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702 commits
| Author | SHA1 | Message | Date | |
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mika-lt
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683b94b462
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fix(fallback): read stored router_settings from the database on fallback writes (#48999)
* fix(fallback): read stored router_settings from the database on fallback writes
POST /fallback and DELETE /fallback/{model} read the current rules through the
config cache. When Redis refuses DEL, the eviction before that read fails and
Redis keeps serving the old copy, so each new rule dropped the earlier ones
The write path now reads the router_settings row straight from the writer
database and stores only that row plus the changed key, so config-file router
settings no longer get copied into the database row
* fix(fallback): edit the config file's fallback list when it owns the key
A delete of a rule only the config file holds answers the config-owned 400 again instead of 404, and restating a rule the file already has answers 200 without copying the file's list into the database row. Adds a writer-read regression test and an integration test against a Redis whose ACL refuses DEL
* fix(fallback): re-read the config file's fallbacks ownership on every fallback write
* test(fallback): run the real config reload in the fallback write tests
* test(fallback): key the refused-DEL Redis double by cache key
* fix(fallback): keep the config-owned 400 for an unset env reference and replace a non-object stored row
* fix(fallback): replace a stored router_settings row that holds a non-JSON string
* fix(fallback): compare a restated config-file rule against its resolved env value
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
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mika-lt
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c21e576a07
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refactor(types): replace Any with proven types in 14 files (#48928)
* refactor(types): replace Any with proven types in 57 files Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(types): hide input values in validation errors and drop secret-bearing seams Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(types): revert the Phoenix prompt seam that rejects string content Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(types): revert files that break mocked tests and Python 3.10 imports Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(types): revert pegasus response validation to main Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(types): revert azure search, milvus and onyx response validation to main Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(proxy): keep accepting numeric keys in /cache/delete * fix(caching): answer 400 for a non-list /cache/delete keys and accept null Stability lists Return pydantic errors without the caller's input on a bad /cache/delete body, let the Bedrock Stability image edit schema take null finish_reasons and images like the old reads did, declare user_id and user_role on the plugin session claim, wrap the retyped Vertex fields in ReadOnly, and drop unused Guardrails AI types * refactor(types): leave provider reply parsers for a follow-up with live provider calls Restores the Bedrock Stability, Bedrock KB, AgentCore, Replicate, Recraft, Aleph Alpha and Guardrails AI parsers to main, since no live provider call could prove the stricter schemas. Also types the cache delete route tests. * refactor(types): keep /cache/delete on main's behavior Restores the cache delete route and its test to main, so this types PR changes no response. Rejecting a non-list keys is left for its own change, since an object keys deletes its keys on main today. * fix(types): keep text completion, batch ids and guardrail callback on main's code * fix(types): keep policy suggestion and auto router parsing on main's code --------- Co-authored-by: mateo <mateo@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> |
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mika-lt
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4212f1af27
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fix(passthrough): forward OpenAI-Beta and default agents=v1 on the OpenAI passthrough (#49009)
* fix(passthrough): forward OpenAI-Beta and default agents=v1 on the OpenAI passthrough The OpenAI-shaped passthrough now forwards the client's own OpenAI-Beta header (agents, chatkit, realtime and any future beta), keeps an operator's extra header over it, and injects agents=v1 on /v1/agents and /v1/vaults paths when the client sent none. Unbilled Agents API calls now log the agent's model on the spend row instead of unknown. * fix(passthrough): keep OpenAI-Beta forwarding on OpenAI only and name the model on streamed and saved agent rows Azure and Azure AI keep main's assistants=v2 rule. ChatKit paths get no route default. Streamed agents rows read session.agent.model from the session events instead of defaulting to gpt-4o, and saved agents name their top-level model. * fix(passthrough): forward the client's OpenAI-Beta only on agents, vaults, and ChatKit paths --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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tessellark
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41602e318d
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fix(proxy): stream a guardrail block on /v1/messages as Anthropic events (#49010)
* fix(proxy): stream a guardrail block on /v1/messages as Anthropic events * fix(proxy): keep post-call guardrails off the streamed messages block The streamed block is sent as-is, like the Responses endpoint's block, so post-call guardrails and streaming hooks never scan the proxy's own block message. Tests cover a post-call rail on the stream and the call id on the JSON block * test(proxy): expect Anthropic events from a streamed messages guardrail block The legacy guardrail suite pinned the old single bare data: line, which is the malformed block this branch replaces. The test now checks the six Anthropic stream events and the block text, so it fails if the bare line comes back * test(proxy): block messages through a real pre-call guardrail in endpoint tests The block tests replaced ProxyBaseLLMRequestProcessing.base_process_llm_request on the class to raise the block. They now send a real request through the endpoint with a pre-call guardrail that raises the passthrough block, and check that the x-litellm-call-id header carries the request's call id --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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tavren27
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3e3362d461
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fix(cohere): price native Cohere embed calls on Foundry relays and the Cohere passthrough (#48978)
* fix(azure_ai): price Foundry relays of the native Cohere v2 embed route A proxy relay of POST /azure_ai/<group>/providers/cohere/v2/embed logged spend 0 because the Foundry relay shapes had no entry for it, so the body fell through to the passthrough object with no usage. The Cohere v2 embed body-to-EmbeddingResponse transform is now one shared function that the direct Cohere path and the relay both call, priced from meta.billed_units and falling back to the echoed texts' token count when billed_units is absent. A body that is not a Cohere embed shape raises ValidationError, so the relay fails open as before. * fix(cohere): price native Cohere embeds on the v1 Foundry relay and the v2 passthrough, billing image tokens per caller The Foundry relay now also prices /providers/cohere/v1/embed, and the shared Cohere embed parser accepts the v1 plain list of float vectors next to the by-type object. Direct Cohere bills billed_units.image_tokens (or v1 images) apart from input_tokens, while Foundry already folds the image into input_tokens, so the parser takes an explicit CohereImageTokenBilling and each caller picks its own. The /cohere passthrough now routes and prices /v2/embed through the same parser as the cohere/ SDK path. The relay prices any body that carries billed units whatever texts holds, and the no-billed-units fallback counts special-token text as plain text instead of raising * fix(cohere): keep embed type labels, per-image v3 pricing, and the plain row for unpriceable passthrough embeds Passthrough and Foundry relay log rows keep each vector's embedding type, text-only usage keeps image_tokens null, a Cohere v3 billed image count is priced per image, and an embed call the cost map cannot price falls back to the plain passthrough row instead of raising inside the logger. * fix(cohere): warn when a passthrough embed cannot be priced and pin test timestamps * test(cohere): cover embed-jobs passthrough calls writing the plain passthrough row --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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tavren27
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e7d3b24d7c
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fix(logging): mark mirrored silent_model calls with is_silent_experiment in spend logs (#48888)
* fix(logging): mark mirrored silent_model calls with is_silent_experiment in spend logs and logging payloads * fix(router): keep the mirror flag when litellm_metadata is set and strip a client-sent is_silent_experiment Spend rows read router metadata from litellm_metadata when a call carries it, so the router now sets the flag there too. The proxy drops a client-sent is_silent_experiment with the other router-owned keys, so real traffic cannot be recorded as mirrored. * fix(logging): mark mirrored calls with a router-only key so clients cannot set is_silent_experiment Spend rows and the standard logging payload now derive is_silent_experiment from _litellm_silent_experiment, which only the router writes and the proxy and pass-through routes strip from client metadata. A caller's own is_silent_experiment key stays untouched, so it keeps its current effect on mirroring and on provider metadata, and unmirrored calls log false in both places. * test(langfuse): expect is_silent_experiment false on every logged generation * fix(logging): mark mirrored calls with a router-only enum value so no client route can set is_silent_experiment The router stamps SilentExperimentMarker.MIRRORED, which client JSON can never produce, so batches, Responses WebSocket frames and pass-through need no strip. Langfuse layers the flag after its requester-metadata dedupe. * fix(logging): set is_silent_experiment only on mirrored calls so real calls keep their payloads Real calls no longer gain an is_silent_experiment key in spend rows or the standard logging payload, so OTEL, S3, GCS and every other logger receive what they did before. The Langfuse regression test now runs the real prompt helper. * fix(logging): keep the mirror marker out of callback metadata and read the spend-row flag from the logging payload Logging now strips the router's mirror marker from the request metadata buckets before any callback or vendor sees it, without touching the router's own dicts, so retries keep it. Spend rows read is_silent_experiment from the standard logging payload, which survives the collector's JSON round trip, so collector-mode rows keep the flag too. * fix(logging): mark mirrored calls through a context variable so router usage counts them once * fix(router): leave callback-started calls unmarked and stop client flags from turning mirroring off Logging handlers now run outside the mirrored-call context, so an LLM call a success or failure callback starts while logging a mirrored call is not flagged as mirrored. The guard against mirroring a mirror reads the same context variable instead of request metadata, so a client-sent is_silent_experiment no longer skips the mirrors. The unused module-level get_standard_logging_metadata stops copying resolved keys from raw metadata. * fix(router): keep main's client flag mirroring skip and the other logging metadata keys A client's is_silent_experiment: true skips mirroring again as on main, the module-level get_standard_logging_metadata drops only the flag, and the silent experiment tests wait on a logged-call signal instead of polling a deadline * fix(logging): take the mirror flag in Prometheus labels and Datadog cost tags only from the router * test: type the new parametrized tests and keep the recorder from changing its argument * fix(datadog): keep client request tags from setting the silent experiment cost tag A request tag is_silent_experiment:true set the Datadog cost tag on a direct call. Only the router flag sets it now. Also covers the sync failure handler's mirrored-call reset with a failure function callback test. --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Priyansh Nandwana
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67ae3fb527
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fix(websearch)!: keep the provider prefix on the agentic follow-up model (#38885)
