The bridges derived prompt_cache_key as the first 64 chars of metadata.user_id.
Claude Code packs a JSON object into that field whose prefix is the per-install
device_id, so every session and subagent on one machine shared a single key,
and a plain end-user id pinned all of that user's conversations to one slot.
Parse the JSON and use session_id; send no key otherwise so the provider falls
back to its own prompt-prefix hashing. An explicit prompt_cache_key still wins.
Fixes#39145
Direct Anthropic http image inlining, Vertex AI Anthropic forced base64 conversion, Ollama completion image download and the watsonx GPT-OSS Hugging Face chat template lookup all ran synchronous HTTP inside the async request path. Each provider config now transforms through async_inline_remote_media on the async path, the Anthropic handler awaits the config's async_transform_request before dispatch and in the Rust fallback, and watsonx text exposes async_transform_request and always awaits ahf_chat_template
Resolves LIT-7028
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Fireworks renders a Responses request through a chat template that only
accepts a system message at the very beginning, so a request carrying
`instructions`, a developer item, and a replayed reasoning item (the shape
Codex CLI sends from its second prompt on) came back 400 with "System
message must be at the beginning".
The leading system or developer items, and any developer item later in
the conversation, now fold their text into top-level `instructions`,
joined with blank lines, and leave `input`. A developer item that closes
the conversation right after an assistant turn stays where it is as a
system item, as does any system or developer item with an image or file
part, so those parts still reach Fireworks. Mid-conversation system items
stay untouched. Non-string `instructions` pass through unchanged.
Folding into `instructions` rather than a leading system item keeps
`previous_response_id` chaining working, since Fireworks prepends the
stored history to `input` and a leading system item would land after it.
This supersedes the leading system item approach from deaadc21d3 and
4807630c2a on this branch. The leading and closing block rules match the
chat path change in #39852.
litellm's azure_ai config rejects reasoning_effort for gpt-chat-latest and Azure documents a fixed reasoning level for it, so the entry no longer advertises reasoning_effort_levels. The catalog metadata tests compare cost_per_token and the whisper transcription cost with the entry the calculator read instead of with list-price literals, the pattern #40195 removed
Direct litellm.cost_per_token callers that name a Model Router deployment as
the model get the routing fee again, as they did before this branch, and the
fee is still charged exactly once on every completion_cost path. The
grok-4-20 entries bill cached prompt tokens at the input rate, since Azure has
no cached-input meter for them, and the model_router twin carries the same
limits and retirement date as model-router. The catalog test now exercises
the cost calculator and map relations instead of pinning map fields.
Restores the public keyword removed at 415bdbfd8f. A direct caller that
names the Model Router as the request model gets the routing fee folded
into the prompt cost once; completion_cost never passes it and charges
the fee through the additional-costs hook as before
The router fee was folded into azure_ai.cost_per_token and then added again
by the additional_costs hook, so every routed request paid it twice. The hook
now owns the fee, the entry named by the deployment supplies the price, and a
response priced as the router entry itself is not charged again
model-router, gpt-chat-latest and cohere-command-a carry the limits from the
Foundry models page, and model-router and grok-4-20-* carry their retirement
dates. The router tests now run at the completion_cost level with a Logging
object, which is the path the proxy takes, and fail at the merge base
Marengo 3.0 requests now get a 400 naming any textTruncate, lengthSec,
useFixedLengthSec, or minClipSec parameter, and any video or audio option
sent with a text, image, text_image, or multi_input request, instead of
silently dropping them. drop_params (global, per deployment, or per
request) drops them instead. Pydantic validation errors name the field
and the reason, and the 3.0 marker is the exact "marengo-embed-3-" model
id segment.
The new metadata test sat at the top of tests/test_litellm. The azure_ai metadata tests live in tests/test_litellm/llms/azure_ai next to the cost calculator test, so this moves it there and bumps its repo-root lookup by the two extra directory levels. No test changes.
