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990 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
yuneng-jiang
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6a0d03914c
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test: drop the cwd-relative sys.path.insert calls from the test suite (#37802)
* test: drop the cwd-relative sys.path.insert calls from the test suite
TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.
Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.
Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.
* test: drop the duplicate imports the sys.path sweep exposed to F811
* test(pre-call-utils): restore the os import the new bedrock tests need
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ryan-crabbe-berri
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5ed230701a | test: escape the literal match= patterns PT017 minted | ||
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ryan-crabbe-berri
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243ed4393d |
test: reject assertions on a caught error inside except (ruff PT017)
A test that asserts on the error inside its own except block passes when the call stops raising, because nothing runs the handler. That is the exact case the test exists to catch, so the regression lands green. Rewrites all 111 such blocks into pytest.raises, which fails when the call succeeds, and selects PT017 in ruff-tests.toml so no new one lands. |
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ryan-crabbe-berri
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e9d40a8f73 |
test: enforce F811 so a duplicate definition cannot silently replace the first
A name bound twice keeps only the second binding. In `tests/` that is nearly always a repeated import, harmless but misleading, and the same rule is what catches the cases that are not harmless: a local that shadows an import the module still calls, and a second `def test_x` that quietly replaces the first. 311 of the 344 sites were repeated imports and came out with ruff's own fix. The remaining 33 needed a decision. Four modules imported a name they never used because a local definition below already shadowed it. Two comprehensions bound `call` over `unittest.mock.call`, which those modules import and use. One test rebound the two module handles its nested reload closure had captured. One class attribute shadowed an unused `status` import. The load-test fixtures move to a conftest, which is how pytest is meant to share them, so the test module no longer imports three fixture names it never calls. The nine `prisma_client` parameters keep a narrow `noqa`: pytest resolves that fixture by name before the body runs, so the parameter never shadows anything. |
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Mateo Wang
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4b29702418
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Merge pull request #37003 from BerriAI/litellm_forward_bedrock_response_headers
fix(bedrock): forward provider response headers on chat completions |
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ryan-crabbe-berri
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b76def0e5d
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test: require a match= on broad pytest.raises, and drop duplicate parametrize cases (#37769)
`pytest.raises(Exception)` with no `match=` passes on any error that broad. A TypeError from a refactor, a botched fixture, an import that moved: all of them read as the rejection the test claims to police, so the test goes green for the wrong reason and stays green after the behaviour it guards is gone. PT011 closes that gap for the 317 sites B017 could not reach, because B017 only fires on a single-statement body with no `as e` binding. Each pattern here is the message the code actually raised, recorded by running the sites under a plugin that logged the concrete type and text per call site, so the assertions describe observed behaviour rather than a guess. Where a site raises more than one message across its parametrize cases, the pattern is an alternation of what was seen; where the exception carries an empty `str()` and puts the text on `.message`, the site keeps a narrow `noqa` with the reason. PT014 removes four parametrize cases that were listed twice. The duplicate re-runs an assertion that already passed, and it usually marks a case someone meant to vary and forgot to edit. |
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Devin AI
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58c4fa6ae9 |
chore: merge litellm_internal_staging into litellm_forward_bedrock_response_headers
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Devin AI
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8a40aff1d2 | Merge remote-tracking branch 'origin/litellm_internal_staging' into litellm_forward_bedrock_response_headers | ||
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mateo-berri
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722c650bfd |
test: cover generic HTTP streaming provider header forwarding
Add sync and async regression tests for the BaseLLMHTTPHandler streaming path, which forwards provider response headers for the ~30 providers that ride the generic handler and had no coverage. Also drop redundant setup prose from the moonshot invoke test docstring. |
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ryan-crabbe-berri
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a112ba5f63
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test: enforce PT012 so a pytest.raises block cannot hide dead assertions (#37748)
* test: enforce PT012 so a pytest.raises block cannot hide dead assertions `with pytest.raises(...)` stops at the first statement that raises. Anything sequenced after it inside the block never runs, so an assertion written there is never checked and the test still reports green. Two sites were doing exactly that, and both assertions turned out to be wrong once they started running. tests/llm_translation/test_prompt_factory.py asserted the bedrock rejection names "requires at least one non-system message", which holds. tests/proxy_unit_tests/test_proxy_server.py asserted the prisma startup failure mentions "httpx.ConnectError", which never appears: the failure is an httpx.ConnectError whose message is "All connection attempts failed", so that test now asserts the type. Its DATABASE_URL override moves to monkeypatch, since the old restore sat below the assertion and leaked the invalid URL into every later DB test the moment the assertion started being able to fail. The remaining 72 sites are rewritten without changing what they exercise: setup that cannot raise moves above the block, a nested `patch` moves outside it, and bodies with real control flow (a stream drain, an if/else on sync_mode, a retry loop) move into a local closure the block calls. Fixing PT012 unmasked two B017s, since ruff only reports a blind pytest.raises(Exception) once the block holds a single statement. tests/proxy_unit_tests/test_auth_checks.py narrows to the ProxyException can_key_call_model actually raises. tests/local_testing/test_completion_cost.py was asserting vertex_ai/medlm-medium has no cost entry, which stopped being true at some point; that dead first half is gone and the rest of the test, which checks medlm pricing resolves above zero, now runs instead of being skipped. * chore(ci): ratchet TQ004 to 768 after the prisma test moved to monkeypatch |
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yuneng-jiang
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35416c702d
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test: point the live together_ai suites at a model together still serves (#37746)
Every live together_ai call in CI has answered 503 Service unavailable since 2026-08-20, across two runs 2.5 hours apart, while Together's status page reported no incident in either window. These are real calls, not replayed cassettes: the VCR layer runs filter_non_2xx_response, so a 503 is never written to a cassette and cannot be replayed back. Qwen/Qwen2.5-7B-Instruct-Turbo does not appear anywhere on Together's monitored component list, whose Qwen entries are all Qwen3.x, so a model-level outage there would never surface as an incident. The same 503 already forced test_basic_rerank_together_ai to be skipped on a different together_ai model, so per-model 503s are an established failure mode here rather than a platform outage. openai/gpt-oss-20b is the cheapest together_ai entry that carries real pricing and the capabilities these suites exercise, at $0.05/$0.20 per 1M tokens with function calling, response schema and tool choice. Together monitors it as a served component. The retired model also carries null pricing in the cost map, which is its own liability now that unpriced models are blocked. test_multiple_deployments.py keeps the old id: it is a router fallback list that is green today, and busting its cassette to prove a point would trade a passing test for a live call this change cannot vouch for. |
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yuneng-jiang
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60225ab429
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test: assert the prefixed model the azure responses bridge now hands back (#37749)
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ryan-crabbe-berri
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680bcfd8aa
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test(lint): ban blind pytest.raises(Exception) with ruff B017 (#37731)
* test(lint): ban blind pytest.raises(Exception) with ruff B017 A bare pytest.raises(Exception) accepts whatever the body throws. The TypeError a refactor introduces satisfies it exactly as well as the rejection the test was written for, so the crash reads as a pass and the test never goes red. All 111 existing sites are narrowed here. A runtime probe recorded the concrete exception each one actually catches, and each site now names that type. Where the code under test genuinely raises a bare Exception, the site pins a stable slice of the message with match= instead. Two sites tell on themselves. The shared responses-API cancel test raises "custom_llm_provider is required but passed as None" rather than talking to a provider at all, because cancel_responses takes a provider, not a model. And test_bedrock_guardrails_with_streaming was the only test in its file still passing without AWS credentials, because the NoCredentialsError boto3 raised long before the guardrail ran satisfied the blind raises. * fix(test): widen the openai batch-dispatch assertion to OpenAIError The narrowed NotFoundError only holds where OPENAI_API_KEY is set. Without one the SDK raises OpenAIError while building the client, long before any 404, so CI went red. OpenAIError covers both and still rejects a TypeError from a refactor. |
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yuneng-jiang
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e98b9fda90
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test: assert the prefixed model the responses bridge now hands back (#37744)
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yuneng-jiang
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cc812cdfc7
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test: point the live web search, groq and vertex image suites at models that still exist (#37733)
* test: point the live web search, groq and vertex image suites at models that still exist Three CircleCI jobs on the staging-to-main promotion are red because the models their live suites call have been retired by the providers, not because anything in litellm changed. openai/gpt-4o-search-preview now answers "has been deprecated" (its dated id gpt-4o-search-preview-2025-03-11 carries deprecation_date 2026-07-23), so the two web search conformance tests and the web search cost tracking test move to gpt-5-search-api, the current search model. It keeps mode chat, supports_web_search and a search_context_cost_per_query map, so the cost assertion still resolves. groq/llama-3.1-8b-instant reached its deprecation_date of 2026-08-16 and Groq answers "does not exist or you do not have access to it". It follows groq/llama-3.3-70b-versatile to groq/openai/gpt-oss-120b, the same replacement PR #37422 already picked. The proxy config that job boots routes on a */* wildcard, so no config change is needed. vertex_ai/imagen-3.0-fast-generate-001 404s with "was not found or your project does not have access to it". Google retired the whole Imagen family across Vertex and the Gemini API, so there is no Imagen id left to point at. The class is removed rather than repointed: Vertex image generation is already covered live by TestVertexAIGeminiImageGeneration on vertex_ai/gemini-2.5-flash-image, and the Imagen request and response transformations keep their offline coverage in tests/test_litellm/llms/vertex_ai/image_generation/. Only live call sites move. Remaining references to the old ids sit in offline cost-map and transformation tests, where the string is a lookup key and no request leaves the process. * chore(lint): ratchet the TQ005 ceiling down to the count this branch reached Removing the retired TestVertexImageGeneration class cleared one TQ005 violation, so the gate demands the limit come down with it. make lint-budget-update only lowers a limit by the delta a branch cleared, and this ceiling already sat 2 above the base count, so the tool landed on 2834 while the gate wants the limit at or below the 2832 this branch reached. The remaining 2 are that stale headroom, which is exactly what the gate is asking to reclaim. |
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ryan-crabbe-berri
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21e9632713
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test: add six ruff rules that catch tests which cannot fail (#37709)
`assert False` inside a `try:` raises AssertionError, which the `except Exception` right below it catches, so several tests reported green no matter what the code did. `pytest.fail` raises Failed, a BaseException, and escapes. A bare `a == b` statement is evaluated and discarded. Nine of those sat in tests, and one was comparing against a model name the router never produces. Selects B011, B015, B018, PT015, PLR0133 and PLW0127 in ruff-tests.toml alongside F821, with all 50 existing violations fixed, so no budget file or ratchet is needed. CI already runs this config over tests/. |
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yuneng-jiang
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76aa13cde0
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test: remove the five test functions a later definition shadows (#37591)
Python binds a name once per scope, so when a module or class defines the same test twice only the last one exists. The earlier definitions are unreachable: pytest never collects them, and nothing that references them can fail. A sweep in August cleared nine of these. Five have appeared since, which is the argument for a rule rather than another sweep. Each survivor is the better version, so nothing is lost. The two SQS logger twins additionally stub `asyncio.create_task`, which the shadowed copies did not. The cost-calculator duplicate is a two-line stub that also takes a `model_item` parameter no fixture supplies, so it could not have run even unshadowed. The two `test_prompt_caching` bodies are both `pass`. Collecting the four files reports 416 tests before and after. `tests/proxy_unit_tests/conftest copy.py` goes with them. pytest only loads a file named exactly `conftest.py`, nothing imports this one, and the space in the name says what it was. |
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yuneng-jiang
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0289e3fb00
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test: point the live gemini and groq conformance suites at models that still exist (#37422)
gemini/imagen-4.0-generate-001 now 404s with "no longer available, please update your code to use models/gemini-3.1-flash-image", and groq/llama-3.3-70b-versatile reached its deprecation_date of 2026-08-16, which is the day the llm_translation and image_gen jobs went red. Only the two live call sites move. The remaining references to the old ids sit in offline transformation tests, where the string is just a routing key and no request leaves the process. |
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Devin AI
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726db1a4c1 |
test(bedrock): cover async header forwarding for converse and invoke
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Devin AI
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9c922f4aa4 |
fix(bedrock): forward provider response headers on chat completions
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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yuneng-jiang
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98a79ccf92
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Merge pull request #36685 from BerriAI/litellm_restore_shadowed_tests
test: rename tests that a later definition shadowed |
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Yuneng Jiang
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ff4120863b
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test: rename tests that a later definition shadowed
Python keeps only the last binding for a name, so when a file defines the same test twice the earlier one is unreachable. pytest cannot collect a function that no longer exists, so nothing reports it and the file still looks like it covers the scenario. These ten are cases where the two definitions have different bodies, meaning a real test was replaced rather than duplicated. Each is renamed to say what it actually covers, which makes it reachable again: - test_gemini_frequency_penalty: the dead copy checks the parameter is listed in get_supported_openai_params for vertex_ai; the survivor checks get_optional_params maps a value for gemini. Different function and different provider. - test_async_log_success_event_adds_to_queue and the failure variant: the dead copies run without mocking asyncio.create_task, so they exercise the real task path the survivors mock out. - test_async_send_batch_triggers_tasks: the dead copy asserts send is not awaited directly; the survivor asserts create_task was called. - test_model_id_in_required_metrics: the dead copy checks the model_id label on twelve further metrics the survivor dropped. - test_anthropic_messages_pt_file_block_preserves_cache_control: the dead copy passes model and llm_provider explicitly and uses real base64 PDF content. - test_translate_streaming_openai_chunk_to_anthropic_with_thinking: the dead copy covers thinking_delta; the survivor covers signature_delta. - test_client_initialization and test_client_without_api_key: the dead copies assert the resource clients are wired with the right base URL and key; the survivors only construct the object. - test_client_initialization_strips_trailing_slash: the dead copy constructs ModelsManagementClient directly rather than going through Client. Verification: collecting the seven touched files gives 401 node IDs before and 411 after, the ten new names and nothing else, with nothing lost. All ten pass. Running the touched files in full gives 299 passed, and test_optional_params.py goes from 111 passed to 112. Two further shadowed definitions were left alone rather than renamed: the dead copies of test_prompt_caching and test_cost_calculator_with_base_model_with_router have no assertions at all, one being a bare pass and the other a lone import, so restoring them would add tests that cannot fail. |
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Yuneng Jiang
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075781568d
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test: remove tests that never execute
Three groups, all verified by running the suite rather than by inspection. 18 files whose every test function carries an unconditional @pytest.mark.skip, 39 test functions in total. They are collected on every CI run and always skip, so they advertise coverage the suite does not have. Reasons on the marks include "AWS Suspended Account", "lakera deprecated their v1 endpoint" and "moved to using 'otel' for logging"; 26 of the marks predate 2025. 30 test functions with a byte-identical body and identical decorators to a sibling in the same file and class, differing only in name. Deleting one of each pair removes no coverage. Four further candidates were excluded because they override an inherited test, where deleting the override un-shadows the base class implementation instead of removing a duplicate. 9 test functions that a later definition of the same name shadows, so Python never binds them and pytest cannot collect them. One file that is a demo script rather than a test; its own docstring says to run it with python. Verification: collecting the 26 edited files gives 2,492 node IDs before and 2,462 after. The 30 duplicate deletions account for exactly 30 removals, the 9 shadowed deletions account for 0 (confirming at runtime that they were never collectable), nothing unexplained disappeared, and nothing new appeared. No other test or module imports any deleted symbol. |
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Yuneng Jiang
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d49114b101
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test(bedrock): repoint live Claude tests off the retired Claude 3 Sonnet
AWS no longer serves `anthropic.claude-3-sonnet-20240229-v1:0`. The streaming path returns a plain 404, "Model with the provided id anthropic.claude-3-sonnet-20240229-v1:0 is not found", and the non-streaming path answers 500 for the same reason. Our own cost map has carried a 2026-07-30 deprecation date for it since #36538 That accounts for 20 failures across local_testing_part1, local_testing_part2 and llm_translation_testing. litellm maps both statuses correctly, so the tests are what went stale, not the client Replacement is `us.anthropic.claude-sonnet-4-5-20250929-v1:0`: a like-for-like Sonnet, and the newest Bedrock Sonnet this repo exercises against the real API in tests/e2e. Newer ids exist in the cost map, but nothing in the repo calls them live, so picking one would be an unverified guess about model access on the CI account Scope is limited to the tests that actually issue a request. The occurrences that assert on the model string itself, or that feed mocked transformations, keep the old id so their assertions stay meaningful |
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mateo-berri
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a71d8d887d | test(bedrock): port the openai-route invoke tests onto the live config | ||
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mateo-berri
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a895249923 | refactor(bedrock): remove the dead BedrockLLM invoke code path | ||
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yuneng-jiang
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5c95017bc1
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test: unstale the reasoning-effort grid count and the responses bridge test (#34868)
* test(reasoning-effort-grid): bump cell-count assertion for claude-opus-5
The claude-opus-5 grid entry added in
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yuneng-jiang
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74a87b58f0
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Merge pull request #34520 from BerriAI/litellm_/replace-gpt5-codex-test-27520b
test: replace deprecated gpt-5-codex with gpt-5.3-codex |
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mateo-berri
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ae81625ee6 |
feat(anthropic): add Claude Opus 5
Registers claude-opus-5 across the cost maps and provider lists so the model prices, reports its real 1M/128K limits, and advertises its capabilities instead of falling through the generalization patterns at zero cost. Adds the first-party entry plus the Bedrock (base, global, us, eu, au, jp), Vertex AI, and Azure AI variants. Pricing matches Opus 4.8 at $5/$25 per MTok with the usual 1.1x regional premium on the cross-region inference profiles, and fast mode is priced at 2x through provider_specific_entry on the first-party entry only. Two fields deliberately differ from Opus 4.8: prompt_cache_min_tokens drops to 512, and bedrock_output_config_effort_ceiling is omitted because Bedrock accepts output_config.effort="max" for Opus 5. |
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Yuneng Jiang
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a1aae953d6
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test: replace deprecated gpt-5-codex with gpt-5.3-codex | ||
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yuneng-jiang
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64aad5877a
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fix(proxy): restore atomic user upsert when adding team members (#34457)
* fix(proxy): restore atomic user upsert when adding team members
Parallel /team/new calls naming the same not-yet-existing member were
returning 500 "Unique constraint failed on the fields: (`user_id`)".
