthropic): answer safeguards on web search short circuits and widen the classifier reply cap
* fix(anthropic): answer unsupported when the proxy serves no deployment for the safeguards classifier
* fix(anthropic): keep a backend's stream verdicts and join split SSE data lines before stamping
* fix(anthropic): map only the router's no-healthy-deployments refusal to unsupported
* fix(anthropic): answer unavailable for the turn on a transient caller denial and let the classifier deployment's timeout apply
* fix(anthropic): keep the user's whole opening message in the classifier transcript past its budget
* fix(anthropic): run the proxy's own access checks for the safeguards classifier call
The classifier and compaction sub-calls now pass through the same model access
checks as the parent request (key, team, team member, user, project, end user,
managed agent grants), with budgets left to the per-model budget check so
nothing is reserved. They also carry the parent's logging params and headers,
so a redaction header or per-request callback params apply to their own rows.
Only per-model RPM limits count against a sub-call now, since a key-wide or
team-wide RPM window is already spent by the parent request. The MCP
auto-execute loop classifies only its final response, a chat-bridge stream
without safeguards yields frames directly, and a classifier provider failure
logs one warning line instead of a traceback
* fix(anthropic): keep safeguards sub-calls off the parent's session routing and carry a body trace id
The classifier and compaction summary calls no longer get the parent's proxy_server_request, so a Claude Code session bound to a router no longer pulls them off their configured model. Redaction still reads the headers carried in metadata.
Their access checks now use the parent's resolved litellm_trace_id when the parent metadata has no trace_id, so a team that requires a trace id accepts a sub-call whose parent sent it in the body.
* fix(anthropic): keep the disable-callbacks header on safeguards sub-calls
* fix(proxy): charge per-model budgets from litellm_metadata when metadata holds only routing keys
* test(integration): add safeguard_results cells for the bridges, native routes, caller checks, and classifier failures
* fix(proxy): check the caller's budgets on the classifier call and keep failed access reads retryable
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* feat(decisions): send images to Cloudflare Clef
OpenAI-format input_image parts and the System One images field now reach Cloudflare clef and clef-flash in its images field, with remote URLs downloaded and inlined first. Providers that cannot take images drop them under drop_params and answer 400 otherwise.
* fix(decisions): follow drop_params for images and use the call timeout for image downloads
* fix(decisions): drop a malformed images entry on its own under drop_params
* fix(decisions): refuse images next to input whatever drop_params says
* fix(decisions): bound clef image fetches, honor drop_params for images, and log caller URLs
Refuse more than 4 images for Cloudflare clef before any download, keep the
caller's timeout as one deadline across image fetch retries, accept images
next to input under drop_params, validate image strings and objects, and pass
the caller's image URLs (not inlined bytes) to logging callbacks.
* fix(decisions): check input_image URLs and clef image sizes
* fix(decisions): accept data URL media type parameters and check clef image types
* fix(decisions): count clef images after dropping unsupported types and flag clef vision
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(databricks): send a single plain text block as a string for non-Claude models
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(databricks): simplify single-text-block helper
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* docs(databricks): clarify json_object validation wording
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(databricks): integration cells for single text block content shape
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(databricks): rename list-content cells to stays_list
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(responses): expect plain string content for bridged Databricks messages
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(databricks): cover the customer's Unity Catalog model from #45617 and #45618
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(databricks): drop casts from _collapse_single_text_block
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(databricks): assert the whole outgoing messages list in the collapse unit test
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(databricks): cover system joins, fallback, SDK clients, and outage burst for one-block content
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(anthropic): put a tool_result cache breakpoint on its hoisted images on the Responses bridge
A marked tool_result whose images get moved into a trailing user message now carries
the breakpoint on the last moved image instead of the function_call_output, so the
prefix OpenAI caches includes the images
* test(anthropic): cover a marked tool_result with two screenshots on the Responses bridge
* fix(anthropic): keep every tool result screenshot inside the breakpoint that covers it on the Responses bridge
A marked tool_result's screenshots now go right after its output, so earlier unmarked screenshots land inside the breakpoint and later tool outputs stay after it. A cache_control mark on a block inside a tool_result is read too.
