* refactor(typing): replace pass-through Any with object in 13 annotations
* docs(client): say dict[str, object] in the models get docstring
* refactor(typing): drop the unused Span aliases in base_cache and levo
* refactor(typing): take a Mapping in delete_nested_value and cover the fresh copy
* fix(typing): deep-copy a read-only Mapping in delete_nested_value through a dict
* fix(proxy): buffer tool discoveries in redis and let the leader write LiteLLM_ToolTable in one statement
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): declare the redis family on the tool discovery buffer and drop the blind except and cast
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): satisfy LIT010 and LIT014 in the tool discovery buffer
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): mark the aggregation fold Final
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): requeue drained tool rows to redis when the write provably did not apply
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): drain tool discoveries on their own lock and scheduler job, not the spend commit
* chore(proxy): suppress the untyped apscheduler add_job on the tool discovery job
* refactor(proxy): move the LiteLLM_ToolTable bulk upsert into its own module next to the other bulk upserts
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): name the LiteLLM_ToolTable bulk write span so the prisma span check resolves it
* refactor(proxy): move the tool discovery drain out of DBSpendUpdateWriter into tool_registry_writer
* refactor(proxy): share db_write_failure_is_requeue_safe from exception_handler so the spend writer stays untouched
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Add OpenCode Go model discovery, protocol routing, session affinity, and published pricing metadata. Cover Chat Completions, Responses, Messages, and Gemini through the existing provider adapters.
Fixes#31568
* feat(ui): configure the default Usage time range
* fix(ui): retain the selected calendar preset during input sync
* fix(ui): preserve fallback dates when applying across DST
* fix(router): route decisions calls only to deployments whose provider supports decisions
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(router): account for indirect route helper coverage
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(router): pin the chat leg so the mixed-group check is deterministic
Weights pick the Bedrock leg for chat instead of relying on simple-shuffle landing on both legs. The route filter now passes the candidate list through unchanged in type, so the pre-existing selector lines lose their pyright ignores
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: joshua <joshua@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(typing): narrow Any sources across providers, proxy and guardrails (wave 1)
* refactor(typing): narrow Any sources across hubs, providers and proxy (wave 2)
* refactor(typing): validate RunwayML task JSON and let dotprompt loaders accept exported prompts
* fix: restore pass-through behavior on paths the typing pass tightened
MCP client IP extraction trusts X-Forwarded-For only when use_x_forwarded_for is True, dotprompt skips a malformed JSON prompt entry instead of raising, RunwayML task payloads pass through unvalidated, and a malformed provider body raises the same AttributeError as before at the CompactifAI, Gemini, and mock transport parse sites
* fix(runwayml): keep a malformed 2xx task body on the 500 path
* fix(edenai,cli): keep a non-object body in the error and fail on a malformed migration report
EdenAI's embedding and image generation parse dropped a non-object body from the error
they raised, so the message no longer named what the provider sent. A helper now raises
with the body in the message, and both paths have a regression test.
`lite encryption migrate` exited 0 with no message when the proxy's report was not an
object or its residual_legacy was not a number, where base crashed. It now fails with a
message naming the value.
The CompactifAI and Gemini image-edit non-object body tests accept TypeError or
AttributeError, which both base and this branch raise, so they guard against a
ValidationError instead of pinning one class. RunwayML's async parse gets the same
non-object body test as the sync one.
* fix: keep base's error classes, duck-typed checks and fail-closed guardrails the typing pass changed
* test: annotate the new regression tests' locals with Final
* test: annotate the remaining new test locals with Final
* feat: add OpenAI 12-hour cache write pricing
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix: omit empty cache-write response fields
* style: format cache-write fields
* fix(cost): bill openai 30m and 12h cache writes from the usage split, add gpt-5.6 rates
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(cost): apply the 12h long-context write rate only above the token threshold
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(cost): key cache write rates by TTL instead of growing the rate tuple
_get_token_base_cost returned a positional 6-tuple and every cache lifetime added a slot to it, to the off-peak helper and to every caller. It now returns a TokenBaseCosts dataclass whose cache write rates live in one mapping keyed by TTL. The TTLs come from the ephemeral_*_input_tokens fields on CacheCreationTokenDetails, so supporting a new lifetime only means adding that usage field and its rate key
A lifetime with no rate of its own now always falls back to the cache creation rate after off-peak pricing is applied. Before this the non-tiered 12h rate and the tiered 1h rate fell back to the rate from before off-peak, so the same request billed differently depending on the pricing path
* refactor(cost): hardcode the cache write TTLs as a Literal instead of parsing usage field names
Parsing ephemeral_*_input_tokens names saved nothing, since a new TTL still needs a usage field and provider parsing code. The TTLs are now a CacheWriteTTL Literal with an explicit rate key per TTL
* refactor(cost): resolve cache write lifetime rates to floats at lookup
Decide the 1h and 12h cost map key in the threshold loop, then resolve each
rate once after off-peak with the base write rate as the default, so no None
flows through the cost code. Add an integration case for a 12h write on a
row without a 12h rate
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(cost): name every cache write TTL, marking 5m and 30m as base rate
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* refactor(cost): list every cache write TTL in one rate key table, None meaning the base write rate
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* refactor(cost): drop base rate entries from the cache write rate table and rely on fallback
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* refactor(cost): key cache write rates by plain string TTL
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* refactor(cost): fill missing cache write TTL rates with the base write rate after off-peak
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* refactor(cost): default missing cache write TTL rates to the base write rate inline