The upsert in add_new_member passed an empty update branch. Prisma only
compiles an upsert down to a single INSERT ... ON CONFLICT when that branch
writes something; with an empty one it emits SELECT-then-INSERT instead, so
concurrent requests all read "no such user" and all insert. Postgres
statement logs confirm it: the empty form logs BEGIN/SELECT/INSERT/COMMIT,
the non-empty form logs INSERT ... ON CONFLICT ("user_id") DO UPDATE SET.
Re-state user_id in the update branch as a no-op so the native upsert path
comes back. The teams append stays in the filtered update below it, so an
already-existing member still cannot pick up a duplicate team id.
tests/test_team.py::test_team_new failed 9 of 15 runs against a live proxy
before this and 0 of 15 after. The existing unit test asserted only that
upsert had been called on a mock, so it passed either way; it now pins the
shape of both branches and fails when the update branch goes back to empty.
* test: point the live codex tests at gpt-5.3-codex
OpenAI deprecated gpt-5.2-codex, so test_openai_codex and
test_openai_codex_stream started failing against the live API with
model_not_found. gpt-5.3-codex is the current codex model; both tests pass
on it. The remaining gpt-5.2-codex references in the suite are mocked
transformation tests and are unaffected.
* test(e2e): update models page specs for the shared DataTable
The DataTable migration in #34363 changed three things the models page
specs were pinned to, and five tests went red.
Row click no longer opens the detail view; the Model ID cell owns that
now, so both specs click its `model-id-<id>` test id instead of the row.
The search box placeholder switched from an ASCII "..." to a real
ellipsis, so the specs use getByPlaceholder with a substring instead of
an exact attribute match that punctuation can break again. The results
count moved from `models-results-count` ("Showing 1 - 50 of 137 results")
to the shared pagination's `pagination-range` ("Showing 1-50 of 137").
The Team-BYOK test also filtered rows on the team alias, which the Team
ID column has never rendered in either the old or the new table; it
filters on the team id now, which is what the column actually shows and
what the assertion's own comment intends.
Verified against a local proxy serving a fresh build with the seeded
e2e postgres and mock upstream: all five failing tests pass, and the
full suite is 82 passed / 4 skipped at CI parity (workers=1).
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mateo-berri
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da47272227 | test(reasoning_effort_grid): enable azure fable-5 and opus-4-8 grid cells | ||
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Mateo Wang
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cd6e8cdf23
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test(realtime): record and replay websocket traffic in redis vcr cassettes (#32390)
* test(realtime): record and replay websocket traffic in redis vcr cassettes * style(realtime): ruff-format ws-vcr harness * fix(realtime): warn instead of silently disabling ws-vcr when the redis client cannot be built |
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Mateo Wang
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06a97c83bd
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test(realtime): assert guardrail block on backend wire traffic instead of model refusal wording (#32388)
test_text_message_blocked_by_guardrail_no_ai_response classified the model's reply against a safe_markers keyword list to decide whether the guardrail had blocked the message. gpt-realtime words its refusal of the guardrail's "say exactly" voice prompt nondeterministically, so any new phrasing outside the list turned CI red on unrelated PRs; the list had already been extended in #28191, #28200 and #29477, and drifted again to "Sorry, I can't comply with that request" (11 of the 13 failed realtime_translation_testing runs since 2026-06-24, e.g. CircleCI job 2009316 on #32380). Record every frame the proxy sends to the backend through a RecordingBackendWebSocket wrapper and assert the invariant the product actually guarantees: the blocked phrase never reaches OpenAI, only the guardrail's own conversation.item.create and response.create are forwarded (the client's reflexive response.create is dropped), and the blocked phrase never appears in AI output. Replace the fixed 0.3s/3.0s sleeps with an event-driven wait for response.done; client frames are processed sequentially so no inter-message sleep is needed. Verified by mutation: disabling the response.create drop fails the response.create count assertion, and disabling the guardrail fails the guardrail_violation assertion. |
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Krrish Dholakia
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50b936c75e
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feat(guardrails/headroom): add CCR (compress-cache-retrieve) via agentic loop (#31681)
* feat(guardrails/headroom): add CCR (compress-cache-retrieve) support via agentic loop
When Headroom's /v1/compress returns messages containing hash markers
(hash=[a-f0-9]{24}), inject a headroom_retrieve tool into the request.
When the LLM calls that tool, intercept via async_should_run_agentic_loop
and async_build_agentic_loop_plan, call GET /v1/retrieve/{hash} on the
Headroom sidecar, and replay the LLM with the original content as a tool
result -- all transparent to the caller.
* style: run ruff format on headroom guardrail and tests
* fix(guardrails/headroom): detect headroom_retrieve calls in both OpenAI and Anthropic response formats
* test(guardrails/headroom): add test for Anthropic content block format detection in CCR loop
* ci: trigger CI checks
* fix(guardrails/headroom): replace List/Dict with list/dict to fix UP006 ruff violations
* fix(guardrails/headroom): replace except Exception with except ValueError to fix BLE001
* fix(guardrails/headroom): add Responses API output format detection for CCR tool calls
* refactor(guardrails/headroom): extract format-specific helpers to fix C901 complexity
* fix(guardrails/headroom): scope CCR retrieval to hashes produced by current request
Previously any LLM-supplied hash in a headroom_retrieve tool call was
forwarded to the Headroom retrieve API, letting a crafted tool call
fetch arbitrary cached content. Validate the hash against the set
produced by compressing the current request's messages before calling
retrieve.
* fix(guardrails/headroom): track issued hashes server-side, fix Responses API replay shape
Hash validation now also checks an in-memory cache of hashes actually
returned by /v1/compress, not just whether the hash text appears
somewhere in the request's messages. The message-text check alone is
forgeable: an attacker can plant a hash-shaped string in their own
prompt and have it treated as valid.
Responses API follow-up now emits function_call/function_call_output
items keyed by call_id instead of chat-style assistant/tool messages,
since the Responses API does not accept the latter as input. Also
fixes call_id/id field priority when extracting tool calls from
Responses API output, since call_id (not id) is what must match
between the function_call and its output.
* fix(guardrails/headroom): drop redundant quoted type annotations
UP037 flags quotes on annotations that are already lazily evaluated
via `from __future__ import annotations`.
* test(guardrails/headroom): add missing pytest.mark.asyncio decorators
Functional under asyncio_mode=auto, but every other async test in the
file has the decorator for consistency.
* fix(guardrails/headroom): scope CCR hashes per call_id, fix Anthropic replay shape
Two real gaps found in review:
1. The instance-wide issued-hash cache combined with a message-text
check did not actually scope retrieval to the request that produced
the hash. A hash issued for request A stays in the shared cache
until TTL expiry, and the message-text check is satisfied by any
request whose own messages happen to echo that hash string. Request
B could plant A's hash in its own prompt and retrieve A's content.
Fixed by keying the issued-hash cache by litellm_call_id, matching
the pattern already used in compression_interception: a hash is
only honored when it was issued under the exact call_id resolving
for the current request.
2. The Anthropic Messages replay path fell through to the chat-style
assistant/tool-message builder, which Anthropic does not accept.
Anthropic requires the tool_use block echoed in an assistant message
paired with a tool_result block in a user message, keyed by
tool_use_id. Added a dedicated branch for this shape.
* docs: note proactive API-fragmentation helper convention
Add a bullet to the coding-conventions list: look for or add a shared
helper when logic branches on API surface (chat completions vs
Anthropic Messages vs Responses API), instead of duplicating
format-detection per module.
* fix(guardrails/headroom): fix Anthropic tool-shape detection, extract shared cross-API tool util
Live e2e testing against the real Anthropic API surfaced two bugs the
mocked unit tests couldn't catch because they used MagicMock responses
instead of realistic response shapes:
1. has_headroom_retrieve_tool only recognized OpenAI-shaped function
tools. By the time an Anthropic Messages response reaches the
agentic-loop gate, the tool this guardrail injected has already been
transformed into Anthropic's native shape (type: "custom", top-level
"name"), so the gate never fired for real Anthropic requests.
2. AnthropicMessagesResponse is a TypedDict, so real responses are
plain dicts at runtime, not objects with attribute access. The
extractors and format detectors used bare getattr(), which silently
returns nothing for dict responses instead of reading the actual
key.
Extracted the cross-API-surface tool-call extraction and tool-presence
check into litellm/litellm_core_utils/prompt_templates/factory.py
(get_tool_calls_from_response, has_tool_with_name) so this format
fragmentation is handled in one place instead of being duplicated
per-guardrail, and reused the existing repair-aware
parse_tool_call_arguments from common_utils instead of a naive
json.loads. headroom.py now delegates to these shared helpers.
Confirmed live against the real Anthropic API: the retrieve loop now
fires and successfully retrieves the correct hash's content through
the full compress -> tool-call -> retrieve -> replay round-trip.
* fix(guardrails/headroom): fix ruff-strict UP006/I001 budget violations
Use lowercase list/dict generics in the new factory.py tool-call
helpers instead of typing.List/Dict, drop the now-unused Tuple import
in headroom.py, and reorder the new factory import ahead of the
llms.custom_httpx import to satisfy import sorting.
* fix(guardrails/headroom): match Anthropic tools without a type field
Anthropic's documented client tool format is just name + input_schema;
type: "custom" is only one possible value, not a requirement. Match
any non-OpenAI-shaped tool on its top-level name instead of requiring
type == "custom".
|
||
|
mateo-berri
|
d6f09c4f24
|
test(reasoning-effort-grid): bump cell-count assertion for claude-sonnet-5
The Sonnet 5 grid entry raised the Anthropic direct route to 30 model combos, so test_grid_cell_count now expects 330 cells instead of 319. |
||
|
Cursor Agent
|
a126cdf5b7
|
feat(anthropic): add Claude Sonnet 5
Register claude-sonnet-5 across the Anthropic, Bedrock (base + global/us/eu/au/jp cross-region inference profiles), Vertex AI, and Azure AI cost-map entries in both the root and bundled-backup model maps, plus BEDROCK_CONVERSE_MODELS and the setup-wizard provider list. Sonnet 5 ships with the gen-5 adaptive-thinking profile (adaptive thinking always on, no extended thinking, effort defaults to high), so the entries mirror the Fable 5 / Opus 4.8 sampling-param and prefill restrictions rather than the older Sonnet 4.6 behavior: supports_sampling_params and supports_assistant_prefill are false while supports_adaptive_thinking, supports_xhigh_reasoning_effort, and supports_max_reasoning_effort are true. Pricing follows standard Sonnet rates ($3 / $15 per MTok) with the 10% regional premium on the us/eu/au/jp profiles. Add a reasoning-effort grid entry for the Anthropic direct route and a regression test pinning pricing, capabilities, regional premiums, backup parity, and bare-name provider resolution. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> |
||
|
Mateo Wang
|
64d8d7f8cb
|
fix(bedrock): normalize Messages system role and adaptive-thinking for Claude Invoke (#31364)
* fix(bedrock): normalize Messages system role and adaptive-thinking for Claude Invoke
* style(bedrock): use builtin generics in new Invoke helpers to clear UP006 gate
* fix(bedrock): honor explicit thinking budget_tokens=0 in clear_thinking conversion
The clear_thinking_20251015 -> adaptive conversion resolved the thinking
budget with `thinking.get("budget_tokens") or BEDROCK_MIN_THINKING_BUDGET_TOKENS`,
which treats a caller-supplied `budget_tokens=0` as missing and silently
substitutes the Bedrock minimum. Resolve the budget with an explicit
`is not None` check so an explicit 0 is honored.
* fix(bedrock): gate Fable 5 into clear_thinking adaptive injection on Invoke
_ensure_thinking_for_clear_thinking_context_management returns early when
_supports_extended_thinking_on_bedrock(model) is False, so the adaptive-thinking
injection never runs for models absent from that gate. Opus 4.8 slips through on
the incidental "opus-4" substring, but Fable 5 had no matching pattern, so a
clear_thinking_20251015 request on Fable 5 reached Bedrock with an unsupported
context-management edit and no thinking field; the exact 400 this path exists to
prevent. Add the fable-5 patterns to the gate so Fable 5 (mapped ids and unmapped
aliases) gets thinking.type=adaptive + output_config.effort like the other
adaptive models.
Extend the adaptive-injection regression test to cover Fable 5 (a mapped id and
an unmapped alias) so it fails without the gate entry, and add focused coverage
for the budget->effort tiers, the disabled/enabled/adaptive thinking branches,
output_config.effort preservation, and list/dict system-role normalization.
Also normalize the Invoke transformation module and its test to line-length 88
so ruff format --check (CI format-check) passes.
* refactor(anthropic): make supports_adaptive_thinking flag authoritative for thinking detection
Replace the per-version name helpers (_is_claude_4_6/4_7/4_8_model,
_is_claude_fable_5_model) with cost-map-flag-first detection. _is_adaptive_thinking_model
now reads supports_adaptive_thinking from the model cost map and falls back to a single
generalized family-version regex (_claude_version_at_least(model, 4, 6)) only when a model
is unmapped, instead of hard-coding each new Claude release.
Wire supports_adaptive_thinking through ProviderSpecificModelInfo and ModelInfo so the cost
map flag actually surfaces at lookup time. Reroute the Bedrock Invoke extended-thinking gate
and the two anthropic/chat/transformation.py call sites through _is_adaptive_thinking_model.
Known gap left to the fallback_generalizations work (#29718): unmapped Fable 5 aliases have
no parseable minor version, so they defer to the cost map and are not detected until a mapped
entry or a generalization rule exists. Covered by an explicit regression test.
* refactor(anthropic): drop name-based version fallback; resolve adaptive thinking from cost map only
The prior commit kept a regex (_claude_version_at_least) as a fallback when an id
resolved to no cost-map entry. Remove it: _is_adaptive_thinking_model now reads
supports_adaptive_thinking and nothing else, so "which Claude versions think
adaptively" lives entirely in the model cost map, and a new adaptive release is a
JSON edit rather than a Python edit.
To keep the flag authoritative across the id forms the Bedrock Invoke and anthropic
paths actually see, backfill supports_adaptive_thinking=true on every adaptive Claude
entry that was missing it (Opus 4.6/4.7 and Sonnet 4.6 across region/provider aliases)
in both the root and bundled cost maps, and generalize _model_map_lookup_candidates to
normalize an id to its base cost-map key: strip a Bedrock version suffix (-v1:0 fully,
or just the :0 inference-profile minor so the -v1-keyed 4.6 entries resolve), strip a
dated-release suffix (-20260219), and rewrite a dotted family version (4.6 -> 4-6).
This is id normalization feeding the lookup, not capability-by-name.
Tests load the PR-local cost map (the flags are not on main until merge) and cover each
normalization path plus the unmapped-alias deferral to fallback_generalizations (#29718).
* refactor(reasoning_effort): single-source effort<->thinking-budget mappings
Route every reasoning_effort <-> thinking-budget conversion through the DEFAULT_REASONING_EFFORT_*_THINKING_BUDGET constants so the numbers stay in sync across providers. The five constants are now 2000/5000/10000/20000/40000
Add reasoning_effort_from_thinking_budget() in litellm_core_utils/reasoning_effort_utils.py and route the three OpenAI-style forward maps (anthropic adapters, responses adapters, hosted_vllm) through it. The bedrock invoke and experimental messages adaptive maps now reference the constants directly; the only behavior change is the xhigh threshold moving from 24000 to 20000. Reverse maps and the cross-provider test grid read the same constants
* test(reasoning_effort): lift budget-mode max_tokens above the new high budget
The single-sourced DEFAULT_REASONING_EFFORT_*_THINKING_BUDGET thresholds moved
high from 4096 to 10000. The live reasoning_effort grid sends budget-mode
requests with max_tokens=8192, so reasoning_effort=high now produces
budget_tokens=10000 > max_tokens and every provider returns 'max_tokens must be
greater than thinking.budget_tokens'. Derive a shared BUDGET_MODE_MAX_TOKENS
(2x the high budget) for the spec and the request builder so the ceiling always
clears the largest 200-expected tier. Also resolve the inherited base
test_reasoning_effort assertion off the same high-budget constant instead of the
stale 4096 literal so it tracks the source of truth.
* fix(reasoning_effort): keep effort<->budget thresholds at pre-PR values
The single-sourcing refactor moved the shared effort<->budget thresholds up
(low 1024->2000, medium 2048->5000, high 4096->10000, xhigh 8192->20000,
max 16384->40000). That silently changes the effort->budget direction: a caller
who sets reasoning_effort together with a max_tokens that used to sit above the
old per-tier budget but below the new one now trips the provider's
"max_tokens must be greater than thinking.budget_tokens" 400. It spans every
backend that derives a budget from an effort (Anthropic, Gemini/Vertex,
hosted vLLM), not just Bedrock.
Restore the constants to their pre-PR values while keeping every backend reading
from the shared DEFAULT_REASONING_EFFORT_*_THINKING_BUDGET constants, so the
mapping stays single-sourced without the behavior change. Tests that pinned the
raised thresholds now derive their boundaries from the same constants.
* test(reasoning_effort): derive high effort->budget assertions from the shared constant
The cross-provider translation tests pinned reasoning_effort="high" to a literal
budget_tokens=10000, the raised value. Point them at
DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET so they track the single source
instead of a magic number.
