* fix(credentials): keep credential labels and edits consistent across UI, API, and replicas
The credential modal flattened stored credential values into the same form object as the
credential's own name, label, and provider, so a stored value keyed credential_name or
display_name replaced the real one and the edit went to the wrong credential or relabeled it.
The modal now submits the top-level fields and credential_values separately
PATCH /credentials now reads the row it merges onto from the writer, so an edit inside the read
replica's lag window no longer writes back an older label or key
POST /credentials answers 409 for a name defined in config instead of creating a DB copy that
shadows it
display_name rules live on the server only. It rejects control, formatting, and line separator
characters, and the UI stops enforcing its own length check and shows the server's error
The models table, model detail view, and model edit picker show the credential's technical name
under its label whenever the two differ
* fix(ui): show credential source in its own column like models
The config marker sat under the credential name. The credentials table now has a Source column
with a Config or DB status badge, matching the Source column on the models table
* test(credentials): unit-test label and prefill helpers and keep the full credential name on hover
Moves displayNameChange and initialFormValues out of the modal so their rules are unit tested,
keeps only the wiring cases in the modal's integration test, makes the label acceptance test
write through the real credentials repository, and adds the full credential name as a hover
title where the models pages truncate it
* 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>
* feat(guardrails): add decision_model guardrail for decisions-API models
Adds a new guardrail provider that scores request and response text with a
decisions-API model (e.g. Jev) against named predicate checks. Each check
carries its own threshold and action (block or log); a flagged block check
rejects the request with a 400 naming the flagged checks, and call failures
honor unreachable_fallback (fail_open or fail_closed).
Ships four preset checks for the Admin UI (prompt injection, jailbreak,
system prompt extraction, data exfiltration) served from
/guardrails/ui/add_guardrail_settings, plus a Decision Model step in the
add-guardrail form filtered to evaluation-mode models.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(guardrails): fail closed on verdict errors and drop comments in decision_model
* feat(guardrails): cap decision_model input with head and tail elision
* fix(guardrails): list only block checks in decision_model 400s and tighten inputs
* feat(guardrails): honor timeout, cap checks at MAX_DECISION_QUESTIONS, and reject duplicate check names
* fix(guardrails): decorate decision_model apply_guardrail with log_guardrail_information
* fix(guardrails): suppress untyped-decorator pyright error on log_guardrail_information import
* feat(guardrails): screen each text in overlapping chunks with a shared decisions-call semaphore
Replace head/tail elision with per-text screening: each non-empty text is
sent to the decision model on its own, split into overlapping chunks when
it exceeds max_input_chars, and all calls run under a per-event-loop
semaphore shared across requests (max_concurrent_decision_calls,
default 8). Check verdicts aggregate the max probability across calls; a
missing predicate answer on a Block check blocks under fail_closed and
only warns under fail_open, while log-only checks are never flagged for
it. The 400 detail now lists flagged check names only. The provider
combobox also matches Decision Model on jev, typesafe, prompt injection,
jailbreak, and decision
* fix(ui): align decision model custom check rows with preset rows
* fix(ui): move decision model custom check remove button to the row end
* style(ui): run prettier on decision model check files
* fix(ui): inline decision model evaluation-mode filter after lens module removal
* fix(guardrails): keep decision model detections when a chunk call fails
* fix(ui): require an enabled decision model check before create
* fix(guardrails): screen decision model texts sequentially like presidio
* feat(guardrails): replace decision model presets with provider picker and per-question test
* fix(ui): render decision model question test as a full-width row
* feat(ui): add run-history test section to decision model form
* fix(ui): prevent threshold overlap and show test shortcut
* feat(guardrails): default decision model threshold to 0.7
