* test(e2e): add gemini tool call id length repro for LIT-8912
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(gemini): add thought_signature_in_tool_call_id to keep tool call ids short for clients that cap them
* fix(gemini): pair a tool result with its call when the client re-adds the thought signature to the call id
Strands, which the AgentCore harness runs, puts provider_specific_fields.thought_signature back on the
assistant tool call id as <id>__thought__<signature> while its tool result keeps the bare id, so the Gemini
conversion found no matching call and answered 500. Ids now match with the signature suffix stripped.
Also adds thought_signature_in_tool_call_id to the litellm_params inventory test and gives the pass-through
tests' mocked logging objects the litellm_params a real Logging object always has.
* fix(gemini): keep the thought signature on streamed Messages and Responses tool calls and accept a string setting
* fix(gemini): read the thought signature back from Responses input and coerce the per-request setting
A Responses function_call input item now keeps its provider_specific_fields, so a
client that sends the item back gets its real thought signature replayed to Gemini.
A per-request thought_signature_in_tool_call_id of "false" or 0 now keeps ids short,
and a value that is not a boolean answers 400, or is dropped under drop_params,
instead of a 500.
The e2e test now uses the sdk fixture and @meta subjects
* fix(caching): key the response cache on thought_signature_in_tool_call_id
* fix(responses): send thought signature fields only with short ids and drop malformed routing kwargs whole
* fix(main): validate routing kwargs after the locals snapshot so fallbacks and model_list calls keep working
The e2e test now uses gemini-3.8-flash with enough max_tokens to finish thinking before the tool call.
* fix(gemini): keep non-chat routes tolerant of a malformed thought_signature_in_tool_call_id, match tool results by exact id first, and keep the deployment's value when drop_params drops a malformed override
* fix(router): validate strict litellm params through a helper so star imports cannot re-bind a public Final
* perf(router): skip strict validation when a request overrides no deployment param
* fix(responses): keep a dict tool call's thought signature on the streamed function_call done item
* refactor(main): return invalid supplemental params as a value and test the streaming tool call with a real response
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(anthropic): rename llms/anthropic/pass_through to llms/anthropic/messages
Move the unified /v1/messages code out of a directory called pass_through, since it is not the
passthrough feature. The inner messages sub-package becomes native, and the Vertex AI partner
model experimental_pass_through package becomes messages. The old packages stay as import shims
that warn once and alias every submodule in sys.modules, so old deep imports still resolve
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(anthropic): keep Final variables out of loops in the rename shim
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(anthropic): match the warning text in the rename shim test
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(anthropic): resolve old pass_through imports lazily through a meta path finder
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(anthropic): mark the alias loader spec restore for the type discipline gate
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(anthropic): format the renamed package shim
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(anthropic): keep the rebind-ok marker on the spec restore line
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(anthropic): carry the new package search path on alias specs
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(anthropic): declare the shim test locals Final
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
gpt_5_transformation.py becomes gpt_reasoning_transformation.py (OpenAI and Azure),
the Bedrock anthropic_claude3_transformation.py files become anthropic_claude_transformation.py,
anthropic_claude2_transformation.py becomes anthropic_legacy_text_completions_transformation.py,
and vertex_ai_partner_models/llama3 becomes vertex_ai_partner_models/llama. Class and symbol
names are unchanged.
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(completion): send prompt_cache_retention to OpenAI instead of silently dropping it
litellm.completion(), Router.completion() and the proxy's /v1/chat/completions
accepted prompt_cache_retention and listed it as a supported OpenAI param, but
completion() never handed it to get_optional_params, and the leftover-kwargs
paths skip known OpenAI params, so it never reached the request body. Make it a
named argument of completion() and acompletion() like prompt_cache_key and pass
it on.
A unit guard sends every OpenAI body param through completion() and fails when a
param added to OPENAI_CHAT_COMPLETION_PARAMS has no case proving it reaches the
wire, the same hole store and prompt_cache_key fell into in #33184. Live e2e
tests capture the body OpenAI receives for the proxy, the SDK and the Router on
both openai/gpt-5.5 and openai/responses/gpt-5.5.
* chore(completion): type the param-forwarding unit test and keep the basedpyright count flat
Every new completion() kwarg adds one unknown-argument error at each call
site that splats an untyped dict into it; typing print_verbose's argument
and Chat's params removes more than prompt_cache_retention added.
* test: freeze the outbound-body model and read captured requests from the mock
Addresses review: the e2e body model is frozen, the backend params are a
tuple, and the unit test reads the request from the respx route instead of
appending to a list.
