+ ⚠️ Note: Your API requests will continue to work, but you should monitor your usage closely.
+ If you reach your maximum budget, requests will be rejected.
+
+
+ You can view your usage and manage your budget in the LiteLLM Dashboard.
+
+ If you have any questions, please send an email to {email_support_contact}
+
+ Best,
+ The LiteLLM team
+"""
+
MAX_BUDGET_ALERT_EMAIL_TEMPLATE = """
diff --git a/litellm/integrations/langfuse/langfuse_otel.py b/litellm/integrations/langfuse/langfuse_otel.py
index 08493a0e8ec..8955d3619f7 100644
--- a/litellm/integrations/langfuse/langfuse_otel.py
+++ b/litellm/integrations/langfuse/langfuse_otel.py
@@ -8,9 +8,8 @@
from litellm.integrations.langfuse.langfuse_otel_attributes import (
LangfuseLLMObsOTELAttributes,
)
-from litellm.integrations.opentelemetry import OpenTelemetry
+from litellm.integrations.opentelemetry import OpenTelemetry, OpenTelemetryConfig
from litellm.types.integrations.langfuse_otel import (
- LangfuseOtelConfig,
LangfuseSpanAttributes,
)
from litellm.types.utils import StandardCallbackDynamicParams
@@ -18,17 +17,8 @@
if TYPE_CHECKING:
from opentelemetry.trace import Span as _Span
- from litellm.integrations.opentelemetry import (
- OpenTelemetryConfig as _OpenTelemetryConfig,
- )
- from litellm.types.integrations.arize import Protocol as _Protocol
-
- Protocol = _Protocol
- OpenTelemetryConfig = _OpenTelemetryConfig
Span = Union[_Span, Any]
else:
- Protocol = Any
- OpenTelemetryConfig = Any
Span = Any
@@ -37,8 +27,12 @@
class LangfuseOtelLogger(OpenTelemetry):
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
+ def __init__(self, config=None, *args, **kwargs):
+ # Prevent LangfuseOtelLogger from modifying global environment variables by constructing config manually
+ # and passing it to the parent OpenTelemetry class
+ if config is None:
+ config = self._create_open_telemetry_config_from_langfuse_env()
+ super().__init__(config=config, *args, **kwargs)
@staticmethod
def set_langfuse_otel_attributes(span: Span, kwargs, response_obj):
@@ -114,6 +108,10 @@ def _set_metadata_attributes(span: Span, metadata: dict):
for key, enum_attr in mapping.items():
if key in metadata and metadata[key] is not None:
value = metadata[key]
+ if key == "trace_id" and isinstance(value, str):
+ # trace_id must be 32 hex char no dashes for langfuse : Litellm sends uuid with dashes (might be breaking at some point)
+ value = value.replace("-", "")
+
if isinstance(value, (list, dict)):
try:
value = json.dumps(value)
@@ -265,8 +263,47 @@ def _get_langfuse_otel_host() -> Optional[str]:
"""
return os.environ.get("LANGFUSE_OTEL_HOST") or os.environ.get("LANGFUSE_HOST")
+ def _create_open_telemetry_config_from_langfuse_env(self) -> OpenTelemetryConfig:
+ """
+ Creates OpenTelemetryConfig from Langfuse environment variables.
+ Does NOT modify global environment variables.
+ """
+ from litellm.integrations.opentelemetry import OpenTelemetryConfig
+
+ public_key = os.environ.get("LANGFUSE_PUBLIC_KEY", None)
+ secret_key = os.environ.get("LANGFUSE_SECRET_KEY", None)
+
+ if not public_key or not secret_key:
+ # If no keys, return default from env (likely logging to console or something else)
+ return OpenTelemetryConfig.from_env()
+
+ # Determine endpoint - default to US cloud
+ langfuse_host = LangfuseOtelLogger._get_langfuse_otel_host()
+
+ if langfuse_host:
+ # If LANGFUSE_HOST is provided, construct OTEL endpoint from it
+ if not langfuse_host.startswith("http"):
+ langfuse_host = "https://" + langfuse_host
+ endpoint = f"{langfuse_host.rstrip('/')}/api/public/otel"
+ verbose_logger.debug(f"Using Langfuse OTEL endpoint from host: {endpoint}")
+ else:
+ # Default to US cloud endpoint
+ endpoint = LANGFUSE_CLOUD_US_ENDPOINT
+ verbose_logger.debug(f"Using Langfuse US cloud endpoint: {endpoint}")
+
+ auth_header = LangfuseOtelLogger._get_langfuse_authorization_header(
+ public_key=public_key, secret_key=secret_key
+ )
+ otlp_auth_headers = f"Authorization={auth_header}"
+
+ return OpenTelemetryConfig(
+ exporter="otlp_http",
+ endpoint=endpoint,
+ headers=otlp_auth_headers,
+ )
+
@staticmethod
- def get_langfuse_otel_config() -> LangfuseOtelConfig:
+ def get_langfuse_otel_config() -> "OpenTelemetryConfig":
"""
Retrieves the Langfuse OpenTelemetry configuration based on environment variables.
@@ -276,7 +313,7 @@ def get_langfuse_otel_config() -> LangfuseOtelConfig:
LANGFUSE_HOST: Optional. Custom Langfuse host URL. Defaults to US cloud.
Returns:
- LangfuseOtelConfig: A Pydantic model containing Langfuse OTEL configuration.
+ OpenTelemetryConfig: A Pydantic model containing Langfuse OTEL configuration.
Raises:
ValueError: If required keys are missing.
@@ -308,12 +345,14 @@ def get_langfuse_otel_config() -> LangfuseOtelConfig:
)
otlp_auth_headers = f"Authorization={auth_header}"
- # Set standard OTEL environment variables
- os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = endpoint
- os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = otlp_auth_headers
+ # Prevent modification of global env vars which causes leakage
+ # os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = endpoint
+ # os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = otlp_auth_headers
- return LangfuseOtelConfig(
- otlp_auth_headers=otlp_auth_headers, protocol="otlp_http"
+ return OpenTelemetryConfig(
+ exporter="otlp_http",
+ endpoint=endpoint,
+ headers=otlp_auth_headers,
)
@staticmethod
diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py
index 18898be7dce..296a88f9a0b 100644
--- a/litellm/integrations/opentelemetry.py
+++ b/litellm/integrations/opentelemetry.py
@@ -599,9 +599,9 @@ def get_tracer_to_use_for_request(self, kwargs: dict) -> Tracer:
def _get_dynamic_otel_headers_from_kwargs(self, kwargs) -> Optional[dict]:
"""Extract dynamic headers from kwargs if available."""
- standard_callback_dynamic_params: Optional[StandardCallbackDynamicParams] = (
- kwargs.get("standard_callback_dynamic_params")
- )
+ standard_callback_dynamic_params: Optional[
+ StandardCallbackDynamicParams
+ ] = kwargs.get("standard_callback_dynamic_params")
if not standard_callback_dynamic_params:
return None
@@ -619,7 +619,9 @@ def _get_tracer_with_dynamic_headers(self, dynamic_headers: dict):
# Prevents thread exhaustion by reusing providers for the same credential sets (e.g. per-team keys)
cache_key = str(sorted(dynamic_headers.items()))
if cache_key in self._tracer_provider_cache:
- return self._tracer_provider_cache[cache_key].get_tracer(LITELLM_TRACER_NAME)
+ return self._tracer_provider_cache[cache_key].get_tracer(
+ LITELLM_TRACER_NAME
+ )
# Create a temporary tracer provider with dynamic headers
temp_provider = TracerProvider(resource=self._get_litellm_resource(self.config))
@@ -674,7 +676,10 @@ def _handle_success(self, kwargs, response_obj, start_time, end_time):
kwargs, response_obj, start_time, end_time, span
)
# Ensure proxy-request parent span is annotated with the actual operation kind
- if parent_span is not None and parent_span.name == LITELLM_PROXY_REQUEST_SPAN_NAME:
+ if (
+ parent_span is not None
+ and parent_span.name == LITELLM_PROXY_REQUEST_SPAN_NAME
+ ):
self.set_attributes(parent_span, kwargs, response_obj)
else:
# Do not create primary span (keep hierarchy shallow when parent exists)
@@ -1003,14 +1008,11 @@ def _emit_semantic_logs(self, kwargs, response_obj, span: Span):
# TODO: Refactor to use the proper OTEL Logs API instead of directly creating SDK LogRecords
from opentelemetry._logs import SeverityNumber, get_logger, get_logger_provider
+
try:
- from opentelemetry.sdk._logs import (
- LogRecord as SdkLogRecord, # type: ignore[attr-defined] # OTEL < 1.39.0
- )
+ from opentelemetry.sdk._logs import LogRecord as SdkLogRecord # type: ignore[attr-defined] # OTEL < 1.39.0
except ImportError:
- from opentelemetry.sdk._logs._internal import (
- LogRecord as SdkLogRecord, # OTEL >= 1.39.0
- )
+ from opentelemetry.sdk._logs._internal import LogRecord as SdkLogRecord # type: ignore[attr-defined, no-redef] # OTEL >= 1.39.0
otel_logger = get_logger(LITELLM_LOGGER_NAME)
@@ -1618,7 +1620,6 @@ def set_attributes( # noqa: PLR0915
for idx, choice in enumerate(response_obj.get("choices")):
if choice.get("finish_reason"):
-
message = choice.get("message")
tool_calls = message.get("tool_calls")
if tool_calls:
@@ -1631,7 +1632,9 @@ def set_attributes( # noqa: PLR0915
)
except Exception as e:
- self.handle_callback_failure(callback_name=self.callback_name or "opentelemetry")
+ self.handle_callback_failure(
+ callback_name=self.callback_name or "opentelemetry"
+ )
verbose_logger.exception(
"OpenTelemetry logging error in set_attributes %s", str(e)
)
@@ -1722,6 +1725,7 @@ def _transform_choices_to_otel_semantic_conventions(
def set_raw_request_attributes(self, span: Span, kwargs, response_obj):
try:
+ self.set_attributes(span, kwargs, response_obj)
kwargs.get("optional_params", {})
litellm_params = kwargs.get("litellm_params", {}) or {}
custom_llm_provider = litellm_params.get("custom_llm_provider", "Unknown")
diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py
index 2c897cb0692..0a61dab0680 100644
--- a/litellm/integrations/prometheus.py
+++ b/litellm/integrations/prometheus.py
@@ -1,6 +1,7 @@
# used for /metrics endpoint on LiteLLM Proxy
#### What this does ####
# On success, log events to Prometheus
+import asyncio
import os
import sys
from datetime import datetime, timedelta
@@ -1188,28 +1189,34 @@ async def _increment_remaining_budget_metrics(
_user_spend = _metadata.get("user_api_key_user_spend", None)
_user_max_budget = _metadata.get("user_api_key_user_max_budget", None)
- await self._set_api_key_budget_metrics_after_api_request(
- user_api_key=user_api_key,
- user_api_key_alias=user_api_key_alias,
- response_cost=response_cost,
- key_max_budget=_api_key_max_budget,
- key_spend=_api_key_spend,
- )
-
- await self._set_team_budget_metrics_after_api_request(
- user_api_team=user_api_team,
- user_api_team_alias=user_api_team_alias,
- team_spend=_team_spend,
- team_max_budget=_team_max_budget,
- response_cost=response_cost,
- )
-
- await self._set_user_budget_metrics_after_api_request(
- user_id=user_id,
- user_spend=_user_spend,
- user_max_budget=_user_max_budget,
- response_cost=response_cost,
+ results = await asyncio.gather(
+ self._set_api_key_budget_metrics_after_api_request(
+ user_api_key=user_api_key,
+ user_api_key_alias=user_api_key_alias,
+ response_cost=response_cost,
+ key_max_budget=_api_key_max_budget,
+ key_spend=_api_key_spend,
+ ),
+ self._set_team_budget_metrics_after_api_request(
+ user_api_team=user_api_team,
+ user_api_team_alias=user_api_team_alias,
+ team_spend=_team_spend,
+ team_max_budget=_team_max_budget,
+ response_cost=response_cost,
+ ),
+ self._set_user_budget_metrics_after_api_request(
+ user_id=user_id,
+ user_spend=_user_spend,
+ user_max_budget=_user_max_budget,
+ response_cost=response_cost,
+ ),
+ return_exceptions=True,
)
+ for i, r in enumerate(results):
+ if isinstance(r, Exception):
+ verbose_logger.debug(
+ f"[Non-Blocking] Prometheus: Budget metric lookup {['key', 'team', 'user'][i]} failed: {r}"
+ )
def _increment_top_level_request_and_spend_metrics(
self,
@@ -1683,6 +1690,108 @@ async def async_post_call_success_hook(
)
pass
+ def _safe_get(self, obj: Any, key: str, default: Any = None) -> Any:
+ """Get value from dict or Pydantic model."""
+ if obj is None:
+ return default
+ if isinstance(obj, dict):
+ return obj.get(key, default)
+ return getattr(obj, key, default)
+
+ def _extract_deployment_failure_label_values(
+ self, request_kwargs: dict
+ ) -> Dict[str, Optional[str]]:
+ """
+ Extract label values for deployment failure metrics from all available
+ sources in request_kwargs. Falls back to litellm_params metadata and
+ user_api_key_auth when standard_logging_payload has None values.
+ """
+ standard_logging_payload = (
+ request_kwargs.get("standard_logging_object", {}) or {}
+ )
+ _litellm_params = request_kwargs.get("litellm_params", {}) or {}
+ _metadata_raw = self._safe_get(standard_logging_payload, "metadata") or {}
+ if isinstance(_metadata_raw, dict):
+ _metadata = _metadata_raw
+ else:
+ _metadata = {
+ "user_api_key_alias": getattr(
+ _metadata_raw, "user_api_key_alias", None
+ ),
+ "user_api_key_team_id": getattr(
+ _metadata_raw, "user_api_key_team_id", None
+ ),
+ "user_api_key_team_alias": getattr(
+ _metadata_raw, "user_api_key_team_alias", None
+ ),
+ "user_api_key_hash": getattr(_metadata_raw, "user_api_key_hash", None),
+ "requester_ip_address": getattr(
+ _metadata_raw, "requester_ip_address", None
+ ),
+ "user_agent": getattr(_metadata_raw, "user_agent", None),
+ }
+ _litellm_params_metadata = _litellm_params.get("metadata", {}) or {}
+
+ # Extract user_api_key_auth if present (proxy injects this, skipped in merge)
+ user_api_key_auth = _litellm_params_metadata.get("user_api_key_auth")
+
+ def _get_api_key_alias() -> Optional[str]:
+ val = _metadata.get("user_api_key_alias")
+ if val is not None:
+ return val
+ val = _litellm_params_metadata.get("user_api_key_alias")
+ if val is not None:
+ return val
+ if user_api_key_auth is not None:
+ return getattr(user_api_key_auth, "key_alias", None)
+ return None
+
+ def _get_team_id() -> Optional[str]:
+ val = _metadata.get("user_api_key_team_id")
+ if val is not None:
+ return val
+ val = _litellm_params_metadata.get("user_api_key_team_id")
+ if val is not None:
+ return val
+ if user_api_key_auth is not None:
+ return getattr(user_api_key_auth, "team_id", None)
+ return None
+
+ def _get_team_alias() -> Optional[str]:
+ val = _metadata.get("user_api_key_team_alias")
+ if val is not None:
+ return val
+ val = _litellm_params_metadata.get("user_api_key_team_alias")
+ if val is not None:
+ return val
+ if user_api_key_auth is not None:
+ return getattr(user_api_key_auth, "team_alias", None)
+ return None
+
+ def _get_hashed_api_key() -> Optional[str]:
+ val = _metadata.get("user_api_key_hash")
+ if val is not None:
+ return val
+ val = _litellm_params_metadata.get("user_api_key_hash")
+ if val is not None:
+ return val
+ if user_api_key_auth is not None:
+ return getattr(user_api_key_auth, "api_key", None) or getattr(
+ user_api_key_auth, "api_key_hash", None
+ )
+ return None
+
+ return {
+ "api_key_alias": _get_api_key_alias(),
+ "team": _get_team_id(),
+ "team_alias": _get_team_alias(),
+ "hashed_api_key": _get_hashed_api_key(),
+ "client_ip": _metadata.get("requester_ip_address")
+ or _litellm_params_metadata.get("requester_ip_address"),
+ "user_agent": _metadata.get("user_agent")
+ or _litellm_params_metadata.get("user_agent"),
+ }
+
def set_llm_deployment_failure_metrics(self, request_kwargs: dict):
"""
Sets Failure metrics when an LLM API call fails
@@ -1707,6 +1816,21 @@ def set_llm_deployment_failure_metrics(self, request_kwargs: dict):
model_id = standard_logging_payload.get("model_id", None)
exception = request_kwargs.get("exception", None)
+ # Fallback: model_id from litellm_metadata.model_info
+ if model_id is None:
+ _model_info = (
+ (_litellm_params.get("litellm_metadata") or {}).get("model_info")
+ or (_litellm_params.get("metadata") or {}).get("model_info")
+ or {}
+ )
+ model_id = _model_info.get("id")
+
+ # Fallback: model_group from litellm_metadata
+ if model_group is None:
+ model_group = (_litellm_params.get("litellm_metadata") or {}).get(
+ "model_group"
+ ) or (_litellm_params.get("metadata") or {}).get("model_group")
+
llm_provider = _litellm_params.get("custom_llm_provider", None)
if self._should_skip_metrics_for_invalid_key(
@@ -1714,9 +1838,37 @@ def set_llm_deployment_failure_metrics(self, request_kwargs: dict):
standard_logging_payload=standard_logging_payload,
):
return
- hashed_api_key = standard_logging_payload.get("metadata", {}).get(
+
+ # Extract context labels from all available sources (fix for None labels)
+ fallback_values = self._extract_deployment_failure_label_values(
+ request_kwargs
+ )
+ _metadata = standard_logging_payload.get("metadata", {}) or {}
+ hashed_api_key = fallback_values.get("hashed_api_key") or _metadata.get(
"user_api_key_hash"
)
+ api_key_alias = fallback_values.get("api_key_alias") or _metadata.get(
+ "user_api_key_alias"
+ )
+ team = fallback_values.get("team") or _metadata.get("user_api_key_team_id")
+ team_alias = fallback_values.get("team_alias") or _metadata.get(
+ "user_api_key_team_alias"
+ )
+ client_ip = fallback_values.get("client_ip") or _metadata.get(
+ "requester_ip_address"
+ )
+ user_agent = fallback_values.get("user_agent") or _metadata.get(
+ "user_agent"
+ )
+
+ # exception_status: prefer status_code, fallback to exception class for known types
+ exception_status = None
+ if exception is not None:
+ exception_status = str(getattr(exception, "status_code", None))
+ if exception_status == "None" or not exception_status:
+ code = getattr(exception, "code", None)
+ if code is not None:
+ exception_status = str(code)
# Create enum_values for the label factory (always create for use in different metrics)
enum_values = UserAPIKeyLabelValues(
@@ -1724,26 +1876,18 @@ def set_llm_deployment_failure_metrics(self, request_kwargs: dict):
model_id=model_id,
api_base=api_base,
api_provider=llm_provider,
- exception_status=(
- str(getattr(exception, "status_code", None)) if exception else None
- ),
+ exception_status=exception_status,
exception_class=(
self._get_exception_class_name(exception) if exception else None
),
- requested_model=model_group,
+ requested_model=model_group or litellm_model_name,
hashed_api_key=hashed_api_key,
- api_key_alias=standard_logging_payload["metadata"][
- "user_api_key_alias"
- ],
- team=standard_logging_payload["metadata"]["user_api_key_team_id"],
- team_alias=standard_logging_payload["metadata"][
- "user_api_key_team_alias"
- ],
+ api_key_alias=api_key_alias,
+ team=team,
+ team_alias=team_alias,
tags=standard_logging_payload.get("request_tags", []),
- client_ip=standard_logging_payload["metadata"].get(
- "requester_ip_address"
- ),
- user_agent=standard_logging_payload["metadata"].get("user_agent"),
+ client_ip=client_ip,
+ user_agent=user_agent,
)
"""
@@ -2761,12 +2905,14 @@ async def _assemble_user_object(
max_budget=max_budget,
)
try:
+ # Note: Setting check_db_only=True bypasses cache and hits DB on every request,
+ # causing huge latency increase and CPU spikes. Keep check_db_only=False.
user_info = await get_user_object(
user_id=user_id,
prisma_client=prisma_client,
user_api_key_cache=user_api_key_cache,
user_id_upsert=False,
- check_db_only=True,
+ check_db_only=False,
)
except Exception as e:
verbose_logger.debug(
diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py
index 00695cbfb5b..7c8e2ebeaff 100644
--- a/litellm/litellm_core_utils/core_helpers.py
+++ b/litellm/litellm_core_utils/core_helpers.py
@@ -94,8 +94,8 @@ def map_finish_reason(
return "length"
elif finish_reason == "tool_use": # anthropic
return "tool_calls"
- elif finish_reason == "content_filtered":
- return "content_filter"
+ elif finish_reason == "compaction":
+ return "length"
return finish_reason
diff --git a/litellm/litellm_core_utils/initialize_dynamic_callback_params.py b/litellm/litellm_core_utils/initialize_dynamic_callback_params.py
index c425319b4d4..ff521d47804 100644
--- a/litellm/litellm_core_utils/initialize_dynamic_callback_params.py
+++ b/litellm/litellm_core_utils/initialize_dynamic_callback_params.py
@@ -1,8 +1,35 @@
from typing import Dict, Optional
-
from litellm.secret_managers.main import get_secret_str
from litellm.types.utils import StandardCallbackDynamicParams
+# Hardcoded list of supported callback params to avoid runtime inspection issues with TypedDict
+_supported_callback_params = [
+ "langfuse_public_key",
+ "langfuse_secret",
+ "langfuse_secret_key",
+ "langfuse_host",
+ "langfuse_prompt_version",
+ "gcs_bucket_name",
+ "gcs_path_service_account",
+ "langsmith_api_key",
+ "langsmith_project",
+ "langsmith_base_url",
+ "langsmith_sampling_rate",
+ "langsmith_tenant_id",
+ "humanloop_api_key",
+ "arize_api_key",
+ "arize_space_key",
+ "arize_space_id",
+ "posthog_api_key",
+ "posthog_host",
+ "braintrust_api_key",
+ "braintrust_project",
+ "braintrust_host",
+ "slack_webhook_url",
+ "lunary_public_key",
+ "turn_off_message_logging",
+]
+
def initialize_standard_callback_dynamic_params(
kwargs: Optional[Dict] = None,
@@ -15,13 +42,10 @@ def initialize_standard_callback_dynamic_params(
standard_callback_dynamic_params = StandardCallbackDynamicParams()
if kwargs:
- _supported_callback_params = (
- StandardCallbackDynamicParams.__annotations__.keys()
- )
-
+ # 1. Check top-level kwargs
for param in _supported_callback_params:
if param in kwargs:
- _param_value = kwargs.pop(param)
+ _param_value = kwargs.get(param)
if (
_param_value is not None
and isinstance(_param_value, str)
@@ -30,4 +54,22 @@ def initialize_standard_callback_dynamic_params(
_param_value = get_secret_str(secret_name=_param_value)
standard_callback_dynamic_params[param] = _param_value # type: ignore
+ # 2. Fallback: check "metadata" or "litellm_params" -> "metadata"
+ metadata = (kwargs.get("metadata") or {}).copy()
+ litellm_params = kwargs.get("litellm_params") or {}
+ if isinstance(litellm_params, dict):
+ metadata.update(litellm_params.get("metadata") or {})
+
+ if isinstance(metadata, dict):
+ for param in _supported_callback_params:
+ if param not in standard_callback_dynamic_params and param in metadata:
+ _param_value = metadata.get(param)
+ if (
+ _param_value is not None
+ and isinstance(_param_value, str)
+ and "os.environ/" in _param_value
+ ):
+ _param_value = get_secret_str(secret_name=_param_value)
+ standard_callback_dynamic_params[param] = _param_value # type: ignore
+
return standard_callback_dynamic_params
diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py
index 4ad2d1002bc..14015225f38 100644
--- a/litellm/litellm_core_utils/litellm_logging.py
+++ b/litellm/litellm_core_utils/litellm_logging.py
@@ -2435,6 +2435,36 @@ async def async_success_handler( # noqa: PLR0915
standard_built_in_tools_params=self.standard_built_in_tools_params,
)
+ # print standard logging payload
+ if (
+ standard_logging_payload := self.model_call_details.get(
+ "standard_logging_object"
+ )
+ ) is not None:
+ emit_standard_logging_payload(standard_logging_payload)
+ elif self.call_type == "pass_through_endpoint":
+ print_verbose(
+ "Async success callbacks: Got a pass-through endpoint response"
+ )
+
+ self.model_call_details["async_complete_streaming_response"] = result
+
+ # cost calculation not possible for pass-through
+ self.model_call_details["response_cost"] = None
+
+ ## STANDARDIZED LOGGING PAYLOAD
+ self.model_call_details[
+ "standard_logging_object"
+ ] = get_standard_logging_object_payload(
+ kwargs=self.model_call_details,
+ init_response_obj=result,
+ start_time=start_time,
+ end_time=end_time,
+ logging_obj=self,
+ status="success",
+ standard_built_in_tools_params=self.standard_built_in_tools_params,
+ )
+
# print standard logging payload
if (
standard_logging_payload := self.model_call_details.get(
@@ -3887,18 +3917,6 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
return langfuse_logger # type: ignore
elif logging_integration == "langfuse_otel":
from litellm.integrations.langfuse.langfuse_otel import LangfuseOtelLogger
- from litellm.integrations.opentelemetry import (
- OpenTelemetry,
- OpenTelemetryConfig,
- )
-
- langfuse_otel_config = LangfuseOtelLogger.get_langfuse_otel_config()
-
- # The endpoint and headers are now set as environment variables by get_langfuse_otel_config()
- otel_config = OpenTelemetryConfig(
- exporter=langfuse_otel_config.protocol,
- headers=langfuse_otel_config.otlp_auth_headers,
- )
for callback in _in_memory_loggers:
if (
@@ -3906,8 +3924,10 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
and callback.callback_name == "langfuse_otel"
):
return callback # type: ignore
+ # Allow LangfuseOtelLogger to initialize its own config safely
+ # This prevents startup crashes if LANGFUSE keys are not in env (e.g. for dynamic usage)
_otel_logger = LangfuseOtelLogger(
- config=otel_config, callback_name="langfuse_otel"
+ config=None, callback_name="langfuse_otel"
)
_in_memory_loggers.append(_otel_logger)
return _otel_logger # type: ignore
diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py
index fe06641a389..2308dc7beca 100644
--- a/litellm/litellm_core_utils/llm_cost_calc/utils.py
+++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py
@@ -215,6 +215,9 @@ def _get_token_base_cost(
cache_creation_tiered_key = (
f"cache_creation_input_token_cost_above_{threshold_str}_tokens"
)
+ cache_creation_1hr_tiered_key = (
+ f"cache_creation_input_token_cost_above_1hr_above_{threshold_str}_tokens"
+ )
cache_read_tiered_key = (
f"cache_read_input_token_cost_above_{threshold_str}_tokens"
)
@@ -229,6 +232,16 @@ def _get_token_base_cost(
),
)
+ if cache_creation_1hr_tiered_key in model_info:
+ cache_creation_cost_above_1hr = cast(
+ float,
+ _get_cost_per_unit(
+ model_info,
+ cache_creation_1hr_tiered_key,
+ cache_creation_cost_above_1hr,
+ ),
+ )
+
if cache_read_tiered_key in model_info:
cache_read_cost = cast(
float,
diff --git a/litellm/litellm_core_utils/logging_callback_manager.py b/litellm/litellm_core_utils/logging_callback_manager.py
index 4f76a5bad03..435ae078a65 100644
--- a/litellm/litellm_core_utils/logging_callback_manager.py
+++ b/litellm/litellm_core_utils/logging_callback_manager.py
@@ -114,6 +114,27 @@ def remove_callback_from_list_by_object(
for c in remove_list:
callback_list.remove(c)
+ def remove_callbacks_by_type(self, callback_list, callback_type):
+ """
+ Remove all callbacks of a specific type from a callback list.
+
+ Args:
+ callback_list: The list to remove callbacks from (e.g., litellm.callbacks)
+ callback_type: The class type to match (e.g., SemanticToolFilterHook)
+
+ Example:
+ litellm.logging_callback_manager.remove_callbacks_by_type(
+ litellm.callbacks, SemanticToolFilterHook
+ )
+ """
+ if not isinstance(callback_list, list):
+ return
+
+ remove_list = [c for c in callback_list if isinstance(c, callback_type)]
+
+ for c in remove_list:
+ callback_list.remove(c)
+
def _add_string_callback_to_list(
self, callback: str, parent_list: List[Union[CustomLogger, Callable, str]]
):
diff --git a/litellm/litellm_core_utils/model_param_helper.py b/litellm/litellm_core_utils/model_param_helper.py
index 91f2f1341cf..4d45c47c224 100644
--- a/litellm/litellm_core_utils/model_param_helper.py
+++ b/litellm/litellm_core_utils/model_param_helper.py
@@ -17,15 +17,16 @@
class ModelParamHelper:
+ # Cached at class level — deterministic set built from static OpenAI type annotations
+ _relevant_logging_args: frozenset = frozenset()
+
@staticmethod
def get_standard_logging_model_parameters(
model_parameters: dict,
) -> dict:
""" """
standard_logging_model_parameters: dict = {}
- supported_model_parameters = (
- ModelParamHelper._get_relevant_args_to_use_for_logging()
- )
+ supported_model_parameters = ModelParamHelper._relevant_logging_args
for key, value in model_parameters.items():
if key in supported_model_parameters:
@@ -172,3 +173,8 @@ def _get_exclude_kwargs() -> Set[str]:
Get the kwargs to exclude from the cache key
"""
return set(["metadata"])
+
+
+ModelParamHelper._relevant_logging_args = frozenset(
+ ModelParamHelper._get_relevant_args_to_use_for_logging()
+)
diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py
index 7790fb83361..b1c2d0a52f5 100644
--- a/litellm/litellm_core_utils/prompt_templates/common_utils.py
+++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py
@@ -443,13 +443,21 @@ def update_messages_with_model_file_ids(
def update_responses_input_with_model_file_ids(
input: Any,
+ model_id: Optional[str] = None,
+ model_file_id_mapping: Optional[Dict[str, Dict[str, str]]] = None,
) -> Union[str, List[Dict[str, Any]]]:
"""
Updates responses API input with provider-specific file IDs.
File IDs are always inside the content array, not as direct input_file items.
- For managed files (unified file IDs), decodes the base64-encoded unified file ID
- and extracts the llm_output_file_id directly.
+ For managed files (unified file IDs), uses model_file_id_mapping if provided,
+ otherwise decodes the base64-encoded unified file ID and extracts the llm_output_file_id directly.
+
+ Args:
+ input: The responses API input parameter
+ model_id: The model ID to use for looking up provider-specific file IDs
+ model_file_id_mapping: Dictionary mapping litellm file IDs to provider file IDs
+ Format: {"litellm_file_id": {"model_id": "provider_file_id"}}
"""
from litellm.proxy.openai_files_endpoints.common_utils import (
_is_base64_encoded_unified_file_id,
@@ -479,22 +487,35 @@ def update_responses_input_with_model_file_ids(
):
file_id = content_item.get("file_id")
if file_id:
- # Check if this is a managed file ID (base64-encoded unified file ID)
- is_unified_file_id = _is_base64_encoded_unified_file_id(file_id)
- if is_unified_file_id:
- unified_file_id = convert_b64_uid_to_unified_uid(file_id)
- if "llm_output_file_id," in unified_file_id:
- provider_file_id = unified_file_id.split(
- "llm_output_file_id,"
- )[1].split(";")[0]
- else:
- # Fallback: keep original if we can't extract
- provider_file_id = file_id
+ provider_file_id = file_id # Default to original
+
+ # Check if we have a mapping for this file ID
+ if model_file_id_mapping and model_id and file_id in model_file_id_mapping:
+ # Use the model-specific file ID from mapping
+ provider_file_id = (
+ model_file_id_mapping.get(file_id, {}).get(model_id)
+ or file_id
+ )
updated_content_item = content_item.copy()
updated_content_item["file_id"] = provider_file_id
updated_content.append(updated_content_item)
else:
- updated_content.append(content_item)
+ # Check if this is a base64-encoded unified file ID without mapping
+ is_unified_file_id = _is_base64_encoded_unified_file_id(file_id)
+ if is_unified_file_id:
+ # Fallback: decode unified file ID
+ unified_file_id = convert_b64_uid_to_unified_uid(file_id)
+ if "llm_output_file_id," in unified_file_id:
+ provider_file_id = unified_file_id.split(
+ "llm_output_file_id,"
+ )[1].split(";")[0]
+
+ updated_content_item = content_item.copy()
+ updated_content_item["file_id"] = provider_file_id
+ updated_content.append(updated_content_item)
+ else:
+ # Not a managed file, keep as-is
+ updated_content.append(content_item)
else:
updated_content.append(content_item)
else:
@@ -506,6 +527,68 @@ def update_responses_input_with_model_file_ids(
return updated_input
+def update_responses_tools_with_model_file_ids(
+ tools: Optional[List[Dict[str, Any]]],
+ model_id: Optional[str] = None,
+ model_file_id_mapping: Optional[Dict[str, Dict[str, str]]] = None,
+) -> Optional[List[Dict[str, Any]]]:
+ """
+ Updates responses API tools with provider-specific file IDs.
+
+ Handles code_interpreter tools with container.file_ids.
+
+ Args:
+ tools: The responses API tools parameter
+ model_id: The model ID to use for looking up provider-specific file IDs
+ model_file_id_mapping: Dictionary mapping litellm file IDs to provider file IDs
+ Format: {"litellm_file_id": {"model_id": "provider_file_id"}}
+ """
+ if not tools or not isinstance(tools, list):
+ return tools
+
+ if not model_file_id_mapping or not model_id:
+ return tools
+
+ updated_tools = []
+ for tool in tools:
+ if not isinstance(tool, dict):
+ updated_tools.append(tool)
+ continue
+
+ updated_tool = tool.copy()
+
+ # Handle code_interpreter with container file_ids
+ if tool.get("type") == "code_interpreter":
+ container = tool.get("container")
+ if isinstance(container, dict):
+ container_file_ids = container.get("file_ids")
+ if isinstance(container_file_ids, list):
+ updated_file_ids = []
+ for file_id in container_file_ids:
+ if isinstance(file_id, str):
+ # Check if we have a mapping for this file ID
+ if file_id in model_file_id_mapping:
+ # Map to provider-specific file ID
+ provider_file_id = (
+ model_file_id_mapping.get(file_id, {}).get(model_id)
+ or file_id
+ )
+ updated_file_ids.append(provider_file_id)
+ else:
+ updated_file_ids.append(file_id)
+ else:
+ updated_file_ids.append(file_id)
+
+ # Update the tool with new file IDs
+ updated_container = container.copy()
+ updated_container["file_ids"] = updated_file_ids
+ updated_tool["container"] = updated_container
+
+ updated_tools.append(updated_tool)
+
+ return updated_tools
+
+
def extract_file_data(file_data: FileTypes) -> ExtractedFileData:
"""
Extracts and processes file data from various input formats.
diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py
index 0e1637a65ba..f9ecd78ff1c 100644
--- a/litellm/litellm_core_utils/prompt_templates/factory.py
+++ b/litellm/litellm_core_utils/prompt_templates/factory.py
@@ -2190,6 +2190,16 @@ def anthropic_messages_pt( # noqa: PLR0915
while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
assistant_content_block: ChatCompletionAssistantMessage = messages[msg_i] # type: ignore
+ # Extract compaction_blocks from provider_specific_fields and add them first
+ _provider_specific_fields_raw = assistant_content_block.get(
+ "provider_specific_fields"
+ )
+ if isinstance(_provider_specific_fields_raw, dict):
+ _compaction_blocks = _provider_specific_fields_raw.get("compaction_blocks")
+ if _compaction_blocks and isinstance(_compaction_blocks, list):
+ # Add compaction blocks at the beginning of assistant content : https://platform.claude.com/docs/en/build-with-claude/compaction
+ assistant_content.extend(_compaction_blocks) # type: ignore
+
thinking_blocks = assistant_content_block.get("thinking_blocks", None)
if (
thinking_blocks is not None
@@ -3399,6 +3409,59 @@ def _convert_to_bedrock_tool_call_result(
return content_block
+def _deduplicate_bedrock_content_blocks(
+ blocks: List[BedrockContentBlock],
+ block_key: str,
+ id_key: str = "toolUseId",
+) -> List[BedrockContentBlock]:
+ """
+ Remove duplicate content blocks that share the same ID under ``block_key``.
+
+ Bedrock requires all toolResult and toolUse IDs within a single message to
+ be unique. When merging consecutive messages, duplicates can occur if the
+ same tool_call_id appears multiple times in conversation history.
+
+ When duplicates exist, the first occurrence is retained and subsequent ones
+ are discarded. A warning is logged for every dropped block so that
+ upstream duplication bugs remain visible.
+
+ Blocks that do not contain ``block_key`` (e.g., cachePoint, text) are
+ always preserved.
+
+ Args:
+ blocks: The list of Bedrock content blocks to deduplicate.
+ block_key: The dict key to inspect (e.g. ``"toolResult"`` or ``"toolUse"``).
+ id_key: The nested key that holds the unique ID (default ``"toolUseId"``).
+ """
+ seen_ids: Set[str] = set()
+ deduplicated: List[BedrockContentBlock] = []
+ for block in blocks:
+ keyed = block.get(block_key)
+ if keyed is not None and isinstance(keyed, dict):
+ block_id = keyed.get(id_key)
+ if block_id:
+ if block_id in seen_ids:
+ verbose_logger.warning(
+ "Bedrock Converse: dropping duplicate %s block with "
+ "%s=%s. This may indicate duplicate tool messages in "
+ "conversation history.",
+ block_key,
+ id_key,
+ block_id,
+ )
+ continue
+ seen_ids.add(block_id)
+ deduplicated.append(block)
+ return deduplicated
+
+
+def _deduplicate_bedrock_tool_content(
+ tool_content: List[BedrockContentBlock],
+) -> List[BedrockContentBlock]:
+ """Convenience wrapper: deduplicate ``toolResult`` blocks by ``toolUseId``."""
+ return _deduplicate_bedrock_content_blocks(tool_content, "toolResult")
+
+
def _insert_assistant_continue_message(
messages: List[BedrockMessageBlock],
assistant_continue_message: Optional[
@@ -3867,6 +3930,8 @@ async def _bedrock_converse_messages_pt_async( # noqa: PLR0915
tool_content.append(cache_point_block)
msg_i += 1
+ # Deduplicate toolResult blocks with the same toolUseId
+ tool_content = _deduplicate_bedrock_tool_content(tool_content)
if tool_content:
# if last message was a 'user' message, then add a blank assistant message (bedrock requires alternating roles)
if len(contents) > 0 and contents[-1]["role"] == "user":
@@ -3932,10 +3997,12 @@ async def _bedrock_converse_messages_pt_async( # noqa: PLR0915
assistant_parts=assistants_parts,
)
elif element["type"] == "text":
- assistants_part = BedrockContentBlock(
- text=element["text"]
- )
- assistants_parts.append(assistants_part)
+ # Skip completely empty strings to avoid blank content blocks
+ if element.get("text", "").strip():
+ assistants_part = BedrockContentBlock(
+ text=element["text"]
+ )
+ assistants_parts.append(assistants_part)
elif element["type"] == "image_url":
if isinstance(element["image_url"], dict):
image_url = element["image_url"]["url"]
@@ -3960,9 +4027,12 @@ async def _bedrock_converse_messages_pt_async( # noqa: PLR0915
elif _assistant_content is not None and isinstance(
_assistant_content, str
):
- assistant_content.append(
- BedrockContentBlock(text=_assistant_content)
- )
+ # Skip completely empty strings to avoid blank content blocks
+ if _assistant_content.strip():
+ assistant_content.append(
+ BedrockContentBlock(text=_assistant_content)
+ )
+ # If content is empty/whitespace, skip it (don't add a placeholder)
# Add cache point block for assistant string content
_cache_point_block = (
litellm.AmazonConverseConfig()._get_cache_point_block(
@@ -3980,6 +4050,8 @@ async def _bedrock_converse_messages_pt_async( # noqa: PLR0915
msg_i += 1
+ assistant_content = _deduplicate_bedrock_content_blocks(assistant_content, "toolUse")
+
if assistant_content:
contents.append(
BedrockMessageBlock(role="assistant", content=assistant_content)
@@ -4230,6 +4302,8 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915
tool_content.append(cache_point_block)
msg_i += 1
+ # Deduplicate toolResult blocks with the same toolUseId
+ tool_content = _deduplicate_bedrock_tool_content(tool_content)
if tool_content:
# if last message was a 'user' message, then add a blank assistant message (bedrock requires alternating roles)
if len(contents) > 0 and contents[-1]["role"] == "user":
@@ -4289,12 +4363,11 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915
assistant_parts=assistants_parts,
)
elif element["type"] == "text":
- # AWS Bedrock doesn't allow empty or whitespace-only text content, so use placeholder for empty strings
- text_content = (
- element["text"] if element["text"].strip() else "."