* test(ui): expect decision model default threshold 0.7 in characterization payload
* feat(guardrails): screen tool call arguments with the decision model
* refactor(guardrails): freeze the decision model config model
* feat(guardrails): list only evaluation-mode deployments in the decision model picker
* feat(guardrails): hide the decision model questions table until a question exists
* style(ui): format DecisionModelFields
* style(ui): format decisionModelQuestion tests
* refactor(guardrails): type the tool call function extraction
* refactor(guardrails): build tool call texts without a mutable accumulator
* refactor(ui): share isDecisionMode for the decision model picker
* fix(guardrails): screen prior Responses function call arguments in the decision model guardrail
The Responses input handler leaves function_call arguments out of texts and tool_calls, so a prompt injection in a prior call's arguments passed unscreened. Assistant tool calls from structured messages are now screened too, deduplicated against the request's tool calls
* test(integration): send instructions with the decision model guardrail check
Checks require instructions, so creating the guardrail returned 422 and the test never sent a chat request
* fix(ui): score the decision model test preview the way the guardrail does
An unanswered block question now shows Block, disabled questions no longer count, and a run is hidden once the model or a question's text changes since it was scored
* fix(ui): clear the decision model error on pick and list questions under Add a question
Picking a model after a failed Create left "Select a decision model" on screen, because the form only validates on submit. The field now revalidates when it already shows an error. The Questions table and its heading appear only once a question exists, below the Add a question box
* test(ui): move the decision model form tests into an integration test file
They render the whole AddGuardrailForm, so they belong in a .integration.test.tsx file. Plain fields are now filled with fireEvent.change, and the two existing form test files go back to main apart from the modelHubCall mock the form now needs
* test(guardrails): build the decision model test params without mutating a local
* fix(guardrails): reject list checks outside decision_model and honor streaming scan settings
- LitellmParams now rejects a list `checks` for any guardrail other than
decision_model, so a Bedrock guardrail with list checks gets 422 again
instead of being stored and failing every request
- The decision model guardrail reads streaming_end_of_stream_only and
streaming_sampling_rate, so a streamed post_call response can be scored
once at end of stream instead of every 5 chunks
- max_concurrent_decision_calls is described as a per-worker limit
* fix(ui): match guardrail providers like the default picker filter
- The provider search uses the Base UI collator filter on the label and
the aliases, so searches such as presidio_pii or llm_as_a_judge match
again, as they did before the alias filter
- Model groups from /model_group/info are parsed defensively and after the
providers are populated, so a malformed response cannot empty the
provider list
* fix(ui): edit decision model questions in place and show passing test chips in green
Questions start as an Add question button. Each click adds a blank question
card with its name, text, action and threshold, editable until the guardrail
is created, mirroring the per-model budget editor. Create and Next reject a
blank or duplicate enabled question. Test run verdicts and chips use the
success tone for Pass, the error tone for Block and the warning tone for a
flagged Log only question
* fix(ui): drop the per-question enable checkbox from the decision model form
A question is either in the list or removed with its X, so every listed
question is sent and scored. The card now labels Name and Question the way
it labels Action and Threshold
* fix(ui): give the question name field the full card width
* fix(ui): show the provider logo on the selected decision provider
* fix(ui): show the full threshold slider track on decision model questions
* feat(ui): add decision model cards to the guardrail garden
Adds a Decision Model section to the guardrail garden with TypeSafe Jev,
Perplexity, Microsoft, OpenAI and Databricks cards. Each card opens the
create form scoped to that provider's decision models.
The create form drops the Decision Provider picker and lists every
decision model on the proxy with its provider logo, prompts to add a
model when none is available, and says that test runs are billed and
logged.