* fix(bedrock): apply the 16 maxTokens floor to Kimi K3 on Converse
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(e2e): use lane AWS credentials for the Kimi K3 messages test
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(e2e): bypass the proxy cache and declare test metadata for the Kimi K3 probe
---------
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>
* test: migrate local_testing SDK and provider tests out of build_and_test
* test: cover sagemaker region, hf template and nova paths end to end
Region rows now go through litellm.completion on the sagemaker/ handler that
the legacy tests exercised, the prompt template test mocks the Hugging Face
tokenizer config instead of switching to custom_prompt_dict, streaming tests
assert role, finish reason and body, and Nova single turn and streaming run
through litellm.completion with a recorded request body
* test: tighten migrated integration tests for local OpenAI routes, timeouts and air-gapped boot
Split the catch-all providers migration file by behavior. The fake endpoint
route tests now hit the owned upstream directly, as the legacy harness tests
did. LM Studio asserts the outbound key, path and stripped model. The embedding
timeout test asserts the proxy maps a deployment timeout to a 408 and forwards
the read timeout to the provider client, instead of timing out the test client
The Azure streaming tool-call test also asserts the second identical stream is
served from the Redis cache with the same reassembled tool call
The Docker network test always skipped in CI because the security job has no
litellm/litellm image and no egress to pull one, and it tested a published image
rather than the checkout. It now boots the checkout proxy with every outbound
HTTP connection refused and asserts readiness, the bundled cost map fallback and
that the cost map host is the only egress attempted
* test: tighten the migrated e2e rows for local_testing SDK and provider tests
Drop the duplicated Anthropic covers marker and the Anthropic cell wrongly claimed by
the Bedrock and Vertex cache tests. Keep the file-wide Anthropic model as it is on main.
Send the 2023-06-01 version header to Anthropic through the deployment's extra_headers
so the header actually reaches the provider, and mark the creation-token case 1h then
5m like the legacy test did
Reuse the Bedrock file's own registration for the pydantic response test, swap the
hand-built PNGs for the shared cat.jpg fixture, assert the SageMaker Nova image answer
names the cat and that top_logprobs=3 is honored, and register the SageMaker Nova opt-in
markers the same way as the other opt-in markers
* test: restore lost assertions in migrated test_main tests
Restore the role alternation check in test_completion_anthropic_hanging, the
embedding and streaming response-header checks, the logit_bias drop check and
the gemini system and safety settings checks. Add an azure 429 retry-after
test for test_exception_with_headers, trigger the router fallback with
mock_timeout like the legacy test, and stop the mock timeout tests sleeping 3s
* test: restore lost legacy assertions in migrated vertex ai unit tests
* test: tighten migrated cost, streaming and embedding tests and scope the hosted mock guard
* test: strengthen migrated anthropic, azure, bedrock and cohere provider tests
Assert full request bodies and usage instead of presence checks, restore the
dummy-tool and custom_llm_provider assertions the legacy tests made, add the
async and per-model variants the legacy parametrization covered, split
streamed tool arguments across deltas, and move the Azure chat streaming
tests to the azure.py mirror file
* test: tighten migrated llms provider tests and cover legacy intents lost in the move
Strengthen the migrated fireworks, gemini, mistral, together, openai text completion, openrouter, perplexity and openai error tests so they assert what the legacy tests proved (cost, provider tag, usage-only-on-last-chunk, function_call rebuild, reasoning param, sync/async/stream error fields). Replace the huggingface and jina tests that doubled litellm code with respx end-to-end tests. Add the tests the pre-existing mapped tests never covered: cohere 400 mapping, token-array text completion, hosted_vllm/llamafile/lm_studio env api base embeddings, watsonx embedding auth, ollama ssl_verify client and ollama vision prompt
* test: cover residual legacy paths for anthropic tool streams, watsonx async stream, ollama http images and caller async embedding clients
* test: restore single request assertions in the deep_infra and azure_ai embedding tests
* test: drop a no-op streaming cost flag from the openai streaming cost test
* test: cover residual legacy-test lines in caching, main, utils, replicate, vertex and bedrock invoke
* test: cover streamed audio assembly, choice membership, untranslated responses events and the stream log guard
* test: pin cost rates in unit tests, use sdk for cohere e2e, drop vertex llama e2e
* test: send the vertex maps e2e request through the openai sdk
* test: drop the local_testing jobs that select no tests after the migrations
* test: wait for the streamed cache entry and keep custom Mistral rates test-local
* test: keep untouched Mistral test lines as they were
---------
Co-authored-by: yuneng <yuneng@berri.ai>
* test(e2e): cover ollama and ollama_chat on chat completions, responses and messages
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(e2e): declare Subject metadata and check streamed tool call ids in the ollama suite
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Mubashir Osmani <mubashir@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Sail now rejects completion_window "flex" on synchronous requests with a
400 saying flex is only for background responses or Batch work. The chat