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* refactor(cost): rename cache write rate key table to make clear it only covers long TTLs
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(cost): list every cache write TTL and bill writes of an unknown TTL at the base write rate
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* refactor(cost): resolve tiered cache write TTL rates after off-peak instead of passing None
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* refactor(cost): carry cache write TTL rates on TokenRates and drop TokenBaseCosts
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* refactor(cost): define TokenRates before its first use to drop the string annotation
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(cost): build the TokenRates TTL default with default_factory and type tiers as Mapping
MappingProxyType({}) as a dataclass default is rejected on Python 3.11, where
mappingproxy is unhashable, so import litellm failed there. The tier type
moves from dict[str, object] to Mapping[str, object] (LIT001), with tier_rate
accepting a Mapping to match.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(cost): type select_tier_for_input tiers as Mapping so tier_rate callers stay typed
The tier returned as a bare dict made every tier_rate call site a partially
unknown argument once tier_rate took Mapping[str, object]. The range bounds are
now validated with a TypeAdapter instead of indexing an unknown list.
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
* fix(responses): honor per-call custom pricing on the native Responses API path
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(responses): narrow the custom pricing regression test to the type gates
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(main): expose register_custom_pricing_for_request for the responses path
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(responses): mark custom pricing test locals Final
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): price per-call custom rates request-locally instead of registering them globally
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): bind per-call rates on the request logging object instead of inferring them in Logging
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): keep Router deployments on the cost-map pricing path
Router inherits cache rates onto the deployment's cost-map entry, so a routed
Responses call binds no request-local price and keeps billing cached tokens at
the inherited rate. Moves the deployment-id lookup to router_utils so the
Responses and completion paths share it.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): gate request-local pricing on the typed deployment model_info
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>
* fix(router): keep image requests off deployments declared supports_vision false in pre-call checks
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(router): type the vision pre-call helpers so the basedpyright gate holds
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(router): keep the pre-call index set typed without a mutable annotation
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(router): cover the vision pre-call helpers directly for the router coverage check
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(router): type the vision pre-call test helpers
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(router): cover the no-params and unresolvable-provider vision branches
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: joshua <joshua@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(spend): await the per-request rollup enqueues and report their failures
The daily and entity spend rollups ran in an unreferenced asyncio task that
nothing awaited or drained, so a shutdown after the spend log was enqueued
could drop every rollup for that request, and a malformed metadata payload
dropped the request from all daily tables at debug level. The rollups now run
inline in update_database, each helper failure is logged through
spend_log_error with the request id, and unreadable metadata only costs the
row its metadata-derived fields
Resolves LIT-6027
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(spend): type the rollup failure helpers for the lint gates
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(spend): track the rollup enqueue task so shutdown drains it
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(spend): shutdown drain waits for an in-flight rollup enqueue
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(spend): register the rollup task without blocking the cost callback
Awaiting the rollups inline made a blocked queue put stall increment_spend_counters, so budget checks could run on stale counters. The task is now only registered via spawn_logging_task; drain_logging_tasks still waits for it on shutdown
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: joshua <joshua@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(aws_polly): read the engine from the bare model name
speech() strips the aws_polly/ prefix before AWSPollyTextToSpeechConfig sees
the model, so _extract_engine_from_model got a bare "standard", found no
slash and returned the neural default. Every Polly request went out with
Engine: neural whatever the model named.
Take the last path segment instead, which covers both the bare name and the
prefixed one.
* test(aws_polly): move imports to the top of the file
* docs(aws_polly): update the engine comment to the bare model name
* feat(proxy): add LITELLM_OFFLINE_MODE to stop every request the proxy makes on its own
* fix(ui): name LITELLM_OFFLINE_MODE where the cost map panel says local mode is forced
* test: isolate the offline-mode tokenizer tests from the local Hugging Face cache
* test: cover the offline policy templates and the cached Kimi K3 tokenizer
* test(integration): count Kimi K3 tokens in the offline-mode egress test
* refactor(token_counter): skip non-bundled tokenizers in offline mode instead of listing downloaded ones
---------
Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com>
* feat(chat): port Moyai UI with Model Hub and images
* feat(chat): add Models sidebar with embedded Model Hub
* feat(chat): polish Model Hub with provider cards and logos
* refactor(chat): use a provider dropdown in Model Hub
* feat(chat): keep workspace switching visible with a Chat identity
* Add minimal opt-in Chat usage metrics and prioritize Chat in workspace selector
* fix(chat): enable usage metrics by default only when chat is used
* fix(chat): count requesting users with frontend-only metrics
* feat(chat): send default-on request metrics to litellm-tel
The router plants its deployment fields in litellm_metadata for Interactions
calls, while the proxy put the request's metadata (spend_logs_metadata, tags,
key fields) under metadata. The spend log resolves litellm_metadata and only
carries user_api_key* fields over, so spend_logs_metadata never reached
LiteLLM_SpendLogs. Route /interactions like /responses so both land in
litellm_metadata.