* fix(anthropic): cover only the screenshots up to a mark inside a tool result, and drop raw breakpoints under drop_params
A cache mark on a text block inside a tool result now covers that result only up to the marked block, the way
Anthropic caches it, so screenshots after the marked text stay outside the breakpoint. A mark on the tool result
itself still covers every screenshot in it
A deployment that reads no prompt cache breakpoints now drops raw prompt_cache_breakpoint marks, top-level and
inside tool results, when drop_params is on, and still forwards them as sent without it
* fix(anthropic): keep raw breakpoints under drop_params for models that take them
* docs(anthropic): say a mark inside a tool result covers its whole text and documents
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Non-admin callers previously received deployment-wide cache activity,
error breakdowns, every key alias on the proxy, and could filter the
aggregates by any other tenant's key alias. All four queries now take
the caller's read scope as bind parameters, which also shrinks the
key-alias and model filter options to the caller's own visibility.
The three-way scope resolution (admin / internal user / fallback) now
lives in resolve_spend_log_read_scope, shared with /spend/tags and
/spend/logs/session/ui instead of being duplicated per endpoint.
* feat(claude-code): emit installationPreference in marketplace.json entries
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(claude-code): satisfy marketplace CI checks
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(claude-code): qualify marketplace entry as read only
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(claude-code): suppress typed marketplace assembly warning
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(claude-code): restrict installation_preference to the documented values
Accept only available, auto_install, or required so a typo returns 422 instead of being silently dropped, and cover the PUT full-replace clearing behavior with a test
* fix(claude-code): echo installation_preference on the public skill hub
* refactor(claude-code): read installation_preference with one membership check and cover hand-edited rows
* test(integration): audit installation_preference on the Claude Code marketplace
* test(integration): kill a serving worker in the marketplace chaos cell
* test(integration): give the marketplace worker-kill cell the two-boot timeout
* fix(proxy): keep installationPreference on the MarketplacePluginEntry model
* fix(proxy): refuse self-installing preferences off sha256 archives, accept the camelCase key, and echo the preference on POST and PUT
* fix(claude-code): serve installation_preference only on archive plugins and refuse both key spellings
Claude Code 2.1.75 and older reject a marketplace.json once any plugin carries installationPreference,
and every Claude Code release before archive support rejects a catalog with an archive plugin. Requiring
an archive source for every value, available included, keeps the field off catalogs those clients can read.
A request that sends installation_preference and installationPreference together now gets a 422.
* fix(claude-code): read the preference spelling that carries a value when the other is null
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* feat(proxy): let team admins create keys for team members behind an opt-in setting
A team admin calling POST /key/generate with a team they administer and the
user_id of another member of that team got a 403, and the dashboard only
offered the "Another User" owner option to proxy admins
Add a create_member_keys entry to team_admin_editable_team_fields. When a
proxy admin turns it on, a team admin can create a team key owned by another
member of a team they administer, through the API and through the Create Key
form. It stays off by default, so nothing changes until a proxy admin opts in
With the setting off, the 403 a team admin gets for this call now names the
setting to enable instead of saying they can only create keys for themselves
* fix(ui): order the member owner picker after the team and drop the viewer from it
For a team admin the owner picker depends on the team, so it now sits under the Team field, lists only the other members of that team, and the Team field says why it is required
* fix(proxy): keep keys a team admin creates for a member from carrying more than LLM access
A key authenticates as its owner, so with create_member_keys on a team admin could create a key owned by a proxy admin who sits in their team and act as a proxy admin with it
The member now has to hold the internal_user or internal_user_viewer role, and the key is always an llm_api key, so the team admin never holds a secret that reaches management routes. Asking for a management or read_only key for a member is refused
The unit tests now check the permission decision directly instead of replacing generate_key_helper_fn, and the saved key is checked against a real proxy in the team admin gate matrix
* style(proxy): sort the team admin field permission imports
* refactor(proxy): force the member key type on the request instead of merging a second dict
* test(proxy): drive member key creation through the key generation helper
The pure helpers were covered but the wiring in _common_key_generation_helper was not. Dropping the owner role lookup, the refusal call, or the user_id handed to common_key_access_checks left every unit test green
* fix(proxy): skip the owner role lookup when create_member_keys is off
The owner role was read before the verdict checked the setting, so with the setting off a failing user table read surfaced as a 500 instead of the 403 a team admin got before, and every such request paid an extra writer read
---------
Co-authored-by: Yuneng Jiang <yuneng@berri.ai>
* feat(telemetry): count Admin UI page views and tab clicks through the proxy
* fix(ui): share one telemetry settings save across both tabs so a second toggle waits for the first
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(telemetry): let every dashboard role reach the UI event routes
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): fail with the denial detail when a dashboard role cannot reach UI event routes
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(telemetry): document POST /telemetry/ui_events with a description and a 200 response schema
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): keep the test-quality reason on the line pytest's checker reads
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(ui): pick the telemetry destination on the Telemetry page and warn when none is chosen
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(ui): send the destination along with groups in the telemetry settings hook test
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(ui): add a top-level telemetry switch and gate turning telemetry on behind an enterprise modal
The page can only turn telemetry off. Turning it on from the switch, a group
or a first destination opens a modal: enterprise proxies are told to contact
their LiteLLM enterprise representative, others that telemetry is an
enterprise-only feature. Group switches and the destination stay visible but
disabled while telemetry is off.