* fix(websearch): keep the provider prefix on the agentic follow-up model The agentic follow-up rebuilt its model string with "a slash means it is already qualified". Handlers are handed the provider-stripped model, and for a provider that routes through a sub-path the remainder still holds a slash, so bedrock/mantle/anthropic.claude-sonnet-5 arrived as mantle/anthropic.claude-sonnet-5 and stayed that way. The follow-up acompletion could not resolve a provider from it and raised "LLM Provider NOT provided". Prefix unless the model is already qualified with that same provider, in one shared helper used by both the handler and the websearch patch builder, which carried copies of the same heuristic. This also fixes openrouter, whose stripped model keeps a slash for the same reason, and the old startswith test that read openai as a prefix of openai_like. Fixes #38829 * test(websearch): cover the agentic follow-up call site The helper had unit tests but the call site did not, so the two changed lines in _execute_chat_completion_agentic_plan were uncovered. Drive the real follow-up with mock_response instead of patching litellm, which keeps the test on the behaviour: against the old code the sub-path case raises "LLM Provider NOT provided", and the ordinary-model case passes either way as a guard. * refactor(websearch): trim the comments on the provider-qualify helper * refactor(websearch): cut the helper and test docstrings to one line * fix(websearch): leave a hook-qualified follow-up model on its own provider A hook that patches in its own model owns that choice, so re-qualifying it sent a cross-provider follow-up to the original request's provider instead. Only a bare patched model and the request's provider-stripped model take the prefix now, and the sibling agentic-loop path resolves the model the same way. * fix(websearch): fall back to the request model when a patch carries no model An empty patched model dispatched a provider-only name instead of falling back, and a provider-stripped sub-path copied into the patch lost its prefix. A patched model now keeps its own provider only when its leading segment names one. * fix(websearch): keep the provider prefix on the responses follow-up model too The responses patch builder still used the old "a slash means already qualified" test, so a provider-stripped sub-path model reached the follow-up unqualified on that surface. It now resolves the model the same way the chat completion builder does. * fix(websearch): read custom_llm_provider without a member call on untyped kwargs * fix(websearch): type the chat patch builder kwargs instead of working around them * test(websearch): run the real search path and flatten the qualify helper tests * fix(websearch): route a hook's model on a JSON-configured provider and keep the request provider on an echoed model * fix(websearch): leave a follow-up model alone when its prefix already routes to the request provider * fix(websearch): send the agentic follow-up to the model the caller requested completion() and responses() record the caller's model before routing strips it, and the chat and Responses follow-up builders prefer that model. This keeps bedrock/converse_like/, bedrock/claude_platform/ and openrouter/openrouter/* deployments on their own route for the follow-up. * test(websearch): cover the recorded caller model in Headroom's retrieval follow-up * test(websearch): cover the recorded caller model in Compresr's retrieval follow-up Also merges the repeated provider-prefix check in core_helpers into one helper and drops a test docstring * test(websearch): drive the real search path through a mocked Perplexity HTTP route * refactor(websearch): read the provider from kwargs inside _caller_model * test(http-handler): type the agentic follow-up provider tests * refactor(websearch): record agentic loop params through a Logging method Moving the recording out of completion() keeps it under basedpyright's code-flow complexity limit after the merge with main. --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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zoveki
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c87a25a357
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feat(proxy)!: general_settings.claude_code_marketplace_auth_required puts the Claude Code marketplace behind a LiteLLM key (#48980)
* test(proxy): require a key for the Claude Code marketplace when configured and keep exception text out of its 500s * feat(proxy): general_settings.claude_code_marketplace_auth_required requires a LiteLLM key for the Claude Code marketplace When true, GET /claude-code/marketplace.json answers 401 without a key in Authorization, x-litellm-api-key, the litellm_key_header_name header, or ?key=, and GET /public/skill_hub returns an empty list. Both routes' 500 bodies carry a fixed message instead of the exception text. * fix(proxy): refuse ?key= and send WWW-Authenticate when the Claude Code marketplace requires a key * fix(proxy): close the marketplace key gate's outage, quoted-setting, and dashboard gaps Refuse the database-outage fallback identity with a 401, read a quoted "true" as on and an unreadable value as on, keep the skill hub's 500 detail a fixed string, and have the dashboard setup tab show a settings snippet with a key header when the marketplace requires a key * fix(proxy): keep the no-database 500, validate the marketplace key setting at boot, and show the key snippet until the probe answers * fix(claude-code): read the marketplace key setting from the UI config and answer database outages with 503 * fix(claude-code): keep the no-database hint on skill_hub and warn that the remove step uninstalls plugins --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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simonp9
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e8f0735d8a
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feat(proxy): let a Claude Code gateway user choose the session team (#49001)
* feat(proxy): let a Claude Code gateway user choose the session team The gateway's browser verification page now lists the developer's teams, and the device code grant mints the session for the picked team. A team the developer is not on is refused with a 400, and a form without a team keeps the first-team default. * fix(proxy): refuse a stale gateway team pick and require an explicit choice * fix(gateway): list the browser-picked team first so an older replica mid-deploy issues it --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Mateo Wang
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c489aa7117
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feat(proxy)!: add suppress_startup_output to turn off startup prints (#47708)
* fix(proxy): keep every startup line JSON when json_logs is on Boot-time messages the proxy printed straight to stdout (banner, feedback box, model list, cache and callback notices, policy table, migration notices, CLI launch notes) and the first records of uvicorn- or granian-spawned workers came out as plain text while json_logs was on, breaking log pipelines that parse every line as JSON. - print_startup_message() prints unchanged in plain mode and, with json_logs on, emits one INFO record on the proxy logger (ANSI stripped) that skips the logger and handler level checks like the print it replaces; show_banner() routes to it in JSON mode only - the CLI exports JSON_LOGS=true when the config sets json_logs so spawned workers log JSON from their first record - the --enforce_prisma_migration_check deprecation notice prints after the config is read so a config-only json_logs covers it Resolves LIT-1624 * fix(proxy): keep show_banner() SDK-free, pick the JSON banner in initialize() Since #46803 the `lite` shell calls show_banner() against an uninitialized litellm package, and reading litellm.json_logs inside it initialized the whole SDK at the banner (0.14 s to 0.83 s for a bare `lite` here, the seconds that PR removed on small VMs). show_banner() is back to its main version; the proxy's initialize() now emits the JSON banner record itself and calls show_banner() only in plain mode. The banner unit tests move to the initialize() call site. * fix(proxy): keep the JSON banner when an operator --log_config names the proxy logger uvicorn applies --log_config after proxy_cli has switched JSON logging on and before initialize() switches it on again through load_config(). A logging config that names "LiteLLM Proxy" removes that logger's handlers in the window between the two, which is where initialize() emits the banner and the feedback box, so at one worker both went nowhere; two-worker children import litellm after uvicorn's logging setup and were not affected. print_startup_message() now falls back to a one-off JSON handler, on the stream the JSON handler uses, when the proxy logger has no handler of its own. Live, json_logs on plus a log_config naming the proxy logger, one worker: main prints the banner and the feedback box as plaintext, |
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devin-ai-integration[bot]
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e85cb4052e
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test: fix flaky spend-log cleanup budget, login lockout, and wedged Redis tests (#47108)
* test: stabilize cleanup, lockout, and post-kill timing tests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): bound the wedged Redis burst p95 by the socket timeout the test sets The burst p95 under a fixed 0.75s cap tracked how fast the box ran the 30 concurrent writes, so a loaded runner failed it at unchanged code. A handler that waits on the wedged publish takes at least the coordination Redis socket timeout, so the test now sets that timeout and asserts the p95 stays under it. * test: pin the lockout clock and bound the wedged Redis burst by the proxy's 2s wait cap The lockout tests now set the login throttle's clock instead of reading the real one, so Retry-After is pinned at 77 and a round-down fails on every run The wedged Redis burst p95 is now bounded by the 2s cap the proxy puts on its own Redis waits, so a burst wait capped under the 5s socket timeout still fails the test The fresh post-kill clients keep the harness's 2s keep-alive expiry, here and in the two tests #47080 changed * test: tell ceil from round in the lockout tests and bound the burst by the imported eviction cap The refused attempt now runs with 76.4s left on the 77s block, where ceil gives 77 but round and int give 76, so both lockout tests fail if Retry-After stops rounding up. The burst bound imports _BULK_EVICTION_WAIT_SECONDS instead of copying its 2s value, so lowering the cap moves the bound with it. * test: bound the wedged Redis burst by the shorter of the eviction cap and the socket timeout Any wait the proxy puts on the stopped Redis lasts at least the shorter of the two, so the bound now follows whichever one a later change lowers. * test: drive the tool index cleanup test with the manual clock and keep the post-kill block as on main The post-kill reset is already fixed on main by making kill() wait for the worker's last thread, so the fresh-client and keep-alive edits go back to main's version. The tool index cleanup test now advances the injected clock instead of timing the real one, and the lockout clock helper gets a type hint. * test: advance the spend log cleanup tests' manual clock through its own method * test: freeze the throttle clock in the login_utils lockout tests and drive the run budget tests with the manual clock --------- 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> |
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paveln83
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032342d2b7
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fix(responses): resume background response streams with stream and starting_after (#48819)
* fix(responses): resume background response streams with stream and starting_after
Retrieving a response with stream=true and starting_after=N now returns the
replayed event stream from OpenAI and Azure instead of one JSON object, on the
SDK (get_responses / aget_responses) and on the proxy's GET /v1/responses/{id}
Providers that cannot replay a stored stream drop the params under drop_params
and raise UnsupportedParamsError without it. Malformed values answer 400 with
the param named. A replay that reaches its terminal event settles the
background response's bill once and passes the provider's usage through
without estimating one. An empty replay (cursor at the end) ends normally
* fix(responses): encrypt replayed stream ids and resume Azure AI Foundry streams
A replayed background stream now gets the same response id encryption as a
created one, so the key that made the response can reuse the id. Azure AI
Foundry deployments resume streams like Azure ones, and the chat-bridged
Responses iterator closes its wrapped stream instead of raising.