Fixes#47841
Co-authored-by: Igor Tavares <2163472+igorborgest@users.noreply.github.com>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
* feat(ui): sort every teams table data column
* fix(ui): order unlimited team limits as infinity and verify org scope
* fix(ui): validate team sort filters without recursion
* feat(ui): sort every users table data column
* fix(ui): order unlimited user budgets as infinity and verify org scope
* fix(ui): cover user sort queries and document pagination bound
* fix(ui): preserve Aria usage token breakdown and viewer model access
* fix(ui): make Aria conversations easier to read and reply to
* fix(ui): wrap long headings in assistant tables
* feat(ui): simplify Aria and add usage charts with PDF answer export
* fix(ui): retain full chart values in Aria PDF exports
* fix(deps): upgrade vulnerable Python dependencies
* fix(deps): lock patched Python dependency versions
* fix(otel): preserve JSON export with the updated SDK
* test(otel): cover JSON export with the updated SDK
* test(otel): update SDK log records and worker discovery
* fix(ui): align Lens peer dependency with patched Next.js
* fix(otel): hand the AgentOps exporter a session it owns for the JWT header
OpenTelemetry 1.45 moved the OTLP HTTP exporter's requests.Session behind a
transport, so writing the minted JWT to the exporter's private _session raised,
was swallowed, and every AgentOps batch went out unauthenticated. The exporter
now creates the session, passes it through the public session= argument and
sets the Authorization header on it
The tests now drive a real export, serving the token endpoint with respx and
capturing the OTLP POST with requests_mock, instead of reading SDK internals
* fix(otel): skip FastAPI instrumentation once the app has started
opentelemetry-instrumentation-fastapi 0.66b1 wraps build_middleware_stack instead
of calling add_middleware, so instrumenting an app whose middleware stack is
already built no longer raises. The call silently did nothing while we still
registered the global response propagator. Check the built stack up front and
skip both, which is what the existing test expects
* test(otel): assert exported requests and span flags instead of SDK internals
OpenTelemetry 1.45 dropped the exporter's private _headers and sets the W3C
random-trace-id flag on new traces, so traceresponse ends in -03. The Langtrace
and Langfuse tests now export through requests_mock and check the URL and headers
the collector receives, and the traceresponse tests expect the server span's own
trace flags
* fix(deps): bump dashboard zod to 4.6.6 and make the vendored Lens use it
zod 4.6.5 is flagged by the dependency scan and blocks osv-scan on every PR. A
root bump alone leaves the vendored Lens tarball, which pins zod 4.6.5 exactly,
on its own nested copy, so a top-level zod override points every consumer at the
root version and the lock carries a single zod 4.6.6
* test(ui): stub the tag summary fetch the client panel retry refreshes
Retrying the model breakdown refetches /tag/summary, which this integration test
left on the real network. When that request failed before the assertion, the
panel switched to its summary error and unmounted the model row, so the test
passed or failed by timing
* fix(deps): drop the duplicated Lens override from the dashboard package.json
* style(ui): format the client panel fetch stub
---------
Co-authored-by: Yuneng Jiang <yuneng@berri.ai>
* feat(ui): consolidate gateway and usage assistants as Aria Agent
* refactor(ui): give Aria an original blue sprite mascot
* feat(ui): prompt shared Aria from usage and retire duplicate usage agent
* fix(ui): keep Aria naming consistent and reachable across dashboard
* feat(ui): open Aria in a floating popup with suggested questions
* fix(ui): simplify the shared Aria Agent title
* feat(proxy): add LITELLM_DISABLE_LATEST_RELEASE_CHECK to stop the release check
The dashboard asks the proxy for the latest LiteLLM release, and the proxy fetches it from api.github.com on every cache miss. Deployments that block or audit outbound traffic had no way to turn that off. With LITELLM_DISABLE_LATEST_RELEASE_CHECK=true the endpoint returns null without making a request, which the dashboard already treats as no upgrade banner.