- )
- assistants_part = BedrockContentBlock(text=text_content)
- assistants_parts.append(assistants_part)
+ # AWS Bedrock doesn't allow empty or whitespace-only text content
+ # Skip completely empty strings to avoid blank content blocks
+ if element.get("text", "").strip():
+ assistants_part = BedrockContentBlock(text=element["text"])
+ assistants_parts.append(assistants_part)
elif element["type"] == "image_url":
if isinstance(element["image_url"], dict):
image_url = element["image_url"]["url"]
@@ -4317,9 +4390,9 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915
assistants_parts.append(_cache_point_block)
assistant_content.extend(assistants_parts)
elif _assistant_content is not None and isinstance(_assistant_content, str):
- # AWS Bedrock doesn't allow empty or whitespace-only text content, so use placeholder for empty strings
- text_content = _assistant_content if _assistant_content.strip() else "."
- assistant_content.append(BedrockContentBlock(text=text_content))
+ # Skip completely empty strings to avoid blank content blocks
+ if _assistant_content.strip():
+ assistant_content.append(BedrockContentBlock(text=_assistant_content))
# Add cache point block for assistant string content
_cache_point_block = (
litellm.AmazonConverseConfig()._get_cache_point_block(
@@ -4336,6 +4409,8 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915
msg_i += 1
+ assistant_content = _deduplicate_bedrock_content_blocks(assistant_content, "toolUse")
+
if assistant_content:
contents.append(
BedrockMessageBlock(role="assistant", content=assistant_content)
diff --git a/litellm/litellm_core_utils/redact_messages.py b/litellm/litellm_core_utils/redact_messages.py
index 0effed3db70..aa763dc9899 100644
--- a/litellm/litellm_core_utils/redact_messages.py
+++ b/litellm/litellm_core_utils/redact_messages.py
@@ -130,6 +130,11 @@ def perform_redaction(model_call_details: dict, result):
def should_redact_message_logging(model_call_details: dict) -> bool:
"""
Determine if message logging should be redacted.
+
+ Priority order:
+ 1. Dynamic parameter (turn_off_message_logging in request)
+ 2. Headers (litellm-disable-message-redaction / litellm-enable-message-redaction)
+ 3. Global setting (litellm.turn_off_message_logging)
"""
litellm_params = model_call_details.get("litellm_params", {})
@@ -139,36 +144,36 @@ def should_redact_message_logging(model_call_details: dict) -> bool:
# Get headers from the metadata
request_headers = metadata.get("headers", {}) if isinstance(metadata, dict) else {}
- possible_request_headers = [
+ # Check for headers that explicitly control redaction
+ if request_headers and bool(
+ request_headers.get("litellm-disable-message-redaction", False)
+ ):
+ # User explicitly disabled redaction via header
+ return False
+
+ possible_enable_headers = [
"litellm-enable-message-redaction", # old header. maintain backwards compatibility
"x-litellm-enable-message-redaction", # new header
]
is_redaction_enabled_via_header = False
- for header in possible_request_headers:
+ for header in possible_enable_headers:
if bool(request_headers.get(header, False)):
is_redaction_enabled_via_header = True
break
- # check if user opted out of logging message/response to callbacks
- if (
- litellm.turn_off_message_logging is not True
- and is_redaction_enabled_via_header is not True
- and _get_turn_off_message_logging_from_dynamic_params(model_call_details)
- is not True
- ):
- return False
-
- if request_headers and bool(
- request_headers.get("litellm-disable-message-redaction", False)
- ):
- return False
-
- # user has OPTED OUT of message redaction
- if _get_turn_off_message_logging_from_dynamic_params(model_call_details) is False:
- return False
-
- return True
+ # Priority 1: Check dynamic parameter first (if explicitly set)
+ dynamic_turn_off = _get_turn_off_message_logging_from_dynamic_params(model_call_details)
+ if dynamic_turn_off is not None:
+ # Dynamic parameter is explicitly set, use it
+ return dynamic_turn_off
+
+ # Priority 2: Check if header explicitly enables redaction
+ if is_redaction_enabled_via_header:
+ return True
+
+ # Priority 3: Fall back to global setting
+ return litellm.turn_off_message_logging is True
def redact_message_input_output_from_logging(
diff --git a/litellm/litellm_core_utils/token_counter.py b/litellm/litellm_core_utils/token_counter.py
index a99bd1cd0f3..6b9e51034c0 100644
--- a/litellm/litellm_core_utils/token_counter.py
+++ b/litellm/litellm_core_utils/token_counter.py
@@ -706,7 +706,7 @@ def _count_content_list(
if isinstance(c, str):
num_tokens += count_function(c)
elif c["type"] == "text":
- num_tokens += count_function(c.get("text", ""))
+ num_tokens += count_function(str(c.get("text", "")))
elif c["type"] == "image_url":
image_url = c.get("image_url")
num_tokens += _count_image_tokens(
@@ -722,7 +722,7 @@ def _count_content_list(
elif c["type"] == "thinking":
# Claude extended thinking content block
# Count the thinking text and skip signature (opaque signature blob)
- thinking_text = c.get("thinking", "")
+ thinking_text = str(c.get("thinking", ""))
if thinking_text:
num_tokens += count_function(thinking_text)
else:
diff --git a/litellm/llms/a2a/__init__.py b/litellm/llms/a2a/__init__.py
new file mode 100644
index 00000000000..043efa5e8bf
--- /dev/null
+++ b/litellm/llms/a2a/__init__.py
@@ -0,0 +1,6 @@
+"""
+A2A (Agent-to-Agent) Protocol Provider for LiteLLM
+"""
+from .chat.transformation import A2AConfig
+
+__all__ = ["A2AConfig"]
diff --git a/litellm/llms/a2a/chat/__init__.py b/litellm/llms/a2a/chat/__init__.py
new file mode 100644
index 00000000000..76bf4dd71d9
--- /dev/null
+++ b/litellm/llms/a2a/chat/__init__.py
@@ -0,0 +1,6 @@
+"""
+A2A Chat Completion Implementation
+"""
+from .transformation import A2AConfig
+
+__all__ = ["A2AConfig"]
diff --git a/litellm/llms/a2a/chat/guardrail_translation/README.md b/litellm/llms/a2a/chat/guardrail_translation/README.md
new file mode 100644
index 00000000000..1e18f5cda3a
--- /dev/null
+++ b/litellm/llms/a2a/chat/guardrail_translation/README.md
@@ -0,0 +1,155 @@
+# A2A Protocol Guardrail Translation Handler
+
+Handler for processing A2A (Agent-to-Agent) Protocol messages with guardrails.
+
+## Overview
+
+This handler processes A2A JSON-RPC 2.0 input/output by:
+1. Extracting text from message parts (`kind: "text"`)
+2. Applying guardrails to text content
+3. Mapping guardrailed text back to original structure
+
+## A2A Protocol Format
+
+### Input Format (JSON-RPC 2.0)
+
+```json
+{
+ "jsonrpc": "2.0",
+ "id": "request-id",
+ "method": "message/send",
+ "params": {
+ "message": {
+ "kind": "message",
+ "messageId": "...",
+ "role": "user",
+ "parts": [
+ {"kind": "text", "text": "Hello, my SSN is 123-45-6789"}
+ ]
+ },
+ "metadata": {
+ "guardrails": ["block-ssn"]
+ }
+ }
+}
+```
+
+### Output Formats
+
+The handler supports multiple A2A response formats:
+
+**Direct message:**
+```json
+{
+ "result": {
+ "kind": "message",
+ "parts": [{"kind": "text", "text": "Response text"}]
+ }
+}
+```
+
+**Nested message:**
+```json
+{
+ "result": {
+ "message": {
+ "parts": [{"kind": "text", "text": "Response text"}]
+ }
+ }
+}
+```
+
+**Task with artifacts:**
+```json
+{
+ "result": {
+ "kind": "task",
+ "artifacts": [
+ {"parts": [{"kind": "text", "text": "Artifact text"}]}
+ ]
+ }
+}
+```
+
+**Task with status message:**
+```json
+{
+ "result": {
+ "kind": "task",
+ "status": {
+ "message": {
+ "parts": [{"kind": "text", "text": "Status message"}]
+ }
+ }
+ }
+}
+```
+
+**Streaming artifact-update:**
+```json
+{
+ "result": {
+ "kind": "artifact-update",
+ "artifact": {
+ "parts": [{"kind": "text", "text": "Streaming text"}]
+ }
+ }
+}
+```
+
+## Usage
+
+The handler is automatically discovered and applied when guardrails are used with A2A endpoints.
+
+### Via LiteLLM Proxy
+
+```bash
+curl -X POST 'http://localhost:4000/a2a/my-agent' \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer your-api-key' \
+-d '{
+ "jsonrpc": "2.0",
+ "id": "1",
+ "method": "message/send",
+ "params": {
+ "message": {
+ "kind": "message",
+ "messageId": "msg-1",
+ "role": "user",
+ "parts": [{"kind": "text", "text": "Hello, my SSN is 123-45-6789"}]
+ },
+ "metadata": {
+ "guardrails": ["block-ssn"]
+ }
+ }
+}'
+```
+
+### Specifying Guardrails
+
+Guardrails can be specified in the A2A request via the `metadata.guardrails` field:
+
+```json
+{
+ "params": {
+ "message": {...},
+ "metadata": {
+ "guardrails": ["block-ssn", "pii-filter"]
+ }
+ }
+}
+```
+
+## Extension
+
+Override these methods to customize behavior:
+
+- `_extract_texts_from_result()`: Custom text extraction from A2A responses
+- `_extract_texts_from_parts()`: Custom text extraction from message parts
+- `_apply_text_to_path()`: Custom application of guardrailed text
+
+## Call Types
+
+This handler is registered for:
+- `CallTypes.send_message`: Synchronous A2A message sending
+- `CallTypes.asend_message`: Asynchronous A2A message sending
diff --git a/litellm/llms/a2a/chat/guardrail_translation/__init__.py b/litellm/llms/a2a/chat/guardrail_translation/__init__.py
new file mode 100644
index 00000000000..13c20677485
--- /dev/null
+++ b/litellm/llms/a2a/chat/guardrail_translation/__init__.py
@@ -0,0 +1,11 @@
+"""A2A Protocol handler for Unified Guardrails."""
+
+from litellm.llms.a2a.chat.guardrail_translation.handler import A2AGuardrailHandler
+from litellm.types.utils import CallTypes
+
+guardrail_translation_mappings = {
+ CallTypes.send_message: A2AGuardrailHandler,
+ CallTypes.asend_message: A2AGuardrailHandler,
+}
+
+__all__ = ["guardrail_translation_mappings"]
diff --git a/litellm/llms/a2a/chat/guardrail_translation/handler.py b/litellm/llms/a2a/chat/guardrail_translation/handler.py
new file mode 100644
index 00000000000..770453f2def
--- /dev/null
+++ b/litellm/llms/a2a/chat/guardrail_translation/handler.py
@@ -0,0 +1,315 @@
+"""
+A2A Protocol Handler for Unified Guardrails
+
+This module provides guardrail translation support for A2A (Agent-to-Agent) Protocol.
+It handles both JSON-RPC 2.0 input requests and output responses, extracting text
+from message parts and applying guardrails.
+
+A2A Protocol Format:
+- Input: JSON-RPC 2.0 with params.message.parts containing text parts
+- Output: JSON-RPC 2.0 with result containing message/artifact parts
+"""
+
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
+
+from litellm._logging import verbose_proxy_logger
+from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation
+from litellm.types.utils import GenericGuardrailAPIInputs
+
+if TYPE_CHECKING:
+ from litellm.integrations.custom_guardrail import CustomGuardrail
+ from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+ from litellm.proxy._types import UserAPIKeyAuth
+
+
+class A2AGuardrailHandler(BaseTranslation):
+ """
+ Handler for processing A2A Protocol messages with guardrails.
+
+ This class provides methods to:
+ 1. Process input messages (pre-call hook) - extracts text from A2A message parts
+ 2. Process output responses (post-call hook) - extracts text from A2A response parts
+
+ A2A Message Format:
+ - Input: params.message.parts[].text (where kind == "text")
+ - Output: result.message.parts[].text or result.artifacts[].parts[].text
+ """
+
+ async def process_input_messages(
+ self,
+ data: dict,
+ guardrail_to_apply: "CustomGuardrail",
+ litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None,
+ ) -> Any:
+ """
+ Process A2A input messages by applying guardrails to text content.
+
+ Extracts text from A2A message parts and applies guardrails.
+
+ Args:
+ data: The A2A JSON-RPC 2.0 request data
+ guardrail_to_apply: The guardrail instance to apply
+ litellm_logging_obj: Optional logging object
+
+ Returns:
+ Modified data with guardrails applied to text content
+ """
+ # A2A request format: { "params": { "message": { "parts": [...] } } }
+ params = data.get("params", {})
+ message = params.get("message", {})
+ parts = message.get("parts", [])
+
+ if not parts:
+ verbose_proxy_logger.debug("A2A: No parts in message, skipping guardrail")
+ return data
+
+ texts_to_check: List[str] = []
+ text_part_indices: List[int] = [] # Track which parts contain text
+
+ # Step 1: Extract text from all text parts
+ for part_idx, part in enumerate(parts):
+ if part.get("kind") == "text":
+ text = part.get("text", "")
+ if text:
+ texts_to_check.append(text)
+ text_part_indices.append(part_idx)
+
+ # Step 2: Apply guardrail to all texts in batch
+ if texts_to_check:
+ inputs = GenericGuardrailAPIInputs(texts=texts_to_check)
+
+ # Pass the structured A2A message to guardrails
+ inputs["structured_messages"] = [message]
+
+ # Include agent model info if available
+ model = data.get("model")
+ if model:
+ inputs["model"] = model
+
+ guardrailed_inputs = await guardrail_to_apply.apply_guardrail(
+ inputs=inputs,
+ request_data=data,
+ input_type="request",
+ logging_obj=litellm_logging_obj,
+ )
+
+ guardrailed_texts = guardrailed_inputs.get("texts", [])
+
+ # Step 3: Apply guardrailed text back to original parts
+ if guardrailed_texts and len(guardrailed_texts) == len(text_part_indices):
+ for task_idx, part_idx in enumerate(text_part_indices):
+ parts[part_idx]["text"] = guardrailed_texts[task_idx]
+
+ verbose_proxy_logger.debug("A2A: Processed input message: %s", message)
+
+ return data
+
+ async def process_output_response(
+ self,
+ response: Any,
+ guardrail_to_apply: "CustomGuardrail",
+ litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None,
+ user_api_key_dict: Optional["UserAPIKeyAuth"] = None,
+ ) -> Any:
+ """
+ Process A2A output response by applying guardrails to text content.
+
+ Handles multiple A2A response formats:
+ - Direct message: {"result": {"kind": "message", "parts": [...]}}
+ - Nested message: {"result": {"message": {"parts": [...]}}}
+ - Task with artifacts: {"result": {"kind": "task", "artifacts": [{"parts": [...]}]}}
+ - Task with status message: {"result": {"kind": "task", "status": {"message": {"parts": [...]}}}}
+
+ Args:
+ response: A2A JSON-RPC 2.0 response dict or object
+ guardrail_to_apply: The guardrail instance to apply
+ litellm_logging_obj: Optional logging object
+ user_api_key_dict: User API key metadata
+
+ Returns:
+ Modified response with guardrails applied to text content
+ """
+ # Handle both dict and Pydantic model responses
+ if hasattr(response, "model_dump"):
+ response_dict = response.model_dump()
+ is_pydantic = True
+ elif isinstance(response, dict):
+ response_dict = response
+ is_pydantic = False
+ else:
+ verbose_proxy_logger.warning(
+ "A2A: Unknown response type %s, skipping guardrail", type(response)
+ )
+ return response
+
+ result = response_dict.get("result", {})
+ if not result or not isinstance(result, dict):
+ verbose_proxy_logger.debug("A2A: No result in response, skipping guardrail")
+ return response
+
+ # Find all text-containing parts in the response
+ texts_to_check: List[str] = []
+ # Each mapping is (path_to_parts_list, part_index)
+ # path_to_parts_list is a tuple of keys to navigate to the parts list
+ task_mappings: List[Tuple[Tuple[str, ...], int]] = []
+
+ # Extract texts from all possible locations
+ self._extract_texts_from_result(
+ result=result,
+ texts_to_check=texts_to_check,
+ task_mappings=task_mappings,
+ )
+
+ if not texts_to_check:
+ verbose_proxy_logger.debug("A2A: No text content in response")
+ return response
+
+ # Step 2: Apply guardrail to all texts in batch
+ # Create a request_data dict with response info and user API key metadata
+ request_data: dict = {"response": response_dict}
+
+ # Add user API key metadata with prefixed keys
+ user_metadata = self.transform_user_api_key_dict_to_metadata(user_api_key_dict)
+ if user_metadata:
+ request_data["litellm_metadata"] = user_metadata
+
+ inputs = GenericGuardrailAPIInputs(texts=texts_to_check)
+
+ guardrailed_inputs = await guardrail_to_apply.apply_guardrail(
+ inputs=inputs,
+ request_data=request_data,
+ input_type="response",
+ logging_obj=litellm_logging_obj,
+ )
+
+ guardrailed_texts = guardrailed_inputs.get("texts", [])
+
+ # Step 3: Apply guardrailed text back to original response
+ if guardrailed_texts and len(guardrailed_texts) == len(task_mappings):
+ for task_idx, (path, part_idx) in enumerate(task_mappings):
+ self._apply_text_to_path(
+ result=result,
+ path=path,
+ part_idx=part_idx,
+ text=guardrailed_texts[task_idx],
+ )
+
+ verbose_proxy_logger.debug("A2A: Processed output response")
+
+ # Update the original response
+ if is_pydantic:
+ # For Pydantic models, we need to update the underlying dict
+ # and the model will reflect the changes
+ response_dict["result"] = result
+ return response
+ else:
+ response["result"] = result
+ return response
+
+ def _extract_texts_from_result(
+ self,
+ result: Dict[str, Any],
+ texts_to_check: List[str],
+ task_mappings: List[Tuple[Tuple[str, ...], int]],
+ ) -> None:
+ """
+ Extract text from all possible locations in an A2A result.
+
+ Handles multiple response formats:
+ 1. Direct message with parts: {"parts": [...]}
+ 2. Nested message: {"message": {"parts": [...]}}
+ 3. Task with artifacts: {"artifacts": [{"parts": [...]}]}
+ 4. Task with status message: {"status": {"message": {"parts": [...]}}}
+ 5. Streaming artifact-update: {"artifact": {"parts": [...]}}
+ """
+ # Case 1: Direct parts in result (direct message)
+ if "parts" in result:
+ self._extract_texts_from_parts(
+ parts=result["parts"],
+ path=("parts",),
+ texts_to_check=texts_to_check,
+ task_mappings=task_mappings,
+ )
+
+ # Case 2: Nested message
+ message = result.get("message")
+ if message and isinstance(message, dict) and "parts" in message:
+ self._extract_texts_from_parts(
+ parts=message["parts"],
+ path=("message", "parts"),
+ texts_to_check=texts_to_check,
+ task_mappings=task_mappings,
+ )
+
+ # Case 3: Streaming artifact-update (singular artifact)
+ artifact = result.get("artifact")
+ if artifact and isinstance(artifact, dict) and "parts" in artifact:
+ self._extract_texts_from_parts(
+ parts=artifact["parts"],
+ path=("artifact", "parts"),
+ texts_to_check=texts_to_check,
+ task_mappings=task_mappings,
+ )
+
+ # Case 4: Task with status message
+ status = result.get("status", {})
+ if isinstance(status, dict):
+ status_message = status.get("message")
+ if (
+ status_message
+ and isinstance(status_message, dict)
+ and "parts" in status_message
+ ):
+ self._extract_texts_from_parts(
+ parts=status_message["parts"],
+ path=("status", "message", "parts"),
+ texts_to_check=texts_to_check,
+ task_mappings=task_mappings,
+ )
+
+ # Case 5: Task with artifacts (plural, array)
+ artifacts = result.get("artifacts", [])
+ if artifacts and isinstance(artifacts, list):
+ for artifact_idx, art in enumerate(artifacts):
+ if isinstance(art, dict) and "parts" in art:
+ self._extract_texts_from_parts(
+ parts=art["parts"],
+ path=("artifacts", str(artifact_idx), "parts"),
+ texts_to_check=texts_to_check,
+ task_mappings=task_mappings,
+ )
+
+ def _extract_texts_from_parts(
+ self,
+ parts: List[Dict[str, Any]],
+ path: Tuple[str, ...],
+ texts_to_check: List[str],
+ task_mappings: List[Tuple[Tuple[str, ...], int]],
+ ) -> None:
+ """Extract text from message parts."""
+ for part_idx, part in enumerate(parts):
+ if part.get("kind") == "text":
+ text = part.get("text", "")
+ if text:
+ texts_to_check.append(text)
+ task_mappings.append((path, part_idx))
+
+ def _apply_text_to_path(
+ self,
+ result: Dict[Union[str, int], Any],
+ path: Tuple[str, ...],
+ part_idx: int,
+ text: str,
+ ) -> None:
+ """Apply guardrailed text back to the specified path in the result."""
+ # Navigate to the parts list
+ current = result
+ for key in path:
+ if key.isdigit():
+ # Array index
+ current = current[int(key)]
+ else:
+ current = current[key]
+
+ # Update the text in the part
+ current[part_idx]["text"] = text
diff --git a/litellm/llms/a2a/chat/streaming_iterator.py b/litellm/llms/a2a/chat/streaming_iterator.py
new file mode 100644
index 00000000000..4b689414ddd
--- /dev/null
+++ b/litellm/llms/a2a/chat/streaming_iterator.py
@@ -0,0 +1,103 @@
+"""
+A2A Streaming Response Iterator
+"""
+from typing import Optional, Union
+
+from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
+from litellm.types.utils import GenericStreamingChunk, ModelResponseStream
+
+from ..common_utils import extract_text_from_a2a_response
+
+
+class A2AModelResponseIterator(BaseModelResponseIterator):
+ """
+ Iterator for parsing A2A streaming responses.
+
+ Converts A2A JSON-RPC streaming chunks to OpenAI-compatible format.
+ """
+
+ def __init__(
+ self,
+ streaming_response,
+ sync_stream: bool,
+ json_mode: Optional[bool] = False,
+ model: str = "a2a/agent",
+ ):
+ super().__init__(
+ streaming_response=streaming_response,
+ sync_stream=sync_stream,
+ json_mode=json_mode,
+ )
+ self.model = model
+
+ def chunk_parser(self, chunk: dict) -> Union[GenericStreamingChunk, ModelResponseStream]:
+ """
+ Parse A2A streaming chunk to OpenAI format.
+
+ A2A chunk format:
+ {
+ "jsonrpc": "2.0",
+ "id": "request-id",
+ "result": {
+ "message": {
+ "parts": [{"kind": "text", "text": "content"}]
+ }
+ }
+ }
+
+ Or for tasks:
+ {
+ "jsonrpc": "2.0",
+ "result": {
+ "kind": "task",
+ "status": {"state": "running"},
+ "artifacts": [{"parts": [{"kind": "text", "text": "content"}]}]
+ }
+ }
+ """
+ try:
+ # Extract text from A2A response
+ text = extract_text_from_a2a_response(chunk)
+
+ # Determine finish reason
+ finish_reason = self._get_finish_reason(chunk)
+
+ # Return generic streaming chunk
+ return GenericStreamingChunk(
+ text=text,
+ is_finished=bool(finish_reason),
+ finish_reason=finish_reason or "",
+ usage=None,
+ index=0,
+ tool_use=None,
+ )
+ except Exception:
+ # Return empty chunk on parse error
+ return GenericStreamingChunk(
+ text="",
+ is_finished=False,
+ finish_reason="",
+ usage=None,
+ index=0,
+ tool_use=None,
+ )
+
+ def _get_finish_reason(self, chunk: dict) -> Optional[str]:
+ """Extract finish reason from A2A chunk"""
+ result = chunk.get("result", {})
+
+ # Check for task completion
+ if isinstance(result, dict):
+ status = result.get("status", {})
+ if isinstance(status, dict):
+ state = status.get("state")
+ if state == "completed":
+ return "stop"
+ elif state == "failed":
+ return "stop" # Map failed state to 'stop' (valid finish_reason)
+
+ # Check for [DONE] marker
+ if chunk.get("done") is True:
+ return "stop"
+
+ return None
diff --git a/litellm/llms/a2a/chat/transformation.py b/litellm/llms/a2a/chat/transformation.py
new file mode 100644
index 00000000000..163cd5ab22e
--- /dev/null
+++ b/litellm/llms/a2a/chat/transformation.py
@@ -0,0 +1,370 @@
+"""
+A2A Protocol Transformation for LiteLLM
+"""
+import uuid
+from typing import Any, Dict, Iterator, List, Optional, Union
+
+import httpx
+
+from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
+from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import Choices, Message, ModelResponse
+
+from ..common_utils import (
+ A2AError,
+ convert_messages_to_prompt,
+ extract_text_from_a2a_response,
+)
+from .streaming_iterator import A2AModelResponseIterator
+
+
+class A2AConfig(BaseConfig):
+ """
+ Configuration for A2A (Agent-to-Agent) Protocol.
+
+ Handles transformation between OpenAI and A2A JSON-RPC 2.0 formats.
+ """
+
+ @staticmethod
+ def resolve_agent_config_from_registry(
+ model: str,
+ api_base: Optional[str],
+ api_key: Optional[str],
+ headers: Optional[Dict[str, Any]],
+ optional_params: Dict[str, Any],
+ ) -> tuple[Optional[str], Optional[str], Optional[Dict[str, Any]]]:
+ """
+ Resolve agent configuration from registry if model format is "a2a/".
+
+ Extracts agent name from model string and looks up configuration in the
+ agent registry (if available in proxy context).
+
+ Args:
+ model: Model string (e.g., "a2a/my-agent")
+ api_base: Explicit api_base (takes precedence over registry)
+ api_key: Explicit api_key (takes precedence over registry)
+ headers: Explicit headers (takes precedence over registry)
+ optional_params: Dict to merge additional litellm_params into
+
+ Returns:
+ Tuple of (api_base, api_key, headers) with registry values filled in
+ """
+ # Extract agent name from model (e.g., "a2a/my-agent" -> "my-agent")
+ agent_name = model.split("/", 1)[1] if "/" in model else None
+
+ # Only lookup if agent name exists and some config is missing
+ if not agent_name or (api_base is not None and api_key is not None and headers is not None):
+ return api_base, api_key, headers
+
+ # Try registry lookup (only available in proxy context)
+ try:
+ from litellm.proxy.agent_endpoints.agent_registry import (
+ global_agent_registry,
+ )
+
+ agent = global_agent_registry.get_agent_by_name(agent_name)
+ if agent:
+ # Get api_base from agent card URL
+ if api_base is None and agent.agent_card_params:
+ api_base = agent.agent_card_params.get("url")
+
+ # Get api_key, headers, and other params from litellm_params
+ if agent.litellm_params:
+ if api_key is None:
+ api_key = agent.litellm_params.get("api_key")
+
+ if headers is None:
+ agent_headers = agent.litellm_params.get("headers")
+ if agent_headers:
+ headers = agent_headers
+
+ # Merge other litellm_params (timeout, max_retries, etc.)
+ for key, value in agent.litellm_params.items():
+ if key not in ["api_key", "api_base", "headers", "model"] and key not in optional_params:
+ optional_params[key] = value
+ except ImportError:
+ pass # Registry not available (not running in proxy context)
+
+ return api_base, api_key, headers
+
+ def get_supported_openai_params(self, model: str) -> List[str]:
+ """Return list of supported OpenAI parameters"""
+ return [
+ "stream",
+ "temperature",
+ "max_tokens",
+ "top_p",
+ ]
+
+ def map_openai_params(
+ self,
+ non_default_params: dict,
+ optional_params: dict,
+ model: str,
+ drop_params: bool,
+ ) -> dict:
+ """
+ Map OpenAI parameters to A2A parameters.
+
+ For A2A protocol, we need to map the stream parameter so
+ transform_request can determine which JSON-RPC method to use.
+ """
+ # Map stream parameter
+ for param, value in non_default_params.items():
+ if param == "stream" and value is True:
+ optional_params["stream"] = value
+
+ return optional_params
+
+ def validate_environment(
+ self,
+ headers: dict,
+ model: str,
+ messages: List[AllMessageValues],
+ optional_params: dict,
+ litellm_params: dict,
+ api_key: Optional[str] = None,
+ api_base: Optional[str] = None,
+ ) -> dict:
+ """
+ Validate environment and set headers for A2A requests.
+
+ Args:
+ headers: Request headers dict
+ model: Model name
+ messages: Messages list
+ optional_params: Optional parameters
+ litellm_params: LiteLLM parameters
+ api_key: API key (optional for A2A)
+ api_base: API base URL
+
+ Returns:
+ Updated headers dict
+ """
+ # Ensure Content-Type is set to application/json for JSON-RPC 2.0
+ if "content-type" not in headers and "Content-Type" not in headers:
+ headers["Content-Type"] = "application/json"
+
+ # Add Authorization header if API key is provided
+ if api_key is not None:
+ headers["Authorization"] = f"Bearer {api_key}"
+
+ return headers
+
+ def get_complete_url(
+ self,
+ api_base: Optional[str],
+ api_key: Optional[str],
+ model: str,
+ optional_params: dict,
+ litellm_params: dict,
+ stream: Optional[bool] = None,
+ ) -> str:
+ """
+ Get the complete A2A agent endpoint URL.
+
+ A2A agents use JSON-RPC 2.0 at the base URL, not specific paths.
+ The method (message/send or message/stream) is specified in the
+ JSON-RPC request body, not in the URL.
+
+ Args:
+ api_base: Base URL of the A2A agent (e.g., "http://0.0.0.0:9999")
+ api_key: API key (not used for URL construction)
+ model: Model name (not used for A2A, agent determined by api_base)
+ optional_params: Optional parameters
+ litellm_params: LiteLLM parameters
+ stream: Whether this is a streaming request (affects JSON-RPC method)
+
+ Returns:
+ Complete URL for the A2A endpoint (base URL)
+ """
+ if api_base is None:
+ raise ValueError("api_base is required for A2A provider")
+
+ # A2A uses JSON-RPC 2.0 at the base URL
+ # Remove trailing slash for consistency
+ return api_base.rstrip("/")
+
+ def transform_request(
+ self,
+ model: str,
+ messages: List[AllMessageValues],
+ optional_params: dict,
+ litellm_params: dict,
+ headers: dict,
+ ) -> dict:
+ """
+ Transform OpenAI request to A2A JSON-RPC 2.0 format.
+
+ Args:
+ model: Model name
+ messages: List of OpenAI messages
+ optional_params: Optional parameters
+ litellm_params: LiteLLM parameters
+ headers: Request headers
+
+ Returns:
+ A2A JSON-RPC 2.0 request dict
+ """
+ # Generate request ID
+ request_id = str(uuid.uuid4())
+
+ if not messages:
+ raise ValueError("At least one message is required for A2A completion")
+
+ # Convert all messages to maintain conversation history
+ # Use helper to format conversation with role prefixes
+ full_context = convert_messages_to_prompt(messages)
+
+ # Create single A2A message with full conversation context
+ a2a_message = {
+ "role": "user",
+ "parts": [{"kind": "text", "text": full_context}],
+ "messageId": str(uuid.uuid4()),
+ }
+
+ # Build JSON-RPC 2.0 request
+ # For A2A protocol, the method is "message/send" for non-streaming
+ # and "message/stream" for streaming
+ stream = optional_params.get("stream", False)
+ method = "message/stream" if stream else "message/send"
+
+ request_data = {
+ "jsonrpc": "2.0",
+ "id": request_id,
+ "method": method,
+ "params": {
+ "message": a2a_message
+ }
+ }
+
+ return request_data
+
+ def transform_response(
+ self,
+ model: str,
+ raw_response: httpx.Response,
+ model_response: ModelResponse,
+ logging_obj: Any,
+ request_data: dict,
+ messages: List[AllMessageValues],
+ optional_params: dict,
+ litellm_params: dict,
+ encoding: Any,
+ api_key: Optional[str] = None,
+ json_mode: Optional[bool] = None,
+ ) -> ModelResponse:
+ """
+ Transform A2A JSON-RPC 2.0 response to OpenAI format.
+
+ Args:
+ model: Model name
+ raw_response: HTTP response from A2A agent
+ model_response: Model response object to populate
+ logging_obj: Logging object
+ request_data: Original request data
+ messages: Original messages
+ optional_params: Optional parameters
+ litellm_params: LiteLLM parameters
+ encoding: Encoding object
+ api_key: API key
+ json_mode: JSON mode flag
+
+ Returns:
+ Populated ModelResponse object
+ """
+ try:
+ response_json = raw_response.json()
+ except Exception as e:
+ raise A2AError(
+ status_code=raw_response.status_code,
+ message=f"Failed to parse A2A response: {str(e)}",
+ headers=dict(raw_response.headers),
+ )
+
+ # Check for JSON-RPC error
+ if "error" in response_json:
+ error = response_json["error"]
+ raise A2AError(
+ status_code=raw_response.status_code,
+ message=f"A2A error: {error.get('message', 'Unknown error')}",
+ headers=dict(raw_response.headers),
+ )
+
+ # Extract text from A2A response
+ text = extract_text_from_a2a_response(response_json)
+
+ # Populate model response
+ model_response.choices = [
+ Choices(
+ finish_reason="stop",
+ index=0,
+ message=Message(
+ content=text,
+ role="assistant",
+ ),
+ )
+ ]
+
+ # Set model
+ model_response.model = model
+
+ # Set ID from response
+ model_response.id = response_json.get("id", str(uuid.uuid4()))
+
+ return model_response
+
+ def get_model_response_iterator(
+ self,
+ streaming_response: Union[Iterator, Any],
+ sync_stream: bool,
+ json_mode: Optional[bool] = False,
+ ) -> BaseModelResponseIterator:
+ """
+ Get streaming iterator for A2A responses.
+
+ Args:
+ streaming_response: Streaming response iterator
+ sync_stream: Whether this is a sync stream
+ json_mode: JSON mode flag
+
+ Returns:
+ A2A streaming iterator
+ """
+ return A2AModelResponseIterator(
+ streaming_response=streaming_response,
+ sync_stream=sync_stream,
+ json_mode=json_mode,
+ )
+
+ def _openai_message_to_a2a_message(self, message: Dict[str, Any]) -> Dict[str, Any]:
+ """
+ Convert OpenAI message to A2A message format.
+
+ Args:
+ message: OpenAI message dict
+
+ Returns:
+ A2A message dict
+ """
+ content = message.get("content", "")
+ role = message.get("role", "user")
+
+ return {
+ "role": role,
+ "parts": [{"kind": "text", "text": str(content)}],
+ "messageId": str(uuid.uuid4()),
+ }
+
+ def get_error_class(
+ self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+ ) -> BaseLLMException:
+ """Return appropriate error class for A2A errors"""
+ # Convert headers to dict if needed
+ headers_dict = dict(headers) if isinstance(headers, httpx.Headers) else headers
+ return A2AError(
+ status_code=status_code,
+ message=error_message,
+ headers=headers_dict,
+ )
diff --git a/litellm/llms/a2a/common_utils.py b/litellm/llms/a2a/common_utils.py
new file mode 100644
index 00000000000..116e1205409
--- /dev/null
+++ b/litellm/llms/a2a/common_utils.py
@@ -0,0 +1,152 @@
+"""
+Common utilities for A2A (Agent-to-Agent) Protocol
+"""
+from typing import Any, Dict, List
+
+from pydantic import BaseModel
+
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+ convert_content_list_to_str,
+)
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.types.llms.openai import AllMessageValues
+
+
+class A2AError(BaseLLMException):
+ """Base exception for A2A protocol errors"""
+
+ def __init__(
+ self,
+ status_code: int,
+ message: str,
+ headers: Dict[str, Any] = {},
+ ):
+ super().__init__(
+ status_code=status_code,
+ message=message,
+ headers=headers,
+ )
+
+
+def convert_messages_to_prompt(messages: List[AllMessageValues]) -> str:
+ """
+ Convert OpenAI messages to a single prompt string for A2A agent.
+
+ Formats each message as "{role}: {content}" and joins with newlines
+ to preserve conversation history. Handles both string and list content.
+
+ Args:
+ messages: List of OpenAI-format messages
+
+ Returns:
+ Formatted prompt string with full conversation context
+ """
+ conversation_parts = []
+ for msg in messages:
+ # Use LiteLLM's helper to extract text from content (handles both str and list)
+ content_text = convert_content_list_to_str(message=msg)
+
+ # Get role
+ if isinstance(msg, BaseModel):
+ role = msg.model_dump().get("role", "user")
+ elif isinstance(msg, dict):
+ role = msg.get("role", "user")
+ else:
+ role = dict(msg).get("role", "user") # type: ignore
+
+ if content_text:
+ conversation_parts.append(f"{role}: {content_text}")
+
+ return "\n".join(conversation_parts)
+
+
+def extract_text_from_a2a_message(
+ message: Dict[str, Any], depth: int = 0, max_depth: int = 10
+) -> str:
+ """
+ Extract text content from A2A message parts.
+
+ Args:
+ message: A2A message dict with 'parts' containing text parts
+ depth: Current recursion depth (internal use)
+ max_depth: Maximum recursion depth to prevent infinite loops
+
+ Returns:
+ Concatenated text from all text parts
+ """
+ if message is None or depth >= max_depth:
+ return ""
+
+ parts = message.get("parts", [])
+ text_parts: List[str] = []
+
+ for part in parts:
+ if part.get("kind") == "text":
+ text_parts.append(part.get("text", ""))
+ # Handle nested parts if they exist
+ elif "parts" in part:
+ nested_text = extract_text_from_a2a_message(part, depth + 1, max_depth)
+ if nested_text:
+ text_parts.append(nested_text)
+
+ return " ".join(text_parts)
+
+
+def extract_text_from_a2a_response(
+ response_dict: Dict[str, Any], max_depth: int = 10
+) -> str:
+ """
+ Extract text content from A2A response result.
+
+ Args:
+ response_dict: A2A response dict with 'result' containing message
+ max_depth: Maximum recursion depth to prevent infinite loops
+
+ Returns:
+ Text from response message parts
+ """
+ result = response_dict.get("result", {})
+ if not isinstance(result, dict):
+ return ""
+
+ # A2A response can have different formats:
+ # 1. Direct message: {"result": {"kind": "message", "parts": [...]}}
+ # 2. Nested message: {"result": {"message": {"parts": [...]}}}
+ # 3. Task with artifacts: {"result": {"kind": "task", "artifacts": [{"parts": [...]}]}}
+ # 4. Task with status message: {"result": {"kind": "task", "status": {"message": {"parts": [...]}}}}
+ # 5. Streaming artifact-update: {"result": {"kind": "artifact-update", "artifact": {"parts": [...]}}}
+
+ # Check if result itself has parts (direct message)
+ if "parts" in result:
+ return extract_text_from_a2a_message(result, depth=0, max_depth=max_depth)
+
+ # Check for nested message
+ message = result.get("message")
+ if message:
+ return extract_text_from_a2a_message(message, depth=0, max_depth=max_depth)
+
+ # Check for streaming artifact-update (singular artifact)
+ artifact = result.get("artifact")
+ if artifact and isinstance(artifact, dict):
+ return extract_text_from_a2a_message(
+ artifact, depth=0, max_depth=max_depth
+ )
+
+ # Check for task status message (common in Gemini A2A agents)
+ status = result.get("status", {})
+ if isinstance(status, dict):
+ status_message = status.get("message")
+ if status_message:
+ return extract_text_from_a2a_message(
+ status_message, depth=0, max_depth=max_depth
+ )
+
+ # Handle task result with artifacts (plural, array)
+ artifacts = result.get("artifacts", [])
+ if artifacts and len(artifacts) > 0:
+ first_artifact = artifacts[0]
+ return extract_text_from_a2a_message(
+ first_artifact, depth=0, max_depth=max_depth
+ )
+
+ return ""
diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py
index 71d74121a30..a14e7d118e8 100644
--- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py
+++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py
@@ -34,6 +34,7 @@
)
from litellm.types.utils import (
ChatCompletionMessageToolCall,
+ Choices,
GenericGuardrailAPIInputs,
ModelResponse,
)
@@ -74,9 +75,10 @@ async def process_input_messages(
if messages is None:
return data
- chat_completion_compatible_request = (
+ chat_completion_compatible_request, tool_name_mapping = (
LiteLLMAnthropicMessagesAdapter().translate_anthropic_to_openai(
- anthropic_message_request=cast(AnthropicMessagesRequest, data)
+ # Use a shallow copy to avoid mutating request data (pop on litellm_metadata).