* fix(ui): shorten the decision model test hint to one line
* fix(ui): prefill the decision model from the garden card's provider
* chore(ui): regenerate the dashboard API types after merging main
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com>
* fix(proxy/auth): expand all-team-models sentinel for project model access checks
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(e2e): use CI-served models and tighten project all-team-models denial check
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(e2e): declare coverage registry cells for project all-team-models tests
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci: retrigger e2e after ephemeral stack deploy timeout
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(e2e): wait for team allowlist propagation before project denial check
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(e2e): annotate written_at as Final per LIT010
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: jesus <jesus@berri.ai>
* fix(sagemaker): keep AWS credentials out of the sagemaker_chat request body
sagemaker_chat and sagemaker_nova built the container body from every optional
param, so aws_access_key_id, aws_secret_access_key, aws_session_token and the
other aws_* auth params, plus model_id and sagemaker_base_url, were sent in the
JSON body to the endpoint container. The request is still SigV4-signed with the
same credentials, which stay in optional_params for signing
* fix(sagemaker): limit the sagemaker_chat body filter to aws_ params
model_id and sagemaker_base_url are not credentials, so they stay in the
body as before and the change sticks to what the fix is about
* test(sagemaker): assert the whole sagemaker_chat request body
* fix(sagemaker): filter the sagemaker_chat body with the shared AWS auth param list
* test(sagemaker): cover the sagemaker_chat body on every endpoint, credential source, and outage
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* test(integration): streamed request bills the same SpendLogs row as the non-streamed request
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): write the stream parity cells as StreamParityTestCase literals
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): express stream parity cells as CostTrackingTestCase pairs
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): stream parity asserts exactly one SpendLogs row per leg after a settle window
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): stream parity forces a spend-log flush with a /v1/responses row instead of a settle sleep
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): stream parity covers messages -> chat and messages -> converse bridges
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): stream parity bridges use xai/grok-4.7 and us.anthropic.claude-opus-5-5
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): use latest frontier models in stream parity cells (gpt-5.6, claude-opus-5-5)
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): switch OpenAI parity cells to gpt-6.1-sol and the chat -> responses bridge to gpt-5.5-pro
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: kerry <kerry@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(vertex_ai): parse Vertex timestamps as UTC so created_at is not shifted by the host timezone
convert_vertex_datetime_to_openai_datetime parsed the trailing-Z Vertex and GCS
timestamps into a naive datetime, so .timestamp() read them as host local time
and every created_at it produced was off by the host UTC offset (about 7h ahead
on a PDT host). Attach UTC before converting, and accept the whole-second and
nanosecond forms Google's RFC 3339 timestamps also use, which used to raise.
Refs LIT-9383
* test(vertex_ai): type the UTC epoch test, guard tzset, build the rig config without mutation
* feat(sdk): add litellm.systemone and trim litellm.decisions to the OpenAI shape
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci: run tests/unit/systemone in the core-utils unit shard
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(sdk): keep litellm.decisions(state=...) working with a DeprecationWarning
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test: make tests/unit/systemone a package
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(sdk): surface the litellm.decisions(state=...) deprecation as a UserWarning
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: kerry <kerry@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(guardrails): guardrail Bedrock Converse stream text per content block
The buffered converse-stream guardrail joined every text delta of the same
kind across all content blocks into one string, guardrailed it, then split the
result back over the frames by length. When a guardrail edit changed the
length (a mask), characters moved between blocks: text spilled from one text
block into the next, and parallel toolUse inputs became invalid JSON.
Group fragments per (contentBlockIndex, kind) so each block is guardrailed and
written back on its own.
* test(guardrails): run the Bedrock stream block tests through the real post-call hook
Drive de_anonymize_event_stream with a real ProxyLogging and pass-through logging object, with a fake guardrail at the boundary, and assert the texts the guardrail receives as well as the frames the client gets back
* test(guardrails): pin the logging start time in the Bedrock stream tests
* docs(proxy): describe /v1/decisions and /v1/systemone in Swagger UI
Add summaries, descriptions, inlined request body schemas with examples and typed 200 responses to the
decision routes and their aliases, so the OpenAPI document and the lazy snapshot show both request formats
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): cap _inlined schema walk depth for the recursion check
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci: gate undocumented POST routes in the proxy OpenAPI document
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci: install the proxy extra before the OpenAPI docs gate
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): drop unused TypeIs import from proxy _types
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: kerry <kerry@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): only pass the JSON-RPC method to MCP admission auth
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): expect method-only MCP admission body
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>
PR #45667 removed openrouter/typesafe/jev-1.13 from the cost map after #45640 moved OpenRouter decisions billing to usage.cost. The OpenAI-format wire test still read the removed row and raised KeyError on CircleCI integration-providers
Co-authored-by: mateo <mateo@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(ui): label the System One yes / no question type noul and explain it on hover
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(ui): capitalize the Noul answer type label
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* Revert "feat(ui): capitalize the Noul answer type label"
This reverts commit e79a40fce2.