* fix(proxy): regenerate dashboard types for the release check flag
The endpoint docstring feeds the OpenAPI description, so the ui-api-types
check needs schema.d.ts regenerated. The docstring now says the flag skips
every outbound request, which stays true once the changelog feed is read
too
---------
Co-authored-by: Sujit Nirmal <nirmalsujit891@gmail.com>
Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com>
The Admin UI upgrade notice read GitHub's latest release, which the release
pipeline sets on its own, so admins were offered releases the changelog had
not announced yet and sent to the auto-generated GitHub release page. The
notice now reads the newest stable release from the docs changelog feed and
links to its changelog page. GitHub release notes are still used for the
feature and fix counts of that version's minor line.
Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com>
* test(interactions): assert the live status enum is parsable instead of pinning it
* test(interactions): read the spec from a vendored snapshot and canary the live one daily
* test(interactions): run the canary on PRs touching its inputs, file an issue on a red run, pin the regenerate command
* test(interactions): take the live spec URL as a pytest option, assert its source, split the canary's tracking-issue job
* test(interactions): file the canary issue whenever its compliance step did not pass, match the issue title exactly, cover UrlContextResult
Update certifi, Rust trust roots, and the Docker CA bundle to remove emSign ECC Root CA - G3
Require the fixed certifi release in litellm-core and retain libssl3 in Docker runtimes for Prisma compatibility
Co-authored-by: shin-berri <shin-laptop@berri.ai>
* fix(responses): round-trip Anthropic compaction blocks as compaction items
/v1/responses on Anthropic routes now returns Claude's compaction block as a
compaction output item (and streams it as output_item.added/done), and a
compaction input item is replayed to Claude as its native compaction block,
so the next turn reuses the summary instead of compacting again. Other routes
receive the summary as assistant text. The Anthropic stream handler now keeps
the summary text that arrives in compaction_delta.
Fixes#41456
* fix(responses): replay compaction blocks natively only when the request compacts
* fix(responses): carry compaction through dict-form edits, SDK replay, affinity, and guardrails
Read Anthropic's dict-form context_management, accept SDK ResponseCompactionItem
objects on replay, strip the encrypted_content affinity wrap before decoding,
scan and rewrite compaction summaries in Responses guardrails, drop the history
a summary replay replaces, send the summary as a user message so thinking
ordering holds, and send the streamed compaction .done with the newest block.
* fix(responses): count, route, guard, and redact compaction items consistently
A compaction item now reads as its summary everywhere the proxy counts or routes
a Responses request: pre-call context checks, the TPM limiter, the input token
endpoint, the stream usage estimate, and the previous_response_id session rebuild
all drop the history the item replaces. A Claude compaction item sent to a
non-Anthropic model (including a fallback) becomes a user summary message instead
of an encrypted blob the provider rejects, and Claude on Bedrock Claude Platform
takes the summary path. Output guardrails see the summary of a returned
compaction item, and logging redaction hides it.
* fix(responses): start previous_response_id replay at a stored compaction and count reservations from it
* fix(responses): keep a stored compaction summary whole in spend logs
* fix(responses): keep stored compaction items summary-only, redact compaction_delta, and finish compaction before reasoning
A spend-log row now stores a top-level compaction input item as its summary alone, so extra keys in a
client-built item no longer reach the database whole. Message redaction also nulls the streamed
compaction_delta, and the streaming bridge sends the compaction item's done event before the reasoning
item that follows it. Adds the audit's integration tests for compaction items on the wire.
* test(responses): cover stored compaction items, compaction_delta redaction, and compaction done order
Adds integration cells for the summary-only spend row of a caller-sent compaction item and its previous_response_id replay, a redacted chat stream row that holds no streamed compaction_delta, and a compaction then thinking stream whose done events follow output order.
* fix(spend_logs): honor the redaction header on failure rows for litellm_metadata routes
A failed /v1/responses or /v1/messages call keeps its proxy request headers in
litellm_metadata, but the failure row read redaction headers from metadata only,
so x-litellm-enable-message-redaction never redacted those rows.
* fix(spend_logs): lift only the redaction opt-in header into failure rows
The failure hook on litellm_metadata routes copied every real request header into the caller's own metadata dict, so the stored request body gained the headers, a caller's metadata.headers was replaced, and a disable header loosened global redaction on failed calls. It now copies only the enable headers into a new litellm_params metadata dict, and only when the caller sent no metadata.headers, so no failure row is less redacted than on main
* test(responses): cover failure-row redaction headers and caller metadata on litellm_metadata routes
* fix(responses): keep earlier-turn instructions after compaction and count compaction items by their summary
A previous_response_id rebuild that starts at a compaction now keeps the system and developer
messages of every earlier turn. Token estimates for raw Responses input count a compaction item
as its summary text instead of the serialized item.
* fix(responses): drop the stored history a caller-sent compaction item replaces on previous_response_id
* fix(responses): count only message items with a string role as compaction instructions
* test(responses): flatten the last two-for comprehension in the compaction wire test
* fix(responses): replay from a compaction in stored Responses output and SDK compaction items
A previous_response_id whose stored row is a Responses output holding a compaction item now starts its replay at that compaction, and the handler dumps SDK compaction objects before checking for a summary. Adds tests for the uncovered compaction paths.