+ anthropic_message_request=cast(AnthropicMessagesRequest, data.copy())
)
)
@@ -84,9 +86,9 @@ async def process_input_messages(
texts_to_check: List[str] = []
images_to_check: List[str] = []
- tools_to_check: List[ChatCompletionToolParam] = (
- chat_completion_compatible_request.get("tools", [])
- )
+ tools_to_check: List[
+ ChatCompletionToolParam
+ ] = chat_completion_compatible_request.get("tools", [])
task_mappings: List[Tuple[int, Optional[int]]] = []
# Track (message_index, content_index) for each text
# content_index is None for string content, int for list content
@@ -282,7 +284,10 @@ async def process_output_response(
if hasattr(content_block, "model_dump"):
block_dict = content_block.model_dump()
else:
- block_dict = {"type": block_type, "text": getattr(content_block, "text", None)}
+ block_dict = {
+ "type": block_type,
+ "text": getattr(content_block, "text", None),
+ }
else:
continue
@@ -358,30 +363,40 @@ async def process_output_streaming_response(
"""
has_ended = self._check_streaming_has_ended(responses_so_far)
if has_ended:
-
# build the model response from the responses_so_far
- model_response = cast(
- ModelResponse,
- AnthropicPassthroughLoggingHandler._build_complete_streaming_response(
- all_chunks=responses_so_far,
- litellm_logging_obj=cast("LiteLLMLoggingObj", litellm_logging_obj),
- model="",
- ),
- )
- tool_calls_list = cast(Optional[List[ChatCompletionMessageToolCall]], model_response.choices[0].message.tool_calls) # type: ignore
- string_so_far = model_response.choices[0].message.content # type: ignore
- guardrail_inputs = GenericGuardrailAPIInputs()
- if string_so_far:
- guardrail_inputs["texts"] = [string_so_far]
- if tool_calls_list:
- guardrail_inputs["tool_calls"] = tool_calls_list
-
- _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( # allow rejecting the response, if invalid
- inputs=guardrail_inputs,
- request_data={},
- input_type="response",
- logging_obj=litellm_logging_obj,
+ built_response = AnthropicPassthroughLoggingHandler._build_complete_streaming_response(
+ all_chunks=responses_so_far,
+ litellm_logging_obj=cast("LiteLLMLoggingObj", litellm_logging_obj),
+ model="",
)
+
+ # Check if model_response is valid and has choices before accessing
+ if (
+ built_response is not None
+ and hasattr(built_response, "choices")
+ and built_response.choices
+ ):
+ model_response = cast(ModelResponse, built_response)
+ first_choice = cast(Choices, model_response.choices[0])
+ tool_calls_list = cast(
+ Optional[List[ChatCompletionMessageToolCall]],
+ first_choice.message.tool_calls,
+ )
+ string_so_far = first_choice.message.content
+ guardrail_inputs = GenericGuardrailAPIInputs()
+ if string_so_far:
+ guardrail_inputs["texts"] = [string_so_far]
+ if tool_calls_list:
+ guardrail_inputs["tool_calls"] = tool_calls_list
+
+ _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( # allow rejecting the response, if invalid
+ inputs=guardrail_inputs,
+ request_data={},
+ input_type="response",
+ logging_obj=litellm_logging_obj,
+ )
+ else:
+ verbose_proxy_logger.debug("Skipping output guardrail - model response has no choices")
return responses_so_far
string_so_far = self.get_streaming_string_so_far(responses_so_far)
@@ -648,7 +663,10 @@ async def _apply_guardrail_responses_to_output(
if isinstance(content_block, dict):
if content_block.get("type") == "text":
cast(Dict[str, Any], content_block)["text"] = guardrail_response
- elif hasattr(content_block, "type") and getattr(content_block, "type", None) == "text":
+ elif (
+ hasattr(content_block, "type")
+ and getattr(content_block, "type", None) == "text"
+ ):
# Update Pydantic object's text attribute
if hasattr(content_block, "text"):
content_block.text = guardrail_response
diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py
index 6a9aafd076b..485e95d6489 100644
--- a/litellm/llms/anthropic/chat/handler.py
+++ b/litellm/llms/anthropic/chat/handler.py
@@ -512,6 +512,9 @@ def __init__(
# Accumulate web_search_tool_result blocks for multi-turn reconstruction
# See: https://github.com/BerriAI/litellm/issues/17737
self.web_search_results: List[Dict[str, Any]] = []
+
+ # Accumulate compaction blocks for multi-turn reconstruction
+ self.compaction_blocks: List[Dict[str, Any]] = []
def check_empty_tool_call_args(self) -> bool:
"""
@@ -592,6 +595,12 @@ def _content_block_delta_helper(self, chunk: dict) -> Tuple[
)
]
provider_specific_fields["thinking_blocks"] = thinking_blocks
+ elif "content" in content_block["delta"] and content_block["delta"].get("type") == "compaction_delta":
+ # Handle compaction delta
+ provider_specific_fields["compaction_delta"] = {
+ "type": "compaction_delta",
+ "content": content_block["delta"]["content"]
+ }
return text, tool_use, thinking_blocks, provider_specific_fields
@@ -721,6 +730,20 @@ def chunk_parser(self, chunk: dict) -> ModelResponseStream: # noqa: PLR0915
provider_specific_fields=provider_specific_fields,
)
+ elif content_block_start["content_block"]["type"] == "compaction":
+ # Handle compaction blocks
+ # The full content comes in content_block_start
+ self.compaction_blocks.append(
+ content_block_start["content_block"]
+ )
+ provider_specific_fields["compaction_blocks"] = (
+ self.compaction_blocks
+ )
+ provider_specific_fields["compaction_start"] = {
+ "type": "compaction",
+ "content": content_block_start["content_block"].get("content", "")
+ }
+
elif content_block_start["content_block"]["type"].endswith("_tool_result"):
# Handle all tool result types (web_search, bash_code_execution, text_editor, etc.)
content_type = content_block_start["content_block"]["type"]
diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py
index 1b61b533275..02b8d952445 100644
--- a/litellm/llms/anthropic/chat/transformation.py
+++ b/litellm/llms/anthropic/chat/transformation.py
@@ -170,9 +170,10 @@ def convert_tool_use_to_openai_format(
tool_call["caller"] = cast(Dict[str, Any], anthropic_tool_content["caller"]) # type: ignore[typeddict-item]
return tool_call
- def _is_claude_opus_4_5(self, model: str) -> bool:
+ @staticmethod
+ def _is_claude_opus_4_6(model: str) -> bool:
"""Check if the model is Claude Opus 4.5."""
- return "opus-4-5" in model.lower() or "opus_4_5" in model.lower()
+ return "opus-4-6" in model.lower() or "opus_4_6" in model.lower()
def get_supported_openai_params(self, model: str):
params = [
@@ -659,32 +660,38 @@ def _map_stop_sequences(
@staticmethod
def _map_reasoning_effort(
- reasoning_effort: Optional[Union[REASONING_EFFORT, str]],
+ reasoning_effort: Optional[Union[REASONING_EFFORT, str]],
+ model: str,
) -> Optional[AnthropicThinkingParam]:
- if reasoning_effort is None:
- return None
- elif reasoning_effort == "low":
- return AnthropicThinkingParam(
- type="enabled",
- budget_tokens=DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET,
- )
- elif reasoning_effort == "medium":
- return AnthropicThinkingParam(
- type="enabled",
- budget_tokens=DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET,
- )
- elif reasoning_effort == "high":
- return AnthropicThinkingParam(
- type="enabled",
- budget_tokens=DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET,
- )
- elif reasoning_effort == "minimal":
+ if AnthropicConfig._is_claude_opus_4_6(model):
return AnthropicThinkingParam(
- type="enabled",
- budget_tokens=DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET,
+ type="adaptive",
)
else:
- raise ValueError(f"Unmapped reasoning effort: {reasoning_effort}")
+ if reasoning_effort is None:
+ return None
+ elif reasoning_effort == "low":
+ return AnthropicThinkingParam(
+ type="enabled",
+ budget_tokens=DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET,
+ )
+ elif reasoning_effort == "medium":
+ return AnthropicThinkingParam(
+ type="enabled",
+ budget_tokens=DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET,
+ )
+ elif reasoning_effort == "high":
+ return AnthropicThinkingParam(
+ type="enabled",
+ budget_tokens=DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET,
+ )
+ elif reasoning_effort == "minimal":
+ return AnthropicThinkingParam(
+ type="enabled",
+ budget_tokens=DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET,
+ )
+ else:
+ raise ValueError(f"Unmapped reasoning effort: {reasoning_effort}")
def _extract_json_schema_from_response_format(
self, value: Optional[dict]
@@ -860,13 +867,8 @@ def map_openai_params( # noqa: PLR0915
if param == "thinking":
optional_params["thinking"] = value
elif param == "reasoning_effort" and isinstance(value, str):
- # For Claude Opus 4.5, map reasoning_effort to output_config
- if self._is_claude_opus_4_5(model):
- optional_params["output_config"] = {"effort": value}
-
- # For other models, map to thinking parameter
optional_params["thinking"] = AnthropicConfig._map_reasoning_effort(
- value
+ reasoning_effort=value, model=model
)
elif param == "web_search_options" and isinstance(value, dict):
hosted_web_search_tool = self.map_web_search_tool(
@@ -877,6 +879,9 @@ def map_openai_params( # noqa: PLR0915
)
elif param == "extra_headers":
optional_params["extra_headers"] = value
+ elif param == "context_management" and isinstance(value, dict):
+ # Pass through Anthropic-specific context_management parameter
+ optional_params["context_management"] = value
## handle thinking tokens
self.update_optional_params_with_thinking_tokens(
@@ -1026,9 +1031,37 @@ def _ensure_beta_header(self, headers: dict, beta_value: str) -> None:
if beta_value not in existing_values:
headers["anthropic-beta"] = f"{existing_beta}, {beta_value}"
- def _ensure_context_management_beta_header(self, headers: dict) -> None:
- beta_value = ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
- self._ensure_beta_header(headers, beta_value)
+ def _ensure_context_management_beta_header(
+ self, headers: dict, context_management: dict
+ ) -> None:
+ """
+ Add appropriate beta headers based on context_management edits.
+ - If any edit has type "compact_20260112", add compact-2026-01-12 header
+ - For all other edits, add context-management-2025-06-27 header
+ """
+ edits = context_management.get("edits", [])
+
+ has_compact = False
+ has_other = False
+
+ for edit in edits:
+ edit_type = edit.get("type", "")
+ if edit_type == "compact_20260112":
+ has_compact = True
+ else:
+ has_other = True
+
+ # Add compact header if any compact edits exist
+ if has_compact:
+ self._ensure_beta_header(
+ headers, ANTHROPIC_BETA_HEADER_VALUES.COMPACT_2026_01_12.value
+ )
+
+ # Add context management header if any other edits exist
+ if has_other:
+ self._ensure_beta_header(
+ headers, ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
+ )
def update_headers_with_optional_anthropic_beta(
self, headers: dict, optional_params: dict
@@ -1056,7 +1089,9 @@ def update_headers_with_optional_anthropic_beta(
headers, ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
)
if optional_params.get("context_management") is not None:
- self._ensure_context_management_beta_header(headers)
+ self._ensure_context_management_beta_header(
+ headers, optional_params["context_management"]
+ )
if optional_params.get("output_format") is not None:
self._ensure_beta_header(
headers, ANTHROPIC_BETA_HEADER_VALUES.STRUCTURED_OUTPUT_2025_09_25.value
@@ -1225,6 +1260,7 @@ def extract_response_content(self, completion_response: dict) -> Tuple[
List[ChatCompletionToolCallChunk],
Optional[List[Any]],
Optional[List[Any]],
+ Optional[List[Any]],
]:
text_content = ""
citations: Optional[List[Any]] = None
@@ -1237,6 +1273,7 @@ def extract_response_content(self, completion_response: dict) -> Tuple[
tool_calls: List[ChatCompletionToolCallChunk] = []
web_search_results: Optional[List[Any]] = None
tool_results: Optional[List[Any]] = None
+ compaction_blocks: Optional[List[Any]] = None
for idx, content in enumerate(completion_response["content"]):
if content["type"] == "text":
text_content += content["text"]
@@ -1278,6 +1315,12 @@ def extract_response_content(self, completion_response: dict) -> Tuple[
thinking_blocks.append(
cast(ChatCompletionRedactedThinkingBlock, content)
)
+
+ ## COMPACTION
+ elif content["type"] == "compaction":
+ if compaction_blocks is None:
+ compaction_blocks = []
+ compaction_blocks.append(content)
## CITATIONS
if content.get("citations") is not None:
@@ -1299,7 +1342,7 @@ def extract_response_content(self, completion_response: dict) -> Tuple[
if thinking_content is not None:
reasoning_content += thinking_content
- return text_content, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results, tool_results
+ return text_content, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results, tool_results, compaction_blocks
def calculate_usage(
self,
@@ -1316,6 +1359,10 @@ def calculate_usage(
cache_creation_token_details: Optional[CacheCreationTokenDetails] = None
web_search_requests: Optional[int] = None
tool_search_requests: Optional[int] = None
+ inference_geo: Optional[str] = None
+ if "inference_geo" in _usage and _usage["inference_geo"] is not None:
+ inference_geo = _usage["inference_geo"]
+
if (
"cache_creation_input_tokens" in _usage
and _usage["cache_creation_input_tokens"] is not None
@@ -1399,6 +1446,7 @@ def calculate_usage(
if (web_search_requests is not None or tool_search_requests is not None)
else None
),
+ inference_geo=inference_geo,
)
return usage
@@ -1442,6 +1490,7 @@ def transform_parsed_response(
tool_calls,
web_search_results,
tool_results,
+ compaction_blocks,
) = self.extract_response_content(completion_response=completion_response)
if (
@@ -1469,6 +1518,8 @@ def transform_parsed_response(
provider_specific_fields["tool_results"] = tool_results
if container is not None:
provider_specific_fields["container"] = container
+ if compaction_blocks is not None:
+ provider_specific_fields["compaction_blocks"] = compaction_blocks
_message = litellm.Message(
tool_calls=tool_calls,
@@ -1477,6 +1528,7 @@ def transform_parsed_response(
thinking_blocks=thinking_blocks,
reasoning_content=reasoning_content,
)
+ _message.provider_specific_fields = provider_specific_fields
## HANDLE JSON MODE - anthropic returns single function call
json_mode_message = self._transform_response_for_json_mode(
@@ -1507,18 +1559,7 @@ def transform_parsed_response(
model_response.created = int(time.time())
model_response.model = completion_response["model"]
- context_management_response = completion_response.get("context_management")
- if context_management_response is not None:
- _hidden_params["context_management"] = context_management_response
- try:
- model_response.__dict__["context_management"] = (
- context_management_response
- )
- except Exception:
- pass
-
model_response._hidden_params = _hidden_params
-
return model_response
def get_prefix_prompt(self, messages: List[AllMessageValues]) -> Optional[str]:
diff --git a/litellm/llms/anthropic/cost_calculation.py b/litellm/llms/anthropic/cost_calculation.py
index 8f34eb00ce5..11b61cc92f0 100644
--- a/litellm/llms/anthropic/cost_calculation.py
+++ b/litellm/llms/anthropic/cost_calculation.py
@@ -22,10 +22,17 @@ def cost_per_token(model: str, usage: "Usage") -> Tuple[float, float]:
Returns:
Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
"""
- return generic_cost_per_token(
- model=model, usage=usage, custom_llm_provider="anthropic"
+ # If usage has inference_geo, prepend it as prefix to model name
+ if hasattr(usage, "inference_geo") and usage.inference_geo and usage.inference_geo.lower() not in ["global", "not_available"]:
+ model_with_geo_prefix = f"{usage.inference_geo}/{model}"
+ else:
+ model_with_geo_prefix = model
+ prompt_cost, completion_cost = generic_cost_per_token(
+ model=model_with_geo_prefix, usage=usage, custom_llm_provider="anthropic"
)
+ return prompt_cost, completion_cost
+
def get_cost_for_anthropic_web_search(
model_info: Optional["ModelInfo"] = None,
diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py
index 8fa7bb7e65e..a17eba75b3b 100644
--- a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py
+++ b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py
@@ -6,6 +6,7 @@
Dict,
List,
Optional,
+ Tuple,
Union,
cast,
)
@@ -47,8 +48,14 @@ def _prepare_completion_kwargs(
top_p: Optional[float] = None,
output_format: Optional[Dict] = None,
extra_kwargs: Optional[Dict[str, Any]] = None,
- ) -> Dict[str, Any]:
- """Prepare kwargs for litellm.completion/acompletion"""
+ ) -> Tuple[Dict[str, Any], Dict[str, str]]:
+ """Prepare kwargs for litellm.completion/acompletion.
+
+ Returns:
+ Tuple of (completion_kwargs, tool_name_mapping)
+ - tool_name_mapping maps truncated tool names back to original names
+ for tools that exceeded OpenAI's 64-char limit
+ """
from litellm.litellm_core_utils.litellm_logging import (
Logging as LiteLLMLoggingObject,
)
@@ -80,7 +87,7 @@ def _prepare_completion_kwargs(
if output_format:
request_data["output_format"] = output_format
- openai_request = ANTHROPIC_ADAPTER.translate_completion_input_params(
+ openai_request, tool_name_mapping = ANTHROPIC_ADAPTER.translate_completion_input_params_with_tool_mapping(
request_data
)
@@ -116,7 +123,7 @@ def _prepare_completion_kwargs(
):
completion_kwargs[key] = value
- return completion_kwargs
+ return completion_kwargs, tool_name_mapping
@staticmethod
async def async_anthropic_messages_handler(
@@ -137,7 +144,7 @@ async def async_anthropic_messages_handler(
**kwargs,
) -> Union[AnthropicMessagesResponse, AsyncIterator]:
"""Handle non-Anthropic models asynchronously using the adapter"""
- completion_kwargs = (
+ completion_kwargs, tool_name_mapping = (
LiteLLMMessagesToCompletionTransformationHandler._prepare_completion_kwargs(
max_tokens=max_tokens,
messages=messages,
@@ -164,6 +171,7 @@ async def async_anthropic_messages_handler(
ANTHROPIC_ADAPTER.translate_completion_output_params_streaming(
completion_response,
model=model,
+ tool_name_mapping=tool_name_mapping,
)
)
if transformed_stream is not None:
@@ -172,7 +180,8 @@ async def async_anthropic_messages_handler(
else:
anthropic_response = (
ANTHROPIC_ADAPTER.translate_completion_output_params(
- cast(ModelResponse, completion_response)
+ cast(ModelResponse, completion_response),
+ tool_name_mapping=tool_name_mapping,
)
)
if anthropic_response is not None:
@@ -222,7 +231,7 @@ def anthropic_messages_handler(
**kwargs,
)
- completion_kwargs = (
+ completion_kwargs, tool_name_mapping = (
LiteLLMMessagesToCompletionTransformationHandler._prepare_completion_kwargs(
max_tokens=max_tokens,
messages=messages,
@@ -249,6 +258,7 @@ def anthropic_messages_handler(
ANTHROPIC_ADAPTER.translate_completion_output_params_streaming(
completion_response,
model=model,
+ tool_name_mapping=tool_name_mapping,
)
)
if transformed_stream is not None:
@@ -257,7 +267,8 @@ def anthropic_messages_handler(
else:
anthropic_response = (
ANTHROPIC_ADAPTER.translate_completion_output_params(
- cast(ModelResponse, completion_response)
+ cast(ModelResponse, completion_response),
+ tool_name_mapping=tool_name_mapping,
)
)
if anthropic_response is not None:
diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py
index 24524233ddf..a86820f82e8 100644
--- a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py
+++ b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py
@@ -3,7 +3,7 @@
import json
import traceback
from collections import deque
-from typing import TYPE_CHECKING, Any, AsyncIterator, Iterator, Literal, Optional
+from typing import TYPE_CHECKING, Any, AsyncIterator, Dict, Iterator, Literal, Optional
from litellm import verbose_logger
from litellm._uuid import uuid
@@ -44,9 +44,16 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
pending_new_content_block: bool = False
chunk_queue: deque = deque() # Queue for buffering multiple chunks
- def __init__(self, completion_stream: Any, model: str):
+ def __init__(
+ self,
+ completion_stream: Any,
+ model: str,
+ tool_name_mapping: Optional[Dict[str, str]] = None,
+ ):
super().__init__(completion_stream)
self.model = model
+ # Mapping of truncated tool names to original names (for OpenAI's 64-char limit)
+ self.tool_name_mapping = tool_name_mapping or {}
def _create_initial_usage_delta(self) -> UsageDelta:
"""
@@ -401,6 +408,19 @@ def _should_start_new_content_block(self, chunk: "ModelResponseStream") -> bool:
choices=chunk.choices # type: ignore
)
+ # Restore original tool name if it was truncated for OpenAI's 64-char limit
+ if block_type == "tool_use":
+ # Type narrowing: content_block_start is ToolUseBlock when block_type is "tool_use"
+ from typing import cast
+ from litellm.types.llms.anthropic import ToolUseBlock
+
+ tool_block = cast(ToolUseBlock, content_block_start)
+
+ if tool_block.get("name"):
+ truncated_name = tool_block["name"]
+ original_name = self.tool_name_mapping.get(truncated_name, truncated_name)
+ tool_block["name"] = original_name
+
if block_type != self.current_content_block_type:
self.current_content_block_type = block_type
self.current_content_block_start = content_block_start
@@ -408,9 +428,14 @@ def _should_start_new_content_block(self, chunk: "ModelResponseStream") -> bool:
# For parallel tool calls, we'll necessarily have a new content block
# if we get a function name since it signals a new tool call
- if block_type == "tool_use" and content_block_start.get("name"):
- self.current_content_block_type = block_type
- self.current_content_block_start = content_block_start
- return True
+ if block_type == "tool_use":
+ from typing import cast
+ from litellm.types.llms.anthropic import ToolUseBlock
+
+ tool_block = cast(ToolUseBlock, content_block_start)
+ if tool_block.get("name"):
+ self.current_content_block_type = block_type
+ self.current_content_block_start = content_block_start
+ return True
return False
diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
index 5ba0754b744..169b138a5f7 100644
--- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
+++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
@@ -1,3 +1,4 @@
+import hashlib
import json
from typing import (
TYPE_CHECKING,
@@ -12,6 +13,54 @@
cast,
)
+# OpenAI has a 64-character limit for function/tool names
+# Anthropic does not have this limit, so we need to truncate long names
+OPENAI_MAX_TOOL_NAME_LENGTH = 64
+TOOL_NAME_HASH_LENGTH = 8
+TOOL_NAME_PREFIX_LENGTH = OPENAI_MAX_TOOL_NAME_LENGTH - TOOL_NAME_HASH_LENGTH - 1 # 55
+
+
+def truncate_tool_name(name: str) -> str:
+ """
+ Truncate tool names that exceed OpenAI's 64-character limit.
+
+ Uses format: {55-char-prefix}_{8-char-hash} to avoid collisions
+ when multiple tools have similar long names.
+
+ Args:
+ name: The original tool name
+
+ Returns:
+ The original name if <= 64 chars, otherwise truncated with hash
+ """
+ if len(name) <= OPENAI_MAX_TOOL_NAME_LENGTH:
+ return name
+
+ # Create deterministic hash from full name to avoid collisions
+ name_hash = hashlib.sha256(name.encode()).hexdigest()[:TOOL_NAME_HASH_LENGTH]
+ return f"{name[:TOOL_NAME_PREFIX_LENGTH]}_{name_hash}"
+
+
+def create_tool_name_mapping(
+ tools: List[Dict[str, Any]],
+) -> Dict[str, str]:
+ """
+ Create a mapping of truncated tool names to original names.
+
+ Args:
+ tools: List of tool definitions with 'name' field
+
+ Returns:
+ Dict mapping truncated names to original names (only for truncated tools)
+ """
+ mapping: Dict[str, str] = {}
+ for tool in tools:
+ original_name = tool.get("name", "")
+ truncated_name = truncate_tool_name(original_name)
+ if truncated_name != original_name:
+ mapping[truncated_name] = original_name
+ return mapping
+
from openai.types.chat.chat_completion_chunk import Choice as OpenAIStreamingChoice
from litellm.litellm_core_utils.prompt_templates.common_utils import (
@@ -77,8 +126,29 @@ def translate_completion_input_params(
self, kwargs
) -> Optional[ChatCompletionRequest]:
"""
+ Translate Anthropic request params to OpenAI format.
+
- translate params, where needed
- pass rest, as is
+
+ Note: Use translate_completion_input_params_with_tool_mapping() if you need
+ the tool name mapping for restoring original names in responses.
+ """
+ result, _ = self.translate_completion_input_params_with_tool_mapping(kwargs)
+ return result
+
+ def translate_completion_input_params_with_tool_mapping(
+ self, kwargs
+ ) -> Tuple[Optional[ChatCompletionRequest], Dict[str, str]]:
+ """
+ Translate Anthropic request params to OpenAI format, returning tool name mapping.
+
+ This method handles truncation of tool names that exceed OpenAI's 64-character
+ limit. The mapping allows restoring original names when translating responses.
+
+ Returns:
+ Tuple of (openai_request, tool_name_mapping)
+ - tool_name_mapping maps truncated tool names back to original names
"""
#########################################################
@@ -102,26 +172,51 @@ def translate_completion_input_params(
model=model, messages=messages, **kwargs
)
- translated_body = (
+ translated_body, tool_name_mapping = (
LiteLLMAnthropicMessagesAdapter().translate_anthropic_to_openai(
anthropic_message_request=request_body
)
)
- return translated_body
+ return translated_body, tool_name_mapping
def translate_completion_output_params(
- self, response: ModelResponse
+ self,
+ response: ModelResponse,
+ tool_name_mapping: Optional[Dict[str, str]] = None,
) -> Optional[AnthropicMessagesResponse]:
+ """
+ Translate OpenAI response to Anthropic format.
+
+ Args:
+ response: The OpenAI ModelResponse
+ tool_name_mapping: Optional mapping of truncated tool names to original names.
+ Used to restore original names for tools that exceeded
+ OpenAI's 64-char limit.
+ """
return LiteLLMAnthropicMessagesAdapter().translate_openai_response_to_anthropic(
- response=response
+ response=response,
+ tool_name_mapping=tool_name_mapping,
)
def translate_completion_output_params_streaming(
- self, completion_stream: Any, model: str
+ self,
+ completion_stream: Any,
+ model: str,
+ tool_name_mapping: Optional[Dict[str, str]] = None,
) -> Union[AsyncIterator[bytes], None]:
+ """
+ Translate OpenAI streaming response to Anthropic format.
+
+ Args:
+ completion_stream: The OpenAI streaming response
+ model: The model name
+ tool_name_mapping: Optional mapping of truncated tool names to original names.
+ """
anthropic_wrapper = AnthropicStreamWrapper(
- completion_stream=completion_stream, model=model
+ completion_stream=completion_stream,
+ model=model,
+ tool_name_mapping=tool_name_mapping,
)
# Return the SSE-wrapped version for proper event formatting
return anthropic_wrapper.async_anthropic_sse_wrapper()
@@ -417,8 +512,10 @@ def translate_anthropic_messages_to_openai( # noqa: PLR0915
has_cache_control_in_text = True
assistant_content_list.append(text_block)
elif content.get("type") == "tool_use":
+ # Truncate tool name for OpenAI's 64-char limit
+ tool_name = truncate_tool_name(content.get("name", ""))
function_chunk: ChatCompletionToolCallFunctionChunk = {
- "name": content.get("name", ""),
+ "name": tool_name,
"arguments": json.dumps(content.get("input", {})),
}
signature = (
@@ -587,8 +684,11 @@ def translate_anthropic_tool_choice_to_openai(
elif tool_choice["type"] == "auto":
return "auto"
elif tool_choice["type"] == "tool":
+ # Truncate tool name if it exceeds OpenAI's 64-char limit
+ original_name = tool_choice.get("name", "")
+ truncated_name = truncate_tool_name(original_name)
tc_function_param = ChatCompletionToolChoiceFunctionParam(
- name=tool_choice.get("name", "")
+ name=truncated_name
)
return ChatCompletionToolChoiceObjectParam(
type="function", function=tc_function_param
@@ -600,12 +700,28 @@ def translate_anthropic_tool_choice_to_openai(
def translate_anthropic_tools_to_openai(
self, tools: List[AllAnthropicToolsValues], model: Optional[str] = None
- ) -> List[ChatCompletionToolParam]:
+ ) -> Tuple[List[ChatCompletionToolParam], Dict[str, str]]:
+ """
+ Translate Anthropic tools to OpenAI format.
+
+ Returns:
+ Tuple of (translated_tools, tool_name_mapping)
+ - tool_name_mapping maps truncated names back to original names
+ for tools that exceeded OpenAI's 64-char limit
+ """
new_tools: List[ChatCompletionToolParam] = []
+ tool_name_mapping: Dict[str, str] = {}
mapped_tool_params = ["name", "input_schema", "description", "cache_control"]
for tool in tools:
+ original_name = tool["name"]
+ truncated_name = truncate_tool_name(original_name)
+
+ # Store mapping if name was truncated
+ if truncated_name != original_name:
+ tool_name_mapping[truncated_name] = original_name
+
function_chunk = ChatCompletionToolParamFunctionChunk(
- name=tool["name"],
+ name=truncated_name,
)
if "input_schema" in tool:
function_chunk["parameters"] = tool["input_schema"] # type: ignore
@@ -619,7 +735,7 @@ def translate_anthropic_tools_to_openai(
self._add_cache_control_if_applicable(tool, tool_param, model)
new_tools.append(tool_param) # type: ignore[arg-type]
- return new_tools # type: ignore[return-value]
+ return new_tools, tool_name_mapping # type: ignore[return-value]
def translate_anthropic_output_format_to_openai(
self, output_format: Any
@@ -694,12 +810,18 @@ def _add_system_message_to_messages(
def translate_anthropic_to_openai(
self, anthropic_message_request: AnthropicMessagesRequest
- ) -> ChatCompletionRequest:
+ ) -> Tuple[ChatCompletionRequest, Dict[str, str]]:
"""
This is used by the beta Anthropic Adapter, for translating anthropic `/v1/messages` requests to the openai format.
+
+ Returns:
+ Tuple of (openai_request, tool_name_mapping)
+ - tool_name_mapping maps truncated tool names back to original names
+ for tools that exceeded OpenAI's 64-char limit
"""
# Debug: Processing Anthropic message request
new_messages: List[AllMessageValues] = []
+ tool_name_mapping: Dict[str, str] = {}
## CONVERT ANTHROPIC MESSAGES TO OPENAI
messages_list: List[
@@ -750,7 +872,7 @@ def translate_anthropic_to_openai(
if "tools" in anthropic_message_request:
tools = anthropic_message_request["tools"]
if tools:
- new_kwargs["tools"] = self.translate_anthropic_tools_to_openai(
+ new_kwargs["tools"], tool_name_mapping = self.translate_anthropic_tools_to_openai(
tools=cast(List[AllAnthropicToolsValues], tools),
model=new_kwargs.get("model"),
)
@@ -784,7 +906,7 @@ def translate_anthropic_to_openai(
if k not in translatable_params: # pass remaining params as is
new_kwargs[k] = v # type: ignore
- return new_kwargs
+ return new_kwargs, tool_name_mapping
def _translate_anthropic_image_to_openai(self, image_source: dict) -> Optional[str]:
"""
@@ -813,22 +935,12 @@ def _translate_anthropic_image_to_openai(self, image_source: dict) -> Optional[s
return None
- def _translate_openai_content_to_anthropic(self, choices: List[Choices]) -> List[
- Union[
- AnthropicResponseContentBlockText,
- AnthropicResponseContentBlockToolUse,
- AnthropicResponseContentBlockThinking,
- AnthropicResponseContentBlockRedactedThinking,
- ]
- ]:
- new_content: List[
- Union[
- AnthropicResponseContentBlockText,
- AnthropicResponseContentBlockToolUse,
- AnthropicResponseContentBlockThinking,
- AnthropicResponseContentBlockRedactedThinking,
- ]
- ] = []
+ def _translate_openai_content_to_anthropic(
+ self,
+ choices: List[Choices],
+ tool_name_mapping: Optional[Dict[str, str]] = None,
+ ) -> List[Dict[str, Any]]:
+ new_content: List[Dict[str, Any]] = []
for choice in choices:
# Handle thinking blocks first
if (
@@ -852,7 +964,7 @@ def _translate_openai_content_to_anthropic(self, choices: List[Choices]) -> List
if signature_value is not None
else None
),
- )
+ ).model_dump()
)
elif thinking_block.get("type") == "redacted_thinking":
data_value = thinking_block.get("data", "")
@@ -860,15 +972,27 @@ def _translate_openai_content_to_anthropic(self, choices: List[Choices]) -> List
AnthropicResponseContentBlockRedactedThinking(
type="redacted_thinking",
data=str(data_value) if data_value is not None else "",
- )
+ ).model_dump()
)
+ # Handle reasoning_content when thinking_blocks is not present
+ elif (
+ hasattr(choice.message, "reasoning_content")
+ and choice.message.reasoning_content
+ ):
+ new_content.append(
+ AnthropicResponseContentBlockThinking(
+ type="thinking",
+ thinking=str(choice.message.reasoning_content),
+ signature=None,
+ ).model_dump()
+ )
# Handle text content
if choice.message.content is not None:
new_content.append(
AnthropicResponseContentBlockText(
type="text", text=choice.message.content
- )
+ ).model_dump()
)
# Handle tool calls (in parallel to text content)
if (
@@ -883,13 +1007,21 @@ def _translate_openai_content_to_anthropic(self, choices: List[Choices]) -> List
if signature:
provider_specific_fields["signature"] = signature
+ # Restore original tool name if it was truncated
+ truncated_name = tool_call.function.name or ""
+ original_name = (
+ tool_name_mapping.get(truncated_name, truncated_name)
+ if tool_name_mapping
+ else truncated_name
+ )
+
tool_use_block = AnthropicResponseContentBlockToolUse(
type="tool_use",
id=tool_call.id,
- name=tool_call.function.name or "",
+ name=original_name,
input=parse_tool_call_arguments(
tool_call.function.arguments,
- tool_name=tool_call.function.name,
+ tool_name=original_name,
context="Anthropic pass-through adapter",
),
)
@@ -898,7 +1030,7 @@ def _translate_openai_content_to_anthropic(self, choices: List[Choices]) -> List
tool_use_block.provider_specific_fields = (
provider_specific_fields
)
- new_content.append(tool_use_block)
+ new_content.append(tool_use_block.model_dump())
return new_content
@@ -914,10 +1046,24 @@ def _translate_openai_finish_reason_to_anthropic(
return "end_turn"
def translate_openai_response_to_anthropic(
- self, response: ModelResponse
+ self,
+ response: ModelResponse,
+ tool_name_mapping: Optional[Dict[str, str]] = None,
) -> AnthropicMessagesResponse:
+ """
+ Translate OpenAI response to Anthropic format.
+
+ Args:
+ response: The OpenAI ModelResponse
+ tool_name_mapping: Optional mapping of truncated tool names to original names.
+ Used to restore original names for tools that exceeded
+ OpenAI's 64-char limit.
+ """
## translate content block
- anthropic_content = self._translate_openai_content_to_anthropic(choices=response.choices) # type: ignore
+ anthropic_content = self._translate_openai_content_to_anthropic(
+ choices=response.choices, # type: ignore
+ tool_name_mapping=tool_name_mapping,
+ )
## extract finish reason
anthropic_finish_reason = self._translate_openai_finish_reason_to_anthropic(
openai_finish_reason=response.choices[0].finish_reason # type: ignore
@@ -1036,6 +1182,13 @@ def _translate_streaming_openai_chunk_to_anthropic(
reasoning_content += thinking
reasoning_signature += signature
+ # Handle reasoning_content when thinking_blocks is not present
+ # This handles providers like OpenRouter that return reasoning_content
+ elif isinstance(choice, StreamingChoices) and hasattr(
+ choice.delta, "reasoning_content"
+ ):
+ if choice.delta.reasoning_content is not None:
+ reasoning_content += choice.delta.reasoning_content
if reasoning_content and reasoning_signature:
raise ValueError(
diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py
index 308bf367d06..bb40f9df266 100644
--- a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py
+++ b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py
@@ -2,6 +2,9 @@
import httpx
+from litellm.anthropic_beta_headers_manager import (
+ update_headers_with_filtered_beta,
+)
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.litellm_core_utils.litellm_logging import verbose_logger
from litellm.llms.base_llm.anthropic_messages.transformation import (
@@ -90,6 +93,11 @@ def validate_anthropic_messages_environment(
optional_params=optional_params,
)
+ headers = update_headers_with_filtered_beta(
+ headers=headers,
+ provider="anthropic",
+ )
+
return headers, api_base
def transform_anthropic_messages_request(
@@ -189,8 +197,27 @@ def _update_headers_with_anthropic_beta(
beta_values.update(b.strip() for b in existing_beta.split(","))
# Check for context management
- if optional_params.get("context_management") is not None:
- beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value)
+ context_management_param = optional_params.get("context_management")
+ if context_management_param is not None:
+ # Check edits array for compact_20260112 type
+ edits = context_management_param.get("edits", [])
+ has_compact = False
+ has_other = False
+
+ for edit in edits:
+ edit_type = edit.get("type", "")
+ if edit_type == "compact_20260112":
+ has_compact = True
+ else:
+ has_other = True
+
+ # Add compact header if any compact edits exist
+ if has_compact:
+ beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.COMPACT_2026_01_12.value)
+
+ # Add context management header if any other edits exist
+ if has_other:
+ beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value)
# Check for structured outputs
if optional_params.get("output_format") is not None:
diff --git a/litellm/llms/azure_ai/anthropic/count_tokens/transformation.py b/litellm/llms/azure_ai/anthropic/count_tokens/transformation.py
index e284595cc8a..09b83b7c971 100644
--- a/litellm/llms/azure_ai/anthropic/count_tokens/transformation.py
+++ b/litellm/llms/azure_ai/anthropic/count_tokens/transformation.py
@@ -30,30 +30,32 @@ def get_required_headers(
"""
Get the required headers for the Azure AI Anthropic CountTokens API.
- Uses Azure authentication (api-key header) instead of Anthropic's x-api-key.
+ Azure AI Anthropic uses Anthropic's native API format, which requires the
+ x-api-key header for authentication (in addition to Azure's api-key header).
Args:
api_key: The Azure AI API key
litellm_params: Optional LiteLLM parameters for additional auth config
Returns:
- Dictionary of required headers with Azure authentication
+ Dictionary of required headers with both x-api-key and Azure authentication
"""
- # Start with base headers
+ # Start with base headers including x-api-key for Anthropic API compatibility
headers = {
"Content-Type": "application/json",
"anthropic-version": "2023-06-01",
"anthropic-beta": ANTHROPIC_TOKEN_COUNTING_BETA_VERSION,
+ "x-api-key": api_key, # Azure AI Anthropic requires this header
}
- # Use Azure authentication
+ # Also set up Azure auth headers for flexibility
litellm_params = litellm_params or {}
if "api_key" not in litellm_params:
litellm_params["api_key"] = api_key
litellm_params_obj = GenericLiteLLMParams(**litellm_params)
- # Get Azure auth headers
+ # Get Azure auth headers (api-key or Authorization)
azure_headers = BaseAzureLLM._base_validate_azure_environment(
headers={}, litellm_params=litellm_params_obj
)
@@ -68,7 +70,7 @@ def get_count_tokens_endpoint(self, api_base: str) -> str:
Get the Azure AI Anthropic CountTokens API endpoint.
Args:
- api_base: The Azure AI API base URL
+ api_base: The Azure AI API base URL
(e.g., https://my-resource.services.ai.azure.com or
https://my-resource.services.ai.azure.com/anthropic)
diff --git a/litellm/llms/azure_ai/anthropic/transformation.py b/litellm/llms/azure_ai/anthropic/transformation.py
index 2d8d3b987c7..753bc9c08eb 100644
--- a/litellm/llms/azure_ai/anthropic/transformation.py
+++ b/litellm/llms/azure_ai/anthropic/transformation.py
@@ -3,6 +3,9 @@
"""
from typing import TYPE_CHECKING, Dict, List, Optional, Union
+from litellm.anthropic_beta_headers_manager import (
+ update_headers_with_filtered_beta,
+)
from litellm.llms.anthropic.chat.transformation import AnthropicConfig
from litellm.llms.azure.common_utils import BaseAzureLLM
from litellm.types.llms.openai import AllMessageValues
@@ -87,6 +90,12 @@ def validate_environment(
if "anthropic-version" not in headers:
headers["anthropic-version"] = "2023-06-01"
+ # Filter out unsupported beta headers for Azure AI
+ headers = update_headers_with_filtered_beta(
+ headers=headers,
+ provider="azure_ai",
+ )
+
return headers
def transform_request(
diff --git a/litellm/llms/azure_ai/rerank/transformation.py b/litellm/llms/azure_ai/rerank/transformation.py
index a47b6082c37..f577a42ed58 100644
--- a/litellm/llms/azure_ai/rerank/transformation.py
+++ b/litellm/llms/azure_ai/rerank/transformation.py
@@ -11,6 +11,7 @@
from litellm.llms.cohere.rerank.transformation import CohereRerankConfig
from litellm.secret_managers.main import get_secret_str
from litellm.types.utils import RerankResponse
+from litellm.utils import _add_path_to_api_base
class AzureAIRerankConfig(CohereRerankConfig):
@@ -28,9 +29,34 @@ def get_complete_url(
raise ValueError(
"Azure AI API Base is required. api_base=None. Set in call or via `AZURE_AI_API_BASE` env var."
)
- if not api_base.endswith("/v1/rerank"):
- api_base = f"{api_base}/v1/rerank"
- return api_base
+ original_url = httpx.URL(api_base)
+ if not original_url.is_absolute_url:
+ raise ValueError(
+ "Azure AI API Base must be an absolute URL including scheme (e.g. "
+ "'https://.services.ai.azure.com'). "
+ f"Got api_base={api_base!r}."