flex case and the responses flex case have failed on every scheduled
litellm-e2e run in builds 337, 340 and 341. The chat cases keep balanced and
auto, and the responses case sends a caller metadata.completion_window of
balanced, so both still prove the window reaches Sail and the bill uses
that window's distinct rates
* fix(streaming): keep the provider's served service_tier on streamed chunks and spend rows
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(streaming): satisfy type-discipline and strict ruff budgets
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(streaming): stamp the served service_tier on every Responses bridge chunk
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(anthropic-adapter): expose streamed chunks so disconnects bill partial spend
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(service-tier): cover anthropic and responses served-tier billing paths
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(anthropic-adapter): return a chunks-exposing stream so disconnects bill partial spend
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(service-tier): bill disconnects through the router's anthropic stream wrapper
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style: apply ruff format to the anthropic stream changes
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(coverage): ignore delegating properties the ast scan cannot see
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style: keep the cast-ok reasons on the cast call line
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): cover served service_tier billing for streamed chat and messages, complete and disconnected
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(anthropic-cache): delegate chunks/messages/model through the messages stream cache writer
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(streaming): keep service_tier on OpenAI-compatible parsed chunks
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(streaming): parameterize delegated chunks and messages types
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(tests): follow the anthropic pass_through rename after merging main
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(anthropic): drain the logging worker between response cache tests
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(spend): cover azure, databricks, responses bridge and gemini served tiers in the stream billing integration test
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(databricks): keep the served service_tier on streamed chunks and bill it
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(databricks): type the served service_tier chunk without a loose kwargs dict
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(cost): bill the served service_tier over the requested one
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(cost): drop explanatory comment from the tier resolution
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: kerry <kerry@berri.ai>
Register Sail (providers.json, LlmProviders.SAIL, OpenAI-compatible lists,
ProviderConfigManager) for chat, Responses and /v1/messages, and add its 12
models to both cost maps with asap, balanced and flex price columns.
Sail picks speed and price with metadata.completion_window and rejects
service_tier, so the Sail chat and Responses configs translate the tier:
default and priority to asap, flex to flex, balanced to balanced, auto to no
window. Billing prices the window that was sent. A tier Sail has no window
for, or a window or tier set where billing cannot see it (request metadata,
extra_body), is a 400 unless drop_params is set.
Add balanced to ServiceTier and its _balanced price columns to the model
info types, the Rust catalog and the dashboard schema. A transform_extra_body
hook on the chat and Responses base configs, which returns extra_body
unchanged by default, lets Sail keep the window when a caller also sends
extra_body.metadata. Sail is listed in the Add Model form and model picker.
Co-authored-by: shrey kharbanda <shrey@berri.ai>
* fix(proxy): send a real error event when a /v1/messages stream fails
When a stream failed halfway through, the proxy wrote the error as a plain
`data: {"error": ...}` line with no `event:` in front of it. Anthropic clients
pick stream events by that name, so they skip the line and the request looks
like it simply stopped with nothing in it.
Write the failure as an `event: error` frame with Anthropic's own payload, and
take the error type from the status code
* fix(proxy): use the shared Anthropic error mapping for the stream error frame
The first pass added a third copy of the status to error-type table, and it
disagreed with the documented one: 529 came out as `api_error` rather than
`overloaded_error`, and 413 as `invalid_request_error` rather than
`request_too_large`, which hides the two failures a client can actually act on.
Drop that copy and put the frame builder next to the table litellm already
keeps in anthropic_interface/exceptions. The bridged adapter path was building
the same frame inline, so it uses the shared one now too
* fix(proxy): seal a torn SSE frame before the /v1/messages error event
An upstream that drops mid-frame leaves the client inside an open event,
so the error frame that follows is glued onto the torn data line and the
Anthropic SDK raises a JSON decode error instead of an APIStatusError.
Close the open frame with a ping event the SDK skips before writing the
error event, and add the e2e stream-cut edge with Bedrock, Anthropic
boundary, and Anthropic mid-frame legs.
* fix(proxy): keep the SSE tail unchanged on a chunk that is not text
A serializer that hands a dict or model object through as-is has no bytes
the frame tail can learn from, so advance_sse_tail leaves it alone instead
of slicing it.
