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140 commits
| Author | SHA1 | Message | Date | |
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Mateo Wang
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9dff9cdd9a
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Merge pull request #37979 from BerriAI/litellm_lit5714_adaptive_thinking_display
fix(anthropic/bedrock): request summarized adaptive thinking for reasoning_effort and use provider thinking token counts |
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mateo-berri
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e0511e9384 |
Merge branch 'litellm_internal_staging' into litellm_bedrock_converse_no_trailing_empty_chunk
Resolves the test-file conflict by keeping both sides, extends the finish-reason gate to trace-bearing metadata events so guardrail trace chunks keep their pre-regression delta shape, parametrizes the regression test over tool-call, mixed, and reasoning streams, and repairs the one ant-design icon usage the lucide-react migration left behind in skill_detail.tsx (semantic conflict on the base branch) |
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Devin AI
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418e8ca5e8 |
fix(bedrock): build response field paths as an immutable sequence to satisfy the type discipline gate
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Devin AI
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20e92d1e68 |
fix(anthropic/bedrock): request summarized adaptive thinking for reasoning_effort and use provider thinking token counts
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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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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yuneng-jiang
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693797420d
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test: unwind environment writes in tests/test_litellm with monkeypatch (#37806)
* test: use monkeypatch.setenv for env writes in tests/test_litellm `os.environ["X"] = v` inside a test leaks the value into every test that runs after it in the same worker, so ordering decides the result. 262 of those writes across 40 files now go through pytest's `monkeypatch` fixture, which restores the previous value at teardown. The rewrite skips any test that a mock.patch-family decorator wraps, any test with defaulted positional parameters, any test whose own name is called directly elsewhere, and rebinds nothing inside nested defs, because in each of those cases appending a fixture parameter changes what pytest or mock binds. Ratchets the TQ004 ceiling from 768 to 506. * fix(test): delete the key through monkeypatch instead of popping it first Five tests popped a key straight out of `os.environ`, ran, then restored it with `monkeypatch.setenv`. By the time monkeypatch saw the name it was already gone, so it recorded "absent" as the value to go back to and deleted the key at teardown. On a worker that inherited a real `RESEND_API_KEY`, `SENDGRID_API_KEY`, `UI_PASSWORD`, `LITELLM_SALT_KEY` or `OPENAI_API_KEY`, every test after the first one ran without it. `monkeypatch.delenv(..., raising=False)` removes the key and restores whatever was there, so the try/finally the manual restore needed goes with it. * chore(test): leave the two cost-calc files to the PR that rewrites them fully Both files are also in #37815, which converts the module-global writes as well as the env writes and folds them into one fixture. Two PRs rewriting the same lines differently is a conflict nobody benefits from resolving, so this one drops back to staging on those two and keeps the other 39. TQ004 clears 200 here instead of 275; the rest moves with #37815. |
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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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tin-berri
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4307b34aca
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fix: omit thinking.type=disabled for always-on thinking Claude models (#37510) | ||
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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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Yassin Kortam
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bf59b7e23d
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feat(rust): route /chat/completions through the Rust core for anthropic and bedrock (#37241)
Adds a chat_completions route module to litellm-core, mirroring the messages route, plus Anthropic Messages and Bedrock Converse provider configs. The per-model `rust: true` opt-in now covers /chat/completions for both providers. The core accepts an allowlisted subset (text conversations, non-streaming) and returns CoreError::Unsupported for anything else, so tool calls, multimodal content and streaming fall back to the Python path transparently. Resolves LIT-5698 |
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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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mateo-berri
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46a4eda19e |
Merge remote-tracking branch 'origin/litellm_internal_staging' into litellm_fix_bedrock_adaptive_thinking_token_accounting
# Conflicts: # litellm/llms/bedrock/chat/invoke_handler.py # litellm/responses/litellm_completion_transformation/transformation.py # tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py # tests/test_litellm/responses/litellm_completion_transformation/test_reasoning_content_transformation.py |
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mateo-berri
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54cc988a9e | test: drop restating comment and wrap long call in thinking tests | ||
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mateo-berri
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138c77023a |
fix: accept bool thinking param instead of crashing with AttributeError
litellm.completion(thinking=True) crashed pre-network in is_thinking_enabled
with a retryable APIConnectionError ('bool' object has no attribute 'get'),
so the router burned retries on a deterministic failure and proxy clients got
a traceback instead of a usable response.
validate_and_fix_thinking_param now coerces thinking=True to the enabled dict
with the default medium budget and drops thinking=False, and the remaining
dict-assuming thinking accessors (base config, bedrock converse, deepseek)
guard with isinstance so raw bools can never crash a transform.
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Marty Sullivan
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5fe7793a14 |
refactor(bedrock): own the Converse batch usage shape in the provider layer
Shape detection and block normalization sat in the generic batch layer, which let batch and live parsing of the same wire format drift apart. Both now live on AmazonConverseConfig as is_converse_usage_shape and usage_from_batch_output, so batch_utils asks the provider adapter rather than knowing Bedrock's field names. Adds direct coverage for the shape predicate, the completion of an incomplete block, cache-count inflation, and the streaming usage event that shares the public transform. Drops the narrative banner from the batch tests. |
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Marty Sullivan
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7dbf2d57c5 |
fix(bedrock): read batch usage by payload shape, not by provider name
Every bedrock batch output line went through the Anthropic usage parser, which reads snake_case input_tokens/output_tokens. Converse-family models (Nova and friends) report camelCase inputTokens/outputTokens, so their usage came back 0/0/0 and the batch billed $0 despite real token consumption. Usage is now selected by the shape of the payload: a Converse-shaped block goes through the same transform the live Converse path uses, so a batch and an equivalent non-batch call agree on tokens, including cache reads and writes. Anthropic-shaped bedrock output is unchanged. A shape neither parser understands (an InvokeModel-native payload from Titan, Cohere, or Llama, which name their counts differently again) still reads zero, but now warns with the keys it saw instead of silently billing $0. Exposes the Converse usage transform as public, since batch parsing is a second legitimate caller; that also removes the private-member access invoke_handler was already making. |
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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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mateo
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3db1759d04 |
fix(bedrock): stop emitting an empty assistant delta after the finish_reason chunk
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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mateo-berri
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0f41365c34 | fix(bedrock): forward output_config effort for application inference profile ARNs | ||
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mateo-berri
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929ee52b87 |
fix(bedrock): preserve adaptive thinking effort through the /v1/messages bridge
Claude Code drives Opus 4.7 with thinking {"type": "adaptive"} plus
output_config {"effort": "max"}. The anthropic-to-openai adapter
forwarded thinking verbatim for Claude models but dropped output_config,
and Bedrock Converse streams zero reasoningContent blocks for adaptive
thinking without an effort tier. Forward the effort subset of
output_config for Bedrock targets, accept it in the converse supported
params, and map it with the model's effort ceiling applied. Re-enable
the skipped e2e compat cell that catches this
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Alex Shtof
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280c95ccb0
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fix(bedrock): enable native structured output for GLM 5 and DeepSeek V3.2 (#35669)
* fix(bedrock): enable native structured output for GLM 5 and DeepSeek V3.2 * ci: empty commit --------- Co-authored-by: Alexander Shtoff <alexander.shtoff@tii.ae> |
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Devin AI
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af2246c5b8 |
fix(anthropic,bedrock): report provider thinking tokens instead of classifying them as text
Resolves LIT-5244 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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tin
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d39c557743 |
fix(bedrock): drop conflicting tool_choice.type when toolConfig.toolChoice is set
Converse rejects a request that carries both toolConfig.toolChoice and an
additionalModelRequestFields.tool_choice.type, so any request that pairs
parallel_tool_calls with an explicit tool_choice 400s with "The additional field
tool_choice/type conflicts with the existing field toolConfig.toolChoice.auto".
That pairing is what agentic clients send by default; Codex CLI sends
tool_choice "auto" and parallel_tool_calls false on every turn, so tool calling
was broken outright on Bedrock models that advertise
supports_parallel_tool_use_config.
Drop the type from the Anthropic passthrough once toolChoice carries it, and keep
disable_parallel_tool_use, which has no toolConfig equivalent and is accepted
alongside toolChoice. Measured against Bedrock directly: toolChoice plus
{disable_parallel_tool_use} succeeds for auto, any and tool, while an empty
tool_choice with no toolChoice is rejected for a missing type, so the type still
has to be emitted when the caller sends no tool_choice.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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mateo-berri
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a895249923 | refactor(bedrock): remove the dead BedrockLLM invoke code path | ||
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ryan-crabbe-berri
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998a372417
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test: stop bedrock tool acompletion tests from making real network calls (#34644) | ||
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mateo-berri
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75c0e12dac | fix(bedrock): let parallel_tool_calls-derived disable flag win over raw tool_choice value | ||
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mateo-berri
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6a7454271b | fix(bedrock): include type in tool_choice disable_parallel_tool_use config for Converse | ||
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Mateo Wang
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9ab3847c0b
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fix(tests): remove importlib.reload of http_handler that breaks client injection in later tests (#34336)
The huggingface embedding test fixture reloaded
litellm.llms.custom_httpx.http_handler, creating a new HTTPHandler class
object. llm_http_handler keeps the class captured at import time, so any
test running later in the same process that injects a client built from
the reloaded class fails the isinstance check and the mock is silently
discarded, causing a real network call. Under pytest-xdist loadscope this
surfaced as a deterministic failure of
test_accept_header_in_completion_request_jwt whenever an unrelated PR
shifted worker distribution.
Also removes the same reload pattern from the vertex rerank integration
test (both were previously removed in
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Abhimanyu Kapur
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a5b0f32a84 |
test(bedrock-converse): cover adaptive-thinking drop when max_tokens too small
Adds the regression test for the warning-drop branch in the Converse adaptive-thinking translation, mirroring the chat completions path's test_raw_adaptive_thinking_dropped_when_max_tokens_too_small. |
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Abhimanyu Kapur
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741220c80f |
fix(bedrock-converse): translate adaptive thinking for pre-4.6 models
Follow-up to #32867 (native /v1/messages) and the /chat/completions commit earlier on this branch, extending the same adaptive-thinking translation to the Bedrock Converse path. Clients like Claude Code send thinking={type: "adaptive"} on every request. When routed via Bedrock Converse to pre-4.6 models (claude-haiku-4-5, claude-sonnet-4-5), this was forwarded as-is and rejected by the model. Mirrors the translation already applied on the /chat/completions and /v1/messages paths: map to legacy thinking={type: enabled, budget_tokens}, capped below max_tokens. Also fixes the missing custom_llm_provider arg in the chat completions path's call to AnthropicConfig._map_reasoning_effort. |
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Mateo Wang
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142d5aa12b
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fix(bedrock): honor cache_control ttl on message-level cachePoint blocks (#32551)
Bedrock Converse supports cachePoint ttl (1h GA for Claude 4.5+), and _get_cache_point_block maps cache_control.ttl -> cachePoint.ttl, but the model parameter its allow-list gate requires was only threaded through the system-message path. Every message-level path either called _get_cache_point_block without model= (8 call sites in _bedrock_converse_messages_pt / _pt_async) or hardcoded CachePointBlock(type="default") (tool-result blocks and _convert_to_bedrock_tool_call_invoke), so a requested 1h ttl silently degraded to the 5-minute default - exactly on the conversation-tail breakpoint that long-running agents need to survive tool calls longer than 5 minutes. - pass model= at the 8 _get_cache_point_block call sites - tool-result blocks: capture the cache_control dict (was a boolean) and route through _get_cache_point_block so ttl survives - _convert_to_bedrock_tool_call_invoke: accept optional model and route per-tool-call cache_control through _get_cache_point_block Completes the ttl support added for system messages (#19848, #20326): message-level cache_control now behaves identically. Note: message-level cache_control on a content-less assistant message emits no cachePoint at all today; that pre-existing gap is orthogonal to ttl and left out of scope (per-tool-call placement covers it). Co-authored-by: Arash <arashne@glia-ai.com> |
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Mateo Wang
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138a69bd61
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fix(bedrock): map guardrailConfig to InvokeModel guardrail headers (#31985)