* test(ui): cover the noul banner tooltip and the answer type option title
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: kerry <kerry@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(types): move LitellmUserRoles and KeyManagementRoutes into litellm.types.proxy.auth.user_roles
Part of LIT-9423
* refactor(types): move is_jwt into litellm.types.proxy.auth.user_api_key_auth
Part of LIT-9423
* refactor(auth): alias is_jwt import so JWTHandler.is_jwt is not read as recursive
* refactor(types): type UserAPIKeyAuth, its token view and hash_token in place
Part of LIT-9423
* refactor(types): move UserAPIKeyAuth, its token view and hash_token into litellm.types.proxy.auth.user_api_key_auth
Part of LIT-9423
* fix(types): keep agent, budget snapshot and is_jwt names importable from proxy._types
Part of LIT-9423
* refactor(sdk): import UserAPIKeyAuth from litellm.types.proxy.auth
Part of LIT-9423
* fix(types): restore main's _types.py content alongside the user_api_key_auth move
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
* style(types): drop blank lines between proxy._types re-export imports
Part of LIT-9423
* refactor(proxy): keep only user_api_key_auth re-exports in _types.py
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(proxy): carry main's UserAPIKeyAuth field into moved file
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(types): drop blank lines between proxy._types re-export imports
Part of LIT-9423
* fix(types): drop unused TypeIs import from proxy/_types.py
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(ui): replace Reset example with an Example preset dropdown and a Clear button on the Decisions tab
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(ui): match Decisions presets by content and keep an omitted model out of Clear and preset picks
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: kerry <kerry@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* docs(proxy): describe /v1/decisions and /v1/systemone in Swagger UI
Add summaries, descriptions, inlined request body schemas with examples and typed 200 responses to the
decision routes and their aliases, so the OpenAPI document and the lazy snapshot show both request formats
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): cap _inlined schema walk depth for the recursion check
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: kerry <kerry@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(types): move LitellmUserRoles and KeyManagementRoutes into litellm.types.proxy.auth.user_roles
Part of LIT-9423
* refactor(types): move is_jwt into litellm.types.proxy.auth.user_api_key_auth
Part of LIT-9423
* refactor(auth): alias is_jwt import so JWTHandler.is_jwt is not read as recursive
* refactor(types): type UserAPIKeyAuth, its token view and hash_token in place
Part of LIT-9423
* refactor(types): move UserAPIKeyAuth, its token view and hash_token into litellm.types.proxy.auth.user_api_key_auth
Part of LIT-9423
* fix(types): keep agent, budget snapshot and is_jwt names importable from proxy._types
Part of LIT-9423
* refactor(core_utils): move resource_ownership out of the proxy
Part of LIT-9423
* fix(types): restore main's _types.py content alongside the user_api_key_auth move
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
* style(types): drop blank lines between proxy._types re-export imports
Part of LIT-9423
* refactor(proxy): keep only user_api_key_auth re-exports in _types.py
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(proxy): carry main's UserAPIKeyAuth field into moved file
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(types): drop blank lines between proxy._types re-export imports
Part of LIT-9423
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(types): move LitellmUserRoles and KeyManagementRoutes into litellm.types.proxy.auth.user_roles
Part of LIT-9423
* refactor(types): move is_jwt into litellm.types.proxy.auth.user_api_key_auth
Part of LIT-9423
* refactor(auth): alias is_jwt import so JWTHandler.is_jwt is not read as recursive
* refactor(types): type UserAPIKeyAuth, its token view and hash_token in place
Part of LIT-9423
* refactor(types): move UserAPIKeyAuth, its token view and hash_token into litellm.types.proxy.auth.user_api_key_auth
Part of LIT-9423
* fix(types): keep agent, budget snapshot and is_jwt names importable from proxy._types
Part of LIT-9423
* fix(types): restore main's _types.py content alongside the user_api_key_auth move