* fix(proxy): answer a /v1/messages stream that fails before its first byte as a JSON error carrying its status
* test: move the Anthropic error frame tests into tests/unit
* test(e2e): cut the upstream stream only after content has been relayed
* test(e2e): carry split SSE lines across chunks and always tear a data line mid-frame
* test(e2e): find the next data line across a chunk boundary before tearing it
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* test(e2e): cover Azure code_interpreter container files by native id with a service-account key
* test(e2e): require the code_interpreter tool, skip at collection, and scope the container call timeout
* fix(e2e): fail the containers suite when the Azure credentials are missing instead of skipping
* fix(proxy): record streamed /v1/responses container ownership before the response.completed frame
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* test(e2e): cover Azure code_interpreter container files by native id with a service-account key
* test(e2e): require the code_interpreter tool, skip at collection, and scope the container call timeout
* fix(e2e): fail the containers suite when the Azure credentials are missing instead of skipping
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* feat(anthropic): placement policy for mid-conversation system messages
Pure functions over the OpenAI-format message list: split off the leading
system run, keep later system messages as role=system at a placement Anthropic
accepts on models flagged supports_mid_conversation_system (after a user turn,
before an assistant turn or the end, never adjacent), and convert them to user
turns in place elsewhere, keeping tool_result first in a merged user turn.
* fix(anthropic): keep mid-conversation system out of the chat completions system prompt
translate_system_message hoisted every role=system message, at any index, into
the top-level system block. On a conversation carrying a mid-session reminder
that rewrites the cached prefix, so the provider re-bills the whole history at
cache-write pricing on every turn (#36559). #36968 fixed this on /v1/messages;
the chat completions path, shared by first-party Anthropic, Vertex, Azure AI
and Bedrock Invoke, still hoisted.
Only the leading system run becomes the system prompt now. Later system
messages go through the placement policy, and anthropic_messages_pt emits a
system message instead of rejecting the role. The caller's message list is no
longer mutated. Tests pin the two-turn prefix invariant across all four chat
configs and both flag states.
* refactor(anthropic): single-source the converted system note
The /v1/messages pass-through and the chat completions path must prefix a
converted system turn with the same operator note.
* test(e2e): prove the prompt cache survives a mid-conversation system reminder on chat completions
Same priming and assertions as the /v1/messages cases, through
/v1/chat/completions with OpenAI-format messages, for first-party Anthropic
and Bedrock Invoke on a flagged (Opus 4.8) and an unflagged (Haiku 4.5) model.
The reminder sits between the assistant turn and the next user turn, the shape
OpenAI-style agent frameworks send, which is the placement the chat path has
to translate.
* test(anthropic): cover the cache_control rebuild shapes and type the test helpers
Codecov flagged the 5m ttl branch and the empty-system path of the wire
builder; both now have a test. Greptile asked for full typing on the new
test helpers.
* refactor(anthropic): read the mid-conversation flag through a public supports_ helper
supports_mid_conversation_system joins the other supports_* helpers in
litellm.utils, so the chat transformation stops importing the private
_supports_factory.
* chore(typing): declare the mid-conversation type aliases with TypeAlias
The Final sweep tightened LIT010, which exempts TypeAlias declarations but
counts a bare alias assignment as an unannotated binding.
* fix(anthropic): let add_code_execution_tool take the pass-through message union
The translator now emits role=system inside messages for models that accept it,
so anthropic_messages_pt returns the pass-through union. add_code_execution_tool
still declared the narrower user/assistant union while only ever reading
content, so upstream's strip_advisor_blocks_from_messages call in between made
the mismatch visible to the type checker.
* fix(bedrock): keep mid-conversation system messages in place on converse path
* fix: ruff format + multi tool_result order + regression test
* fix: satisfy type-discipline gate + update osv ignore for mlflow PYSEC-2026-3865
* fix(bedrock): restore role narrowing in hoisted system loop for basedpyright budget
* test(bedrock): cover mid-conversation system conversion branches
- non-dict guard in _opens_with_tool_result
- in-place conversion without tool context
- str/list cache_control preservation in mid-conversation path
- drop unreachable non-system guard in hoisted loop
* Place type-discipline suppressions on the lines the gate scans
* Narrow hoisted loop to system role so basedpyright sees the right TypedDict
* fix(anthropic): place mid-conversation system runs by their neighbours only
A run after an assistant turn now slides behind the user turn that
immediately follows it, and a run that ends the array or precedes an
assistant turn becomes a user turn in place. No later message can move
an earlier run, so a client that replays the conversation with more
turns appended sends a byte-identical prefix and preserved thinking
blocks keep their binding
* refactor(bedrock): share the converted system note with the anthropic module
Converse imports CONVERTED_SYSTEM_NOTE instead of carrying its own copy
of the same text, and the reordering helpers lose their comments
* test: pin the replayed request prefix across preserved-thinking turns
One test per audited feature, through the real entrypoint: the chat
transformations for anthropic, bedrock invoke, vertex and converse, the
modify_params dummy tool result, dotprompt with unchanged variables, and
Presidio masking against an in-process fake. Each serializes system,
tools and the earlier messages of turn N and N+1 and asserts they match.