---------
Co-authored-by: nate <nate@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(telemetry): keep reports in a local table with a persistent instance id and an admin export route
* feat(telemetry): key deployment hashes from a random secret stored in the proxy DB
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(telemetry): retry a locally stored window under the same id so it is never counted twice
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* docs(telemetry): state the overlap and dedupe contract for paging stored reports
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(ui): regenerate dashboard API types for the telemetry reports paging note
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(telemetry): require an explicit LITELLM_TELEMETRY_DESTINATION or saved destination before recording
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(ui): regenerate dashboard API types for the explicit telemetry destination
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(telemetry): register the attempt logger and create its hash secret only once a sink is built
A proxy with a database that never opts in now carries no telemetry callback
and writes no deployment hash secret to LiteLLM_Config.
---------
Co-authored-by: nate <nate@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(telemetry): record proxy requests and provider attempts into the telemetry sink
* fix(telemetry): validate env settings, fix the window and only record the request's own provider calls
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(telemetry): bind the call id from a typed local
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(telemetry): record a finished request into the sink current when it finishes
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(telemetry): record every failed provider attempt, including retries and fallbacks on a shared logging object
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(telemetry): report timeouts and connection failures as their own provider status classes
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(telemetry): record failed attempts from the per-deployment failure hook instead of a new core logging hook
Reverts the async_log_attempt_failure_event hook added to CustomLogger and
litellm_logging in 3421b38685. The router already calls
async_post_call_failure_deployment_hook once per failed attempt, retries and
fallbacks included, so every failed fallback still gets its own row without
touching the shared logging object.
* fix(telemetry): register the attempt logger only once a telemetry group is turned on
A proxy that never opts in now carries no telemetry callback. Once registered
the logger stays, returning early while every group is off.
---------
Co-authored-by: nate <nate@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(telemetry): add AggregatingSink, fixed-bucket histograms, Report and HttpExporter
* fix(telemetry): add records to rows in place, guard concurrent flushes, cap restored rows and count bad histogram values
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(telemetry): give the shared row helper its lint reasons
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(telemetry): retry the same statuses as the proxy's should_retry (408, 409, 429, 5xx)
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(telemetry): cover populated block_types and header_keys in report JSON
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: nate <nate@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(prometheus): skip custom metadata labels a metric already carries
A custom metadata label whose sanitized name matches one of a metric's
built-in labels was appended a second time, so every .labels() call on
that metric raised ValueError. With custom_prometheus_metadata_labels:
[project_alias], the per-project per-model rate limit gauges raised on
every request from a project key with per-model limits, and the rest of
the success event (latency, deployment success, total requests) was
skipped.
* test(prometheus): cover shared custom labels on every endpoint, failure, metadata and outage burst
* test(prometheus): drain the shared upstream at the start of each integration test
* test(prometheus): assert the forwarded route, model and stream per endpoint cell
---------
Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com>
* feat(telemetry): add litellm.telemetry records, consent groups and sink protocol
* docs(telemetry): reword TelemetryGroup docstring and drop module docstring
* fix(telemetry): add timeout and connection_error status classes, check relative imports in the package boundary test
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: nate <nate@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(vertex_ai): unwrap tool-emulated json_schema envelopes and send output_config.format for Claude
Vertex AI Claude deployments that emulate response_format through a forced
tool sometimes nest the schema object under an extra key such as
`parameters` or `input`, and that envelope reached message.content with a
200. The converter now unwraps exactly one nested object that matches the
schema, leaving a payload that is itself schema-shaped alone.
Native structured-output rows sent the deprecated top-level `output_format`,
which Vertex rejects with a 400. The Vertex request now carries it as
`output_config.format`, with an explicit caller value winning.