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
* style(types): drop blank lines between proxy._types re-export imports
Part of LIT-9423
* refactor(proxy): keep only user_api_key_auth re-exports in _types.py
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(proxy): carry main's UserAPIKeyAuth field into moved file
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(types): drop blank lines between proxy._types re-export imports
Part of LIT-9423
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* Add ScaleDown model pricing
Registers the five ScaleDown models in the pricing map. ScaleDown bills on
input tokens only, so output_cost_per_token is zero for every entry. The
classify and decisions models bill at $0.04 per million input tokens, and
extract, summarize, and compress bill at $0.05 per million.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* Price all ScaleDown models at $0.04 per million and drop unverified context limits
Every ScaleDown model now bills $0.04 per million input tokens. The
32000-token max_input_tokens/max_output_tokens values were not confirmed
against the service, so they are removed rather than registered.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* Price all ScaleDown models at $0.05 per million input tokens
This is the standard rate in ScaleDown's billing and matches the usage dashboard.
The Decisions usage.cost field is not used for pricing.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* Add regression tests for the ScaleDown pricing entries
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* Cite the source of the ScaleDown rate in the pricing tests and type them
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* fix(scaledown): type the pricing model registry
* fix(scaledown): address pricing review annotations
---------
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
Co-authored-by: moe-berri <moe@berri.ai>
* Add ScaleDown model pricing
Registers the five ScaleDown models in the pricing map. ScaleDown bills on
input tokens only, so output_cost_per_token is zero for every entry. The
classify and decisions models bill at $0.04 per million input tokens, and
extract, summarize, and compress bill at $0.05 per million.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* Price all ScaleDown models at $0.04 per million and drop unverified context limits
Every ScaleDown model now bills $0.04 per million input tokens. The
32000-token max_input_tokens/max_output_tokens values were not confirmed
against the service, so they are removed rather than registered.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* Price all ScaleDown models at $0.05 per million input tokens
This is the standard rate in ScaleDown's billing and matches the usage dashboard.
The Decisions usage.cost field is not used for pricing.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* Add regression tests for the ScaleDown pricing entries
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* Cite the source of the ScaleDown rate in the pricing tests and type them
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* Add ScaleDown as a chat provider
ScaleDown serves five models over two wire protocols. extract, summarize, and
compress speak OpenAI chat on /v1/chat/completions, while classify and
decisions implement the Jev Decisions API on /v1/scaledown, taking a state
object plus a questions map and returning one typed answer per question.
The provider authenticates with x-api-key rather than a bearer token, and the
decisions models need a non-chat request body, so it routes through the HTTP
handler instead of the OpenAI passthrough. Malformed decisions requests are
rejected locally with a message naming the problem, so callers do not have to
decode an opaque 422 from upstream. Decisions bills one model call per
question, so the cost upstream reports is passed through rather than
re-estimated from token counts.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* Route ScaleDown domain models to the native API and stop trusting usage.cost
The /v1/chat/completions and /v1/models routes return 403 on api.scaledown.xyz,
so extract, summarize and compress now call the native /extract,
/summarization/abstractive and /compress/raw/ endpoints. The default api_base is
the bare host; a trailing /v1 is stripped and re-added only for decisions.
The Decisions usage.cost field was about 600x the dashboard amount, so it is no
longer passed through; cost is computed from input tokens at the registered rate.