+ )
+ normalized_path = original_url.path.rstrip("/")
+
+ # Allow callers to pass either full v1/v2 rerank endpoints:
+ # - https://.services.ai.azure.com/v1/rerank
+ # - https://.services.ai.azure.com/providers/cohere/v2/rerank
+ if normalized_path.endswith("/v1/rerank") or normalized_path.endswith("/v2/rerank"):
+ return str(original_url.copy_with(path=normalized_path or "/"))
+
+ # If callers pass just the version path (e.g. ".../v2" or ".../providers/cohere/v2"), append "/rerank"
+ if (
+ normalized_path.endswith("/v1")
+ or normalized_path.endswith("/v2")
+ or normalized_path.endswith("/providers/cohere/v2")
+ ):
+ return _add_path_to_api_base(
+ api_base=str(original_url.copy_with(path=normalized_path or "/")),
+ ending_path="/rerank",
+ )
+
+ # Backwards compatible default: Azure AI rerank was originally exposed under /v1/rerank
+ return _add_path_to_api_base(api_base=api_base, ending_path="/v1/rerank")
def validate_environment(
self,
diff --git a/litellm/llms/bedrock/chat/converse_handler.py b/litellm/llms/bedrock/chat/converse_handler.py
index d5bd054118d..0a56ea63d36 100644
--- a/litellm/llms/bedrock/chat/converse_handler.py
+++ b/litellm/llms/bedrock/chat/converse_handler.py
@@ -66,7 +66,7 @@ def make_sync_call(
model_response=model_response, json_mode=json_mode
)
else:
- decoder = AWSEventStreamDecoder(model=model)
+ decoder = AWSEventStreamDecoder(model=model, json_mode=json_mode)
completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=stream_chunk_size))
# LOGGING
diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py
index d4e4d3591ba..3d63a701901 100644
--- a/litellm/llms/bedrock/chat/converse_transformation.py
+++ b/litellm/llms/bedrock/chat/converse_transformation.py
@@ -3,6 +3,7 @@
"""
import copy
+import json
import time
import types
from typing import List, Literal, Optional, Tuple, Union, cast, overload
@@ -11,6 +12,9 @@
import litellm
from litellm._logging import verbose_logger
+from litellm.anthropic_beta_headers_manager import (
+ filter_and_transform_beta_headers,
+)
from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
from litellm.litellm_core_utils.core_helpers import (
filter_exceptions_from_params,
@@ -66,6 +70,7 @@
BedrockModelInfo,
get_anthropic_beta_from_headers,
get_bedrock_tool_name,
+ is_claude_4_5_on_bedrock,
)
# Computer use tool prefixes supported by Bedrock
@@ -81,6 +86,7 @@
UNSUPPORTED_BEDROCK_CONVERSE_BETA_PATTERNS = [
"advanced-tool-use", # Bedrock Converse doesn't support advanced-tool-use beta headers
"prompt-caching", # Prompt caching not supported in Converse API
+ "compact-2026-01-12", # The compact beta feature is not currently supported on the Converse and ConverseStream APIs
]
@@ -306,9 +312,7 @@ def _is_nova_lite_2_model(self, model: str) -> bool:
return "nova-2-lite" in model_without_region
def _map_web_search_options(
- self,
- web_search_options: dict,
- model: str
+ self, web_search_options: dict, model: str
) -> Optional[BedrockToolBlock]:
"""
Map web_search_options to Nova grounding systemTool.
@@ -431,7 +435,7 @@ def _handle_reasoning_effort_parameter(
else:
# Anthropic and other models: convert to thinking parameter
optional_params["thinking"] = AnthropicConfig._map_reasoning_effort(
- reasoning_effort
+ reasoning_effort=reasoning_effort, model=model
)
def get_supported_openai_params(self, model: str) -> List[str]:
@@ -617,37 +621,6 @@ def _transform_computer_use_tools(
return transformed_tools
- def _filter_unsupported_beta_headers_for_bedrock(
- self, model: str, beta_list: list
- ) -> list:
- """
- Remove beta headers that are not supported on Bedrock Converse API for the given model.
-
- Extended thinking beta headers are only supported on specific Claude 4+ models.
- Some beta headers are universally unsupported on Bedrock Converse API.
-
- Args:
- model: The model name
- beta_list: The list of beta headers to filter
-
- Returns:
- Filtered list of beta headers
- """
- filtered_betas = []
-
- # 1. Filter out beta headers that are universally unsupported on Bedrock Converse
- for beta in beta_list:
- should_keep = True
- for unsupported_pattern in UNSUPPORTED_BEDROCK_CONVERSE_BETA_PATTERNS:
- if unsupported_pattern in beta.lower():
- should_keep = False
- break
-
- if should_keep:
- filtered_betas.append(beta)
-
- return filtered_betas
-
def _separate_computer_use_tools(
self, tools: List[OpenAIChatCompletionToolParam], model: str
) -> Tuple[
@@ -808,11 +781,11 @@ def map_openai_params(
if param == "web_search_options" and isinstance(value, dict):
# Note: we use `isinstance(value, dict)` instead of `value and isinstance(value, dict)`
# because empty dict {} is falsy but is a valid way to enable Nova grounding
- grounding_tool = self._map_web_search_options(value, model)
- if grounding_tool is not None:
- optional_params = self._add_tools_to_optional_params(
- optional_params=optional_params, tools=[grounding_tool]
- )
+ grounding_tool = self._map_web_search_options(value, model)
+ if grounding_tool is not None:
+ optional_params = self._add_tools_to_optional_params(
+ optional_params=optional_params, tools=[grounding_tool]
+ )
# Only update thinking tokens for non-GPT-OSS models and non-Nova-Lite-2 models
# Nova Lite 2 handles token budgeting differently through reasoningConfig
@@ -926,6 +899,7 @@ def _get_cache_point_block(
ChatCompletionAssistantMessage,
],
block_type: Literal["system"],
+ model: Optional[str] = None,
) -> Optional[SystemContentBlock]:
pass
@@ -939,6 +913,7 @@ def _get_cache_point_block(
ChatCompletionAssistantMessage,
],
block_type: Literal["content_block"],
+ model: Optional[str] = None,
) -> Optional[ContentBlock]:
pass
@@ -951,16 +926,26 @@ def _get_cache_point_block(
ChatCompletionAssistantMessage,
],
block_type: Literal["system", "content_block"],
+ model: Optional[str] = None,
) -> Optional[Union[SystemContentBlock, ContentBlock]]:
- if message_block.get("cache_control", None) is None:
+ cache_control = message_block.get("cache_control", None)
+ if cache_control is None:
return None
+
+ cache_point = CachePointBlock(type="default")
+ if isinstance(cache_control, dict) and "ttl" in cache_control:
+ ttl = cache_control["ttl"]
+ if ttl in ["5m", "1h"] and model is not None:
+ if is_claude_4_5_on_bedrock(model):
+ cache_point["ttl"] = ttl
+
if block_type == "system":
- return SystemContentBlock(cachePoint=CachePointBlock(type="default"))
+ return SystemContentBlock(cachePoint=cache_point)
else:
- return ContentBlock(cachePoint=CachePointBlock(type="default"))
+ return ContentBlock(cachePoint=cache_point)
def _transform_system_message(
- self, messages: List[AllMessageValues]
+ self, messages: List[AllMessageValues], model: Optional[str] = None
) -> Tuple[List[AllMessageValues], List[SystemContentBlock]]:
system_prompt_indices = []
system_content_blocks: List[SystemContentBlock] = []
@@ -972,7 +957,7 @@ def _transform_system_message(
SystemContentBlock(text=message["content"])
)
cache_block = self._get_cache_point_block(
- message, block_type="system"
+ message, block_type="system", model=model
)
if cache_block:
system_content_blocks.append(cache_block)
@@ -983,7 +968,7 @@ def _transform_system_message(
SystemContentBlock(text=m["text"])
)
cache_block = self._get_cache_point_block(
- m, block_type="system"
+ m, block_type="system", model=model
)
if cache_block:
system_content_blocks.append(cache_block)
@@ -1081,10 +1066,16 @@ def _process_tools_and_beta(
user_betas = get_anthropic_beta_from_headers(headers)
anthropic_beta_list.extend(user_betas)
- # Filter out tool search tools - Bedrock Converse API doesn't support them
+ # Separate pre-formatted Bedrock tools (e.g. systemTool from web_search_options)
+ # from OpenAI-format tools that need transformation via _bedrock_tools_pt
filtered_tools = []
+ pre_formatted_tools: List[ToolBlock] = []
if original_tools:
for tool in original_tools:
+ # Already-formatted Bedrock tools (e.g. systemTool for Nova grounding)
+ if "systemTool" in tool:
+ pre_formatted_tools.append(tool)
+ continue
tool_type = tool.get("type", "")
if tool_type in (
"tool_search_tool_regex_20251119",
@@ -1106,7 +1097,28 @@ def _process_tools_and_beta(
# Add computer use tools and anthropic_beta if needed (only when computer use tools are present)
if computer_use_tools:
- anthropic_beta_list.append("computer-use-2024-10-22")
+ # Determine the correct computer-use beta header based on model
+ # "computer-use-2025-11-24" for Claude Opus 4.6, Claude Opus 4.5
+ # "computer-use-2025-01-24" for Claude Sonnet 4.5, Haiku 4.5, Opus 4.1, Sonnet 4, Opus 4, and Sonnet 3.7
+ # "computer-use-2024-10-22" for older models
+ model_lower = model.lower()
+ if "opus-4.6" in model_lower or "opus_4.6" in model_lower or "opus-4-6" in model_lower or "opus_4_6" in model_lower:
+ computer_use_header = "computer-use-2025-11-24"
+ elif "opus-4.5" in model_lower or "opus_4.5" in model_lower or "opus-4-5" in model_lower or "opus_4_5" in model_lower:
+ computer_use_header = "computer-use-2025-11-24"
+ elif any(pattern in model_lower for pattern in [
+ "sonnet-4.5", "sonnet_4.5", "sonnet-4-5", "sonnet_4_5",
+ "haiku-4.5", "haiku_4.5", "haiku-4-5", "haiku_4_5",
+ "opus-4.1", "opus_4.1", "opus-4-1", "opus_4_1",
+ "sonnet-4", "sonnet_4",
+ "opus-4", "opus_4",
+ "sonnet-3.7", "sonnet_3.7", "sonnet-3-7", "sonnet_3_7"
+ ]):
+ computer_use_header = "computer-use-2025-01-24"
+ else:
+ computer_use_header = "computer-use-2024-10-22"
+
+ anthropic_beta_list.append(computer_use_header)
# Transform computer use tools to proper Bedrock format
transformed_computer_tools = self._transform_computer_use_tools(
computer_use_tools
@@ -1116,6 +1128,9 @@ def _process_tools_and_beta(
# No computer use tools, process all tools as regular tools
bedrock_tools = _bedrock_tools_pt(filtered_tools)
+ # Append pre-formatted tools (systemTool etc.) after transformation
+ bedrock_tools.extend(pre_formatted_tools)
+
# Set anthropic_beta in additional_request_params if we have any beta features
# ONLY apply to Anthropic/Claude models - other models (e.g., Qwen, Llama) don't support this field
# and will error with "unknown variant anthropic_beta" if included
@@ -1128,14 +1143,14 @@ def _process_tools_and_beta(
if beta not in seen:
unique_betas.append(beta)
seen.add(beta)
-
- # Filter out unsupported beta headers for Bedrock Converse API
- filtered_betas = self._filter_unsupported_beta_headers_for_bedrock(
- model=model,
- beta_list=unique_betas,
+
+ filtered_betas = filter_and_transform_beta_headers(
+ beta_headers=unique_betas,
+ provider="bedrock_converse",
)
- additional_request_params["anthropic_beta"] = filtered_betas
+ if filtered_betas:
+ additional_request_params["anthropic_beta"] = filtered_betas
return bedrock_tools, anthropic_beta_list
@@ -1187,9 +1202,11 @@ def _transform_request_helper(
)
# Prepare and separate parameters
- inference_params, additional_request_params, request_metadata = self._prepare_request_params(
- optional_params, model
- )
+ (
+ inference_params,
+ additional_request_params,
+ request_metadata,
+ ) = self._prepare_request_params(optional_params, model)
original_tools = inference_params.pop("tools", [])
@@ -1241,7 +1258,9 @@ async def _async_transform_request(
litellm_params: dict,
headers: Optional[dict] = None,
) -> RequestObject:
- messages, system_content_blocks = self._transform_system_message(messages)
+ messages, system_content_blocks = self._transform_system_message(
+ messages, model=model
+ )
# Convert last user message to guarded_text if guardrailConfig is present
messages = self._convert_consecutive_user_messages_to_guarded_text(
@@ -1297,7 +1316,9 @@ def _transform_request(
litellm_params: dict,
headers: Optional[dict] = None,
) -> RequestObject:
- messages, system_content_blocks = self._transform_system_message(messages)
+ messages, system_content_blocks = self._transform_system_message(
+ messages, model=model
+ )
# Convert last user message to guarded_text if guardrailConfig is present
messages = self._convert_consecutive_user_messages_to_guarded_text(
@@ -1475,7 +1496,9 @@ def apply_tool_call_transformation_if_needed(
return message, returned_finish_reason
- def _translate_message_content(self, content_blocks: List[ContentBlock]) -> Tuple[
+ def _translate_message_content(
+ self, content_blocks: List[ContentBlock]
+ ) -> Tuple[
str,
List[ChatCompletionToolCallChunk],
Optional[List[BedrockConverseReasoningContentBlock]],
@@ -1492,9 +1515,9 @@ def _translate_message_content(self, content_blocks: List[ContentBlock]) -> Tupl
"""
content_str = ""
tools: List[ChatCompletionToolCallChunk] = []
- reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = (
- None
- )
+ reasoningContentBlocks: Optional[
+ List[BedrockConverseReasoningContentBlock]
+ ] = None
citationsContentBlocks: Optional[List[CitationsContentBlock]] = None
for idx, content in enumerate(content_blocks):
"""
@@ -1548,7 +1571,71 @@ def _translate_message_content(self, content_blocks: List[ContentBlock]) -> Tupl
return content_str, tools, reasoningContentBlocks, citationsContentBlocks
- def _transform_response( # noqa: PLR0915
+ def _filter_json_mode_tools(
+ self,
+ tools: List[ChatCompletionToolCallChunk],
+ json_mode: Optional[bool],
+ chat_completion_message: ChatCompletionResponseMessage,
+ ) -> Optional[List[ChatCompletionToolCallChunk]]:
+ """
+ Filter out json_tool_call from tools if json_mode is enabled.
+ Handles the case where both real tools and response_format are used together.
+
+ Returns None when json_tool_call was converted to content (caller should not set tool_calls).
+ Returns the (possibly filtered) list of tools otherwise.
+ """
+ if not (json_mode is True and tools):
+ return tools # No filtering needed, return as-is (including empty list)
+
+ # Check if json_tool_call is present
+ json_tool_call_index = None
+ for idx, tool in enumerate(tools):
+ if tool["function"].get("name") == RESPONSE_FORMAT_TOOL_NAME:
+ json_tool_call_index = idx
+ break
+
+ if json_tool_call_index is None:
+ return tools # No json_tool_call found, return as-is
+
+ # If json_tool_call is the ONLY tool, convert it to content (existing behavior)
+ if len(tools) == 1:
+ verbose_logger.debug(
+ "Processing JSON tool call response for response_format (single tool)"
+ )
+ json_mode_content_str: Optional[str] = tools[0]["function"].get(
+ "arguments"
+ )
+ if json_mode_content_str is not None:
+ # Bedrock returns the response wrapped in a "properties" object
+ # We need to extract the actual content from this wrapper
+ try:
+ response_data = json.loads(json_mode_content_str)
+
+ # If Bedrock wrapped the response in "properties", extract the content
+ if (
+ isinstance(response_data, dict)
+ and "properties" in response_data
+ and len(response_data) == 1
+ ):
+ response_data = response_data["properties"]
+ json_mode_content_str = json.dumps(response_data)
+ except json.JSONDecodeError:
+ # If parsing fails, use the original response
+ pass
+
+ chat_completion_message["content"] = json_mode_content_str
+ return None # Signal: don't set tool_calls
+
+ # Multiple tools with json_tool_call present: filter it out
+ verbose_logger.debug(
+ "Filtering out %s from tool calls (multiple tools present)",
+ RESPONSE_FORMAT_TOOL_NAME,
+ )
+ return [
+ tool for idx, tool in enumerate(tools) if idx != json_tool_call_index
+ ]
+
+ def _transform_response( # noqa: PLR0915
self,
model: str,
response: httpx.Response,
@@ -1570,7 +1657,7 @@ def _transform_response( # noqa: PLR0915
additional_args={"complete_input_dict": data},
)
- json_mode: Optional[bool] = optional_params.pop("json_mode", None)
+ json_mode: Optional[bool] = optional_params.get("json_mode", None)
## RESPONSE OBJECT
try:
completion_response = ConverseResponseBlock(**response.json()) # type: ignore
@@ -1621,9 +1708,9 @@ def _transform_response( # noqa: PLR0915
chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"}
content_str = ""
tools: List[ChatCompletionToolCallChunk] = []
- reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = (
- None
- )
+ reasoningContentBlocks: Optional[
+ List[BedrockConverseReasoningContentBlock]
+ ] = None
citationsContentBlocks: Optional[List[CitationsContentBlock]] = None
if message is not None:
@@ -1642,49 +1729,30 @@ def _transform_response( # noqa: PLR0915
provider_specific_fields["citationsContent"] = citationsContentBlocks
if provider_specific_fields:
- chat_completion_message["provider_specific_fields"] = provider_specific_fields
+ chat_completion_message[
+ "provider_specific_fields"
+ ] = provider_specific_fields
if reasoningContentBlocks is not None:
- chat_completion_message["reasoning_content"] = (
- self._transform_reasoning_content(reasoningContentBlocks)
- )
- chat_completion_message["thinking_blocks"] = (
- self._transform_thinking_blocks(reasoningContentBlocks)
- )
+ chat_completion_message[
+ "reasoning_content"
+ ] = self._transform_reasoning_content(reasoningContentBlocks)
+ chat_completion_message[
+ "thinking_blocks"
+ ] = self._transform_thinking_blocks(reasoningContentBlocks)
chat_completion_message["content"] = content_str
- if (
- json_mode is True
- and tools is not None
- and len(tools) == 1
- and tools[0]["function"].get("name") == RESPONSE_FORMAT_TOOL_NAME
- ):
- verbose_logger.debug(
- "Processing JSON tool call response for response_format"
- )
- json_mode_content_str: Optional[str] = tools[0]["function"].get("arguments")
- if json_mode_content_str is not None:
- import json
- # Bedrock returns the response wrapped in a "properties" object
- # We need to extract the actual content from this wrapper
- try:
- response_data = json.loads(json_mode_content_str)
-
- # If Bedrock wrapped the response in "properties", extract the content
- if (
- isinstance(response_data, dict)
- and "properties" in response_data
- and len(response_data) == 1
- ):
- response_data = response_data["properties"]
- json_mode_content_str = json.dumps(response_data)
- except json.JSONDecodeError:
- # If parsing fails, use the original response
- pass
+ # Filter out json_tool_call from tools if json_mode is enabled
+ filtered_tools = self._filter_json_mode_tools(
+ tools=tools,
+ json_mode=json_mode,
+ chat_completion_message=chat_completion_message,
+ )
- chat_completion_message["content"] = json_mode_content_str
- else:
- chat_completion_message["tool_calls"] = tools
+ # None means json_tool_call was the only tool and was converted to content.
+ # Otherwise set tool_calls (including empty list, preserving original behavior).
+ if filtered_tools is not None:
+ chat_completion_message["tool_calls"] = filtered_tools
## CALCULATING USAGE - bedrock returns usage in the headers
usage = self._transform_usage(completion_response["usage"])
diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py
index 1c58a11eebe..f3070d0a83d 100644
--- a/litellm/llms/bedrock/chat/invoke_handler.py
+++ b/litellm/llms/bedrock/chat/invoke_handler.py
@@ -72,6 +72,7 @@
bedrock_tool_name_mappings: InMemoryCache = InMemoryCache(
max_size_in_memory=50, default_ttl=600
)
+from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
from litellm.llms.bedrock.chat.converse_transformation import AmazonConverseConfig
from litellm.llms.bedrock.chat.invoke_transformations.amazon_openai_transformation import (
AmazonBedrockOpenAIConfig,
@@ -252,7 +253,7 @@ async def make_call(
response.aiter_bytes(chunk_size=stream_chunk_size)
)
else:
- decoder = AWSEventStreamDecoder(model=model)
+ decoder = AWSEventStreamDecoder(model=model, json_mode=json_mode)
completion_stream = decoder.aiter_bytes(
response.aiter_bytes(chunk_size=stream_chunk_size)
)
@@ -346,7 +347,7 @@ def make_sync_call(
response.iter_bytes(chunk_size=stream_chunk_size)
)
else:
- decoder = AWSEventStreamDecoder(model=model)
+ decoder = AWSEventStreamDecoder(model=model, json_mode=json_mode)
completion_stream = decoder.iter_bytes(
response.iter_bytes(chunk_size=stream_chunk_size)
)
@@ -1282,7 +1283,7 @@ def get_response_stream_shape():
class AWSEventStreamDecoder:
- def __init__(self, model: str) -> None:
+ def __init__(self, model: str, json_mode: Optional[bool] = False) -> None:
from botocore.parsers import EventStreamJSONParser
self.model = model
@@ -1290,6 +1291,8 @@ def __init__(self, model: str) -> None:
self.content_blocks: List[ContentBlockDeltaEvent] = []
self.tool_calls_index: Optional[int] = None
self.response_id: Optional[str] = None
+ self.json_mode = json_mode
+ self._current_block_is_json_tool_call = False
def check_empty_tool_call_args(self) -> bool:
"""
@@ -1384,6 +1387,7 @@ def _handle_converse_start_event(
] = None
self.content_blocks = [] # reset
+ self._current_block_is_json_tool_call = False
if start_obj is not None:
if "toolUse" in start_obj and start_obj["toolUse"] is not None:
## check tool name was formatted by litellm
@@ -1391,18 +1395,33 @@ def _handle_converse_start_event(
response_tool_name = get_bedrock_tool_name(
response_tool_name=_response_tool_name
)
- self.tool_calls_index = (
- 0 if self.tool_calls_index is None else self.tool_calls_index + 1
- )
- tool_use = {
- "id": start_obj["toolUse"]["toolUseId"],
- "type": "function",
- "function": {
- "name": response_tool_name,
- "arguments": "",
- },
- "index": self.tool_calls_index,
- }
+
+ # If json_mode is enabled and this is the internal json_tool_call,
+ # suppress it from tool_calls and convert arguments to text content
+ if (
+ self.json_mode is True
+ and response_tool_name == RESPONSE_FORMAT_TOOL_NAME
+ ):
+ self._current_block_is_json_tool_call = True
+ verbose_logger.debug(
+ "Suppressing %s from streaming tool calls",
+ RESPONSE_FORMAT_TOOL_NAME,
+ )
+ else:
+ self.tool_calls_index = (
+ 0
+ if self.tool_calls_index is None
+ else self.tool_calls_index + 1
+ )
+ tool_use = {
+ "id": start_obj["toolUse"]["toolUseId"],
+ "type": "function",
+ "function": {
+ "name": response_tool_name,
+ "arguments": "",
+ },
+ "index": self.tool_calls_index,
+ }
elif (
"reasoningContent" in start_obj
and start_obj["reasoningContent"] is not None
@@ -1445,19 +1464,23 @@ def _handle_converse_delta_event(
if "text" in delta_obj:
text = delta_obj["text"]
elif "toolUse" in delta_obj:
- tool_use = {
- "id": None,
- "type": "function",
- "function": {
- "name": None,
- "arguments": delta_obj["toolUse"]["input"],
- },
- "index": (
- self.tool_calls_index
- if self.tool_calls_index is not None
- else index
- ),
- }
+ if self._current_block_is_json_tool_call:
+ # Convert json_tool_call arguments to text content instead of tool_use
+ text = delta_obj["toolUse"]["input"]
+ else:
+ tool_use = {
+ "id": None,
+ "type": "function",
+ "function": {
+ "name": None,
+ "arguments": delta_obj["toolUse"]["input"],
+ },
+ "index": (
+ self.tool_calls_index
+ if self.tool_calls_index is not None
+ else index
+ ),
+ }
elif "reasoningContent" in delta_obj:
provider_specific_fields = {
"reasoningContent": delta_obj["reasoningContent"],
@@ -1494,6 +1517,8 @@ def _handle_converse_stop_event(
) -> Optional[ChatCompletionToolCallChunk]:
"""Handle stop/contentBlockIndex event in converse chunk parsing."""
tool_use: Optional[ChatCompletionToolCallChunk] = None
+ if self._current_block_is_json_tool_call:
+ return None # Don't emit tool_use for suppressed json_tool_call blocks
is_empty = self.check_empty_tool_call_args()
if is_empty:
tool_use = {
diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py
index 65d237bdbdf..4c87f6fa994 100644
--- a/litellm/llms/bedrock/common_utils.py
+++ b/litellm/llms/bedrock/common_utils.py
@@ -446,6 +446,29 @@ def get_bedrock_base_model(model: str) -> str:
return model
+def is_claude_4_5_on_bedrock(model: str) -> bool:
+ """
+ Check if the model is a Claude 4.5 model on Bedrock.
+ Claude 4.5 models support prompt caching with '5m' and '1h' TTL on Bedrock.
+ """
+ model_lower = model.lower()
+ claude_4_5_patterns = [
+ "sonnet-4.5",
+ "sonnet_4.5",
+ "sonnet-4-5",
+ "sonnet_4_5",
+ "haiku-4.5",
+ "haiku_4.5",
+ "haiku-4-5",
+ "haiku_4_5",
+ "opus-4.5",
+ "opus_4.5",
+ "opus-4-5",
+ "opus_4_5",
+ ]
+ return any(pattern in model_lower for pattern in claude_4_5_patterns)
+
+
# Import after standalone functions to avoid circular imports
from litellm.llms.bedrock.count_tokens.bedrock_token_counter import BedrockTokenCounter
@@ -815,21 +838,23 @@ def get_anthropic_beta_from_headers(headers: dict) -> List[str]:
# If it's already a list, return it
if isinstance(anthropic_beta_header, list):
return anthropic_beta_header
-
+
# Try to parse as JSON array first (e.g., '["interleaved-thinking-2025-05-14", "claude-code-20250219"]')
if isinstance(anthropic_beta_header, str):
anthropic_beta_header = anthropic_beta_header.strip()
- if anthropic_beta_header.startswith("[") and anthropic_beta_header.endswith("]"):
+ if anthropic_beta_header.startswith("[") and anthropic_beta_header.endswith(
+ "]"
+ ):
try:
parsed = json.loads(anthropic_beta_header)
if isinstance(parsed, list):
return [str(beta).strip() for beta in parsed]
except json.JSONDecodeError:
pass # Fall through to comma-separated parsing
-
+
# Fall back to comma-separated values
return [beta.strip() for beta in anthropic_beta_header.split(",")]
-
+
return []
diff --git a/litellm/llms/bedrock/embed/cohere_transformation.py b/litellm/llms/bedrock/embed/cohere_transformation.py
index 490cd71b793..d00cb74aae0 100644
--- a/litellm/llms/bedrock/embed/cohere_transformation.py
+++ b/litellm/llms/bedrock/embed/cohere_transformation.py
@@ -15,7 +15,7 @@ def __init__(self) -> None:
pass
def get_supported_openai_params(self) -> List[str]:
- return ["encoding_format"]
+ return ["encoding_format", "dimensions"]
def map_openai_params(
self, non_default_params: dict, optional_params: dict
@@ -23,6 +23,8 @@ def map_openai_params(
for k, v in non_default_params.items():
if k == "encoding_format":
optional_params["embedding_types"] = v
+ elif k == "dimensions":
+ optional_params["output_dimension"] = v
return optional_params
def _is_v3_model(self, model: str) -> bool:
diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
index b1c45ea83a2..19fe7d8c140 100644
--- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
+++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
@@ -12,6 +12,9 @@
import httpx
+from litellm.anthropic_beta_headers_manager import (
+ filter_and_transform_beta_headers,
+)
from litellm.llms.anthropic.common_utils import AnthropicModelInfo
from litellm.llms.anthropic.experimental_pass_through.messages.transformation import (
AnthropicMessagesConfig,
@@ -23,7 +26,10 @@
from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import (
AmazonInvokeConfig,
)
-from litellm.llms.bedrock.common_utils import get_anthropic_beta_from_headers
+from litellm.llms.bedrock.common_utils import (
+ get_anthropic_beta_from_headers,
+ is_claude_4_5_on_bedrock,
+)
from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER
from litellm.types.llms.openai import AllMessageValues
from litellm.types.router import GenericLiteLLMParams
@@ -52,10 +58,6 @@ class AmazonAnthropicClaudeMessagesConfig(
# Beta header patterns that are not supported by Bedrock Invoke API
# These will be filtered out to prevent 400 "invalid beta flag" errors
- UNSUPPORTED_BEDROCK_INVOKE_BETA_PATTERNS = [
- "advanced-tool-use", # Bedrock Invoke doesn't support advanced-tool-use beta headers
- "prompt-caching-scope"
- ]
def __init__(self, **kwargs):
BaseAnthropicMessagesConfig.__init__(self, **kwargs)
@@ -116,15 +118,22 @@ def get_complete_url(
)
def _remove_ttl_from_cache_control(
- self, anthropic_messages_request: Dict
+ self, anthropic_messages_request: Dict, model: Optional[str] = None
) -> None:
"""
Remove `ttl` field from cache_control in messages.
Bedrock doesn't support the ttl field in cache_control.
+ Update: Bedock supports `5m` and `1h` for Claude 4.5 models.
+
Args:
anthropic_messages_request: The request dictionary to modify in-place
+ model: The model name to check if it supports ttl
"""
+ is_claude_4_5 = False
+ if model:
+ is_claude_4_5 = self._is_claude_4_5_on_bedrock(model)
+
if "messages" in anthropic_messages_request:
for message in anthropic_messages_request["messages"]:
if isinstance(message, dict) and "content" in message:
@@ -133,7 +142,14 @@ def _remove_ttl_from_cache_control(
for item in content:
if isinstance(item, dict) and "cache_control" in item:
cache_control = item["cache_control"]
- if isinstance(cache_control, dict) and "ttl" in cache_control:
+ if (
+ isinstance(cache_control, dict)
+ and "ttl" in cache_control
+ ):
+ ttl = cache_control["ttl"]
+ if is_claude_4_5 and ttl in ["5m", "1h"]:
+ continue
+
cache_control.pop("ttl", None)
def _supports_extended_thinking_on_bedrock(self, model: str) -> bool:
@@ -155,10 +171,18 @@ def _supports_extended_thinking_on_bedrock(self, model: str) -> bool:
# Supported models on Bedrock for extended thinking
supported_patterns = [
- "opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5", # Opus 4.5
- "opus-4.1", "opus_4.1", "opus-4-1", "opus_4_1", # Opus 4.1
- "opus-4", "opus_4", # Opus 4
- "sonnet-4", "sonnet_4", # Sonnet 4
+ "opus-4.5",
+ "opus_4.5",
+ "opus-4-5",
+ "opus_4_5", # Opus 4.5
+ "opus-4.1",
+ "opus_4.1",
+ "opus-4-1",
+ "opus_4_1", # Opus 4.1
+ "opus-4",
+ "opus_4", # Opus 4
+ "sonnet-4",
+ "sonnet_4", # Sonnet 4
]
return any(pattern in model_lower for pattern in supported_patterns)
@@ -175,10 +199,27 @@ def _is_claude_opus_4_5(self, model: str) -> bool:
"""
model_lower = model.lower()
opus_4_5_patterns = [
- "opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5",
+ "opus-4.5",
+ "opus_4.5",
+ "opus-4-5",
+ "opus_4_5",
]
return any(pattern in model_lower for pattern in opus_4_5_patterns)
+ def _is_claude_4_5_on_bedrock(self, model: str) -> bool:
+ """
+ Check if the model is Claude 4.5 on Bedrock.
+
+ Claude Sonnet 4.5, Haiku 4.5, and Opus 4.5 support 1-hour prompt caching.
+
+ Args:
+ model: The model name
+
+ Returns:
+ True if the model is Claude 4.5
+ """
+ return is_claude_4_5_on_bedrock(model)
+
def _supports_tool_search_on_bedrock(self, model: str) -> bool:
"""
Check if the model supports tool search on Bedrock.
@@ -199,9 +240,15 @@ def _supports_tool_search_on_bedrock(self, model: str) -> bool:
# Supported models for tool search on Bedrock
supported_patterns = [
# Opus 4.5
- "opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5",
+ "opus-4.5",
+ "opus_4.5",
+ "opus-4-5",
+ "opus_4_5",
# Sonnet 4.5
- "sonnet-4.5", "sonnet_4.5", "sonnet-4-5", "sonnet_4_5",
+ "sonnet-4.5",
+ "sonnet_4.5",
+ "sonnet-4-5",
+ "sonnet_4_5",
]
return any(pattern in model_lower for pattern in supported_patterns)
@@ -228,41 +275,48 @@ def _filter_unsupported_beta_headers_for_bedrock(
model: The model name
beta_set: The set of beta headers to filter in-place
"""
- beta_headers_to_remove = set()
- has_advanced_tool_use = False
-
- # 1. Filter out beta headers that are universally unsupported on Bedrock Invoke and track if advanced-tool-use header is present
- for beta in beta_set:
- for unsupported_pattern in self.UNSUPPORTED_BEDROCK_INVOKE_BETA_PATTERNS:
- if unsupported_pattern in beta.lower():
- beta_headers_to_remove.add(beta)
- has_advanced_tool_use = True
- break
-
+ # 1. Handle header transformations BEFORE filtering
+ # (advanced-tool-use -> tool-search-tool)
+ # This must happen before filtering because advanced-tool-use is in the unsupported list
+ has_advanced_tool_use = "advanced-tool-use-2025-11-20" in beta_set
+ if has_advanced_tool_use and self._supports_tool_search_on_bedrock(model):
+ beta_set.discard("advanced-tool-use-2025-11-20")
+ beta_set.add("tool-search-tool-2025-10-19")
+ beta_set.add("tool-examples-2025-10-29")
+
+ # 2. Apply provider-level filtering using centralized JSON config
+ beta_list = list(beta_set)
+ filtered_list = filter_and_transform_beta_headers(
+ beta_headers=beta_list,
+ provider="bedrock",
+ )
+
+ # Update the set with filtered headers
+ beta_set.clear()
+ beta_set.update(filtered_list)
+
+ # 2.1. Handle model-specific exceptions: structured-outputs is only supported on Opus 4.6
+ # Re-add structured-outputs if it was in the original set and model is Opus 4.6
+ model_lower = model.lower()
+ is_opus_4_6 = any(pattern in model_lower for pattern in ["opus-4.6", "opus_4.6", "opus-4-6", "opus_4_6"])
+ if is_opus_4_6 and "structured-outputs-2025-11-13" in beta_list:
+ beta_set.add("structured-outputs-2025-11-13")
- # 2. Filter out extended thinking headers for models that don't support them
+ # 3. Filter out extended thinking headers for models that don't support them
extended_thinking_patterns = [
"extended-thinking",
"interleaved-thinking",
]
if not self._supports_extended_thinking_on_bedrock(model):
+ beta_headers_to_remove = set()
for beta in beta_set:
for pattern in extended_thinking_patterns:
if pattern in beta.lower():
beta_headers_to_remove.add(beta)
break
-
- # Remove all filtered headers
- for beta in beta_headers_to_remove:
- beta_set.discard(beta)
-
- # 3. Translate advanced-tool-use to Bedrock-specific headers for models that support tool search
- # Ref: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-request-response.html
- # Ref: https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool
- if has_advanced_tool_use and self._supports_tool_search_on_bedrock(model):
- beta_set.add("tool-search-tool-2025-10-19")
- beta_set.add("tool-examples-2025-10-29")
-
+
+ for beta in beta_headers_to_remove:
+ beta_set.discard(beta)
def _get_tool_search_beta_header_for_bedrock(
self,
@@ -290,7 +344,9 @@ def _get_tool_search_beta_header_for_bedrock(
input_examples_used: Whether input examples are used
beta_set: The set of beta headers to modify in-place
"""
- if tool_search_used and not (programmatic_tool_calling_used or input_examples_used):
+ if tool_search_used and not (
+ programmatic_tool_calling_used or input_examples_used
+ ):
beta_set.discard(ANTHROPIC_TOOL_SEARCH_BETA_HEADER)
if "opus-4" in model.lower() or "opus_4" in model.lower():
beta_set.add("tool-search-tool-2025-10-19")
@@ -302,13 +358,13 @@ def _convert_output_format_to_inline_schema(
) -> None:
"""
Convert Anthropic output_format to inline schema in message content.
-
+
Bedrock Invoke doesn't support the output_format parameter, so we embed
the schema directly into the user message content as text instructions.
-
+
This approach adds the schema to the last user message, instructing the model
to respond in the specified JSON format.
-
+
Args:
output_format: The output_format dict with 'type' and 'schema'
anthropic_messages_request: The request dict to modify in-place
@@ -321,35 +377,32 @@ def _convert_output_format_to_inline_schema(
schema = output_format.get("schema")
if not schema:
return
-
+
# Get messages from the request
messages = anthropic_messages_request.get("messages", [])
if not messages:
return
-
+
# Find the last user message
last_user_message_idx = None
for idx in range(len(messages) - 1, -1, -1):
if messages[idx].get("role") == "user":
last_user_message_idx = idx
break
-
+
if last_user_message_idx is None:
return
-
+
last_user_message = messages[last_user_message_idx]
content = last_user_message.get("content", [])
-
+
# Ensure content is a list
if isinstance(content, str):
content = [{"type": "text", "text": content}]
last_user_message["content"] = content
-
+
# Add schema as text content to the message
- schema_text = {
- "type": "text",
- "text": json.dumps(schema)
- }
+ schema_text = {"type": "text", "text": json.dumps(schema)}
content.append(schema_text)
def transform_anthropic_messages_request(
@@ -374,9 +427,9 @@ def transform_anthropic_messages_request(
# 1. anthropic_version is required for all claude models
if "anthropic_version" not in anthropic_messages_request:
- anthropic_messages_request["anthropic_version"] = (
- self.DEFAULT_BEDROCK_ANTHROPIC_API_VERSION
- )
+ anthropic_messages_request[
+ "anthropic_version"
+ ] = self.DEFAULT_BEDROCK_ANTHROPIC_API_VERSION
# 2. `stream` is not allowed in request body for bedrock invoke
if "stream" in anthropic_messages_request:
@@ -386,8 +439,10 @@ def transform_anthropic_messages_request(
if "model" in anthropic_messages_request:
anthropic_messages_request.pop("model", None)
- # 4. Remove `ttl` field from cache_control in messages (Bedrock doesn't support it)
- self._remove_ttl_from_cache_control(anthropic_messages_request)
+ # 4. Remove `ttl` field from cache_control in messages (Bedrock doesn't support it for older models)
+ self._remove_ttl_from_cache_control(
+ anthropic_messages_request=anthropic_messages_request, model=model
+ )
# 5. Convert `output_format` to inline schema (Bedrock invoke doesn't support output_format)
output_format = anthropic_messages_request.pop("output_format", None)
@@ -396,14 +451,14 @@ def transform_anthropic_messages_request(
output_format=output_format,
anthropic_messages_request=anthropic_messages_request,
)
-
+
# 6. AUTO-INJECT beta headers based on features used
anthropic_model_info = AnthropicModelInfo()
tools = anthropic_messages_optional_request_params.get("tools")
messages_typed = cast(List[AllMessageValues], messages)
tool_search_used = anthropic_model_info.is_tool_search_used(tools)
- programmatic_tool_calling_used = anthropic_model_info.is_programmatic_tool_calling_used(
- tools
+ programmatic_tool_calling_used = (
+ anthropic_model_info.is_programmatic_tool_calling_used(tools)
)
input_examples_used = anthropic_model_info.is_input_examples_used(tools)
@@ -436,8 +491,7 @@ def transform_anthropic_messages_request(
if beta_set:
anthropic_messages_request["anthropic_beta"] = list(beta_set)
-
-
+
return anthropic_messages_request
def get_async_streaming_response_iterator(
@@ -455,7 +509,7 @@ def get_async_streaming_response_iterator(
)
# Convert decoded Bedrock events to Server-Sent Events expected by Anthropic clients.
return self.bedrock_sse_wrapper(
- completion_stream=completion_stream,
+ completion_stream=completion_stream,
litellm_logging_obj=litellm_logging_obj,
request_body=request_body,
)
@@ -474,14 +528,14 @@ async def bedrock_sse_wrapper(
from litellm.llms.anthropic.experimental_pass_through.messages.streaming_iterator import (
BaseAnthropicMessagesStreamingIterator,
)
+
handler = BaseAnthropicMessagesStreamingIterator(
litellm_logging_obj=litellm_logging_obj,
request_body=request_body,
)
-
+
async for chunk in handler.async_sse_wrapper(completion_stream):
yield chunk
-
class AmazonAnthropicClaudeMessagesStreamDecoder(AWSEventStreamDecoder):
diff --git a/litellm/llms/bedrock/realtime/handler.py b/litellm/llms/bedrock/realtime/handler.py
new file mode 100644
index 00000000000..9b6a80f4a2f
--- /dev/null
+++ b/litellm/llms/bedrock/realtime/handler.py
@@ -0,0 +1,307 @@
+"""
+This file contains the handler for AWS Bedrock Nova Sonic realtime API.
+
+This uses aws_sdk_bedrock_runtime for bidirectional streaming with Nova Sonic.
+"""
+
+import asyncio
+import json
+from typing import Any, Optional
+
+from litellm._logging import verbose_proxy_logger
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
+
+from ..base_aws_llm import BaseAWSLLM
+from .transformation import BedrockRealtimeConfig
+
+
+class BedrockRealtime(BaseAWSLLM):
+ """Handler for Bedrock Nova Sonic realtime speech-to-speech API."""