The e2e mid-conversation system test imports its content blocks from
models.py again and is marked provider_live
* fix(anthropic): move mid-conversation system placement into prompt_templates
The prompt factory imported the placement helper from the Anthropic provider
package, whose common_utils reads a factory constant at import time, so loading
the factory first raised ImportError. The module now sits next to
anthropic_messages_pt and every consumer imports core utils
A user turn with content [] or None puts no block on the wire, so a system run
anchored to it landed first in messages or behind an assistant turn. Such a run
now converts in place; empty strings and empty text blocks still anchor because
the factory fills them with a placeholder
* fix(anthropic): anchor system messages only on user turns that reach the wire
* fix(bedrock): type the converse system-message helpers over the message TypedDicts
* fix(anthropic): read replayed pydantic messages in the Converse helpers and convert a system run whose assistant follower sends nothing
A history that replays the previous turn as the litellm.Message object
was invisible to the Converse system-message helpers, so a mid-conversation
system stayed between a tool call and its result or reached Converse as
role: system. The helpers now read fields through the shared
message_field and parts_of accessors and drop the local role predicate.
Flagged placement anchored a system run on any assistant follower, but
anthropic_messages_pt drops an assistant turn that puts no block on the
wire (content None, an empty list, an unsigned thinking part), so the
system landed directly before the next user turn, which Anthropic
rejects. Such a run now converts in place. An empty or whitespace text
turn still anchors, since the converter pads it with a placeholder.
* fix(anthropic): treat bridged encrypted reasoning as a vanishing assistant turn for system placement
An assistant turn whose only blocks carry Responses API encrypted reasoning is
dropped by anthropic_messages_pt, so a mid-conversation system run anchored
before it landed directly before the next user turn. The unsignable-thinking
predicate now lives in common_utils and both the factory and the placement
policy consult it.
* fix(anthropic): let an inline thinking part hide separate thinking_blocks in system placement
anthropic_messages_pt skips an assistant turn's separate thinking_blocks as soon
as its content list carries an inline thinking or redacted_thinking part, so a
turn whose inline part is unsigned puts nothing on the wire even when the
separate block is signed. The placement policy now mirrors that rule.
---------
Co-authored-by: Shifat Islam Santo <shifatislamsanto764@gmail.com>
Co-authored-by: ege-arhan <egearhany@gmail.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* test(e2e): add conversational matrix across chat, messages and responses
Parameterizes one behavioral contract (reply, stream, cost log, tool call,
tool round trip) across /v1/chat/completions, /v1/messages and /v1/responses,
OpenAI and Anthropic models, and env-ref vs stored-credential auth, with
record/replay fixtures.
Adds general_settings.disable_model_info_refresh so the proxy fronting a
replay fixture does not poll every OpenAI-compatible deployment's /v1/models
in the background, which otherwise leaves unconsumed interactions in the
recorded bundle.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(e2e): force the weather tool on the first turn and rename Provider to Deployment
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: mateo <mateo@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Register s3_access_key_id, s3_secret_access_key and s3_encryption_key_id as
LiteLLM-owned batch params so they are no longer forwarded to Bedrock as
additionalModelRequestFields (which 400s ordinary chat on a batch-configured
deployment), keep them on CredentialLiteLLMParams so the batch/file paths
still receive them, and redact the S3 credential key names in debug logs.
Resolves LIT-8290
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Adds live e2e coverage for the Bedrock combinations behind recent customer
incidents: llm_provider-* response-header forwarding on /chat/completions
(nonstream and stream), regional us.anthropic.* inference-profile ids over
the invoke route, and the Admin UI Test Connection probe for a
responses-mode Bedrock Mantle deployment. Registers the matching cells in
the coverage registry and publishes the provider x feature matrix table in
its README.
The websocket routes under /openai_passthrough and /openai had no e2e
coverage, so nothing catches the regression from issue #36088, where both
prefixes carried HTTP routes only and refused every upgrade with a 403
before a socket ever existed.
Two tests cover it. The realtime one opens /openai_passthrough/v1/realtime
and asserts OpenAI's own session.created frame comes back, which proves the
route is registered and relayed upstream. The responses one asserts
/openai/v1/responses accepts the upgrade, since a responses.connect socket
waits for the client to speak first and has no opening frame to check.