* fix(bedrock): map guardrailConfig to InvokeModel guardrail headers The InvokeModel API takes the guardrail identifier, version and trace as X-Amzn-Bedrock-* request headers, unlike Converse which takes them in the request body. The invoke transformer never set these headers, so guardrailConfig was silently dropped (or leaked into the request body) and Bedrock guardrails never ran on invoke-route models. Pop guardrailConfig in AmazonInvokeConfig.validate_environment, validate it, and set the headers before SigV4 signing; explicitly passed headers keep winning over guardrailConfig so existing workarounds are unaffected * fix(bedrock): reject guardrailConfig missing guardrailIdentifier A guardrailConfig without guardrailIdentifier (e.g. an empty dict) would validate, produce no guardrail headers, and let the request proceed with guardrails silently not applied; that silent skip is the exact failure mode this fix exists to remove, so fail fast with a 400 instead |
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Shivam Rawat
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1543725916
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fix(bedrock): honor ttl for tool_config cache injection points (#31929)
* fix(bedrock): honor ttl for tool_config cache injection points Pass cache_control_injection_points control.ttl through to Bedrock toolConfig cachePoint blocks, matching message/system cache behavior. Co-authored-by: Cursor <cursoragent@cursor.com> * refactor(bedrock): drive Claude 4.5+ ttl support from pricing JSON, not regex is_claude_4_5_on_bedrock hardcoded a model-name pattern list that needed a manual update for every new Claude release (it already silently missed Sonnet 5 and Fable 5). Replace it with a lookup against cache_creation_input_token_cost_above_1hr in model_prices_and_context_window.json, which AWS docs confirm tracks the same 1h-TTL-capable model set. Also fixes two bedrock Claude 3.5 Sonnet entries that incorrectly carried that pricing field (their own regional variants didn't have it), which would have made the JSON-driven check wrongly grant them 1h TTL support. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(tests): use real Claude Sonnet 4.5 release id in ttl cache-point tests test_add_cache_point_tool_block_passes_ttl_for_claude_4_5 and test_bedrock_tools_pt_passes_ttl_for_claude_4_5 used a fabricated model id (...-20250514-v1:0) that never shipped. This passed under the old regex-based is_claude_4_5_on_bedrock, which matched on substring alone, but fails now that it looks up cache_creation_input_token_cost_above_1hr in litellm.model_cost, since the fake id has no pricing entry. Also force the bundled local cost map in both tests so ttl eligibility reads this branch's pricing data instead of the network-fetched main copy, which lacks the fix until merge. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(bedrock): restore cache and tool config compatibility * fix(bedrock): preserve Sonnet 5 parallel tool config * fix(bedrock): decouple parallel tool support from cache ttl * refactor(bedrock): drive parallel tool use config from JSON, not hardcoded patterns Replace the hardcoded _CLAUDE_BEDROCK_PARALLEL_TOOL_USE_PATTERNS tuple and bedrock_converse_supports_strict_tool_schemas (dead code) with a supports_parallel_tool_use_config key in model_prices_and_context_window.json, matching how is_claude_4_5_on_bedrock already reads cache_creation_input_token_cost_above_1hr from the pricing JSON. New models pick up parallel tool use support automatically when their pricing entry ships with the key set, with no code change required Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(tests): use real model id in parallel-tool-use-without-ttl-pricing test anthropic.claude-opus-4-7-unlisted-v1:0 has no entry in model_prices_and_context_window.json, so bedrock_converse_supports_parallel_tool_use_config returned False and the test died with KeyError on additionalModelRequestFields. Use jp.anthropic.claude-opus-4-7, a real entry that carries supports_parallel_tool_use_config without 1h-TTL cache pricing, which is exactly the decoupling this test exists to cover * test(utils): allow supports_parallel_tool_use_config in pricing schema The misc unit test job validates model_prices_and_context_window.json against the INTENDED_SCHEMA allowlist in test_utils.py, which rejects unknown keys. Add the supports_parallel_tool_use_config key this PR introduced so test_aaamodel_prices_and_context_window_json_is_valid passes again * fix(bedrock): preserve ttl for regional claude models * fix(bedrock): fall back to base model entry when regional pricing lacks capability fields Regional model_cost entries like jp.anthropic.claude-opus-4-7 that omit cache_creation_input_token_cost_above_1hr shadowed the base entry that has it, so is_claude_4_5_on_bedrock returned False and requested cache ttl values were dropped for those deployments. Both capability lookups now consult the full model id and the region-stripped base entry, matching the coverage of the old name-pattern list. Also restores ToolBlock keyword construction for the tool_config cachePoint; PEP 589 TypedDict keyword instantiation works on every supported Python version --------- Co-authored-by: Shivam Rawat <shivamrawat@Shivams-MacBook-Pro.local> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: mateo <mateo@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Sameer Kankute
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e33e2917c6
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chore: litellm oss 170626 (#30637)
* fix(proxy): allow non-admin virtual keys to call GA Realtime WebRTC HTTP routes (#30089) * fix(proxy): allow non-admin virtual keys to call GA Realtime WebRTC HTTP routes Add the realtime WebRTC HTTP sub-routes (/realtime/client_secrets, /realtime/calls and their /v1 + /openai/v1 variants) to LiteLLMRoutes.openai_routes so is_llm_api_route() classifies them as LLM API routes. Without this, non-admin virtual keys received 401 'Only proxy admin can be used to generate, delete, update info for new keys/users/teams' when calling these endpoints. Fixes #29923 * fix(proxy): validate session.model for realtime routes in model-access check The GA Realtime WebRTC HTTP routes resolve the effective model from the nested session.model (falling back to the top-level model), but the auth layer's get_model_from_request() only extracted the top-level model. A model-restricted virtual key could therefore place a disallowed model in session.model, leave the top-level model unset, and skip can_key_call_model() entirely - obtaining an ephemeral token for a model it is not allowed to use. Extract session.model for the realtime client_secrets/calls routes so the model-access check runs against the model the request will actually use. Legitimate callers are unaffected; their permitted model still validates. Relates to https://github.com/BerriAI/litellm/issues/29923 * fix(proxy): classify realtime transcription_sessions routes as LLM API routes Add the GA Realtime WebRTC transcription_sessions HTTP routes to openai_routes so is_llm_api_route() returns True for them, matching the client_secrets and calls routes already fixed. These endpoints are registered with user_api_key_auth in realtime_endpoints/endpoints.py, so without this a non-admin virtual key calling POST /v1/realtime/transcription_sessions would hit the admin-only 401 branch. Extends the regression test parametrization accordingly. --------- Co-authored-by: habonlaci <4699494+habonlaci@users.noreply.github.com> * feat(proxy): surface max_input_tokens/max_output_tokens on /v1/models (#30272) * feat(proxy): surface max_input_tokens/max_output_tokens on /v1/models * fix(proxy): degrade /v1/models gracefully when model-group lookup fails --------- Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix: sort tiered token-cost thresholds numerically (#30375) * fix: sort tiered token-cost thresholds numerically _get_token_base_cost iterated input_cost_per_token_above_<N>_tokens keys with a lexicographic sort, so for tiers whose thresholds have different digit lengths (e.g. 90k vs 128k) a request crossing both was billed at the lower tier that sorted first. Sort by the parsed numeric threshold instead, so the highest tier the request actually crosses is applied. * refactor: reuse _parse_above_token_threshold for inline threshold parse --------- Co-authored-by: Eric (GabiDevFamily) <271972409+santino18727-debug@users.noreply.github.com> * fix(openai): preserve cache_control for openai-compatible custom endpoints (#30387) * fix(openai): preserve cache_control for openai-compatible custom endpoints * fix(openai): use parsed hostname to detect real OpenAI for cache_control preservation * fix(proxy): drain all daily-spend batches per flush cycle (#30281) (#30505) * fix(types): prevent internal parallel_request_limiter fields from leaking to upstream providers (#30545) * fix(types): add internal parallel_request_limiter fields to all_litellm_params to prevent forwarding to upstream providers * test(types): add regression test for internal rate-limit fields in all_litellm_params * fix(init): add bool type annotation to suppress_debug_info (#30531) Module-level `suppress_debug_info = False` had no annotation, so strict type checkers (e.g. ty) infer it as `Literal[False]`. Reassigning it to `True` (as done in proxy_server.py and router.py) then fails with an invalid-assignment error. Annotate it as `bool` to match every other flag in this module. * fix: coalesce null aggregates in update_metrics for no-spend keys (#29945) * feat(team_endpoints): add query parameter `key_limit` to `/team/info` endpoint (#30006) * feat(team_endpoints): Add query parameter key_limit to /team/info * feat(team_endpoints): update schema.d.ts to include the new query parameter * feat(team_endpoints): add tests for limitting key count in /team/info response * feat(team_endpoints): Apply suggestions from greptile * Set greater-than constraint on key-limit * Fix type * fix(router): release aiohttp connection when stream iteration ends abnormally (#30271) * fix(router): release aiohttp connection when stream iteration ends abnormally A streaming response that terminates with a mid-stream read timeout, a task cancellation (client disconnect), or GeneratorExit never closed the underlying aiohttp ClientResponse. aiohttp only auto-releases the connector slot at body EOF, so each abnormally terminated stream permanently leaked one slot from the shared TCPConnector pool. During a backend traffic spike the pool drains; once exhausted every subsequent request to that host waits for a slot, times out and surfaces as a 408, indefinitely, even after the backend recovers. Only a proxy restart cleared the in-memory sessions, which matched the reported symptom of a router stuck returning 408 for a healthy vLLM backend. Close the response in a finally clause when iteration ends. On a fully read response the connection was already released at EOF and close() is a no-op, so keep-alive reuse for normal requests is unchanged. Fixes #30192 * test(aiohttp): cover GeneratorExit path with a mock instead of a live socket The previous slot-release test started a real aiohttp TCP server, which can flake in offline CI and does not exercise this fix's code path directly. Replace it with a dependency-injected mock that closes the stream generator (GeneratorExit) and asserts the response is closed, covering the third abnormal-exit path the finally block handles * feat(proxy): serve Anthropic-native /v1/models for Claude Code gateway discovery (#30273) * feat(proxy): serve Anthropic-native /v1/models for Claude Code gateway discovery * refactor(proxy): move Anthropic model-list formatter into llms/anthropic/common_utils * fix(proxy): make model_list request param optional for direct callers * feat(dashscope): add Responses API support (#30286) * feat(dashscope): add Responses API support DashScope's OpenAI-compatible endpoint serves /responses, so register a DashScopeResponsesAPIConfig that routes dashscope/* responses calls to {api_base}/responses without rewriting the upstream model id, instead of falling back to the chat-completions -> responses emulation pipeline. Closes #29780 * feat(dashscope): mark responses API as not supporting native websocket Matches the hosted_vllm/perplexity/openrouter responses configs, which all override supports_native_websocket() to False since the OpenAI-compatible endpoint has no native wss:// responses transport. --------- Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(spend-logs): preserve error_message on ProxyException failures (#30381) * fix(spend-logs): preserve error_message on ProxyException failures `StandardLoggingPayloadSetup.get_error_information` used `str(original_exception)` to populate the human-readable error message stored in `spend_logs.metadata.error_information.error_message`. `ProxyException` (litellm/proxy/_types.py:3453) sets `self.message` in its constructor but does NOT call `super().__init__(message)` and does NOT define `__str__`. As a result, `str(ProxyException(...))` returns the empty string, and every auth/budget/quota rejection was landing in spend_logs with `error_message=""` despite a fully populated traceback. Operator impact: dashboard "LLM Failure" rows became untriageable — the only way to tell a 401 from a 429 was to manually unpack the traceback JSON via psql. Burst failure patterns (e.g. a UI session polling with a stale token) produced 20-30 indistinguishable `error_code=401` rows per second. Fix: prefer the `.message` attribute (set by ProxyException and every litellm.exceptions.* class) over `str(exc)`. The `str(exc)` fallback is retained for non-litellm exception types, preserving prior behavior. Test plan: - 2 new unit tests in tests/test_litellm/litellm_core_utils/ test_litellm_logging.py: * test_get_error_information_prefers_message_attribute_over_str * test_get_error_information_falls_back_to_str_when_no_message_attr - Existing test_get_error_information_error_code_priority still passes - End-to-end verified: bad-key 401 now stores full "Authentication Error, Invalid proxy server token passed..." message in spend_logs.metadata.error_information.error_message * fix(spend-logs): preserve explicit empty .message + drop dead reference Greptile P2 on #30381. The truthiness check `if message_attr:` silently skipped an explicit empty-string `.message` and fell through to `str(original_exception)`. For ProxyException-shaped objects both produce empty, so the bug was latent; for other exception types it would inject a different string into error_information.error_message and corrupt the signal. Use `is not None` so an empty string survives verbatim. Also drop the stale `See e2e/cases/11.` comment reference — that path does not exist anywhere in the repo and confuses future readers. Regression test added: an exception with `.message=""` and a non-empty `super().__init__()` arg must yield error_message == "". * ci: retrigger workflows after base branch change to litellm_internal_staging * fix(anthropic): strip LiteLLM-injected total_tokens from /v1/messages response (#30382) * fix(anthropic): strip LiteLLM-injected total_tokens from /v1/messages response The non-streaming /v1/messages response carries a LiteLLM-injected usage.total_tokens = input_tokens + output_tokens that is not part of the Anthropic API spec. This caused three problems: 1. Shape divergence with streaming on the same endpoint. message_delta.usage in the SSE path never carries total_tokens. Clients parsing both paths get two different schemas from one endpoint. 2. Shape divergence with upstream. Direct calls to https://api.anthropic.com/v1/messages return no total_tokens field, so clients using the official Anthropic SDK couldn't rely on it, and clients that did rely on the LiteLLM-injected one broke when bypassing the proxy. 