Native responses are returned unchanged as JSON, including the _value/_span_anchor
wrappers on nested extraction. They carry no output token count, so
completion_tokens is reported as 0 and documented as unmeasured.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* Move ScaleDown translation into llms/ and address review findings
- Dispatch moves to llms/scaledown/chat/handler.py; main.py keeps a thin call
and get_llm_provider_logic.py no longer has a ScaleDown branch, since the
config resolves key and base URL itself.
- questions passed via extra_body are merged and validated after the shared
merge (sign_request), so the documented extra_body path works.
- extra_body can no longer change the upstream model, and decisions state may
carry a document but not text, so guardrails always see the text.
- A custom api_base requires its own api_key; the environment key is only sent
to the default host or SCALEDOWN_API_BASE.
- Image content parts become the decisions document; max_completion_tokens maps
to max_tokens; stream is served as one chunk for every model.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* Refuse prompt-bearing extra_body keys and empty-key fallback to the env credential
extra_body is merged after guardrails inspect the messages, so each model now
accepts only option keys there (extract threshold/top_n, compress
compression_rate, decisions questions/state); text, instructions, prompt,
context and model are rejected. An empty api_key no longer lets the
SCALEDOWN_API_KEY from the environment go to a custom api_base.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* Read compress token usage from the real response shape
/compress/raw/ nests the per-prompt counts under results and reports the total
as a top-level input_tokens; the provider read a top-level original_prompt_tokens
that is not there, so compress reported zero tokens and zero cost.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* Accept the router's max_retries and word the extra_body error clearly
The proxy adds max_retries to every call, which map_openai_params rejected, so
every proxied ScaleDown request failed with a 400. It is now accepted and not
forwarded.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* Return extracted fields from extract, follow schema refs, and meet the type-discipline gate
- extract content is now the extracted fields (span anchors dropped, wrappers
unwrapped), so it validates against the caller's response_format; the raw
payload stays on _hidden_params['scaledown_response'].
- Local $ref/$defs in the response_format schema are resolved.
- The adapter is rewritten with Final locals, read-only annotations, one raising
helper and a typed handler; check_type_discipline reports no violations.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* Fix stream_options, schema-guided extract cleaning, document input and multi-image handling
- stream_options is accepted and not sent upstream, so include_usage streams work.
- Extract output is cleaned against the requested entities, so a field named
_value or *_span_anchor is no longer dropped.
- Extract and summarize send an image or PDF as the native document fields.
- More than one image is rejected instead of silently using the first.
- The cost test derives its rate from the cost map.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* Cut recursive schema refs at the cycle and cap the expanded entity count
A small response_format schema with a self-referencing $def expanded to 4^16
leaves before any request was sent. Refs that point back into themselves are now
cut off at that point, and the total expanded entities are capped at 1000 so
repeated acyclic refs are rejected with a clear error.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* Bound ref chains and schema nesting so long schemas fail cleanly
Resolving a chain of thousands of distinct $refs recursed until RecursionError.