* fix(responses): drop malformed resume params under drop_params and keep JSON answers to a streamed retrieve
A bad stream flag drops to a plain retrieve and a bad cursor to a full replay when drop_params is on, and a cursor without stream is ignored even when malformed. A streamed retrieve that the upstream answers with JSON returns the response object instead of an empty stream. get_responses forwards only the include keys from extra_query.
* fix(responses): give proxy stream resumes the deployment stream_timeout and document retrieve query params
* fix(responses): keep deployment stream off retrieves and reject oversized cursors
* fix(responses): keep router default stream off plain retrieves
* test(responses): black box coverage for resume refusals, drop_params, stream timeout and billing
* test(responses): replay a real stream in the billing test and type the fake wrapper chunks
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>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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AaronHowell
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904c092ffe
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feat(providers): add BigModel support and model pricing (#48563)
* feat: add BigModel provider with native Chat, Responses, and Messages APIs Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com> * test(providers): make BigModel unit tests a package Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com> * docs(providers): clarify BigModel parameter interface Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com> * fix(bigmodel): keep the Coding Plan base, surface Responses auth errors, and scope routes Chat Completions keeps a GLM Coding Plan api_base. Responses raises the status from BigModel's success=false body instead of returning an empty 200, and a JSON body answering a stream request is replayed as a one-shot stream. Messages streams keep ping and [DONE] lines. bigmodel leaves openai_compatible_providers so speech, image, and transcription routes send it no request * fix(responses): replay a JSON body on a stream request only when it has a Content-Length A gateway that labels a chunked SSE stream as application/json kept streaming before the replay, so only a sized body is read in full * fix(bigmodel): scope the JSON stream replay to BigModel, map its business error codes, and echo requested tools * fix(bigmodel): forward Codex's include and parallel_tool_calls, keep the OpenAI org header off BigModel, and pass unchanged Messages stream events through * fix(bigmodel): map disabled thinking to low effort, keep Messages temperature, raise context window and content policy errors, and default parallel_tool_calls to true * fix(bigmodel): keep the caller's reasoning_effort when Messages rewrites disabled thinking * fix(bigmodel): keep context-window and content-policy errors on streamed Responses replays --------- Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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devin-ai-integration[bot]
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3c1eb73a06
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fix(proxy)!: list every priced Vertex family under a vertex_ai/* grant (#41959)
* fix(proxy): list every priced Vertex family under a vertex_ai/* grant A vertex_ai/* key listed none of the 68 cost map rows priced under the Mistral, Llama, Qwen, AI21, OpenAI, image, video and embedding labels because their sets were never part of the vertex_ai union, and Qwen rows had no ingestion branch at all. Wildcard expansion also dropped a meta/ or openai/ publisher segment as if it were a provider prefix, and the wildcard health check would have picked $0-per-token rows first once the union grew, so it now sorts paid rows ahead of unpriced ones. The VERTEX_FAMILIES_A_VERTEX_WILDCARD_GRANT_DOES_NOT_LIST allowlist is gone so the cost map guard enforces every label from now on. * fix(health): route every wildcard health-check pick through the listed provider * fix(health-check): keep per-character-billed rows ahead of rows priced $0 everywhere in the wildcard pick * fix(health-check): break $0-per-token ties toward per-character-billed rows in the wildcard pick * fix(health): only health-check ids the router sends to the provider The wildcard health check picker now skips ids that get_llm_provider routes to a different provider or to none, so the longer vertex_ai union cannot pull partner rows into the check. Drops the forced custom_llm_provider and the per-character tie-break from earlier revisions, and pins the replicate and wandb wildcard listing to each row's full owner id. * fix(health): match wildcard health picks against the provider's own route The previous check kept a bare id only when get_llm_provider routed it to the exact provider name, which dropped every ai21 row because bare ai21 ids route to the ai21_chat alias, so ai21/* health checks sent no request. Compare the bare id's route with the route of the provider-prefixed id instead, so aliases match. Resolve routes with inferred_provider, which returns the declared provider for github_copilot and chatgpt without starting their device-code login. * fix(health): keep wildcard health picks that only route through a saved custom_llm_provider Rank picks routed to the checked provider first and ids routed to no provider second, each by cost, and skip only ids routed to a different provider, so a wildcard saved with custom_llm_provider still sends its health request * refactor(vertex_ai): mark the new Vertex registry sets Final * chore: re-run CI against current main --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Mateo Wang
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65d912fc2e
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feat(responses): support Codex client tool_search on Anthropic and other bridged providers (#48972)
* feat(responses): support Codex client tool_search on Anthropic and other bridged providers * test(router): cover get_search_tool_support provider defaults and model_info overrides * fix(responses): keep tools loaded by earlier Codex tool searches across previous_response_id * perf(responses): read stored turns once on client tool search continuations * fix(router): avoid OAuth refreshes when listing search capabilities * test(cli): expect disabled search on fallback Codex models |
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rj-cairn
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5eb1042bbf
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fix(token_counter): count OpenAI models newer than tiktoken's table with o200k_base (#48728)
* fix(token_counter): count OpenAI models newer than tiktoken's table with o200k_base Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(token_counter): keep tiktoken's encoding for suffixed variants of models it knows Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(token_counter): move the o200k provenance note into the assert message Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(token_counter): strip openai/ and azure/ prefixes before picking the encoding Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(rust_bridge): azure/gpt-4o now resolves to o200k_base Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(token_counter): check the provider prefix through _fix_model_name to keep the type gate flat Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(token_counter): check o200k on the gpt-6 models tiktoken 0.14 still does not know * fix(token_counter): count Azure deployment names and OpenAI alias groups with their OpenAI tokenizer * fix(token_counter): count mixed, alias, and base-model deployments with every served model's tokenizer A group that serves OpenAI and non-OpenAI models now reserves at least each served model's count, model_group_alias names count as their target group, custom Azure deployment names count with their base model, and azure_ai/, chatgpt/ and similar prefixes resolve to the listed OpenAI model. * fix(token_counter): keep mixed groups on OpenAI tokenizers, cache served models, and count context checks, failed rows and stream fallback with the served model * fix(token-counter): keep mixed groups at their old reservation and count new OpenAI release names with o200k A group that serves an OpenAI model next to another vendor now counts its own name the way tiktoken reads it as written, plus its OpenAI models, so it reserves what main did instead of the lower o200k count base_model picks the tokenizer for estimated usage only when it names an OpenAI model, now also in the Cursor stream counter and the output-limit response Unlisted gpt-5+ and o-series names count with o200k_base, and wildcard routes run the context-window check with the matched OpenAI model * fix(token-counter): count an alias to a group no OpenAI model serves by the alias as written Keeps the count main gives such an alias, so its budget reservation and rate limit match main and no other vendor's tokenizer loads for it. Also sorts the import the main merge left unsorted. * test: type the new token-counting tests' parameters * fix(token-counter): count a release-named group no OpenAI model serves as main does, and snapshot the OpenAI model set before iterating it * fix(token-counter): read served models through the router's allowlisted deployments_named, and coordinate the registration race test * perf(token-counter): classify each distinct served model once when walking a model group Budgeted requests walk every deployment in their model group, since the router's cached copy is outside the router surface the proxy may use. Classifying each distinct served model once instead of once per deployment cuts a 200-deployment group from 2.3-4.5 ms to about 1.4 ms with the same result. --------- Co-authored-by: mateo <mateo@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> |
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paveln83
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3f5e42d032
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fix(proxy): enforce team and key Allowed Vector Stores when no vector store registry is loaded (#48915)
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* test(proxy): cover team and key vector store allowlists when no vector store registry is loaded * fix(proxy): enforce team and key Allowed Vector Stores when no vector store registry is loaded * fix(proxy): skip non-string vector_store_ids entries instead of failing auth * fix(proxy): answer 400 on a malformed vector_store_ids in legacy mode too Legacy vector store access checks now parse the request the same way vector_store_deny_by_default does, so a vector_store_ids that is not a list of strings is a 400 with or without a registry. * fix(proxy): honor drop_params and a tool's string vector_store_ids in the legacy vector store check Under drop_params the check reads the stores registry retrieval uses instead of answering 400, and retrieval reads only the string entries of a top-level list. Without it, a tool's single string id is checked as one store, the way the registry reads it. * fix(proxy): honor deployment and router drop settings for a malformed vector_store_ids * test(proxy): drop vector store registry mocks the access check no longer reads * fix(auth): answer 400 for a malformed vector_store_ids only under a key or team allowlist * fix(auth): match managed vector store ids by their provider store and read a string id as one store A LiteLLM-managed store id now matches an allowlist entry for the provider store it decodes to, in both directions, so an allowlist naming store A no longer refuses A's managed id. A top-level vector_store_ids string, which is how ?vector_store_ids= arrives, is checked as one store id instead of answering 400. Registry retrieval reads an SDK tuple of ids again --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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tessellark
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b7da9a2755
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fix(credentials): keep the stored secret when PATCH echoes its masked read-back (#48950)