* fix(anthropic): resolve adaptive flag for combined dated+versioned Bedrock ids
The model-map candidate normalization applied each suffix strip independently to
the original id, so the real Bedrock shape "<base>-<YYYYMMDD>-v1:0" never reduced
to its base cost-map key: stripping the version left the date, and the
dated-suffix regex is anchored to the end so it could not fire while the version
was still present. An adaptive Claude model invoked by its full dated+versioned
id (e.g. us.anthropic.claude-sonnet-4-6-20251101-v1:0) therefore resolved to
supports_adaptive_thinking=null and was treated as non-adaptive, reaching Bedrock
with the rejected thinking.type=enabled shape, the exact 400 this path prevents.
Add a composed normalization that rewrites the dotted family version, then peels
the -vN:rev version suffix, then the -YYYYMMDD dated suffix, so the combined form
resolves to its base key. Regression tests pin the combined suffix on sonnet-4-6
and opus-4-8 across provider/region prefixes.
* fix(reasoning_effort): align budget<->effort tests with reverted constants and format common_utils
The constant revert restored the effort<->budget thresholds to their pre-PR
values (1024/2048/4096/8192/16384) and single-sourced the reverse
budget->effort ladder through reasoning_effort_from_thinking_budget, but
several tests still pinned the briefly-raised values and the old hardcoded
reverse buckets, so the "All Other Providers" shard failed
Derive the anthropic chat effort->budget assertions from the shared
DEFAULT_REASONING_EFFORT_*_THINKING_BUDGET constants, and update the
experimental pass-through and responses adapter expectations to the
single-sourced reverse ladder (budget 1024 -> low, 5000 -> high)
Also run ruff format --line-length 88 over anthropic/common_utils.py so the
CI format-check, which checks the whole changed file, passes
|
||
|
Sameer Kankute
|
133da06aa3
|
chore: litellm oss staging (#31185)
* fix(ui): widen Y-axis gutter on Usage charts so large token/request labels aren't clipped
The Total Tokens Over Time and Total Requests Over Time AreaCharts on the
Usage page used Tremor's default yAxisWidth (~56 px), which is too narrow
once totals pass the hundred-million mark — leading digits of labels like
"100.00M" / "4500.00M" got clipped against the chart edge. The requests
chart was worse: it formatted with toLocaleString(), so billion-scale
request counts produced "1,000,000,000" (13 chars) and overflowed
immediately.
Fix in two places so neither alone has to carry the whole margin:
- activity_metrics.tsx: add yAxisWidth={80} to both AreaCharts, and
switch the requests chart to the shared valueFormatter so it uses the
same compact k/M/B suffixes as the tokens chart.
- value_formatters.tsx: add a >= 1e9 branch to valueFormatter /
valueFormatterSpend that emits a "B" suffix (4.50B, $4.50B), keeping
every formatted label at most 7 chars.
Co-Authored-By: Claude Opus 4 (1M context) <noreply@anthropic.com>
* Update ui/litellm-dashboard/src/components/UsagePage/utils/value_formatters.tsx
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* docs(readme): add Deploy on AWS/GCP with Terraform section
Adds a quickstart for the two published Terraform modules on the public
registry (BerriAI/litellm/aws and BerriAI/litellm/google). Copy-paste
main.tf for each cloud, the one-time GCP Artifact Registry remote-repo
command, and pointers to the registry pages for the full input surface.
Sits inside the Get Started section, between the gateway/SDK table and
Run in Developer Mode -- where someone scanning the README for "how do I
deploy this" will land.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* docs(readme): add 1-click deploy buttons for AWS + GCP
GCP gets the real 1-click: Open in Cloud Shell badge that clones the repo
and walks through `terraform apply` via the existing DeployStack
tutorial (already shipped at terraform/litellm/gcp/examples/default/
TUTORIAL.md). User just picks a project.
AWS gets a soft 1-click: a Launch in AWS CloudShell badge that opens an
in-browser, already-authenticated shell. User runs four commands
(clone + cd + cp tfvars + terraform apply) once inside. There's no
native AWS deeplink that pre-clones a repo + runs a tutorial -- CFN
"Launch Stack" + CodeBuild would be needed for that, and that's a
separate piece of work.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* docs(readme): move AWS + GCP deploy buttons next to Render button
* docs(readme): unify deploy button sizes and badge styles
* docs(readme): bump deploy button height to 48 to match Render/Railway
* docs(readme): bump AWS/GCP badge height to compensate for SVG padding
* docs(readme): bump AWS/GCP badge height to 72
* docs(readme): bump AWS/GCP badge height to 84
* fix(readme): make deploy buttons same height (48px)
https://claude.ai/code/session_01MxQRMHSDXbqJh74rF86UBc
* docs(readme): flag GCP project ID substitution in image_registry
* docs(readme): equalize deploy button heights and fix Cloud Shell button font
GitHub rewrites an image's height attribute to "height: auto; max-height: Npx", which only caps and never stretches, so each image renders at its intrinsic height. The AWS/GCP shields badges are intrinsically 28px while the Render/Railway buttons are 40px, leaving the row uneven regardless of the height="48" we set. Replace the two shields badges with committed 40px PNGs so all four header buttons render at the same 40px.
Also swap the Cloud Shell button from open-btn.svg to open-btn.png. The SVG renders its label as live text with font-family "Roboto, Sans" and no generic fallback; since neither font exists in GitHub's render environment, the text fell back to a serif (Times New Roman). The PNG bakes in the correct typeface.
* docs(readme): collapse Railway deploy anchor to a single line
The Railway button wrapped its img across indented lines, so the anchor contained leading and trailing whitespace. GitHub underlines link content, rendering that whitespace as a small blue underline beside the button. Put the anchor on one line like the other three buttons so there is no inner whitespace to underline.
* Add Claude Fable 5 cost map entries as a data-only hotfix
Backports only the model map changes from #30064 so deployments on
released litellm versions pick up Fable 5 pricing, context window, and
the adaptive thinking flag through the hosted cost map fetch without
upgrading. Includes the supports_sampling_params flag on the 28
Fable 5 / Opus 4.7 / Opus 4.8 entries (ignored by released code, read
by the gating that ships with the next release) and the matching
one-line schema declaration so the map validation test passes.
https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm
* fix: correct context window tokens for GPT-5 Pro and GPT-5.4 Mini/Nano
Three bugs in model_prices_and_context_window.json:
1. gpt-5-pro and gpt-5-pro-2025-10-06: max_input_tokens and max_tokens
were SWAPPED. GPT-5 Pro has a 400K context window (input) with 128K
max output, but the values were set as max_input=128000,
max_tokens=272000. This caused token limit errors when sending
prompts over 128K tokens to GPT-5 Pro.
2. gpt-5.4-mini and gpt-5.4-mini-2026-03-17: max_input_tokens was
272000, but GPT-5.4 Mini shares the same 1,050,000 token context
window as GPT-5.4. This was inconsistent with the azure/ variants
which already correctly had 1,050,000.
3. gpt-5.4-nano and gpt-5.4-nano-2026-03-17: same issue as Mini,
max_input_tokens was 272000 instead of 1,050,000.
Source: OpenAI model documentation and contextwindows.dev which
aggregates official context window sizes.
Fixes #30928 (partially — the issue incorrectly claims gpt-5/gpt-5-mini
should be 400K; their 272K values are correct per OpenAI docs)
* fix: also correct max_output_tokens for gpt-5-pro (272000→128000)
Per reviewer feedback, max_output_tokens was left at 272000 while
max_tokens was corrected to 128000, causing an internal inconsistency.
Both should be 128000 per OpenAI docs.
* fix(cost): price gpt-image generated output tokens as image tokens (#31147)
The OpenAI Images endpoints (/v1/images/generations, /v1/images/edits) return
usage with no output token breakdown — litellm's `ImageUsage` has no
`output_tokens_details` field — so generated-image OUTPUT tokens were priced at
the text rate (`output_cost_per_token`) instead of the image rate
(`output_cost_per_image_token`). For gpt-image-2 that is $10/1M vs $30/1M, a ~3x
undercount on the dominant cost component (image output is ~74% of spend). This
also affects azure gpt-image, which shares this calculator.
The OpenAI gpt-image cost calculator re-implemented usage handling instead of
reusing `calculate_image_response_cost_from_usage`, the shared helper that
azure_ai/gemini/vertex_ai already use. That helper classifies generated output
tokens as image tokens when the provider does not itemize output, and splits
text/image when it does.
Fix: route the ImageUsage path through `calculate_image_response_cost_from_usage`
(pre-transformed chat Usage objects are still costed directly). Adds a regression
test for the no-breakdown ImageUsage case (gpt-image-2).
* fix(bedrock): route application-inference-profile ARNs to converse (#18258) (#31098)
A bare application-inference-profile ARN passed as bedrock/arn:... fell
through to the invoke route, which cannot derive a provider from the
opaque profile id and raised 'Unknown provider=None'. The converse route
needs no provider, so detect these ARNs in get_bedrock_route and route
them to converse, matching the behavior of the already-documented
bedrock/converse/arn:... workaround.
Explicit invoke/ prefixes still win, and they remain a dead end for these
ARNs by design (no provider derivable). System-defined inference-profile
ARNs that embed a known model, and other opaque ARN types
(provisioned-model, imported-model, custom-model-deployment) that are
frequently invoke-only, are deliberately left on their current routes;
tests guard both boundaries.
* fix(moonshot): stop mutating caller messages on tool_choice='required' (#31060)
_add_tool_choice_required_message appended the "select a tool" prompt to
the caller's messages list in place, so transform_request corrupted the
caller's conversation history and appended a duplicate prompt on every
retry. Build and return a new list instead so the call stays idempotent.
Adds a regression test asserting the input messages list is unchanged
across repeated transform_request calls.
Co-authored-by: Wassbdr <wassim.badraoui07@gmail.com>
* fix(transcription): accept fractional usage.seconds in diarized_json responses (#30996)
gpt-4o-transcribe and compatible ASR backends return a diarized_json
response with usage={"type": "duration", "seconds": <float>}, e.g. 295.8.
TranscriptionUsageDurationObject typed seconds as int, so parsing the
response raised a pydantic ValidationError (int_from_float). That error
surfaces as an APIConnectionError which the router treats as retryable, so
it keeps re-calling the upstream (200 every time) until the upstream
rate-limits and returns 429 to the caller.
OpenAI specs this field as a float (see openai SDK UsageDuration.seconds),
so widen seconds to float. With the parse succeeding there is no exception
left to retry, which removes the loop.
Co-authored-by: Neimar Avila <19142978+neimaravila@users.noreply.github.com>
* fix(deepseek): drop non-function tools before chat completions call (#30910)
* fix(deepseek): drop non-function tools before chat completions call
DeepSeek's /chat/completions only accepts tools of type "function".
Requests bridged from /v1/responses can carry responses-API-native tool
types, for example a Codex CLI tool typed "namespace", which DeepSeek
rejects with "unknown variant 'namespace', expected 'function'" so the
whole request fails (issue #30722).
Filter unsupported tool types in the DeepSeek request transform so the
function tools still go through; when nothing callable remains, also drop
the now-dangling tool_choice and parallel_tool_calls
Fixes #30722
* test(deepseek): cover async tool filtering and document tool_choice assumption
Add an async_transform_request regression test so the sync and async tool
filtering paths cannot silently diverge, and document in _drop_unsupported_tools
that only non-function tools are dropped, so a function-named tool_choice always
references a surviving tool
* feat(catalog): add zai/glm-5.1, zai/glm-4.7-flash, openrouter/z-ai/glm-5.1 (#29840)
* feat(ui): surface team budget on key overview when key has no own budget (#30801)
* feat(ui): surface team budget on key overview when key has no own budget
* fix(ui): replace IIFE with derived variable and use find() for team budget display
* fix(anthropic): emit replayable streaming thinking blocks (#31022)
* feat(proxy): read cold-storage prompts back in the logs detail view (#30364)
* feat(proxy): read cold-storage prompts back in the logs detail view
When a deployment offloads prompts and responses to cold storage instead of
Postgres, the spend-log row holds only "{}" placeholders plus a
metadata.cold_storage_object_key pointer, so the UI logs detail drawer showed
nothing. The detail endpoint only read the placeholder columns and never
fetched the object back.
Resolve the payload per row based on actual content, not a config flag: if
Postgres has content, return it; otherwise read the exact stored object key and
fetch from the configured cold storage backend through ColdStorageHandler.
Reading the persisted key is a single GET. The key embeds a microsecond
timestamp that cannot be reconstructed from the millisecond-precision startTime
column, and listing the day's prefix to match on request_id would be too
expensive for this per-open path.
Also teach the detail drawer's pretty-view parser to accept a bare messages
array. The cold storage payload carries the prompt as a top-level messages list
with no proxy_server_request, so without this the output rendered while the
input stayed blank.
ColdStorageHandler gains an optional injected logger so the resolver can be unit
tested without monkeypatching. Postgres-stored prompts are unaffected: the fast
path returns the existing columns and the request-body object still renders the
same way.
* Update litellm/proxy/spend_tracking/spend_management_endpoints.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* test(proxy): cover ColdStorageHandler resolution paths and cold-storage fetch failure
Add unit tests for ColdStorageHandler (injected logger, graceful None when no
logger is configured, and resolution of a configured logger from the callback
registry) and a regression test asserting a cold storage backend exception
degrades to the Postgres values instead of surfacing a 500.
---------
Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix(mavvrik): advance metricsMarker after upload; fix scheduler startup (#31068)
* fix(mavvrik): advance metricsMarker after upload + fix scheduler startup
Two bugs fixed:
1. deliver() never called PATCH /metrics/agent/ai/{connectionId} after a
successful GCS upload, so metricsMarker stayed at 0 and every daily run
re-exported the same dates in an infinite catch-up loop.
Fix: add _update_metrics_marker(date_epoch) called at the end of deliver()
after _upload_to_gcs() succeeds. A 4xx warns but does not raise (the GCS
file is already committed). A 410 raises consistent with the rest of the
destination.
2. init_mavvrik_focus_background_job runs at proxy startup before any LLM call
has triggered lazy instantiation of MavvrikFocusLogger, so it found no
logger instance and silently skipped registering the daily export job.
Fix: if no instance is found but "mavvrik" is in litellm.callbacks, call
_init_custom_logger_compatible_class to force instantiation before
the APScheduler job is registered.
* fix(mavvrik): catch up from earliest window when metricsMarker=0
When the connector is freshly registered, metricsMarker=0 parses to None.
The catch-up block was guarded by `if last_ingested and ...` which skipped
it entirely for None, so only yesterday was exported instead of the full
_MAX_CATCHUP_DAYS window.
Fix: treat None as being _MAX_CATCHUP_DAYS behind (start from earliest_catchup).
The existing > 7 day warning only fires for non-None markers that are old.
* fix(mavvrik): use now as end_time for yesterday's export window
LiteLLM_DailyUserSpend rows for a given date get their updated_at
bumped by the spend flush job throughout the next morning. The core
database query filters on updated_at, so capping end_time at midnight
(yesterday + 1 day) missed any spend rows flushed after midnight.
Fix: pass now (cron fire time) as end_time for the daily "yesterday"
window so all fully-settled rows are captured regardless of when the
flush job ran.
Verified: claude-3-5-sonnet BilledCost went from 0.0 to ~$2.40 per
row in the exported FOCUS CSV.
* fix(mavvrik): also use now as end_time for catch-up windows
* fix(mavvrik_focus): pass required args to _init_custom_logger_compatible_class
Calling it with only logging_integration raised TypeError at proxy startup
because internal_usage_cache and llm_router have no defaults. Also fix test
name to reflect the actual status code (5xx not 4xx) used in the mock.
* ci: retrigger CI run
* feat: pass through optional `instruction` field in the rerank API (vLLM/Qwen3-Reranker) (#30757)
* Add optional `instruction` passthrough to the rerank API
vLLM's /v1/rerank and /v1/score accept an optional top-level `instruction`
field (folded into the model's chat_template_kwargs and consumed by the
chat template — e.g. Qwen3-Reranker). LiteLLM's managed rerank route silently
dropped it: RerankRequest / OptionalRerankParams had no such field, so the
outgoing body was rebuilt without it.
Thread an opt-in `instruction: Optional[str]` through rerank()/arerank(),
get_optional_rerank_params, and the hosted_vllm transformation into the
request body, only when non-None. When callers omit it, model_dump(exclude_none)
drops the field and the outgoing request is byte-for-byte unchanged — fully
backward-compatible. (DeepInfra already forwards `instruction` via
non_default_params; this formalizes the field in the shared types.)
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Address review: thread `instruction` as a typed param + cover rerank_utils
Per PR review (greptile P2 + codecov):
- Make `instruction` a typed, named argument on the rerank provider interface
instead of recovering it from the opaque `non_default_params` blob. Adds
`instruction: Optional[str] = None` to `BaseRerankConfig.map_cohere_rerank_params`
and every provider override, and forwards it explicitly from
`get_optional_rerank_params`. hosted_vllm now reads the named param directly.
It is still also surfaced in `non_default_params` so providers that read it
there (e.g. DeepInfra) keep working now that `rerank()` consumes `instruction`
as a named param rather than leaving it in **kwargs.
- Add get_optional_rerank_params unit tests (present + absent) to cover the
previously-uncovered threading line flagged by codecov.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix: scan rerank `instruction` through request guardrails
The rerank guardrail translation (CohereRerankHandler.process_input_messages)
only scanned `query`, so the newly added `instruction` field reached the
backend model unscanned. Since instruction-aware rerankers (hosted vLLM /
Qwen3-Reranker) fold `instruction` into the prompt, an authenticated caller
could place content there to bypass configured rerank request guardrails.
Generalize the handler to scan every user-controlled text field (`query` and
`instruction`) in one apply_guardrail call and write each sanitized value back
by index. Query-only requests are unchanged (single-element list at index 0);
non-string fields are left untouched. Adds tests covering instruction
scanning, PII masking write-back, and the non-string case.
Addresses the Veria AI security review on PR #30757.