Ref resolution is now an iterative walk capped at 32 hops, and nested properties
are capped at 32 levels; both raise a clear 400 instead.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
* fix(scaledown): support native classify and preserve extraction results
* test(scaledown): include provider regressions in CI shard
* fix(scaledown): pass registry typing and bounded recursion checks
* docs(scaledown): capture provider setup before and after
* fix(scaledown): preserve nullable schemas and reject malformed requests
* fix(scaledown): isolate provider dispatch from existing providers
* fix(scaledown): preserve the shared dispatch return contract
* fix(scaledown): preserve failure logging and reject lossy schemas
* fix(scaledown): keep timeout typing annotation on its expression
* fix(scaledown): resolve escaped extraction schema references
* fix(scaledown): annotate the bounded reference walk
* chore(scaledown): remove unused loop suppression
---------
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
Co-authored-by: moe-berri <moe@berri.ai>
* refactor(types): move LitellmUserRoles and KeyManagementRoutes into litellm.types.proxy.auth.user_roles
Part of LIT-9423
* refactor(types): annotate GenerateRequestBase containers and check_max_budget
Part of LIT-9423
* refactor(types): move GenerateRequestBase and LiteLLM_ObjectPermissionBase into litellm.types.proxy.management_endpoints.request_base
Part of LIT-9423
* refactor(types): annotate user request metadata and check_user_info validators
Part of LIT-9423
* refactor(types): move user request models into litellm.types.proxy.management_endpoints.user_requests
Part of LIT-9423
* refactor(types): move MemberDeleteRequest into litellm.types.proxy.management_endpoints.team_endpoints
Part of LIT-9423
* fix(types): restore main's _types.py content alongside the request_base move
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
* style(types): merge request_base re-exports into one import block
Part of LIT-9423
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(types): move LitellmUserRoles and KeyManagementRoutes into litellm.types.proxy.auth.user_roles
Part of LIT-9423
* refactor(types): annotate GenerateRequestBase containers and check_max_budget
Part of LIT-9423
* refactor(types): move GenerateRequestBase and LiteLLM_ObjectPermissionBase into litellm.types.proxy.management_endpoints.request_base
Part of LIT-9423
* refactor(types): annotate user request metadata and check_user_info validators
Part of LIT-9423
* refactor(types): move user request models into litellm.types.proxy.management_endpoints.user_requests
Part of LIT-9423
* fix(types): restore main's _types.py content alongside the request_base move
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
* style(types): merge request_base re-exports into one import block
Part of LIT-9423
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(types): move LitellmUserRoles and KeyManagementRoutes into litellm.types.proxy.auth.user_roles
Part of LIT-9423
* refactor(types): annotate GenerateRequestBase containers and check_max_budget
Part of LIT-9423
* refactor(types): move GenerateRequestBase and LiteLLM_ObjectPermissionBase into litellm.types.proxy.management_endpoints.request_base
Part of LIT-9423
* refactor(types): annotate key request containers and drop_blank_team_id
Part of LIT-9423
* refactor(types): move key request models into litellm.types.proxy.management_endpoints.key_requests
Part of LIT-9423
* fix(types): keep MappingProxyType exported from litellm.proxy._types for star importers
Part of LIT-9423
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
* feat(otel): let a team strip captured message content from its own OTel v2 destination
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(proxy): split the shared callback option conflict check and sync dashboard API types
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(otel): address review feedback on team capture_message_content
Classify capture_message_content as NotSecret in the credential slot registry, inject test exporters through register_exporter_factory instead of patching _exporter_from_spec, type the test helpers, and stop reassigning processor in build_tracer_provider
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(otel): add integration cells for team capture_message_content
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(otel): cover weave_otel and newrelic team destinations in capture_message_content cells
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(otel): stop rejecting client-sent capture_message_content at the request body boundary
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(otel): let a team capture_message_content override the global setting in both directions
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(otel): keep a team's capture_message_content on its own OTel v2 destination
The setting no longer rides the flattened team callback vars, so a sibling destination and a routed provider follow the global mode. Request bodies get the base 400 back, a failure-only entry rejects the setting, and entries for one backend must agree
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(otel): type the capture_message_content callback validation tests
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(otel): type the remaining capture_message_content test signatures
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(otel): mark the new capture_message_content test locals Final
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(otel): default tenant capture to callback_settings.otel and keep CaptureMessageContent formatting bare
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(otel): keep CaptureMessageContent a plain str like main
The str-to-Enum switch changed the public type: the config field's type,
repr and model_dump, a constructor that now raised on unknown values, and
unknown env values coerced to no_content. CaptureMessageContent is the plain
str subclass main ships again, the field is a str, and a
captures_span_content helper replaces the enum property. A test pins that a
published preset's capture mode stays the default for an unowned
destination, which is what main forwards.
* test(otel): drop the module-level CaptureMessageContent import the rewritten test no longer uses
* fix(lint): keep the merged branch within main's type budgets
Freeze the capture-mode settings model (LIT015) and read stored logging
entries through one typed sequence helper, so the conflicting-capture checks
add no unknown types.