* test(credentials): pin that PATCH keeps the stored secret when it echoes the masked read-back * fix(credentials): keep the stored secret when PATCH echoes its masked read-back * fix(credentials): only treat a mask on a key GET masks as a placeholder PATCH now skips mask-shaped values only on the keys get_masked_values masks, using the masker's own keyword check, so a value like api_base ab****cd is saved as sent again. Also drops the new test docstrings and tightens the new test helper types --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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tavren27
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cbf4e40d38
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fix(proxy)!: stop labelling management and health rejections as auth failures (#48739)
* test(proxy): cover management and health routes labelling rejections as auth failures * fix(proxy): name management and health route errors by their status instead of auth_error * fix(proxy): keep auth_error for 401 and 403 and answer database outages with 503 in route catch-alls /key/list and /key/aliases now share the catch-all helper too, so they stop adding the Authentication Error prefix and typing a 400 as internal_server_error * fix(proxy): answer 503 when the database drops mid-query and prisma fails to name the error A real outage surfaces from prisma's query engine as a builtin AttributeError, which the prisma-module check skipped, so the route catch-alls still answered 500 or 400 * fix(proxy): report wrapped and stalled database outages as 503 and label upstream failures by their own status * fix(proxy): answer the upstream status on the catch-all, keep auth_error on tag denials, report model insert outages as 503 * fix(proxy): answer 503 for database failovers and label provider auth errors like chat A database that refuses connections while starting up, shutting down, in recovery, or read-only after a failover now answers 503 no_db_connection instead of 400. A provider's 401 or 403 keeps the type /chat/completions gives it, while a 401 or 403 the proxy raises keeps auth_error. An exception with no message is named by its type. * fix(proxy): scope failover 503s to management route errors and keep a re-raised error's cause * test(proxy): expect the OpenAI error shape from management route refusals Team-not-found 404s now answer invalid_request_error, and every handler-raised refusal answers param null instead of the string None. --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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zoveki
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a11e76fe8f
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fix(google_genai): read one-object and null spellings of repeated fields in countTokens and generateContent (#48771)
* fix(count_tokens): normalize Google system parts and null tools * fix(count_tokens): preserve typed validator return * fix(count_tokens): validate single system part without typing.cast * fix(count_tokens): accept every Google spelling of a repeated field Google countTokens now reads a repeated field sent as one object, null, or a list with null items the same way as its list form, for system parts, tools, and function declarations, matching what Vertex accepts. * fix(count_tokens): read every Google spelling of contents and skip null system text contents and each content's parts now read one object, null, and null items like their list form, the same as system parts and tools. A one-object parts used to translate to no messages, so non-Google models counted it as empty. A system part whose text is null is skipped, as Vertex AI does. * fix(google_genai): read null-text media system parts as media and one-object content parts on generateContent * fix(google_proxy): count null-text system parts that carry only part metadata, like Vertex AI A system part with null or missing text and only metadata such as thought or thoughtSignature, or an empty part, now counts as empty instead of answering 400. Parts that carry an image or file with null text still answer 400. --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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mika-lt
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de42b67252
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fix(proxy)!: enforce an end user's per-model budget from their linked budget (#48815)
* fix(proxy): enforce an end user's per-model budget from their linked budget The shared auth checks now refuse a request once the end user's spend on that model reaches the model_max_budget of their budget (a linked budget or the default applied to them), for virtual keys and the master key alike. The cap is read from the end-user row, since the master key's cached auth object keeps whichever end user's limits it was cached with. The per-model end-user check inside virtual-key validation is removed: it read a token field that is never set there, so it never ran. * fix(auth): enforce the end user's per-model budget on JWT auth and skip it on budget-exempt routes JWT tokens now carry the end user's per-model caps so spend is charged against them and over-cap requests are refused. Routes that skip budget checks, like /spend/calculate, skip this check too. The proxy default end-user budget is covered by a test. * fix(auth): write the end user's per-model cap onto every token and check it after the member fallback The shared auth checks now resolve the end user's per-model cap, falling back to the key's or the proxy's default end-user budget when the end user has no row yet, and write it onto the token so the spend hook charges the cap the check enforces on every auth path. The check runs after the team-member budget fallback, on the model it picked, and the JWT builder's duplicate check is removed * fix(auth): keep only a per-model end-user cap that custom auth itself set A key-auth token reached through custom auth's auto hand-back now takes the current end user's cap, so a cached master-key or virtual-key object no longer carries an earlier end user's cap into another end user's request. * test(auth): build the custom-auth token with model_copy instead of setting the flag in place * test(auth): hold an end user to their own or their key's budget cap when the proxy has no default budget * fix(proxy): give each request its own copy of a custom-auth token so end-user caps and ids stay per request --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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tessellark
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5bd47442fb
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fix(cli): give lite codex aliases of a Codex-known model that model's catalog entry (#48894)
* fix(cli): give lite codex aliases of a Codex-known model that model's catalog entry lite codex built its Codex model catalog from /v1/models ids only, so a proxy alias of a model Codex knows (api-gpt-6-luna for openai/gpt-6-luna) got the fallback entry. That entry has no supports_search_tool or tool_mode, so Codex sent every MCP and app tool inline and each turn cost far more input tokens. lite now reads each listed model's upstream from the proxy's /v1/model/info (team_public_model_name when set, else model_name, first deployment wins) and passes it to the shared catalog builder, the same way the proxy's own Codex route does. If that request fails, the catalog is built as before. * fix(cli): give a lite codex alias an upstream only when all its deployments route to one OpenAI model * fix(cli): cap a lite codex alias's context window at its listed max_input_tokens and count only OpenAI-hosted deployments * fix(cli): keep Codex's tier, upgrade and reasoning defaults off lite codex aliases * fix(cli): keep Codex's own effort, verbosity and promo off lite codex aliases and skip credential-hosted deployments --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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devin-ai-integration[bot]
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a2ef18b257
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test(unit): keep unix socket paths in unit tests under the macOS AF_UNIX limit (#45040)
* test(proxy): keep the spend sidecar socket path under the macOS AF_UNIX limit * test(unit): share the short socket fixtures across unit tests Move socket_dir and socket_path to tests/unit/conftest.py so the datadog redis test's absent socket fails for the right reason on a Mac, rename the pgbouncer helper's directory argument, and record the rule in tests/unit/AGENTS.md * test(unit): fall back to /tmp when TMPDIR leaves no room for a socket path socket_dir now uses /tmp when TMPDIR plus the longest socket suffix would pass macOS's sun_path limit, and its teardown ignores errors so a failed test reports only its own failure * test(unit): keep socket fixtures inside tmp_path behind a short link socket_dir now links a short temp directory to tmp_path, so artifacts keep pytest's retention and a killed run leaks only an empty link. The fallback to /tmp counts the temp directory in bytes, and socket_tempdir points tempfile at it for the pooler tests whose socket directory production picks --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Mateo Wang
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f46adf80e4
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fix(proxy): let typesafe/* allowlists cover /typesafe Jev calls and list Jev in the model picker (#47697)
* fix(proxy): list TypeSafe Jev models in the admin model picker * fix(proxy): let typesafe/* allowlists cover bare /typesafe Jev ids Adds a typesafe-jev-ids routing rule to the cost map so a bare jev-* id resolves to TypeSafe, which lets typesafe/* on a key, team or project allowlist match /typesafe calls. The picker gate now only checks that a TypeSafe key is set, so an unresolvable OIDC reference cannot fail the listing, and the /v1/models route tests mock deployments with litellm_params like real ones. * fix(typesafe): match only published Jev ids and keep picker loads off the secret manager Narrows the typesafe-jev-ids rule to jev-latest, jev-preview and versioned ids so a model group such as jev-internal-router never resolves to TypeSafe, checks TYPESAFE_API_KEY in the environment instead of through the secret manager on each picker load, and moves the picker tests into the mapped test file. * test(typesafe): type the picker test fixture and tests * fix(typesafe): match only published Jev versions and cover non-version suffixes in tests * fix(typesafe): match only priced Jev ids and offer TypeSafe in the no-team key picker * fix(proxy): offer TypeSafe allowlist names only to the Admin UI allowlist pickers The dashboard sends include_model_access_groups on every /models call, so gate the TypeSafe names on wildcard routes too and skip access-group-only listings. Also say the typesafe-jev-ids rule matches case-insensitively. * fix(ui): offer only the TypeSafe names the cost-map rule routes, and keep /v1/models listing as on main A typesafe/* router deployment registers typesafe/* in the cost map, so the allowlist pickers offered a bare *, which allows every model. The pickers now list only priced names the rule sends to TypeSafe. The non-expand list also stops de-duplicating every entry and only skips TypeSafe names already listed. |
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kailian zhou
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3a350cacc9
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feat!: add StreamLake provider metadata (#41006)