3. Numerical misuse. total = input + output undercounts when cache_read_input_tokens and cache_creation_input_tokens are non-zero, because cache tokens are reported in their own fields. A 100k-token cached prompt with 1 non-cache input token + 200 output tokens reports total_tokens = 201, off by ~99.8% from any reasonable definition of "total." Fix: add _strip_total_tokens_from_anthropic_response in litellm/proxy/anthropic_endpoints/endpoints.py and invoke it in the success path of anthropic_response right before returning. Only mutates dict-shaped responses; streaming (which already lacks the field) is left untouched. spend_logs / Prometheus continue to compute total_tokens internally for billing — this fix only strips the field from the wire response. Scope: only the Anthropic passthrough endpoint /v1/messages. The OpenAI-shape /v1/chat/completions is unaffected. * fix(anthropic): gate total_tokens strip behind flag + handle Pydantic .usage Two P1 greptile threads on #30382: P1 — **Backwards-incompatible removal without a feature flag** Stripping `usage.total_tokens` unconditionally breaks any client currently reading the LiteLLM-shaped non-streaming /v1/messages response. Per the codebase's policy (mirrors #30418), gate behind a new flag. - `litellm.strip_anthropic_total_tokens: bool = False` (default — backward-compat: clients keep seeing total_tokens). - Env override: `LITELLM_STRIP_ANTHROPIC_TOTAL_TOKENS=true`. - Docstring: planned to flip to True in a future major release; opt in early. P1 — **Silent no-op if `result` is a Pydantic model** `base_process_llm_request` may return a Pydantic-style object whose `.usage` is a plain dict (the most common shape — e.g. objects wrapping raw upstream JSON). The original `isinstance(response, dict)` guard skipped strip on those, so `total_tokens` would still hit the wire. Helper now also reads `getattr(response, "usage", None)` and strips when that's a dict. Strongly-typed Pydantic `Usage` sub-models with required `total_tokens` fields are still skipped — those impose type constraints the helper doesn't try to subvert. Tests: - `test_strips_total_tokens_on_pydantic_model_with_dict_usage` - `test_flag_defaults_off` 8/8 pass locally. * fix(anthropic): drop env var for strip flag (docs CI) Mirrors #30418's pattern (`expose_router_debug_in_errors: bool = True`, no `os.getenv`). The `LITELLM_STRIP_ANTHROPIC_TOTAL_TOKENS` env var introduced in the prior commit was flagged by `tests/documentation_tests/test_env_keys.py` because the documentation file `docs/my-website/docs/proxy/config_settings.md` lives in `BerriAI/litellm-docs` (separate repo) and registering a new env key requires a parallel docs PR — a friction we avoid here by exposing the flag only as a Python attribute + `litellm_settings` config key, both of which load through the existing proxy config plumbing without needing the env-var registry to be updated. No semantic change: default still False, behavior identical when set via `litellm.strip_anthropic_total_tokens = True` or `litellm_settings.strip_anthropic_total_tokens: true` in config.yaml. Verified locally: env scan no longer surfaces the key; 8/8 tests pass. * ci: retrigger workflows after base branch change to litellm_internal_staging * fix(pricing): correct swapped input/output token costs for command-r7b-12-2024 (#30413) * fix(pricing): correct swapped input/output token costs for command-r7b-12-2024 * test: resolve model prices JSON relative to test file for pip installs * fix(exception-mapping): map Gemini upstream-error body code 429 to RateLimitError (#30417) * fix(exception-mapping): map Gemini upstream-error body code 429 to RateLimitError Some Gemini-compatible gateways (e.g. new-api) wrap a 429 rate-limit signal from upstream inside an HTTP 500/503 envelope, with the real code only surfaced in the JSON body: {"error":{"message":"...high demand...","type":"upstream_error", "param":"","code":429}} Previously LiteLLM only looked at the HTTP status and mapped this to InternalServerError, which Router treats as non-retryable for many configs — so users got hard 500s instead of fallback/retry. Now the Gemini/Vertex exception mapper parses error.code from the body and routes code 429 to RateLimitError before falling through to the HTTP-status branches. Other body codes fall through unchanged. Tests cover: - new-api gateway's `code:429` payload now maps to RateLimitError - Genuine 500-body responses stay InternalServerError - Non-JSON body strings fall through to status-code mapping unchanged * fix(exception-mapping): scope body-code 429 promotion to 5xx envelopes Addresses greptile P1/P2 + @Sameerlite's review on #30417. The new elif branch was firing for any HTTP status, so a gateway response of HTTP 400 with body {"error":{"code":429,...}} would be incorrectly promoted to RateLimitError (retryable) instead of falling through to BadRequestError. Same trap for 401 -> AuthenticationError. Scoped the body-code 429 check to `500 <= status_code < 600` — covers 500/502/503/504 (gateways wrapping upstream 429 in any 5xx envelope) without inviting the 4xx misclassification. Tests: parametrized table now covers 5xx (500/502/503), 4xx (400/401), and the existing fall-through cases, asserting each maps to the exception type that matches the HTTP status code. 50/50 pass locally. * ci: retrigger workflows after base branch change to litellm_internal_staging * feat(router): add expose_router_debug_in_errors flag (default True) to redact internal model_group/fallback names (#30418) * feat(router)!: redact internal model_group/fallback names from exception messages The Router was unconditionally appending internal config names onto exception.message: - "Received Model Group=..." - "Available Model Group Fallbacks=..." - "No fallback model group found... Fallbacks={...}" - "context_window_fallbacks={...}" - Deployment-timeout messages including model_group - Fallback failure detail listing fallback chain ProxyException forwards .message verbatim to clients, so gateways were leaking their model_name / fallback wiring in every failed call. Fix: gate all five mutation sites on a new `litellm.expose_router_debug_in_errors` flag (default False). Set to True to restore upstream debug behavior for local debugging. Why: matches the redaction posture this codebase already has for upstream model identifiers (cf. _litellm_returned_model_name) and removes the last common error-path leak of internal model_group names. Breaking change marker (!): if anything parses "Received Model Group=" out of client error messages, flip the flag on or migrate to the x-litellm-* response headers instead. Tests: 7 cases covering each of the 5 redaction sites + the flag-on inverse path, plus a "default off" sanity check. * test(router): cover sites 1 + 3 of expose_router_debug_in_errors gate Addresses Greptile / codecov feedback on #30418: patch coverage was 55.6% with 4 lines uncovered in litellm/router.py. The existing tests exercised sites 2 (ContextWindowExceededError), 4 (no-fallback-found), and 5 (Received Model Group) — both default and flag-on. Sites 1 and 3 were declared in the PR description as covered by "site 5 also fires" but the gate body lines for each (the `e.message +=` inside the `if litellm.expose_router_debug_in_errors:` branch) only execute when the flag is on AND the specific exception path is taken, which neither existing test triggered. Added 4 new tests (default + flag-on × 2 sites): - test_default_does_not_leak_deployment_timeout_debug - test_flag_on_leaks_deployment_timeout_debug - test_default_does_not_leak_content_policy_fallback_hint - test_flag_on_leaks_content_policy_fallback_hint Trigger details: - Site 1 (litellm.Timeout in _acompletion) is reached via the Router-supported `mock_timeout=True` + `timeout=0.001` kwargs on `acompletion(...)`. Cannot embed a Timeout instance in model_list because Router.__init__ deep-copies it and Timeout.__reduce__ does not preserve the required positional args. - Site 3 (ContentPolicyViolationError without content_policy_fallbacks set, in async_function_with_fallbacks_common_utils) is reached by passing a `mock_response=litellm.ContentPolicyViolationError(...)` instance via the call-site kwarg — same deepcopy-avoidance reason. 11/11 tests pass locally. Patch coverage on litellm/router.py for this PR's diff should now be 100%. * chore(router): flip expose_router_debug_in_errors default to True Addresses @Sameerlite's review on #30418 — maintain backward compat on the wire. Redact becomes opt-in via setting the flag to False; the historical behavior (leak internal model_group / fallback wiring through exception messages) is preserved as the default. - litellm/__init__.py: default flipped to True, docstring rewritten with deprecation note pointing at a future flip to False (redact by default) in a major release. - tests/test_litellm/test_router_exception_redaction.py: fixture resets to True (was False); the "off" tests now explicitly set False; the "default_leaks_*" tests rely on the fixture default. test_flag_defaults_off -> test_flag_defaults_on. - No router.py change needed; the gate keys off the same flag, only the default changes. - PR title no longer needs the breaking-change `!` marker — no client sees a behavior change at default settings. 11/11 pass locally. * ci: retrigger workflows after base branch change to litellm_internal_staging * feat(guardrails): integrate Repelloai Argus guardrail (#30465) * feat(guardrails): add RepelloAI Argus guardrail integration (#1) * feat(guardrails): add RepelloAI Argus guardrail integration Add a new guardrail hook backed by RepelloAI Argus, with dashboard-managed asset policies enforced via an asset_id and X-API-Key auth. * fix(guardrails): harden RepelloAI Argus guardrail - scan streaming responses on output (was bypassing the guardrail) - log blocked verdicts as guardrail_intervened instead of success - treat auth/config errors (401/403/404/422) as misconfiguration that always blocks, not a fail-open-able unreachable error - default unreachable_fallback to fail_closed and read it directly; block on unknown/malformed verdicts so an API change can't silently disable enforcement - type unreachable_fallback as a Literal, drop the duplicate config model, expose unreachable_fallback in the config schema, and stop leaking the raw provider response / exception strings to the client * fix(guardrails): address RepelloAI Argus review feedback - support ARGUS_API_KEY (with REPELLOAI_API_KEY fallback) - make asset_id required in the config model - normalize unreachable_fallback so only fail_open opens; block on 400 misconfig - correct the shared unreachable_fallback field description * docs(guardrails): add RepelloAI Argus docs page and dashboard listing - add docs page covering config, env vars, modes, verdicts, failure semantics - list RepelloAI Argus in the Guardrail Garden with provider/logo mappings - add a regression test for the provider logo and display-name resolution * fix(guardrails): keep RepelloAI asset_id optional in config model A required asset_id leaked onto the shared LitellmParams (which inherits RepelloAIGuardrailConfigModel), breaking validation for every other guardrail. Keep it optional like sibling models; the guardrail __init__ still raises when asset_id is missing, which is the real enforcement. * Add comment for last user turn scanning * feat(guardrails): harden repelloai scanning * feat(guardrails): expand repelloai scanning to include tool definitions Add extraction of tool definitions and tool call arguments to the RepelloAI guardrail scanning. Improves detection coverage by including function schemas and parameters in the prompt sent to the guardrail service. Also captures detailed error responses in logs and adds guardrail header to streaming responses. * refactor(guardrails): fix and harden repelloai schema text extraction - Fix duplicate text in _iter_schema_text: previously all dict values were re-queued onto the stack even after scalar/list keys were already extracted explicitly, causing names/descriptions to appear twice in the scanned prompt - Extract schema key frozensets to module-level constants so they are not reconstructed on every call - Change _iter_schema_text from @classmethod to @staticmethod (cls unused) - Narrow _call_analyze stage param from str to Literal["prompt", "response"] - Add HttpxResponse type annotation to _raise_for_config_error - Add LLMResponseTypes annotation to async_post_call_success_hook response param * fix(guardrails): resolve pyright type errors in repelloai guardrail - Narrow async_handler.post return from Response|None to Response with explicit None guard before calling raise_for_status/json - Fix list comprehension returning str|None by switching to explicit loop with isinstance guard so pyright tracks the narrowing - Cast model_dump() result to Dict since hasattr does not narrow object type in pyright * fix(guardrails/repello): include Responses API instructions field in prompt scan The /v1/responses top-level `instructions` field was not included in _extract_prompt_text, allowing a caller to bypass guardrail policy checks by putting blocked content in `instructions` while keeping `input` benign. * feat: add api_key to config model and read prompt from data dict * fix(guardrails/repello): plug input_text and tool-call response bypass gaps Responses API input content parts with type 'input_text' were silently dropped by build_inspection_messages (which only handles type='text'), allowing callers to send blocked content via that path without triggering the pre-call scan. Fix: add _extract_input_text_parts to RepelloAIGuardrail and call it when walking the Responses API input messages. Post-call scanning skipped responses whose choices contained only tool_calls or function_call (message.content=None), letting models put blocked output in function arguments undetected. Fix: _extract_chat_completion_text now calls _extract_tool_call_args_from_message on each choice message. Also replace typing.Dict/List with builtin dict/list to clear TID251 strict ruff violations introduced by this file. * fix(guardrails/repello): scan Responses API function_call output arguments Output items with type 'function_call' in a /v1/responses response were skipped by _extract_responses_api_text; only 'message' items were walked. A model could return blocked content in function_call.arguments undetected. Now extract arguments from function_call output items before scanning. * fix(anthropic): drop orphaned server_tool_use on multi-turn replay from generic OpenAI clients (#30486) * fix(anthropic): drop orphaned server_tool_use on multi-turn replay from generic OpenAI clients When an Anthropic server-side tool (web_search, id `srvtoolu_...`) is used, its result is carried in `provider_specific_fields.web_search_results` — PRs #17746 / #17798 restore it for callers that round-trip provider_specific_fields. A generic OpenAI client that does NOT preserve provider_specific_fields (e.g. Open WebUI talking to a Vertex/Anthropic model over /chat/completions) drops it on replay and instead sends back an assistant `tool_call` + a `tool` message both keyed to the `srvtoolu_` id. The transform then produced a bare `server_tool_use` (with no following *_tool_result) plus a user `tool_result` for the same id — both invalid, so the next turn 400s: messages.N.content.0: unexpected `tool_use_id` found in `tool_result` blocks: srvtoolu_... Each `tool_result` block must have a corresponding `tool_use` block in the previous message. This is the commonly-reported vertex_ai symptom where Gemini works but Claude 400s on the 2nd turn of a web-search chat. Fix (litellm/litellm_core_utils/prompt_templates/factory.py): - convert_to_anthropic_tool_invoke: only emit a server_tool_use when its matching *_tool_result is available to pair with it; otherwise skip it (a bare server_tool_use is itself rejected). - anthropic_messages_pt: drop a replayed `tool`/`function` message whose tool_call_id starts with `srvtoolu_` (a server-executed tool produces no client result; a user tool_result for it is invalid). The existing reconstruction path (provider_specific_fields present, e.g. the litellm SDK) is unchanged, as is regular client tool_use/tool_result. Tests (tests/llm_translation/test_prompt_factory.py): - update test_convert_to_anthropic_tool_invoke_server_tool -> test_convert_to_anthropic_tool_invoke_server_tool_without_result_is_dropped - add test_anthropic_messages_pt_generic_client_drops_orphan_server_tool Follow-up to #17746 / #17798; addresses the generic-client (no provider_specific_fields) case of #17737. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * test(anthropic): cover the srvtoolu_ round-trip fix in the test_litellm unit suite The regression tests added in tests/llm_translation/test_prompt_factory.py aren't run by the coverage CI job (it runs tests/test_litellm), so the new factory.py branches showed as uncovered (codecov patch coverage). Add equivalent focused tests in the unit suite so both new branches are exercised there: - convert_to_anthropic_tool_invoke drops a srvtoolu_ server_tool_use when no matching *_tool_result is available. - anthropic_messages_pt drops the orphaned srvtoolu_ tool message a generic OpenAI client replays. Refs #17737 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * test(anthropic): cover the server_tool_use + result valid-pair path in unit suite Covers the remaining patch-coverage lines codecov flagged: convert_to_anthropic_tool_invoke emitting server_tool_use followed by its web_search_tool_result when the matching result is present (the litellm-SDK round-trip path). Refs #17737 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * style(anthropic): flatten srvtoolu_ tool-message guard to a negated if Addresses the Greptile style nit: replace the if-pass/else with a single negated `if not (...)