+
+ def __init__(self):
+ super().__init__()
+
+ async def async_realtime(
+ self,
+ model: str,
+ websocket: Any,
+ logging_obj: LiteLLMLogging,
+ api_base: Optional[str] = None,
+ api_key: Optional[str] = None,
+ timeout: Optional[float] = None,
+ aws_region_name: Optional[str] = None,
+ aws_access_key_id: Optional[str] = None,
+ aws_secret_access_key: Optional[str] = None,
+ aws_session_token: Optional[str] = None,
+ aws_role_name: Optional[str] = None,
+ aws_session_name: Optional[str] = None,
+ aws_profile_name: Optional[str] = None,
+ aws_web_identity_token: Optional[str] = None,
+ aws_sts_endpoint: Optional[str] = None,
+ aws_bedrock_runtime_endpoint: Optional[str] = None,
+ aws_external_id: Optional[str] = None,
+ **kwargs,
+ ):
+ """
+ Establish bidirectional streaming connection with Bedrock Nova Sonic.
+
+ Args:
+ model: Model ID (e.g., 'amazon.nova-sonic-v1:0')
+ websocket: Client WebSocket connection
+ logging_obj: LiteLLM logging object
+ aws_region_name: AWS region
+ Various AWS authentication parameters
+ """
+ try:
+ from aws_sdk_bedrock_runtime.client import (
+ BedrockRuntimeClient,
+ InvokeModelWithBidirectionalStreamOperationInput,
+ )
+ from aws_sdk_bedrock_runtime.config import Config
+ from smithy_aws_core.identity.environment import (
+ EnvironmentCredentialsResolver,
+ )
+ except ImportError:
+ raise ImportError(
+ "Missing aws_sdk_bedrock_runtime. Install with: pip install aws-sdk-bedrock-runtime"
+ )
+
+ # Get AWS region
+ if aws_region_name is None:
+ optional_params = {
+ "aws_region_name": aws_region_name,
+ }
+ aws_region_name = self._get_aws_region_name(optional_params, model)
+
+ # Get endpoint URL
+ if api_base is not None:
+ endpoint_uri = api_base
+ elif aws_bedrock_runtime_endpoint is not None:
+ endpoint_uri = aws_bedrock_runtime_endpoint
+ else:
+ endpoint_uri = f"https://bedrock-runtime.{aws_region_name}.amazonaws.com"
+
+ verbose_proxy_logger.debug(
+ f"Bedrock Realtime: Connecting to {endpoint_uri} with model {model}"
+ )
+
+ # Initialize Bedrock client with aws_sdk_bedrock_runtime
+ config = Config(
+ endpoint_uri=endpoint_uri,
+ region=aws_region_name,
+ aws_credentials_identity_resolver=EnvironmentCredentialsResolver(),
+ )
+ bedrock_client = BedrockRuntimeClient(config=config)
+
+ transformation_config = BedrockRealtimeConfig()
+
+ try:
+ # Initialize the bidirectional stream
+ bedrock_stream = await bedrock_client.invoke_model_with_bidirectional_stream(
+ InvokeModelWithBidirectionalStreamOperationInput(model_id=model)
+ )
+
+ verbose_proxy_logger.debug(
+ "Bedrock Realtime: Bidirectional stream established"
+ )
+
+ # Track state for transformation
+ session_state = {
+ "current_output_item_id": None,
+ "current_response_id": None,
+ "current_conversation_id": None,
+ "current_delta_chunks": None,
+ "current_item_chunks": None,
+ "current_delta_type": None,
+ "session_configuration_request": None,
+ }
+
+ # Create tasks for bidirectional forwarding
+ client_to_bedrock_task = asyncio.create_task(
+ self._forward_client_to_bedrock(
+ websocket,
+ bedrock_stream,
+ transformation_config,
+ model,
+ session_state,
+ )
+ )
+
+ bedrock_to_client_task = asyncio.create_task(
+ self._forward_bedrock_to_client(
+ bedrock_stream,
+ websocket,
+ transformation_config,
+ model,
+ logging_obj,
+ session_state,
+ )
+ )
+
+ # Wait for both tasks to complete
+ await asyncio.gather(
+ client_to_bedrock_task,
+ bedrock_to_client_task,
+ return_exceptions=True,
+ )
+
+ except Exception as e:
+ verbose_proxy_logger.exception(
+ f"Error in BedrockRealtime.async_realtime: {e}"
+ )
+ try:
+ await websocket.close(code=1011, reason=f"Internal error: {str(e)}")
+ except Exception:
+ pass
+ raise
+
+ async def _forward_client_to_bedrock(
+ self,
+ client_ws: Any,
+ bedrock_stream: Any,
+ transformation_config: BedrockRealtimeConfig,
+ model: str,
+ session_state: dict,
+ ):
+ """Forward messages from client WebSocket to Bedrock stream."""
+ try:
+ from aws_sdk_bedrock_runtime.models import (
+ BidirectionalInputPayloadPart,
+ InvokeModelWithBidirectionalStreamInputChunk,
+ )
+
+ while True:
+ # Receive message from client
+ message = await client_ws.receive_text()
+ verbose_proxy_logger.debug(
+ f"Bedrock Realtime: Received from client: {message[:200]}"
+ )
+
+ # Transform OpenAI format to Bedrock format
+ transformed_messages = transformation_config.transform_realtime_request(
+ message=message,
+ model=model,
+ session_configuration_request=session_state.get(
+ "session_configuration_request"
+ ),
+ )
+
+ # Send transformed messages to Bedrock
+ for bedrock_message in transformed_messages:
+ event = InvokeModelWithBidirectionalStreamInputChunk(
+ value=BidirectionalInputPayloadPart(
+ bytes_=bedrock_message.encode("utf-8")
+ )
+ )
+ await bedrock_stream.input_stream.send(event)
+ verbose_proxy_logger.debug(
+ f"Bedrock Realtime: Sent to Bedrock: {bedrock_message[:200]}"
+ )
+
+ except Exception as e:
+ verbose_proxy_logger.debug(
+ f"Client to Bedrock forwarding ended: {e}", exc_info=True
+ )
+ # Close the Bedrock stream input
+ try:
+ await bedrock_stream.input_stream.close()
+ except Exception:
+ pass
+
+ async def _forward_bedrock_to_client(
+ self,
+ bedrock_stream: Any,
+ client_ws: Any,
+ transformation_config: BedrockRealtimeConfig,
+ model: str,
+ logging_obj: LiteLLMLogging,
+ session_state: dict,
+ ):
+ """Forward messages from Bedrock stream to client WebSocket."""
+ try:
+ while True:
+ # Receive from Bedrock
+ output = await bedrock_stream.await_output()
+ result = await output[1].receive()
+
+ if result.value and result.value.bytes_:
+ bedrock_response = result.value.bytes_.decode("utf-8")
+ verbose_proxy_logger.debug(
+ f"Bedrock Realtime: Received from Bedrock: {bedrock_response[:200]}"
+ )
+
+ # Transform Bedrock format to OpenAI format
+ from litellm.types.realtime import RealtimeResponseTransformInput
+
+ realtime_response_transform_input: RealtimeResponseTransformInput = {
+ "current_output_item_id": session_state.get(
+ "current_output_item_id"
+ ),
+ "current_response_id": session_state.get("current_response_id"),
+ "current_conversation_id": session_state.get(
+ "current_conversation_id"
+ ),
+ "current_delta_chunks": session_state.get(
+ "current_delta_chunks"
+ ),
+ "current_item_chunks": session_state.get("current_item_chunks"),
+ "current_delta_type": session_state.get("current_delta_type"),
+ "session_configuration_request": session_state.get(
+ "session_configuration_request"
+ ),
+ }
+
+ transformed_response = (
+ transformation_config.transform_realtime_response(
+ message=bedrock_response,
+ model=model,
+ logging_obj=logging_obj,
+ realtime_response_transform_input=realtime_response_transform_input,
+ )
+ )
+
+ # Update session state
+ session_state.update(
+ {
+ "current_output_item_id": transformed_response.get(
+ "current_output_item_id"
+ ),
+ "current_response_id": transformed_response.get(
+ "current_response_id"
+ ),
+ "current_conversation_id": transformed_response.get(
+ "current_conversation_id"
+ ),
+ "current_delta_chunks": transformed_response.get(
+ "current_delta_chunks"
+ ),
+ "current_item_chunks": transformed_response.get(
+ "current_item_chunks"
+ ),
+ "current_delta_type": transformed_response.get(
+ "current_delta_type"
+ ),
+ "session_configuration_request": transformed_response.get(
+ "session_configuration_request"
+ ),
+ }
+ )
+
+ # Send transformed messages to client
+ openai_messages = transformed_response.get("response", [])
+ for openai_message in openai_messages:
+ message_json = json.dumps(openai_message)
+ await client_ws.send_text(message_json)
+ verbose_proxy_logger.debug(
+ f"Bedrock Realtime: Sent to client: {message_json[:200]}"
+ )
+
+ except Exception as e:
+ verbose_proxy_logger.debug(
+ f"Bedrock to client forwarding ended: {e}", exc_info=True
+ )
+ # Close the client WebSocket
+ try:
+ await client_ws.close()
+ except Exception:
+ pass
diff --git a/litellm/llms/bedrock/realtime/transformation.py b/litellm/llms/bedrock/realtime/transformation.py
new file mode 100644
index 00000000000..1dde1b47fe3
--- /dev/null
+++ b/litellm/llms/bedrock/realtime/transformation.py
@@ -0,0 +1,1156 @@
+"""
+This file contains the transformation logic for Bedrock Nova Sonic realtime API.
+
+Transforms between OpenAI Realtime API format and Bedrock Nova Sonic format.
+"""
+
+import json
+import uuid as uuid_lib
+from typing import Any, List, Optional, Union
+
+from litellm._logging import verbose_logger
+from litellm._uuid import uuid
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.base_llm.realtime.transformation import BaseRealtimeConfig
+from litellm.types.llms.openai import (
+ OpenAIRealtimeContentPartDone,
+ OpenAIRealtimeDoneEvent,
+ OpenAIRealtimeEvents,
+ OpenAIRealtimeOutputItemDone,
+ OpenAIRealtimeResponseAudioDone,
+ OpenAIRealtimeResponseContentPartAdded,
+ OpenAIRealtimeResponseDelta,
+ OpenAIRealtimeResponseDoneObject,
+ OpenAIRealtimeResponseTextDone,
+ OpenAIRealtimeStreamResponseBaseObject,
+ OpenAIRealtimeStreamResponseOutputItemAdded,
+ OpenAIRealtimeStreamSession,
+ OpenAIRealtimeStreamSessionEvents,
+)
+from litellm.types.realtime import (
+ ALL_DELTA_TYPES,
+ RealtimeResponseTransformInput,
+ RealtimeResponseTypedDict,
+)
+from litellm.utils import get_empty_usage
+
+
+class BedrockRealtimeConfig(BaseRealtimeConfig):
+ """Configuration for Bedrock Nova Sonic realtime transformations."""
+
+ def __init__(self):
+ # Track session state
+ self.prompt_name = str(uuid_lib.uuid4())
+ self.content_name = str(uuid_lib.uuid4())
+ self.audio_content_name = str(uuid_lib.uuid4())
+
+ # Default configuration values
+ # Inference configuration
+ self.max_tokens = 1024
+ self.top_p = 0.9
+ self.temperature = 0.7
+
+ # Audio output configuration
+ self.output_sample_rate_hertz = 24000
+ self.output_sample_size_bits = 16
+ self.output_channel_count = 1
+ self.voice_id = "matthew"
+ self.output_encoding = "base64"
+ self.output_audio_type = "SPEECH"
+ self.output_media_type = "audio/lpcm"
+
+ # Audio input configuration
+ self.input_sample_rate_hertz = 16000
+ self.input_sample_size_bits = 16
+ self.input_channel_count = 1
+ self.input_encoding = "base64"
+ self.input_audio_type = "SPEECH"
+ self.input_media_type = "audio/lpcm"
+
+ # Text configuration
+ self.text_media_type = "text/plain"
+
+ def validate_environment(
+ self, headers: dict, model: str, api_key: Optional[str] = None
+ ) -> dict:
+ """Validate environment - no special validation needed for Bedrock."""
+ return headers
+
+ def get_complete_url(
+ self, api_base: Optional[str], model: str, api_key: Optional[str] = None
+ ) -> str:
+ """Get complete URL - handled by aws_sdk_bedrock_runtime."""
+ return api_base or ""
+
+ def requires_session_configuration(self) -> bool:
+ """Bedrock requires session configuration."""
+ return True
+
+ def session_configuration_request(self, model: str, tools: Optional[List[dict]] = None) -> str:
+ """
+ Create initial session configuration for Bedrock Nova Sonic.
+
+ Args:
+ model: Model ID
+ tools: Optional list of tool definitions
+
+ Returns JSON string with session start and prompt start events.
+ """
+ session_start = {
+ "event": {
+ "sessionStart": {
+ "inferenceConfiguration": {
+ "maxTokens": self.max_tokens,
+ "topP": self.top_p,
+ "temperature": self.temperature,
+ }
+ }
+ }
+ }
+
+ prompt_start_config = {
+ "promptName": self.prompt_name,
+ "textOutputConfiguration": {"mediaType": self.text_media_type},
+ "audioOutputConfiguration": {
+ "mediaType": self.output_media_type,
+ "sampleRateHertz": self.output_sample_rate_hertz,
+ "sampleSizeBits": self.output_sample_size_bits,
+ "channelCount": self.output_channel_count,
+ "voiceId": self.voice_id,
+ "encoding": self.output_encoding,
+ "audioType": self.output_audio_type,
+ },
+ }
+
+ # Add tool configuration if tools are provided
+ if tools:
+ prompt_start_config["toolUseOutputConfiguration"] = {
+ "mediaType": "application/json"
+ }
+ prompt_start_config["toolConfiguration"] = {
+ "tools": self._transform_tools_to_bedrock_format(tools)
+ }
+
+ prompt_start = {"event": {"promptStart": prompt_start_config}}
+
+ # Return as a marker that we've sent the configuration
+ return json.dumps(
+ {"session_start": session_start, "prompt_start": prompt_start}
+ )
+
+ def _transform_tools_to_bedrock_format(self, tools: List[dict]) -> List[dict]:
+ """
+ Transform OpenAI tool format to Bedrock tool format.
+
+ Args:
+ tools: List of OpenAI format tools
+
+ Returns:
+ List of Bedrock format tools
+ """
+ bedrock_tools = []
+ for tool in tools:
+ if tool.get("type") == "function":
+ function = tool.get("function", {})
+ bedrock_tool = {
+ "toolSpec": {
+ "name": function.get("name", ""),
+ "description": function.get("description", ""),
+ "inputSchema": {
+ "json": json.dumps(function.get("parameters", {}))
+ }
+ }
+ }
+ bedrock_tools.append(bedrock_tool)
+ return bedrock_tools
+
+ def _map_audio_format_to_sample_rate(self, audio_format: str, is_output: bool = True) -> int:
+ """
+ Map OpenAI audio format to sample rate.
+
+ Args:
+ audio_format: OpenAI audio format (pcm16, g711_ulaw, g711_alaw)
+ is_output: Whether this is for output (True) or input (False)
+
+ Returns:
+ Sample rate in Hz
+ """
+ # OpenAI uses 24kHz for output and can vary for input
+ # Bedrock Nova Sonic uses 24kHz for output and 16kHz for input by default
+ if audio_format == "pcm16":
+ return 24000 if is_output else 16000
+ elif audio_format in ["g711_ulaw", "g711_alaw"]:
+ return 8000 # G.711 typically uses 8kHz
+ return 24000 if is_output else 16000
+
+ def transform_session_update_event(self, json_message: dict) -> List[str]:
+ """
+ Transform session.update event to Bedrock session configuration.
+
+ Args:
+ json_message: OpenAI session.update message
+
+ Returns:
+ List of Bedrock format messages (JSON strings)
+ """
+ verbose_logger.debug("Handling session.update")
+ messages: List[str] = []
+
+ session_config = json_message.get("session", {})
+
+ # Update inference configuration from session if provided
+ if "max_response_output_tokens" in session_config:
+ self.max_tokens = session_config["max_response_output_tokens"]
+ if "temperature" in session_config:
+ self.temperature = session_config["temperature"]
+
+ # Update audio output configuration from session if provided
+ if "voice" in session_config:
+ self.voice_id = session_config["voice"]
+ if "output_audio_format" in session_config:
+ output_format = session_config["output_audio_format"]
+ self.output_sample_rate_hertz = self._map_audio_format_to_sample_rate(
+ output_format, is_output=True
+ )
+
+ # Update audio input configuration from session if provided
+ if "input_audio_format" in session_config:
+ input_format = session_config["input_audio_format"]
+ self.input_sample_rate_hertz = self._map_audio_format_to_sample_rate(
+ input_format, is_output=False
+ )
+
+ # Allow direct override of sample rates if provided (custom extension)
+ if "output_sample_rate_hertz" in session_config:
+ self.output_sample_rate_hertz = session_config["output_sample_rate_hertz"]
+ if "input_sample_rate_hertz" in session_config:
+ self.input_sample_rate_hertz = session_config["input_sample_rate_hertz"]
+
+ # Send session start
+ session_start = {
+ "event": {
+ "sessionStart": {
+ "inferenceConfiguration": {
+ "maxTokens": self.max_tokens,
+ "topP": self.top_p,
+ "temperature": self.temperature,
+ }
+ }
+ }
+ }
+ messages.append(json.dumps(session_start))
+
+ # Send prompt start
+ prompt_start_config = {
+ "promptName": self.prompt_name,
+ "textOutputConfiguration": {"mediaType": self.text_media_type},
+ "audioOutputConfiguration": {
+ "mediaType": self.output_media_type,
+ "sampleRateHertz": self.output_sample_rate_hertz,
+ "sampleSizeBits": self.output_sample_size_bits,
+ "channelCount": self.output_channel_count,
+ "voiceId": self.voice_id,
+ "encoding": self.output_encoding,
+ "audioType": self.output_audio_type,
+ },
+ }
+
+ # Add tool configuration if tools are provided
+ tools = session_config.get("tools")
+ if tools:
+ prompt_start_config["toolUseOutputConfiguration"] = {
+ "mediaType": "application/json"
+ }
+ prompt_start_config["toolConfiguration"] = {
+ "tools": self._transform_tools_to_bedrock_format(tools)
+ }
+
+ prompt_start = {"event": {"promptStart": prompt_start_config}}
+ messages.append(json.dumps(prompt_start))
+
+ # Send system prompt if provided
+ instructions = session_config.get("instructions")
+ if instructions:
+ text_content_name = str(uuid_lib.uuid4())
+
+ # Content start
+ text_content_start = {
+ "event": {
+ "contentStart": {
+ "promptName": self.prompt_name,
+ "contentName": text_content_name,
+ "type": "TEXT",
+ "interactive": False,
+ "role": "SYSTEM",
+ "textInputConfiguration": {"mediaType": self.text_media_type},
+ }
+ }
+ }
+ messages.append(json.dumps(text_content_start))
+
+ # Text input
+ text_input = {
+ "event": {
+ "textInput": {
+ "promptName": self.prompt_name,
+ "contentName": text_content_name,
+ "content": instructions,
+ }
+ }
+ }
+ messages.append(json.dumps(text_input))
+
+ # Content end
+ text_content_end = {
+ "event": {
+ "contentEnd": {
+ "promptName": self.prompt_name,
+ "contentName": text_content_name,
+ }
+ }
+ }
+ messages.append(json.dumps(text_content_end))
+
+ return messages
+
+ def transform_input_audio_buffer_append_event(self, json_message: dict) -> List[str]:
+ """
+ Transform input_audio_buffer.append event to Bedrock audio input.
+
+ Args:
+ json_message: OpenAI input_audio_buffer.append message
+
+ Returns:
+ List of Bedrock format messages (JSON strings)
+ """
+ verbose_logger.debug("Handling input_audio_buffer.append")
+ messages: List[str] = []
+
+ # Check if we need to start audio content
+ if not hasattr(self, "_audio_content_started"):
+ audio_content_start = {
+ "event": {
+ "contentStart": {
+ "promptName": self.prompt_name,
+ "contentName": self.audio_content_name,
+ "type": "AUDIO",
+ "interactive": True,
+ "role": "USER",
+ "audioInputConfiguration": {
+ "mediaType": self.input_media_type,
+ "sampleRateHertz": self.input_sample_rate_hertz,
+ "sampleSizeBits": self.input_sample_size_bits,
+ "channelCount": self.input_channel_count,
+ "audioType": self.input_audio_type,
+ "encoding": self.input_encoding,
+ },
+ }
+ }
+ }
+ messages.append(json.dumps(audio_content_start))
+ self._audio_content_started = True
+
+ # Send audio chunk
+ audio_data = json_message.get("audio", "")
+ audio_event = {
+ "event": {
+ "audioInput": {
+ "promptName": self.prompt_name,
+ "contentName": self.audio_content_name,
+ "content": audio_data,
+ }
+ }
+ }
+ messages.append(json.dumps(audio_event))
+
+ return messages
+
+ def transform_input_audio_buffer_commit_event(self, json_message: dict) -> List[str]:
+ """
+ Transform input_audio_buffer.commit event to Bedrock audio content end.
+
+ Args:
+ json_message: OpenAI input_audio_buffer.commit message
+
+ Returns:
+ List of Bedrock format messages (JSON strings)
+ """
+ verbose_logger.debug("Handling input_audio_buffer.commit")
+ messages: List[str] = []
+
+ if hasattr(self, "_audio_content_started"):
+ audio_content_end = {
+ "event": {
+ "contentEnd": {
+ "promptName": self.prompt_name,
+ "contentName": self.audio_content_name,
+ }
+ }
+ }
+ messages.append(json.dumps(audio_content_end))
+ delattr(self, "_audio_content_started")
+
+ return messages
+
+ def transform_conversation_item_create_event(self, json_message: dict) -> List[str]:
+ """
+ Transform conversation.item.create event to Bedrock text input or tool result.
+
+ Args:
+ json_message: OpenAI conversation.item.create message
+
+ Returns:
+ List of Bedrock format messages (JSON strings)
+ """
+ verbose_logger.debug("Handling conversation.item.create")
+ messages: List[str] = []
+
+ item = json_message.get("item", {})
+ item_type = item.get("type")
+
+ # Handle tool result
+ if item_type == "function_call_output":
+ return self.transform_conversation_item_create_tool_result_event(json_message)
+
+ # Handle regular message
+ if item_type == "message":
+ content = item.get("content", [])
+ for content_part in content:
+ if content_part.get("type") == "input_text":
+ text_content_name = str(uuid_lib.uuid4())
+
+ # Content start
+ text_content_start = {
+ "event": {
+ "contentStart": {
+ "promptName": self.prompt_name,
+ "contentName": text_content_name,
+ "type": "TEXT",
+ "interactive": True,
+ "role": "USER",
+ "textInputConfiguration": {
+ "mediaType": self.text_media_type
+ },
+ }
+ }
+ }
+ messages.append(json.dumps(text_content_start))
+
+ # Text input
+ text_input = {
+ "event": {
+ "textInput": {
+ "promptName": self.prompt_name,
+ "contentName": text_content_name,
+ "content": content_part.get("text", ""),
+ }
+ }
+ }
+ messages.append(json.dumps(text_input))
+
+ # Content end
+ text_content_end = {
+ "event": {
+ "contentEnd": {
+ "promptName": self.prompt_name,
+ "contentName": text_content_name,
+ }
+ }
+ }
+ messages.append(json.dumps(text_content_end))
+
+ return messages
+
+ def transform_response_create_event(self, json_message: dict) -> List[str]:
+ """
+ Transform response.create event to Bedrock format.
+
+ Args:
+ json_message: OpenAI response.create message
+
+ Returns:
+ List of Bedrock format messages (JSON strings)
+ """
+ verbose_logger.debug("Handling response.create")
+ # Bedrock starts generating automatically, no explicit trigger needed
+ return []
+
+ def transform_response_cancel_event(self, json_message: dict) -> List[str]:
+ """
+ Transform response.cancel event to Bedrock format.
+
+ Args:
+ json_message: OpenAI response.cancel message
+
+ Returns:
+ List of Bedrock format messages (JSON strings)
+ """
+ verbose_logger.debug("Handling response.cancel")
+ # Send interrupt signal if needed
+ return []
+
+ def transform_realtime_request(
+ self,
+ message: str,
+ model: str,
+ session_configuration_request: Optional[str] = None,
+ ) -> List[str]:
+ """
+ Transform OpenAI realtime request to Bedrock Nova Sonic format.
+
+ Args:
+ message: OpenAI format message (JSON string)
+ model: Model ID
+ session_configuration_request: Previous session config
+
+ Returns:
+ List of Bedrock format messages (JSON strings)
+ """
+ try:
+ json_message = json.loads(message)
+ except json.JSONDecodeError:
+ verbose_logger.warning(f"Invalid JSON message: {message[:200]}")
+ return []
+
+ message_type = json_message.get("type")
+
+ # Route to appropriate transformation method
+ if message_type == "session.update":
+ return self.transform_session_update_event(json_message)
+ elif message_type == "input_audio_buffer.append":
+ return self.transform_input_audio_buffer_append_event(json_message)
+ elif message_type == "input_audio_buffer.commit":
+ return self.transform_input_audio_buffer_commit_event(json_message)
+ elif message_type == "conversation.item.create":
+ return self.transform_conversation_item_create_event(json_message)
+ elif message_type == "response.create":
+ return self.transform_response_create_event(json_message)
+ elif message_type == "response.cancel":
+ return self.transform_response_cancel_event(json_message)
+ else:
+ verbose_logger.warning(f"Unknown message type: {message_type}")
+ return []
+
+ def transform_session_start_event(
+ self,
+ event: dict,
+ model: str,
+ logging_obj: LiteLLMLoggingObj,
+ ) -> OpenAIRealtimeStreamSessionEvents:
+ """
+ Transform Bedrock sessionStart event to OpenAI session.created.
+
+ Args:
+ event: Bedrock sessionStart event
+ model: Model ID
+ logging_obj: Logging object
+
+ Returns:
+ OpenAI session.created event
+ """
+ verbose_logger.debug("Handling sessionStart")
+
+ session = OpenAIRealtimeStreamSession(
+ id=logging_obj.litellm_trace_id,
+ modalities=["text", "audio"],
+ )
+ if model is not None and isinstance(model, str):
+ session["model"] = model
+
+ return OpenAIRealtimeStreamSessionEvents(
+ type="session.created",
+ session=session,
+ event_id=str(uuid.uuid4()),
+ )
+
+ def transform_content_start_event(
+ self,
+ event: dict,
+ current_response_id: Optional[str],
+ current_output_item_id: Optional[str],
+ current_conversation_id: Optional[str],
+ ) -> tuple[
+ List[OpenAIRealtimeEvents],
+ Optional[str],
+ Optional[str],
+ Optional[str],
+ Optional[ALL_DELTA_TYPES],
+ ]:
+ """
+ Transform Bedrock contentStart event to OpenAI response events.
+
+ Args:
+ event: Bedrock contentStart event
+ current_response_id: Current response ID
+ current_output_item_id: Current output item ID
+ current_conversation_id: Current conversation ID
+
+ Returns:
+ Tuple of (events, response_id, output_item_id, conversation_id, delta_type)
+ """
+ content_start = event["contentStart"]
+ role = content_start.get("role")
+
+ if role != "ASSISTANT":
+ return [], current_response_id, current_output_item_id, current_conversation_id, None
+
+ verbose_logger.debug("Handling ASSISTANT contentStart")
+
+ # Initialize IDs if needed
+ if not current_response_id:
+ current_response_id = f"resp_{uuid.uuid4()}"
+ if not current_output_item_id:
+ current_output_item_id = f"item_{uuid.uuid4()}"
+ if not current_conversation_id:
+ current_conversation_id = f"conv_{uuid.uuid4()}"
+
+ # Determine content type
+ content_type = content_start.get("type", "TEXT")
+ current_delta_type: ALL_DELTA_TYPES = "text" if content_type == "TEXT" else "audio"
+
+ returned_messages: List[OpenAIRealtimeEvents] = []
+
+ # Send response.created
+ response_created = OpenAIRealtimeStreamResponseBaseObject(
+ type="response.created",
+ event_id=f"event_{uuid.uuid4()}",
+ response={
+ "object": "realtime.response",
+ "id": current_response_id,
+ "status": "in_progress",
+ "output": [],
+ "conversation_id": current_conversation_id,
+ },
+ )
+ returned_messages.append(response_created)
+
+ # Send response.output_item.added
+ output_item_added = OpenAIRealtimeStreamResponseOutputItemAdded(
+ type="response.output_item.added",
+ response_id=current_response_id,
+ output_index=0,
+ item={
+ "id": current_output_item_id,
+ "object": "realtime.item",
+ "type": "message",
+ "status": "in_progress",
+ "role": "assistant",
+ "content": [],
+ },
+ )
+ returned_messages.append(output_item_added)
+
+ # Send response.content_part.added
+ content_part_added = OpenAIRealtimeResponseContentPartAdded(
+ type="response.content_part.added",
+ content_index=0,
+ output_index=0,
+ event_id=f"event_{uuid.uuid4()}",
+ item_id=current_output_item_id,
+ part=(
+ {"type": "text", "text": ""}
+ if current_delta_type == "text"
+ else {"type": "audio", "transcript": ""}
+ ),
+ response_id=current_response_id,
+ )
+ returned_messages.append(content_part_added)
+
+ return (
+ returned_messages,
+ current_response_id,
+ current_output_item_id,
+ current_conversation_id,
+ current_delta_type,
+ )
+
+ def transform_text_output_event(
+ self,
+ event: dict,
+ current_output_item_id: Optional[str],
+ current_response_id: Optional[str],
+ current_delta_chunks: Optional[List[OpenAIRealtimeResponseDelta]],
+ ) -> tuple[List[OpenAIRealtimeEvents], Optional[List[OpenAIRealtimeResponseDelta]]]:
+ """
+ Transform Bedrock textOutput event to OpenAI response.text.delta.
+
+ Args:
+ event: Bedrock textOutput event
+ current_output_item_id: Current output item ID
+ current_response_id: Current response ID
+ current_delta_chunks: Current delta chunks
+
+ Returns:
+ Tuple of (events, updated_delta_chunks)
+ """
+ verbose_logger.debug("Handling textOutput")
+ text_content = event["textOutput"].get("content", "")
+
+ if not current_output_item_id or not current_response_id:
+ return [], current_delta_chunks
+
+ text_delta = OpenAIRealtimeResponseDelta(
+ type="response.text.delta",
+ content_index=0,
+ event_id=f"event_{uuid.uuid4()}",
+ item_id=current_output_item_id,
+ output_index=0,
+ response_id=current_response_id,
+ delta=text_content,
+ )
+
+ # Track delta chunks
+ if current_delta_chunks is None:
+ current_delta_chunks = []
+ current_delta_chunks.append(text_delta)
+
+ return [text_delta], current_delta_chunks
+
+ def transform_audio_output_event(
+ self,
+ event: dict,
+ current_output_item_id: Optional[str],
+ current_response_id: Optional[str],
+ ) -> List[OpenAIRealtimeEvents]:
+ """
+ Transform Bedrock audioOutput event to OpenAI response.audio.delta.
+
+ Args:
+ event: Bedrock audioOutput event
+ current_output_item_id: Current output item ID
+ current_response_id: Current response ID
+
+ Returns:
+ List of OpenAI events
+ """
+ verbose_logger.debug("Handling audioOutput")
+ audio_content = event["audioOutput"].get("content", "")
+
+ if not current_output_item_id or not current_response_id:
+ return []
+
+ audio_delta = OpenAIRealtimeResponseDelta(
+ type="response.audio.delta",
+ content_index=0,
+ event_id=f"event_{uuid.uuid4()}",
+ item_id=current_output_item_id,
+ output_index=0,
+ response_id=current_response_id,
+ delta=audio_content,
+ )
+
+ return [audio_delta]
+
+ def transform_content_end_event(
+ self,
+ event: dict,
+ current_output_item_id: Optional[str],
+ current_response_id: Optional[str],
+ current_delta_type: Optional[str],
+ current_delta_chunks: Optional[List[OpenAIRealtimeResponseDelta]],
+ ) -> tuple[List[OpenAIRealtimeEvents], Optional[List[OpenAIRealtimeResponseDelta]]]:
+ """
+ Transform Bedrock contentEnd event to OpenAI response done events.
+
+ Args:
+ event: Bedrock contentEnd event
+ current_output_item_id: Current output item ID
+ current_response_id: Current response ID
+ current_delta_type: Current delta type (text or audio)
+ current_delta_chunks: Current delta chunks
+
+ Returns:
+ Tuple of (events, reset_delta_chunks)
+ """
+ content_end = event["contentEnd"]
+ verbose_logger.debug(f"Handling contentEnd: {content_end}")
+
+ if not current_output_item_id or not current_response_id:
+ return [], current_delta_chunks
+
+ returned_messages: List[OpenAIRealtimeEvents] = []
+
+ # Send appropriate done event based on type
+ if current_delta_type == "text":
+ # Accumulate text
+ accumulated_text = ""
+ if current_delta_chunks:
+ accumulated_text = "".join(
+ [chunk.get("delta", "") for chunk in current_delta_chunks]
+ )
+
+ text_done = OpenAIRealtimeResponseTextDone(
+ type="response.text.done",
+ content_index=0,
+ event_id=f"event_{uuid.uuid4()}",
+ item_id=current_output_item_id,
+ output_index=0,
+ response_id=current_response_id,
+ text=accumulated_text,
+ )
+ returned_messages.append(text_done)
+
+ # Send content_part.done
+ content_part_done = OpenAIRealtimeContentPartDone(
+ type="response.content_part.done",
+ content_index=0,
+ event_id=f"event_{uuid.uuid4()}",
+ item_id=current_output_item_id,
+ output_index=0,
+ part={"type": "text", "text": accumulated_text},
+ response_id=current_response_id,
+ )
+ returned_messages.append(content_part_done)
+
+ elif current_delta_type == "audio":
+ audio_done = OpenAIRealtimeResponseAudioDone(
+ type="response.audio.done",
+ content_index=0,
+ event_id=f"event_{uuid.uuid4()}",
+ item_id=current_output_item_id,
+ output_index=0,
+ response_id=current_response_id,
+ )
+ returned_messages.append(audio_done)
+
+ # Send content_part.done
+ content_part_done = OpenAIRealtimeContentPartDone(
+ type="response.content_part.done",
+ content_index=0,
+ event_id=f"event_{uuid.uuid4()}",
+ item_id=current_output_item_id,
+ output_index=0,
+ part={"type": "audio", "transcript": ""},
+ response_id=current_response_id,
+ )
+ returned_messages.append(content_part_done)
+
+ # Send output_item.done
+ output_item_done = OpenAIRealtimeOutputItemDone(
+ type="response.output_item.done",
+ event_id=f"event_{uuid.uuid4()}",
+ output_index=0,
+ response_id=current_response_id,
+ item={
+ "id": current_output_item_id,
+ "object": "realtime.item",
+ "type": "message",
+ "status": "completed",
+ "role": "assistant",
+ "content": [],
+ },
+ )
+ returned_messages.append(output_item_done)
+
+ # Reset delta chunks
+ return returned_messages, None
+
+ def transform_prompt_end_event(
+ self,
+ event: dict,
+ current_response_id: Optional[str],
+ current_conversation_id: Optional[str],
+ ) -> tuple[List[OpenAIRealtimeEvents], Optional[str], Optional[str], Optional[ALL_DELTA_TYPES]]:
+ """
+ Transform Bedrock promptEnd event to OpenAI response.done.
+
+ Args:
+ event: Bedrock promptEnd event
+ current_response_id: Current response ID
+ current_conversation_id: Current conversation ID
+
+ Returns:
+ Tuple of (events, reset_output_item_id, reset_response_id, reset_delta_type)
+ """
+ verbose_logger.debug("Handling promptEnd")
+
+ if not current_response_id or not current_conversation_id:
+ return [], None, None, None
+
+ usage_obj = get_empty_usage()
+ response_done = OpenAIRealtimeDoneEvent(
+ type="response.done",
+ event_id=f"event_{uuid.uuid4()}",
+ response=OpenAIRealtimeResponseDoneObject(
+ object="realtime.response",
+ id=current_response_id,
+ status="completed",
+ output=[],
+ conversation_id=current_conversation_id,
+ usage={
+ "prompt_tokens": usage_obj.prompt_tokens,
+ "completion_tokens": usage_obj.completion_tokens,
+ "total_tokens": usage_obj.total_tokens,
+ },
+ ),
+ )
+
+ # Reset state for next response
+ return [response_done], None, None, None
+
+ def transform_tool_use_event(
+ self,
+ event: dict,
+ current_output_item_id: Optional[str],
+ current_response_id: Optional[str],
+ ) -> tuple[List[OpenAIRealtimeEvents], str, str]:
+ """
+ Transform Bedrock toolUse event to OpenAI format.
+
+ Args:
+ event: Bedrock toolUse event
+ current_output_item_id: Current output item ID
+ current_response_id: Current response ID
+
+ Returns:
+ Tuple of (events, tool_call_id, tool_name) for tracking
+ """
+ verbose_logger.debug("Handling toolUse")
+ tool_use = event["toolUse"]
+
+ if not current_output_item_id or not current_response_id:
+ return [], "", ""
+
+ # Parse the tool input
+ tool_input = {}
+ if "input" in tool_use:
+ try:
+ tool_input = json.loads(tool_use["input"]) if isinstance(tool_use["input"], str) else tool_use["input"]
+ except json.JSONDecodeError:
+ tool_input = {}
+
+ tool_call_id = tool_use.get("toolUseId", "")
+ tool_name = tool_use.get("toolName", "")
+
+ # Create a function call arguments done event
+ # This is a custom event format that matches what clients expect
+ from typing import cast
+ function_call_event: dict[str, Any] = {
+ "type": "response.function_call_arguments.done",
+ "event_id": f"event_{uuid.uuid4()}",
+ "response_id": current_response_id,
+ "item_id": current_output_item_id,
+ "output_index": 0,
+ "call_id": tool_call_id,
+ "name": tool_name,
+ "arguments": json.dumps(tool_input),
+ }
+
+ return [cast(OpenAIRealtimeEvents, function_call_event)], tool_call_id, tool_name
+
+ def transform_conversation_item_create_tool_result_event(self, json_message: dict) -> List[str]:
+ """
+ Transform conversation.item.create with tool result to Bedrock format.
+
+ Args:
+ json_message: OpenAI conversation.item.create message with tool result
+
+ Returns:
+ List of Bedrock format messages (JSON strings)
+ """
+ verbose_logger.debug("Handling conversation.item.create for tool result")
+ messages: List[str] = []
+
+ item = json_message.get("item", {})
+ if item.get("type") == "function_call_output":
+ tool_content_name = str(uuid_lib.uuid4())
+ call_id = item.get("call_id", "")
+ output = item.get("output", "")
+
+ # Content start for tool result
+ tool_content_start = {
+ "event": {
+ "contentStart": {
+ "promptName": self.prompt_name,
+ "contentName": tool_content_name,
+ "interactive": False,
+ "type": "TOOL",
+ "role": "TOOL",
+ "toolResultInputConfiguration": {
+ "toolUseId": call_id,
+ "type": "TEXT",
+ "textInputConfiguration": {
+ "mediaType": "text/plain"
+ }
+ }
+ }
+ }
+ }
+ messages.append(json.dumps(tool_content_start))
+
+ # Tool result
+ tool_result = {
+ "event": {
+ "toolResult": {
+ "promptName": self.prompt_name,
+ "contentName": tool_content_name,
+ "content": output if isinstance(output, str) else json.dumps(output)
+ }
+ }
+ }
+ messages.append(json.dumps(tool_result))
+
+ # Content end
+ tool_content_end = {
+ "event": {
+ "contentEnd": {
+ "promptName": self.prompt_name,
+ "contentName": tool_content_name,
+ }
+ }
+ }
+ messages.append(json.dumps(tool_content_end))
+
+ return messages
+
+ def transform_realtime_response(
+ self,
+ message: Union[str, bytes],
+ model: str,
+ logging_obj: LiteLLMLoggingObj,
+ realtime_response_transform_input: RealtimeResponseTransformInput,
+ ) -> RealtimeResponseTypedDict:
+ """
+ Transform Bedrock Nova Sonic response to OpenAI realtime format.