* feat: add StreamLake provider metadata * fix(streamlake): complete provider discovery and test request flows * test(streamlake): assert cost and tool-param invariants instead of pinning vendor prices * fix(streamlake): accept tools and reasoning on endpoint ids and match the current price page Endpoint ids have no cost-map row, so tools and reasoning_effort were rejected before reaching StreamLake. The provider now declares both capabilities, and named non-reasoning rows opt out explicitly. The rows now follow StreamLake's 2026-09-17 price page: drop the KAT-Coder and Qwen3 rows it no longer serves, add the served models that had no price, drop account-level rpm and tpm, and fill missing context windows. StreamLake is also registered for streamlake/* wildcards and its messages and responses support. * fix(streamlake): regenerate the endpoint matrix and type the new tests The backup endpoint matrix advertised chat only while the root map listed Messages and Responses, so /public/endpoints hid both. Regenerating with scripts/generate_provider_endpoints_support.py makes the two files match. Types the StreamLake tests and sets the Add Model test's API key with fireEvent.change, per the dashboard test guide. * fix(streamlake): drop the retired max_tokens field from the StreamLake cost-map rows * fix(streamlake): price the four served models StreamLake publishes rates for and add the Qwen3.8 cache prices * fix(streamlake): send token caps as max_completion_tokens and mark image models StreamLake ignores max_tokens on kimi-k2.6, kimi-k2.7-code, qwen3.6-27b and qwen3.6-35b-a3b, while every served model honors max_completion_tokens, so the JSON provider now maps max_tokens to max_completion_tokens instead of the reverse. Rows whose models read an image in a live probe set supports_vision, and the Qwen3.8 rows drop the explicit cache write price, which this route never uses since cache_control is dropped * fix(streamlake): keep cache_control marks, price explicit cache reads, and use API input limits StreamLake reads cache_control, so the provider keeps the marks through a preserve_cache_control flag. Qwen3.8 explicit cache hits now use their own read price, rows with a cache read price declare prompt caching, three models StreamLake serves without reasoning drop reasoning_effort, and max_input_tokens follows the limits the API reports. * fix(utils): warn when a custom_provider_map name matches a built-in provider --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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simonp9
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0f68b9919c
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fix(guardrails): skip embeddings input in google_text_moderation (#48934)
* fix(guardrails): skip embeddings input in google_text_moderation and openai_moderations Since v1.103.0 the proxy dispatches these legacy moderation callbacks on every route, and they read the embeddings input field as text, so benign embedding inputs were blocked. Return early for embedding call types, matching the Aim and Cato guardrails. * test(guardrails): type the embeddings moderation test parameters * fix(guardrails): keep openai_moderations moderating embeddings, skip only google_text_moderation Google's moderation scores topic categories like Finance and Health, so it blocks documents being indexed by subject. OpenAI's moderation scores harm categories only, so it keeps moderating embedding input as it has since v1.103.0 * fix(guardrails): skip Gigachat passthrough embeddings in google_text_moderation Treat an allm_passthrough_route call whose endpoint ends in embeddings as non-conversational, log the skip, return None from the early exit, and type the hook's data so the basedpyright gate does not grow * fix(guardrails): match only plain embeddings passthrough paths in google_text_moderation --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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mika-lt
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4e5f585c2a
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fix(guardrails): scan Responses tool results in texts-mode guardrails (#48827)
* fix(guardrails): scan Responses tool results in texts-mode guardrails * fix(guardrails): keep guardrail-added tools and non-text parts on Responses tool scans Tool result texts now come from the same conversion that builds the chat tool message, so items without a call_id, dict outputs, and outputs with no text parts stay aligned for Bedrock and PANW. A changed joined text replaces the first text part and keeps the other parts. scan_only_tool_results appends tools the guardrail returns with new names, like chat, and tool_result and computer_call_output items are scanned through the shared type list. * fix(guardrails): skip empty and screenshot tool outputs and keep text part types on Responses scans * fix(guardrails): keep Bedrock latest-turn masking aligned when tool outputs send no text * fix(guardrails): scan each Responses tool output text part on its own * fix(guardrails): split a Responses tool output's structured row when only textless outputs share its call_id * fix(guardrails): pair each Responses tool output with its own structured row --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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ryan-crabbe-berri
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019a9de603
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fix(logging): stop dumping the request body at every layer under --detailed_debug (#48973)
* fix(logging): stop dumping the request body at every layer under --detailed_debug The proxy dumped the full request body at every layer it passed through (proxy entry, pre-call, router, SDK call echo, param mapping, logging object), so one Codex "hi" wrote over 500KB of debug log. Keep the inbound request line and the outgoing provider request, summarize large values in the SDK call echo, and stop per-request lines that report nothing (callback disable checks with no disable header, cost map name probes, response id decode misses, vector store path misses). * fix(logging): summarize tools in the param mapping debug lines Non-Default params and Final returned optional params still printed full tool definitions, so a Codex-style request repeated them twice more. Use the same size summary as the SDK call echo and cover tools in the test. * perf(logging): build the param mapping debug summary only when debug is on |
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Mateo Wang
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7aa7948a47
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fix(health): ask OpenAI and Azure audio chat models for audio output in the health check probe (#47730)
* fix(health): ask OpenAI and Azure audio chat models for audio output in the health check probe * fix(health): check the probed model when health_check_model is set, and mark Azure audio models as audio output A deployment with an audio base_model and a text health_check_model got the audio fields sent to its text probe model. The Azure gpt-audio and gpt-4o audio preview cost-map entries now set supports_audio_output, so a dated Azure name like azure/gpt-audio-1.5-2026-02-23 gets the audio probe too * fix(health): read litellm_params.base_model, and drop the stored base_model when Test Connect swaps the model * fix(health): judge the audio probe on a swapped-in health_check_params model, and keep Test Connect's stored base_model * fix(health): look up a provider-prefixed base_model under its own provider and keep text-only operator modalities |
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Nate Armstrong
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7650f766b0
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refactor(router): fence the router surface and document the router-core boundary (#48839)
Add rustdoc overviews for a new litellm-router-core crate and for litellm-model-catalog, a module docstring on litellm/router.py, and a shrink-only allowlist of the Router members and router_utils modules used outside the router, checked in CI by router_surface_check.py. Move the router-free helpers out of litellm/router_utils: batch_utils to litellm_core_utils, and the auto-router naming contract, tuning baseline and baseline request next to the auto-router plugin. No behaviour change. |
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rj-cairn
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d06b889d41
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fix(streaming): keep merged think tags balanced and split SSE lines only on CR/LF (#48755)
* fix(vertex_ai): split Gemini SSE lines on CR/LF only so U+2028 in text keeps the stream The Gemini and Vertex AI streaming path read the SSE body with httpx iter_lines()/aiter_lines(). Those follow str.splitlines(), which also breaks on U+0085, U+2028, U+2029, \x0b, \x0c and \x1c-\x1e. JSON leaves U+0085, U+2028 and U+2029 unescaped inside strings, so a data: line whose text holds one of them was cut in two. The tail is not a data: line and the head never parses, and the accumulate-and-retry fallback then carries the unparsed prefix forward, so every later event is appended to it and the stream ends with no more content and no usage. SSE lines end only at CRLF, LF or CR. The new iter_sse_lines/aiter_sse_lines split decoded text on exactly those, treating a CR at the end of one chunk and an LF at the start of the next as one break. The first request and every long-decode hop now read through them. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * fix(streaming): keep merged think tags balanced and split shared SSE lines only on CR/LF Close an open <think> block on the first content or tool-call delta and at stream end, reopen it when reasoning returns, and route the shared chat completions stream through the SSE line splitter so U+2028 and U+2029 inside JSON no longer drop chunks. * fix(streaming): exact-match the SSE [DONE] marker in BaseModelResponseIterator * fix(streaming): split every SSE reader on CR/LF only and keep the logged think block closed Switch the remaining httpx stream readers (Anthropic chat, Google GenAI, Interactions, Codestral, openai_like, Predibase, LangGraph, Vertex Agent Engine, A2A providers, Azure AI agents, sandboxes, the A2A endpoint, and others) from iter_lines/aiter_lines to iter_sse_lines/aiter_sse_lines, so a raw U+2028 or U+2029 inside a JSON string no longer splits an event. Keep the chunk that closes a think block in the logged and cached response when a stream ends without a finish_reason, close the block before a legacy function_call, and make sync Codestral FIM streaming send stream=true and use the text-completion-codestral stream parser. Test fakes that only mocked iter_lines now stream real httpx bytes and assert the content they feed in. * fix(streaming): split buffered SSE parsers only on CR and LF, shard the codestral tests Buffered SSE and JSONL parsers used str.splitlines(), which also breaks on U+2028, U+2029 and U+0085 that JSON allows raw inside strings. They now use split_sse_lines from sse_lines.py, which also gains a str.split fast path. Adds tests/unit/llms/codestral to the unit workflow shard. * fix(streaming): keep U+2028 in restamped Anthropic frames and fold CRLF before splitting SSE lines * test: type the new stream tests, drop a comment, and benchmark the SSE line splitter * test: type the iterator class parameters in the line-separator stream tests * fix(responses): stream tool call arguments opened in a text chunk * fix(responses): hold a tool call that shares a chunk with text until the text ends * test: type the a2a SSE handler helper and the sandbox fake responses --------- Co-authored-by: Tim Vink <vinktim@gmail.com> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Co-authored-by: michelligabriele <gabriele.michelli@icloud.com> |
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joshua-berri
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d30c702c5c
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feat(accounting): add durable event ledger writer (#48883)
* feat(accounting): add durable event ledger writer * fix(accounting): validate delegated transaction managers locally * docs(accounting): clarify ledger integration contracts * fix(accounting): return ledger conflicts as values and keep cleanup on the kind index Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(accounting): import assert_never from typing_extensions for Python 3.10 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(accounting): annotate ledger integration test locals as Final Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(accounting): finish ledger test conventions --------- Co-authored-by: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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mubashir1osmani
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8bbce867d0