A refused upgrade is an HTTP response rather than a close frame, so both
assert on the handshake. ws_base_url moves into e2e_config now that a
second suite needs it
The passthrough tests and their coverage registry rows pointed at the internal
ticket id, which does not resolve for anyone following a link from
status.litellm.ai. Each test docstring and registry rationale now names the
GitHub issue it pins: #36086 for the two prefix routing cases, #36087 for the
file list cursors, #36523 for streamed Responses cost, and #36646 for
embeddings spend.
Five e2e tests over routes a customer drives through the gateway, each one
pinning a fix that currently has no live coverage.
The dedicated /openai_passthrough prefix used to be swallowed by the
provider-scoped /{provider}/v1/files and /{provider}/v1/batches routes, which
bound "openai_passthrough" as a provider name and failed inside the gateway
before ever reaching OpenAI. Two tests now upload a file and list batches
through that prefix and assert OpenAI's own objects come back.
Streamed /openai_passthrough/v1/responses and /openai_passthrough/v1/embeddings
are relayed to OpenAI but still have to be costed, since the customer budgets
against this traffic. Both used to land a row the gateway could not use: the
streamed responses call logged a zero-cost row under a random id, and
embeddings wrote no row at all. Each test now reconciles the logged spend and
token counts against the response the caller was actually served.
GET /v1/files narrowed its data to the caller's own rows but left first_id and
last_id addressing the shared provider account's page, handing any caller raw
provider file ids belonging to other tenants. The new test asserts both cursors
address rows in the page the caller can see.
ResourceManager.defer now accepts any callable rather than one returning None,
so a delete that answers with a response model can be deferred as-is.
On models without supports_mid_conversation_system, a system entry between
an assistant tool_use turn and the user tool_result turn became a user turn
in that position and the provider rejected the request ("tool_use ids were
found without tool_result blocks immediately after"). That run of entries
now goes right after the tool_result turn, where consecutive user turns
merge upstream. The converted turn also carries only role and content, as
the hoist did, so an entry with extra keys no longer 400s with "Extra
inputs are not permitted".
The e2e cache priming re-sends the identical first turn until its own cache
entry reads back before the reminder turn goes out, since Vertex can take a
few seconds to serve a freshly written entry.
* test(e2e): cover vendor strategy gaps for chat contract, image edits, auth, team activity
Resolves the first slice of LIT-4778 (vendor API testing strategy): image edits happy path, chat multi-turn + validation + sanitization, LLM-route auth header matrix, and /team/daily/activity structure
* test(e2e): expand vendor API strategy coverage across endpoints
Adds validation cases on existing endpoint suites, plus vector stores, search,
bedrock native, realtime HTTP secrets/calls, responses retrieve, files/batches
contract, and chat stream SSE. Registers coverage cells for LIT-4778
* test(e2e): finish vendor strategy open items
Audio transcription negatives, vector-store file attach/poll/search,
OpenAI moderation category matrix across chat/messages/responses, and
smoke model matrix for chat (LIT-4778)
* test(e2e): harden vendor strategy suite against live env edges
Fix stream [DONE] tracking, XSS no-crash contract, realtime model routing,
vector store list/search models, responses validation, and provider-denied
Bedrock paths so the suite is stable against a live proxy
* test(e2e): rename suites, drop vendor_contract, fix greptile gaps
Move shared status helpers into e2e_http, rename chat auth headers and
chat security suites, remove vendor_contract and dev_config files_settings,
and tighten transcription validation plus vector-store search assertions
* test(e2e): route bedrock stream disconnects through e2e_http
Catch mid-stream RequestException in the shared harness so bedrock native
tests do not import requests directly
The existing web_search cells drive Claude Code's client-side WebSearch
tool, which the CLI executes itself and feeds back as a tool_result. The
CLI never emits a web_search_20250305 definition, so those cells stayed
green while the Anthropic-managed server tool 400'd on Bedrock.
Add a cell that posts the server tool to a Bedrock deployment over
/v1/messages and asserts a web_search_tool_result block comes back, and
reword the compat row so it no longer reads as coverage of the server
tool. Model the server tool as a composed base shared with tool_search.
Resolves LIT-5391
This reverts commit dcb4e5033c.