* fix(otel): reject a destination capture_message_content while OTel v2 is off
The legacy OTel callbacks never read the per-destination setting, so a team
could register no_content and still export prompts and replies under the
global capture mode. Registration now refuses the var unless the proxy runs
with LITELLM_OTEL_V2=true, the same rule the Arize transport and New Relic
routing vars follow.
* test(otel): type the OTel v2 fixtures and the v2-off regression test
* fix(otel): accept only no_content and span_only on a team or key destination
OTel v2 writes captured content only to span attributes, so event_only
behaved like no_content and span_and_event like span_only on a
destination. The proxy-wide setting keeps all four values
* docs(otel): say an omitted capture setting follows the proxy's own callback for that backend
* test(otel): describe the general capture fallback in the unowned destination test
---------
Co-authored-by: mrinal <mrinal@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: yucheng-berri <yucheng@berri.ai>
Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com>
* refactor(anthropic): move passthrough SSE stream assembly into litellm/llms
Part of LIT-9423
* refactor(anthropic): move raw SSE stream assembly helpers out of proxy guardrails
Part of LIT-9423
* refactor(anthropic): give the anthropic_sse re-exports reasoned noqa codes
Part of LIT-9423
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
* test(anthropic): cover the raw SSE helpers in stream_assembly
Part of LIT-9423
* refactor(types): move LitellmUserRoles and KeyManagementRoutes into litellm.types.proxy.auth.user_roles
Part of LIT-9423
* refactor(types): annotate GenerateRequestBase containers and check_max_budget
Part of LIT-9423
* refactor(types): move GenerateRequestBase and LiteLLM_ObjectPermissionBase into litellm.types.proxy.management_endpoints.request_base
Part of LIT-9423
* fix(types): restore main's _types.py content alongside the request_base move
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
* style(types): merge request_base re-exports into one import block
Part of LIT-9423
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(types): annotate key request containers and drop_blank_team_id
Part of LIT-9423
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(constants): move AUTH_OBJECTS_TARGET into litellm.constants
Part of LIT-9423
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(skills): move NewSkillRequest to litellm.models.skills
Part of LIT-9423
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(terraform): stop sending blocked on litellm_user updates
/user/update writes blocked into the user table upsert, but blocked only
exists as a column on keys, so every litellm_user update failed with a 400
from Prisma. Updates no longer send it. Create sends it only when set to
true, since /user/new applies it to the auto-created key
blocked only applies at creation; litellm_key_block covers changes after that
* fix(terraform): always send blocked on litellm_user create
/user/new fills an omitted blocked from default_internal_user_params, so
leaving it out on create let a proxy-side blocked=true default block the
auto-created key. Create sends the configured value again, including
false, while updates still never send it
* chore(terraform): drop comment on litellm_user create blocked
* fix(terraform): keep litellm_user blocked undeprecated
Create sends blocked again, so it still controls the auto-created key.
Whether to deprecate it in favor of litellm_key_block is left to the
provider maintainers; the docs describe it as creation-only instead
* refactor(anthropic): move prompt cache pricing helper out of the proxy package
Part of LIT-9423
* fix(sdk): resolve litellm.proxy lazily on attribute access
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The layer cleanup stopped import litellm from loading any proxy module, so litellm.proxy was no longer bound and litellm.proxy._types.X raised AttributeError. The module __getattr__ now imports litellm.proxy on first access, which keeps import litellm free of proxy._types and fixes the docs and code-quality checks on main
* refactor(types): move LitellmUserRoles and KeyManagementRoutes into litellm.types.proxy.auth.user_roles
Part of LIT-9423
* refactor(types): move is_jwt into litellm.types.proxy.auth.user_api_key_auth
Part of LIT-9423
* refactor(auth): alias is_jwt import so JWTHandler.is_jwt is not read as recursive