* fix(anthropic): unwrap tool-emulated json_schema envelopes on streams and tool-name wrappers
* test(anthropic): expect the json tool description through a shared constant
* fix(anthropic): drop the json tool description that makes sonnet-5 omit required keys
* fix(anthropic): keep schema-accepted answers whole and decide held streams at the second key
An answer the json_schema accepts is returned as is before any envelope rule runs, the tool-name
wrapper is unwrapped only when the schema rejects it, a malformed schema turns the unwrap off, a
streamed answer whose first key is a schema property named name or tool_name waits for its second
key before streaming, the held fragments go through a StringIO, and a prefix that stays undecided
past 4 KB is held to the end
* test(anthropic): keep the stream family cells to providers whose stream iterator gets the schema
* fix(anthropic): unwrap json tool envelopes by structure instead of schema validation
The unwrap now reads only the schema's top-level property names. A lone
undeclared key holding an object, alone or beside a name equal to the json
tool's own name, is the envelope, and the unwrap repeats while the inner
object shares no key with the schema. jsonschema and referencing are no
longer imported on the request path. The stream holds only an answer that
opens with the tool's name or with an undeclared key holding an object, and a
schema without top-level properties streams as it arrives.
* fix(anthropic): walk json tool envelopes iteratively
* fix(anthropic): keep json tool arguments too deep to decode instead of raising
* fix(anthropic): unwrap json tool envelopes beside undeclared string labels
* fix(anthropic): keep json answers whole on schemas that allow extra keys and stream held text as sent
A schema that allows undeclared keys and requires none now keeps an undeclared wrapper key on both the stream and non-stream paths. When no envelope is found, the held stream text is now sent exactly as the model wrote it. On Vertex /v1/messages, output_format now wins over output_config.format when both are set, which matches the Anthropic adapter.
* test(anthropic): cover label envelopes, dynamic-key schemas, as-written held streams and output_format precedence
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(cost_calculator): price an explicit base_model from its own row when the deployment provider row has no rates
A chatgpt/ deployment with model_info.base_model recorded zero spend because the
base model was looked up under the chatgpt provider, which resolves to the
deliberately unpriced chatgpt/ row. When the deployment provider's row for the
base model declares no rates, price it under the provider of the base model's
own row instead. Deployments whose provider row declares rates are unchanged.
(cherry picked from commit 42d9a8693e)
* test(integration): bill a chatgpt base_model deployment from the priced row on a live proxy
Co-authored-by: sla55er <stiliyanmarkov@gmail.com>
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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Co-authored-by: sla55er <stiliyanmarkov@gmail.com>
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(openai): carry cache_control to prompt_cache_breakpoint on chat completions to gpt-5.6
A cache_control mark on a system, developer, user or tool message sent to /v1/chat/completions now reaches an OpenAI deployment that reads prompt_cache_breakpoint (gpt-5.6+ on api.openai.com, or one opted in through prompt_cache_options) as a prompt_cache_breakpoint on the same content part, instead of being stripped. Marks on assistant turns and tool definitions are still dropped, and deployments that do not read breakpoints keep today's behavior.
* test(openai): rewrite chat prompt_cache_breakpoint wire test as sent-vs-received literals
Each test now shows the request the caller sends and the exact body OpenAI receives as plain JSON, with one helper that returns the upstream body. Drops the per-test marker plumbing and the spend-log check, which did not test the cache mark translation.
* test(openai): cover a marked chat message with null content on the breakpoint path
A system, developer, user or tool message that carries cache_control but whose content is neither a string nor a list of parts is handed to the existing strip untouched instead of raising inside the transform. The new case pins that the request still goes out with the mark stripped and no breakpoint added.
* refactor(openai): reuse the cache control hook's breakpoint placement on chat completions
The chat transform had its own copy of how a message-level cache_control lands on a part for OpenAI's prompt_cache_breakpoint. It now calls one hook helper that the injection path also uses, so which roles and part types can carry a breakpoint is decided in one place. A message-level mark on list content now lands on the last part OpenAI accepts a breakpoint on, matching injection.
* test(e2e): cover cache_control reaching gpt-5.6 explicit prompt caching on chat completions
Four live cases through the proxy: a marked system part, a marked string system message, a marked user part and a marked tool result. Each must write the cache on the first call and read it on the second.