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feat(proxy): accept request priority from the x-litellm-priority header (#47618)
* feat(proxy): accept request priority from the x-litellm-priority header Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(proxy): only apply x-litellm-priority on scheduler-queued routes Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(proxy): apply x-litellm-priority only where the router schedules the request The header now maps onto priority only for chat and text completions that a router serves, so the Cursor route's responses call and proxies started without a config never forward it to the provider. /queue/chat/completions now honors the header too, with the header winning over a body priority. * test(proxy): use a real ProxyLogging in the priority header routing test * fix(proxy): ignore a malformed priority header under drop_params instead of dropping the body priority * fix(proxy): treat an empty priority header as unset and pass a valid one on as an integer --------- Co-authored-by: Mubashir Osmani <mubashir@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> |
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Mateo Wang
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567383c503
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feat(proxy)!: model family and access group entries in model_max_budget (#47722)
* feat(ui): let the per-model budget field take a model pattern or access group * feat(proxy): let model_max_budget entries name a model family or access group, shared across replicas * fix(proxy): open each shared model budget window once for every replica, and match patterns in linear time Without Redis, the first replica to charge an entry in a new window now opens it in the window spend table and the others join it, so no flush replaces spend another replica already added. Budget patterns match by splitting on `*` instead of a regular expression, a refused request names the entry that refused it, and the key form no longer offers a typed pattern another row already budgets. * fix(proxy): read the shared budget window from the writer, and keep concurrent first charges on one replica Opening a shared window now reads the row from the writer, since a lagging read replica could hand a replica its own start and let a flush replace the others' spend. A charge that waited on the open no longer resets a local window another charge on the same replica opened meanwhile. Window tests use fixed timestamps. * fix(proxy): stop a non proxy admin from lifting a team model cap with a looser group or pattern entry Group and pattern entries now take precedence over other entries, so a team update by anyone but a proxy admin must keep each new or changed entry within every existing cap it can take precedence over. A pattern can match a provider-prefixed or wildcard-routed name, so any two patterns count as overlapping, and a model or group entry counts as outranking every pattern. * fix(proxy): narrow model budget pattern overlap, keep team caps under key patterns, and accept only typed patterns in the UI * fix(proxy): keep model family budgets through database blips, team models and key entries A failed window open no longer restarts the window at this replica's clock, so it cannot wipe shared spend. Spend charged meanwhile stays local and joins the shared window once the database answers A key entry named after a model replaces only the team entry of that name, so team pattern and group caps still apply. Team-scoped models are gated and charged under their public name, and group lookups see team deployments The team update guard compares caps by window length and only ranks patterns that can match the same name, including Bedrock vendor patterns behind a bedrock wildcard route * fix(proxy): refuse team entries that take precedence over a capped model_max_budget entry A non-proxy-admin could add a carve-out (an exact model, a narrower pattern or a group) at or under the family cap, and its own counter then multiplied the team's spend on that family. Any new entry that takes precedence over a capped entry now needs a proxy admin, and group members include the team's own public model names so team deployments cannot hide a group overlap. * test(router): cover team_public_model_names_of * docs(proxy): name pattern and access group keys in model_max_budget docstrings * fix(proxy): name the entry holding the cap in the team model budget raise refusal * fix(proxy): check precedence when a team edit makes an unenforced model_max_budget entry take effect * fix(proxy): let an org admin give a legacy model_max_budget row a budget_duration at the same or a lower cap * fix(proxy): keep a legacy model_max_budget row's stored cap when an org admin gives it a budget_duration |
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mrinal-berri
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0b57b9976a
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feat(daily-activity): per-guardrail spend breakdown in user, team and organization daily activity (#48702)
* feat(daily-activity): read guardrail spend per key from the daily guardrail usage table Adds DailyActivityRepository.guardrail_spend, which sums LiteLLM_DailyGuardrailUsageUnits.cost by date, guardrail and key. A guardrail row counts only when its key has a spend row on that day under the same scope filters as the rest of the daily activity response, so user, key and date filters apply to guardrail cost too * feat(daily-activity): add a per-guardrail spend breakdown to /user/daily/activity/aggregated Each day now has breakdown.guardrails[<guardrail_id>] with metrics.spend and an api_key_breakdown carrying each key's spend and metadata. Guardrail entries only report spend, since that is the one metric stored per key. The breakdown is on the user endpoint only, is empty when a model filter is set because guardrail cost is not stored per model, and lists only keys within api_key_limit while the guardrail total keeps every key. spend and budgets are unchanged Resolves LIT-9785 * feat(daily-activity): add the guardrail breakdown to team and organization activity, with guardrail names /team/daily/activity/aggregated and /organization/daily/activity/aggregated now return breakdown.guardrails the same way the user endpoint does, since each key belongs to one team and one organization. Each guardrail entry also carries metadata.guardrail_name, read from LiteLLM_GuardrailsTable for guardrails created through the UI or API and falling back to the breakdown key for config.yaml guardrails, whose key is already their name * fix(daily-activity): keep daily activity up when the guardrail breakdown query fails, and treat an empty model filter as none A database error from the guardrail spend query now logs a warning and returns the response without guardrails, where it used to fail the whole aggregated response with a 500. An empty model= no longer drops the guardrail breakdown, matching how the rest of the endpoint already ignores it. The daily activity golden files and fixtures gain the empty guardrails key every breakdown now carries * refactor(daily-activity): fold guardrail breakdown in a worker thread and drop docstrings Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * feat(daily-activity): report untracked guardrail units next to spend 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> |
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yucheng-berri
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c6ca22ebc5
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fix(guardrails)!: make hide-secrets honor skip_tool_message_in_guardrail (#48689)
* fix(guardrails): make hide-secrets honor skip_tool_message_in_guardrail With the flag on, per guardrail or in litellm_settings, hide-secrets leaves role: tool messages and Anthropic tool_result blocks as sent, as the unified guardrails do. Tool call arguments are still masked. * test(guardrails): type the skip-tool walker test and assert the whole request |
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Mateo Wang
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9007e0fa70
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fix(files)!: scope provider and model file lists to the caller's managed files under require_managed_files (#47682)
* test(files): cover owner-scoped file listing under require_managed_files * fix(files): list only the caller's managed files on every list route under require_managed_files * fix(files): narrow owner-scoped managed file lists to the named provider or model and page with after_id and before_id * fix(files): narrow scoped lists the way uploads route, and refuse lists that cannot be scoped A model name now matches every deployment that serves it, wildcards and aliases included, and a provider matches deployments by the provider uploads resolve, so anthropic/* and unprefixed Claude models count. The OpenAI and Azure lists also return the caller's pass-through uploads. An unknown provider and different after and after_id cursors answer 400, and a list with no matching deployment skips the database read. * fix(files): match stored files by model name and keep flag-off lists as on main Under require_managed_files, a narrowed list now matches a file's model mappings by deployment id or model name, so files LiteLLM stores in its own database show on their model and provider lists. The Anthropic list defaults to 20 rows when no limit is sent, after_id, before_id and the cursor 400s apply only under the flag, and an admin keeps the provider list when the managed files hook is not loaded. List errors are returned as values and mapped to HTTP errors in one place. * fix(files): keep admin provider lists and index the owner-scoped walk Under require_managed_files, an admin keeps the provider list as on main, and only a non-admin list reads the caller's own and team managed files. A stored file now also shows on the provider its model name routes to through a wildcard, with exact model names first, and a list routed to a model takes the default page size of the provider serving it. A new index on created_by and created_at keeps each chunk of the walk an index read, and the model check runs before the file object is parsed. * fix(files): filter provider and model lists in the query A non-admin list scoped to a provider or model now asks Postgres only for the caller's rows that map to one of that scope's deployments or models, or that live in file storage, instead of reading every owned row and filtering them in Python. A caller with many files on other providers no longer waits for the whole set to be read. Team members' files are matched in the same query. * test(files): type the fake raw query in the files list tests * refactor(proxy): list managed files through one typed database handle * fix(files): list alias and routing-group storage uploads on their provider |
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Mateo Wang
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dfd9ad4103
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fix(responses)!: bill partial usage on mid-stream failures and report bridged /v1/messages failures to the proxy (#47537)
A Responses API stream that fails partway now bills the usage it already consumed, ends with an error instead of a clean stop, and honors stream_timeout between chunks. Bridged /v1/messages calls report those failures to the proxy's failure hooks, so spend, budgets and logs see them |
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yujonglee
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1f68cc5649
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refactor(rust-bridge): require the native extension (#48852)
* refactor(rust-bridge): require the native extension _native ships in every wheel and the sdist will not install without it, yet the bridge loaded it optionally and carried None paths that could fail open for fork safety, exception mapping and OTLP errors. Delete loader.py and import _native directly. Tests inject a fake module through NativeBinding(source=...) or with_source() instead of patching the loader The install smoke checks now verify the compiled module, and tests fail instead of skipping when _native is missing. The interactions spec canary installs the project so it has the extension Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * fix(ci): build Rust bridge in Windows smoke tests --------- Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
