Number("") and Number(" ") both return 0, which passed the finite check, so a
provider reporting an empty cost got a fabricated $0.000000 metric instead of
having the unusable value omitted.
Both ingestion sites carried the same inline parsing, so this pulls it into one
parseUsageCost helper that keeps finite numbers and non-blank numeric strings and
drops everything else, including booleans, arrays and breakdown objects.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01YNw8WvkvCSeTcE5qvergu3
Persistently route Claude Code through a LiteLLM proxy with a long-lived virtual key or the stored lite login, turn on gateway model discovery so /model lists the proxy's models, optionally pick the model Claude Code starts on, and record what changed so unconfigure restores only the keys the user has not touched since. lite login --config-claude writes through the same receipt and is undoable too. The two settings merges (lite up / --config-claude and lite autoroute) collapse into one credential-aware merge
get_candidate_model_ids_for_route (added in #40280 for the encrypted-content affinity
check) reconstructed the candidate pool by unioning the model_name and team indexes with
pattern_router.route. That diverged from how the router actually resolves a route: it took
a union instead of the first matching path, and pattern_router.route only matches the
literal name, so a provider-qualified pattern (matched by get_deployments_by_pattern, which
retries the {provider}/{model} form) was missed and the default deployment was ignored.
For an affinity follow-up on a wildcard or team-public route, that mismatch could strip
encrypted reasoning on a same-group cooldown, or return a 503 on a real cross-path switch.
Delegate the non-model_name case to _try_early_resolve_deployments_for_model_not_in_names,
the same resolver _common_checks_available_deployment uses, so candidate membership follows
the router's real precedence. With include_team_models left off it stays read-only and does
not raise. Behavior for concrete model groups and routing groups is unchanged.
Claude-Session: https://claude.ai/code/session_01KAumQbhzk6jdWWHFLA8Jar
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
The FK cascade drops the LiteLLM_JWTKeyMapping row, but the cached
jwt_key_mapping:{claim}:{value} entry still resolved to the deleted token
hash, so every JWT call from that identity failed until
virtual_key_mapping_cache_ttl expired instead of auto-registering against a
recreated key. delete_verification_tokens now snapshots the mapping cache
keys before the delete and evicts them across replicas afterwards, the same
way /key/regenerate already does.
Claude-Session: https://claude.ai/code/session_011Tn3657NkV6ojLqewL64Kb
Guardrails that hand back tool_calls in their own shape (vendor JSON, user code output) raised a KeyError on the non-stream Responses write-back. Returned tool calls are now validated before comparison; a shape or count that does not line up leaves every tool-call item unchanged and logs a warning naming the guardrail. A tool call's name is written back only when the guardrail changed it, so a nameless custom_tool_call no longer picks up the custom_tool placeholder.
Perplexity's Responses payload fails ResponsesAPIResponse validation on
truncation "" and is kept as an unvalidated model, so its usage stays a
plain dict and _stamp_responses_usage_cost raised AttributeError on every
streamed completion once reasoning made the cost non-zero. Validate the
dict into ResponseAPIUsage before stamping, keeping a provider-reported
cost when it carries one.
Resolves LIT-7391
The converse reasoning gate only matched openai.gpt-5, so gpt-6-astra fell through to
Anthropic's thinking block and Bedrock rejected the call with 400 Unknown parameter:
'thinking'. Match any openai.gpt-<digit> model at the three gate sites instead.
Nova 2 lite and pro accept forced tool_choice on Converse (verified live on
us.amazon.nova-2-lite-v1:0), so the nine Nova 2 registry keys now advertise
supports_tool_choice. The invoke dispatcher also forwards json_mode to Nova like it
already does for Anthropic and TwelveLabs.
* fix(langfuse): give session-header calls their own trace id
A client that sends only a session header (x-litellm-session-id, a vendor
x-<name>-session-id such as Claude Code's X-Claude-Code-Session-Id, a bare
x-session-id, or the Codex session/thread/conversation family) has that value
stamped into both trace_id and session_id by the proxy. Langfuse upserts a
trace by id, so every turn of a session collapsed into one growing trace and
the Sessions view showed "Total traces: 1"
Detect that aliasing in the Langfuse callback from the request headers the
callback already receives, and use litellm_call_id as the trace id for those
calls. session_id still carries the header value, so the turns stay grouped
under one session. An explicit x-litellm-trace-id, langfuse_trace_id, or
langfuse_existing_trace_id keeps its trace id, including when the caller sets
it to the same value as the session id
Co-authored-by: jesus <jesus@berri.ai>
* test(langfuse): cover direct-SDK callers without proxy request headers
* fix(langfuse): preserve session trace provenance
---------
Co-authored-by: jesus <jesus@berri.ai>
A team-scoped auto-router is stored under an internal
model_name_{team_id}_{uuid} with the caller-facing name in
model_info.team_public_model_name, and the four pre-routing strategy
registries key on that internal name. A team key asks for the public name,
so the strategy lookup missed, the team early-resolve exit handed back the
marker deployment itself, and every call 400'd with "Unmapped LLM provider".