The suites landed without the proof-of-fix and QA runbook the PR body
itself flagged as outstanding, so the coverage they claim is unverified
against a live proxy
* test(e2e): cover vendor strategy gaps for chat contract, image edits, auth, team activity
Resolves the first slice of LIT-4778 (vendor API testing strategy): image edits happy path, chat multi-turn + validation + sanitization, LLM-route auth header matrix, and /team/daily/activity structure
* test(e2e): expand vendor API strategy coverage across endpoints
Adds validation cases on existing endpoint suites, plus vector stores, search,
bedrock native, realtime HTTP secrets/calls, responses retrieve, files/batches
contract, and chat stream SSE. Registers coverage cells for LIT-4778
* test(e2e): finish vendor strategy open items
Audio transcription negatives, vector-store file attach/poll/search,
OpenAI moderation category matrix across chat/messages/responses, and
smoke model matrix for chat (LIT-4778)
* test(e2e): harden vendor strategy suite against live env edges
Fix stream [DONE] tracking, XSS no-crash contract, realtime model routing,
vector store list/search models, responses validation, and provider-denied
Bedrock paths so the suite is stable against a live proxy
* test(e2e): rename suites, drop vendor_contract, fix greptile gaps
Move shared status helpers into e2e_http, rename chat auth headers and
chat security suites, remove vendor_contract and dev_config files_settings,
and tighten transcription validation plus vector-store search assertions
* test(e2e): route bedrock stream disconnects through e2e_http
Catch mid-stream RequestException in the shared harness so bedrock native
tests do not import requests directly
* fix(e2e): address greptile and veria review on vendor strategy suite
Store search tool keys as os.environ refs and resolve them in SearchAPIRouter.
Tighten validation helpers and assertions so 5xx/empty/unrelated failures no longer pass coverage cells
* fix(e2e): drop search_api_router os.environ expansion from vendor suite
Keep the PR test-only. Search tools register without an api_key so the
proxy falls back to its own PERPLEXITY/TAVILY env, same pattern as a2a.
* test(e2e): drop search e2e suite from vendor strategy PR
Remove the /v1/search coverage file and its registry rows so this PR
no longer carries search endpoint testing.
The /openai/{endpoint} passthrough forwards a raw OpenAI-format request to
api.openai.com (or OPENAI_API_BASE) with the proxy's OPENAI_API_KEY swapped in,
and still logs a costed pass_through_endpoint SpendLogs row. Nothing exercised
that path end to end.
Adds a live /openai/v1/chat/completions passthrough test that asserts a 2xx
completion and a costed row with custom_llm_provider=openai, mirroring the
gemini and anthropic passthrough cost tests, and registers
llm.chat_completions.openai.passthrough.nonstream.cost_logged.
/vllm/batches and /vllm/files are high-volume passthrough routes with no e2e
coverage. They ride litellm's generic /vllm/{endpoint} forwarder, so the test
uploads a JSONL through /vllm/v1/files and creates a batch through
/vllm/v1/batches (BatchClient with provider=vllm), asserting the forwarded file
and batch objects come back. Lives next to TestHostedVllmBatch and is skip-marked
for the same reason: no live vLLM server (HOSTED_VLLM_API_BASE) in the e2e env.
Adds the two llm-translation registry cells.
Ran the before/after proof live against Vertex Claude (global endpoint,
project vertex-check-481318): base transform 400s an unflagged model
(claude-opus-4-7) on a mid-conversation role:system reminder, the fix
hoists it to a 200, and a flagged model (claude-opus-4-8) keeps the
reminder in messages with cache_read held at 15615 across the reminder
turn. Flip both vertex.mid_conversation_system rows to fail_before_fix:
proven.
Azure AI Foundry and Vertex AI serve Claude on the first-party Anthropic
Messages contract, which was verified live to be byte-identical to
api.anthropic.com: a leading role:"system" entry in messages is rejected on
every model ("messages.0: use the top-level 'system' parameter"), and a
mid-conversation role:"system" reminder is accepted in place on Claude 4.8+/5
but 400s on Claude 4.7 and older ("role 'system' is not supported on this
model"). This is the same contract Bedrock Invoke already handles model-aware
(PRs #32578/#32831/#32882); Vertex and Azure did no hoisting at all, so a Claude
Code session on an older Vertex/Azure Claude model hard-400s on its reminder
turns, and the only thing sparing 4.8+/5 was that nothing was hoisted
Extract Bedrock's model-gated normalization into the shared
AnthropicMessagesConfig base as _normalize_system_role_messages and call it from
the Vertex and Azure messages configs. Flagged models (4.8+/5) hoist only the
leading run of system entries and keep mid-conversation reminders in place so
the top-level system prefix stays byte-identical and the prompt cache is
preserved; unflagged models hoist every system entry so the request returns a
completion instead of a 400
Add supports_mid_conversation_system to the azure_ai and vertex_ai Claude 4.8+/5
cost-map entries. Exact cost-map hits win over the claude-mid-conversation-system
fallback rule, so without the explicit flag those models would be treated as
unsupported and hoist every reminder, collapsing the prompt cache (the exact
customer regression). A per-provider test guards this so future 4.8+/5 entries
cannot silently miss the flag
Closes the Vertex/Azure gap from the customer RCA
* test(e2e): cover passthrough headers, batch assume-role, gemini, vllm, bedrock guardrails, batch rate-limit mapping
Add parent-package e2e suites for the six feature gaps: pass-through header forwarding via /config/pass_through_endpoint, Bedrock batch STS assume-role, Gemini chat + files, hosted_vllm batch/files, Bedrock guardrail pre_call blocks (plus restored content-filter team opt-out), and OpenAI batch RPM 429 body mapping. Registry cells and LiteLLMParamsBody/TeamMetadata fields updated so markers collect cleanly.