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26e53cb738
|
fix(cli): let the proxy decide when a classic lite login token has expired (#47712)
* test(cli): print-token and whoami let the proxy judge a classic token past the client window * fix(cli): let the proxy decide when a classic lite login token has expired * fix(cli): treat 403 and 404 as a rejected token and judge lite up logins the same way * fix(cli): keep lite up on the client window and bound the proxy check to 3 seconds * test(cli): prove the proxy check gives up on a silent proxy and pin stale fixtures Point the timeout test at a local socket that never answers, use fixed timestamps for stale CLI token fixtures, and correct the print-token docs for tokens younger than the client window. * fix(cli): expire a CLI token with no recorded proxy and stop treating 404 as rejected A token file without base_url cannot be checked, so it keeps the client window's verdict instead of printing forever. A 404 also comes from a proxy that does not serve /v1/models, so only 401 and 403 count as a rejected key, matching the CLI's existing key check. The timeout test now runs in process. * test(cli): judge the whoami proxy check on a fixed timestamp and name the client window for lite up |
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|
Amaan
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7c7304c22b
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feat(providers): add ZeroGPU as an OpenAI-compatible provider (#43874)
* feat(providers): add ZeroGPU as an OpenAI-compatible provider Registers zerogpu through the JSON provider registry so zerogpu/<model> routes to https://api.zerogpu.ai/v1/chat/completions with ZEROGPU_API_KEY and an optional ZEROGPU_API_BASE. Adds LlmProviders.ZEROGPU, the constants.py provider and endpoint lists, cost map entries for the 10 ZeroGPU text generation models, the endpoint matrix, the Add Model form fields, the README row and the dashboard provider entry * feat(zerogpu): add capability flags verified against the live API Live calls to https://api.zerogpu.ai/v1/chat/completions confirmed tools, tool_choice and json_schema response_format on 8 of the 10 catalog text models, cached token reporting on deepseek-v4.1-flash, gpt-4.1-mini, gpt-5.4-nano and gpt-5.6-luna, and reasoning_effort on gpt-5.6-luna. LFM2.5-1.2B-Instruct answers stream requests with plain JSON instead of SSE and LFM2.5-1.2B-Thinking returns 500 on every request, so both stay out of the cost map until the API serves them correctly * feat(zerogpu): price LFM2.5-1.2B-Instruct Adds zerogpu/LFM2.5-1.2B-Instruct to the cost map with the catalog prices and context window. It gets no capability flags because the live API ignores tools, response_format and max_completion_tokens for this model, so LiteLLM drops tool params for it instead of sending them. LFM2.5-1.2B-Thinking stays out of the cost map * fix(zerogpu): reject transcription requests before calling the upstream API ZeroGPU only serves chat completions, so drop it from the OpenAI transcription transport set. A zerogpu transcription request now fails locally instead of hitting an /audio/transcriptions endpoint that does not exist * test(zerogpu): cover the responses and messages bridges Both endpoints reach ZeroGPU through chat completions, so assert the request each one sends upstream and the reply it returns * refactor(zerogpu): move zerogpu out of the provider list tails New upstream providers append to the end of openai_compatible_endpoints, openai_compatible_providers and providers.json, so an entry at the tail conflicts with every one of them. Placing zerogpu next to long-stable entries keeps those merges clean. No behavior change * fix(zerogpu): price cached input for qwen3-30b-a3b-fp8 The ZeroGPU model catalog (https://docs.zerogpu.ai/docs/model-catalog, checked 2026-10-09) lists cached input for qwen3-30b-a3b-fp8 at $0.05 per 1M tokens. Without cache_read_input_token_cost, cached tokens were billed at the full $0.10 input rate * revert(zerogpu): leave the audio transcription provider set unchanged Per review, keep OPENAI_AUDIO_TRANSCRIPTION_PROVIDERS as upstream defines it. This PR only covers chat completions, and ZeroGPU now serves /audio/transcriptions anyway, so the local refusal and its test are removed * fix(zerogpu): add max_tokens so trim_messages can size ZeroGPU history trim_messages reads max_tokens as the fallback for max_input_tokens and evaluates it eagerly, so without it the lookup raised, got swallowed, and the full history came back untrimmed. max_tokens matches each model's maxTokens in ZeroGPU's models API (https://api-dashboard.zerogpu.ai/api/models, checked 2026-10-10) * test(zerogpu): record mocked requests on a recorder object The mock handler appended to a list the caller passed in, which AGENTS.md forbids. A recorder now owns the captured requests as a tuple * fix(zerogpu): cap max_tokens at each model's output limit Each cap is the max_output_tokens ZeroGPU stores for the model in its production models table (read 2026-10-20), the same column its /v1/models serves as max_length. That gives 65536 for deepseek-v4.1-flash, 32768 for gpt-4.1-mini and gpt-oss-120b, 128000 for gpt-5.4-nano and gpt-5.6-luna, 8192 for LFM2.5-1.2B-Instruct and llama-3.1-8b-instruct-fast, and 16384 for qwen3-30b-a3b-fp8. glm-5.3-flash is stored as 32786, which reads as a typo for 32768, so that row uses 32768 trim_messages sizes history from max_input_tokens, so lowering max_tokens leaves it unaffected * fix(zerogpu): flag prompt caching on glm-5.3-flash Repeating the same ~4.3K-token prompt against ZeroGPU on 2026-10-20 returned cached_tokens of 4320 on some glm-5.3-flash calls. gpt-oss-120b and llama-3.1-8b-instruct-fast returned 0 on all nine calls, and qwen3-30b-a3b-fp8 never returned cached_tokens, so they stay unflagged. Their cached prices stay as ZeroGPU publishes them, so any cache hit is still billed at the right rate * fix(zerogpu): set gpt-5.4-nano context window to 272000 ZeroGPU accepts a 271001-token gpt-5.4-nano prompt and rejects 273000 tokens as over the context window (checked 2026-10-21), so max_input_tokens is 272000 rather than the 400000 its catalog lists * fix(zerogpu): list zerogpu models for zerogpu/* wildcards zerogpu had no models_by_provider entry, so get_provider_models("zerogpu") returned None and a zerogpu/* wildcard listed no models. zerogpu_models is now filled from the cost map rows like openinfer_models and registered in models_by_provider * fix(zerogpu): flag gpt-5.4-nano as a reasoning model ZeroGPU honors reasoning_effort on gpt-5.4-nano (checked 2026-10-21). Without it the model spends 0 reasoning tokens, and with low or high it spends 32 or more. LiteLLM only treated it as a reasoning model by falling back to OpenAI's bare gpt-5.4-nano row, so get_model_info and /model/info reported supports_reasoning as null. The row now carries the flag itself * fix(zerogpu): flag deepseek, glm, gpt-oss and qwen3 as reasoning models ZeroGPU's /v1/models marks these four as reasoning models and accepts reasoning_effort on all of them (checked 2026-10-21), but LiteLLM rejected the param with UnsupportedParamsError because their rows had no supports_reasoning flag. glm reports reasoning_tokens under reasoning_effort, and gpt-oss and qwen3 spend hidden reasoning tokens on every reply. The gpt-oss test case now expects reasoning_effort to reach ZeroGPU * fix(zerogpu): set gpt-oss-120b context window to 128000 ZeroGPU routes gpt-oss-120b across several upstream backends, and at least one of them returns 413 past a 128000-token window, so max_input_tokens uses that as the safe floor. The backends reached on 2026-10-21 accepted up to about 131K prompt tokens, so this only gives up a little headroom on the larger ones * feat(zerogpu): call ZeroGPU's Responses API natively ZeroGPU serves /v1/responses for all nine catalog models, so LiteLLM no longer bridges Responses requests through chat completions. Native calls return ZeroGPU's reasoning output items for glm, gpt-oss and qwen3, which the bridge dropped. Checked live on 2026-10-22 through litellm.responses for every model, non-streaming and streaming, plus a gpt-oss tool call. LFM2.5-1.2B-Instruct streams correctly now that ZeroGPU's text_generation lane emits Responses SSE * fix(zerogpu): stop inferring the zerogpu provider from api_base alone Listing https://api.zerogpu.ai/v1 in openai_compatible_endpoints rerouted bare model names on that api_base from openai to zerogpu. A bare gpt-5.6-luna call with verbosity used to return 200 and started raising UnsupportedParamsError. Drop the entry and its api_base-alone test, so zerogpu is only selected through the zerogpu/ prefix * fix(zerogpu): price gpt-6-luna, llama-3.2-3b, LFM2.5-1.2B-Thinking and qwen3.6 A zerogpu/* wildcard served these four chat models with no cost-map row, so their calls logged $0 spend. Add rows at ZeroGPU's catalog prices from https://api-dashboard.zerogpu.ai/api/models, with context windows and output caps from ZeroGPU's models table. LFM2.5-1.2B-Thinking has no cached price, like LFM2.5-1.2B-Instruct. gpt-6-luna is flagged supports_reasoning because LiteLLM already routes gpt-6 names as reasoning models. Checked live on 2026-10-28, all four now report nonzero response_cost * fix(zerogpu): drop the retired max_tokens field from the zerogpu rows Upstream #48471 retired max_tokens from the cost map in favor of max_input_tokens and max_output_tokens, and test_no_row_carries_the_retired_max_tokens_field failed on all 13 zerogpu rows. Every row already carries the same value in max_output_tokens, so nothing is lost. The zerogpu limits test now checks that each row's output limit fits its context window * chore(zerogpu): regenerate provider endpoint support for zerogpu Upstream #48619 generates provider_endpoints_support.json from the cost map and checks it in CI, and the hand-written zerogpu entry no longer matched. Regenerated with scripts/generate_provider_endpoints_support.py. Only the zerogpu entry changed, and it now matches what the generator emits for other JSON-registry providers such as openinfer * fix(zerogpu): flag tools, json_schema and prompt caching on gpt-6-luna ZeroGPU serves function calling, tool_choice, json_schema response_format and prompt caching on gpt-6-luna, so set supports_function_calling, supports_tool_choice, supports_response_schema and supports_prompt_caching on its row in both cost maps. Checked live on 2026-10-28, a weather tool call came back with the right arguments, json_schema output validated, and repeated prompts reported 3260 of 3263 tokens cached * fix(zerogpu): set qwen3.6-35b-a3b context to 262144 and flag its tools and reasoning ZeroGPU accepts up to 262,144 input tokens on qwen3.6-35b-a3b and serves tools, tool_choice, json_schema response_format and reasoning_effort on it. Raise max_input_tokens from 16384 and set supports_function_calling, supports_tool_choice, supports_response_schema and supports_reasoning in both cost maps. Before this, LiteLLM rejected tools and reasoning_effort for the model. Checked live on 2026-10-28, a 209,905-token prompt succeeded, an oversized one was refused with a 262144-token limit, and tool calls, json_schema output and reasoning_effort all worked * fix(zerogpu): flag vision on gpt-6-luna and qwen3.6-35b-a3b ZeroGPU reads image inputs on gpt-6-luna and qwen3.6-35b-a3b, so set supports_vision on both rows in both cost maps. Checked live on 2026-10-28, both models named the animal in an image_url photo, and the image showed up in their prompt token counts |
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tin-berri
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ca6c830221
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fix(proxy): account for media in combined TPM reservations (#48865) | ||
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zoveki
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41fcce62ff