* fix(responses): keep the stored tool call a function_call_output answers when a compaction item replaces history
* fix(responses): keep a stored tool call on every turn after a compaction item and bound the stored summary
A previous_response_id rebuild that starts at a turn holding a compaction item now sends again each stored call a
replayed tool output answers, so a retry or a later turn no longer loses the turn-1 call. The start is picked only from
rows up to the referenced response. A spend row keeps a compaction summary up to the larger of 64K chars and
MAX_STRING_LENGTH_PROMPT_IN_DB instead of with no bound.
* fix(responses): replay a compacted session only through the referenced response
A later sibling turn that sent a compaction item put its summary between a stored tool call and its tool output, so Claude answered 400 on a turn that continued the first of two retried turns, and on a retry sent without the item
* fix(spend-logs): keep main's stored value for a prompt length limit under 2
At a MAX_STRING_LENGTH_PROMPT_IN_DB of 0 or 1 the shared truncation helper stored only the truncation marker, where
main stores the marker followed by the whole value
* fix(spend-logs): keep main's stored tail for a negative prompt length limit
At a negative MAX_STRING_LENGTH_PROMPT_IN_DB the shared truncation helper stored one more character of each value than main, since it dropped main's clamp of the end slice to the limit minus the start
* fix(responses): keep a compaction item when a guardrail rewrites its summary through structured_messages
* test(responses): run the compaction wire tests on the worker database under isolated workers
* fix(router): keep the encrypted content affinity pin out of provider requests
* fix(proxy): inspect compaction summaries in guardrails, strip a client-sent affinity pin, and price failures from the newest compaction
* fix(proxy): count a failed chat or /v1/messages call on its whole history, not from a compaction-typed message
* fix(proxy): trim a failed call's input at the newest compaction only when the client called /v1/responses
* fix(proxy): trim a failed call's input at the newest compaction when the proxy dispatched it as a Responses call
* test(proxy): run the dispatched-call-type failure test through the real request setup
* fix(proxy): count a failed Cursor stream's input from the newest compaction item
The Cursor route maps to no call type, so the stream generator's failure hook fell
back to the route and counted the whole history. Pass the processor's route_type.
* fix(proxy): count a broken Cursor stream's prompt from the newest compaction item
* fix(responses): keep a tool call a later output answers when trimming before a compaction item
* fix(responses): move a compaction-split tool call's turn to right before its output on both compaction routes
* perf(responses): look up moved turn items in a set so a long compaction-split turn trims in linear time
* test(responses): measure the compaction-split turn move as a CodSpeed benchmark instead of a clock read in unit tests
* fix(responses): move only calls still open at the compaction, and bill a disconnected stream from the newest compaction item
* fix(responses): restore only the newest stored call when a reused call_id is answered after a compaction item
* fix(spend logs): bound a Responses compaction item's summary like a chat block instead of cutting its encrypted_content
* test(proxy): price a failed dispatched call through a real ProxyLogging instead of mocked hooks
* fix(responses): finish a stream whose Claude compaction failed and group answered call ids by turn once
* fix(responses): count input_tokens after a compaction item the way inference sends it
* fix(llms): sanitize trailing slashes in provider get_complete_url
* test(llms): move trailing-slash url tests into each provider's mapped unit file
* fix(llms): strip trailing slashes in Perplexity search and Ollama embedding URLs and run the Topaz tests in CI
* fix(llms): strip trailing slashes in search, DashScope, VolcEngine, GigaChat and Gradient AI URL builders
* fix(fastcrw): strip a trailing slash from api_base and expect one slash in the GigaChat tests
* fix(gigachat): strip a trailing slash from the pass-through api_base
* fix(llms): join api_base and route with one shared helper
A deployment api_base that ends in / made several get_complete_url builders emit a double slash, which Envoy and other gateways answer with 404. Add join_api_base in url_utils and use it in those builders, with a regression test that drives every get_complete_url under litellm/llms with a trailing-slash api_base
* refactor(llms): move per-provider api_base strips onto join_api_base
Use the shared join_api_base helper in the search, chat, embedding, image variation, GigaChat and VolcEngine URL builders instead of a per-provider rstrip and suffix check, so one helper owns the single-slash join
* ci: run the trailing-slash URL builder test in the llms shard
* test(integration): cover trailing-slash api_base across provider URL builders
Wire cells for chat, streaming, messages, responses, embeddings, search and pass-through with and without a trailing slash, an owned-proxy module for the flag-gated providers, env bases and a worker kill, two chaos bursts, and a Topaz SDK cell
* fix(llms): send every provider route through join_api_base
Every place under litellm/llms and litellm/main.py that joined a caller-settable api_base with a route by concatenation or f-string now goes through join_api_base, so an api_base with a trailing slash sends the same upstream path as one without it
Routes made only of a caller-supplied id, model, or passthrough path stay on a plain join over an already normalized base, because the helper's suffix dedup would turn a lookup by id into a list or create call