* fix(openai): parse content parts instead of a banned TypeGuard when renaming cache marks
The strict ruff gate bans typing.TypeGuard as unverified narrowing. The part rename now validates an accepted part with pydantic before handing it to the shared rename helper.
* chore(typing): validate provider payloads at the boundary to clear 464 basedpyright Any errors
Replaces untyped reads of decoded JSON in 48 provider, guardrail and proxy
modules with one pydantic validation at the point the payload enters the
module, so every value read afterwards carries a real type.
reportAny 6111 -> 5663 and reportExplicitAny 1416 -> 1400. No basedpyright,
ruff-strict or type-discipline rule is higher repo-wide or in any single
file, and no cast, ignore or suppression marker was added.
* chore(typing): replace 47 Any annotations that every caller already satisfies with object
Each annotation here is a parameter or return type whose value is only
stored, compared, logged or forwarded, so object describes it exactly and
no function body changes. A site was kept only if no basedpyright,
ruff-strict or type-discipline rule rose in any file that references it.
reportExplicitAny 1400 -> 1353 and reportAny 5663 -> 5655.
* chore(typing): type upstream responses and loose annotations across providers, proxy and integrations
Second pass of the Any reduction. Decoded upstream JSON is validated once at
the provider boundary and read through typed views afterwards, annotations
that every caller already satisfies move from Any to a concrete type, and the
nine search transformations validate each result item on its own so error
text no longer carries the item index.
No cast, no type: ignore, no suppression marker and no budget file edit
* ci(unit): run the brave, google_pse, nlp_cloud and opensandbox tests in the providers shard
* fix(openai): carry cache_control to prompt_cache_breakpoint on /v1/responses to gpt-5.6
Native /v1/responses used to strip every Anthropic cache_control mark, so a
gpt-5.6 deployment in explicit mode never wrote a cache. Marks on system,
developer and user parts and on function_call_output now become
prompt_cache_breakpoint on the same block when the deployment reads
breakpoints, and are stripped exactly as before everywhere else
* fix(openai): read str providers and base_url in the prompt cache breakpoint gate
Responses configs generated from a provider JSON file, and openai_like
itself, name their provider as a plain str, so .value crashed every native
Responses request through them. The gate also only read api_base, while
responses() keeps a caller's base_url as an extra field on the params, so an
injected mark on a custom host was rewritten to a breakpoint
* test(openai): cover the pass-through branches of the prompt cache breakpoint swap
A part type OpenAI does not cache keeps no breakpoint and an item mark skips
it, and an item or content of a shape OpenAI will reject reaches the strip
untouched instead of failing inside the proxy
* fix(prompt_caching): keep a client-written prompt_cache_breakpoint when a cache_control mark lands on the same block
A JSON-RPC tools/call on /{server_name}/mcp (or /mcp with x-mcp-servers naming one server) that uses the upstream's bare tool name answered "Tool not found" on every proxy process that had not yet listed that server, because the per-process tool-name map stays empty for a server whose upstream only lists tools with the caller's own credentials, and mcp SDK 2 no longer lists before an uncached call. call_mcp_tool now hands the single scoped server to execute_mcp_tool as requested_server_id, so the existing REST contract lists the server with the caller's headers and routes the bare name to it, and a bare name shared by two servers goes to the one the path names
Co-authored-by: aayushbaluni <73417844+aayushbaluni@users.noreply.github.com>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): broadcast team cache invalidation on team writes
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): cover cross-instance team cache invalidation
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(ui): source Home What's new from day 0 support blog posts
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(ui): Home cards read card_title and card_summary from blog tags with provider logo
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(unit): wait on the work itself instead of fixed sleeps in 21 flaky files
Replace sleeps that raced background logging, cache writes, refreshes and
thread work with either awaiting that work directly (logging worker flush,
the batch logger's flush_queue, the tasks the call spawned, thread joins) or
an Event the test's own fake sets when it runs. Each wait keeps a 5s hang
guard that never decides pass or fail. Tests that need a fake clock, real
sockets, or measure latency are left unchanged, and no production code moves
* test(unit): await the tasks each completion spawns instead of yielding once
The logging tests yielded once with asyncio.sleep(0) so the task that
enqueues the success log could run before the worker flush. That relied
on the enqueue happening before the task's first await. Snapshot the
running tasks around each acompletion call and await the ones it spawned,
so the guarantee holds however many steps sit before the enqueue