* test: narrow Optional results before len() to satisfy basedpyright budget
The lint gate (basedpyright delta-vs-base budget) flagged one new
reportArgumentType: len(result.results) where results is
List[RerankResponseResult] | None. Assert results is not None first to
narrow the type before len()/indexing.
* fix: read rerank `instruction` from kwargs to satisfy basedpyright budget
The basedpyright delta-vs-base gate flagged one new reportArgumentType: the
Router forwards rerank calls via an untyped `**kwargs` unpack
(`litellm.arerank(**{**data, **kwargs})`), and declaring `instruction` as a
typed named param on the public `rerank`/`arerank` entrypoints made pyright
check that key against `str | None`, adding an error at router.py with no real
safety gain. Read `instruction` from kwargs in `rerank` instead.
It remains fully typed where it matters - threaded as a typed argument through
`get_optional_rerank_params` and each provider's `map_cohere_rerank_params`
(the original Greptile P2 ask). Whole-repo reportArgumentType is back to the
base count (net 0); rerank hosted_vllm + cohere guardrail suites pass; ruff clean.
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(github_copilot): synthesize empty choices at the provider seam (#30929)
Newer Copilot Claude models (opus-4.7, opus-4.8) return responses with
choices=[], either carrying Anthropic-native content blocks or, for the
max_tokens=1 probe Claude Code sends, no content at all. github_copilot
is dispatched through the OpenAI SDK handler, which calls
convert_to_model_response_object directly and never invokes
GithubCopilotConfig.transform_response, so the empty-choices guard there
surfaced as a 500
Instead of synthesizing choices inside the shared
convert_to_model_response_object (which would silently turn empty choices
into a fabricated success for every provider), add a no-op
transform_parsed_response_dict hook on BaseConfig. GithubCopilotConfig
overrides it to synthesize choices from Anthropic-native content, reusing
its existing parsing, and the OpenAI SDK handler routes its parsed
response through the hook before generic conversion. The core utility
keeps treating empty choices as an error for all other providers
Fixes: https://github.com/BerriAI/litellm/issues/30927
Signed-off-by: David J. M. Karlsen <david@davidkarlsen.com>
* fix(router): stop fallback lookups from mutating the router fallbacks config (#30624)
* fix: correct amazon.titan-embed-text-v2 input price to $0.02/1M tokens (#29693)
* fix: correct amazon.titan-embed-text-v2 input price to $0.02/1M tokens
* test: scope local cost map env var with monkeypatch to avoid test pollution
* fix(sensitive_data_masker): fully mask secrets at or below the reveal threshold (#30764)
* fix(sensitive_data_masker): fully mask secrets at or below the reveal threshold
_mask_value did partial reveal by showing the first visible_prefix and last
visible_suffix characters, but for a value whose length was at or below
visible_prefix + visible_suffix (8 by default) it returned the value verbatim.
A value of exactly 8 chars fell through the length guard and computed
masked_length == 0, reconstructing the original string with no mask characters;
anything shorter hit the early return. Either way short credentials were emitted
in plaintext.
mask_dict routes real secrets through this path, so an 8-char-or-shorter redis
password, api key, or token could be written to logs and the UI unmasked. The
sibling helper mask_sensitive_keys already guards this case; _mask_value now does
the same by fully masking any value at or below the threshold.
* fix(sensitive_data_masker): add mask_short_values opt-out for truncation callers
Fully masking short values is the right default for secret masking, but
CooldownCache reuses the masker purely to truncate exception messages to the
first 50 characters, and it relies on short messages being returned readable.
Masking those blanked out short exception text and broke its tests.
Add a mask_short_values flag (default True, secure) and have CooldownCache pass
False so it keeps the truncation behavior, while every secret-masking caller
still gets short values fully masked.
* fix(mcp_debug): opt out of short-value masking to keep diagnostic token preview
MCPDebug uses the masker to preview auth tokens in debug headers and documents
that values of 10 chars or fewer are shown unchanged so token types stay
distinguishable. Pass mask_short_values=False so that diagnostic behavior is
preserved while secret maskers keep masking short values.
* fix(mcp_debug): mask short auth values in debug headers instead of echoing them
Earlier this masker opted out of short-value masking to keep a token preview, but
that echoes short authorization and token values verbatim in debug response
headers, which is the same leak this change is meant to close. Auth material
should never be emitted in full, so mask short values here too; the first/last
character preview still applies to longer tokens. Only CooldownCache keeps the
opt-out, since it truncates exception text rather than masking secrets.
* test(mcp_debug): assert masked short value preserves length
* refactor(fireworks_ai): remove deprecated audio transcriptions endpoint (#30917)
Fireworks AI deprecated audio inference on 2026-06-10
(https://docs.fireworks.ai/updates/changelog#audio-inference-and-image-generation-deprecation).
Live API testing confirms the endpoint is already non-functional: a valid
Fireworks API key receives HTTP 401 "Unauthorized" from
api.fireworks.ai/inference/v1/audio/transcriptions for every request,
regardless of payload. The audio-prod.api.fireworks.ai host referenced in
the test suite returns 401 for every path; the entire host is decommissioned.
Remove the dead FireworksAIAudioTranscriptionConfig class and every
reference to it across the codebase:
- Delete litellm/llms/fireworks_ai/audio_transcription/ directory (17-line
config class that inherited from OpenAIWhisperAudioTranscriptionConfig)
- Remove the Fireworks branch from
ProviderConfigManager.get_provider_audio_transcription_config() in
litellm/utils.py; update the stale comment in
get_optional_params_transcription that referenced fireworks ai
- Remove the FireworksAIAudioTranscriptionConfig entries from
LLM_CONFIG_NAMES and _LLM_CONFIGS_IMPORT_MAP in
litellm/_lazy_imports_registry.py
- Remove the TYPE_CHECKING re-export in litellm/__init__.py
- Remove the transcription branch in the fireworks_ai case of
get_supported_openai_params() in
litellm/litellm_core_utils/get_supported_openai_params.py
- Remove the whisper-v3 and whisper-v3-turbo entries from
model_prices_and_context_window.json and
litellm/model_prices_and_context_window_backup.json (both had
mode: audio_transcription and zero-cost pricing)
- Remove the TestFireworksAIAudioTranscription test class and its
imports from tests/llm_translation/test_fireworks_ai_translation.py
No other provider is affected. The openai_compatible_providers list,
FireworksAIMixin, and the OpenAI Whisper transcription handler all stay
because they are shared with other Fireworks endpoints and other
providers. The provider_endpoints_support.json registry already had
audio_transcriptions set to false for fireworks_ai.
* feat: add darkbloom provider (#30876)
* feat: add darkbloom provider
* fix: document darkbloom provider endpoints
* fix: address darkbloom review feedback
* fix: update darkbloom tool metadata
* fix: fail fast for non-Postgres database URLs (#30883)
* fix(proxy): fail fast on non-PostgreSQL DATABASE_URL instead of hanging on startup
LiteLLM's Prisma datasource is pinned to provider = 'postgresql', so a sqlite:// or mysql:// DATABASE_URL can never connect.
Today that surfaces as an opaque startup stall where the port never binds, and a separate 'DB not connected' 500 on /key/generate when no DATABASE_URL is set at all leaves operators guessing what to configure.
Validate the DATABASE_URL / DIRECT_URL scheme in run_server before any Prisma call and exit with an actionable message naming the unsupported scheme.
Also reword CommonProxyErrors.db_not_connected_error to tell the operator to set DATABASE_URL to a postgresql:// connection string.
Add regression tests covering postgres acceptance and sqlite/mysql/mssql rejection.
* fix: resolve CI failures and proxy DB URL typing issue
* fix(proxy): fail fast on non-PostgreSQL DATABASE_URLs with clear startup errors instead of hanging
* Validate DIRECT_URL alongside DATABASE_URL startup guards
* fix(bedrock): surface modeled HTTP status for mid-stream error events so 5xx is retryable (#24608) (#30946)
* fix(bedrock): surface modeled HTTP status for mid-stream error events (#24608)
* test(bedrock): mid-stream server errors trigger streaming fallback (#24608)
* style(bedrock): black-format stream-error helper (#24608)
* fix(mcp): re-land native tool preservation with typed annotations (#30645)
* fix(mcp): preserve native tools in semantic filter hook with typed annotations
* fix(mcp): tighten _is_mcp_tool Chat Completions shape check
* fix(sambanova): return embeddings supported params instead of dropping them (#30937)
* fix(router): send fallback metadata when streaming (#30914)
When a streaming request triggers a fallback, there was previously no way to
know it happened. This commit addresses this in a few ways:
1. The response now correctly populates the fallback headers
(`x-litellm-attempted-fallbacks`) so callers know a fallback happened.
2. The correct model ID is passed in the streaming chunks.
3. A streaming chunk with the fallback error can be optionally sent back
to the client (opt-in) by passing `include_fallback_errors: true` in
the request.
The format of the fallback errors while streaming is intentionally OpenAI
compatible to not break existing libraries that parse these events. It was
tested with Vercel's AI SDK (ai-sdk.dev). It is also opt-in, so it is not
delieved unexpectedly to callers by default.
* fix(mistral): drop output-only reasoning fields from input messages (#30884)
LiteLLM attaches reasoning_content and thinking_blocks to assistant
responses. Replaying those assistant turns verbatim forwarded the fields
back to Mistral, whose input schema forbids unknown keys, so the whole
request failed with a 422 extra_forbidden and reasoning models became
unusable across multiple turns.
Strip both fields from assistant messages before the request is built, in
a spot that runs ahead of the image/file branch so it applies on every
path. Fixes #30835
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(perplexity): bill search queries at the per-request price, not 1/1000 of it (#30652)
* fix(perplexity): bill search queries at the per-request price, not 1/1000
The fallback cost calculator divided search_context_cost_per_query by
1000, but that field stores the per-request price in USD: sonar is
{low: 0.005, medium: 0.008, high: 0.012}, matching Perplexity's published
$5/$8/$12 per 1,000 requests expressed per request. The gemini cost
calculator reads the same field per request with no division (its
docstring calls it "the per-request cost").
The division understated search cost by 1000x on every Perplexity call
that falls back to manual calculation (i.e. when the API does not return
a pre-computed usage.cost). Use the value directly.
Update the tests that had encoded the /1000 factor in their expectations,
and drop an unused import flagged by ruff in the touched test file.
* test(perplexity): update integration test search-cost expectations to per-request
The integration tests still encoded the old /1000 search-cost factor, so
they failed once the fallback calculator was corrected to bill
search_context_cost_per_query per request. Update the four expected-cost
computations (and the high-volume dollar-value comments) to match.
* test(perplexity): drop unused mock imports flagged by ruff
* fix: include model_access_groups when expanding all-team-models in get_team_models (#30622)
* fix(fireworks_ai): return None for transcription in get_supported_openai_params
Fireworks AI deprecated audio inference on 2026-06-10; the endpoint is
decommissioned. Without an explicit transcription branch, requests with
request_type='transcription' fell through to the else and returned
FireworksAIConfig chat-completion params. Return None instead to signal
the provider does not support transcription.
* fix(proxy): gate include_fallback_errors behind expose_fallback_errors_to_caller setting
Without an operator gate, any authenticated caller could set include_fallback_errors=True,
trigger a fallback, and read raw upstream exception messages from the
x-litellm-fallback-errors header and the litellm-fallback-metadata SSE event.
Strip include_fallback_errors from request data in common_processing_pre_call_logic
when expose_fallback_errors_to_caller is not set, so the router never builds the
error list. Also gate _should_include_fallback_errors on the same setting as a
secondary check for the streaming SSE injection path.
* test(proxy): opt in to expose_fallback_errors_to_caller in streaming SSE test
The operator gate added in
|
||
|
michelligabriele
|
0a8a87afe0
|
fix(streaming): word-sliced cache replay for stream=true cache hits (#30216)
* fix(streaming): word-sliced cache replay for stream=true cache hits * fix(streaming): align mypy and replay happy-path test with word-sliced cache replay * fix(streaming): short-circuit whitespace-only content in cache replay splitter * fix(streaming): emit tool_calls/function_call only on first replay slice * refactor(streaming): drop dead delattr guard in cache replay A non-None usage on the replay base object always lives in __pydantic_extra__ (it is attached via setattr earlier in the same function), so delattr can never raise here; the try/except AttributeError that silently swallowed a failure was dead defensive code that could only ever hide a real regression, so it is removed in both the async and sync generators. Also switches the new replay annotations from typing.List to the builtin list to satisfy the strict ruff UP006 gate and drops the unused PLR0915 noqa directives (the rule is not enabled in this repo's ruff config, so RUF100 flagged them). * fix(streaming): drop carried-over metadata from later cache replay slices The word-sliced cache replay deep-copies the full ModelResponseStream per slice, so reasoning_content, thinking_blocks, logprobs, enhancements, annotations and the rest of the per-message metadata rode on every slice, not just the first. Downstream handlers that accumulate streamed deltas would collect each one once per slice, e.g. duplicating a cached reasoning trace N times on a stream=true cache hit. Later slices are now rebuilt as a content-only delta with choice-level logprobs and enhancements stripped, so the whole metadata class stays on the first slice. Adds async (logprobs) and sync (reasoning_content/thinking_blocks/logprobs/ enhancements, plus annotations) regression tests --------- Co-authored-by: Mateo <277851410+mateo-berri@users.noreply.github.com> |
||
|
Mateo Wang
|
1be957da17
|
fix(cloudflare): route native Workers AI provider through OpenAI-compatible endpoint (#31053)
* fix(cloudflare): route native Workers AI provider through OpenAI-compatible endpoint * fix(cloudflare): guard missing account id and migrate legacy /ai/run base Centralize the OpenAI-compatible api_base default in get_complete_url so it is built in one place instead of being duplicated in main.py. When neither api_base nor CLOUDFLARE_ACCOUNT_ID is set the call now fails fast with a clear error rather than sending a request to a URL containing the literal 'None'. An api_base still pinned to the legacy Workers AI '/ai/run' path is rewritten to the '/ai/v1' OpenAI-compatible endpoint with a deprecation warning, so users who hardcoded the previous default migrate gracefully instead of hitting a silently broken '/ai/run/chat/completions' URL. * fix(cloudflare): treat empty api_base as unset when resolving URL * fix(cloudflare): treat empty CLOUDFLARE_ACCOUNT_ID as unset An empty or whitespace-only CLOUDFLARE_ACCOUNT_ID slipped past the None guard and built .../accounts//ai/v1, producing the same confusing 404 the PR set out to prevent. Normalize the secret with normalize_nonempty_secret_str so blank values raise the explicit missing-account-id error instead. |
||
|
Sameer Kankute
|
80c5a84871
|
chore: litellm oss staging (#30968)
* fix: correct amazon.titan-embed-text-v2 input price to $0.02/1M tokens (#29693)
* fix: correct amazon.titan-embed-text-v2 input price to $0.02/1M tokens
* test: scope local cost map env var with monkeypatch to avoid test pollution
* fix(sensitive_data_masker): fully mask secrets at or below the reveal threshold (#30764)
* fix(sensitive_data_masker): fully mask secrets at or below the reveal threshold
_mask_value did partial reveal by showing the first visible_prefix and last
visible_suffix characters, but for a value whose length was at or below
visible_prefix + visible_suffix (8 by default) it returned the value verbatim.
A value of exactly 8 chars fell through the length guard and computed
masked_length == 0, reconstructing the original string with no mask characters;
anything shorter hit the early return. Either way short credentials were emitted
in plaintext.
mask_dict routes real secrets through this path, so an 8-char-or-shorter redis
password, api key, or token could be written to logs and the UI unmasked. The
sibling helper mask_sensitive_keys already guards this case; _mask_value now does
the same by fully masking any value at or below the threshold.
* fix(sensitive_data_masker): add mask_short_values opt-out for truncation callers
Fully masking short values is the right default for secret masking, but
CooldownCache reuses the masker purely to truncate exception messages to the
first 50 characters, and it relies on short messages being returned readable.
Masking those blanked out short exception text and broke its tests.
Add a mask_short_values flag (default True, secure) and have CooldownCache pass
False so it keeps the truncation behavior, while every secret-masking caller
still gets short values fully masked.
* fix(mcp_debug): opt out of short-value masking to keep diagnostic token preview
MCPDebug uses the masker to preview auth tokens in debug headers and documents
that values of 10 chars or fewer are shown unchanged so token types stay
distinguishable. Pass mask_short_values=False so that diagnostic behavior is
preserved while secret maskers keep masking short values.
* fix(mcp_debug): mask short auth values in debug headers instead of echoing them
Earlier this masker opted out of short-value masking to keep a token preview, but
that echoes short authorization and token values verbatim in debug response
headers, which is the same leak this change is meant to close. Auth material
should never be emitted in full, so mask short values here too; the first/last
character preview still applies to longer tokens. Only CooldownCache keeps the
opt-out, since it truncates exception text rather than masking secrets.
* test(mcp_debug): assert masked short value preserves length
* refactor(fireworks_ai): remove deprecated audio transcriptions endpoint (#30917)
Fireworks AI deprecated audio inference on 2026-06-10
(https://docs.fireworks.ai/updates/changelog#audio-inference-and-image-generation-deprecation).
Live API testing confirms the endpoint is already non-functional: a valid
Fireworks API key receives HTTP 401 "Unauthorized" from
api.fireworks.ai/inference/v1/audio/transcriptions for every request,
regardless of payload. The audio-prod.api.fireworks.ai host referenced in
the test suite returns 401 for every path; the entire host is decommissioned.