The strategy lookup now resolves the requested name through the same
team-first, then global, then admin-across-teams deployment resolution the
deployment path uses, and looks the registries up under the model_name of
whatever that resolves to. Both exits of _common_checks_available_deployment
drop strategy markers through one helper, so a marker-only resolution is
rejected as uncallable on every path. The request team id has one reader.
Resolves LIT-7363
Claude-Session: https://claude.ai/code/session_01NU97S7d2FUDDvTk59k53Wp
Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com>
* fix(proxy): accept non-string callback vars in default_team_settings
A YAML boolean such as turn_off_message_logging: true in a
default_team_settings block failed TeamCallbackMetadata's str-only
callback_vars validation and errored the request before any callback
ran. Stringify the value the same way AddTeamCallback does.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): drop docstring from default_team_settings bool regression test
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): move default_team_settings bool regression test to mapped pre_call_utils suite
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: yucheng <yucheng@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
LiteLLM_JWTKeyMapping_token_fkey was created ON DELETE RESTRICT, so deleting
a virtual key that a JWT mapping pointed at failed with a foreign key
violation on every deletion path (/key/delete, Admin UI, alias delete,
team and user cascades). Declaring onDelete: Cascade on the relation lets
the database clean the mapping up uniformly, so the next JWT call from that
identity re-registers against the newly created key.
Rebase of #33703 onto current staging.
Claude-Session: https://claude.ai/code/session_011Tn3657NkV6ojLqewL64Kb
When a provider returns choices as null, an object, a string or a number, the
converter said the response had no 'choices' even though the key was present in
the raw keys it listed. A shared message now keeps the old wording for a missing
key and names the offending type otherwise.
The cached-stream regression test also pins the chunk count so a leaked extra
chunk fails it.
* fix(proxy): keep a body litellm_session_id in SpendLogs under missing_session_id omit
Under general_settings.missing_session_id: omit, apply_missing_session_id_policy now
mirrors a client-supplied top-level litellm_session_id into metadata.session_id when the
client did not set one there, so SpendLogs.session_id and Langfuse agree with the session
callbacks already report through StandardLoggingPayload.session_id
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): keep client metadata.session_id ahead of body litellm_session_id on litellm_metadata routes
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(proxy): drop docstrings from the missing_session_id omit regression tests
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: yucheng <yucheng@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
initialize_azure_sdk_client now falls back to litellm.constants.DEFAULT_MAX_RETRIES
when litellm_params carries no max_retries, so off-router Azure clients (files,
batches, fine-tuning, assistants, audio) honor the env var like OpenAI clients do.
Router paths already default max_retries to 0 and are unchanged.
Regression tests cover the default, explicit 0/5/None values, and the env var
reaching the SDK client in a fresh interpreter.
When litellm_params does not include max_retries (the common case for
deployments configured without explicit retry settings),
initialize_azure_sdk_client() previously passed None through the
'if max_retries is not None' guard, resulting in AsyncAzureOpenAI being
created without a max_retries argument. The OpenAI SDK then uses its
own hardcoded default of 2, ignoring the DEFAULT_MAX_RETRIES env var.
Fix: fall back to litellm.constants.DEFAULT_MAX_RETRIES when
litellm_params has no max_retries. This ensures the SDK client
respects the configured retry count.
Steps to reproduce:
1. Set env var DEFAULT_MAX_RETRIES=0
2. Configure a deployment without explicit max_retries in litellm_params
3. Make a request that triggers a timeout
4. Observe: SDK retries (x-stainless-retry-count=1) despite env var=0
Related: https://github.com/BerriAI/litellm/issues/5124
A streamed /v1/responses request with tool_choice {"type": "function", "name": ...}
that reaches a chat-completions-only deployment failed with HTTP 500 before the
first byte: the synthetic response.created and response.in_progress events copied
the chat-shaped tool_choice into ResponsesAPIResponse, whose ToolChoice type expects
the flat Responses API shape. The non-streamed path echoed "auto" regardless of the
request.
Both paths now normalize the request's tool_choice through the existing chat
transform and map it back to the Responses API vocabulary, validated by a
TypeAdapter(ToolChoice), so a named function is echoed as {"type": "function",
"name": ...} and a missing tool_choice is echoed as "auto".
Fixes#33689
Skipping the name write-back in either handler left every test green; a
guardrail that renames a tool call now has a regression test on both the
chat chunk path and the Anthropic SSE path
Preset #40341 pointed the Anthropic family REASONING tier at claude-fable-5-1,
but this test still hardcoded claude-opus-5, so the payload it saw no longer
matched. Rebase the assertion on ANTHROPIC_PRESET.complexity_router_config.tier_model_configs
so a preset refresh flows through instead of redding the suite on staging.
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Krrish Dholakia <krrish-berri-2@users.noreply.github.com>
A stream cache hit on an entry stored with choices == [] indexed choices[0]
in the cached_response branch and failed with IndexError, so the streaming
converters' empty chunk had no working consumer. The branch now treats a
chunk without choices as empty and lets the wrapper close the stream with
its usual finish_reason stop chunk