+
+ Args:
+ message: Bedrock format message (JSON string)
+ model: Model ID
+ logging_obj: Logging object
+ realtime_response_transform_input: Current state
+
+ Returns:
+ Transformed response with updated state
+ """
+ try:
+ json_message = json.loads(message)
+ except json.JSONDecodeError:
+ message_preview = message[:200].decode('utf-8', errors='replace') if isinstance(message, bytes) else message[:200]
+ verbose_logger.warning(f"Invalid JSON message: {message_preview}")
+ return {
+ "response": [],
+ "current_output_item_id": realtime_response_transform_input.get(
+ "current_output_item_id"
+ ),
+ "current_response_id": realtime_response_transform_input.get(
+ "current_response_id"
+ ),
+ "current_delta_chunks": realtime_response_transform_input.get(
+ "current_delta_chunks"
+ ),
+ "current_conversation_id": realtime_response_transform_input.get(
+ "current_conversation_id"
+ ),
+ "current_item_chunks": realtime_response_transform_input.get(
+ "current_item_chunks"
+ ),
+ "current_delta_type": realtime_response_transform_input.get(
+ "current_delta_type"
+ ),
+ "session_configuration_request": realtime_response_transform_input.get(
+ "session_configuration_request"
+ ),
+ }
+
+ # Extract state
+ current_output_item_id = realtime_response_transform_input.get(
+ "current_output_item_id"
+ )
+ current_response_id = realtime_response_transform_input.get(
+ "current_response_id"
+ )
+ current_conversation_id = realtime_response_transform_input.get(
+ "current_conversation_id"
+ )
+ current_delta_chunks = realtime_response_transform_input.get(
+ "current_delta_chunks"
+ )
+ current_delta_type = realtime_response_transform_input.get("current_delta_type")
+ session_configuration_request = realtime_response_transform_input.get(
+ "session_configuration_request"
+ )
+
+ returned_messages: List[OpenAIRealtimeEvents] = []
+
+ # Parse Bedrock event
+ event = json_message.get("event", {})
+
+ # Route to appropriate transformation method
+ if "sessionStart" in event:
+ session_created = self.transform_session_start_event(
+ event, model, logging_obj
+ )
+ returned_messages.append(session_created)
+ session_configuration_request = json.dumps({"configured": True})
+
+ elif "contentStart" in event:
+ (
+ events,
+ current_response_id,
+ current_output_item_id,
+ current_conversation_id,
+ current_delta_type,
+ ) = self.transform_content_start_event(
+ event,
+ current_response_id,
+ current_output_item_id,
+ current_conversation_id,
+ )
+ returned_messages.extend(events)
+
+ elif "textOutput" in event:
+ events, current_delta_chunks = self.transform_text_output_event(
+ event,
+ current_output_item_id,
+ current_response_id,
+ current_delta_chunks,
+ )
+ returned_messages.extend(events)
+
+ elif "audioOutput" in event:
+ events = self.transform_audio_output_event(
+ event, current_output_item_id, current_response_id
+ )
+ returned_messages.extend(events)
+
+ elif "contentEnd" in event:
+ events, current_delta_chunks = self.transform_content_end_event(
+ event,
+ current_output_item_id,
+ current_response_id,
+ current_delta_type,
+ current_delta_chunks,
+ )
+ returned_messages.extend(events)
+
+ elif "toolUse" in event:
+ events, tool_call_id, tool_name = self.transform_tool_use_event(
+ event, current_output_item_id, current_response_id
+ )
+ returned_messages.extend(events)
+ # Store tool call info for potential use
+ verbose_logger.debug(f"Tool use event: {tool_name} (ID: {tool_call_id})")
+
+ elif "promptEnd" in event:
+ (
+ events,
+ current_output_item_id,
+ current_response_id,
+ current_delta_type,
+ ) = self.transform_prompt_end_event(
+ event, current_response_id, current_conversation_id
+ )
+ returned_messages.extend(events)
+
+ return {
+ "response": returned_messages,
+ "current_output_item_id": current_output_item_id,
+ "current_response_id": current_response_id,
+ "current_delta_chunks": current_delta_chunks,
+ "current_conversation_id": current_conversation_id,
+ "current_item_chunks": realtime_response_transform_input.get(
+ "current_item_chunks"
+ ),
+ "current_delta_type": current_delta_type,
+ "session_configuration_request": session_configuration_request,
+ }
diff --git a/litellm/llms/cerebras/chat.py b/litellm/llms/cerebras/chat.py
index 4e9c6811a77..9929e2ab9a2 100644
--- a/litellm/llms/cerebras/chat.py
+++ b/litellm/llms/cerebras/chat.py
@@ -7,6 +7,7 @@
from typing import Optional
from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
+from litellm.utils import supports_reasoning
class CerebrasConfig(OpenAIGPTConfig):
@@ -24,6 +25,7 @@ class CerebrasConfig(OpenAIGPTConfig):
tool_choice: Optional[str] = None
tools: Optional[list] = None
user: Optional[str] = None
+ reasoning_effort: Optional[str] = None
def __init__(
self,
@@ -37,6 +39,7 @@ def __init__(
tool_choice: Optional[str] = None,
tools: Optional[list] = None,
user: Optional[str] = None,
+ reasoning_effort: Optional[str] = None,
) -> None:
locals_ = locals().copy()
for key, value in locals_.items():
@@ -53,7 +56,7 @@ def get_supported_openai_params(self, model: str) -> list:
"""
- return [
+ supported_params = [
"max_tokens",
"max_completion_tokens",
"response_format",
@@ -67,6 +70,12 @@ def get_supported_openai_params(self, model: str) -> list:
"user",
]
+ # Only add reasoning_effort for models that support it
+ if supports_reasoning(model=model, custom_llm_provider="cerebras"):
+ supported_params.append("reasoning_effort")
+
+ return supported_params
+
def map_openai_params(
self,
non_default_params: dict,
diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py
index 4f86877a6c0..ac9dd5998e2 100644
--- a/litellm/llms/custom_httpx/http_handler.py
+++ b/litellm/llms/custom_httpx/http_handler.py
@@ -50,9 +50,21 @@
except Exception:
version = "0.0.0"
-headers = {
- "User-Agent": f"litellm/{version}",
-}
+def get_default_headers() -> dict:
+ """
+ Get default headers for HTTP requests.
+
+ - Default: `User-Agent: litellm/{version}`
+ - Override: set `LITELLM_USER_AGENT` to fully override the header value.
+ """
+ user_agent = os.environ.get("LITELLM_USER_AGENT")
+ if user_agent is not None:
+ return {"User-Agent": user_agent}
+
+ return {"User-Agent": f"litellm/{version}"}
+
+# Initialize headers (User-Agent)
+headers = get_default_headers()
# https://www.python-httpx.org/advanced/timeouts
_DEFAULT_TIMEOUT = httpx.Timeout(timeout=5.0, connect=5.0)
@@ -371,13 +383,16 @@ def create_client(
shared_session=shared_session,
)
+ # Get default headers (User-Agent, overridable via LITELLM_USER_AGENT)
+ default_headers = get_default_headers()
+
return httpx.AsyncClient(
transport=transport,
event_hooks=event_hooks,
timeout=timeout,
verify=ssl_config,
cert=cert,
- headers=headers,
+ headers=default_headers,
follow_redirects=True,
)
@@ -899,6 +914,9 @@ def __init__(
# /path/to/client.pem
cert = os.getenv("SSL_CERTIFICATE", litellm.ssl_certificate)
+ # Get default headers (User-Agent, overridable via LITELLM_USER_AGENT)
+ default_headers = get_default_headers() if not disable_default_headers else None
+
if client is None:
transport = self._create_sync_transport()
@@ -908,7 +926,7 @@ def __init__(
timeout=timeout,
verify=ssl_config,
cert=cert,
- headers=headers if not disable_default_headers else None,
+ headers=default_headers,
follow_redirects=True,
)
else:
diff --git a/litellm/llms/custom_httpx/httpx_handler.py b/litellm/llms/custom_httpx/httpx_handler.py
index 6f684ba01c2..491cd97f7db 100644
--- a/litellm/llms/custom_httpx/httpx_handler.py
+++ b/litellm/llms/custom_httpx/httpx_handler.py
@@ -1,3 +1,4 @@
+import os
from typing import Optional, Union
import httpx
@@ -7,13 +8,22 @@
except Exception:
version = "0.0.0"
-headers = {
- "User-Agent": f"litellm/{version}",
-}
+def get_default_headers() -> dict:
+ """
+ Get default headers for HTTP requests.
+ - Default: `User-Agent: litellm/{version}`
+ - Override: set `LITELLM_USER_AGENT` to fully override the header value.
+ """
+ user_agent = os.environ.get("LITELLM_USER_AGENT")
+ if user_agent is not None:
+ return {"User-Agent": user_agent}
+
+ return {"User-Agent": f"litellm/{version}"}
class HTTPHandler:
def __init__(self, concurrent_limit=1000):
+ headers = get_default_headers()
# Create a client with a connection pool
self.client = httpx.AsyncClient(
limits=httpx.Limits(
diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py
index 2b7f5dd5995..e9ae94307d4 100644
--- a/litellm/llms/databricks/chat/transformation.py
+++ b/litellm/llms/databricks/chat/transformation.py
@@ -298,7 +298,8 @@ def map_openai_params(
if "reasoning_effort" in non_default_params and "claude" in model:
optional_params["thinking"] = AnthropicConfig._map_reasoning_effort(
- non_default_params.get("reasoning_effort")
+ reasoning_effort=non_default_params.get("reasoning_effort"),
+ model=model
)
optional_params.pop("reasoning_effort", None)
## handle thinking tokens
diff --git a/litellm/llms/deprecated_providers/palm.py b/litellm/llms/deprecated_providers/palm.py
index 3039222c0e2..657a6fdb229 100644
--- a/litellm/llms/deprecated_providers/palm.py
+++ b/litellm/llms/deprecated_providers/palm.py
@@ -139,7 +139,7 @@ def completion(
)
## COMPLETION CALL
try:
- response = palm.generate_text(prompt=prompt, **inference_params)
+ response = palm.generate_text(prompt=prompt, **inference_params) # type: ignore[attr-defined]
except Exception as e:
raise PalmError(
message=str(e),
diff --git a/litellm/llms/fireworks_ai/chat/transformation.py b/litellm/llms/fireworks_ai/chat/transformation.py
index 86bcd94450f..7ec32fecc46 100644
--- a/litellm/llms/fireworks_ai/chat/transformation.py
+++ b/litellm/llms/fireworks_ai/chat/transformation.py
@@ -236,6 +236,10 @@ def _transform_messages_helper(
disable_add_transform_inline_image_block=disable_add_transform_inline_image_block,
)
filter_value_from_dict(cast(dict, message), "cache_control")
+ # Remove fields not permitted by FireworksAI that may cause:
+ # "Not permitted, field: 'messages[n].provider_specific_fields'"
+ if isinstance(message, dict) and "provider_specific_fields" in message:
+ cast(dict, message).pop("provider_specific_fields", None)
return messages
diff --git a/litellm/llms/gemini/files/transformation.py b/litellm/llms/gemini/files/transformation.py
index 37f1376c2b1..cc799cfd6aa 100644
--- a/litellm/llms/gemini/files/transformation.py
+++ b/litellm/llms/gemini/files/transformation.py
@@ -210,7 +210,7 @@ def transform_retrieve_file_request(
We expect file_id to be the URI (e.g. https://generativelanguage.googleapis.com/v1beta/files/...)
as returned by the upload response.
"""
- api_key = litellm_params.get("api_key")
+ api_key = litellm_params.get("api_key") or self.get_api_key()
if not api_key:
raise ValueError("api_key is required")
@@ -222,7 +222,8 @@ def transform_retrieve_file_request(
api_base = api_base.rstrip("/")
url = "{}/v1beta/{}?key={}".format(api_base, file_id, api_key)
- return url, {"Content-Type": "application/json"}
+ # Return empty params dict - API key is already in URL, no query params needed
+ return url, {}
def transform_retrieve_file_response(
self,
@@ -299,7 +300,7 @@ def transform_delete_file_request(
# Extract the file path from full URI
file_name = file_id.split("/v1beta/")[-1]
else:
- file_name = file_id
+ file_name = file_id if file_id.startswith("files/") else f"files/{file_id}"
# Construct the delete URL
url = f"{api_base}/v1beta/{file_name}"
diff --git a/litellm/llms/gemini/image_generation/transformation.py b/litellm/llms/gemini/image_generation/transformation.py
index 63b835df9d0..73aef15e4c7 100644
--- a/litellm/llms/gemini/image_generation/transformation.py
+++ b/litellm/llms/gemini/image_generation/transformation.py
@@ -255,9 +255,11 @@ def transform_image_generation_response(
if "inlineData" in part:
inline_data = part["inlineData"]
if "data" in inline_data:
+ thought_sig = part.get("thoughtSignature")
model_response.data.append(ImageObject(
b64_json=inline_data["data"],
url=None,
+ provider_specific_fields={"thought_signature": thought_sig} if thought_sig else None,
))
# Extract usage metadata for Gemini models
diff --git a/litellm/llms/gigachat/chat/transformation.py b/litellm/llms/gigachat/chat/transformation.py
index ba14de1f65d..f546f356e11 100644
--- a/litellm/llms/gigachat/chat/transformation.py
+++ b/litellm/llms/gigachat/chat/transformation.py
@@ -386,33 +386,7 @@ def _transform_messages(self, messages: List[AllMessageValues]) -> List[dict]:
transformed.append(message)
- # Collapse consecutive user messages
- return self._collapse_user_messages(transformed)
-
- def _collapse_user_messages(self, messages: List[dict]) -> List[dict]:
- """Collapse consecutive user messages into one."""
- collapsed: List[dict] = []
- prev_user_msg: Optional[dict] = None
- content_parts: List[str] = []
-
- for msg in messages:
- if msg.get("role") == "user" and prev_user_msg is not None:
- content_parts.append(msg.get("content", ""))
- else:
- if content_parts and prev_user_msg:
- prev_user_msg["content"] = "\n".join(
- [prev_user_msg.get("content", "")] + content_parts
- )
- content_parts = []
- collapsed.append(msg)
- prev_user_msg = msg if msg.get("role") == "user" else None
-
- if content_parts and prev_user_msg:
- prev_user_msg["content"] = "\n".join(
- [prev_user_msg.get("content", "")] + content_parts
- )
-
- return collapsed
+ return transformed
def transform_response(
self,
diff --git a/litellm/llms/github_copilot/chat/transformation.py b/litellm/llms/github_copilot/chat/transformation.py
index 50f18cedf9b..be8ad7d0877 100644
--- a/litellm/llms/github_copilot/chat/transformation.py
+++ b/litellm/llms/github_copilot/chat/transformation.py
@@ -1,11 +1,16 @@
-from typing import Any, Optional, Tuple, cast, List
+from typing import List, Optional, Tuple
+
from litellm.exceptions import AuthenticationError
from litellm.llms.openai.openai import OpenAIConfig
from litellm.types.llms.openai import AllMessageValues
from ..authenticator import Authenticator
-from ..common_utils import GetAPIKeyError, GITHUB_COPILOT_API_BASE
+from ..common_utils import (
+ GITHUB_COPILOT_API_BASE,
+ GetAPIKeyError,
+ get_copilot_default_headers,
+)
class GithubCopilotConfig(OpenAIConfig):
@@ -25,9 +30,7 @@ def _get_openai_compatible_provider_info(
api_key: Optional[str],
custom_llm_provider: str,
) -> Tuple[Optional[str], Optional[str], str]:
- dynamic_api_base = (
- self.authenticator.get_api_base() or GITHUB_COPILOT_API_BASE
- )
+ dynamic_api_base = self.authenticator.get_api_base() or GITHUB_COPILOT_API_BASE
try:
dynamic_api_key = self.authenticator.get_api_key()
except GetAPIKeyError as e:
@@ -45,14 +48,24 @@ def _transform_messages(
):
import litellm
- disable_copilot_system_to_assistant = (
- litellm.disable_copilot_system_to_assistant
- )
- if not disable_copilot_system_to_assistant:
- for message in messages:
- if "role" in message and message["role"] == "system":
- cast(Any, message)["role"] = "assistant"
- return messages
+ # Check if system-to-assistant conversion is disabled
+ if litellm.disable_copilot_system_to_assistant:
+ # GitHub Copilot API now supports system prompts for all models (Claude, GPT, etc.)
+ # No conversion needed - just return messages as-is
+ return messages
+
+ # Default behavior: convert system messages to assistant for compatibility
+ transformed_messages = []
+ for message in messages:
+ if message.get("role") == "system":
+ # Convert system message to assistant message
+ transformed_message = message.copy()
+ transformed_message["role"] = "assistant"
+ transformed_messages.append(transformed_message)
+ else:
+ transformed_messages.append(message)
+
+ return transformed_messages
def validate_environment(
self,
@@ -69,6 +82,14 @@ def validate_environment(
headers, model, messages, optional_params, litellm_params, api_key, api_base
)
+ # Add Copilot-specific headers (editor-version, user-agent, etc.)
+ try:
+ copilot_api_key = self.authenticator.get_api_key()
+ copilot_headers = get_copilot_default_headers(copilot_api_key)
+ validated_headers = {**copilot_headers, **validated_headers}
+ except GetAPIKeyError:
+ pass # Will be handled later in the request flow
+
# Add X-Initiator header based on message roles
initiator = self._determine_initiator(messages)
validated_headers["X-Initiator"] = initiator
@@ -87,7 +108,7 @@ def get_supported_openai_params(self, model: str) -> list:
For other models, returns standard OpenAI parameters (which may include reasoning_effort for o-series models).
"""
from litellm.utils import supports_reasoning
-
+
# Get base OpenAI parameters
base_params = super().get_supported_openai_params(model)
@@ -118,7 +139,7 @@ def _has_vision_content(self, messages: List[AllMessageValues]) -> bool:
"""
Check if any message contains vision content (images).
Returns True if any message has content with vision-related types, otherwise False.
-
+
Checks for:
- image_url content type (OpenAI format)
- Content items with type 'image_url'
diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py
index fb00aa28f45..c406f502b45 100644
--- a/litellm/llms/openai/chat/guardrail_translation/handler.py
+++ b/litellm/llms/openai/chat/guardrail_translation/handler.py
@@ -21,7 +21,13 @@
from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation
from litellm.main import stream_chunk_builder
from litellm.types.llms.openai import ChatCompletionToolParam
-from litellm.types.utils import Choices, GenericGuardrailAPIInputs, ModelResponse, ModelResponseStream, StreamingChoices
+from litellm.types.utils import (
+ Choices,
+ GenericGuardrailAPIInputs,
+ ModelResponse,
+ ModelResponseStream,
+ StreamingChoices,
+)
if TYPE_CHECKING:
from litellm.integrations.custom_guardrail import CustomGuardrail
@@ -80,9 +86,9 @@ async def process_input_messages(
if tool_calls_to_check:
inputs["tool_calls"] = tool_calls_to_check # type: ignore
if messages:
- inputs["structured_messages"] = (
- messages # pass the openai /chat/completions messages to the guardrail, as-is
- )
+ inputs[
+ "structured_messages"
+ ] = messages # pass the openai /chat/completions messages to the guardrail, as-is
# Pass tools (function definitions) to the guardrail
tools = data.get("tools")
if tools:
@@ -362,14 +368,17 @@ async def process_output_streaming_response(
# check if the stream has ended
has_stream_ended = False
for chunk in responses_so_far:
- if chunk.choices[0].finish_reason is not None:
+ if chunk.choices and chunk.choices[0].finish_reason is not None:
has_stream_ended = True
break
if has_stream_ended:
# convert to model response
model_response = cast(
- ModelResponse, stream_chunk_builder(chunks=responses_so_far, logging_obj=litellm_logging_obj)
+ ModelResponse,
+ stream_chunk_builder(
+ chunks=responses_so_far, logging_obj=litellm_logging_obj
+ ),
)
# run process_output_response
await self.process_output_response(
diff --git a/litellm/llms/openai/common_utils.py b/litellm/llms/openai/common_utils.py
index 8bcecd35232..ce470f04aca 100644
--- a/litellm/llms/openai/common_utils.py
+++ b/litellm/llms/openai/common_utils.py
@@ -15,14 +15,12 @@
from aiohttp import ClientSession
import litellm
-from litellm._logging import verbose_logger
from litellm.llms.base_llm.chat.transformation import BaseLLMException
from litellm.llms.custom_httpx.http_handler import (
_DEFAULT_TTL_FOR_HTTPX_CLIENTS,
AsyncHTTPHandler,
get_ssl_configuration,
)
-from litellm.types.utils import LlmProviders
class OpenAIError(BaseLLMException):
@@ -205,67 +203,30 @@ def _get_async_http_client(
if litellm.aclient_session is not None:
return litellm.aclient_session
- # Use the global cached client system to prevent memory leaks (issue #14540)
- # This routes through get_async_httpx_client() which provides TTL-based caching
- from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
+ # Get unified SSL configuration
+ ssl_config = get_ssl_configuration()
- try:
- # Get SSL config and include in params for proper cache key
- ssl_config = get_ssl_configuration()
- params = {"ssl_verify": ssl_config} if ssl_config is not None else {}
- params["disable_aiohttp_transport"] = litellm.disable_aiohttp_transport
-
- # Get a cached AsyncHTTPHandler which manages the httpx.AsyncClient
- cached_handler = get_async_httpx_client(
- llm_provider=LlmProviders.OPENAI, # Cache key includes provider
- params=params, # Include SSL config in cache key
+ return httpx.AsyncClient(
+ verify=ssl_config,
+ transport=AsyncHTTPHandler._create_async_transport(
+ ssl_context=ssl_config
+ if isinstance(ssl_config, ssl.SSLContext)
+ else None,
+ ssl_verify=ssl_config if isinstance(ssl_config, bool) else None,
shared_session=shared_session,
- )
- # Return the underlying httpx client from the handler
- return cached_handler.client
- except (ImportError, AttributeError, KeyError) as e:
- # Fallback to creating a client directly if caching system unavailable
- # This preserves backwards compatibility
- verbose_logger.debug(
- f"Client caching unavailable ({type(e).__name__}), using direct client creation"
- )
- ssl_config = get_ssl_configuration()
- return httpx.AsyncClient(
- verify=ssl_config,
- transport=AsyncHTTPHandler._create_async_transport(
- ssl_context=ssl_config
- if isinstance(ssl_config, ssl.SSLContext)
- else None,
- ssl_verify=ssl_config if isinstance(ssl_config, bool) else None,
- shared_session=shared_session,
- ),
- follow_redirects=True,
- )
+ ),
+ follow_redirects=True,
+ )
@staticmethod
def _get_sync_http_client() -> Optional[httpx.Client]:
if litellm.client_session is not None:
return litellm.client_session
- # Use the global cached client system to prevent memory leaks (issue #14540)
- from litellm.llms.custom_httpx.http_handler import _get_httpx_client
-
- try:
- # Get SSL config and include in params for proper cache key
- ssl_config = get_ssl_configuration()
- params = {"ssl_verify": ssl_config} if ssl_config is not None else None
-
- # Get a cached HTTPHandler which manages the httpx.Client
- cached_handler = _get_httpx_client(params=params)
- # Return the underlying httpx client from the handler
- return cached_handler.client
- except (ImportError, AttributeError, KeyError) as e:
- # Fallback to creating a client directly if caching system unavailable
- verbose_logger.debug(
- f"Client caching unavailable ({type(e).__name__}), using direct client creation"
- )
- ssl_config = get_ssl_configuration()
- return httpx.Client(
- verify=ssl_config,
- follow_redirects=True,
- )
+ # Get unified SSL configuration
+ ssl_config = get_ssl_configuration()
+
+ return httpx.Client(
+ verify=ssl_config,
+ follow_redirects=True,
+ )
diff --git a/litellm/llms/openai/embeddings/guardrail_translation/__init__.py b/litellm/llms/openai/embeddings/guardrail_translation/__init__.py
new file mode 100644
index 00000000000..a60662282ca
--- /dev/null
+++ b/litellm/llms/openai/embeddings/guardrail_translation/__init__.py
@@ -0,0 +1,13 @@
+"""OpenAI Embeddings handler for Unified Guardrails."""
+
+from litellm.llms.openai.embeddings.guardrail_translation.handler import (
+ OpenAIEmbeddingsHandler,
+)
+from litellm.types.utils import CallTypes
+
+guardrail_translation_mappings = {
+ CallTypes.embedding: OpenAIEmbeddingsHandler,
+ CallTypes.aembedding: OpenAIEmbeddingsHandler,
+}
+
+__all__ = ["guardrail_translation_mappings", "OpenAIEmbeddingsHandler"]
diff --git a/litellm/llms/openai/embeddings/guardrail_translation/handler.py b/litellm/llms/openai/embeddings/guardrail_translation/handler.py
new file mode 100644
index 00000000000..7458020e109
--- /dev/null
+++ b/litellm/llms/openai/embeddings/guardrail_translation/handler.py
@@ -0,0 +1,179 @@
+"""
+OpenAI Embeddings Handler for Unified Guardrails
+
+This module provides guardrail translation support for OpenAI's embeddings endpoint.
+The handler processes the 'input' parameter for guardrails.
+"""
+
+from typing import TYPE_CHECKING, Any, List, Optional, Union
+
+from litellm._logging import verbose_proxy_logger
+from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation
+from litellm.types.utils import GenericGuardrailAPIInputs
+
+if TYPE_CHECKING:
+ from litellm.integrations.custom_guardrail import CustomGuardrail
+ from litellm.types.utils import EmbeddingResponse
+
+
+class OpenAIEmbeddingsHandler(BaseTranslation):
+ """
+ Handler for processing OpenAI embeddings requests with guardrails.
+
+ This class provides methods to:
+ 1. Process input text (pre-call hook)
+ 2. Process output response (post-call hook) - embeddings don't typically need output guardrails
+
+ The handler specifically processes the 'input' parameter which can be:
+ - A single string
+ - A list of strings (for batch embeddings)
+ - A list of integers (token IDs - not processed by guardrails)
+ - A list of lists of integers (batch token IDs - not processed by guardrails)
+ """
+
+ async def process_input_messages(
+ self,
+ data: dict,
+ guardrail_to_apply: "CustomGuardrail",
+ litellm_logging_obj: Optional[Any] = None,
+ ) -> Any:
+ """
+ Process input text by applying guardrails to text content.
+
+ Args:
+ data: Request data dictionary containing 'input' parameter
+ guardrail_to_apply: The guardrail instance to apply
+ litellm_logging_obj: Optional logging object
+
+ Returns:
+ Modified data with guardrails applied to input
+ """
+ input_data = data.get("input")
+ if input_data is None:
+ verbose_proxy_logger.debug(
+ "OpenAI Embeddings: No input found in request data"
+ )
+ return data
+
+ if isinstance(input_data, str):
+ data = await self._process_string_input(
+ data, input_data, guardrail_to_apply, litellm_logging_obj
+ )
+ elif isinstance(input_data, list):
+ data = await self._process_list_input(
+ data, input_data, guardrail_to_apply, litellm_logging_obj
+ )
+ else:
+ verbose_proxy_logger.warning(
+ "OpenAI Embeddings: Unexpected input type: %s. Expected string or list.",
+ type(input_data),
+ )
+
+ return data
+
+ async def _process_string_input(
+ self,
+ data: dict,
+ input_data: str,
+ guardrail_to_apply: "CustomGuardrail",
+ litellm_logging_obj: Optional[Any],
+ ) -> dict:
+ """Process a single string input through the guardrail."""
+ inputs = GenericGuardrailAPIInputs(texts=[input_data])
+ if model := data.get("model"):
+ inputs["model"] = model
+
+ guardrailed_inputs = await guardrail_to_apply.apply_guardrail(
+ inputs=inputs,
+ request_data=data,
+ input_type="request",
+ logging_obj=litellm_logging_obj,
+ )
+
+ if guardrailed_texts := guardrailed_inputs.get("texts"):
+ data["input"] = guardrailed_texts[0]
+ verbose_proxy_logger.debug(
+ "OpenAI Embeddings: Applied guardrail to string input. "
+ "Original length: %d, New length: %d",
+ len(input_data),
+ len(data["input"]),
+ )
+
+ return data
+
+ async def _process_list_input(
+ self,
+ data: dict,
+ input_data: List[Union[str, int, List[int]]],
+ guardrail_to_apply: "CustomGuardrail",
+ litellm_logging_obj: Optional[Any],
+ ) -> dict:
+ """Process a list input through the guardrail (if it contains strings)."""
+ if len(input_data) == 0:
+ return data
+
+ first_item = input_data[0]
+
+ # Skip non-text inputs (token IDs)
+ if isinstance(first_item, (int, list)):
+ verbose_proxy_logger.debug(
+ "OpenAI Embeddings: Input is token IDs, skipping guardrail processing"
+ )
+ return data
+
+ if not isinstance(first_item, str):
+ verbose_proxy_logger.warning(
+ "OpenAI Embeddings: Unexpected input list item type: %s",
+ type(first_item),
+ )
+ return data
+
+ # List of strings - apply guardrail
+ inputs = GenericGuardrailAPIInputs(texts=input_data) # type: ignore
+ if model := data.get("model"):
+ inputs["model"] = model
+
+ guardrailed_inputs = await guardrail_to_apply.apply_guardrail(
+ inputs=inputs,
+ request_data=data,
+ input_type="request",
+ logging_obj=litellm_logging_obj,
+ )
+
+ if guardrailed_texts := guardrailed_inputs.get("texts"):
+ data["input"] = guardrailed_texts
+ verbose_proxy_logger.debug(
+ "OpenAI Embeddings: Applied guardrail to %d inputs",
+ len(guardrailed_texts),
+ )
+
+ return data
+
+ async def process_output_response(
+ self,
+ response: "EmbeddingResponse",
+ guardrail_to_apply: "CustomGuardrail",
+ litellm_logging_obj: Optional[Any] = None,
+ user_api_key_dict: Optional[Any] = None,
+ ) -> Any:
+ """
+ Process output response - embeddings responses contain vectors, not text.
+
+ For embeddings, the output is numerical vectors, so there's typically
+ no text content to apply guardrails to. This method is a no-op but
+ is included for interface consistency.
+
+ Args:
+ response: Embedding response object
+ guardrail_to_apply: The guardrail instance to apply
+ litellm_logging_obj: Optional logging object
+ user_api_key_dict: User API key metadata
+
+ Returns:
+ Unmodified response (embeddings don't have text output to guard)
+ """
+ verbose_proxy_logger.debug(
+ "OpenAI Embeddings: Output response processing skipped - "
+ "embeddings contain vectors, not text"
+ )
+ return response
diff --git a/litellm/llms/openai/realtime/handler.py b/litellm/llms/openai/realtime/handler.py
index fd04ac4d458..ef9cc43c3e1 100644
--- a/litellm/llms/openai/realtime/handler.py
+++ b/litellm/llms/openai/realtime/handler.py
@@ -16,6 +16,62 @@
class OpenAIRealtime(OpenAIChatCompletion):
+ """
+ Base handler for OpenAI-compatible realtime WebSocket connections.
+
+ Subclasses can override template methods to customize:
+ - _get_default_api_base(): Default API base URL
+ - _get_additional_headers(): Extra headers beyond Authorization
+ - _get_ssl_config(): SSL configuration for WebSocket connection
+ """
+
+ def _get_default_api_base(self) -> str:
+ """
+ Get the default API base URL for this provider.
+ Override this in subclasses to set provider-specific defaults.
+ """
+ return "https://api.openai.com/"
+
+ def _get_additional_headers(self, api_key: str) -> dict:
+ """
+ Get additional headers beyond Authorization.
+ Override this in subclasses to customize headers (e.g., remove OpenAI-Beta).
+
+ Args:
+ api_key: API key for authentication
+
+ Returns:
+ Dictionary of additional headers
+ """
+ return {
+ "Authorization": f"Bearer {api_key}",
+ "OpenAI-Beta": "realtime=v1",
+ }
+
+ def _get_ssl_config(self, url: str) -> Any:
+ """
+ Get SSL configuration for WebSocket connection.
+ Override this in subclasses to customize SSL behavior.
+
+ Args:
+ url: WebSocket URL (ws:// or wss://)
+
+ Returns:
+ SSL configuration (None, True, or SSLContext)
+ """
+ if url.startswith("ws://"):
+ return None
+
+ # Use the shared SSL context which respects custom CA certs and SSL settings
+ ssl_config = get_shared_realtime_ssl_context()
+
+ # If ssl_config is False (ssl_verify=False), websockets library needs True instead
+ # to establish connection without verification (False would fail)
+ if ssl_config is False:
+ return True
+
+ return ssl_config
+
def _construct_url(self, api_base: str, query_params: RealtimeQueryParams) -> str:
"""
Construct the backend websocket URL with all query parameters (including 'model').
@@ -45,8 +101,9 @@ async def async_realtime(
):
import websockets
from websockets.asyncio.client import ClientConnection
+
if api_base is None:
- api_base = "https://api.openai.com/"
+ api_base = self._get_default_api_base()
if api_key is None:
raise ValueError("api_key is required for OpenAI realtime calls")
@@ -56,30 +113,27 @@ async def async_realtime(
url = self._construct_url(api_base, query_params)
try:
- # Only use SSL context for secure websocket connections (wss://)
- # websockets library doesn't accept ssl argument for ws:// URIs
- ssl_context = None if url.startswith("ws://") else get_shared_realtime_ssl_context()
+ # Get provider-specific SSL configuration
+ ssl_config = self._get_ssl_config(url)
+
+ # Get provider-specific headers
+ headers = self._get_additional_headers(api_key)
+
# Log a masked request preview consistent with other endpoints.
logging_obj.pre_call(
input=None,
api_key=api_key,
additional_args={
"api_base": url,
- "headers": {
- "Authorization": f"Bearer {api_key}",
- "OpenAI-Beta": "realtime=v1",
- },
+ "headers": headers,
"complete_input_dict": {"query_params": query_params},
},
)
async with websockets.connect( # type: ignore
url,
- additional_headers={
- "Authorization": f"Bearer {api_key}", # type: ignore
- "OpenAI-Beta": "realtime=v1",
- },
+ additional_headers=headers, # type: ignore
max_size=REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES,
- ssl=ssl_context,
+ ssl=ssl_config,
) as backend_ws:
realtime_streaming = RealTimeStreaming(
websocket, cast(ClientConnection, backend_ws), logging_obj
diff --git a/litellm/llms/openai/responses/guardrail_translation/handler.py b/litellm/llms/openai/responses/guardrail_translation/handler.py
index d943662f9e4..ad3d4c932d4 100644
--- a/litellm/llms/openai/responses/guardrail_translation/handler.py
+++ b/litellm/llms/openai/responses/guardrail_translation/handler.py
@@ -319,9 +319,7 @@ async def process_output_response(
return response
if not response_output:
- verbose_proxy_logger.debug(
- "OpenAI Responses API: Empty output in response"
- )
+ verbose_proxy_logger.debug("OpenAI Responses API: Empty output in response")
return response
# Step 1: Extract all text content and tool calls from response output
@@ -427,27 +425,30 @@ async def process_output_streaming_response(
handle_raw_dict_callback=None,
)
- tool_calls = model_response_choices[0].message.tool_calls
- text = model_response_choices[0].message.content
- guardrail_inputs = GenericGuardrailAPIInputs()
- if text:
- guardrail_inputs["texts"] = [text]
- if tool_calls:
- guardrail_inputs["tool_calls"] = cast(
- List[ChatCompletionToolCallChunk], tool_calls
- )
- # Include model information from the response if available
- response_model = final_chunk.get("response", {}).get("model")
- if response_model:
- guardrail_inputs["model"] = response_model
- if tool_calls or text:
- _guardrailed_inputs = await guardrail_to_apply.apply_guardrail(
- inputs=guardrail_inputs,
- request_data={},
- input_type="response",
- logging_obj=litellm_logging_obj,
- )
- return responses_so_far
+ if model_response_choices:
+ tool_calls = model_response_choices[0].message.tool_calls
+ text = model_response_choices[0].message.content
+ guardrail_inputs = GenericGuardrailAPIInputs()
+ if text:
+ guardrail_inputs["texts"] = [text]
+ if tool_calls:
+ guardrail_inputs["tool_calls"] = cast(
+ List[ChatCompletionToolCallChunk], tool_calls
+ )
+ # Include model information from the response if available
+ response_model = final_chunk.get("response", {}).get("model")
+ if response_model:
+ guardrail_inputs["model"] = response_model
+ if tool_calls or text:
+ _guardrailed_inputs = await guardrail_to_apply.apply_guardrail(
+ inputs=guardrail_inputs,
+ request_data={},
+ input_type="response",
+ logging_obj=litellm_logging_obj,
+ )
+ return responses_so_far
+ else:
+ verbose_proxy_logger.debug("Skipping output guardrail - model response has no choices")
# model_response_stream = OpenAiResponsesToChatCompletionStreamIterator.translate_responses_chunk_to_openai_stream(final_chunk)
# tool_calls = model_response_stream.choices[0].tool_calls
# convert openai response to model response
@@ -513,11 +514,9 @@ def _has_text_content(self, response: "ResponsesAPIResponse") -> bool:
# Check if it's an OutputText with text
if isinstance(content_item, OutputText):
if content_item.text:
-
return True
elif isinstance(content_item, dict):
if content_item.get("text"):
-
return True
return False
diff --git a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py
index 289963e917a..ed4d2d6a740 100644
--- a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py
+++ b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py
@@ -27,6 +27,8 @@
type=LiteLLMCacheType.LOCAL
) # only used for calling 'get_cache_key' function
+MAX_PAGINATION_PAGES = 100 # Reasonable upper bound for pagination
+
class ContextCachingEndpoints(VertexBase):
"""
@@ -115,7 +117,7 @@ def check_cache(
- None
"""
- _, url = self._get_token_and_url_context_caching(
+ _, base_url = self._get_token_and_url_context_caching(
gemini_api_key=api_key,
custom_llm_provider=custom_llm_provider,
api_base=api_base,
@@ -123,43 +125,63 @@ def check_cache(
vertex_location=vertex_location,
vertex_auth_header=vertex_auth_header
)
- try:
- ## LOGGING
- logging_obj.pre_call(
- input="",
- api_key="",
- additional_args={
- "complete_input_dict": {},
- "api_base": url,
- "headers": headers,
- },
- )
- resp = client.get(url=url, headers=headers)
- resp.raise_for_status()
- except httpx.HTTPStatusError as e:
- if e.response.status_code == 403:
+ page_token: Optional[str] = None
+
+ # Iterate through all pages
+ for _ in range(MAX_PAGINATION_PAGES):
+ # Build URL with pagination token if present
+ if page_token:
+ separator = "&" if "?" in base_url else "?"
+ url = f"{base_url}{separator}pageToken={page_token}"
+ else:
+ url = base_url
+
+ try:
+ ## LOGGING
+ logging_obj.pre_call(
+ input="",
+ api_key="",
+ additional_args={
+ "complete_input_dict": {},
+ "api_base": url,
+ "headers": headers,
+ },
+ )
+
+ resp = client.get(url=url, headers=headers)
+ resp.raise_for_status()
+ except httpx.HTTPStatusError as e:
+ if e.response.status_code == 403:
+ return None
+ raise VertexAIError(
+ status_code=e.response.status_code, message=e.response.text
+ )
+ except Exception as e:
+ raise VertexAIError(status_code=500, message=str(e))
+
+ raw_response = resp.json()
+ logging_obj.post_call(original_response=raw_response)
+
+ if "cachedContents" not in raw_response:
return None
- raise VertexAIError(
- status_code=e.response.status_code, message=e.response.text
- )
- except Exception as e:
- raise VertexAIError(status_code=500, message=str(e))
- raw_response = resp.json()
- logging_obj.post_call(original_response=raw_response)
- if "cachedContents" not in raw_response:
- return None
+ all_cached_items = CachedContentListAllResponseBody(**raw_response)
- all_cached_items = CachedContentListAllResponseBody(**raw_response)
+ if "cachedContents" not in all_cached_items:
+ return None
- if "cachedContents" not in all_cached_items:
- return None
+ # Check current page for matching cache_key
+ for cached_item in all_cached_items["cachedContents"]:
+ display_name = cached_item.get("displayName")
+ if display_name is not None and display_name == cache_key:
+ return cached_item.get("name")
- for cached_item in all_cached_items["cachedContents"]:
- display_name = cached_item.get("displayName")
- if display_name is not None and display_name == cache_key:
- return cached_item.get("name")
+ # Check if there are more pages
+ page_token = all_cached_items.get("nextPageToken")
+ if not page_token:
+ # No more pages, cache not found
+ break
return None
@@ -187,7 +209,7 @@ async def async_check_cache(
- None
"""
- _, url = self._get_token_and_url_context_caching(
+ _, base_url = self._get_token_and_url_context_caching(
gemini_api_key=api_key,
custom_llm_provider=custom_llm_provider,
api_base=api_base,
@@ -195,43 +217,63 @@ async def async_check_cache(
vertex_location=vertex_location,
vertex_auth_header=vertex_auth_header
)
- try:
- ## LOGGING
- logging_obj.pre_call(
- input="",
- api_key="",
- additional_args={
- "complete_input_dict": {},
- "api_base": url,
- "headers": headers,
- },
- )
- resp = await client.get(url=url, headers=headers)
- resp.raise_for_status()
- except httpx.HTTPStatusError as e:
- if e.response.status_code == 403:
+ page_token: Optional[str] = None
+
+ # Iterate through all pages
+ for _ in range(MAX_PAGINATION_PAGES):
+ # Build URL with pagination token if present
+ if page_token:
+ separator = "&" if "?" in base_url else "?"