` guard around the tool_result append. Behavior unchanged. Refs #17737 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(proxy): require premium only when enabling premium metadata fields (#30285) (#30506) Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(perplexity): stop double-billing reasoning tokens in manual cost fallback (#30488) * fix(perplexity): stop double-billing reasoning tokens in manual cost fallback When perplexity_cost_per_token cannot use the API-provided usage.cost.total_cost short-circuit and falls back to manual calculation, it multiplies the full usage.completion_tokens by output_cost_per_token and then adds reasoning_tokens * output_cost_per_reasoning_token on top. Per the OpenAI/Perplexity usage convention codified for the central path in PR #18607, completion_tokens already INCLUDES reasoning_tokens, so the manual fallback double-bills reasoning at both the output and reasoning rate. Concrete impact on perplexity/sonar-deep-research (input 2e-6, output 8e-6, reasoning 3e-6): for the exact usage shape exercised by the live response fixture in tests/llm_translation/test_perplexity_reasoning.py (prompt_tokens=9, completion_tokens=20, reasoning_tokens=15) the current code charges 0.000223 vs the convention-correct 0.000103, a 2.165x overcharge. The bug is reachable whenever Perplexity omits the cost object (streaming chunks, fixture-driven paths, older API versions). Subtracts reasoning_tokens (clamped at zero) from completion_tokens before applying the output rate, mirroring how dashscope/cost_calculator.py and the central generic_cost_per_token already handle it. Preserves the existing fallback behaviour when output_cost_per_reasoning_token is unset (all completion_tokens stay at the output rate). Existing tests in tests/test_litellm/llms/perplexity/test_perplexity_cost_calculator.py asserted the buggy math and are updated to the convention-correct math. Adds a focused regression test using the exact usage shape from the live response fixture so this class of bug cannot be silently reintroduced. * style(perplexity): drop redundant type annotation on else branch to satisfy mypy mypy [no-redef] flagged 'completion_cost' as declared in both if and else arms; keeping the annotation only on the first declaration matches existing patterns in this file. * fix(perplexity): update integration test expected costs for non-double-billed math Three tests in test_perplexity_integration.py asserted the old buggy expectation that reasoning_tokens are billed in addition to the full completion_tokens count. After the fix in cost_per_token, reasoning_tokens are billed at the reasoning rate and the remaining (completion_tokens - reasoning_tokens) at the standard output rate, matching OpenAI/Perplexity convention (PR #18607). Updates: test_end_to_end_cost_calculation_with_transformation, test_main_cost_calculator_integration, test_high_volume_cost_calculation. The high-volume sanity threshold drops to 0.25 to reflect the corrected total. * fix(ui): use dynamic proxy base URL in MCP usage examples (#30487) Replace hardcoded http://localhost:4000 with getProxyBaseUrl() in the MCP server usage example and copy-to-clipboard snippet so the generated configuration works for non-local deployments. Fixes #30466 * feat: add missing UK PII entity types to Presidio guardrail (#30537) * feat: add missing UK PII entity types to Presidio guardrail Add UK_PASSPORT, UK_POSTCODE, and UK_VEHICLE_REGISTRATION to PiiEntityType enum and PII_ENTITY_CATEGORIES_MAP. These entity types are supported by Microsoft Presidio but were missing from litellm's type definitions, preventing users from configuring UK-specific PII detection. * test: remove fragile hardcoded entity count test Remove test_uk_category_entity_count which hardcodes len() == 5. The test_uk_entities_match_presidio_recognizers test already verifies exact set equality, making the count test redundant and fragile to future Presidio additions. * style: apply Black formatting to match CI requirements * fix: route volcengine (Doubao) tiered-pricing models to the tiered cost handler (#30357) Volcengine (Doubao) models define `tiered_pricing` but no flat per-token cost, so cost_per_token fell through to generic_cost_per_token (which only reads flat costs) and tracked them at $0 Route custom_llm_provider == "volcengine" to the shared tiered-pricing handler in litellm/llms/dashscope/cost_calculator.py, which already computes graduated tier costs. Make that handler provider-agnostic by adding a custom_llm_provider argument (default "dashscope" preserves existing behavior) so get_model_info resolves the correct model map entry Fixes #30346 * feat(mcp): make MCP gateway name and description configurable via env vars (#30473) * feat(mcp): make MCP gateway name and description configurable via env vars * Rename function _restore_env to _apply_env * docs(mcp): document import-time capture of env-backed identity constants Address Greptile review feedback: clarify that LITELLM_MCP_SERVER_NAME and LITELLM_MCP_SERVER_DESCRIPTION are read once at import and require a module reload to observe env changes after import. Generated with AI assistance Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: Yevhen Luhovtsov <yevhen.luhovtsov@intapp.com> Co-authored-by: Claude <noreply@anthropic.com> * fix(mcp): preserve native tools in semantic filter hook (#26650) * fix(mcp): preserve native tools in semantic filter hook The SemanticToolFilterHook.async_pre_call_hook passed ALL tools (MCP + native) to filter_tools(), which only knows MCP-registered tool names. Native tools silently failed the name match in _get_tools_by_names() and were dropped from the request. Fix: partition tools into native and MCP-registered before filtering. Run the semantic filter only on MCP tools, then merge native tools back unconditionally. Changes: - Robust _is_mcp_tool() using shape-based detection for OpenAI-format dicts, safe regardless of future _extract_tool_info changes - Single-pass partition loop (no double _is_mcp_tool calls) - Preserve native tools in MCP expansion path (mixed requests) - Track MCP expansion to prevent expanded tools bypassing filtering - filter_stats reports MCP-only counts for accurate metrics - Extracted _emit_filter_metadata() helper - Skip spurious filter headers for all-native tool requests Closes #26212 * remove stale docstring note referencing tools_expanded_from_mcp * fix: handle Responses API name collision and preserve tool ordering - Classify Responses API tools ({type: 'function', name: '...'}) as native to prevent name collisions with MCP canonical names - Preserve original request tool ordering using id()-based merge instead of naive native+mcp concatenation - Add 2 regression tests: name collision and ordering preservation * style: apply black formatting * fix(mcp): harden semantic filter — preserve all native tool formats, safe metadata access, graceful expansion failure, name-based merge * lint: suppress PLR0915 on async_pre_call_hook (matches codebase convention) * ci: retrigger checks after rebase onto litellm_internal_staging * feat(fireworks): sync Fireworks AI model registry with current platform catalog (#30616) Adds 12 new Fireworks serverless models and updates 3 existing entries in model_prices_and_context_window.json and its bundled backup to match the current Fireworks platform model list. New direct models: glm-5p2, qwen3p7-plus, minimax-m3, minimax-m2p7, kimi-k2p7-code, kimi-k2p6, deepseek-v4-pro, deepseek-v4-flash. New router endpoints: glm-5p1-fast, kimi-k2p6-fast, kimi-k2p7-code-fast. Updated: glm-5p1, gpt-oss-120b, and gpt-oss-20b now carry correct output token caps, cache-read pricing, and explicit capability flags max_tokens is set equal to max_output_tokens (not the full context window) for models whose generation cap is below their context window. This avoids the shared input+output budget path in get_modified_max_tokens, which would otherwise let callers request output sizes the model cannot produce. The same fix corrects the pre-existing glm-5p1, gpt-oss-120b, and gpt-oss-20b entries that had max_tokens equal to the full context window Short-form aliases (fireworks_ai/<model>) are added for every direct accounts/fireworks/models/ entry so cost attribution works for callers using bare model names. Router endpoints get short-form aliases too, and transform_request now routes bare names ending in -fast to the accounts/fireworks/routers/ path instead of defaulting every bare name to models/. This keeps the kimi-k2p6-fast router from being misrouted to the nonexistent models/kimi-k2p6-fast endpoint kimi-k2p6-turbo is intentionally excluded; kimi-k2p6-fast is its replacement. Context windows for deepseek-v4 and kimi models use the power-of-two values (1048576 and 262144) published on the Fireworks model pages, matching the convention already used by existing entries Two regression tests in test_utils.py assert the exact per-token costs, token limits, capability flags, and short-form-to-long-form equality for all 15 models against both the main and backup cost maps. Two routing tests in test_fireworks_ai_chat_transformation.py verify bare -fast names route to routers/ and bare direct-model names route to models/ * fix(bedrock): handle role:"system" inside the messages array on /v1/messages (#29698) (#30443) * feat(anthropic): hoist leading in-array system to top-level (helper) * test(anthropic): cover _system_content_to_blocks edge cases; deepcopy cache_control * test(anthropic): mid-conversation system normalization cases * feat: add supports_mid_conversation_system flag to Claude Opus 4.8 Add supports_mid_conversation_system: true to all 9 claude-opus-4-8 cost-map entries (Anthropic-native, Bedrock, Vertex, Azure AI) in both the root cost map and the bundled package backup, since the runtime helper and tests read the backup in local/offline mode. Pin the mid-system passthrough regression test to the local cost map via the existing local_model_cost_map fixture so it reads the branch-local flag rather than the network-fetched main copy. * fix(bedrock): normalize in-array system in /v1/messages handler (#29698) Wire normalize_system_messages_for_anthropic into anthropic_messages_handler so all Bedrock /v1/messages paths (Invoke / Mantle / ClaudePlatform / Converse-bridge) hoist leading in-array system entries (and demote mid-conversation ones on models lacking supports_mid_conversation_system) into the top-level system field. The normalized messages/system are written back into the local_vars snapshot the base_llm branch reads from, otherwise the Invoke/Mantle fix would silently no-op. Also fix the helper to resolve supports_mid_conversation_system through the prefix-aware AnthropicModelInfo._supports_model_capability resolver. The raw _supports_factory could not see the flag once get_llm_provider left the invoke/ prefix on the model id, which would have wrongly demoted mid-conversation system on a Bedrock invoke opus-4-8 path. * fix(bedrock): resolve mid-conversation-system flag through mantle/invoke/converse route prefixes; drop unused param * fix(types): widen system param to Union[str, List] for hoisted system blocks * refactor(bedrock): drop dead local_vars messages writeback * fix(bedrock/converse): translate in-array system in anthropic->openai adapter (#29698) * fix(bedrock/converse): preserve cache_control on in-array system; test drop-empty * fix(bedrock/converse): rename colliding local to satisfy mypy; test handler system-merge branches * fix(types): register supports_mid_conversation_system in model-info schema The cost-map JSON-schema validation test (test_aaamodel_prices_and_context_window_json_is_valid) rejects unknown properties, so adding supports_mid_conversation_system to the opus-4-8 cost-map entries failed CI with 'Additional properties are not allowed'. Register the flag in the INTENDED_SCHEMA allow-list and in the ProviderSpecificModelInfo TypedDict so it is a typed, first-class capability flag alongside its peers (supports_output_config, etc.). --------- Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(bedrock/agentcore): optionally forward multimodal content blocks in InvokeAgentRuntime payload (#28885) * fix(bedrock/agentcore): optionally forward multimodal content blocks in InvokeAgentRuntime payload By default the agentcore provider flattens the last message to a text-only {"prompt": "..."} payload via convert_content_list_to_str, silently dropping OpenAI multimodal blocks (image_url, file, input_audio, ...). This adds an opt-in `forward_multimodal_content` litellm param. When truthy and the last message's content is a list containing a non-text block, the original OpenAI content list is forwarded verbatim under a new "content" field so an attachment-aware AgentCore agent can read it. Default off keeps the payload byte-identical to the legacy {"prompt": "..."} shape — existing agents are unaffected. The flag is read from optional_params (where other AgentCore params land) with a litellm_params fallback, and accepts a bool or a config/env string ('true', '1', ...). AgentCore Runtime is schemaless on the agent side — the agent's @app.entrypoint parses arbitrary JSON up to 100 MB (per https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/runtime-invoke-agent.html), so this is a purely upstream change; no AgentCore-side schema is asserted. * fix(bedrock/agentcore): shallow-copy forwarded multimodal content list Address review feedback (Sameerlite): payload["content"] = last_content aliased the caller's mutable messages[-1]["content"] list. Harmless today because the payload is JSON-serialized immediately, but a latent footgun if a future caller mutates the returned payload before serialization. Forward list(last_content) so the payload owns its own list. Block dicts stay shared on purpose — a deep copy would clone potentially large base64 media on the request hot path, and the flagged risk was the shared list, not the blocks. Update the passthrough tests to assert equality + distinct identity, and add a regression test that mutating the payload list can't leak back into the original message content. * Revert "fix(mcp): preserve native tools in semantic filter hook (#26650)" This reverts commit |
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Sameer Kankute
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chore(oss): litellm oss staging 120626 (#30292)