AWS prices Marengo 2.7 and 3.0 text and image embeddings per request, never per
token, and their responses carry no token count. The old transform estimated
prompt tokens from the vector length, which billed a text request at 128 tokens
times the per-token rate (0.00896 instead of 0.00007). Marengo responses now
report zero tokens with query_count and image_count derived from the request
batch, and all six Marengo cost-map entries price per request (with the video
and audio per-second and per-image rates on the base entries). query_count is a
new prompt_tokens_details field wired to input_cost_per_query in the cost
calculator.
Add cost map entries for azure_ai/gpt-chat-latest, codex-mini, whisper,
model-router, cohere-command-a, grok-4-20-reasoning, and
grok-4-20-non-reasoning, priced from the live Azure AI Foundry and Azure
OpenAI pricing pages and the Azure Retail Prices API.
Skip the model router flat fee when the response model is the router
entry itself, since the generic cost already priced that fee. Before,
azure_ai/model_router charged it twice.
Resolves LIT-3157
* fix(bedrock): keep x-amzn-RequestId on chat error responses
Bedrock chat error paths built BedrockError from only a status code and a
message, so the provider response headers were gone before exception mapping
ran and the proxy had nothing to forward. AWS support needs x-amzn-RequestId
to investigate a server-side error.
- converse and invoke chat handlers pass the real headers and response when
they turn an httpx.HTTPStatusError into a BedrockError, and read the body
through error_response_text so a streamed body nobody read does not throw
- every bedrock chat get_error_class honors the headers it is already handed:
invoke, moonshot, bedrock-hosted openai, agentcore and the invoke agent
- BedrockError carries those headers into the response it synthesizes when a
caller has headers but no response, skipping values httpx cannot carry
- the bedrock 500 mapping forwards the provider response like its 4xx and 503
siblings instead of fabricating a blank one
The proxy now returns llm_provider-x-amzn-requestid on Bedrock chat errors.
* fix(bedrock): keep request-id on text-classified errors
The context-window and image branches of _map_bedrock_exception built their
litellm exception without the provider response, so a Bedrock 400 classified
by its body text lost x-amzn-RequestId while the sibling branches kept it.
Also narrows the new BedrockError types and trims its docstrings.
* chore(bedrock): drop the docstrings on the new error helpers
* fix(bedrock): keep request-id on every error path that has one
The ticket's root cause is that every BedrockError raise site under
litellm/llms/bedrock/ was built from status and message alone. The first
commits covered the chat and invoke handlers; this covers the rest.
Embeddings, rerank, image generation, image edit, count tokens, search and
the transformation layers now hand on the provider response or its headers,
and both bedrock_mantle configs return a BedrockError instead of the
OpenAI error that drops them.
Two blockers surfaced while verifying the streaming path. The trailing
`except Exception` in make_call and make_sync_call swallowed the BedrockError
raised a few lines above, relabelling a provider status as a 500, and the
non-200 branch read an unread streamed body, which throws.
The raise sites left alone have no provider response to carry: timeouts,
credential and config errors, and mid-stream event frames.
* fix(bedrock): forward provider headers from the count tokens route
The count tokens route converts BedrockError into an HTTPException, and dropped
the headers the handler had just kept, so that route still lost the request id.
get_response_headers now takes a Mapping so an httpx.Headers can be handed to it
without a copy.
* fix(bedrock): classify every bedrock surface through BedrockError
Eleven bedrock configs still inherited a provider-agnostic get_error_class
that builds a blank response, so the request id was gone before the proxy
read it. Claude platform, bedrock anthropic-messages, both image edit
configs, passthrough, realtime, vector stores and agentcore search now
return BedrockError, and a parametrized audit drives all 36 configs.
* fix(proxy): keep provider headers on the httpx status error branch
_handle_llm_api_exception forwards safe_headers on every branch except the
httpx.HTTPStatusError one, which the bedrock passthrough route reaches, so
the request id was dropped before the client saw the response.