+ url = f"{base_url}{separator}pageToken={page_token}"
+ else:
+ url = base_url
+
+ try:
+ ## LOGGING
+ logging_obj.pre_call(
+ input="",
+ api_key="",
+ additional_args={
+ "complete_input_dict": {},
+ "api_base": url,
+ "headers": headers,
+ },
+ )
+
+ resp = await client.get(url=url, headers=headers)
+ resp.raise_for_status()
+ except httpx.HTTPStatusError as e:
+ if e.response.status_code == 403:
+ return None
+ raise VertexAIError(
+ status_code=e.response.status_code, message=e.response.text
+ )
+ except Exception as e:
+ raise VertexAIError(status_code=500, message=str(e))
+
+ raw_response = resp.json()
+ logging_obj.post_call(original_response=raw_response)
+
+ if "cachedContents" not in raw_response:
return None
- raise VertexAIError(
- status_code=e.response.status_code, message=e.response.text
- )
- except Exception as e:
- raise VertexAIError(status_code=500, message=str(e))
- raw_response = resp.json()
- logging_obj.post_call(original_response=raw_response)
- if "cachedContents" not in raw_response:
- return None
+ all_cached_items = CachedContentListAllResponseBody(**raw_response)
- all_cached_items = CachedContentListAllResponseBody(**raw_response)
+ if "cachedContents" not in all_cached_items:
+ return None
- if "cachedContents" not in all_cached_items:
- return None
+ # Check current page for matching cache_key
+ for cached_item in all_cached_items["cachedContents"]:
+ display_name = cached_item.get("displayName")
+ if display_name is not None and display_name == cache_key:
+ return cached_item.get("name")
- for cached_item in all_cached_items["cachedContents"]:
- display_name = cached_item.get("displayName")
- if display_name is not None and display_name == cache_key:
- return cached_item.get("name")
+ # Check if there are more pages
+ page_token = all_cached_items.get("nextPageToken")
+ if not page_token:
+ # No more pages, cache not found
+ break
return None
@@ -501,4 +543,4 @@ def get_cache(self):
pass
async def async_get_cache(self):
- pass
+ pass
\ No newline at end of file
diff --git a/litellm/llms/vertex_ai/files/transformation.py b/litellm/llms/vertex_ai/files/transformation.py
index b3612113ec2..2470c59bbac 100644
--- a/litellm/llms/vertex_ai/files/transformation.py
+++ b/litellm/llms/vertex_ai/files/transformation.py
@@ -165,7 +165,7 @@ def get_complete_file_url(
"""
Get the complete url for the request
"""
- bucket_name = litellm_params.get("bucket_name") or os.getenv("GCS_BUCKET_NAME")
+ bucket_name = litellm_params.get("bucket_name") or litellm_params.get("litellm_metadata", {}).pop("gcs_bucket_name", None) or os.getenv("GCS_BUCKET_NAME")
if not bucket_name:
raise ValueError("GCS bucket_name is required")
file_data = data.get("file")
diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
index a9ac21bb56f..04ae4b6beb8 100644
--- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
+++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
@@ -478,6 +478,13 @@ def _map_function( # noqa: PLR0915
if "type" in tool and tool["type"] == "computer_use":
computer_use_config = {k: v for k, v in tool.items() if k != "type"}
tool = {VertexToolName.COMPUTER_USE.value: computer_use_config}
+ # Handle OpenAI-style web_search and web_search_preview tools
+ # Transform them to Gemini's googleSearch tool
+ elif "type" in tool and tool["type"] in ("web_search", "web_search_preview"):
+ verbose_logger.info(
+ f"Gemini: Transforming OpenAI-style '{tool['type']}' tool to googleSearch"
+ )
+ tool = {VertexToolName.GOOGLE_SEARCH.value: {}}
# Handle tools with 'type' field (OpenAI spec compliance) Ignore this field -> https://github.com/BerriAI/litellm/issues/14644#issuecomment-3342061838
elif "type" in tool:
tool = {k: tool[k] for k in tool if k != "type"}
@@ -1725,6 +1732,52 @@ def _check_finish_reason(
else:
return "stop"
+ @staticmethod
+ def _check_prompt_level_content_filter(
+ processed_chunk: GenerateContentResponseBody,
+ response_id: Optional[str],
+ ) -> Optional["ModelResponseStream"]:
+ """
+ Check if prompt is blocked due to content filtering at the prompt level.
+
+ This handles the case where Vertex AI blocks the prompt before generation begins,
+ indicated by promptFeedback.blockReason being present.
+
+ Args:
+ processed_chunk: The parsed response chunk from Vertex AI
+ response_id: The response ID from the chunk
+
+ Returns:
+ ModelResponseStream with content_filter finish_reason if blocked, None otherwise.
+
+ Note:
+ This is consistent with non-streaming _handle_blocked_response() behavior.
+ Candidate-level content filtering (SAFETY, RECITATION, etc.) is handled
+ separately via _process_candidates() → _check_finish_reason().
+ """
+ from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices
+
+ # Check if prompt is blocked due to content filtering
+ prompt_feedback = processed_chunk.get("promptFeedback")
+ if prompt_feedback and "blockReason" in prompt_feedback:
+ verbose_logger.debug(
+ f"Prompt blocked due to: {prompt_feedback.get('blockReason')} - {prompt_feedback.get('blockReasonMessage')}"
+ )
+
+ # Create a content_filter response (consistent with non-streaming _handle_blocked_response)
+ choice = StreamingChoices(
+ finish_reason="content_filter",
+ index=0,
+ delta=Delta(content=None, role="assistant"),
+ logprobs=None,
+ enhancements=None,
+ )
+
+ model_response = ModelResponseStream(choices=[choice], id=response_id)
+ return model_response
+
+ return None
+
@staticmethod
def _calculate_web_search_requests(grounding_metadata: List[dict]) -> Optional[int]:
web_search_requests: Optional[int] = None
@@ -2806,6 +2859,15 @@ def chunk_parser(self, chunk: dict) -> Optional["ModelResponseStream"]:
processed_chunk = GenerateContentResponseBody(**chunk) # type: ignore
response_id = processed_chunk.get("responseId")
model_response = ModelResponseStream(choices=[], id=response_id)
+
+ # Check if prompt is blocked due to content filtering
+ blocked_response = VertexGeminiConfig._check_prompt_level_content_filter(
+ processed_chunk=processed_chunk,
+ response_id=response_id,
+ )
+ if blocked_response is not None:
+ model_response = blocked_response
+
usage: Optional[Usage] = None
_candidates: Optional[List[Candidates]] = processed_chunk.get("candidates")
grounding_metadata: List[dict] = []
diff --git a/litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py b/litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py
index 89ed9f1a8a5..ba3df88be14 100644
--- a/litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py
+++ b/litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py
@@ -295,9 +295,11 @@ def transform_image_generation_response(
if "inlineData" in part:
inline_data = part["inlineData"]
if "data" in inline_data:
+ thought_sig = part.get("thoughtSignature")
model_response.data.append(ImageObject(
b64_json=inline_data["data"],
url=None,
+ provider_specific_fields={"thought_signature": thought_sig} if thought_sig else None,
))
if usage_metadata := response_data.get("usageMetadata", None):
diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py
index 9b8ff3ecc2d..918b8ecc225 100644
--- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py
+++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py
@@ -1,5 +1,8 @@
from typing import Any, Dict, List, Optional, Tuple
+from litellm.anthropic_beta_headers_manager import (
+ update_headers_with_filtered_beta,
+)
from litellm.llms.anthropic.common_utils import AnthropicModelInfo
from litellm.llms.anthropic.experimental_pass_through.messages.transformation import (
AnthropicMessagesConfig,
@@ -7,7 +10,6 @@
from litellm.types.llms.anthropic import (
ANTHROPIC_BETA_HEADER_VALUES,
ANTHROPIC_HOSTED_TOOLS,
- ANTHROPIC_PROMPT_CACHING_SCOPE_BETA_HEADER,
)
from litellm.types.llms.anthropic_tool_search import get_tool_search_beta_header
from litellm.types.llms.vertex_ai import VertexPartnerProvider
@@ -65,10 +67,6 @@ def validate_anthropic_messages_environment(
existing_beta = headers.get("anthropic-beta")
if existing_beta:
beta_values.update(b.strip() for b in existing_beta.split(","))
-
- # Use the helper to remove unsupported beta headers
- self.remove_unsupported_beta(headers)
- beta_values.discard(ANTHROPIC_PROMPT_CACHING_SCOPE_BETA_HEADER)
# Check for web search tool
for tool in tools:
@@ -84,6 +82,12 @@ def validate_anthropic_messages_environment(
if beta_values:
headers["anthropic-beta"] = ",".join(beta_values)
+ # Filter out unsupported beta headers for Vertex AI
+ headers = update_headers_with_filtered_beta(
+ headers=headers,
+ provider="vertex_ai",
+ )
+
return headers, api_base
def get_complete_url(
@@ -128,23 +132,3 @@ def transform_anthropic_messages_request(
) # do not pass output_format in request body to vertex ai - vertex ai does not support output_format as yet
return anthropic_messages_request
-
- def remove_unsupported_beta(self, headers: dict) -> None:
- """
- Helper method to remove unsupported beta headers from the beta headers.
- Modifies headers in place.
- """
- unsupported_beta_headers = [
- ANTHROPIC_PROMPT_CACHING_SCOPE_BETA_HEADER
- ]
- existing_beta = headers.get("anthropic-beta")
- if existing_beta:
- filtered_beta = [
- b.strip()
- for b in existing_beta.split(",")
- if b.strip() not in unsupported_beta_headers
- ]
- if filtered_beta:
- headers["anthropic-beta"] = ",".join(filtered_beta)
- elif "anthropic-beta" in headers:
- del headers["anthropic-beta"]
diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py
index 1df07f405e6..0b728d88e76 100644
--- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py
+++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py
@@ -51,6 +51,40 @@ class VertexAIAnthropicConfig(AnthropicConfig):
def custom_llm_provider(self) -> Optional[str]:
return "vertex_ai"
+ def _add_context_management_beta_headers(
+ self, beta_set: set, context_management: dict
+ ) -> None:
+ """
+ Add context_management beta headers to the beta_set.
+
+ - If any edit has type "compact_20260112", add compact-2026-01-12 header
+ - For all other edits, add context-management-2025-06-27 header
+
+ Args:
+ beta_set: Set of beta headers to modify in-place
+ context_management: The context_management dict from optional_params
+ """
+ from litellm.types.llms.anthropic import ANTHROPIC_BETA_HEADER_VALUES
+
+ edits = context_management.get("edits", [])
+ has_compact = False
+ has_other = False
+
+ for edit in edits:
+ edit_type = edit.get("type", "")
+ if edit_type == "compact_20260112":
+ has_compact = True
+ else:
+ has_other = True
+
+ # Add compact header if any compact edits exist
+ if has_compact:
+ beta_set.add(ANTHROPIC_BETA_HEADER_VALUES.COMPACT_2026_01_12.value)
+
+ # Add context management header if any other edits exist
+ if has_other:
+ beta_set.add(ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value)
+
def transform_request(
self,
model: str,
@@ -86,6 +120,11 @@ def transform_request(
beta_set = set(auto_betas)
if tool_search_used:
beta_set.add("tool-search-tool-2025-10-19") # Vertex requires this header for tool search
+
+ # Add context_management beta headers (compact and/or context-management)
+ context_management = optional_params.get("context_management")
+ if context_management:
+ self._add_context_management_beta_headers(beta_set, context_management)
if beta_set:
data["anthropic_beta"] = list(beta_set)
diff --git a/litellm/llms/vertex_ai/vertex_llm_base.py b/litellm/llms/vertex_ai/vertex_llm_base.py
index a185370e376..4613b6a5715 100644
--- a/litellm/llms/vertex_ai/vertex_llm_base.py
+++ b/litellm/llms/vertex_ai/vertex_llm_base.py
@@ -20,6 +20,7 @@
_get_vertex_url,
all_gemini_url_modes,
get_vertex_base_model_name,
+ get_vertex_base_url,
is_global_only_vertex_model,
)
@@ -200,12 +201,7 @@ def get_api_base(
) -> str:
if api_base:
return api_base
- elif vertex_location == "global":
- return "https://aiplatform.googleapis.com"
- elif vertex_location:
- return f"https://{vertex_location}-aiplatform.googleapis.com"
- else:
- return f"https://{self.get_default_vertex_location()}-aiplatform.googleapis.com"
+ return get_vertex_base_url(vertex_location or self.get_default_vertex_location())
@staticmethod
def create_vertex_url(
@@ -218,7 +214,8 @@ def create_vertex_url(
) -> str:
"""Return the base url for the vertex partner models"""
- api_base = api_base or f"https://{vertex_location}-aiplatform.googleapis.com"
+ if api_base is None:
+ api_base = get_vertex_base_url(vertex_location)
if partner == VertexPartnerProvider.llama:
return f"{api_base}/v1/projects/{vertex_project}/locations/{vertex_location}/endpoints/openapi/chat/completions"
elif partner == VertexPartnerProvider.mistralai:
@@ -247,11 +244,13 @@ def get_complete_vertex_url(
stream: Optional[bool],
model: str,
) -> str:
+ # Use get_vertex_region to handle global-only models
+ resolved_location = self.get_vertex_region(vertex_location, model)
api_base = self.get_api_base(
- api_base=custom_api_base, vertex_location=vertex_location
+ api_base=custom_api_base, vertex_location=resolved_location
)
default_api_base = VertexBase.create_vertex_url(
- vertex_location=vertex_location or "us-central1",
+ vertex_location=resolved_location,
vertex_project=vertex_project or project_id,
partner=partner,
stream=stream,
@@ -274,7 +273,7 @@ def get_complete_vertex_url(
url=default_api_base,
model=model,
vertex_project=vertex_project or project_id,
- vertex_location=vertex_location or "us-central1",
+ vertex_location=resolved_location,
vertex_api_version="v1", # Partner models typically use v1
)
return api_base
diff --git a/litellm/llms/xai/chat/transformation.py b/litellm/llms/xai/chat/transformation.py
index 245e10e45c1..21782fc6fbf 100644
--- a/litellm/llms/xai/chat/transformation.py
+++ b/litellm/llms/xai/chat/transformation.py
@@ -4,6 +4,7 @@
import litellm
from litellm._logging import verbose_logger
+from litellm.constants import XAI_API_BASE
from litellm.litellm_core_utils.prompt_templates.common_utils import (
filter_value_from_dict,
strip_name_from_messages,
@@ -14,8 +15,6 @@
from ...openai.chat.gpt_transformation import OpenAIGPTConfig
-XAI_API_BASE = "https://api.x.ai/v1"
-
class XAIChatConfig(OpenAIGPTConfig):
@property
diff --git a/litellm/llms/xai/realtime/__init__.py b/litellm/llms/xai/realtime/__init__.py
new file mode 100644
index 00000000000..3b0d345f2c2
--- /dev/null
+++ b/litellm/llms/xai/realtime/__init__.py
@@ -0,0 +1,5 @@
+"""xAI Realtime API handler."""
+
+from .handler import XAIRealtime
+
+__all__ = ["XAIRealtime"]
diff --git a/litellm/llms/xai/realtime/handler.py b/litellm/llms/xai/realtime/handler.py
new file mode 100644
index 00000000000..c79477ba1df
--- /dev/null
+++ b/litellm/llms/xai/realtime/handler.py
@@ -0,0 +1,38 @@
+"""
+This file contains the handler for xAI's Grok Voice Agent API `/v1/realtime` endpoint.
+
+xAI's Realtime API is fully OpenAI-compatible, so we inherit from OpenAIRealtime
+and only override the configuration differences.
+
+This requires websockets, and is currently only supported on LiteLLM Proxy.
+"""
+
+from litellm.constants import XAI_API_BASE
+
+from ...openai.realtime.handler import OpenAIRealtime
+
+
+class XAIRealtime(OpenAIRealtime):
+ """
+ Handler for xAI Grok Voice Agent API.
+
+ xAI's Realtime API uses the same WebSocket protocol as OpenAI but with:
+ - Different endpoint: wss://api.x.ai/v1/realtime (via _get_default_api_base)
+ - No OpenAI-Beta header required (via _get_additional_headers)
+ - Model: grok-4-1-fast-non-reasoning
+
+ All WebSocket logic is inherited from OpenAIRealtime.
+ """
+
+ def _get_default_api_base(self) -> str:
+ """xAI uses a different API base URL."""
+ return XAI_API_BASE
+
+ def _get_additional_headers(self, api_key: str) -> dict:
+ """
+ xAI does NOT require the OpenAI-Beta header.
+ Only send Authorization header.
+ """
+ return {
+ "Authorization": f"Bearer {api_key}",
+ }
diff --git a/litellm/llms/xai/responses/transformation.py b/litellm/llms/xai/responses/transformation.py
index 82b4771fb4d..95873aab846 100644
--- a/litellm/llms/xai/responses/transformation.py
+++ b/litellm/llms/xai/responses/transformation.py
@@ -2,6 +2,7 @@
import litellm
from litellm._logging import verbose_logger
+from litellm.constants import XAI_API_BASE
from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig
from litellm.secret_managers.main import get_secret_str
from litellm.types.llms.openai import ResponsesAPIOptionalRequestParams
@@ -16,8 +17,6 @@
else:
LiteLLMLoggingObj = Any
-XAI_API_BASE = "https://api.x.ai/v1"
-
class XAIResponsesAPIConfig(OpenAIResponsesAPIConfig):
"""
diff --git a/litellm/main.py b/litellm/main.py
index 13361c644cb..bca023e65ec 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -1199,6 +1199,13 @@ def completion( # type: ignore # noqa: PLR0915
headers = {}
if extra_headers is not None:
headers.update(extra_headers)
+ # Inject proxy auth headers if configured
+ if litellm.proxy_auth is not None:
+ try:
+ proxy_headers = litellm.proxy_auth.get_auth_headers()
+ headers.update(proxy_headers)
+ except Exception as e:
+ verbose_logger.warning(f"Failed to get proxy auth headers: {e}")
num_retries = kwargs.get(
"num_retries", None
) ## alt. param for 'max_retries'. Use this to pass retries w/ instructor.
@@ -2199,6 +2206,48 @@ def completion( # type: ignore # noqa: PLR0915
logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements
client=client,
)
+ elif custom_llm_provider == "a2a":
+ # A2A (Agent-to-Agent) Protocol
+ # Resolve agent configuration from registry if model format is "a2a/"
+ api_base, api_key, headers = litellm.A2AConfig.resolve_agent_config_from_registry(
+ model=model,
+ api_base=api_base,
+ api_key=api_key,
+ headers=headers,
+ optional_params=optional_params,
+ )
+
+ # Fall back to environment variables and defaults
+ api_base = api_base or litellm.api_base or get_secret_str("A2A_API_BASE")
+
+ if api_base is None:
+ raise Exception(
+ "api_base is required for A2A provider. "
+ "Either provide api_base parameter, set A2A_API_BASE environment variable, "
+ "or register the agent in the proxy with model='a2a/'."
+ )
+
+ headers = headers or litellm.headers
+
+ response = base_llm_http_handler.completion(
+ model=model,
+ stream=stream,
+ messages=messages,
+ acompletion=acompletion,
+ api_base=api_base,
+ model_response=model_response,
+ optional_params=optional_params,
+ litellm_params=litellm_params,
+ shared_session=shared_session,
+ custom_llm_provider=custom_llm_provider,
+ timeout=timeout,
+ headers=headers,
+ encoding=_get_encoding(),
+ api_key=api_key,
+ logging_obj=logging,
+ client=client,
+ provider_config=provider_config,
+ )
elif custom_llm_provider == "gigachat":
# GigaChat - Sber AI's LLM (Russia)
api_key = (
@@ -2455,6 +2504,20 @@ def completion( # type: ignore # noqa: PLR0915
headers = headers or litellm.headers
+ # Add GitHub Copilot headers (same as /responses endpoint does)
+ if custom_llm_provider == "github_copilot":
+ from litellm.llms.github_copilot.common_utils import (
+ get_copilot_default_headers,
+ )
+ from litellm.llms.github_copilot.authenticator import Authenticator
+
+ copilot_auth = Authenticator()
+ copilot_api_key = copilot_auth.get_api_key()
+ copilot_headers = get_copilot_default_headers(copilot_api_key)
+ if extra_headers:
+ copilot_headers.update(extra_headers)
+ extra_headers = copilot_headers
+
if extra_headers is not None:
optional_params["extra_headers"] = extra_headers
@@ -3113,8 +3176,8 @@ def completion( # type: ignore # noqa: PLR0915
api_key
or litellm.api_key
or litellm.openrouter_key
- or get_secret("OPENROUTER_API_KEY")
- or get_secret("OR_API_KEY")
+ or get_secret_str("OPENROUTER_API_KEY")
+ or get_secret_str("OR_API_KEY")
)
openrouter_site_url = get_secret("OR_SITE_URL") or "https://litellm.ai"
@@ -4555,6 +4618,13 @@ def embedding( # noqa: PLR0915
headers = {}
if extra_headers is not None:
headers.update(extra_headers)
+ # Inject proxy auth headers if configured
+ if litellm.proxy_auth is not None:
+ try:
+ proxy_headers = litellm.proxy_auth.get_auth_headers()
+ headers.update(proxy_headers)
+ except Exception as e:
+ verbose_logger.warning(f"Failed to get proxy auth headers: {e}")
### CUSTOM MODEL COST ###
input_cost_per_token = kwargs.get("input_cost_per_token", None)
output_cost_per_token = kwargs.get("output_cost_per_token", None)
@@ -4709,11 +4779,11 @@ def embedding( # noqa: PLR0915
litellm_params=litellm_params_dict,
)
elif (
- model in litellm.open_ai_embedding_models
- or custom_llm_provider == "openai"
+ custom_llm_provider == "openai"
or custom_llm_provider == "together_ai"
or custom_llm_provider == "nvidia_nim"
or custom_llm_provider == "litellm_proxy"
+ or (model in litellm.open_ai_embedding_models and custom_llm_provider is None)
):
api_base = (
api_base
@@ -4884,8 +4954,8 @@ def embedding( # noqa: PLR0915
api_key
or litellm.api_key
or litellm.openrouter_key
- or get_secret("OPENROUTER_API_KEY")
- or get_secret("OR_API_KEY")
+ or get_secret_str("OPENROUTER_API_KEY")
+ or get_secret_str("OR_API_KEY")
)
openrouter_site_url = get_secret("OR_SITE_URL") or "https://litellm.ai"
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 0f84bba941d..4704549e716 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -744,12 +744,13 @@
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": true,
- "tool_use_system_prompt_tokens": 346
+ "tool_use_system_prompt_tokens": 346,
+ "supports_native_streaming": true
},
"anthropic.claude-3-5-sonnet-20240620-v1:0": {
"input_cost_per_token": 3e-06,
"litellm_provider": "bedrock",
- "max_input_tokens": 200000,
+ "max_input_tokens": 1000000,
"max_output_tokens": 4096,
"max_tokens": 4096,
"mode": "chat",
@@ -758,14 +759,22 @@
"supports_pdf_input": true,
"supports_response_schema": true,
"supports_tool_choice": true,
- "supports_vision": true
+ "supports_vision": true,
+ "input_cost_per_token_above_200k_tokens": 6e-06,
+ "output_cost_per_token_above_200k_tokens": 3e-05,
+ "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06,
+ "cache_read_input_token_cost_above_200k_tokens": 6e-07,
+ "cache_creation_input_token_cost_above_1hr": 7.5e-06,
+ "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.5e-05,
+ "cache_creation_input_token_cost": 3.75e-06,
+ "cache_read_input_token_cost": 3e-07
},
"anthropic.claude-3-5-sonnet-20241022-v2:0": {
"cache_creation_input_token_cost": 3.75e-06,
"cache_read_input_token_cost": 3e-07,
"input_cost_per_token": 3e-06,
"litellm_provider": "bedrock",
- "max_input_tokens": 200000,
+ "max_input_tokens": 1000000,
"max_output_tokens": 8192,
"max_tokens": 8192,
"mode": "chat",
@@ -777,7 +786,13 @@
"supports_prompt_caching": true,
"supports_response_schema": true,
"supports_tool_choice": true,
- "supports_vision": true
+ "supports_vision": true,
+ "input_cost_per_token_above_200k_tokens": 6e-06,
+ "output_cost_per_token_above_200k_tokens": 3e-05,
+ "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06,
+ "cache_read_input_token_cost_above_200k_tokens": 6e-07,
+ "cache_creation_input_token_cost_above_1hr": 7.5e-06,
+ "cache_creation_input_token_cost_above_1hr_above_200k_tokens": 1.5e-05
},
"anthropic.claude-3-7-sonnet-20240620-v1:0": {
"cache_creation_input_token_cost": 4.5e-06,
@@ -948,6 +963,336 @@
"supports_vision": true,
"tool_use_system_prompt_tokens": 159
},
+ "anthropic.claude-opus-4-6-v1": {
+ "cache_creation_input_token_cost": 6.25e-06,
+ "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
+ "cache_read_input_token_cost": 5e-07,
+ "cache_read_input_token_cost_above_200k_tokens": 1e-06,
+ "input_cost_per_token": 5e-06,
+ "input_cost_per_token_above_200k_tokens": 1e-05,
+ "litellm_provider": "bedrock_converse",
+ "max_input_tokens": 1000000,
+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
+ "mode": "chat",
+ "output_cost_per_token": 2.5e-05,
+ "output_cost_per_token_above_200k_tokens": 3.75e-05,
+ "search_context_cost_per_query": {
+ "search_context_size_high": 0.01,
+ "search_context_size_low": 0.01,
+ "search_context_size_medium": 0.01
+ },
+ "supports_assistant_prefill": false,
+ "supports_computer_use": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_reasoning": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 346
+ },
+ "anthropic.claude-opus-4-6-v1": {
+ "cache_creation_input_token_cost": 6.25e-06,
+ "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
+ "cache_read_input_token_cost": 5e-07,
+ "cache_read_input_token_cost_above_200k_tokens": 1e-06,
+ "input_cost_per_token": 5e-06,
+ "input_cost_per_token_above_200k_tokens": 1e-05,
+ "litellm_provider": "bedrock_converse",
+ "max_input_tokens": 1000000,
+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
+ "mode": "chat",
+ "output_cost_per_token": 2.5e-05,
+ "output_cost_per_token_above_200k_tokens": 3.75e-05,
+ "search_context_cost_per_query": {
+ "search_context_size_high": 0.01,
+ "search_context_size_low": 0.01,
+ "search_context_size_medium": 0.01
+ },
+ "supports_assistant_prefill": false,
+ "supports_computer_use": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_reasoning": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 346
+ },
+ "global.anthropic.claude-opus-4-6-v1": {
+ "cache_creation_input_token_cost": 6.25e-06,
+ "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
+ "cache_read_input_token_cost": 5e-07,
+ "cache_read_input_token_cost_above_200k_tokens": 1e-06,
+ "input_cost_per_token": 5e-06,
+ "input_cost_per_token_above_200k_tokens": 1e-05,
+ "litellm_provider": "bedrock_converse",
+ "max_input_tokens": 1000000,
+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
+ "mode": "chat",
+ "output_cost_per_token": 2.5e-05,
+ "output_cost_per_token_above_200k_tokens": 3.75e-05,
+ "search_context_cost_per_query": {
+ "search_context_size_high": 0.01,
+ "search_context_size_low": 0.01,
+ "search_context_size_medium": 0.01
+ },
+ "supports_assistant_prefill": false,
+ "supports_computer_use": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_reasoning": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 346
+ },
+ "global.anthropic.claude-opus-4-6-v1": {
+ "cache_creation_input_token_cost": 6.25e-06,
+ "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
+ "cache_read_input_token_cost": 5e-07,
+ "cache_read_input_token_cost_above_200k_tokens": 1e-06,
+ "input_cost_per_token": 5e-06,
+ "input_cost_per_token_above_200k_tokens": 1e-05,
+ "litellm_provider": "bedrock_converse",
+ "max_input_tokens": 1000000,
+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
+ "mode": "chat",
+ "output_cost_per_token": 2.5e-05,
+ "output_cost_per_token_above_200k_tokens": 3.75e-05,
+ "search_context_cost_per_query": {
+ "search_context_size_high": 0.01,
+ "search_context_size_low": 0.01,
+ "search_context_size_medium": 0.01
+ },
+ "supports_assistant_prefill": false,
+ "supports_computer_use": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_reasoning": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 346
+ },
+ "us.anthropic.claude-opus-4-6-v1:0": {
+ "cache_creation_input_token_cost": 6.875e-06,
+ "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05,
+ "cache_read_input_token_cost": 5.5e-07,
+ "cache_read_input_token_cost_above_200k_tokens": 1.1e-06,
+ "input_cost_per_token": 5.5e-06,
+ "input_cost_per_token_above_200k_tokens": 1.1e-05,
+ "litellm_provider": "bedrock_converse",
+ "max_input_tokens": 1000000,
+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
+ "mode": "chat",
+ "output_cost_per_token": 2.75e-05,
+ "output_cost_per_token_above_200k_tokens": 4.125e-05,
+ "search_context_cost_per_query": {
+ "search_context_size_high": 0.01,
+ "search_context_size_low": 0.01,
+ "search_context_size_medium": 0.01
+ },
+ "supports_assistant_prefill": false,
+ "supports_computer_use": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_reasoning": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 346
+ },
+ "us.anthropic.claude-opus-4-6-v1": {
+ "cache_creation_input_token_cost": 6.875e-06,
+ "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05,
+ "cache_read_input_token_cost": 5.5e-07,
+ "cache_read_input_token_cost_above_200k_tokens": 1.1e-06,
+ "input_cost_per_token": 5.5e-06,
+ "input_cost_per_token_above_200k_tokens": 1.1e-05,
+ "litellm_provider": "bedrock_converse",
+ "max_input_tokens": 1000000,
+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
+ "mode": "chat",
+ "output_cost_per_token": 2.75e-05,
+ "output_cost_per_token_above_200k_tokens": 4.125e-05,
+ "search_context_cost_per_query": {
+ "search_context_size_high": 0.01,
+ "search_context_size_low": 0.01,
+ "search_context_size_medium": 0.01
+ },
+ "supports_assistant_prefill": false,
+ "supports_computer_use": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_reasoning": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 346
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+ "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05,
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+ "tool_use_system_prompt_tokens": 346
+ },
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"cache_read_input_token_cost": 3e-07,
@@ -1429,6 +1774,33 @@
"supports_tool_choice": true,
"supports_vision": true
},
+ "azure_ai/claude-opus-4-6": {
+ "input_cost_per_token": 5e-06,
+ "output_cost_per_token": 2.5e-05,
+ "litellm_provider": "azure_ai",
+ "max_input_tokens": 200000,
+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
+ "mode": "chat",
+ "search_context_cost_per_query": {
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+ "search_context_size_medium": 0.01
+ },
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+ "cache_creation_input_token_cost_above_1hr": 1e-05,
+ "cache_read_input_token_cost": 5e-07,
+ "supports_assistant_prefill": false,
+ "supports_computer_use": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_reasoning": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 159
+ },
"azure_ai/claude-opus-4-1": {
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"cache_creation_input_token_cost_above_1hr": 3e-05,
@@ -6715,13 +7087,13 @@
"supports_tool_choice": true
},
"cerebras/gpt-oss-120b": {
- "input_cost_per_token": 2.5e-07,
+ "input_cost_per_token": 3.5e-07,
"litellm_provider": "cerebras",
"max_input_tokens": 131072,
"max_output_tokens": 32768,
"max_tokens": 32768,
"mode": "chat",
- "output_cost_per_token": 6.9e-07,
+ "output_cost_per_token": 7.5e-07,
"source": "https://www.cerebras.ai/blog/openai-gpt-oss-120b-runs-fastest-on-cerebras",
"supports_function_calling": true,
"supports_parallel_function_calling": true,
@@ -6739,6 +7111,7 @@
"output_cost_per_token": 8e-07,
"source": "https://inference-docs.cerebras.ai/support/pricing",
"supports_function_calling": true,
+ "supports_reasoning": true,
"supports_tool_choice": true
},
"cerebras/zai-glm-4.6": {
@@ -7439,6 +7812,130 @@
"supports_vision": true,
"tool_use_system_prompt_tokens": 159
},
+ "claude-opus-4-6": {
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+ "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
+ "cache_creation_input_token_cost_above_1hr": 1e-05,
+ "cache_read_input_token_cost": 5e-07,
+ "cache_read_input_token_cost_above_200k_tokens": 1e-06,
+ "input_cost_per_token": 5e-06,
+ "input_cost_per_token_above_200k_tokens": 1e-05,
+ "litellm_provider": "anthropic",
+ "max_input_tokens": 1000000,
+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
+ "mode": "chat",
+ "output_cost_per_token": 2.5e-05,
+ "output_cost_per_token_above_200k_tokens": 3.75e-05,
+ "search_context_cost_per_query": {
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+ "supports_computer_use": true,
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+ "supports_pdf_input": true,
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+ "supports_reasoning": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 346
+ },
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+ "cache_creation_input_token_cost": 6.875e-06,
+ "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05,
+ "cache_creation_input_token_cost_above_1hr": 1.1e-05,
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+ "cache_read_input_token_cost_above_200k_tokens": 1.1e-06,
+ "input_cost_per_token": 5.5e-06,
+ "input_cost_per_token_above_200k_tokens": 1.1e-05,
+ "litellm_provider": "anthropic",
+ "max_input_tokens": 200000,
+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
+ "mode": "chat",
+ "output_cost_per_token": 2.75e-05,
+ "output_cost_per_token_above_200k_tokens": 4.125e-05,
+ "search_context_cost_per_query": {
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+ "supports_computer_use": true,
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+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 346
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+ "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
+ "cache_creation_input_token_cost_above_1hr": 1e-05,
+ "cache_read_input_token_cost": 5e-07,
+ "cache_read_input_token_cost_above_200k_tokens": 1e-06,
+ "input_cost_per_token": 5e-06,
+ "input_cost_per_token_above_200k_tokens": 1e-05,
+ "litellm_provider": "anthropic",
+ "max_input_tokens": 1000000,
+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
+ "mode": "chat",
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+ "search_context_cost_per_query": {
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+ "supports_tool_choice": true,
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+ "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05,
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+ "input_cost_per_token_above_200k_tokens": 1.1e-05,
+ "litellm_provider": "anthropic",
+ "max_input_tokens": 200000,
+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
+ "mode": "chat",
+ "output_cost_per_token": 2.75e-05,
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+ "search_context_cost_per_query": {
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+ "search_context_size_low": 0.01,
+ "search_context_size_medium": 0.01
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+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 346
+ },
"claude-sonnet-4-20250514": {
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"cache_creation_input_token_cost": 3.75e-06,
@@ -10559,6 +11056,32 @@
"/v1/audio/transcriptions"
]
},
+ "elevenlabs/eleven_v3": {
+ "input_cost_per_character": 0.00018,
+ "litellm_provider": "elevenlabs",
+ "metadata": {
+ "calculation": "$0.18/1000 characters (Scale plan pricing, 1 credit per character)",
+ "notes": "ElevenLabs Eleven v3 - most expressive TTS model with 70+ languages and audio tags support"
+ },
+ "mode": "audio_speech",
+ "source": "https://elevenlabs.io/pricing",
+ "supported_endpoints": [
+ "/v1/audio/speech"
+ ]
+ },
+ "elevenlabs/eleven_multilingual_v2": {
+ "input_cost_per_character": 0.00018,
+ "litellm_provider": "elevenlabs",
+ "metadata": {
+ "calculation": "$0.18/1000 characters (Scale plan pricing, 1 credit per character)",
+ "notes": "ElevenLabs Eleven Multilingual v2 - default TTS model with 29 languages support"
+ },
+ "mode": "audio_speech",
+ "source": "https://elevenlabs.io/pricing",
+ "supported_endpoints": [
+ "/v1/audio/speech"
+ ]
+ },
"embed-english-light-v2.0": {
"input_cost_per_token": 1e-07,
"litellm_provider": "cohere",
@@ -12835,6 +13358,40 @@
"supports_vision": true,
"supports_web_search": true
},
+ "deep-research-pro-preview-12-2025": {
+ "input_cost_per_image": 0.0011,
+ "input_cost_per_token": 2e-06,
+ "input_cost_per_token_batches": 1e-06,
+ "litellm_provider": "vertex_ai-language-models",
+ "max_input_tokens": 65536,
+ "max_output_tokens": 32768,
+ "max_tokens": 32768,
+ "mode": "image_generation",
+ "output_cost_per_image": 0.134,
+ "output_cost_per_image_token": 0.00012,
+ "output_cost_per_token": 1.2e-05,
+ "output_cost_per_token_batches": 6e-06,
+ "source": "https://ai.google.dev/gemini-api/docs/pricing",
+ "supported_endpoints": [
+ "/v1/chat/completions",
+ "/v1/completions",
+ "/v1/batch"
+ ],
+ "supported_modalities": [
+ "text",
+ "image"
+ ],
+ "supported_output_modalities": [
+ "text",
+ "image"
+ ],
+ "supports_function_calling": false,
+ "supports_prompt_caching": true,
+ "supports_response_schema": true,
+ "supports_system_messages": true,
+ "supports_vision": true,
+ "supports_web_search": true
+ },
"gemini-2.5-flash-lite": {
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"input_cost_per_audio_token": 3e-07,
@@ -13289,7 +13846,8 @@
"supports_tool_choice": true,
"supports_video_input": true,
"supports_vision": true,
- "supports_web_search": true
+ "supports_web_search": true,
+ "supports_native_streaming": true
},
"vertex_ai/gemini-3-pro-preview": {
"cache_read_input_token_cost": 2e-07,
@@ -13337,7 +13895,8 @@
"supports_tool_choice": true,
"supports_video_input": true,
"supports_vision": true,
- "supports_web_search": true
+ "supports_web_search": true,
+ "supports_native_streaming": true
},
"vertex_ai/gemini-3-flash-preview": {
"cache_read_input_token_cost": 5e-08,
@@ -13380,7 +13939,8 @@
"supports_tool_choice": true,
"supports_video_input": true,
"supports_vision": true,
- "supports_web_search": true
+ "supports_web_search": true,
+ "supports_native_streaming": true
},
"gemini-2.5-pro-exp-03-25": {
"cache_read_input_token_cost": 1.25e-07,
@@ -14747,25 +15307,22 @@
"supports_vision": true,
"supports_web_search": true
},
- "gemini/gemini-2.5-flash-lite": {
- "cache_read_input_token_cost": 1e-08,
- "input_cost_per_audio_token": 3e-07,
- "input_cost_per_token": 1e-07,
+ "gemini/deep-research-pro-preview-12-2025": {
+ "input_cost_per_image": 0.0011,
+ "input_cost_per_token": 2e-06,
+ "input_cost_per_token_batches": 1e-06,
"litellm_provider": "gemini",
- "max_audio_length_hours": 8.4,
- "max_audio_per_prompt": 1,
- "max_images_per_prompt": 3000,
- "max_input_tokens": 1048576,
- "max_output_tokens": 65535,
- "max_pdf_size_mb": 30,
- "max_tokens": 65535,
- "max_video_length": 1,
- "max_videos_per_prompt": 10,
- "mode": "chat",
- "output_cost_per_reasoning_token": 4e-07,
- "output_cost_per_token": 4e-07,
- "rpm": 15,
- "source": "https://ai.google.dev/gemini-api/docs/models#gemini-2.5-flash-lite",
+ "max_input_tokens": 65536,
+ "max_output_tokens": 32768,
+ "max_tokens": 32768,
+ "mode": "image_generation",
+ "output_cost_per_image": 0.134,
+ "output_cost_per_image_token": 0.00012,
+ "output_cost_per_token": 1.2e-05,
+ "rpm": 1000,
+ "tpm": 4000000,
+ "output_cost_per_token_batches": 6e-06,
+ "source": "https://ai.google.dev/gemini-api/docs/pricing",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/completions",
@@ -14773,28 +15330,20 @@
],
"supported_modalities": [
"text",
- "image",
- "audio",
- "video"
+ "image"
],
"supported_output_modalities": [
- "text"
+ "text",
+ "image"
],
- "supports_audio_output": false,
- "supports_function_calling": true,
- "supports_parallel_function_calling": true,
- "supports_pdf_input": true,
+ "supports_function_calling": false,
"supports_prompt_caching": true,
- "supports_reasoning": true,
"supports_response_schema": true,
"supports_system_messages": true,
- "supports_tool_choice": true,
- "supports_url_context": true,
"supports_vision": true,
- "supports_web_search": true,
- "tpm": 250000
+ "supports_web_search": true
},
- "gemini/gemini-2.5-flash-lite-preview-09-2025": {
+ "gemini/gemini-2.5-flash-lite": {
"cache_read_input_token_cost": 1e-08,
"input_cost_per_audio_token": 3e-07,
"input_cost_per_token": 1e-07,
@@ -14812,7 +15361,7 @@
"output_cost_per_reasoning_token": 4e-07,
"output_cost_per_token": 4e-07,
"rpm": 15,
- "source": "https://developers.googleblog.com/en/continuing-to-bring-you-our-latest-models-with-an-improved-gemini-2-5-flash-and-flash-lite-release/",
+ "source": "https://ai.google.dev/gemini-api/docs/models#gemini-2.5-flash-lite",