* feat(bedrock): add bedrock mantle gemma 4 models (#30264) * feat(bedrock): add bedrock mantle gemma 4 models * test(bedrock): harden mantle local cost fixture * feat(responses): enable the responses API for the Tensormesh provider (#30209) * feat(responses): enable the responses API for the Tensormesh provider * Update litellm/llms/openai_like/providers.json Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> --------- Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * fix(langfuse_otel): mark LLM spans as generations (#30250) * fix(bedrock): stop stream_chunk_size leaking into invoke request bodies (#30240) stream_chunk_size is a LiteLLM-internal knob for re-chunking the HTTP response stream. The invoke transformations splat optional_params into the provider request body without dropping it, and Bedrock rejects unknown fields, so any bedrock/invoke request that sets the parameter fails with ValidationException: stream_chunk_size: Extra inputs are not permitted. Drop it in the invoke dispatcher (covers cohere, titan, mistral, meta, ai21) and in the Claude messages-format request builder (the route used for bedrock/invoke Anthropic models) * fix(bedrock): stop buffering streamed tool-call argument deltas (#30231) * fix(bedrock): stop buffering streamed tool-call argument deltas Two issues made Bedrock tool-use streaming arrive as a single end-of-stream burst through LiteLLM while plain text streamed fine. First, the anthropic-beta allowlist mapped fine-grained-tool-streaming-2025-05-14 to null for bedrock and bedrock_converse, so the header was silently stripped. Without that beta, Anthropic models on Bedrock buffer tool input server-side and emit all toolUse.input deltas at once (verified against converse-stream and invoke-with-response-stream directly). Bedrock accepts the beta via additionalModelRequestFields.anthropic_beta, so it is now forwarded. Second, the streaming reads re-chunked the AWS event stream with iter_bytes(chunk_size=1024). httpx's ByteChunker only releases full 1024-byte blocks, so the small early events (messageStart, contentBlockStart, first deltas) sat in the buffer until enough bytes accumulated, pushing time-to-first-byte from ~1.4s to ~8.5s on buffered tool-use streams. The default is now no re-chunking; an explicit stream_chunk_size is still honored. * test(bedrock): cover explicit stream_chunk_size on sync invoke path * test(bedrock): cover stream_chunk_size plumbing through converse completion * test(bedrock): cover stream_chunk_size default in legacy BedrockLLM streaming * test(bedrock): merge converse handler tests into existing mapped test file pytest imports test modules by basename in non-package test dirs, so the new tests/test_litellm/llms/bedrock/chat/test_converse_handler.py collided with the pre-existing tests/test_litellm/llms/chat/test_converse_handler.py and broke collection in CI. Move the new tests into the existing file * feat(otel): emit v2 cost breakdown + stamp tracer scope version (#30156) Read the StandardLoggingPayload cost_breakdown into a typed LLMCost on LLMCallSpanData and emit each component under litellm.cost.* (absent components omitted, so spans stay sparse). Stamp litellm.__version__ as the instrumentation scope version so every v2 span carries a deterministic scope.version. Tests under tests/test_litellm/integrations/otel/. * fix(proxy): cancel in-flight upstream LLM request on client disconnect (opt-in) (#30223) * fix(proxy): cancel in-flight upstream LLM request on client disconnect (opt-in) On the non-streaming path, base_process_llm_request awaited the LLM call with no disconnect monitoring; when the HTTP client went away the upstream request kept running until completion or request_timeout (6000s default), holding a backend slot (e.g. a vLLM GPU slot) for output nobody would read Add an opt-in general_settings.cancel_on_disconnect flag, default off, so the default code path is unchanged. When enabled, a receive-based watcher task observes http.disconnect and cancels the asyncio.gather driving the upstream call. The resulting CancelledError is converted to HTTPException 499 only when the disconnect event is set, so server-initiated cancellations still propagate as-is. The 499 then flows through _handle_llm_api_exception like any other failure, meaning post_call_failure_hook still releases max_parallel_requests slots and fires spend and alerting callbacks; it is logged at info level instead of a full traceback Also removes the dead check_request_disconnection helper in proxy_server.py (zero call sites) along with its behavior-pin tests Builds on the receive-based design from #25776 Addresses #13774. Re-fixes #22805 (regressed after the #14295 revert) Co-authored-by: CreateRandom <18438707+CreateRandom@users.noreply.github.com> * fix(proxy): scope 499 quiet logging to disconnects and harden watcher Address the two P2 findings from the Greptile review on #30223. The info-level logging in _log_llm_api_exception now applies only to the disconnect-specific HTTPException (status 499 plus the shared _CLIENT_DISCONNECT_DETAIL message), so any other 499 raised by hooks or guardrails keeps its full traceback. The disconnect watcher now catches exceptions from request.receive() (e.g. a transport reset) and logs a warning instead of dying silently, making the degradation to no-op visible; a test pins that the LLM call is not cancelled in that case --------- Co-authored-by: kursad <kursad.lacin@brado.net> Co-authored-by: CreateRandom <18438707+CreateRandom@users.noreply.github.com> * fix(bedrock): grant aws-external-anthropic:* in OIDC session policy for claude_platform (#30200) (#30205) The inline STS session policy passed to assume_role_with_web_identity acts as an IAM PERMISSION CEILING — effective permissions are the intersection of the role's identity policies and this policy. Any action not listed is silently denied even when the IAM role grants it. #27678 added the bedrock/claude_platform/<model> route but its service-side action namespace is aws-external-anthropic:*, not bedrock:*. Without a matching statement here, every claude_platform request via OIDC (GCP federation, EKS Pod Identity webhook, etc.) 403s with 'no session policy allows the aws-external-anthropic:CreateInference action' — even with a fully permissive identity policy. Add a second ClaudePlatformLiteLLM statement covering CreateInference, CreateBatchInference, CancelBatchInference, DeleteBatchInference, CountTokens, Get*, List*. Keep aws:SecureTransport=true parity with the bedrock statement. Static creds + IRSA flow through different code paths and are not affected. Fixes #30200 * fix(proxy): set Retry-After header on RouterRateLimitError 429 responses (#30098) * Set Retry-After header on RouterRateLimitError responses When all deployments for a model are in cooldown, the proxy returns a 429 whose cooldown timing is only available by parsing the error message string. RouterRateLimitError already carries cooldown_time, so expose it as a standard retry-after header in _handle_llm_api_exception. The value is rounded up so clients never retry before the cooldown window ends. Fixes #27823. * Set Retry-After after response-headers hook so cooldown wins The cooldown-derived retry-after was assigned before the post_call_response_headers_hook merge, so a callback returning a retry-after key (including a stale or empty value) silently clobbered it. Move the RouterRateLimitError block after the callback merge so the cooldown value is authoritative for this error type. * fix(router): route aspeech through async_function_with_fallbacks (#30104) * fix(router): route aspeech through async_function_with_fallbacks Router.aspeech selected a deployment and awaited litellm.aspeech directly, so TTS requests got no retry on failure and no failover to backup deployments; the except block only fired an exception alert and re-raised. Every other router endpoint (acompletion, aembedding, atranscription, arerank) already delegates to async_function_with_fallbacks Mirror the atranscription pattern: move deployment selection and the litellm.aspeech call into a private _aspeech method, then have the public aspeech set kwargs["original_function"] = self._aspeech and await self.async_function_with_fallbacks(**kwargs). _aspeech also picks up the shared _get_async_openai_model_client helper and the same total/success/fail call accounting the sibling endpoints use Fixes #27778. * fix(router): apply deployment kwargs and rpm semaphore in _aspeech Bring _aspeech fully in line with _atranscription: call _update_kwargs_with_deployment so deployment metadata, model_info, timeout, and default litellm params flow into the request, and wrap the litellm.aspeech call with the max_parallel_requests semaphore plus async_routing_strategy_pre_call_checks so TTS respects rpm limits the same way the other router endpoints do Also add a unit test that exercises _aspeech directly and asserts the deployment metadata reaches the underlying call * fix(slack_alerting): stop false-positive hanging request alerts for requests below the alerting threshold (#30106) * fix(slack_alerting): skip hanging request alerts below the threshold The hanging request check alerted on any cached request whose completion status was not yet recorded, with no minimum age check. Since the background loop runs every alerting_threshold / 2 seconds, any request that happened to be in flight at a check fired a "hanging - Ns+ request time" alert even if it was only seconds old, producing a steady stream of false positives. Add a created_at timestamp to HangingRequestData, stamped when the request enters the hanging request cache, and skip requests younger than alerting_threshold without evicting them, so a later check can still alert if they never complete. Extend the cache TTL from threshold + 60s to 1.5x threshold + 60s; with the age check, entries only become alertable after threshold seconds, and the check period is threshold / 2, so the old TTL could evict a genuinely hanging request before any check saw it cross the threshold. Fixes #27855. * fix(slack_alerting): alert once per hanging request The min-age gate stops false positives for young in-flight requests, but a genuinely hanging request still re-alerted on every checker tick within the cache TTL. With the wider TTL (1.5x threshold + 60s) that is 1-2 extra Slack notifications per stuck request at the default 600s threshold. Flag a HangingRequestData entry as alerted once its alert fires and skip flagged entries on later ticks, so each hang produces exactly one alert. The cache reference is mutated in place, so the TTL is untouched and still handles cleanup. Adds a regression test asserting one alert across multiple ticks. Fixes #27855. * fix(health): treat all-proxy-models keys as unrestricted in /health (#30087) * fix(health): treat all-proxy-models keys as unrestricted in /health A key granted all model permissions stores the literal "all-proxy-models" marker in its models list. The /health access filter compared that marker against real model_names, so the model list filtered down to nothing and the WebUI health check returned healthy_count=0, unhealthy_count=0 with HTTP 503. Skip the filter (both the live path and the background-cache model_id scoping) when the marker is present, matching how auth_checks treats SpecialModelNames.all_proxy_models. Fixes #29744. * fix(health): resolve all-team-models sentinel to the team allowlist Same failure shape as the all-proxy-models case: a key carrying the literal "all-team-models" entry matches no real model_name, so the /health access filter would zero out the model list. Resolve the sentinel to the key's team models when team_id is set, matching get_key_models in model_checks.py. Without a team_id the sentinel stays unresolved and matches nothing, denying rather than widening access, mirroring _resolve_key_models_for_auth_check. * feat(proxy): auto-enable drop_params for Claude Code requests (#30218) * feat(proxy): auto-enable drop_params for Claude Code requests Claude Code identifies itself with a claude-cli/<version> user agent and sends Anthropic-specific params (top_k, thinking, etc.) on every request. When the proxy routes those requests to a non-Anthropic provider, the unsupported params fail the call unless drop_params is configured. Detect the Claude Code user agent in add_litellm_data_to_request and default drop_params to true for those requests, without overriding an explicit drop_params value sent by the caller. * feat(proxy): respect operator litellm_settings drop_params over Claude Code default An explicit drop_params in the operator's litellm_settings (true or false) now suppresses the Claude Code user agent default, so an operator who deliberately configured drop_params: false keeps strict param validation for Claude Code clients too. The auto-default only fills the gap when neither the request body nor the config sets a value. * fix(snowflake): migrate to native endpoints with auto-routing for Claude models (#29964) * fix(snowflake): migrate to native Cortex REST API endpoints Replaces the legacy /api/v2/cortex/inference:complete endpoint with the native OpenAI-compatible /api/v2/cortex/v1/chat/completions endpoint, fixing error 390142 (Incoming request does not contain a valid payload) when using model: snowflake/<model> in LiteLLM proxy. Changes: - litellm/llms/snowflake/chat/transformation.py: route to native /cortex/v1/chat/completions, remove Snowflake-specific tool_spec payload transformation, remove content_list response handling, add stream to supported params - litellm/llms/snowflake/anthropic/transformation.py (new): SnowflakeCortexAnthropicConfig routes Claude models to /cortex/v1/messages with anthropic-version header and Anthropic->OpenAI response transform - tests: 29 unit tests covering URL routing, auth headers, payload format, and response parsing * fix(snowflake): map max_tokens to max_completion_tokens for native endpoint * fix: handle multi-turn tool conversations and OpenAI→Anthropic tool format conversion - _extract_system_and_messages now preserves tool_calls from assistant messages and converts them to Anthropic tool_use content blocks - tool role messages are converted to user role with tool_result content blocks (as required by Anthropic Messages API) - Added _transform_tools_to_anthropic() to convert OpenAI tool format (type/function/parameters) to Anthropic format (name/input_schema) - Added comprehensive tests for multi-turn tool conversations Addresses review feedback on PR #29964 * test: add coverage for malformed JSON and non-string tool arguments * fix(tests): update chat transformation tests for native OpenAI-compatible endpoint * style: apply black formatting * fix: resolve mypy type errors in anthropic transformation * fix: correct mypy type: ignore error codes (attr-defined) * fix: use max_tokens instead of max_completion_tokens for Snowflake endpoint compatibility * refactor: merge Anthropic config into unified SnowflakeConfig with auto-routing - Remove separate SnowflakeCortexAnthropicConfig and anthropic/ directory - SnowflakeConfig now auto-routes based on model name: - Claude models → /messages endpoint (Anthropic format) - All others → /chat/completions endpoint (OpenAI format) - No new provider needed (stays as SNOWFLAKE = 'snowflake') - Tool message transformation for Claude: tool_calls → tool_use blocks, tool role → user with tool_result - OpenAI → Anthropic tool format conversion (parameters → input_schema) - Addresses Greptile feedback about unwired SnowflakeCortexAnthropicConfig * fix: use max_completion_tokens for /chat/completions (Snowflake deprecated max_tokens on this endpoint) * fix(tests): update assertions for Claude auto-routing to /messages endpoint * fix(snowflake): add tool_choice conversion and preserve max_completion_tokens in Anthropic path * fix(snowflake): use ChatCompletionMessageToolCall objects and strip model prefix on OpenAI path * fix(snowflake): collect multiple system messages to prevent guardrail override * chore: remove committed .pyc files and add __pycache__ to .gitignore * fix: remove unused Union import * fix: restore original .gitignore (accidentally replaced in earlier commit) * feat(snowflake): add streaming response handler for both Anthropic and OpenAI SSE formats * fix: remove unused AsyncIterator and Iterator imports * fix: add missing total_tokens to ChatCompletionUsageBlock * fix(snowflake): coalesce consecutive tool results into single user message for Anthropic * fix(snowflake): handle message_start event for streaming input_tokens tracking * fix: evict last deleted model in multi-instance deployments (#28608) * fix: evict last deleted model in multi-instance deployments _delete_deployment had an early return when db_models was empty, preventing eviction of the last deleted model during reconciliation. - Remove len(db_models)==0 early return from _delete_deployment - Return None (not []) from _get_models_from_db on DB failure so callers can distinguish a transient failure from a genuinely empty DB - Guard _update_llm_router against None to skip updates on DB failure Fixes #28443 * test: remove dead MagicMock assignment in type_mismatch test * fix: update test to pass [] not None to _update_llm_router test_ProxyConfig__update_llm_router_bad_proxy_logging_raises was passing None as new_models to get through to the proxy_logging_obj check, but the None guard we added now returns early before reaching that path. Pass [] instead so the test exercises the intended AttributeError case. Signed-off-by: Rudra Dudhat <contact.rdudhat@gmail.com> * chore: regenerate API types to sync schema.d.ts with proxy OpenAPI spec Signed-off-by: Rudra Dudhat <contact.rdudhat@gmail.com> --------- Signed-off-by: Rudra Dudhat <contact.rdudhat@gmail.com> * fix: invalidate Redis spend counter on /key/reset_spend (#29694) * fix: set Redis spend counter to reset_to value on /key/reset_spend Previously, the Redis spend counter was always set to 0.0 after a reset, even when reset_to was a non-zero value (partial reset). This caused the budget to be under-enforced for up to 60 seconds until the counter expired and fell through to the DB. Now the counter is set to the actual reset_to value, so partial resets are reflected correctly and budget enforcement is consistent. * test: update reset_key_spend test to match direct cache set The implementation now sets spend_counter_cache directly instead of calling _invalidate_spend_counter. Update the test to verify the in_memory_cache.set_cache call with the correct key, value, and ttl. --------- Co-authored-by: michaelxer <michaelxer@users.noreply.github.com> * fix: add scaleway models pricing (#27659) * fix: Add embeddings support for Scaleway provider * fix: resolve merge conflicts * fix(main): clarify backend route handling for Swagger static assets (#30196) * fix(main): clarify backend route handling for Swagger static assets * fix(allowlist): add BACKEND_MOUNT_PATHS for Swagger static assets * fix(voyage): route multimodal embeddings to correct endpoint (#30193) * fix(voyage): route multimodal embeddings to correct endpoint * test(voyage): cover multimodal embedding edge cases * test(voyage): cover api key fallback * fix(voyage): raise early on missing api key and malformed image url * test(voyage): cover utils routing and helper * fix(voyage): route supported openai params for multimodal models * style: apply black formatting * fix(ui): infer Azure API version from API base (#30204) * fix(ui): infer Azure API version from API base * fix(ui): address Azure API version feedback * Update litellm/llms/snowflake/chat/transformation.py Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * feat(datadog): add team-scoped Datadog callback support (#29947) Enable teams to configure their own Datadog credentials via POST /team/{team_id}/callback, following the same pattern as Langfuse. * Merge pull request #29528 from aanchal22/litellm_byok-alias-merge fix(proxy): atomic merge for team model aliases and team.models on BYOK create * feat: add EmpirioLabs as an OpenAI-compatible provider (#30278) Co-authored-by: Adam Dalloul <adam.d.developer@gmail.com> * fix: resolve failing tests and lint in snowflake/team endpoints - Black-format snowflake/chat/transformation.py to fix lint failure - Update Anthropic config test to expect default max_tokens of 4096 (matches implementation) - Add AsyncMock + execute_raw mock to team_model_add cache-refresh pin test - Add model_dump mock and patch cache/logging in test_uses_atomic_array_append_with_dedup Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(test): update test_db_error_new_model_check for new _delete_deployment logic _delete_deployment no longer short-circuits on empty db_models — it now treats [] as a valid empty-DB state and proceeds to check config models. Mock get_config to return the two router deployments so they appear in combined_id_list and are protected, which matches the real-world scenario where a DB error occurs but the models are config-backed. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * feat(proxy): register cancel_on_disconnect in ConfigGeneralSettings and config list (#30295) * feat(proxy): register cancel_on_disconnect in ConfigGeneralSettings and config list Follow-up to #30223 per maintainer review: documents the flag in ConfigGeneralSettings with a short description and adds it to allowed_args in get_config_list so the UI and /config/list expose it. A test pins that /config/list returns the field with type Boolean, which requires both registrations to be present * chore(ui): regenerate schema.d.ts for cancel_on_disconnect --------- Co-authored-by: kursad <kursad.lacin@brado.net> * fix(datadog): never fall back to env DD_API_KEY for caller-supplied destinations Team/key-scoped Datadog loggers could be pointed at an arbitrary dd_agent_host or dd_site while omitting dd_api_key, causing the proxy's global DD_API_KEY to be sent as the DD-API-KEY header to that destination. Gate the env-var fallback behind an allow_env_credentials flag, set to False when the destination is caller-supplied, mirroring the existing langfuse/langsmith pattern. --------- Signed-off-by: Rudra Dudhat <contact.rdudhat@gmail.com> Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com> Co-authored-by: daitran-tensormesh <dai@tensormesh.ai> Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Co-authored-by: Muspi Merol <me@promplate.dev> Co-authored-by: fangkang <fangkangm@gmail.com> Co-authored-by: Chris Hoogeboom <chris.hoogeboom@gmail.com> Co-authored-by: kursadlacin <kursadlacin@gmail.com> Co-authored-by: kursad <kursad.lacin@brado.net> Co-authored-by: CreateRandom <18438707+CreateRandom@users.noreply.github.com> Co-authored-by: hcl <chenglunhu@gmail.com> Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com> Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com> Co-authored-by: sfc-gh-nashukla <navnit.shukla@snowflake.com> Co-authored-by: Rudra Dudhat <contact.rdudhat@gmail.com> Co-authored-by: Michael <52305679+michaelxer@users.noreply.github.com> Co-authored-by: michaelxer <michaelxer@users.noreply.github.com> Co-authored-by: Quentin Champenois <26109239+Quentinchampenois@users.noreply.github.com> Co-authored-by: mauriceberentsen <mauriceberentsen@live.nl> Co-authored-by: lost9999 <56498264+lost9999@users.noreply.github.com> Co-authored-by: GaetanVDB07 <86427581+GaetanVDB07@users.noreply.github.com> Co-authored-by: Aanchal Khandelwal <aan2210khandelwal@gmail.com> Co-authored-by: Adam Dalloul <adam_dalloul@icloud.com> Co-authored-by: Adam Dalloul <adam.d.developer@gmail.com> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> |
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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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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.
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
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Sameer Kankute
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32c88ca74f
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Litellm oss staging 080626 (#29932)
* feat(bedrock_mantle): add SigV4/IAM auth to Responses API route (fixes #29665) (#29788) * feat(responses): add default no-op sign_request to BaseResponsesAPIConfig * feat(responses): call sign_request after body is final, send signed bytes when signed * feat(bedrock_mantle): add SigV4 sign_request via composed BaseAWSLLM (bearer path) * test(bedrock_mantle): cover SigV4 access-key, AssumeRole, body bytes, region/auth consistency * feat(bedrock_mantle): defer auth to sign_request; validate_environment no longer requires bearer * docs(bedrock_mantle): document SigV4 + Bearer auth on Responses route * test(responses): cover fake-stream signing order and mantle bearer arg/env precedence * fix(bedrock_mantle): wrap all botocore credential errors with both-paths guidance * fix(bedrock_mantle): catch specific credential errors, not all BotoCoreError, so STS transport failures are not masked * fix(bedrock_mantle): sign the compact Responses route too, not just create * fix(github-copilot): route per-model on /v1/responses based on model info (#29747) * feat(focus): add GCS destination for FOCUS export (#29751) * test: add failing tests for FocusGCSDestination * feat: add FocusGCSDestination reusing GCSBucketBase auth * feat: register FocusGCSDestination in factory; export from __init__ * fix(focus): preserve GCS_PATH_SERVICE_ACCOUNT when service_account_json not in config * style: apply Black formatting to gcs_destination and tests * style: apply Black formatting to factory.py * fix(bedrock): omit empty additionalModelRequestFields and system from Converse API payload (#29565) Amazon Nova Pro (and other strict Bedrock models) return 400 Malformed input request when additionalModelRequestFields: {} or system: [] are present in the payload. Both fields are optional in CommonRequestObject (total=False) and must be omitted rather than sent as empty structures. Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(proxy): recognize *.cognitiveservices.azure.com as OpenAI-compatible in pass-through cost tracking (#29730) * fix(proxy): recognize *.cognitiveservices.azure.com as OpenAI-compatible Azure OpenAI resources created via the newer "Azure AI Foundry" / Cognitive Services pathway live on `*.cognitiveservices.azure.com` subdomains, not the older `openai.azure.com`. Both are valid Azure OpenAI surfaces in production today. The OpenAI pass-through cost-tracking handler hard-codes only the older hostname in five places (four `is_openai_*_route` methods on OpenAIPassthroughLoggingHandler, plus is_openai_route on PassThroughEndpointLogging). As a result, calls from newer Azure deployments are silently classified as "not an OpenAI route", the dispatch into the cost-tracking handler is skipped, and tokens/cost never get extracted into LiteLLM_SpendLogs — the row gets written with prompt_tokens=0, completion_tokens=0, spend=0, model='unknown'. Reproduced 2026-06-04 against a real Azure OpenAI deployment on `*.cognitiveservices.azure.com` proxied through LiteLLM v1.88.0. Fix: factor the hostname check into a single helper `_is_openai_compatible_host` listing all three recognized surfaces (api.openai.com, openai.azure.com, cognitiveservices.azure.com), and have all five call sites delegate to it. Purely additive — never weakens recognition for the originally-supported hostnames. Adds a test `test_is_openai_route_recognizes_cognitiveservices_azure_com` that exercises all four `is_openai_*_route` static methods against `*.cognitiveservices.azure.com` URLs (positive cases per route + a small cross-route negative to confirm route-specific path matching still works on the new hostname). Out of scope for this PR (separate followup): - `openai_passthrough_handler` calls chat/completions `transform_response` on Responses API payloads (`output:` not `choices:`), which throws inside the dispatch and drops the SpendLogs row entirely. Recognized + tracked separately. * ci: trigger fresh run Empty commit to re-run checks. The previous auth-and-jwt failure was a transient HuggingFace Hub 429 rate-limit hitting tokenizer downloads in tests/proxy_unit_tests/test_custom_tokenizer_bug.py — unrelated to this PR's scope (hostname recognition in pass-through cost tracking). No code change. --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * fix(responses): preserve forced-function tool_choice name in Responses to Chat transform (#29812) The Responses API forces a specific function with a top-level name ({"type": "function", "name": "X"}), but _transform_tool_choice only handled the nested Chat Completions shape and fell through to returning "required" for the flat form, silently dropping the function name and degrading a forced function call to force-any-tool. Map the flat Responses shape to the nested Chat shape, keeping the "required" fallback when no name is present. * Preserve x-anthropic-billing-header system blocks for first-party Anthropic (#29584) * Preserve x-anthropic-billing-header system blocks for first-party Anthropic PR #20951 strips system blocks beginning with "x-anthropic-billing-header:" for every Anthropic target. That block is how the first-party Anthropic API recognizes Claude Code subscription (OAuth) traffic, so dropping it makes requests that carry only that block, such as the auto-mode tool-safety classifier, fail with a misleading 429 rate_limit_error; normal turns still work because they also carry the "You are Claude Code" identity block. Gate the strip behind should_strip_billing_metadata(), defaulting to False on the first-party AnthropicConfig and AnthropicMessagesConfig so the block is kept, and overridden to True on the providers that reach these transforms and reject the block (Bedrock platform, Vertex, Azure for the chat path; Minimax, Azure, DeepSeek for the messages path). Behavior for those providers is unchanged. * Strip billing header on Bedrock invoke and Vertex messages pass-through Two more subclasses reach the gated strip but inherited keep-by-default. AmazonAnthropicClaudeConfig (Bedrock invoke) calls AnthropicConfig.transform_request, which calls translate_system_message, and VertexAIPartnerModelsAnthropicMessagesConfig (Vertex messages pass-through) calls super().transform_anthropic_messages_request. Override should_strip_billing_metadata() to True on both. Add a parametrized test asserting the flag for every first-party base (False) and provider subclass (True), covering all overrides, plus a translate_system_message regression test for the Bedrock invoke path. * fix(cache): log hashed cache keys (#29890) * fix(ui): save routing groups as list (#29889) * Revert "fix(ui): save routing groups as list (#29889)" (#29928) This reverts commit |
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Mateo Wang
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f11c12d157
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Revert "chore(tests): migrate Bedrock CI to AWS account 941277531214 (#28728)" (#29326)
This reverts the Bedrock CI account migration (#28728). The original account (888602223428) was put under an AWS security restriction after a leaked key and has since been reactivated, while the replacement account (941277531214) lacks access to several models the suites exercise (legacy Bedrock Claude 3 models, Cohere, Nova Canvas image gen, Bedrock batch inference, and flagship Opus). Pointing CI back at the reactivated account restores that coverage. This is the exact inverse of #28728: all hardcoded 941277531214 references go back to 888602223428 (provisioned/imported-model ARNs, AgentCore runtime ARNs and their suffixes, batch execution role ARN, and the example proxy config), the S3 buckets revert to litellm-proxy and load-testing-oct, the guardrail IDs revert to wf0hkdb5x07f and ff6ujrregl1q, the SageMaker endpoint and Knowledge Base revert to their original ids, and the live-call tests go back to the legacy model strings. The grid_spec fail_reason workaround for the unentitled Opus cells is dropped while keeping the unrelated bedrock_effort_ceiling field added after the migration. The CircleCI AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY env vars still point at 941277531214 and must be set to the reactivated account's fresh credentials separately via the CircleCI API; AWS_REGION_NAME stays us-west-2. |
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Sameer Kankute
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1d9095f914
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fix(bedrock): support tool search results + chat annotations (#29120)
* Fix overiding of fastapi_response headers * fix(bedrock): support tool search results and surface citations as annotations Add an optional tool-message search_results path that maps directly to Bedrock toolResult.searchResult blocks, and convert Converse citationsContent into chat completion annotations for user-facing citation metadata. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(format): align bedrock prompt factory with black Reformat the updated bedrock prompt template conversion file so CI black --check passes. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(bedrock): harden citations, search_results mapping, and token counting Resolve mypy issues in citation parsing, only attach url_citation annotations when citation text is stitched into content, fall back to tool content when search_results is empty, and count search_results text in token/TPM preflight paths. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(bedrock): extract tool result helpers to satisfy PLR0915 Refactor _convert_to_bedrock_tool_call_result into smaller helpers so lint passes without changing Bedrock tool result behavior. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(bedrock): count all forwarded search_results fields in token estimates Include source, title, content text, and citations when estimating tokens so large metadata cannot bypass TPM preflight checks. Reformat factory.py with black. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(managed-files): skip content blocks without a type key in get_file_ids_from_messages * fix(bedrock): stitch citations for any punctuation-only text block * fix(bedrock): map null citation source/title to empty annotation strings * fix(bedrock): advance citation offset for text-only citationsContent blocks * fix(bedrock): complete citation TypedDicts for grounding annotations --------- Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Mateo Wang
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95015de733
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feat: add support for claude code goal mode for bedrock opus output config (#28898)
* feat: support goal mode for claude on bedrock
* fix failing lint test
* addressing greptile comments
* fixing failed test
* address greptile: copy output_config and warn on dropped converse format
* fix(bedrock): skip redundant output_config normalization on Converse reasoning_effort path
When reasoning_effort is mapped via _handle_reasoning_effort_parameter, the
resulting output_config is already normalized via
normalize_bedrock_opus_output_config_effort. Mark it as normalized so
_prepare_request_params can skip the redundant call (and the associated
get_model_info lookup) on every request.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* test(reasoning-effort-grid): reflect Bedrock opus-4-6 xhigh→max clamping
* fix(bedrock): stop leaking output_config marker and message-content mutation
* fix(bedrock): guard effort key access in normalize_bedrock_opus_output_config_effort
Defensively check that 'effort' is a valid key in _BEDROCK_OUTPUT_CONFIG_EFFORT_ORDER
before indexing, to prevent a KeyError if the hardcoded guard tuple ever drifts from
the order dict's keys.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(bedrock): drop dead second clause in effort normalization guard
The 'effort not in _BEDROCK_OUTPUT_CONFIG_EFFORT_ORDER' check is
unreachable once 'effort not in ("xhigh", "max")' has been ruled out,
since both literals are present in the order dict. Keep the literal
membership check and let the dict lookups below speak for themselves.