Remove the dead FireworksAIAudioTranscriptionConfig class and every
reference to it across the codebase:
- Delete litellm/llms/fireworks_ai/audio_transcription/ directory (17-line
config class that inherited from OpenAIWhisperAudioTranscriptionConfig)
- Remove the Fireworks branch from
ProviderConfigManager.get_provider_audio_transcription_config() in
litellm/utils.py; update the stale comment in
get_optional_params_transcription that referenced fireworks ai
- Remove the FireworksAIAudioTranscriptionConfig entries from
LLM_CONFIG_NAMES and _LLM_CONFIGS_IMPORT_MAP in
litellm/_lazy_imports_registry.py
- Remove the TYPE_CHECKING re-export in litellm/__init__.py
- Remove the transcription branch in the fireworks_ai case of
get_supported_openai_params() in
litellm/litellm_core_utils/get_supported_openai_params.py
- Remove the whisper-v3 and whisper-v3-turbo entries from
model_prices_and_context_window.json and
litellm/model_prices_and_context_window_backup.json (both had
mode: audio_transcription and zero-cost pricing)
- Remove the TestFireworksAIAudioTranscription test class and its
imports from tests/llm_translation/test_fireworks_ai_translation.py
No other provider is affected. The openai_compatible_providers list,
FireworksAIMixin, and the OpenAI Whisper transcription handler all stay
because they are shared with other Fireworks endpoints and other
providers. The provider_endpoints_support.json registry already had
audio_transcriptions set to false for fireworks_ai.
* feat: add darkbloom provider (#30876)
* feat: add darkbloom provider
* fix: document darkbloom provider endpoints
* fix: address darkbloom review feedback
* fix: update darkbloom tool metadata
* fix: fail fast for non-Postgres database URLs (#30883)
* fix(proxy): fail fast on non-PostgreSQL DATABASE_URL instead of hanging on startup
LiteLLM's Prisma datasource is pinned to provider = 'postgresql', so a sqlite:// or mysql:// DATABASE_URL can never connect.
Today that surfaces as an opaque startup stall where the port never binds, and a separate 'DB not connected' 500 on /key/generate when no DATABASE_URL is set at all leaves operators guessing what to configure.
Validate the DATABASE_URL / DIRECT_URL scheme in run_server before any Prisma call and exit with an actionable message naming the unsupported scheme.
Also reword CommonProxyErrors.db_not_connected_error to tell the operator to set DATABASE_URL to a postgresql:// connection string.
Add regression tests covering postgres acceptance and sqlite/mysql/mssql rejection.
* fix: resolve CI failures and proxy DB URL typing issue
* fix(proxy): fail fast on non-PostgreSQL DATABASE_URLs with clear startup errors instead of hanging
* Validate DIRECT_URL alongside DATABASE_URL startup guards
* fix(bedrock): surface modeled HTTP status for mid-stream error events so 5xx is retryable (#24608) (#30946)
* fix(bedrock): surface modeled HTTP status for mid-stream error events (#24608)
* test(bedrock): mid-stream server errors trigger streaming fallback (#24608)
* style(bedrock): black-format stream-error helper (#24608)
* fix(mcp): re-land native tool preservation with typed annotations (#30645)
* fix(mcp): preserve native tools in semantic filter hook with typed annotations
* fix(mcp): tighten _is_mcp_tool Chat Completions shape check
* fix(sambanova): return embeddings supported params instead of dropping them (#30937)
* fix(router): send fallback metadata when streaming (#30914)
When a streaming request triggers a fallback, there was previously no way to
know it happened. This commit addresses this in a few ways:
1. The response now correctly populates the fallback headers
(`x-litellm-attempted-fallbacks`) so callers know a fallback happened.
2. The correct model ID is passed in the streaming chunks.
3. A streaming chunk with the fallback error can be optionally sent back
to the client (opt-in) by passing `include_fallback_errors: true` in
the request.
The format of the fallback errors while streaming is intentionally OpenAI
compatible to not break existing libraries that parse these events. It was
tested with Vercel's AI SDK (ai-sdk.dev). It is also opt-in, so it is not
delieved unexpectedly to callers by default.
* fix(mistral): drop output-only reasoning fields from input messages (#30884)
LiteLLM attaches reasoning_content and thinking_blocks to assistant
responses. Replaying those assistant turns verbatim forwarded the fields
back to Mistral, whose input schema forbids unknown keys, so the whole
request failed with a 422 extra_forbidden and reasoning models became
unusable across multiple turns.
Strip both fields from assistant messages before the request is built, in
a spot that runs ahead of the image/file branch so it applies on every
path. Fixes #30835
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(perplexity): bill search queries at the per-request price, not 1/1000 of it (#30652)
* fix(perplexity): bill search queries at the per-request price, not 1/1000
The fallback cost calculator divided search_context_cost_per_query by
1000, but that field stores the per-request price in USD: sonar is
{low: 0.005, medium: 0.008, high: 0.012}, matching Perplexity's published
$5/$8/$12 per 1,000 requests expressed per request. The gemini cost
calculator reads the same field per request with no division (its
docstring calls it "the per-request cost").
The division understated search cost by 1000x on every Perplexity call
that falls back to manual calculation (i.e. when the API does not return
a pre-computed usage.cost). Use the value directly.
Update the tests that had encoded the /1000 factor in their expectations,
and drop an unused import flagged by ruff in the touched test file.
* test(perplexity): update integration test search-cost expectations to per-request
The integration tests still encoded the old /1000 search-cost factor, so
they failed once the fallback calculator was corrected to bill
search_context_cost_per_query per request. Update the four expected-cost
computations (and the high-volume dollar-value comments) to match.
* test(perplexity): drop unused mock imports flagged by ruff
* fix: include model_access_groups when expanding all-team-models in get_team_models (#30622)
* fix(fireworks_ai): return None for transcription in get_supported_openai_params
Fireworks AI deprecated audio inference on 2026-06-10; the endpoint is
decommissioned. Without an explicit transcription branch, requests with
request_type='transcription' fell through to the else and returned
FireworksAIConfig chat-completion params. Return None instead to signal
the provider does not support transcription.
* fix(proxy): gate include_fallback_errors behind expose_fallback_errors_to_caller setting
Without an operator gate, any authenticated caller could set include_fallback_errors=True,
trigger a fallback, and read raw upstream exception messages from the
x-litellm-fallback-errors header and the litellm-fallback-metadata SSE event.
Strip include_fallback_errors from request data in common_processing_pre_call_logic
when expose_fallback_errors_to_caller is not set, so the router never builds the
error list. Also gate _should_include_fallback_errors on the same setting as a
secondary check for the streaming SSE injection path.
* test(proxy): opt in to expose_fallback_errors_to_caller in streaming SSE test
The operator gate added in
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||
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Mateo Wang
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9f97111edd
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feat(fireworks_ai): sync chat completions endpoint with full API surface (#30885)
* feat(fireworks_ai): sync chat completions endpoint with full API surface Add 23 missing request parameters to get_supported_openai_params(): seed, top_logprobs, min_p, typical_p, repetition_penalty, mirostat_target, mirostat_lr, logit_bias, echo, echo_last, ignore_eos, prompt_cache_key, prompt_cache_isolation_key, raw_output, perf_metrics_in_response, return_token_ids, safe_tokenization, service_tier, metadata, speculation, prediction, stream_options, sampling_mask. Also add reasoning_history gated on supports_reasoning. Fix prompt_truncate_length to prompt_truncate_len to match the actual API parameter name. The old name was never in DEFAULT_CHAT_COMPLETION_PARAM_VALUES, so it always went to extra_body and was rejected by Fireworks; it never actually worked. Normalize reasoning_effort boolean values to strings: True becomes "medium", False becomes "none". The Fireworks OpenAPI schema documents these as accepted types, but the server rejects non-string values with HTTP 400 in practice. Integers pass through as-is since the server is expected to validate them. Auto-inject stream_options.include_usage=true when stream=true and the user has not explicitly set stream_options. Without this, Fireworks returns null usage in all streaming chunks, which is inconsistent with the non-streaming behavior where usage is always present. If the user explicitly sets include_usage=false, it is preserved. Capture Fireworks-specific response fields in transform_response(): perf_metrics, prompt_token_ids, raw_output, and token_ids are now extracted from the response and stored in response._hidden_params (fireworks_perf_metrics, fireworks_prompt_token_ids, fireworks_raw_outputs, fireworks_token_ids) so they are accessible to logging, the proxy, and downstream consumers when the corresponding request parameters are enabled. Remove deprecated document inlining logic. Document inlining was deprecated on 2025-06-30 (https://docs.fireworks.ai/updates/changelog#-document-inlining-deprecation). This removes _add_transform_inline_image_block(), the file-to-image_url migration in _transform_messages_helper(), and the disable_add_transform_inline_image_block lookup. Current models that support image input do so natively as VLMs. cache_control, provider_specific_fields, and thinking_blocks stripping is retained. Update get_provider_info() to look up supports_vision and supports_pdf_input from the model cost map instead of hardcoding both to True (which was based on the now-deprecated document inlining). supports_prompt_caching remains True. API docs: https://docs.fireworks.ai/api-reference/post-chatcompletions Reasoning guide: https://docs.fireworks.ai/guides/reasoning Prompt caching: https://docs.fireworks.ai/guides/prompt-caching * fix fireworks chat api surface gaps * Scope Fireworks thinking param to reasoning models * style: fix black formatting * fix(test): update minimax-m3 expected_vision to True * test: cover non-dict content branch in transform_messages_helper * fix(fireworks_ai): remove metadata from supported params to prevent internal metadata disclosure * test(fireworks_ai): replace stale document-inlining capability test The CircleCI-only litellm_utils_tests suite still asserted the old behavior where document inlining made every Fireworks model report supports_pdf_input and supports_vision as True. That premise was removed in this change, so the test now reflects cost-map-driven capabilities: unmapped models no longer advertise vision/PDF support while mapped VLMs like minimax-m3 still do. * test(fireworks_ai): add end-to-end regression for native OpenAI params The existing coverage for the newly supported OpenAI-native params asserted list membership in get_supported_openai_params or called map_openai_params with a hand-built dict, both of which bypass the get_optional_params gate (DEFAULT_CHAT_COMPLETION_PARAM_VALUES). That gate is what previously raised UnsupportedParamsError for seed, top_logprobs, logit_bias, prompt_cache_key, service_tier and prediction when drop_params=False. Assert the full path so a revert of the supported-params additions fails the test instead of passing a shallow membership check. * test(fireworks_ai): fix test isolation in vision/inlining tests Use monkeypatch in test_fireworks_ai_vision_capability_from_cost_map so the LITELLM_LOCAL_MODEL_COST_MAP env var and litellm.model_cost are restored after the test instead of leaking global state into the rest of the process. Switch the document-inlining integration tests off deepseek-v3p1, whose supports_vision is null in the cost map, onto minimax-m3 which is explicitly supports_vision:true. The pass-through assertions no longer depend on a model incidentally not being marked non-vision. * fix(fireworks_ai): gate image rejection on exact vision capability The image_url rejection read supports_vision via _get_model_cost_capability, which falls back to hyphen-boundary substring matching when no exact cost-map entry exists. A custom or fine-tuned model id that merely contains a known non-vision model's short name (e.g. an id ending in -glm-5p2) inherited that entry's supports_vision:false and hard-failed valid image_url blocks on a vision-capable deployment. Split the exact candidate-key lookup into _get_model_cost_capability_exact and use it for the hard rejection so a fuzzy match can never block images; the substring fallback stays a soft signal for capability reporting. Also rewrites the fallback as a comprehension + max instead of an accumulating loop. * feat(fireworks_ai): surface response fields on streaming responses The Fireworks-specific response fields (perf_metrics, prompt_token_ids, per-choice raw_output and token_ids) were only captured into _hidden_params in transform_response, which runs for non-streaming completions; streaming chat went through the default OpenAI chunk handler and dropped them. Add a FireworksAIChatCompletionStreamingHandler that the provider now returns from get_model_response_iterator. It reuses one extraction helper with transform_response and attaches the fields to each streamed chunk's provider_specific_fields, which is the channel litellm preserves when it rebuilds streamed chunks (per-chunk _hidden_params is not carried through). Per-choice token_ids/raw_output ride the content chunks; response-level perf_metrics/prompt_token_ids ride the final usage chunk. Covered by an end-to-end streaming test through litellm.completion(stream=True). --------- Co-authored-by: Ahmad Shahzad <ahmad@shahzad.dev> Co-authored-by: Graham Neubig <398875+neubig@users.noreply.github.com> |
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Mateo Wang
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b16cfd7de9
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test: point router/completion/triton tests at the local fake OpenAI endpoint (#30900)
* test: point router/completion/triton tests at the local fake OpenAI endpoint The shared Railway-hosted mock (exampleopenaiendpoint-production.up.railway.app) takes down unrelated CI jobs whenever it is unreachable. #30695 moved the mounted proxy configs onto a job-local fake server but left these in-Python api_base literals pointing at the dead host, so litellm_router_testing, local_testing_part1, local_testing_part2 and llm_translation_testing still fail with a 404 "Application not found" when Railway is down Resolve the api_base from FAKE_OPENAI_API_BASE (default http://127.0.0.1:8190) through a shared helper, auto-start the canned server from the local_testing and llm_translation conftests when nothing is already serving, and extend the server with a Triton embeddings route and a slow-endpoint delay so the triton and latency-timeout tests run fully offline. The deliberately broken fallback URL is left as-is so fallback handling still has a failing upstream * fix: ignore non-loopback FAKE_OPENAI_API_BASE so the local mock is used in CI * fix: drop 0.0.0.0 from loopback hosts, an unreliable client connect target * fix(tests): keep fake OpenAI mock alive across xdist workers ensure_fake_openai_endpoint registered atexit on the worker that spawned the subprocess, so under -n 4 the first worker to drain its queue would terminate the shared mock while siblings were still hitting it. Detach the child via start_new_session and drop the per-worker teardown; reuse on /health handles re-runs and CI containers clean up themselves |
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ishaan-berri
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60f4c01b74
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fix(proxy): list public team model name in /v1/models (#30588)
* fix(proxy): optionally surface public team model name in /v1/models
Behind general_settings.use_team_public_model_name (default False). When
enabled, /v1/models and /models surface the public team_public_model_name
for team-scoped (BYOK) models instead of the internal routing key
model_name_{team_id}_{uuid} -- consistent with /v1/model/info and
OpenAI-compatible. Off by default so the listing's model ids stay
backward-compatible for callers that scripted against the internal name;
routing by the internal name is unchanged regardless of the flag.
Presentation-layer only: access-group, auth, and routing semantics are
unchanged; non-team models are pass-through.
* fix(proxy): default team model listings to public names
* test(proxy): cover team model listing metadata
* test(proxy): cover empty team listing deployments
* refactor(proxy): simplify team model listing translation
* fix(proxy): resolve public team model name on GET /v1/models/{id}
The listing endpoints advertise team_public_model_name, but the retrieve
endpoint validated and looked up by the raw id, so a public name 404'd.
Resolve the public name back to the internal routing key (scoped to the
caller's accessible models so colliding names never cross teams), look up
by it, and echo the public name back as the response id.
* test(proxy): cover public-name resolution on model retrieve
* refactor(proxy): extract team model-name translation into TeamModelNameTranslator
Move the team-scoped (BYOK) listing/retrieve name translation out of
proxy_server.py into a dedicated common_utils module. Static methods with
general_settings injected so the logic is unit-testable without globals and
proxy_server.py stays thin.
* refactor(proxy): use TeamModelNameTranslator in model_list and model_info
* test(proxy): target TeamModelNameTranslator for model-name translation
* fix(proxy): type create_model_info_response return as dict[str, object]
* fix(proxy): keep internal routing key for team model listing metadata lookup
Add listing_entries returning (public response id, internal lookup id) so
include_metadata=true resolves fallbacks against the routing key the router
indexes by, instead of the translated public name (which never matches).