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fix(gemini)!: accept Google's current Managed Agents fields on create and interactions (#48750)
* fix(gemini): send Google's current Managed Agents fields on create and forward agent_config on interactions * fix(gemini): accept id on managed agent get, delete, and list_versions * fix(gemini): drop unsupported agent fields under drop_params and keep agent_config keyword-only A model interaction drops agent_config under drop_params, agent create drops wrongly typed optional fields under drop_params and otherwise answers 400 naming the field the caller sent, and agent_config on interactions create is keyword-only so positional response settings stay in place * fix(gemini): drop a non-object agent_config under drop_params and name the id alias the caller sent * fix(interactions): reject agent_config on bridged providers and drop a typeless one under drop_params Bridged providers answer 400 for agent_config without drop_params and drop it with drop_params. A Gemini agent run drops an agent_config without a string type under drop_params. The Gemini agents wire test expects Google's current upstream field names. * fix(interactions): honor additional_drop_params for agent_config on bridged providers * fix(agents): drop a clashing instructions alias under drop_params --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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moe-berri
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365d3d3729
|
feat(decisions): support Ollama decision models (#48871)
* test: cover local Ollama Decisions wire contracts Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(decisions): support ollama providers Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(decisions): account for Ollama model-info requests 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> |
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tessellark
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a0c9a5993b
|
fix(batches): stop unbillable batches from blocking file deletes and new-batch polling (#48752)
* test(batches): cover the batch cost poller gaps left after #47190 * fix(batches): keep unbillable batches from blocking file deletes and new-batch polling The file delete check now ignores batches past MANAGED_OBJECT_STALENESS_CUTOFF_DAYS, a provider 404 for the output object retires the batch, each poll page takes the arrival lane first, a batch expires only after the poller visits it past the cutoff, and the compare-and-swap matches batch_processed instead of updated_at * test(batches): cover the full arrival page and a failed expiry write in the batch cost poller Old batches still get polled every other cycle while new batches fill the page, old batches that fill a page never push a new batch out after boot, and a failed stale_expired write no longer stops the rest of the page from being polled * fix(batches): share the poll page between old and new batches and hold output files until billed Each lane now gets at least half of the poll page, a lane with nothing left gives its half to the other, and a lane read that resumed mid-lane wraps to the start, so neither old nor new batches can starve the other. The delete guard applies the 7 day cutoff to input files only. Output and error files stay locked until the batch is billed or retired, and stale_expired rows no longer lock anything. Only a lasting provider failure expires a batch past the cutoff, so a timeout or a 5xx no longer gives up on a batch that may still bill. Past the cutoff a lasting failure, a lasting cost-tracking failure, or an output file id that never appeared retires a finished row instead of polling it forever. * fix(batches): retry refused finished batches past 7 days, expire unfinished ones on a failed retrieve A finished batch the provider refuses after 7 days (rotated key, bucket 403, a 404 that does not name the batch) now keeps retrying so its spend is written once the config is fixed, instead of retiring unbilled. A batch the provider still reports running keeps polling. A batch that is not finished in the database becomes stale_expired once a retrieve fails after 7 days, so an unreachable provider is not polled forever. An S3 NoSuchKey for the output object now counts as gone. * fix(batches): expire an unfinished old batch only after 3 failed retrieves in a row * fix(batches): keep retrying an unfinished old batch through timeouts, 429 and 5xx * fix(batches): wait 3 polls before expiring an old batch whose output id lags * fix(batches): match a resource id in a 404 message followed by punctuation * fix(batches): keep refusal and pending-output expiry streaks apart, and void a pending-output retirement on a client write * fix(batches): give up on unsettled batches past twice the staleness cutoff Replace the per-reason refusal and pending-output streaks with one give-up age, and inject a clock into the poller and the delete check so tests pin exact age boundaries * fix(batches): keep polling finished batches past the give-up age so their spend stays recoverable * test(batches): expect a restarted poller's first page to split between old and new batches * test(batches): pin the delete-guard tests to a fixed clock and type a new journal list * test(batches): type the provider helper and the storage 404 cases --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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tessellark
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43ce3e22aa
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fix(responses)!: answer 400 before counting a lone Responses built-in tool on Claude (#48798)
* test(responses): audit #47363 Adds integration cells for the input token count paths #47363 changed: the provider id of a managed file and the 403 for another team's file on OpenAI, file_search and additional_tools parity plus cache-mark stripping and untranslatable tools on Claude, and the Bedrock runtime count leaving out the options it refuses. * fix(count-tokens): reject a lone tool with no chat equivalent before the Claude count call A Claude input token count whose only tool was mcp or computer_use_preview skipped the pre-call check and went to the provider, so the caller got the provider error instead of the 400 naming the tool. The check now runs whenever a tool would get a placeholder name. * fix(responses): count a lone Anthropic toolset and name the uncountable tool The lone-tool 400 now fires only for OpenAI Responses built-in tool types, so a nameless Anthropic toolset still counts. The error says LiteLLM cannot count that tool type for the model. Adds a Bedrock unit cell for the lone-tool 400 and pins the runtime wire test's tool shape per request type. * fix(responses): take the uncountable tool types from the OpenAI SDK and keep the region hint off LiteLLM's 400 The Responses built-in tool types now come from openai's ToolParam union, so a type the SDK adds is refused before the count call too. The AWS region hint no longer lands on an error LiteLLM raised before calling the provider. Tests cover two more lone types and the hint. * fix(responses): keep LiteLLM's own list of uncountable tool types and pass a non-string tool type through * fix(responses): count tool_search and namespace tools the way Claude chat inference sends them The count refused a lone tool_search with LiteLLM's 400 and sent a lone namespace raw or squashed it into an empty tool, while Claude chat inference maps tool_search to Anthropic's tool search tool and unpacks a namespace into its function tools. Both now go through the deployment's own map_openai_params, so the count sends what inference sends, and tool_search leaves the list of uncountable types. * fix(bedrock): count a Claude request with its deployment's own chat translation Bedrock invoke and Mantle inference turn a chat tool_search tool into Bedrock's tool search tool, but the Bedrock counter always translated with the Anthropic config, so the count answered 400 for a request inference serves. --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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rj-cairn
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ff5bf4826d
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fix(count_tokens)!: audit #47441 and keep the framed estimate for Bedrock count models from the cost map (#48805)
* fix(count_tokens): keep the framed estimate when a region-less token_count_model cannot count (audit #47441) * fix(count_tokens): let framing stand in for a shipped token_count_model, keep a configured one's error The framed local estimate now replaces a 400/404 from any count model the cost map ships, Mantle-pinned rows included, and an admin-set token_count_model keeps its error as the docs say. * fix(count_tokens): warn with the region when framing stands in for a pinned count model Test a Mantle row #47441 pinned in its own region, and keep the region hint check on an admin-set count model. * fix(count_tokens): name the deployment in the framing stand-in warning * fix(count_tokens): let framing stand in when a cost map count model's region denies the account A count model the cost map pins to a region can answer 401 or 403 when the account has not enabled that region or lacks Mantle count access there. With framing set, the estimate now stands in, as it does for 400 and 404. An admin set count model and a count model with no pinned region keep the error. The warning now names the model group, the count model and its region, and says the proxy falls back to the estimate. * test(count_tokens): cover framed 401 and 403 from a shipped count model region on the wire --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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tin-berri
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c40bdc475f
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fix(router)!: resolve trusted JEV connection environment references (#48717) | ||
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tin-berri
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54a6a5fb3a
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fix(otel): suppress spans for excluded proxy requests (#48720) | ||
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Mateo Wang
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ea6dbd2109
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feat(proxy): log one line per token count call (#47657)
* feat(proxy): log one line per token count call Each count call on /v1/messages/count_tokens, /utils/token_counter, /v1/responses/input_tokens and the Gemini countTokens routes now logs one INFO line with the litellm call id, the requested model, the model the count ran against, the count, the count source and the status code, so it joins the inference call that sends the same x-litellm-call-id * test(google_endpoints): give the count test's fake request a headers list * fix(proxy): keep token count log lines one per call and trusted in JSON logs, and log Bedrock native counts Quote log values that carry whitespace, quotes, equals, or control characters so a model name can't split or forge a line. Let JsonFormatter record extras win over a dict parsed from the message. Log the Bedrock native count-tokens route through the same line. * fix(proxy): escape braces in token count log values, log the Bedrock model AWS counts, and skip the line when there is nothing to count * fix(proxy): log the deployment id the count routed to on every token count line * fix(proxy): log count requests rejected before counting with the status they answer A count route that rejects a request before the shared count function runs now logs the same token count line with its 400. An unsupported content block on the Messages count route becomes its 400 inside the logged wrapper, so its line says 400 instead of 500. A count that ends in a BaseException other than cancellation logs a 500. * fix(proxy): compute the Bedrock counted model id only after a successful count * revert(logging): keep the JSON log formatter's field order as on main The count line already escapes braces in its values, so the formatter never parses a dict from it. Changing the order for every logger is a separate decision. |