The sweep test now also reaches the factory-made JSON provider builders, and a new loopback test drives the public entrypoints with an api_base ending in /gw/ and checks that every request path keeps /gw/ with no double slash
* fix(llms): keep the route skip only where a builder already had it
join_api_base is a pure join again. The new ensure_api_route skips the route only when the URL path
already ends in it, and it is used only where the merge base or the trailing-slash test PR head
already skipped. Evals cancel, Predibase, and Azure AI threads append their route again, EdenAI
model listing and Gemini RAG ingestion join through join_api_base, and Azure AI Anthropic checks the
path so a host named anthropic-* builds /anthropic/v1/messages
* fix(azure_ai): read /anthropic from the URL path when building the count_tokens URL
* test(llms): run the Gemini RAG trailing-slash case through aingest
The sync ingest wrapper needs a current event loop, which an earlier async test in the same worker clears
* test(llms): cover the Azure realtime, Bedrock rerank, and oobabooga joins with a trailing-slash api_base
* test(integration): cover api_base joins across providers, query strings and named hosts
* feat(url_utils): add join_api_base and ensure_api_route helpers
* fix(url_utils): keep the scheme slashes when a stripped suffix was the whole host
join_api_base and ensure_api_route now keep the // of a base left with only a scheme,
so http://v1 with /v1 stripped joins to http://v1/files as before. The Azure v1 client
base_url goes through ensure_api_route so a base ending in /openai/v1 no longer doubles it
* fix(azure_ai): build the count_tokens URL from the messages URL
* fix(azure_ai): match anthropic as a whole path segment in the messages URL
* fix(llms): keep main's Azure AI anthropic match and non-string Bedrock endpoints
Azure AI chat and messages go back to cutting the base at the first
/anthropic, as on main, and count_tokens keeps its path check, so a
gateway path like /anthropic-proxy builds the same URLs as main. A base
that already ends in /v1/messages still gets count_tokens next to it.
A non-string aws_bedrock_runtime_endpoint (an httpx.URL VPC endpoint) is
used as its text again in AgentCore, Bedrock responses and the RAGFlow
base, instead of falling back to the default host.
get_volcengine_base_url and GigaChat's get_api_base strip a trailing
slash again for outside callers. The cache-hit wire test is skipped as a
known proxy bug, since one cache hit writes two cache_hit spend rows.
* fix(llms): drop a slash before the api_base query and tighten the route test checks
* test(llms): annotate new test locals as Final and flatten the builder generator
* fix(url_utils): keep a scheme-only api_base hostless so it fails before sending
---------
Co-authored-by: Raushankumar0720 <raushan.singh.cg@gmail.com>
A guardrail hook plants a litellm_metadata key on a chat completions request after the proxy
stamped litellm_received_at into metadata. get_litellm_metadata_from_kwargs prefers that key
when both exist and only copied the user_api_key_* fields across, so the response timing
headers lost their receive anchor and x-litellm-overhead-duration-ms reported 2 ms on
/v1/chat/completions against 58 ms on /v1/responses for the same proxy work. The receive stamp
now rides along with the user_api_key_* fields.
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(perf): add small_bodies_30rps and mixed_size_requests_4rps load cases
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(perf): run the load cases at 50 rps small and 5 rps mixed, the pod's knee
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(perf): ramp the load cases until the pod stops keeping up and report sustained_rps
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(perf): hand the ramp to the observation and widen the mixed ramp to 14 rps
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(perf): floor the small ramp at 20 rps and emit the per-step readings
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(perf): emit the ramp's hold bound and each step's unexpected statuses
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(perf): read each ramp step's cpu, redis latency and connections from the samplers' windows
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(perf): floor mixed_size_requests_ramp at 8 rps from the lane's 10 rps sustained step
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(perf): compare step readings with math.isclose so the e2e basedpyright gate stays clean
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: kerry <kerry@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): keep a retried /v1/messages stream's spend log by logging the superseded attempt as a failure
A /v1/messages stream the router retries before any content shares one logging object across both attempts. The superseded attempt's end-of-stream logging ran as a success (a usage-only or placeholder result) and claimed the request's one-shot success log, so the recovered stream's spend row was either missing or written under the superseded attempt's message id. The passthrough logging path now logs an Anthropic stream whose frames carry an `event: error` as a failure with whatever usage the provider reported, the way a dropped stream already does, and the router closes the superseded stream before it opens the retry so that logging is queued first.
* fix(proxy): log a provider error frame through an attempt-scoped logging copy
The error-frame failure stashed the superseded attempt's usage on the logging
object the retry shares, so the retry's success log carried the failed
attempt's token counts. The failure now logs through a copy of the logging
object with its own model_call_details, and the helper moves into the
Anthropic passthrough logging handler so the proxy streaming handler only
routes by endpoint type.