* fix(bedrock): clamp output_config.effort against ceiling for any known value
The early return when effort was not 'xhigh'/'max' meant a ceiling of
'low' or 'medium' would silently forward an out-of-range value. Gate on
the known effort ordering instead so the ceiling comparison runs for
every recognized effort.
* test(grid_spec): use _CAPS_OPUS_4_7 for non-Bedrock opus-4-6 entries
claude-opus-4-6 now declares supports_xhigh_reasoning_effort in the model
map, so production accepts xhigh on Azure AI and Vertex AI routes. Update
those grid_spec entries to match production capabilities so expected()
predicts 200 for xhigh instead of 400.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* test(grid_spec): revert xhigh caps for non-Bedrock opus-4-6
azure_ai/claude-opus-4-6 and vertex_ai/claude-opus-4-6 do not declare
supports_xhigh_reasoning_effort in model_prices_and_context_window.json.
Azure AI upstream rejects xhigh with HTTP 400 ("Supported levels: high,
low, max, medium"). Restore _CAPS_4_6 so the grid predicts 400 for
xhigh, matching production capabilities.
* fix: stop advertising xhigh effort on Opus 4.5/4.6
Only Opus 4.7 supports the xhigh reasoning effort level. Remove the
supports_xhigh_reasoning_effort flag from every Opus 4.5 and Opus 4.6
entry (direct Anthropic, Bedrock, and regional variants) in both model
catalog files.
On the direct Anthropic path there is no effort clamp, so flagging 4.5/4.6
as xhigh-capable caused litellm to forward xhigh to a model that rejects it
(and made get_model_info misreport the capability). xhigh now correctly
degrades to high / raises on those models.
Bedrock graceful degradation for Claude Code goal mode is unaffected: it
relies solely on the bedrock_output_config_effort_ceiling clamp (4.5->high,
4.6->max, 4.7->xhigh), which runs before validation, so xhigh requests to
older Bedrock Opus models are still silently lowered rather than rejected.
Update effort-gating tests to reflect that 4.5/4.6 no longer accept xhigh.
* fix: clamp xhigh effort on Bedrock Invoke /v1/messages instead of rejecting
Claude Code "goal mode" sends output_config.effort=xhigh over the Anthropic
/v1/messages API, which routes Bedrock models through
AmazonAnthropicClaudeMessagesConfig. That path validated effort against the
model's native capability and raised 400 for xhigh on Opus 4.6, while the
chat-completions paths (Converse + Invoke) already clamp xhigh to the model's
bedrock_output_config_effort_ceiling. That asymmetry broke goal mode on the
exact API surface Claude Code uses.
Apply the same ceiling clamp on the messages path before the shared effort
gate runs, so xhigh degrades to max on Opus 4.6 (and stays xhigh on 4.7).
Scoped to adaptive-thinking models and to models that declare a ceiling, so
Sonnet 4.6 (no ceiling) and Opus 4.5 (budget mode) are unaffected and still
reject xhigh.
* fix(bedrock): preserve user output_config when applying reasoning_effort
- Converse path: merge mapped effort into existing output_config via
setdefault instead of overwriting it, matching the Anthropic Messages
path. Prevents user-supplied output_config.format from being silently
dropped when reasoning_effort is also provided.
- tests: clear _get_local_model_cost_map lru_cache in the autouse
fixture alongside get_bedrock_response_stream_shape to avoid stale
cache leakage between tests.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(bedrock): pre-clamp reasoning_effort for chat invoke; correct test caps
- Add _clamp_adaptive_reasoning_effort_for_bedrock to AmazonAnthropicClaudeConfig
so raw reasoning_effort=xhigh degrades to the model's bedrock effort ceiling
before AnthropicConfig.map_openai_params converts it to output_config.
Mirrors converse path (_handle_reasoning_effort_parameter) and messages path
(_clamp_adaptive_reasoning_effort_for_bedrock) so the three Bedrock paths
are consistent.
- grid_spec: restore caps=_CAPS_4_6 for Bedrock converse/invoke Opus 4.6 entries
so the test reflects the model's actual JSON capabilities. Teach expected()
to bypass the xhigh/max cap check when bedrock_effort_ceiling will clamp
the wire effort, so the test still passes for Bedrock's graceful degradation
contract without lying about native model caps.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
---------
Co-authored-by: Dennis Henry <dennis.henry@okta.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
|
||
|
Mateo Wang
|
f9407bc036
|
chore(tests): migrate Bedrock CI to AWS account 941277531214 (#28728)
* chore(tests): migrate Bedrock CI from AWS account 888602223428 to 941277531214
The original account (888602223428) was put under a security restriction by
AWS after a root access key leaked in a PR comment. While that account works
its way through the AWS Support unlock process, Bedrock-touching CI tests have
been migrated to a fresh account (941277531214).
Changes:
- Replace 26 hardcoded references to 888602223428 with 941277531214 across
8 files (provisioned-model ARNs, imported-model ARNs, AgentCore runtime
ARNs, batch execution role ARN, and example proxy config).
- The provisioned-model and imported-model ARNs are referenced only from
mocked unit tests — no AWS resources to recreate.
- The batch execution IAM role has been recreated in the new account with
the same name and equivalent permissions.
- The two AgentCore runtimes (hosted_agent_r9jvp-3ySZuRHjLC,
hosted_agent_13sf6-cALnp38iZD) are being recreated in the new account
under the same names — see tools/agentcore-deploy/ in a follow-up.
CircleCI env vars AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY / AWS_REGION_NAME
were updated separately via the CircleCI API to point at the new account.
Smoke-tested locally against the new account:
aws bedrock-runtime converse --region us-west-2 \
--model-id us.anthropic.claude-sonnet-4-5-20250929-v1:0 \
--messages '[{"role":"user","content":[{"text":"ping"}]}]'
→ 200, model returned 'pong'
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* chore(tests): refresh AgentCore ARN suffixes to match newly-deployed runtimes
The first migration commit replaced just the account ID, but AgentCore
auto-assigns a random 10-char suffix to every runtime on creation — we
can't reuse the original suffixes (`3ySZuRHjLC`, `cALnp38iZD`) in the
new account. Updated the AgentCore-runtime ARNs in the three files that
reference real runtime IDs (not the mock-based unit-test ARNs).
Deployed runtimes:
arn:aws:bedrock-agentcore:us-west-2:941277531214:runtime/hosted_agent_r9jvp-Rq79QFC2fp
arn:aws:bedrock-agentcore:us-west-2:941277531214:runtime/hosted_agent_13sf6-4046UzHSwy
Both runtimes are status=READY and pass a smoke invoke:
$ aws bedrock-agentcore invoke-agent-runtime --agent-runtime-arn ... --payload '{"prompt":"ping"}'
→ 200, {"result": "echo: ping"}
The agent is a minimal echo (see /tmp/agentcore_deploy/agent.py for the
deploy artifacts). Tests that only verify the SDK wiring will pass; if any
test asserts on agent output content, swap the echo for the real agent.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* chore(tests): point Bedrock batch tests at new-account S3 bucket
The account migration (888602223428 -> 941277531214) was a flat
account-ID swap, which only rewrites ARNs that embed the account
number. S3 bucket names carry no account ID, so the live Bedrock
batch tests still uploaded to `litellm-proxy` — a bucket that lives
in the old account. S3 names are globally unique, and the old account
still holds that name, so it can't be recreated in the new account.
Rename to `litellm-proxy-941277531214` (account-ID suffix guarantees
global uniqueness). The bucket must be created in 941277531214 and the
batch execution role granted s3:GetObject/PutObject/ListBucket on it
before this job is run in CI.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* chore(tests): point live S3 logging test at new-account bucket
Same account-ID-free blind spot as the batch bucket: `load-testing-oct`
lives in the old account and its name can't be reused globally. The
`logging_testing` CI job is wired into the workflow and runs
test_basic_s3_logging, which uploads to this bucket with the CI env
creds, then lists and deletes objects — a live dependency.
Rename to `load-testing-oct-941277531214`. The bucket must exist in the
new account with the CI IAM principal granted
s3:PutObject/GetObject/ListBucket/DeleteObject before this job runs.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* chore(tests): repoint Bedrock guardrail IDs to new-account guardrails
The migration left guardrail IDs untouched (no account ID in them), so
all live guardrail tests failed with "guardrail identifier or version
does not exist" against 941277531214. Recreated both guardrails in the
new account and updated the hardcoded IDs:
- wf0hkdb5x07f -> zgkmukebruil (PII mask: PHONE + CREDIT_DEBIT_CARD,
with explicit inputAction=ANONYMIZE so masking applies to INPUT,
which is the source litellm's moderation hook sends)
- ff6ujrregl1q -> 4w3d1di3snt5 (blocks "coffee"; blocked message set
to the exact string the tests assert on)
Updated test_bedrock_guardrails.py, otel_test_config.yaml, and the
guardrailConfig in test_bedrock_completion.py. Verified locally: the 5
previously-failing guardrail tests now pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* test(bedrock): migrate legacy models to current inference profiles
The new CI account (941277531214) cannot invoke legacy Bedrock models
(AWS gates them: "marked by provider as Legacy... not actively using in
the last 30 days"). Migrated the live-call tests:
- anthropic.claude-3-sonnet-20240229 -> us.anthropic.claude-sonnet-4-5-20250929-v1:0
- anthropic.claude-3-haiku-20240307 -> us.anthropic.claude-haiku-4-5-20251001-v1:0
Current Claude models on Bedrock require the us. inference-profile prefix
(bare on-demand ids are rejected).
cohere.command-r-plus has no working replacement (all Cohere is legacy-
gated in the new account): swapped to claude-haiku-4-5 in provider-
agnostic param lists. amazon.titan-image-generator skipped (no working
replacement). Mocked/transformation/cost tests that reference the legacy
strings are intentionally left unchanged. Verified live against the new
account.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* test(bedrock): repoint SageMaker + Knowledge Base to new-account resources
These referenced account-scoped resources by hardcoded id that only
existed in the old account, so the migration's account-ID swap missed
them. Recreated in 941277531214 and repointed:
- SageMaker endpoint jumpstart-dft-hf-textgeneration1-mp-20240815-185614
-> litellm-ci-textgen (gpt2 on a TGI container, ml.g5.xlarge)
- Bedrock Knowledge Base T37J8R4WTM -> LCYXFBR2TU (OpenSearch Serverless
vector store + titan-embed-text-v2, seeded with a LiteLLM doc)
Verified live: test_sagemaker.py (12 passed) and
test_bedrock_knowledgebase_hook.py (12 passed).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* test(reasoning_effort_grid): skip bedrock claude-opus-4-7 cells (not entitled on 941277531214)
claude-opus-4-7 is listed in the new Bedrock CI account's foundation
models but invoke is denied (AccessDeniedException: "not available for
this account"). Bedrock access to the flagship Opus requires an AWS
Sales request, not the self-serve model-access toggle, so it can't be
enabled inline with the rest of the account migration.
Add an optional `skip_reason` to ModelEntry and set it on the
bedrock-claude-opus-4-7 entry; the grid test honors it via pytest.skip.
Cell count (231) and route coverage are unchanged, so the structural
asserts still pass. Restore coverage by deleting the one skip_reason
line once access is granted.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* test(bedrock): swap/skip legacy-gated models unavailable on new CI account
The migrated AWS account (941277531214) cannot access several models that
the old account could, so the remaining red CI jobs were hitting real
Bedrock "Access denied / Legacy" and "account not authorized" errors:
- image_gen: skip both Nova Canvas test classes (amazon.nova-canvas-v1:0 is
legacy-gated), matching the existing titan skip.