* fix(bedrock): keep the request id on the timeout mappings
Timeout takes no response argument, so the three bedrock timeout branches
dropped the provider headers even when the upstream answered 408 or 504
with an x-amzn-RequestId. They now ride on the exception, already
llm_provider-prefixed, which is the form the proxy emits.
* fix(bedrock): keep the provider response on mapped timeouts
The previous round attached llm_provider-prefixed headers directly to the
Timeout. That shadowed the raw upstream headers for _get_response_headers,
so router cooldown and fallback cooldown stopped honouring retry-after on
bedrock 408/504 replies.
Give Timeout an optional response instead, the way every other mapped
bedrock exception already carries one. Retry logic reads the raw
retry-after off the response, and the proxy prefixes those headers on the
way out, so clients still see llm_provider-x-amzn-requestid.
* chore(bedrock): drop the explanatory comment on Timeout.response
* feat(skills): semantic search over the LiteLLM-hosted skill registry
Adds GET /v1/skills?query= (custom_llm_provider=litellm_proxy) and a
skill_search MCP virtual tool, ranking the caller's accessible skills by
semantic similarity, mirroring the A2A agent registry search (LIT-6309).
Also fixes a pre-existing bug where create_skill() dropped description and
instructions for the litellm_proxy provider, which left every LiteLLM-hosted
skill with no searchable text.
* fix(mcp): coerce skill_search top_k instead of raising 500 on malformed input
The MCP-REST skill_search dispatch validated raw tool arguments through a
pydantic model directly, so a non-numeric top_k raised a ValidationError
that the endpoint's catch-all turned into an HTTP 500. Mirrors the
agent_search branch's tolerant coerce_top_k handling instead.
* fix(skills): enforce key limits on search embeddings and bound the semantic index
Semantic search embeddings now run the same pre_call_hook the /embeddings
route runs, so key rate limits, budgets and guardrails apply before the
embedding model is called. The shared SemanticTextIndex caps cached vectors
and evicts the least recently searched entries, and each skill's embedded
text is capped so one skill cannot inflate the embedding batch
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(skills): surface proxy 429s from search embeddings instead of a 503
ProxyRateLimitError is also an OpenAIError, so the search engine was folding
a key rate limit into skill_search_unavailable. Proxy HTTPExceptions now
propagate so the caller gets the same 429 the /embeddings route returns
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(skills): import assert_never from typing_extensions for Python 3.10
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(skills): embed the request as the pre-call hooks returned it, not the original text
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(skills): keep the litellm_proxy provider check for GET /v1/skills?query= inside llms/
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(skills): move the GET /v1/skills?query= endpoint tests under tests/test_litellm/proxy
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>
A fake-streamed provider hands the adapter one chunk carrying both the
delta payload and the finish_reason, which is exactly what the combined
chunk splitter exists for, but its content check never listed the refusal.
The translation short-circuits on finish_reason, so that refusal text was
dropped and the client got `stop_reason: refusal` over an empty content
array, the symptom this PR set out to fix.
Both refusal accumulators also drop their `mutable-ok` lists for a plain
string attribute
The first-delta guard read `delta.refusal` directly, while the translation
three lines later goes through `openai_chat_refusal_text`, which also reads
the `provider_specific_fields` LiteLLM parks unrecognized fields in. A
provider that sends the refusal that way had its only refusal delta skipped
as blank, so the client got `stop_reason: refusal` over an empty content
array, which is the symptom this PR set out to fix
The /v1/messages adapter lowers a tier the entry does not accept, so dropping max from the astra
rows moves that path from Foundry's 400 to a request at xhigh. Nothing pinned that, and the guard
test's docstring named gpt-6-astra as the only gpt-5 name with an azure_ai row, which 11 rows
contradict.
The new Lyria passthrough branch runs before the image-generation branch
and keys on the same `predictions[0].bytesBase64Encoded` shape imagen
returns, so only the cost-map lookup separates them. Cover an imagen
predict response end to end so a future change that drops that lookup
fails here instead of misbilling images as audio.