* fix(proxy): scope the error-frame failure log to streams closed at the frame
A stream that ran to its end at the provider's error frame kept logging
through the success route before this PR, and the attempt-scoped failure
log is now taken only for a stream the router closed at that frame, with
the copy made at the close so the retry's deployment never reaches it
* fix(router): log each dropped /v1/messages attempt through its own logging copy and open the retry without its usage
A /v1/messages stream dropped after message_start and retried in its model group shares one Logging object
across attempts, so the retry's success log inherited the dropped attempt's stashed usage and exception,
and a second drop never reached the failure handlers because the first had already marked the object as
logged. The dropped attempt now dispatches its failure handlers through an attempt-scoped copy, and the
router discards the stashed usage right before it opens the next attempt, on same-group retries and on
every fallback hop, so a request that runs out of attempts still bills the last attempt's partial usage.
* fix(router): dispatch chat stream failures through the attempt-scoped logging copy
The router now clears a dropped attempt's stashed usage before every fallback hop, so the chat stream wrapper's queued failure log must read that usage from its own copy of the logging object, like the /v1/messages path already does. The stash test asserts through a real success log, the cooldown test reads a real Router's cooldown list, and the integration helpers carry full types.
* test(vertex): give the stream wrapper's logging stub an attempt_scoped_copy
* test: route the pass-through logging mock's attempt copy to itself and read the callback sink through a reader
The pass-through failure test stubbed dispatch_failure_handlers on a spec'd MagicMock, which the attempt-scoped copy no longer reached, so the logging worker never finished. The integration tests kept earlier sink batches in a caller-owned list the helper mutated; a small reader keeps them instead.
* test(router): use real Logging in fallback cooldown test
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(router): preserve superseded retry usage for billing
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): bill retry input after prior stream usage
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): track retry dispatch for terminal billing
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(router): use deterministic retry timestamp
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(router): avoid real clock in fallback tests
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(tests): remove duplicate streaming logger import
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): retain error-frame usage for exhausted retries
* test(proxy): fix retry billing expectations and helper annotations
* fix(proxy): preserve failure estimate without carried usage
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(proxy): audit retry helpers and fixtures
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): fix retry fixture timestamps
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): observe retry stash at attempt boundary
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(logging): keep terminal failure attribution when dropped attempts log on copies
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(logging): publish a dropped attempt's payload only while it is still the current attempt
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): cover spend-log encoding error propagation
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(anthropic): skip adapter failure dispatch when the wrapped stream already logged it
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(router): cool down the final fallback attempt when its failure callbacks were blocked
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(anthropic): import Final in adapter regression
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): drop spend-log encoding expectation superseded by main's poison isolation
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(anthropic): finish the wrapped stream's failure log before surfacing the error
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(anthropic): wait for the wrapped stream's failure log to finish like the inline dispatch did
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(anthropic): wait for the executor-run sync failure callback separately from the async caller boundary
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): pin pass-through stream fixture start times
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(logging): publish failed attempt payload before awaiting failure callbacks
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(logging): publish failed attempt payload from the failure handler after baseline invalidation
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(logging): drop unused discard_partial_usage_for_failure
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(vertex-ai): support gemini 3.1 flash tts
* fix(vertex-ai): keep TTS metadata compatible with the cost map guard
* fix(vertex-ai): preserve existing Gemini TTS models and voice mappings
* fix(vertex-ai): recognize provider-stripped TTS model names
* test(vertex-ai): match current speech HTTP error response
* fix(vertex-ai): preserve nested speech locale and voice
* fix(vertex-ai): use provider speech media type in proxy
* fix(gemini): preserve TTS dispatch and nested speech settings
* test(gemini): run TTS regressions in CI shards
* fix(vertex-ai): account for Gemini Cloud TTS audio cost
* fix(vertex-ai): bill Gemini TTS without optional audio decoder
* test(gemini): cover TTS mapping and billing edge cases
* test(vertex-ai): cover Gemini voice normalization in provider shard
* fix(vertex-ai): keep Gemini TTS routing with generic provider metadata
* chore(vertex-ai): remove obsolete lint suppressions
* fix(gemini): strip audio format from speech configuration
* chore(vertex-ai): remove obsolete mutation suppressions
* fix(vertex-ai): preserve FLAC billing and wrapped audio headers
* fix(vertex-ai): reject unmeasurable FLAC before billing
* fix(vertex-ai): reconcile speech support with current provider mappings
* fix(vertex-ai): validate speech mappings and usage types
* fix(vertex-ai): preserve speech mapper override forwarding
* fix(gemini): map voice aliases across speech request forms
* fix(vertex-ai): send only formats the speech bridge cannot serve to Cloud TTS
Gemini TTS detection now goes by model name, so newer Vertex Gemini TTS
models keep working, and only mp3, opus, alaw, mulaw and other container
formats move to Cloud Text-to-Speech. wav and pcm requests stay on
generateContent as on main.