* test(e2e): cover LIT-4587 gaps for redis, responses, tpm cache, apply_guardrail, langfuse
Adds customer-shaped live e2e for apply_guardrail, responses store+metadata TTL,
TPM excluding cached tokens, redis-backed RPM, redis circuit-breaker path,
Langfuse spend, Cohere chat, virtual-key auth, file content download, hosted_vllm
chat, and Nova Sonic realtime. Registry cells updated for the new markers.
* test(e2e): drive LIT-4587 gap suites on Anthropic to avoid Gemini quota flakes
Redis RPM, circuit-breaker path, virtual-key auth, responses metadata, and
Langfuse driver models now use Anthropic haiku so local runs stay green when
Gemini daily quota is exhausted.
* test(e2e): drop Langfuse spend suite; feature is being deprecated
Remove test_langfuse_e2e.py, logging.langfuse registry cells, and the
langfuse-only conftest driver/credentials fixtures.
* test(e2e): fold provider/batch feature tests into their endpoint suites
Keep the e2e layout endpoint- and suite-scoped instead of one file per
provider or feature
Move the virtual-key auth case into access_control/test_access_control_e2e.py
as TestVirtualKeyAuth (replacing an incomplete stub) and drop the standalone
test_virtual_key_auth_e2e.py
Fold the five per-file batch suites (file content, RPM 429 mapping, Bedrock
assume-role, Gemini files, hosted_vllm batch) into batches/test_batches_e2e.py.
The hosted_vllm batch case is skipped for now since it needs a live vLLM server
(HOSTED_VLLM_API_BASE) the e2e environment does not provision; it and the
gemini-files and RPM-mapping cases reference LIT-3382 / LIT-3266 where relevant
Merge the cohere, gemini and hosted_vllm chat cases into
llm_translation/test_chat_completions_regression_e2e.py so /chat/completions
coverage lives in one endpoint file, and repoint the coverage_registry source
fields to the new homes
Move the shared CacheControl / TextBlock / RichMessage request blocks into the
root models.py (re-exported from endpoints_client) so quota_management can use
them without a cross-suite import, which also clears the basedpyright errors in
test_tpm_excludes_cached_tokens_e2e.py; type the httpbin echo body in
test_passthrough_headers_e2e.py with a pydantic model to drop the Any-typed
json.loads path
* test(e2e): address review feedback and re-home virtual-key coverage
Replace the tautological Bedrock assume-role batch id assertion (`startswith(...)
or batch.id`, always true) with a managed-id shape check, since the unified
target_model_names path re-encodes the id rather than returning a raw ARN
Raise the batch RPM-mapping test's rpm_limit above one so the file upload can no
longer consume the key's sole request unit before batch create runs; the batch
create then clears the generic per-request limiter and the batch limiter is what
returns the "Batch rate limit exceeded" body the assertions check
Set exercised_on to [] on the pass-through header test; it drives a pass-through
endpoint, not /chat/completions
Move the virtual-key valid_allows / invalid_denied cells from other.yaml to
mgmt.yaml as mgmt.virtual_key.* so TestVirtualKeyAuth rolls up under Management,
and point its covers marker at the new ids
Introduce the e2e coverage denominator: 282 behavior cells across the six
tracking modules (LLMs, MCPs, Management/UI, Reliability & Performance,
Logging & Guardrails, Other), one validated YAML row each, plus a collector
that diffs the registry against @pytest.mark.covers markers and reports
coverage per module.
The registry rows validate against a pydantic discriminated union so a row
cannot carry a field from another module. The collector is static: a
collect-only pass reads the markers, so it runs no test and needs no live
proxy. Register the covers marker suite-wide so that pass works under
--strict-markers.
This is a draft for review. Tiers are proposed rather than signed off, and a
few cells still need a support check or a prune.