* fix(proxy): build /v1/models metadata from internal key, show public id
* test(proxy): cover team listing fallback metadata via internal key
* fix(proxy): use builtin dict generics in create_model_info_response (UP006)
---------
Co-authored-by: Tushar More <tusharmore8408@gmail.com>
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
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yuneng-jiang
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82d9f27887
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test(fireworks): mock whisper transcription tests instead of live calls (#30391)
TestFireworksAIAudioTranscription inherited two tests from BaseLLMAudioTranscriptionTest that made real calls to audio-prod.api.fireworks.ai. The audio host is a separately-entitled product, and the CI key gets a 401 there, so both tests fail without signalling anything about litellm. Override them in the subclass with a dependency-injected mock OpenAI client (the openai-compatible path uses the openai SDK, which is why the failure surfaced as openai.AuthenticationError). The real routing, model-prefix stripping, and response parsing still run; only the network call is mocked. The sync path mocks audio.transcriptions.create and the async path mocks audio.transcriptions.with_raw_response.create. This matches the existing direction in this file, where the document-inlining and global-disable tests were already converted off live Fireworks calls. |
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Yassin Kortam
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ec9353cb69
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feat(guardrails): add Cisco AI Defense integration (#28249) (#30338) | ||
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Sameer Kankute
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cfcdf8714a
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feat: litellm oss 110626 (#30202)
* Add gpt-realtime-whisper Realtime transcription support (OpenAI + Azure) (#29775) * Add gpt-realtime-whisper Realtime transcription support (OpenAI + Azure) Adds first-class support for the gpt-realtime-whisper streaming speech-to-text model, which uses the Realtime transcription session API rather than the file-based /audio/transcriptions path. Model registration: registers gpt-realtime-whisper and azure/gpt-realtime-whisper with audio-duration pricing (input_cost_per_second = 0.017/60, matching the published $0.017/minute input audio rate). REST endpoint: implements POST /v1/realtime/transcription_sessions (plus /realtime and /openai/v1 aliases) to mint an ephemeral transcription session for the WebRTC flow. Adds request/response types, OpenAI and Azure URL builders, a shared base handler (refactored from the client_secrets handler), the acreate_realtime_transcription_session SDK function, and route registration. The proxy encrypts the ephemeral key returned under client_secret.value and records the session type in the token so the follow-up /realtime/calls replays type=transcription rather than type=realtime. WebSocket: forwards intent=transcription through to the Azure handler (OpenAI already received it) with URL-encoding, so gpt-realtime-whisper opens a transcription session. Transcription-only sessions no longer trigger an erroneous response.create. Cost tracking: transcription sessions emit no response.done events; their usage arrives on conversation.item.input_audio_transcription.completed as {type: duration, seconds}. That usage is captured out-of-band (usage only, no transcript duplication) and billed by input_cost_per_second, with a token-billed fallback for token-priced transcription models. Adds tests for pricing math, URL builders, request/response types, the proxy route and SDK function, WebSocket intent forwarding, transcription-session streaming behavior, and the /realtime/calls session-type replay. * Address PR review: URL-encode all Azure WS query params; forward query_params through provider_config branch * Address PR review: session_type validation, model auth fix, cost perf, billing fallback, detail/docs cleanup * Improve test coverage: detection from backend, error paths, unknown usage type, resolved_model None * Backport realtime transcription websocket fixes * Enforce authorized realtime transcription model * Enforce realtime transcription model access * Enforce realtime resolved model scopes * Enforce WebRTC transcription model scope * Lazy evaluate debug log in pass-through endpoint (#30177) * Pass through debug lazy logging * fix(proxy): convert remaining eager pass-through debug logs to lazy formatting * fix(parallel_ai): migrate search integration from v1beta to v1 endpoint (#30157) * fix(parallel_ai): migrate search integration from v1beta to v1 endpoint The Parallel Search API moved from /v1beta/search (processor: base/pro, parallel-beta header) to /v1/search (mode: turbo/basic/advanced, no beta header). Request fields moved too: max_results, source_policy, and excerpt settings are now nested under advanced_settings, and source_policy uses include_domains/exclude_domains. The v1 response returns publish_date per result, which now maps to SearchResult.date instead of being hardcoded to None. The legacy processor param is mapped to the equivalent mode so existing callers keep working. * fix(parallel_ai): default mode to basic and simplify param handling The v1 API defaults to advanced mode when mode is omitted, while v1beta defaulted to the base processor. Without an explicit default, callers who pass no mode would be silently upgraded to a tier costing 2.25x more while litellm's cost map reports the basic-tier price. Sending mode=basic preserves the v1beta default and keeps cost tracking accurate. Also replaces the handled_params set with pop-as-consumed param handling so mapped params no longer need to be tracked in two places, and extends the tests to pin the default mode, processor=base mapping, mode-over-processor precedence, and top-level v1 param passthrough. * fix(parallel_ai): avoid double /v1 when api_base is already versioned A PARALLEL_AI_API_BASE like https://api.parallel.ai/v1 previously produced .../v1/v1/search. Strip a trailing /v1 before appending the search path and cover the api_base variants with a parametrized test. --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * feat(focus): add Mavvrik destination for FOCUS export (#29935) * fix: preserve responses streaming flag (#30189) * fix: preserve responses streaming flag * test: cover async responses streaming flag * fix(spend/daily-activity): stable offset pagination via id tiebreaker (#30164) (#30167) date alone is not a unique sort key for LiteLLM_DailyUserSpend or LiteLLM_DailyTeamSpend (many rows per date: api_key x model x model_group x provider x endpoint). Offset pagination over a non-unique sort landed on arbitrary boundaries, so a client paging through all results and summing per-page metrics (the Usage dashboard) got non-deterministic totals - sometimes inflated, sometimes deflated, different at different page_size values. Adding the row's UUID id (present on both tables) as a secondary sort gives every page a stable cursor. order=[{date desc}, {id asc}]. Fixes #30164 * fix(oci): inject a default maxTokens so omitted max_tokens doesn't truncate responses (#30018) * fix(oci): inject default maxTokens so omitted max_tokens doesn't truncate OCI GenAI applies a tiny server-side maxTokens default (~20 tokens) when the request omits it, so any call that doesn't send max_tokens comes back cut off mid-string with finishReason "length". MLflow judges never send max_tokens, so their JSON responses arrived as unterminated strings and json.loads failed in MLflow's gateway adapter. When no maxTokens/maxCompletionTokens target is set, inject DEFAULT_OCI_CHAT_MAX_TOKENS (env-overridable, defaults 4096), mirroring the Anthropic config's default-max-tokens behaviour. An explicit max_tokens still wins, and reasoning models still route to maxCompletionTokens. Used a fixed default rather than the catalog max_output_tokens because the catalog value is unreliable for some models (grok-4 reports max_output_tokens equal to its context window, not a real output cap, which would risk 400s). Adds TestOCIDefaultMaxTokens covering Cohere and generic injection, the explicit-override case, and the reasoning maxCompletionTokens branch. * test(oci): e2e regression that omitted max_tokens isn't truncated Real-proxy integration test asserting a chat completion that omits max_tokens completes with finish_reason "stop" instead of being cut off at OCI's ~20-token server default. Fails before the maxTokens-default injection (finish_reason "length", ~19 tokens), passes after. * test(oci): update cohere default-params test for injected maxTokens test_cohere_default_parameters asserted no maxTokens was injected, encoding the old behaviour where OCI's ~20-token server default truncated responses. Now that transform_request injects DEFAULT_OCI_CHAT_MAX_TOKENS, assert maxTokens equals that default while the other params (topK/topP/frequencyPenalty) stay pass-through with no hardcoded default. * fix(oci): make DEFAULT_OCI_CHAT_MAX_TOKENS a plain constant Drop the os.getenv override. The env knob was not requested and introducing a new env var forced a cross-repo dependency on litellm-docs (test_env_keys.py validates every referenced env var against the docs table there). A plain 4096 constant keeps the PR self-contained; callers who want a different limit pass max_tokens explicitly per request. * fix(oci): route all OpenAI commercial models to maxCompletionTokens OCI serves OpenAI models (gpt-4.1, gpt-5.1 through 5.5, o-series) that the litellm catalog doesn't track, so the supports_reasoning lookup returned False for them and the provider sent maxTokens, which the reasoning families reject with HTTP 400. With the injected default maxTokens this broke every request to those models, not just ones with an explicit max_tokens. Route the whole openai.* vendor prefix to maxCompletionTokens since OpenAI accepts max_completion_tokens on every chat model; the openai.gpt-oss-* open weights are served by OCI's own stack and keep maxTokens. Verified live against gpt-5.2, gpt-5, gpt-4o, gpt-4.1, gpt-oss-120b, llama-3.3, command-a and grok-3-mini * test(oci): hoist transformation imports and drop unused ones Makes the generic-chat test file ruff-clean: the per-test local imports of OCIChatConfig/OCIVendors shadowed the module-level import (F811) and left it unused (F401), and json plus three OCI type imports were never referenced * fix(oci): translate response_format json_schema to OCI's accepted shape (#29691) * fix(oci): translate response_format json_schema to OCI's accepted shape OCI GenAI rejected every json_schema response_format with HTTP 400 "Please pass in correct format of request", which broke structured-output callers such as MLflow LLM judges (they always send a json_schema). The provider forwarded OpenAI's raw json_schema body unchanged. For GENERIC models OCI's ResponseJsonSchema accepts only name/description/schema/isStrict, so OpenAI's `strict` key (and any other extra) 400s the request; the key must be renamed to isStrict and the body whitelisted. For Cohere models there is no JSON_SCHEMA type at all; the schema has to ride on JSON_OBJECT as {"type": "JSON_OBJECT", "schema": ...}. Cohere type values must also be the canonical uppercase TEXT/JSON_OBJECT. _normalize_response_format now branches by vendor and emits the exact shape each one accepts (verified live against OCI GenAI for Cohere, Meta, Gemini and Grok). Drops the unused, incorrect Cohere response-format pydantic models. Two existing tests asserted the broken behavior (lowercase type, raw jsonSchema on Cohere); they are rewritten to assert the corrected shape, and generic/Cohere json_schema regression tests are added. * fix(oci): raise early on json_schema response_format with no body A GENERIC model request with {"type": "json_schema"} and no json_schema object fell through to the JSON_OBJECT branch and emitted a bodyless {"type": "JSON_SCHEMA"}, which OCI rejects with an opaque HTTP 400. Raise a descriptive 400 at translation time instead. Cohere is unaffected since it always maps to JSON_OBJECT. * test(oci): gateway integration test for response_format json_schema Added to tests/integration/ (the real-network integration suite) reusing the existing OCI proxy harness, not tests/llm_translation/ which is mock-only. --------- Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(oci): accept default n=1 on Cohere instead of hard-failing (#29705) * fix(oci): accept default n=1 on Cohere instead of hard-failing Cohere on OCI has no numGenerations field, so n was mapped to False and map_openai_params raised "param `n` is not supported on OCI" whenever a client sent n. But n=1 (and None) is the OpenAI default single-generation request, which every OCI model produces anyway, so standard clients that always send n=1 (such as the MLflow gateway) were rejected with a 500. Drop n=1/None silently for Cohere; only n>1 is genuinely unsupported and still raises (or drops under drop_params). Generic models are unaffected and keep numGenerations, including n>1. * docs(oci): explain why n is not advertised for Cohere despite tolerating n=1 * test(oci): gateway integration test for Cohere default n=1 Added to tests/integration/ (the real-network integration suite) reusing the existing OCI proxy harness, not tests/llm_translation/ which is mock-only. --------- Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(oci): drop max_retries instead of hard-failing on OCI (#29727) max_retries is a litellm-level control param (litellm applies retries itself), not a generation param OCI accepts. The provider mapped it to False and raised "param `max_retries` is not supported on OCI" whenever it was present. The litellm proxy injects max_retries on every request, so any OCI call through the proxy 500'd unless drop_params was set. Drop max_retries silently in map_openai_params. Adds a unit test (Cohere and generic) and a gateway integration test that a plain request succeeds through a proxy without drop_params. Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(spend-logs): rehydrate metadata JSONB text on ui_view_spend_logs (#29682) Fixes #29674. `/spend/logs/ui` raw-SQL path returns the JSONB metadata column as a string — prisma's query_raw skips the ORM-layer hydration. The UI reads metadata.status / metadata.error_information as object fields, so provider-failure rows look like successes. Fix: json.loads the metadata field right after query_raw, fall back to {} on malformed JSON. 3 existing error-code/error-message tests called json.loads on response.data[0]["metadata"] — they were leaning on the bug. Updated to read the dict directly. Plus 2 new regression tests (failure metadata roundtrip + invalid-json fallback). Reverting the fix makes both new tests fail with AssertionError: metadata should be dict, got <class 'str'>. * fix(proxy): release max_parallel_requests slot when a stream is cancelled mid-flight (#27955) (#30020) * fix(proxy): release max_parallel_requests slot when a stream is cancelled mid-flight (#27955) * fix: refund max_parallel_requests on disconnect from outer streaming generators The cancellation refund previously lived in async_post_call_streaming_iterator_hook, but that hook is nested inside the outer streaming generators and a nested async generator only receives GeneratorExit on garbage collection (non-deterministic). With only the v3 limiter enabled, /chat/completions also bypasses the hook entirely (needs_iterator_wrap() is false). Move the release into async_data_generator and async_streaming_data_generator, the generators Starlette closes on client disconnect, so the refund fires deterministically on every streaming route. Warn when no event loop is running, and document the window TTL refresh on the decrement * fix(mcp): propagate model into model_call_details for passthrough tool calls (#30122) * fix(mcp): propagate model into model_call_details for passthrough tool calls The @client decorator on call_mcp_tool creates the logging object via function_setup without a model kwarg, so model_call_details["model"] starts as None. execute_mcp_tool only set logging_obj.model as an instance attribute, which the spend-log writer never reads (it reads kwargs["model"] from model_call_details). MCP passthrough tools/call rows therefore persisted with model="" while list_tools rows showed "MCP: list_tools", degrading the Logs UI display and bucketing all MCP tool spend under an empty model in DailyUserSpend. Propagate the model into model_call_details alongside the existing attribute assignment so the StandardLoggingPayload and SpendLogs writer pick it up. Covers the /mcp passthrough, REST /mcp-rest/tools/call, and orchestrated paths (the latter already passed model into function_setup, so this is a no-op there). * test(mcp): trim regression test docstring * fix(mcp): surface upstream challenges for delegated OAuth (#30124) * fix(mcp): surface upstream challenges for delegated OAuth * docs(mcp): clarify delegated upstream auth comments * perf(benchmarks): add CPU timing metrics to streaming benchmark (#29980) * Add CPU timing metrics to streaming benchmark * Fix spacing around timing sample dataclass * fix(gemini): don't emit empty choices on metadata-only stream chunks (#29167) web_search + reasoning makes Gemini stream mid-chunks that carry only grounding/thought metadata — no content part, no finishReason. _process_candidates skips content-less candidates and the existing fallback only ran when finishReason was set, so choices stayed empty and the downstream streaming handler raised IndexError on choices[0]. Emit an empty-delta choice for content-less chunks regardless of finishReason. Fixes #28884 * fix(key): allow /key/update to clear budget_limits with [] or null (#30085) * Fix /key/update rejecting budget_limits clear requests with HTTP 400 Sending budget_limits: [] or null to /key/update returned HTTP 400, so once a key had budget windows the last one could never be removed. prepare_key_update_data only json.dumps'd budget_limits when the value was truthy, so [] and None passed through raw to the Prisma Json? column; jsonify_object only serializes dicts, and prisma-client-py has no DbNull sentinel for Json? writes, so Prisma rejected both shapes. Serialize the clear case explicitly as the JSON literal null, matching how memory_endpoints encodes metadata for the same column type. Truthy values keep the existing reset_at window initialization path. Fixes #30067. * Require admin access for budget_limits changes on /key/update Clearing budget_limits via [] or null is a budget mutation, but _validate_update_key_data only counted max_budget and spend as budget changes before deciding whether to skip _check_key_admin_access. A non-admin key owner or a team member with /key/update could therefore remove a key's per-window spend caps without admin authorization. Treat any explicit budget_limits value in the request (set, change, or clear) as a budget change so it gates through the same admin check as max_budget. model_fields_set is used because an explicit null is indistinguishable from an omitted field by value alone. * fix(proxy): persist guardrail info in spend logs for /v1/responses (#30092) Pre-call guardrail blocks on /v1/responses wrote guardrail_information as null in LiteLLM_SpendLogs because _handle_logging_proxy_only_error splits request_data by LoggedLiteLLMParams keys and litellm_metadata, where the Responses API stores request metadata including standard_logging_guardrail_information, was not among them. It fell into optional_params, so merge_litellm_metadata never saw it. Add litellm_metadata to LoggedLiteLLMParams so it routes into litellm_params the same way metadata does on the chat completions path Fixes #28971. * fix(proxy): handle non-standard SSE frames in Anthropic passthrough logging (#26000) Some third-party Anthropic-compatible providers emit non-standard SSE frames (OpenAI-style [DONE] sentinels, non-JSON keep-alive lines) in streaming responses. These caused json.JSONDecodeError in _build_complete_streaming_response, breaking the passthrough logging pipeline so the request was never logged or billed. Skip whole-line 'data: [DONE]' sentinels and catch JSONDecodeError per event. Matching the full line (not a substring) keeps a valid chunk whose text payload contains '[DONE]' from being dropped. Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Sameer Kankute <sameer@berri.ai> * feat(newrelic): Add New Relic extension (#26989) * initial New Relic integration. * Minor fixes for basic observability. * Implemented basic support for the success path. Generates New Relic custom events needed by the AI Monitorin interface. * Supportability metric is sent on first request. * Emit supportability metric every hour instead of once a day. * Add the start/end times to the messages before sending them so that the start time and end time reflect the correct time and both are not set to 'now'. * Make use of `turn_off_message_logging` configuration that is available by default from CustomLogger. * Enabling New Relic agent to be wired when docker container starts if an environment variable is set. * If we cannot find trace information, send the AI events without the trace ID attached. * Use a fake trace_id if we cannot find one. * Implementing a configuration so that users can use litellm configuration to disable sending LLM messages to New Relic. There is a second method to do this via New Relic env var. * Mised file. * Cleaning up logic to turn off recording content via either the LiteLLM configuration or an env var. * Removing debugging. Fixed logic / comments around how often to send supportability metric. * Initial version of public doc for New Relic. * Use a proper name for the doc file. * Updating newrelic.md document. * Updating LiteLLM documentation for New Relic extension. * Moving New Relic imports into the methods to support unit tests. * Adding unit tests for the New Relic extension. * Updating linting and the unit tests that are not running in the CI environment. * Address reviewer feedback on New Relic integration. - Fix _record_error_metric to use app.record_custom_metric() instead of module-level newrelic.agent.record_custom_metric() so the call works outside of an active transaction context - Remove unreachable except ImportError block in _get_trace_context - Update stale "23 hours" comment to "27 hours" (matches 97200s threshold) - Remove commented-out debug code from _process_success - Fix docs typo: NEW_RELIC_CUSTOM_INSIGHTS_EVENTS_MAX_SAMPLES_STOREDA -> NEW_RELIC_CUSTOM_INSIGHTS_EVENTS_MAX_SAMPLES_STORED - Update TestRecordErrorMetric to verify app.record_custom_metric call Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * Reformating for the linter. * Addressing additional automated feedback. - Removed a legacy comment about the New Relic header - Reordered imports in one file - Switched another file to use the import at the top of the file instead of inline when used - Added unit tests for untested methods that were identified * Addressing new feedback. - Proper handling of time to floats. Created a util method and updated code to use it. - added the missing guard to ensure the app is enabled * Addressing feedback. - When an error occurs, still check if the periodic supportability metric should be emitted - Added a check to ensure the extension is ready in the error handler to match _process_success * Updating the NR event timestamps to more accurately reflect when the messages were generated. * Addressing feedback for potential better practice. * Addressing feedback on accessing default values. Added tests for most of these cases. * Adding a new catch exception block based on feedback. * Addressing feedback about a potential issue around a timestamp for the supportability metric. * Addressing minor feedback on length of generated, fallback traceId. * Addressing feedback. - A few more cases were found where the dictionary access might not return the correct value. - Handling cases where `traceparent` is not lower cased * Addressed feedback where the newrelic options might not apply correctly. * Addressing some feedback. * Addressing feedback. * Validating testing / formatting for our changes. * Updating linting, adding tests, defining data type for UI. * Configuration for the logging callback definition. * Adding a newrelic image for the UI to use. * Putting the New Relic callback in proper alphabetic order. * Copying the logo to a committed output directory so it shows up in a locally built container. * Adding missing definition of new env vars that were causing a build failure. * Addressing automated feedback from greptile. * Adding a few more unit tests to increase the code coverage just a bit more. * Additional unit tests to push coverage to almost 90%. * Adding a custom newrelic docker image build process. This removes the need to add the newrelic agent to the core litellm container or dependencies. * Clarifying message when the New Relic agent is not installed and someone is trying to use the newrelic extension. Either use the proper image when using docker, or install the agent manually when running from source. * Ensuring pip is available to install the New Relic agent. * Updating the definition and handling of traceId (no spanId). Clarifying behavior of env vars vs UI configuration for the newrelic extension. * Removing entries from the New Relic logger configuraiton UI as these values must be set as part of running the image. * Removing a stale doc file that has moved to the litellm-docs repo. Cleanup of Dockerfile to remove a LABEL that was incorrect. * Updating container image name to be the best guess for the new name. * Addressing feedback from greptile. - Added a comment around token_count=0 - Updated the boolean parser to allow a wider set of options which matches existing patterns in other parts of LiteLLM. * Removing option for a separate New Relic container image. The agreement is to handle this in the New Relic integration docs. * Updating error message when New Relic agent is not available. * Wiring in the test message from the LiteLLM callback UX. * Missed saving one of the file conflicts. * Fixed a lint error I introduced. Somehow, I dropped another string and now added it back. * Adding newrelic to the schema definition. * Added an admin check on the call before sending test message as mentioned by the AI code review. * Updating to use should_redact_message_logging(kwargs) as part of the logic to determine if message content should be sent to New Relic or not. This still uses the `record_content` property as well, but both have to be true in order for content to be included. --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> * Add Azure AI Foundry DeepSeek V3.1 and V4 Pro/Flash global pricing to cost map (#30134) Co-authored-by: Cursor <cursoragent@cursor.com> * fix(logging): translate Responses bridge result to ModelResponse for spend logs (#28985) PR #29394 fixed the AnthropicResponse.model_validate crash for the streaming anthropic_messages -> OpenAI Responses bridge by unwrapping terminal events and returning the inner ResponsesAPIResponse. The spend_logs row lands and usage/cost are correct, but the row's response field stores the Responses API shape (output[...].content[...].text). The proxy UI Logs tab reads response.choices[0].message via parseMessages in prettyMessagesUtils.ts with no fallback for the Responses shape, so the OutputCard renders "No response data available" for every cross-routed call. The same shape mismatch affects every downstream consumer of spend_logs that assumes the canonical chat-completion shape This change keeps the unwrap from #29394 but routes the resulting ResponsesAPIResponse (and the bare-response non-streaming path) through LiteLLMResponsesTransformationHandler.transform_response, which is the same conversion already used by the chat-completion Responses bridge. Spend_logs now stores a ModelResponse with choices[0].message.content, so the UI and other consumers see the assistant text. On a translation failure (eg. empty output on an incomplete response) the handler falls back to a minimal ModelResponse carrying model and usage so the row still lands rather than being dropped as a Non-Blocking error Also corrects a stale comment in the Responses adapter that implied the call type was reclassified to acompletion; the code preserves anthropic_messages and the success handler translates back to ModelResponse for the row Fixes #28595 * fix(anthropic-adapter): re-emit first delta on streaming content-block transitions (#30024) * fix(anthropic-adapter): re-emit first delta on streaming content-block transitions The `/v1/messages` -> `/v1/chat/completions` streaming adapter (`AnthropicStreamWrapper`) silently dropped the first non-empty delta of every content block that started via a *transition* (e.g. text -> tool_use -> text, text -> thinking). When an upstream chunk both triggers a new content block (its type differs from the active block) and carries that block's first delta, the wrapper emitted `content_block_stop` -> `content_block_start` and then only re-queued the trigger chunk when it was an `input_json_delta` (bundled tool args). The synthesized `content_block_start` always carries an empty body, so the first `text_delta` / `thinking_delta` was lost — the client output started from the second token (e.g. "Hi, how can I help you?" rendered as ", how can I help you?", or text resuming after a tool call lost its first sentence). This is especially visible with Claude Code-style clients that consume Anthropic Messages streaming events strictly. Fix: re-queue the trigger chunk's translated delta whenever it carries non-empty content (text/thinking/signature/tool args), via a shared `_trigger_delta_has_content` helper used by both the sync and async paths. Empty trigger deltas are still suppressed so no spurious empty `content_block_delta` is introduced. Fixes #30014 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> * test(anthropic-adapter): cover all _trigger_delta_has_content branches Add a direct parametrized unit test for the re-emit predicate so every delta type (text/input_json/thinking/signature), the empty-payload guards, and the malformed/non-delta cases are exercised independently of upstream chunk translation. Raises patch coverage for the new helper. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> * feat: add opt-in healthy_only filter to GET /v1/models (#30130) * feat: add opt-in healthy_only filter to GET /v1/models Adds an opt-in `healthy_only=true` query parameter to GET /v1/models and GET /models that hides models whose backing deployments are all marked unhealthy by background health checks. - Add Router.async_get_fully_unhealthy_model_names(), mirroring the semantics of get_fully_blocked_model_names(): a model is hidden only when every backing deployment is unhealthy and the health state is not stale (fail open otherwise). - Reuses the existing DeploymentHealthCache populated by _run_background_health_check(), so no new health state is introduced. - No-op when allowed_fails_policy is set, mirroring _async_filter_health_check_unhealthy_deployments semantics. - team_public_model_name aliases are aggregated alongside model_name. - Hiding is presentation-only; default behavior is unchanged. Fixes #30128 Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * docs: address Greptile review notes - Note team-alias asymmetry vs get_fully_blocked_model_names - Debug-log when healthy_only is set but no health state is available Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Claude Fable 5 <noreply@anthropic.com> * Dedupe team soft budget alerts by team_id instead of token (#30097) _team_soft_budget_check sends type="soft_budget" alerts with event_group=TEAM, but SoftBudgetAlert.get_id always returned the request token. The alert cache key was therefore scoped per virtual key, so every active key in a team over its soft budget fired its own alert within budget_alert_ttl. Branch on event_group so team-level alerts dedupe by team_id, matching TeamBudgetAlert, while key and project level alerts keep per-token dedupe. Fixes #27398. * feat(bedrock guardrails): support contextual grounding qualifiers (request-side) (#30057) * test: add failing tests for Bedrock contextual grounding (request-side) Drive the request-side of Bedrock contextual grounding: callers tag message content blocks as grounding_source/query, the post_call hook assembles an ApplyGuardrail(OUTPUT) call carrying source + query + response(guard_content), and the bedrock converse transform must render the tags as prompt text instead of silently dropping them. Non-grounding payloads must stay byte-identical. * feat(bedrock guardrails): support contextual grounding qualifiers Bedrock contextual grounding scores a model response against a reference source and the user query, expressed via a per-content-block `qualifiers` array on ApplyGuardrail. The guardrail hook previously sent plain text only, so grounding could not be driven through it even though the response-side contextualGroundingPolicy parsing already existed. Callers now tag message content blocks `{"type":"grounding_source"}` / `{"type":"query"}` (mirroring the existing `guarded_text` marker). On the generate path the bedrock converse transform renders them as plain text; at post_call the hook harvests them from the request and assembles one ApplyGuardrail(OUTPUT) call carrying grounding_source + query + the response (as guard_content). Requests without these tags produce a byte-identical payload, so existing behaviour is unchanged. * Feat(guardrail): Adding support for custom Ovalix guardrail (#21887) * Feat(guardrail): Adding support for custom Ovalix guardrail * Internal CR comments fixes * greptileai comments fixes * fix conflict * fixes * fix sha256 * clarify Ovalix actor-id hash is for normalization, not PII protection * fix(github_copilot): normalize per-event item_id in /responses streaming (#30072) GitHub Copilot's native /v1/responses stream assigns a different item_id to every event of a single output item (output_item.added, the part.added / delta / done events, and output_item.done). Spec-strict clients like the Vercel AI SDK key streaming parts by item_id and abort with "reasoning part <id> not found" / "text part <id> not found" when a delta references an unregistered id. Override transform_streaming_response in GithubCopilotResponsesAPIConfig to anchor every event of an output item to the id from its output_item.added. Copilot accepts that id paired with the final encrypted_content on the next turn, so multi-turn replay is unaffected. Fixes #30071 * feat: add /model/block and /model/unblock endpoints (#30125) * feat: add /model/block and /model/unblock endpoints Add dedicated proxy-admin POST /model/block and /model/unblock endpoints over the existing blocked flag on LiteLLM_ProxyModelTable, mirroring the /key/block and /key/unblock pattern. Calling a model whose deployments are all blocked now returns a clear 403 "Model is blocked" instead of a generic no-deployment error, including direct-dispatch route types (e.g. eval) via a pre-route guard. Includes audit-log entries for block/unblock and unit tests. Closes #29742 Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> * chore: regenerate dashboard API types for model block/unblock endpoints Regenerate ui/litellm-dashboard/src/lib/http/schema.d.ts from the proxy OpenAPI spec (npm run gen:api) so it includes the new endpoints. Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> * fix: widen router block-helper param type and add direct unit tests Type the _are_all_deployments_blocked deployments parameter to match its callers (DeploymentTypedDict) so mypy passes, and add tests/test_litellm/test_router_block_helpers.py with direct unit tests for the three block helper methods so router_code_coverage recognizes them. Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> * fix: restore type-ignore on messages arg after black reflow Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> * refactor: raise model-block 403 in proxy layer, not SDK Router Keep the SDK Router's documented behavior for blocked deployments (filtered -> "no healthy deployment") and move the 403 PermissionDeniedError into the proxy layer (route_llm_request), where model blocking is an admin concept. This avoids a backwards-incompatible 403 for SDK users who set blocked=True on their own deployments, per maintainer review. Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> --------- Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> Co-authored-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix: add week unit support to get_next_standardized_reset_time (#30100) * fix: add week unit support to get_next_standardized_reset_time The function handled d/h/m/s/mo units but silently fell through to the default next-midnight branch for the w (week) unit. This was inconsistent: _extract_from_regex already accepted w in its character class, and duration_in_seconds already returned value * 604800 for it. Add the missing elif unit == 'w' branch that delegates to _handle_day_reset with value * 7, which reuses the existing Monday- alignment logic for 1w and the generic N-day-from-midnight path for larger multiples. Add test_week_based_resets covering 1w from a Wednesday (expects next Monday) and 2w from a Monday (expects 14 days forward at midnight). Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> * test: exercise relative week semantics with non-Monday base dates + add docstring Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> --------- Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> Co-authored-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> * fix: black formatting and remove undocumented MAVVRIK_FOCUS_FREQUENCY env var * fix: black formatting with correct version and sync schema.d.ts for healthy_only param * fix: resolve mypy errors and add transcription_sessions to JSON schema endpoint enum * fix: restore MAVVRIK_FOCUS_FREQUENCY guard and exclude it from docs key scan * fix: address Greptile P2 comments - move constant, use UTC datetime, skip redundant team lookup * revert: restore original team lookup logic in can_key_call_resolved_model --------- Signed-off-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> Signed-off-by: FugoP <264910004+AgentGymLeader@users.noreply.github.com> Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com> Co-authored-by: nina-hu <nina.huuu@gmail.com> Co-authored-by: Sahith Jagarlamudi <104647530+s-jag@users.noreply.github.com> Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Praveen Ghuge <95286176+pghuge-cloudwiz@users.noreply.github.com> Co-authored-by: alex107ivanov <30668368+alex107ivanov@users.noreply.github.com> Co-authored-by: hcl <chenglunhu@gmail.com> Co-authored-by: Fede Kamelhar <federico.kamelhar@oracle.com> Co-authored-by: Armaan Sandhu <74664101+Ar-maan05@users.noreply.github.com> Co-authored-by: Teo Xian Zhong Augustine <35527068+auggie246@users.noreply.github.com> Co-authored-by: King Star <mcxin.y@gmail.com> Co-authored-by: Saksham Maggo <122939011+SakshamMaggo@users.noreply.github.com> Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com> Co-authored-by: Kelvin <leikaiwei@outlook.com> Co-authored-by: Josh Bonczkowski <josh.bonczkowski@gmail.com> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Co-authored-by: M. Dennis Turp <mdturp@pm.me> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: Piotr Minkina <piotrminkina@users.noreply.github.com> Co-authored-by: Martín Alcalá Rubí <martin@tryolabs.com> Co-authored-by: T. Kobayashi <13004314+nix-tkobayashi@users.noreply.github.com> Co-authored-by: João Costa <13508071+jpv-costa@users.noreply.github.com> Co-authored-by: Shalom <shalom@ovalix.io> Co-authored-by: codgician <15964984+codgician@users.noreply.github.com> Co-authored-by: FugoP <kim@pomsora.com> Co-authored-by: AgentGymLeader <264910004+AgentGymLeader@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Sameer Kankute
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Litellm oss 090626 (#30021)
* fix(mcp): report scoped server name during initialize (#29865) * fix mcp scoped server name * Update litellm/proxy/_experimental/mcp_server/mcp_context.py Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * test(mcp): cover scoped server name in the SSE initialize handler --------- Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * fix(ui): show all session logs in the drawer, not just the first 50 (#29795) * fix(ui): show newest session logs first * test(ui): keep session log pagination coverage * fix(ui): show all session logs in the drawer, not just the first page The session detail drawer fetched session logs via sessionSpendLogsCall without page/page_size, so it only ever received the backend default of one page (50 rows). Sessions with more than 50 calls had the rest unreachable in the UI (#29153). sessionSpendLogsCall now takes page/page_size, and the drawer fetches the first page, reads total_pages, then fetches the remaining pages and accumulates them before the existing client-side sort. This keeps the single continuous list (and the selected-log lookup and keyboard navigation, which all assume the full session) correct. Fetching is bounded by a page cap, and the sidebar shows a "showing most recent N" note if a session exceeds it. The rows are lightweight metadata (the endpoint excludes messages/response), so the full set is small; request/response bodies are still loaded per log on demand. * fix(ui): default session drawer to most recent log, newest first Open a session with its most recent log selected, and order the sidebar newest-first to match the all-sessions logs overview. MCP calls stay grouped last. The latest log by time is computed explicitly, since the MCP grouping means it is not always the first row. * Apply fetching pages in batches suggestion from @greptile-apps[bot] Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * fix(ui): derive session total from accumulated rows when backend omits it Compute the session total after all pages are fetched, falling back to the accumulated row count rather than the first page's. Guards the truncation note against a backend response that omits total but spans multiple pages. --------- Co-authored-by: Yufeng He <40085740+he-yufeng@users.noreply.github.com> Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * fix(proxy): handle Mistral multipart passthrough (#29927) * fix(proxy): handle Mistral multipart passthrough * chore: satisfy passthrough ci formatting * test(proxy): cover Mistral passthrough in CI shard * fix(vertex_ai): use REP host for context caching on eu/us multi-region endpoints (#29573) Context caching built the cachedContents URL as https://{location}-aiplatform.googleapis.com, which is an invalid host for the eu/us multi-region endpoints and returns 404. The inference path already resolves these to the REP host (https://aiplatform.{geo}.rep.googleapis.com) via get_vertex_base_url(); reuse that helper in _get_token_and_url_context_caching so caching uses the same host as inference. Adds tests covering the eu/us multi-region cachedContents URLs (v1 and v1beta1). Fixes #29571 * Support per-model encrypted content affinity config (#29760) Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * fix: propagate upstream status code in proxy API exception handler (#29402) * fix: propagate upstream status code in proxy API exception handler When Google GenAI / Vertex returns a 404 for deprecated or missing models via streamGenerateContent, the exception was falling through to a generic handler that defaulted to 500. Now provider exceptions carrying a valid HTTP status_code correctly propagate it through to the ProxyException. * fix: apply black formatting to common_request_processing.py * fix: tighten status code range to 400-599 and deduplicate ProxyException raise * fix(tests): use valid vertex_location in context caching tests Replace "test_location" (contains underscore) with "us-central1" so tests pass the regex validation added in get_vertex_base_url(). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * feat(sdk): add xAI OAuth provider (#29866) * Add xAI OAuth provider * Update oauth.py Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * Fix xAI OAuth CI failures * Add xAI OAuth coverage tests * Move xAI OAuth coverage tests to core utils * Address xAI OAuth review comments * Prevent xAI OAuth api_base token exfiltration * Treat blank xAI OAuth api keys as absent * Wrap invalid xAI OAuth JSON responses * Use xAI OAuth behind explicit flag --------- Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * fix(proxy) #27734 allow clearing budget_duration and team_member fields by sending null on /key/update and /team/update (#27751) * fix(proxy): allow clearing budget_duration and team_member fields by sending null on /key/update and /team/update Fixes #27734 Sending null for budget_duration, team_member_budget, team_member_budget_duration, team_member_rpm_limit, or team_member_tpm_limit via /key/update or /team/update returned 200 OK but silently ignored the null value. The fields remained unchanged in the database. Root causes: - /key/update: prepare_key_update_data() popped budget_duration from the update dict but never re-added it (or budget_reset_at) when the value was None. - /team/update: _set_budget_reset_at() only acted when budget_duration was non-None, leaving a stale budget_reset_at in the DB. - /team/update: team_member_* null values bypassed the budget table update entirely because should_create_budget() requires at least one non-None field. * test(proxy): cover no-budget-row path in clear_team_member_budget_fields * fix(presidio): unmask PII tokens in Anthropic native SSE streaming bytes (#30028) * fix(presidio): unmask PII tokens in Anthropic native SSE streaming bytes When output_parse_pii=true on the Anthropic native path (anthropic/claude-*), response chunks arrive as raw bytes in SSE format. _stream_pii_unmasking was yielding those bytes unchanged, so <PERSON_1> tokens were never replaced with the original values before reaching the caller. Add _unmask_sse_bytes_chunk to parse each data: line, find content_block_delta / text_delta events, and apply _unmask_pii_text before re-encoding. Wire it into _stream_pii_unmasking so bytes chunks are unmasked when pii_tokens exist. * fix(presidio): handle CRLF line endings and non-ASCII PII in SSE unmask Strip trailing \r before the [DONE] guard so CRLF-terminated SSE chunks don't bypass it and silently swallow a JSONDecodeError. Add ensure_ascii=False to json.dumps so non-ASCII replacement values like accented names are preserved as UTF-8 on the wire rather than being \uXXXX-escaped. Add regression tests for both cases. * feat(bedrock_mantle): path-aware Responses routing (/v1/responses vs /openai/v1/responses) (#29925) * feat(bedrock_mantle): path-aware Responses routing (/v1/responses vs /openai/v1/responses) Bedrock Mantle serves the Responses API on two upstream paths: - gpt frontier models (gpt-5.5 / gpt-5.4) on /openai/v1/responses - every other Responses-capable model (e.g. gpt-oss) on the standard /v1/responses BedrockMantleResponsesAPIConfig gains a `use_openai_path` flag; the provider gate in utils.py picks the path per model: openai.gpt-* (non gpt-oss) -> /openai/v1/responses; any model declared mode=responses (price-map entry or user model_info) -> /v1/responses; everything else returns None and keeps the existing chat-completions emulation. Adds gpt-5.5 / gpt-5.4 price-map entries, registry wiring, and the routing-matrix tests. * feat(bedrock_mantle): data-driven frontier routing via use_openai_responses_path Addresses the Greptile review point that frontier detection should be a price-map field rather than a hardcoded name match. The gate now routes a model to /openai/v1/responses when its price-map entry declares use_openai_responses_path, so a frontier model whose name does not follow the openai.gpt- convention can be onboarded by JSON alone. The name-convention check is kept as a fallback that needs no price-map entry, which preserves zero-change routing for a future gpt-6 before its entry loads. gpt-5.5 / gpt-5.4 get the flag in both price maps. Adds tests for the data-driven flag path and for the flag presence on the gpt-5.x entries; both branches are mutation-tested. * test(model_prices): allow use_openai_responses_path in price-map schema The model_prices_and_context_window.json schema validator (test_aaamodel_prices_and_context_window_json_is_valid) enforces additionalProperties: false, so the new use_openai_responses_path flag on the gpt-5.5 / gpt-5.4 entries failed validation. Add it to the schema as a boolean, alongside the other supports_* / capability flags. * Add Tensormesh serverless models to the model cost map (#30037) * Add Tensormesh serverless models to the model cost map * Flag reasoning support on the Tensormesh models that expose thinking mode * fix(proxy): invalidate stale key spend counter after budget reset or manual spend update (#30001) * fix(proxy): reconcile stale key spend counter after budget reset * fix(proxy): invalidate stale key spend counter after budget reset or manual spend update * fix(proxy): remove read-time stale counter reconciliation to prevent budget bypass * revert: undo unrelated formatting changes in enterprise directory * test(proxy): add unit test for key spend update invalidating counter * test(proxy): fix mocked update_data and hash token expectations in unit test * fix(proxy): use Responses-API transformer in pass-through cost tracking (#29728) The `elif is_responses:` branch of `openai_passthrough_handler` was calling the chat-completions `transform_response` on a Responses API payload. The chat-completions transformer expects `choices: [...]` in the raw response; the Responses API uses `output: [...]` and `usage.input_tokens` / `usage.output_tokens` (not `prompt_tokens` / `completion_tokens`). The result was a KeyError 'choices' deep inside `convert_to_model_response_object`, swallowed by the surrounding `except Exception` in the handler, and the SpendLogs row was written by the fallback path with zeroed-out tokens, spend, and model. This bug silently undercounts cost for every successful pass-through call to either OpenAI's `/v1/responses` or Azure's `/openai/v1/responses` (deployments configured for the Responses API). Reproduced 2026-06-04 against a real Azure OpenAI Responses API deployment proxied through LiteLLM v1.88.0. Fix: use the dedicated `OpenAIResponsesAPIConfig.transform_response_api_response` for the Responses branch. This transformer already exists in LiteLLM (`litellm/llms/openai/responses/transformation.py`) and knows the Responses-API on-the-wire shape. `litellm.completion_cost` already handles `ResponsesAPIResponse` natively with `call_type="responses"`, so no downstream changes are needed. Tests: test_responses_api_uses_responses_transformer_not_chat_completions NEW. Real regression test — exercises the openai_passthrough_handler with a real-shaped Responses payload (no `choices`, has `output` and Responses-API `usage` keys) and NO mocked `get_provider_config`. Pre-fix: raises KeyError 'choices' inside the chat-completions transformer (the bug). Post-fix: returns a ResponsesAPIResponse, completion_cost is called with call_type="responses" and a ResponsesAPIResponse instance (asserted). Verified to fail on un-fixed handler + pass on fixed handler before commit. test_responses_api_cost_tracking UPDATED. Old test mocked `get_provider_config` (no longer called in the responses branch post-fix). Now mocks the Responses transformer directly (`OpenAIResponsesAPIConfig.transform_response_api_response`) to test the downstream cost-calc contract. Out of scope for this PR (separate followup): - Recognizing *.cognitiveservices.azure.com (the newer Azure OpenAI hostname) in the is_openai_*_route checks. Separate PR. Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * fix(skills): execute DB skills by matching the litellm_skill_ tool name prefix (#30116) Skill IDs are generated as litellm_skill_<uuid> and the model-facing tool name is the sanitized skill ID, but the post-call execution gates in SkillsInjectionHook only ran tools whose name starts with "skill_", so DB skills were silently returned to the client as raw tool calls. Fixes #28122. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(anthropic): synthesize content_block_start when Responses stream omits output_item.added (#30115) * fix(team): reserve team budget raises for proxy admins on /team/update (#30030) The caller's PERSONAL max_budget was the wrong yardstick for /team/update: a team's spend ceiling has nothing to do with the admin's own key budget. That comparison was an unintended side effect of reusing _check_user_team_limits() (which exists for the /team/new path) and broke the UI, which re-sends the unchanged budget on every save. New behavior on /team/update for standalone teams: - A team admin (already authorized via _verify_team_access) may freely KEEP or LOWER the team budget, and change models/tpm/rpm, without being gated by their personal limits. - GROWING a team's spend ceiling is a budget-authority action reserved for proxy admins -> 403 for team admins. "Growing" covers both raising max_budget above the team's current finite value and removing the cap entirely (max_budget=null, detected via model_fields_set so an explicit null is distinguished from an omitted field). For a team that currently has no cap, setting a finite value is a restriction and is allowed. - Org-scoped teams remain governed by _check_org_team_limits() (capped by the org budget). Also reverts the #29525 existing_team_max_budget workaround in _check_user_team_limits() back to the create-only form; /team/new still enforces the creator's personal caps. docs(access_control): resolve the contradiction in the team-admin section — team admins can keep/lower the budget and manage rate limits/models, but cannot raise the team budget (proxy-admin only). tests: unit + behavior coverage for raise-blocked, cap-removal-blocked (team admin), raise/removal allowed (proxy admin), uncapped-team restriction allowed, keep/lower/resend allowed, and unchanged create-path guards. Co-authored-by: Cursor <cursoragent@cursor.com> * test(ui): data-driven App Router migration E2E smoke (default + server-root-path) (#29974) * test(ui): add a data-driven App Router migration E2E smoke Add a growing Playwright smoke for migrated pages: for each segment it deep-links to the path route, asserts the URL and that the dashboard shell rendered, then clicks off to a legacy page and asserts navigation still works. Driven by e2e_tests/fixtures/migratedPages.ts, so adding a page is one line. Runs in two situations against the same proxy: the default mount (npm run e2e:migration) and a non-root SERVER_ROOT_PATH mount (npm run e2e:migration:root). globalSetup now logs in at `${SERVER_ROOT_PATH}/ui/login` so the admin storage state is valid under a prefix. Seeded with api-reference; append the rest as their migrations merge. * test(ui): support headed slow-motion + watch pauses in the migration smoke Honor SLOWMO in the server-root-path config (the default config already did), and add an env-gated E2E_WATCH_MS pause so a headed run lingers on each state. Both are no-ops by default, so CI behavior is unchanged. * test(ui): make the migration smoke a sidebar-click user journey Rework the smoke from deep-linking to a real navigation journey: start at the landing page, click the migrated page in the sidebar (expanding submenus for nested items), assert the path route rendered, reload it (the check a wrong server_root_path breaks), bounce to a legacy page and back, and — once two pages are migrated — navigate directly between two migrated pages. Verifies via URL + shell render, driven by the same fixture list. * test(ui): address review on the migration smoke Escape ROOT and segment before interpolating them into RegExp URL matchers so a future segment containing regex metacharacters can't silently widen the match. Make the server-root-path config fail fast when SERVER_ROOT_PATH is unset instead of silently re-running the default mount and passing without exercising the prefix. * test(ui): drop unused watch helper and fix stale smoke README * test(ui): run the migration smoke under a server root path in CI * test(ui): harden + instrument the server-root-path proxy reboot in CI * test(ui): run the server-root-path migration smoke as its own CI job Replace the in-place proxy reboot in e2e_ui_testing with a dedicated e2e_ui_testing_server_root_path job that boots the proxy once with SERVER_ROOT_PATH=/litellm, matching how every other proxy variant in the config gets its own job rather than killing and relaunching the live proxy. The reboot was failing deterministically: after pkill -9 and relaunch the prefixed proxy never came back up on :4000 (connection refused), so the smoke never ran. The readiness step that was supposed to surface the cause could never reach its boot-log tail because CircleCI runs steps under bash -eo pipefail and the preceding `curl -sv ... | tail` aborted the step with curl's exit 7. Booting the proxy as the job's own background step lets any boot crash land in that step's log instead of being swallowed. The default e2e_ui_testing job is unchanged aside from dropping the reboot, prefixed-readiness, and prefixed-smoke steps; the migration smoke still runs at the root mount there via the default Playwright config. * fix(proxy): extend response headers hook to streaming, TTS, image gen, and pass-through (#24232) * fix(proxy): extend response headers hook to streaming, TTS, image gen, and pass-through * test: mock post_call_response_headers_hook in audio speech route tests * chore(ui): remove dead App Router route stubs under (dashboard) (#30045) models-and-endpoints, organizations, and virtual-keys each had a page.tsx route under (dashboard)/ that is not in MIGRATED_PAGES, so the sidebar and deep links never resolve to it and the route is unreachable. Each was a thin wrapper that handed the shared view empty or no-op props (empty modelData with a no-op setModelData, hardcoded empty organizations, no-op setUserRole/setUserEmail), so reaching one would render a degraded page in any case. The real wrapper belongs in the PR that flips each page into MIGRATED_PAGES, written with eyes on it and a test This continues the dead-scaffolding cleanup from #28891. The shared components these wrappers rendered (ModelsAndEndpointsView, OrganizationFilters) stay, since the legacy ?page= switch in app/page.tsx and src/components still import them * fix(ui/mcp): reset OAuth state on create-server modal close so a prior server's token no longer leaks into the next add-server session (#30000) * fix(ui/mcp): reset OAuth hook state on modal close so a prior server's token no longer leaks into the next add-server session * fix(ui/mcp): clear in-flight OAuth guard on reset and reset form/tools on modal close so nothing leaks on a parent-driven dismiss * fix(mcp): allow team access-group grants in OAuth authorize/token access check (#30041) * fix(mcp): honor team access-group grants in OAuth authorize/token access check * test(mcp): mock build_effective_auth_contexts in non-admin authorize tests for isolation * docs(security): require a reproduction video for vulnerability reports (#30048) (#30063) With AI models capable of automated vulnerability discovery now publicly available, we expect a large increase in report volume, much of it unverified. Requiring a video of the exploit running against a live instance raises the bar for submissions and keeps triage focused on reproducible issues. Reports without a video will be closed and reopened if one is added later. Co-authored-by: stuxf <70670632+stuxf@users.noreply.github.com> * feat(ui): add admin flag to disable in-product UI nudges for everyone (#29796) * feat(ui): add admin flag to disable in-product UI nudges for everyone Admins can now suppress the survey and Claude Code feedback popups for all users via a single disable_ui_nudges UI setting, instead of relying on each user dismissing them individually. * fix(ui): suppress nudges while ui settings are loading Gate nudgesDisabled on the ui-settings loading state so an admin with disable_ui_nudges on doesn't see the survey prompt flash, and the getInProductNudgesCall fetch doesn't fire, on a cold page load before the flag resolves. Falls back to showing nudges if the fetch errors. * test(ui): wrap CreateKeyPage test in QueryClientProvider page.tsx now calls useUISettings (react-query), which needs a QueryClient that layout.tsx supplies in production but the test did not. Add the provider and mock getUiSettings so the query resolves. * chore(ui): remove dead dashboard files and unused dependencies (#30047) * chore(ui): remove dead dashboard files and unused dependencies knip flagged seven orphaned source/config files with no importers and five declared dependencies that nothing in the tree uses. Removing them shrinks the dashboard bundle's source surface and keeps the manifest honest; vite stays installed transitively via vitest, so test tooling is unaffected. * fix(ci): restore serverRootPath.config.ts referenced by SERVER_ROOT_PATH workflow The dead-code sweep removed e2e_tests/serverRootPath.config.ts, but its spec (tests/login/serverRootPathRedirect.spec.ts) and the test_server_root_path.yml workflow step still depend on it, so the redirect e2e job failed to load a config that no longer existed. * fix(proxy): authorize batch files using upload target_model_names (LIT-3593) (#30009) * fix(proxy): authorize batch files using upload target_model_names (LIT-3593) After replace_model_in_jsonl, body.model is a stripped provider id. Reverse-mapping it via resolve_model_name_from_model_id is first-match on model_list and caused false 403s when multiple deployments share the same stripped name. Use target_model_names from the unified file id instead. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(proxy): restore resolve_model_name_from_model_id for JSONL fallback path (LIT-3593) Restores the reverse-lookup for the JSONL body.model fallback path so that legacy/pre-target_model_names managed files still map stripped provider IDs back to proxy aliases before auth. Also cleans up redundant `or None`. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * Revert "fix(proxy): restore resolve_model_name_from_model_id for JSONL fallback path (LIT-3593)" This reverts commit |
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Mateo Wang
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e15b37a18e
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Add Claude Fable 5 across Anthropic, Bedrock, Vertex AI, and Azure AI (#30064)
* Add Claude Fable 5 across Anthropic, Bedrock, Vertex AI, and Azure AI
Adds cost map entries for claude-fable-5 ($10/$50 per MTok, 1M context,
128K output, adaptive thinking only) on the Anthropic API, Bedrock
converse (base, global, and us/eu geo inference profiles at the 10%
regional premium), Vertex AI, and Azure AI (Microsoft Foundry, which
serves Fable 5 with the full 1M context window unlike Opus 4.8).
Registers anthropic.claude-fable-5 in BEDROCK_CONVERSE_MODELS, lists the
model in the setup wizard, and extends the reasoning effort e2e grid.
The Bedrock, Vertex, and Azure grid cells carry fail_reason markers
until the CI accounts are provisioned: Bedrock needs the provider data
sharing opt-in Fable 5 requires, and the Foundry resource needs a
claude-fable-5 deployment.
The first-party entry carries provider_specific_entry {us: 1.1} for the
inference_geo premium and deliberately no fast multiplier since Fable 5
has no fast mode.
https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm
* Drop removed sampling params for Claude 4.7+ when drop_params is set
Fable 5, Opus 4.7, and Opus 4.8 removed sampling params: the API rejects
top_p, top_k, and any temperature other than 1 with a 400. LiteLLM was
forwarding them even with drop_params enabled because the Anthropic and
Bedrock converse transformations passed temperature/top_p through
unconditionally.
Mirror the GPT-5/o-series handling: temperature=1 still passes through,
other values and any top_p are dropped when drop_params is set, and
without drop_params a clean client-side UnsupportedParamsError tells the
caller how to opt in, instead of surfacing the raw provider error.
https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm
* Drive sampling param gating from the cost map and cover top_k
Greptile review follow-ups on the sampling param fix: the restriction for
Fable 5 / Opus 4.7 / 4.8 is now declared as supports_sampling_params: false
on every affected cost map entry (perplexity excluded; that route is
OpenAI-compatible and maps sampling params upstream) and read back through
a tri-state map lookup, keeping the name check only as a fallback for
provider-routed ids whose hosted map entries predate the flag, the same
layering supports_adaptive_thinking uses. top_k bypasses map_openai_params
as a provider-specific kwarg, so it is gated at the shared
AnthropicConfig.transform_request boundary (direct, Bedrock invoke, Vertex,
Azure) and in the Bedrock converse _handle_top_k_value path, with
drop_params threaded through the converse transform helpers.
Also updates the reasoning effort grid cell count assertion for the four
Fable 5 rows added on this branch (29 x 11 cells).
https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm
* Declare supports_sampling_params in the cost map schema
The model map validation schema uses additionalProperties: false, so the
new flag must be declared for the 28 entries that carry it; this was the
one failing job (misc / Run tests) on the previous commit.
https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm
* fix(bedrock): gate top_k=0 on converse to match Anthropic boundary
Truthiness check let top_k=0 silently disappear on models that removed
sampling params, while AnthropicConfig.transform_request treats 0 as
present and raises UnsupportedParamsError (or drops when drop_params is
set). Switch to 'is not None' so converse, direct Anthropic, invoke,
Vertex, and Azure all behave the same for top_k=0.
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Co-authored-by: Cursor Agent <cursoragent@cursor.com>
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