A Cloud TTS response whose duration cannot be measured now returns the
audio unbilled with a warning instead of raising after a paid call. FLAC
handling is removed because Cloud TTS has no FLAC encoding.
The cost map, the google_genai mappers and the proxy error path go back
to main, since those changes were outside this feature.
* fix(vertex_ai): keep Gemini TTS formats, region and instructions on routes that serve them
Only mp3, opus, alaw and mulaw go to Cloud Text-to-Speech. Every other
format gets a local 400 that lists the supported ones, before Google is
called.
Cloud TTS uses the deployment's us or eu regional host. Other regions
and a custom api_base get a 400 that says to request wav or pcm.
instructions is sent as the Cloud TTS style prompt, and a request with
no voice keeps the Gemini model.
get_usage_object is back to main's code, so dict usages still go
through Usage.__init__, and the proxy no longer labels pcm16 or
linear16 output as WAV.
* fix(vertex_ai): send Gemini TTS to every regional Cloud TTS host and keep speak tags as text
Cloud Text-to-Speech serves Gemini TTS on a host named after each Vertex
location, so a non-global vertex_location now uses
{location}-texttospeech.googleapis.com instead of only the us and eu hosts.
Google answers for regions it does not serve.
Gemini TTS voices reject SSML, so input that contains <speak> is sent as
text for Gemini TTS models. Chirp keeps the SSML auto-detect.
Removes the proxy alaw and mulaw media-type entries. Cloud TTS returns
those encodings with a WAV header, so the response is labeled audio/wav
and the entries never applied.
Adds a regression test that custom per-token pricing bills the usage a
speech response reports.
* fix(vertex_ai): map Gemini TTS regions to Google's Cloud TTS locations and keep a bad voice from cooling down the deployment
US and EU Vertex regions go to the us and eu Cloud TTS hosts, northamerica-northeast1 to its own, global or unset to global, and any other region gets a local 400. A Cloud TTS 404 on the Gemini path, such as a misspelled voice, comes back as a 400 so the router keeps the deployment serving wav. Usage reads the sent request from the response instead of the logging object.
* fix(vertex_ai): default Gemini TTS Cloud formats to a Gemini voice and validate speakers
Parse only usage fields from the sent Cloud TTS request, so a numeric speakingRate no longer fails after Google bills it.
Send the Gemini voice Kore when no voice is named, and reject Cloud TTS voice names unless drop_params is set.
Reject malformed speaker entries unless drop_params is set, and translate Cloud TTS speaker fields on wav and chat audio.
Use the Vertex default region when no location is set, so mp3 and wav share a region.
* test(vertex_ai): cover two-speaker Gemini TTS mp3 through aspeech with fake credentials
Replaces two tests that patched the Vertex TTS config's own token and URL methods.
* fix(vertex_ai): reject malformed Gemini TTS voices and keep beta Chirp and Lyria params unmapped
* fix(vertex_ai): drop unknown Gemini TTS voice keys on wav and pcm instead of rejecting them
* fix(vertex_ai): reject unknown Gemini TTS voice keys on wav, pcm, and chat audio unless drop_params is set
* fix(vertex_ai): honor every drop_params setting in the Gemini TTS voice key check
Global drop_params, batch row and deployment drop_params on Vertex batch
file uploads now drop unknown voice keys. A voice object with both
speechConfig and speech_config gets a 400 for the ignored speech_config.
* fix(gemini): drop unknown nested voice fields under drop_params and detect TTS by served model
* fix(gemini): filter unknown speech config fields with fixed-depth helpers and pass audio params through unchanged
* fix(gemini): camel-case snake replicated voice keys so drop_params keeps them
* fix(vertex-ai): reject voice fields Cloud TTS cannot send unless drop_params is set
* fix(vertex-ai): reject wrong-typed Cloud TTS voice values and check speakers as sent
Gemini TTS on mp3, opus, alaw, or mulaw now answers 400 for a voice field whose value Cloud TTS cannot send, such as a list languageCode or a string voiceConfig, and for nested voice fields under a speaker that also names speakerId. drop_params still drops them.
* fix(vertex-ai): reject wrong-typed speech config wrappers and speakerId overriding a prebuilt voice on Cloud TTS
* fix(vertex-ai): keep speakers and voice_config beside a speech_config wrapper on Cloud TTS
* fix(vertex-ai): pick Cloud TTS voices from the same speech config the WAV path sends to Gemini
* fix(files): carry the deployment's drop_params on model= file uploads so Vertex batch rows drop unknown voice keys
* fix(gemini): keep a structured speech voice when the model is a model_alias_map alias for a Gemini TTS model
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(cli): send pi's session id from lite pi so sessions group and stick
* docs(cli): drop the provider_block docstring lines the README already covers
* docs(cli): note the session id format the proxy reads for lite pi