- batches: skip test_async_file_and_batch (Bedrock batch inference is not
authorized on the new account; requires an AWS support case).
- litellm_overhead: swap legacy claude-3-5-haiku for the active
us.anthropic.claude-haiku-4-5 inference profile.
- test_completion_claude_3_function_call: swap legacy claude-3-sonnet for the
active us.anthropic.claude-sonnet-4-5 inference profile.
https://claude.ai/code/session_01Y7zgHYu9GX29YRwV4yiWAa
* test(bedrock): fix remaining e2e legacy-model + batch failures on new CI account
- e2e_openai_endpoints: skip test_bedrock_batches_api (Bedrock batch inference
is not authorized on account 941277531214) and migrate the missed
s3_bucket_name in oai_misc_config.yaml to litellm-proxy-941277531214.
- build_and_test: swap legacy bedrock claude-3-sonnet for the active
us.anthropic.claude-sonnet-4-5 inference profile in the proxy structured
output e2e test.
https://claude.ai/code/session_01Y7zgHYu9GX29YRwV4yiWAa
* test(bedrock): make opus-4-7 + batch cells fail loudly and mock image-gen (#28791)
Replace the silent skips added for the new CI account with noisier behavior:
- reasoning-effort grid: opus-4-7 cells now fail (when AWS creds are present)
instead of skipping, so the missing entitlement stays visible in CI; they
still skip when AWS creds are absent (local dev)
- Bedrock batch inference tests: drop the skip so they run and fail until
batch access is granted
- Titan + Nova Canvas image-gen tests: mock the Bedrock HTTP call so the
transform + cost-tracking path stays under test without live model access
https://claude.ai/code/session_01MT7SWDnXUjv6e6EPG7BDjT
Co-authored-by: Claude <noreply@anthropic.com>
* test(bedrock): use pytest.xfail for known-failing opus-4-7 cells
Replace pytest.fail with pytest.xfail when a model has a fail_reason,
so known-broken cells stay visible as XFAIL without keeping CI red.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
---------
Co-authored-by: Mateo <mateo@Mateos-MacBook-Pro.local>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
|
||
|
Sameer Kankute
|
b7e978a5c3
|
Litellm oss staging 04 21 2026 2 (#26569)
* fix(bedrock): use model info lookup for output_config support instead of hardcoded check Replace hardcoded _is_claude_4_6_model() string matching with supports_output_config flag in model_prices_and_context_window.json, accessed via _supports_factory(). This follows the project's established pattern for model capability checks (per AGENTS.md rule #8). Bedrock Invoke now conditionally preserves output_config for models that declare supports_output_config=true (currently Claude 4.6 models), while stripping it for older models to avoid request rejection. Ref: https://github.com/BerriAI/litellm/issues/22797 * fix(vertex_ai): single-flight credential refresh to prevent thundering herd (#26024) * fix(vertex_ai): single-flight credential refresh to prevent thundering herd When GCP credentials expire under high concurrency, all requests simultaneously call credentials.refresh() via asyncify, saturating the 40-thread anyio pool and blocking the proxy for 20+ seconds. This adds: - Per-credential asyncio.Lock in get_access_token_async for single-flight refresh (1 coroutine refreshes, others wait on the lock) - Background refresh when token_state is STALE (usable but near expiry), returning the current token immediately with zero added latency - threading.Lock on the sync get_access_token path - Uses google-auth's TokenState enum (FRESH/STALE/INVALID) instead of reimplementing expiry logic Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: address PR review comments - Use asyncio.create_task() instead of deprecated get_event_loop().create_task() - Track in-flight background refresh tasks to prevent duplicate refreshes when multiple STALE-path callers pass through the lock before the first background task completes - Add token validation in the STALE branch (consistent with FRESH/INVALID) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: lazy-import TokenState to avoid breaking when google-auth is not installed Also extract helper methods to bring get_access_token_async under the PLR0915 statement limit (50). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * chore: apply Black formatting to test file and update uv.lock Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: remove user-provided project_id from log messages (CodeQL log injection) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: avoid leaking token value in error message, log type instead Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * chore: restore uv.lock to match litellm_oss_branch Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: remove project_id from remaining log message (CodeQL log injection) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: remove remaining project_id from log and error messages Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: reuse cached credentials in VertexAIPartnerModels (#26065) * fix: reuse cached credentials in VertexAIPartnerModels instead of creating new VertexLLM per request VertexAIPartnerModels.completion() was creating a throwaway VertexLLM() instance on every call to get an access token, bypassing the credential cache inherited from VertexBase. This caused a fresh token fetch for every single request, adding significant latency overhead. Fix: call super().__init__() to initialize VertexBase's credential cache, and use self._ensure_access_token() instead of a new VertexLLM instance. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: apply same credential caching fix to VertexAIGemmaModels and VertexAIModelGardenModels Same bug as VertexAIPartnerModels: both classes had `pass` in __init__ instead of `super().__init__()`, and created throwaway VertexLLM() instances per request instead of using self._ensure_access_token(). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix(fireworks): add glm-5p1 metadata and parallel_tool_calls (#26069) * fix(chatgpt): preserve responses routing and recover empty output (#25403) (#26219) - preserve existing shared backend `mode` when router deployment registration reuses a provider/model key already in `litellm.model_cost` (prevents alias with `mode: chat` from downgrading shared `chatgpt/gpt-5.4` from `responses` to `chat` and triggering 403s on /v1/chat/completions) - teach the ChatGPT Responses parser to recover `response.output_item.done` entries when `response.completed.output` is empty - add defensive /responses -> /chat/completions bridge fallback that reconstructs output items from raw SSE when `raw_response.output` is empty - regression coverage for shared alias routing, empty completed.output parsing, and SSE bridge recovery Closes #25403 Co-authored-by: afoninsky <andrey.afoninsky@gmail.com> Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(deps): relax core runtime dependency pins from exact == to ranges When litellm migrated from Poetry to uv (PR #24905, v1.83.1), the core dependency specifications in pyproject.toml changed from Poetry bare-version strings (e.g. openai = "2.30.0") to PEP 621 exact pins (openai==2.24.0). Poetry bare-version strings are actually caret ranges (^X.Y.Z == >=X.Y.Z,<X+1), but PEP 621 == is exact. This means every downstream package that installs litellm as a library dependency is now forced to downgrade aiohttp, pydantic, openai, click, and 8 other common packages to exact old versions. Fix: restore range specifiers for the 12 core runtime dependencies. The optional extras (proxy, proxy-runtime, etc.) are consumed primarily by Docker images where exact pins are appropriate and are left unchanged. The uv.lock file continues to provide exact reproducibility for Docker builds and CI. Fixes: #26154 * Add Rubrik as officially-supported guardrail plugin (#25305) * Add Rubrik as officially-supported guardrail plugin Adds tool blocking and batch logging integration with an external Rubrik webhook service. The plugin validates LLM tool calls against a policy service (fail-open on errors) and batch-logs all requests/responses. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Update Rubrik docs: config.yaml as primary, env vars as fallback Restructures the Quick Start to present config.yaml as the recommended approach with tabbed UI, and environment variables as an alternative fallback. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add Rubrik env vars to config_settings reference Fixes documentation validation by adding RUBRIK_API_KEY, RUBRIK_BATCH_SIZE, RUBRIK_SAMPLING_RATE, and RUBRIK_WEBHOOK_URL to the environment settings reference table. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add fallback message when blocking service returns empty explanation Prevents whitespace-only violation message when the tool blocking service blocks tools but returns an empty content field. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(ocr): add Reducto parse OCR support (#26068) * feat(ocr): add Reducto parse OCR support * fix(reducto): address OCR review feedback * chore: refresh uv lockfile * Revert "chore: refresh uv lockfile" This reverts commit |
||
|
Cursor Agent
|
2cb3f0f027
|
refactor: remove unnecessary comments from #27074
Strip out the explanatory and historical comments that don't carry business-logic justification. Comments that simply narrate what code does — or that explain prior behavior, what was changed, or which PR introduced a fix — are removed. Docstrings are reduced to a one-line summary where the long form repeated information already evident from the code or test data. No code-behavior changes. All 643 affected unit tests still pass. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> |
||
|
mateo-berri
|
108b87fb24 |
fix(anthropic,bedrock,databricks): four reasoning_effort follow-ups
- claude-sonnet-4-6 + reasoning_effort=max no longer 400s. Renamed _is_opus_4_6_model to _is_claude_4_6_model at three sites and added supports_max_reasoning_effort: true to 12 model entries in the JSON cost map (10 sonnet 4.6 ids + OpenRouter opus 4.6/4.7). - _map_reasoning_effort now raises BadRequestError(400) directly with llm_provider, instead of letting Databricks (and similar callers) surface its raw ValueError as a 500. - output_config.effort on Opus 4.5 over Bedrock no longer 400s for missing effort-2025-11-24 beta. Flipped JSON to "effort-2025-11-24" for bedrock + bedrock_converse and added an auto-attach branch in _process_tools_and_beta for non-adaptive Anthropic + output_config on Converse. - reasoning_effort=xhigh / =max on legacy budget-mode models (Haiku 4.5, Sonnet 4.5, Opus 4.5) now map to thinking.budget_tokens 8192 / 16384 instead of returning 400. Added two constants in litellm/constants.py. Tests updated for all four flips. Validated end-to-end via 306-cell live proxy matrix (6 model families x 3 routes x 17 effort cases), all pass. |
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|
Claude
|
09d37db8d9 |
feat(anthropic,bedrock): strip output_config under drop_params for non-effort models
When a proxy fronts Claude Code (which always sends `output_config.effort`)
at a pre-4.5 Anthropic model — haiku-3, sonnet-3.5, opus-3, sonnet-4 — the
forwarded knob causes a forced 400 the client can't fix. Gating a strip
behind the existing `drop_params` flag lets operators opt into silent
fixup once and stop worrying about per-model param hygiene.
Default (`drop_params=False`) still forwards and surfaces the provider's
error, preserving the strict, debuggable contract from #27074.
Per https://platform.claude.com/docs/en/build-with-claude/effort the
supporting set is Opus 4.5+, Sonnet 4.6+, and Mythos Preview; everything
else is dropped (with a verbose_logger warning so the strip is visible).
Recognition uses model-name patterns plus a fallback to any
`supports_*_reasoning_effort` flag in the model map for forward
compatibility with new entries.
https://claude.ai/code/session_01WjHq31rvXT6xYNdVmSJvRp
(cherry picked from commit
|
||
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Cursor Agent
|
a6c673e7b9 |
fix(anthropic,bedrock,vertex): forward output_config.effort + 400 on garbage reasoning_effort
Follow-up bugs surfaced by the QA sweep on PR #27039 (https://github.com/BerriAI/litellm/pull/27039#issuecomment-4363363610). 1. Stop stripping output_config.effort on Bedrock + Vertex adaptive routes. - Vertex AI Claude 4.6/4.7 accepts output_config.effort on rawPredict (verified end-to-end against us-east5 / global). The strip helper now no-ops for effort. - Bedrock Converse routes output_config into additionalModelRequestFields for anthropic base models so the requested adaptive tier (low/medium/ high/xhigh/max) actually reaches the wire instead of all collapsing to identical thinking. - Bedrock Invoke chat transformation (AmazonAnthropicClaudeConfig) stops popping output_config from the post-AnthropicConfig request body. - Bedrock Invoke /v1/messages allowlist (BedrockInvokeAnthropicMessagesRequest) now lists output_config so the runtime allowlist filter forwards it. 2. Validate effort across Bedrock Converse so 'disabled' / 'invalid' / '' / unsupported tiers (xhigh/max on Sonnet 4.6 or budget-mode 4.5 models) surface as a clean 400 BadRequestError instead of 500. 3. ValueError -> BadRequestError throughout (AnthropicConfig.map_openai_params, _apply_output_config, AmazonConverseConfig._handle_reasoning_effort_parameter). Empty-string effort is now rejected (was silently passing the 'if effort and ...' short-circuit). 4. Floor reasoning_effort='minimal' at the Anthropic provider minimum (1024 budget_tokens) via new ANTHROPIC_MIN_THINKING_BUDGET_TOKENS so it's a usable tier on direct Anthropic / Azure AI Anthropic / Vertex AI Anthropic / Bedrock Invoke (all of which 400 below 1024). 5. model_prices: dedupe duplicate supports_max_reasoning_effort key on claude-opus-4-7 / claude-opus-4-7-20260416. Adds regression tests across all five affected paths; existing tests asserting the silent-strip behavior were updated to reflect the new pass-through and clean 400 surfaces. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> |
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mateo-berri
|
3835306c83 |
fix(anthropic,bedrock): omit thinking/output_config when reasoning_effort="none"
Setting reasoning_effort="none" on Anthropic chat models (direct, Bedrock
Invoke, Bedrock Converse, Vertex AI Anthropic, Azure AI Anthropic) crashed
LiteLLM with:
litellm.APIConnectionError: 'NoneType' object has no attribute 'get'
Both the Anthropic chat transformation and Bedrock Converse called
``AnthropicConfig._map_reasoning_effort`` and assigned the ``None`` it returns
for ``"none"`` directly to ``optional_params["thinking"]``. Downstream
``is_thinking_enabled`` then did ``optional_params["thinking"].get("type")``
and crashed.
Pop ``thinking`` (and on Claude 4.6/4.7, ``output_config``) instead of
assigning ``None``, restoring the documented contract that
``reasoning_effort="none"`` means "do not enable thinking". This also
prevents downstream Anthropic 400s ("thinking: Input should be an object",
"output_config.effort: Input should be ...") if the bug were ever masked.
Verified end-to-end against the live Anthropic API and Bedrock Converse
on claude-opus-4-{5,6,7} and claude-sonnet-4-6, plus Bedrock Invoke for
Claude 4.5/4.6. Vertex AI Anthropic and Azure AI Anthropic inherit the
fixed ``map_openai_params`` from ``AnthropicConfig`` and need no further
changes.
|