"supported_endpoints": [
"/v1/chat/completions",
"/v1/completions",
@@ -14841,10 +15390,10 @@
"supports_web_search": true,
"tpm": 250000
},
- "gemini/gemini-2.5-flash-preview-09-2025": {
- "cache_read_input_token_cost": 7.5e-08,
- "input_cost_per_audio_token": 1e-06,
- "input_cost_per_token": 3e-07,
+ "gemini/gemini-2.5-flash-lite-preview-09-2025": {
+ "cache_read_input_token_cost": 1e-08,
+ "input_cost_per_audio_token": 3e-07,
+ "input_cost_per_token": 1e-07,
"litellm_provider": "gemini",
"max_audio_length_hours": 8.4,
"max_audio_per_prompt": 1,
@@ -14856,8 +15405,8 @@
"max_video_length": 1,
"max_videos_per_prompt": 10,
"mode": "chat",
- "output_cost_per_reasoning_token": 2.5e-06,
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@@ -14888,7 +15437,7 @@
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@@ -15331,6 +15927,7 @@
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@@ -15376,7 +15973,8 @@
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@@ -24314,6 +24926,31 @@
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@@ -26319,13 +26956,13 @@
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@@ -27084,6 +27721,34 @@
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"max_tokens": 8192,
"mode": "chat",
- "output_cost_per_token": 7.2e-07
+ "output_cost_per_token": 7.2e-07,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/meta/llama-4-maverick": {
"input_cost_per_token": 2e-07,
@@ -28158,7 +28906,8 @@
"max_output_tokens": 8192,
"max_tokens": 8192,
"mode": "chat",
- "output_cost_per_token": 6e-07
+ "output_cost_per_token": 6e-07,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/meta/llama-4-scout": {
"input_cost_per_token": 1e-07,
@@ -28167,7 +28916,10 @@
"max_output_tokens": 8192,
"max_tokens": 8192,
"mode": "chat",
- "output_cost_per_token": 3e-07
+ "output_cost_per_token": 3e-07,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/mistral/codestral": {
"input_cost_per_token": 3e-07,
@@ -28176,7 +28928,9 @@
"max_output_tokens": 4000,
"max_tokens": 4000,
"mode": "chat",
- "output_cost_per_token": 9e-07
+ "output_cost_per_token": 9e-07,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/mistral/codestral-embed": {
"input_cost_per_token": 1.5e-07,
@@ -28194,7 +28948,10 @@
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
- "output_cost_per_token": 2.8e-07
+ "output_cost_per_token": 2.8e-07,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true
},
"vercel_ai_gateway/mistral/magistral-medium": {
"input_cost_per_token": 2e-06,
@@ -28203,7 +28960,10 @@
"max_output_tokens": 64000,
"max_tokens": 64000,
"mode": "chat",
- "output_cost_per_token": 5e-06
+ "output_cost_per_token": 5e-06,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true
},
"vercel_ai_gateway/mistral/magistral-small": {
"input_cost_per_token": 5e-07,
@@ -28212,7 +28972,8 @@
"max_output_tokens": 64000,
"max_tokens": 64000,
"mode": "chat",
- "output_cost_per_token": 1.5e-06
+ "output_cost_per_token": 1.5e-06,
+ "supports_function_calling": true
},
"vercel_ai_gateway/mistral/ministral-3b": {
"input_cost_per_token": 4e-08,
@@ -28221,7 +28982,9 @@
"max_output_tokens": 4000,
"max_tokens": 4000,
"mode": "chat",
- "output_cost_per_token": 4e-08
+ "output_cost_per_token": 4e-08,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/mistral/ministral-8b": {
"input_cost_per_token": 1e-07,
@@ -28230,7 +28993,10 @@
"max_output_tokens": 4000,
"max_tokens": 4000,
"mode": "chat",
- "output_cost_per_token": 1e-07
+ "output_cost_per_token": 1e-07,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/mistral/mistral-embed": {
"input_cost_per_token": 1e-07,
@@ -28248,7 +29014,9 @@
"max_output_tokens": 4000,
"max_tokens": 4000,
"mode": "chat",
- "output_cost_per_token": 6e-06
+ "output_cost_per_token": 6e-06,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/mistral/mistral-saba-24b": {
"input_cost_per_token": 7.9e-07,
@@ -28266,7 +29034,10 @@
"max_output_tokens": 4000,
"max_tokens": 4000,
"mode": "chat",
- "output_cost_per_token": 3e-07
+ "output_cost_per_token": 3e-07,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true
},
"vercel_ai_gateway/mistral/mixtral-8x22b-instruct": {
"input_cost_per_token": 1.2e-06,
@@ -28275,7 +29046,8 @@
"max_output_tokens": 2048,
"max_tokens": 2048,
"mode": "chat",
- "output_cost_per_token": 1.2e-06
+ "output_cost_per_token": 1.2e-06,
+ "supports_function_calling": true
},
"vercel_ai_gateway/mistral/pixtral-12b": {
"input_cost_per_token": 1.5e-07,
@@ -28284,7 +29056,11 @@
"max_output_tokens": 4000,
"max_tokens": 4000,
"mode": "chat",
- "output_cost_per_token": 1.5e-07
+ "output_cost_per_token": 1.5e-07,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true
},
"vercel_ai_gateway/mistral/pixtral-large": {
"input_cost_per_token": 2e-06,
@@ -28293,7 +29069,11 @@
"max_output_tokens": 4000,
"max_tokens": 4000,
"mode": "chat",
- "output_cost_per_token": 6e-06
+ "output_cost_per_token": 6e-06,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true
},
"vercel_ai_gateway/moonshotai/kimi-k2": {
"input_cost_per_token": 5.5e-07,
@@ -28302,7 +29082,9 @@
"max_output_tokens": 16384,
"max_tokens": 16384,
"mode": "chat",
- "output_cost_per_token": 2.2e-06
+ "output_cost_per_token": 2.2e-06,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/morph/morph-v3-fast": {
"input_cost_per_token": 8e-07,
@@ -28329,7 +29111,9 @@
"max_output_tokens": 4096,
"max_tokens": 4096,
"mode": "chat",
- "output_cost_per_token": 1.5e-06
+ "output_cost_per_token": 1.5e-06,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/openai/gpt-3.5-turbo-instruct": {
"input_cost_per_token": 1.5e-06,
@@ -28347,7 +29131,10 @@
"max_output_tokens": 4096,
"max_tokens": 4096,
"mode": "chat",
- "output_cost_per_token": 3e-05
+ "output_cost_per_token": 3e-05,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/openai/gpt-4.1": {
"cache_creation_input_token_cost": 0.0,
@@ -28358,7 +29145,11 @@
"max_output_tokens": 32768,
"max_tokens": 32768,
"mode": "chat",
- "output_cost_per_token": 8e-06
+ "output_cost_per_token": 8e-06,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true
},
"vercel_ai_gateway/openai/gpt-4.1-mini": {
"cache_creation_input_token_cost": 0.0,
@@ -28369,7 +29160,11 @@
"max_output_tokens": 32768,
"max_tokens": 32768,
"mode": "chat",
- "output_cost_per_token": 1.6e-06
+ "output_cost_per_token": 1.6e-06,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true
},
"vercel_ai_gateway/openai/gpt-4.1-nano": {
"cache_creation_input_token_cost": 0.0,
@@ -28380,7 +29175,11 @@
"max_output_tokens": 32768,
"max_tokens": 32768,
"mode": "chat",
- "output_cost_per_token": 4e-07
+ "output_cost_per_token": 4e-07,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true
},
"vercel_ai_gateway/openai/gpt-4o": {
"cache_creation_input_token_cost": 0.0,
@@ -28391,7 +29190,11 @@
"max_output_tokens": 16384,
"max_tokens": 16384,
"mode": "chat",
- "output_cost_per_token": 1e-05
+ "output_cost_per_token": 1e-05,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true
},
"vercel_ai_gateway/openai/gpt-4o-mini": {
"cache_creation_input_token_cost": 0.0,
@@ -28402,7 +29205,11 @@
"max_output_tokens": 16384,
"max_tokens": 16384,
"mode": "chat",
- "output_cost_per_token": 6e-07
+ "output_cost_per_token": 6e-07,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true
},
"vercel_ai_gateway/openai/o1": {
"cache_creation_input_token_cost": 0.0,
@@ -28413,7 +29220,11 @@
"max_output_tokens": 100000,
"max_tokens": 100000,
"mode": "chat",
- "output_cost_per_token": 6e-05
+ "output_cost_per_token": 6e-05,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true
},
"vercel_ai_gateway/openai/o3": {
"cache_creation_input_token_cost": 0.0,
@@ -28424,7 +29235,11 @@
"max_output_tokens": 100000,
"max_tokens": 100000,
"mode": "chat",
- "output_cost_per_token": 8e-06
+ "output_cost_per_token": 8e-06,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true
},
"vercel_ai_gateway/openai/o3-mini": {
"cache_creation_input_token_cost": 0.0,
@@ -28435,7 +29250,10 @@
"max_output_tokens": 100000,
"max_tokens": 100000,
"mode": "chat",
- "output_cost_per_token": 4.4e-06
+ "output_cost_per_token": 4.4e-06,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true
},
"vercel_ai_gateway/openai/o4-mini": {
"cache_creation_input_token_cost": 0.0,
@@ -28446,7 +29264,11 @@
"max_output_tokens": 100000,
"max_tokens": 100000,
"mode": "chat",
- "output_cost_per_token": 4.4e-06
+ "output_cost_per_token": 4.4e-06,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true,
+ "supports_response_schema": true
},
"vercel_ai_gateway/openai/text-embedding-3-large": {
"input_cost_per_token": 1.3e-07,
@@ -28518,7 +29340,10 @@
"max_output_tokens": 32000,
"max_tokens": 32000,
"mode": "chat",
- "output_cost_per_token": 1.5e-05
+ "output_cost_per_token": 1.5e-05,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/vercel/v0-1.5-md": {
"input_cost_per_token": 3e-06,
@@ -28527,7 +29352,10 @@
"max_output_tokens": 32768,
"max_tokens": 32768,
"mode": "chat",
- "output_cost_per_token": 1.5e-05
+ "output_cost_per_token": 1.5e-05,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/xai/grok-2": {
"input_cost_per_token": 2e-06,
@@ -28536,7 +29364,9 @@
"max_output_tokens": 4000,
"max_tokens": 4000,
"mode": "chat",
- "output_cost_per_token": 1e-05
+ "output_cost_per_token": 1e-05,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/xai/grok-2-vision": {
"input_cost_per_token": 2e-06,
@@ -28545,7 +29375,10 @@
"max_output_tokens": 32768,
"max_tokens": 32768,
"mode": "chat",
- "output_cost_per_token": 1e-05
+ "output_cost_per_token": 1e-05,
+ "supports_vision": true,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/xai/grok-3": {
"input_cost_per_token": 3e-06,
@@ -28554,7 +29387,9 @@
"max_output_tokens": 131072,
"max_tokens": 131072,
"mode": "chat",
- "output_cost_per_token": 1.5e-05
+ "output_cost_per_token": 1.5e-05,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/xai/grok-3-fast": {
"input_cost_per_token": 5e-06,
@@ -28563,7 +29398,8 @@
"max_output_tokens": 131072,
"max_tokens": 131072,
"mode": "chat",
- "output_cost_per_token": 2.5e-05
+ "output_cost_per_token": 2.5e-05,
+ "supports_function_calling": true
},
"vercel_ai_gateway/xai/grok-3-mini": {
"input_cost_per_token": 3e-07,
@@ -28572,7 +29408,9 @@
"max_output_tokens": 131072,
"max_tokens": 131072,
"mode": "chat",
- "output_cost_per_token": 5e-07
+ "output_cost_per_token": 5e-07,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/xai/grok-3-mini-fast": {
"input_cost_per_token": 6e-07,
@@ -28581,7 +29419,9 @@
"max_output_tokens": 131072,
"max_tokens": 131072,
"mode": "chat",
- "output_cost_per_token": 4e-06
+ "output_cost_per_token": 4e-06,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/xai/grok-4": {
"input_cost_per_token": 3e-06,
@@ -28590,7 +29430,9 @@
"max_output_tokens": 256000,
"max_tokens": 256000,
"mode": "chat",
- "output_cost_per_token": 1.5e-05
+ "output_cost_per_token": 1.5e-05,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/zai/glm-4.5": {
"input_cost_per_token": 6e-07,
@@ -28599,7 +29441,9 @@
"max_output_tokens": 131072,
"max_tokens": 131072,
"mode": "chat",
- "output_cost_per_token": 2.2e-06
+ "output_cost_per_token": 2.2e-06,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/zai/glm-4.5-air": {
"input_cost_per_token": 2e-07,
@@ -28608,7 +29452,9 @@
"max_output_tokens": 96000,
"max_tokens": 96000,
"mode": "chat",
- "output_cost_per_token": 1.1e-06
+ "output_cost_per_token": 1.1e-06,
+ "supports_function_calling": true,
+ "supports_tool_choice": true
},
"vercel_ai_gateway/zai/glm-4.6": {
"litellm_provider": "vercel_ai_gateway",
@@ -28676,7 +29522,9 @@
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
- "supports_tool_choice": true
+ "supports_tool_choice": true,
+ "supports_native_streaming": true,
+ "supports_vision": true
},
"vertex_ai/claude-3-5-sonnet": {
"input_cost_per_token": 3e-06,
@@ -28947,7 +29795,38 @@
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": true,
- "tool_use_system_prompt_tokens": 159
+ "tool_use_system_prompt_tokens": 159,
+ "supports_native_streaming": true
+ },
+ "vertex_ai/claude-opus-4-6": {
+ "cache_creation_input_token_cost": 6.25e-06,
+ "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05,
+ "cache_read_input_token_cost": 5e-07,
+ "cache_read_input_token_cost_above_200k_tokens": 1e-06,
+ "input_cost_per_token": 5e-06,
+ "input_cost_per_token_above_200k_tokens": 1e-05,
+ "litellm_provider": "vertex_ai-anthropic_models",
+ "max_input_tokens": 1000000,
+ "max_output_tokens": 128000,
+ "max_tokens": 128000,
+ "mode": "chat",
+ "output_cost_per_token": 2.5e-05,
+ "output_cost_per_token_above_200k_tokens": 3.75e-05,
+ "search_context_cost_per_query": {
+ "search_context_size_high": 0.01,
+ "search_context_size_low": 0.01,
+ "search_context_size_medium": 0.01
+ },
+ "supports_assistant_prefill": false,
+ "supports_computer_use": true,
+ "supports_function_calling": true,
+ "supports_pdf_input": true,
+ "supports_prompt_caching": true,
+ "supports_reasoning": true,
+ "supports_response_schema": true,
+ "supports_tool_choice": true,
+ "supports_vision": true,
+ "tool_use_system_prompt_tokens": 346
},
"vertex_ai/claude-sonnet-4-5": {
"cache_creation_input_token_cost": 3.75e-06,
@@ -28999,7 +29878,8 @@
"supports_reasoning": true,
"supports_response_schema": true,
"supports_tool_choice": true,
- "supports_vision": true
+ "supports_vision": true,
+ "supports_native_streaming": true
},
"vertex_ai/claude-opus-4@20250514": {
"cache_creation_input_token_cost": 1.875e-05,
@@ -29281,6 +30161,21 @@
"output_cost_per_token_batches": 6e-06,
"source": "https://docs.cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/3-pro-image"
},
+ "vertex_ai/deep-research-pro-preview-12-2025": {
+ "input_cost_per_image": 0.0011,
+ "input_cost_per_token": 2e-06,
+ "input_cost_per_token_batches": 1e-06,
+ "litellm_provider": "vertex_ai-language-models",
+ "max_input_tokens": 65536,
+ "max_output_tokens": 32768,
+ "max_tokens": 32768,
+ "mode": "image_generation",
+ "output_cost_per_image": 0.134,
+ "output_cost_per_image_token": 0.00012,
+ "output_cost_per_token": 1.2e-05,
+ "output_cost_per_token_batches": 6e-06,
+ "source": "https://docs.cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/3-pro-image"
+ },
"vertex_ai/imagegeneration@006": {
"litellm_provider": "vertex_ai-image-models",
"mode": "image_generation",
@@ -29770,6 +30665,9 @@
"mode": "chat",
"output_cost_per_token": 1e-06,
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing",
+ "supported_regions": [
+ "global"
+ ],
"supports_function_calling": true,
"supports_tool_choice": true
},
@@ -29782,6 +30680,9 @@
"mode": "chat",
"output_cost_per_token": 4e-06,
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing",
+ "supported_regions": [
+ "global"
+ ],
"supports_function_calling": true,
"supports_tool_choice": true
},
@@ -29794,6 +30695,9 @@
"mode": "chat",
"output_cost_per_token": 1.2e-06,
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing",
+ "supported_regions": [
+ "global"
+ ],
"supports_function_calling": true,
"supports_tool_choice": true
},
@@ -29806,6 +30710,9 @@
"mode": "chat",
"output_cost_per_token": 1.2e-06,
"source": "https://cloud.google.com/vertex-ai/generative-ai/pricing",
+ "supported_regions": [
+ "global"
+ ],
"supports_function_calling": true,
"supports_tool_choice": true
},
@@ -34754,4 +35661,4 @@
"output_cost_per_token": 0,
"supports_reasoning": true
}
-}
\ No newline at end of file
+}
diff --git a/litellm/proxy/_experimental/mcp_server/auth/litellm_auth_handler.py b/litellm/proxy/_experimental/mcp_server/auth/litellm_auth_handler.py
index 081d83dd1c8..75b75d3ba44 100644
--- a/litellm/proxy/_experimental/mcp_server/auth/litellm_auth_handler.py
+++ b/litellm/proxy/_experimental/mcp_server/auth/litellm_auth_handler.py
@@ -27,6 +27,7 @@ def __init__(
oauth2_headers: Optional[Dict[str, str]] = None,
mcp_protocol_version: Optional[str] = None,
raw_headers: Optional[Dict[str, str]] = None,
+ client_ip: Optional[str] = None,
):
self.user_api_key_auth = user_api_key_auth
self.mcp_auth_header = mcp_auth_header
@@ -35,3 +36,4 @@ def __init__(
self.mcp_protocol_version = mcp_protocol_version
self.oauth2_headers = oauth2_headers
self.raw_headers = raw_headers
+ self.client_ip = client_ip
diff --git a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py
index 49d6ac7d898..786cfbfb008 100644
--- a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py
+++ b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py
@@ -1,11 +1,12 @@
from typing import Dict, List, Optional, Set, Tuple
+from fastapi import HTTPException
from starlette.datastructures import Headers
from starlette.requests import Request
from starlette.types import Scope
from litellm._logging import verbose_logger
-from litellm.proxy._types import LiteLLM_TeamTable, SpecialHeaders, UserAPIKeyAuth
+from litellm.proxy._types import LiteLLM_TeamTable, ProxyException, SpecialHeaders, UserAPIKeyAuth
from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
@@ -63,6 +64,13 @@ async def process_mcp_request(
HTTPException: If headers are invalid or missing required headers
"""
headers = MCPRequestHandler._safe_get_headers_from_scope(scope)
+
+ # Check if there is an explicit LiteLLM API key (primary header)
+ has_explicit_litellm_key = (
+ headers.get(MCPRequestHandler.LITELLM_API_KEY_HEADER_NAME_PRIMARY)
+ is not None
+ )
+
litellm_api_key = (
MCPRequestHandler.get_litellm_api_key_from_headers(headers) or ""
)
@@ -106,16 +114,38 @@ async def mock_body():
request.body = mock_body # type: ignore
if ".well-known" in str(request.url): # public routes
validated_user_api_key_auth = UserAPIKeyAuth()
- # elif litellm_api_key == "":
- # from fastapi import HTTPException
-
- # raise HTTPException(
- # status_code=401,
- # detail="LiteLLM API key is missing. Please add it or use OAuth authentication.",
- # headers={
- # "WWW-Authenticate": f'Bearer resource_metadata=f"{request.base_url}/.well-known/oauth-protected-resource"',
- # },
- # )
+ elif has_explicit_litellm_key:
+ # Explicit x-litellm-api-key provided - always validate normally
+ validated_user_api_key_auth = await user_api_key_auth(
+ api_key=litellm_api_key, request=request
+ )
+ elif oauth2_headers:
+ # No x-litellm-api-key, but Authorization header present.
+ # Could be a LiteLLM key (backward compat) OR an OAuth2 token
+ # from an upstream MCP provider (e.g. Atlassian).
+ # Try LiteLLM auth first; on auth failure, treat as OAuth2 passthrough.
+ try:
+ validated_user_api_key_auth = await user_api_key_auth(
+ api_key=litellm_api_key, request=request
+ )
+ except HTTPException as e:
+ if e.status_code in (401, 403):
+ verbose_logger.debug(
+ "MCP OAuth2: Authorization header is not a valid LiteLLM key, "
+ "treating as OAuth2 token passthrough"
+ )
+ validated_user_api_key_auth = UserAPIKeyAuth()
+ else:
+ raise
+ except ProxyException as e:
+ if str(e.code) in ("401", "403"):
+ verbose_logger.debug(
+ "MCP OAuth2: Authorization header is not a valid LiteLLM key, "
+ "treating as OAuth2 token passthrough"
+ )
+ validated_user_api_key_auth = UserAPIKeyAuth()
+ else:
+ raise
else:
validated_user_api_key_auth = await user_api_key_auth(
api_key=litellm_api_key, request=request
@@ -387,6 +417,9 @@ async def _get_team_object_permission(
user_api_key_cache,
)
+ verbose_logger.debug(
+ f"MCP team permission lookup: team_id={user_api_key_auth.team_id if user_api_key_auth else None}"
+ )
if not user_api_key_auth or not user_api_key_auth.team_id or not prisma_client:
return None
diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py
index 56feff548ad..8b052dd0da1 100644
--- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py
+++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py
@@ -9,13 +9,14 @@
get_async_httpx_client,
httpxSpecialProvider,
)
+from litellm.proxy.auth.ip_address_utils import IPAddressUtils
from litellm.proxy.common_utils.encrypt_decrypt_utils import (
decrypt_value_helper,
encrypt_value_helper,
)
from litellm.proxy.common_utils.http_parsing_utils import _read_request_body
-from litellm.types.mcp_server.mcp_server_manager import MCPServer
from litellm.proxy.utils import get_server_root_path
+from litellm.types.mcp_server.mcp_server_manager import MCPServer
router = APIRouter(
tags=["mcp"],
@@ -300,7 +301,10 @@ async def authorize(
)
lookup_name = mcp_server_name or client_id
- mcp_server = global_mcp_server_manager.get_mcp_server_by_name(lookup_name)
+ client_ip = IPAddressUtils.get_mcp_client_ip(request)
+ mcp_server = global_mcp_server_manager.get_mcp_server_by_name(
+ lookup_name, client_ip=client_ip
+ )
if mcp_server is None:
raise HTTPException(status_code=404, detail="MCP server not found")
return await authorize_with_server(
@@ -342,7 +346,10 @@ async def token_endpoint(
)
lookup_name = mcp_server_name or client_id
- mcp_server = global_mcp_server_manager.get_mcp_server_by_name(lookup_name)
+ client_ip = IPAddressUtils.get_mcp_client_ip(request)
+ mcp_server = global_mcp_server_manager.get_mcp_server_by_name(
+ lookup_name, client_ip=client_ip
+ )
if mcp_server is None:
raise HTTPException(status_code=404, detail="MCP server not found")
return await exchange_token_with_server(
@@ -425,7 +432,10 @@ def _build_oauth_protected_resource_response(
request_base_url = get_request_base_url(request)
mcp_server: Optional[MCPServer] = None
if mcp_server_name:
- mcp_server = global_mcp_server_manager.get_mcp_server_by_name(mcp_server_name)
+ client_ip = IPAddressUtils.get_mcp_client_ip(request)
+ mcp_server = global_mcp_server_manager.get_mcp_server_by_name(
+ mcp_server_name, client_ip=client_ip
+ )
# Build resource URL based on the pattern
if mcp_server_name:
@@ -538,7 +548,10 @@ def _build_oauth_authorization_server_response(
mcp_server: Optional[MCPServer] = None
if mcp_server_name:
- mcp_server = global_mcp_server_manager.get_mcp_server_by_name(mcp_server_name)
+ client_ip = IPAddressUtils.get_mcp_client_ip(request)
+ mcp_server = global_mcp_server_manager.get_mcp_server_by_name(
+ mcp_server_name, client_ip=client_ip
+ )
return {
"issuer": request_base_url, # point to your proxy
@@ -629,7 +642,10 @@ async def register_client(request: Request, mcp_server_name: Optional[str] = Non
if not mcp_server_name:
return dummy_return
- mcp_server = global_mcp_server_manager.get_mcp_server_by_name(mcp_server_name)
+ client_ip = IPAddressUtils.get_mcp_client_ip(request)
+ mcp_server = global_mcp_server_manager.get_mcp_server_by_name(
+ mcp_server_name, client_ip=client_ip
+ )
if mcp_server is None:
return dummy_return
return await register_client_with_server(
diff --git a/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py b/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py
index 4d53ae7059d..14bbb82808d 100644
--- a/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py
+++ b/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py
@@ -1,26 +1,37 @@
"""
MCP Guardrail Handler for Unified Guardrails.
-This handler works with the synthetic "messages" payload generated by
-`ProxyLogging._convert_mcp_to_llm_format`, which always produces a single user
-message whose `content` string encodes the MCP tool name and arguments. The
-handler simply feeds that text through the configured guardrail and writes the
-result back onto the message.
+Converts an MCP call_tool (name + arguments) into a single OpenAI-compatible
+tool_call and passes it to apply_guardrail. Works with the synthetic payload
+from ProxyLogging._convert_mcp_to_llm_format.
+
+Note: For MCP tool definitions (schema) -> OpenAI tools=[], see
+litellm.experimental_mcp_client.tools.transform_mcp_tool_to_openai_tool
+when you have a full MCP Tool from list_tools. Here we only have the call
+payload (name + arguments) so we just build the tool_call.
"""
from typing import TYPE_CHECKING, Any, Dict, Optional
+from mcp.types import Tool as MCPTool
+
from litellm._logging import verbose_proxy_logger
+from litellm.experimental_mcp_client.tools import transform_mcp_tool_to_openai_tool
from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation
+from litellm.types.llms.openai import (
+ ChatCompletionToolParam,
+ ChatCompletionToolParamFunctionChunk,
+)
from litellm.types.utils import GenericGuardrailAPIInputs
if TYPE_CHECKING:
- from litellm.integrations.custom_guardrail import CustomGuardrail
from mcp.types import CallToolResult
+ from litellm.integrations.custom_guardrail import CustomGuardrail
+
class MCPGuardrailTranslationHandler(BaseTranslation):
- """Guardrail translation handler for MCP tool calls."""
+ """Guardrail translation handler for MCP tool calls (passes a single tool_call to guardrail)."""
async def process_input_messages(
self,
@@ -28,56 +39,51 @@ async def process_input_messages(
guardrail_to_apply: "CustomGuardrail",
litellm_logging_obj: Optional[Any] = None,
) -> Dict[str, Any]:
- messages = data.get("messages")
- if not isinstance(messages, list) or not messages:
- verbose_proxy_logger.debug("MCP Guardrail: No messages to process")
- return data
-
- first_message = messages[0]
- content: Optional[str] = None
- if isinstance(first_message, dict):
- content = first_message.get("content")
- else:
- content = getattr(first_message, "content", None)
+ mcp_tool_name = data.get("mcp_tool_name") or data.get("name")
+ mcp_arguments = data.get("mcp_arguments") or data.get("arguments")
+ mcp_tool_description = data.get("mcp_tool_description") or data.get(
+ "description"
+ )
+ if mcp_arguments is None or not isinstance(mcp_arguments, dict):
+ mcp_arguments = {}
- if not isinstance(content, str):
- verbose_proxy_logger.debug(
- "MCP Guardrail: Message content missing or not a string",
- )
+ if not mcp_tool_name:
+ verbose_proxy_logger.debug("MCP Guardrail: mcp_tool_name missing")
return data
- inputs = GenericGuardrailAPIInputs(texts=[content])
- # Include model information if available
- model = data.get("model")
- if model:
- inputs["model"] = model
- guardrailed_inputs = await guardrail_to_apply.apply_guardrail(
+ # Convert MCP input via transform_mcp_tool_to_openai_tool, then map to litellm
+ # ChatCompletionToolParam (openai SDK type has incompatible strict/cache_control).
+ mcp_tool = MCPTool(
+ name=mcp_tool_name,
+ description=mcp_tool_description or "",
+ inputSchema={}, # Call payload has no schema; guardrail gets args from request_data
+ )
+ openai_tool = transform_mcp_tool_to_openai_tool(mcp_tool)
+ fn = openai_tool["function"]
+ tool_def: ChatCompletionToolParam = {
+ "type": "function",
+ "function": ChatCompletionToolParamFunctionChunk(
+ name=fn["name"],
+ description=fn.get("description") or "",
+ parameters=fn.get("parameters")
+ or {
+ "type": "object",
+ "properties": {},
+ "additionalProperties": False,
+ },
+ strict=fn.get("strict", False) or False, # Default to False if None
+ ),
+ }
+ inputs: GenericGuardrailAPIInputs = GenericGuardrailAPIInputs(
+ tools=[tool_def],
+ )
+
+ await guardrail_to_apply.apply_guardrail(
inputs=inputs,
request_data=data,
input_type="request",
logging_obj=litellm_logging_obj,
)
- guardrailed_texts = (
- guardrailed_inputs.get("texts", []) if guardrailed_inputs else []
- )
-
- if guardrailed_texts:
- new_content = guardrailed_texts[0]
- if isinstance(first_message, dict):
- first_message["content"] = new_content
- else:
- setattr(first_message, "content", new_content)
-
- verbose_proxy_logger.debug(
- "MCP Guardrail: Updated content for tool %s",
- data.get("mcp_tool_name"),
- )
- else:
- verbose_proxy_logger.debug(
- "MCP Guardrail: Guardrail returned no text updates for tool %s",
- data.get("mcp_tool_name"),
- )
-
return data
async def process_output_response(
@@ -87,7 +93,6 @@ async def process_output_response(
litellm_logging_obj: Optional[Any] = None,
user_api_key_dict: Optional[Any] = None,
) -> Any:
- # Not implemented: MCP guardrail translation never calls this path today.
verbose_proxy_logger.debug(
"MCP Guardrail: Output processing not implemented for MCP tools",
)
diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
index 4c17a2ff3e0..e7174f943b2 100644
--- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
+++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
@@ -53,6 +53,7 @@
MCPTransportType,
UserAPIKeyAuth,
)
+from litellm.proxy.auth.ip_address_utils import IPAddressUtils
from litellm.proxy.common_utils.encrypt_decrypt_utils import decrypt_value_helper
from litellm.proxy.utils import ProxyLogging
from litellm.types.llms.custom_http import httpxSpecialProvider
@@ -65,20 +66,23 @@
from litellm.types.utils import CallTypes
try:
- from mcp.shared.tool_name_validation import ( # type: ignore
+ from mcp.shared.tool_name_validation import (
+ validate_tool_name, # pyright: ignore[reportAssignmentType]
+ )
+ from mcp.shared.tool_name_validation import (
SEP_986_URL,
- validate_tool_name,
)
except ImportError:
from pydantic import BaseModel
+
SEP_986_URL = "https://github.com/modelcontextprotocol/protocol/blob/main/proposals/0001-tool-name-validation.md"
- class ToolNameValidationResult(BaseModel):
+ class _ToolNameValidationResult(BaseModel):
is_valid: bool = True
warnings: list = []
- def validate_tool_name(name: str) -> ToolNameValidationResult: # type: ignore[misc]
- return ToolNameValidationResult()
+ def validate_tool_name(name: str) -> _ToolNameValidationResult: # type: ignore[misc]
+ return _ToolNameValidationResult()
# Probe includes characters on both sides of the separator to mimic real prefixed tool names.
@@ -324,6 +328,9 @@ async def load_servers_from_config(
access_groups=server_config.get("access_groups", None),
static_headers=server_config.get("static_headers", None),
allow_all_keys=bool(server_config.get("allow_all_keys", False)),
+ available_on_public_internet=bool(
+ server_config.get("available_on_public_internet", False)
+ ),
)
self.config_mcp_servers[server_id] = new_server
@@ -469,12 +476,12 @@ def _register_openapi_tools(self, spec_path: str, server: MCPServer, base_url: s
)
# Update tool name to server name mapping (for both prefixed and base names)
- self.tool_name_to_mcp_server_name_mapping[
- base_tool_name
- ] = server_prefix
- self.tool_name_to_mcp_server_name_mapping[
- prefixed_tool_name
- ] = server_prefix
+ self.tool_name_to_mcp_server_name_mapping[base_tool_name] = (
+ server_prefix
+ )
+ self.tool_name_to_mcp_server_name_mapping[prefixed_tool_name] = (
+ server_prefix
+ )
registered_count += 1
verbose_logger.debug(
@@ -622,6 +629,9 @@ async def build_mcp_server_from_table(
allowed_tools=getattr(mcp_server, "allowed_tools", None),
disallowed_tools=getattr(mcp_server, "disallowed_tools", None),
allow_all_keys=mcp_server.allow_all_keys,
+ available_on_public_internet=bool(
+ getattr(mcp_server, "available_on_public_internet", False)
+ ),
updated_at=getattr(mcp_server, "updated_at", None),
)
return new_server
@@ -696,6 +706,23 @@ async def get_allowed_mcp_servers(
verbose_logger.warning(f"Failed to get allowed MCP servers: {str(e)}.")
return allow_all_server_ids
+ def filter_server_ids_by_ip(
+ self, server_ids: List[str], client_ip: Optional[str]
+ ) -> List[str]:
+ """
+ Filter server IDs by client IP — external callers only see public servers.
+
+ Returns server_ids unchanged when client_ip is None (no filtering).
+ """
+ if client_ip is None:
+ return server_ids
+ return [
+ sid
+ for sid in server_ids
+ if (s := self.get_mcp_server_by_id(sid)) is not None
+ and self._is_server_accessible_from_ip(s, client_ip)
+ ]
+
async def get_tools_for_server(self, server_id: str) -> List[MCPTool]:
"""
Get the tools for a given server
@@ -1929,7 +1956,9 @@ async def _call_regular_mcp_tool(
)
async def _call_tool_via_client(client, params):
- return await client.call_tool(params, host_progress_callback=host_progress_callback)
+ return await client.call_tool(
+ params, host_progress_callback=host_progress_callback
+ )
tasks.append(
asyncio.create_task(_call_tool_via_client(client, call_tool_params))
@@ -1967,7 +1996,6 @@ async def call_tool(
oauth2_headers: Optional[Dict[str, str]] = None,
raw_headers: Optional[Dict[str, str]] = None,
host_progress_callback: Optional[Callable] = None,
-
) -> CallToolResult:
"""
Call a tool with the given name and arguments
@@ -2200,6 +2228,42 @@ def get_mcp_servers_from_ids(self, server_ids: List[str]) -> List[MCPServer]:
servers.append(server)
return servers
+ def _get_general_settings(self) -> Dict[str, Any]:
+ """Get general_settings, importing lazily to avoid circular imports."""
+ try:
+ from litellm.proxy.proxy_server import (
+ general_settings as proxy_general_settings,
+ )
+ return proxy_general_settings
+ except ImportError:
+ # Fallback if proxy_server not available
+ return {}
+
+ def _is_server_accessible_from_ip(
+ self, server: MCPServer, client_ip: Optional[str]
+ ) -> bool:
+ """
+ Check if a server is accessible from the given client IP.
+
+ - If client_ip is None, no IP filtering is applied (internal callers).
+ - If the server has available_on_public_internet=True, it's always accessible.
+ - Otherwise, only internal/private IPs can access it.
+ """
+ if client_ip is None:
+ return True
+ if server.available_on_public_internet:
+ return True
+ # Check backwards compat: litellm.public_mcp_servers
+ public_ids = set(litellm.public_mcp_servers or [])
+ if server.server_id in public_ids:
+ return True
+ # Non-public server: only accessible from internal IPs
+ general_settings = self._get_general_settings()
+ internal_networks = IPAddressUtils.parse_internal_networks(
+ general_settings.get("mcp_internal_ip_ranges")
+ )
+ return IPAddressUtils.is_internal_ip(client_ip, internal_networks)
+
def get_mcp_server_by_id(self, server_id: str) -> Optional[MCPServer]:
"""
Get the MCP Server from the server id
@@ -2212,27 +2276,72 @@ def get_mcp_server_by_id(self, server_id: str) -> Optional[MCPServer]:
def get_public_mcp_servers(self) -> List[MCPServer]:
"""
- Get the public MCP servers
+ Get the public MCP servers (available_on_public_internet=True flag on server).
+ Also includes servers from litellm.public_mcp_servers for backwards compat.
"""
servers: List[MCPServer] = []
- if litellm.public_mcp_servers is None:
- return servers
- for server_id in litellm.public_mcp_servers:
- server = self.get_mcp_server_by_id(server_id)
- if server:
+ public_ids = set(litellm.public_mcp_servers or [])
+ for server in self.get_registry().values():
+ if server.available_on_public_internet or server.server_id in public_ids:
servers.append(server)
return servers
- def get_mcp_server_by_name(self, server_name: str) -> Optional[MCPServer]:
+ def get_mcp_server_by_name(
+ self, server_name: str, client_ip: Optional[str] = None
+ ) -> Optional[MCPServer]:
"""
- Get the MCP Server from the server name
+ Get the MCP Server from the server name.
+
+ Uses priority-based matching to avoid collisions:
+ 1. First pass: exact alias match (highest priority)
+ 2. Second pass: exact server_name match
+ 3. Third pass: exact name match (lowest priority)
+
+ Args:
+ server_name: The server name to look up.
+ client_ip: Optional client IP for access control. When provided,
+ non-public servers are hidden from external IPs.
"""
registry = self.get_registry()
+ # Pass 1: Match by alias (highest priority)
+ for server in registry.values():
+ if server.alias == server_name:
+ if not self._is_server_accessible_from_ip(server, client_ip):
+ return None
+ return server
+ # Pass 2: Match by server_name
for server in registry.values():
if server.server_name == server_name:
+ if not self._is_server_accessible_from_ip(server, client_ip):
+ return None
+ return server
+ # Pass 3: Match by name (lowest priority)
+ for server in registry.values():
+ if server.name == server_name:
+ if not self._is_server_accessible_from_ip(server, client_ip):
+ return None
return server
return None
+ def get_filtered_registry(
+ self, client_ip: Optional[str] = None
+ ) -> Dict[str, MCPServer]:
+ """
+ Get registry filtered by client IP access control.
+
+ Args:
+ client_ip: Optional client IP. When provided, non-public servers
+ are hidden from external IPs. When None, returns all servers.
+ """
+ registry = self.get_registry()
+ if client_ip is None:
+ return registry
+ return {
+ k: v
+ for k, v in registry.items()
+ if self._is_server_accessible_from_ip(v, client_ip)
+ }
+
def _generate_stable_server_id(
self,
server_name: str,
@@ -2462,6 +2571,7 @@ def _build_mcp_server_table(self, server: MCPServer) -> LiteLLM_MCPServerTable:
token_url=server.token_url,
registration_url=server.registration_url,
allow_all_keys=server.allow_all_keys,
+ available_on_public_internet=server.available_on_public_internet,
)
async def get_all_mcp_servers_unfiltered(self) -> List[LiteLLM_MCPServerTable]:
diff --git a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
index d93f852f22d..2cd56e0ff3f 100644
--- a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
+++ b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py
@@ -10,8 +10,10 @@
)
from litellm.proxy._experimental.mcp_server.utils import merge_mcp_headers
from litellm.proxy._types import UserAPIKeyAuth
+from litellm.proxy.auth.ip_address_utils import IPAddressUtils
from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
from litellm.types.mcp import MCPAuth
+from litellm.types.utils import CallTypes
MCP_AVAILABLE: bool = True
try:
@@ -28,6 +30,7 @@
if MCP_AVAILABLE:
from mcp.types import Tool as MCPTool
+
from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
global_mcp_server_manager,
)
@@ -76,6 +79,26 @@ def _create_tool_response_objects(tools, server_mcp_info):
for tool in tools
]
+ def _extract_mcp_headers_from_request(
+ request: Request,
+ mcp_request_handler_cls,
+ ) -> tuple:
+ """
+ Extract MCP auth headers from HTTP request.
+
+ Returns:
+ Tuple of (mcp_auth_header, mcp_server_auth_headers, raw_headers)
+ """
+ headers = request.headers
+ raw_headers = dict(headers)
+ mcp_auth_header = mcp_request_handler_cls._get_mcp_auth_header_from_headers(
+ headers
+ )
+ mcp_server_auth_headers = (
+ mcp_request_handler_cls._get_mcp_server_auth_headers_from_headers(headers)
+ )
+ return mcp_auth_header, mcp_server_auth_headers, raw_headers
+
async def _get_tools_for_single_server(
server,
server_auth_header,
@@ -96,6 +119,35 @@ async def _get_tools_for_single_server(
return _create_tool_response_objects(tools, server.mcp_info)
+ async def _resolve_allowed_mcp_servers_for_tool_call(
+ user_api_key_dict: UserAPIKeyAuth,
+ server_id: str,
+ ) -> List[MCPServer]:
+ """Resolve allowed MCP servers for the given user and validate server_id access."""
+ auth_contexts = await build_effective_auth_contexts(user_api_key_dict)
+ allowed_server_ids_set = set()
+ for auth_context in auth_contexts:
+ servers = await global_mcp_server_manager.get_allowed_mcp_servers(
+ user_api_key_auth=auth_context
+ )
+ allowed_server_ids_set.update(servers)
+ if server_id not in allowed_server_ids_set:
+ raise HTTPException(
+ status_code=403,
+ detail={
+ "error": "access_denied",
+ "message": f"The key is not allowed to access server {server_id}",
+ },
+ )
+ allowed_mcp_servers: List[MCPServer] = []
+ for allowed_server_id in allowed_server_ids_set:
+ server = global_mcp_server_manager.get_mcp_server_by_id(
+ allowed_server_id
+ )
+ if server is not None:
+ allowed_mcp_servers.append(server)
+ return allowed_mcp_servers
+
########################################################
@router.get("/tools/list", dependencies=[Depends(user_api_key_auth)])
async def list_tool_rest_api(
@@ -142,21 +194,25 @@ async def list_tool_rest_api(
auth_contexts = await build_effective_auth_contexts(user_api_key_dict)
+ _rest_client_ip = IPAddressUtils.get_mcp_client_ip(request)
+
allowed_server_ids_set = set()
for auth_context in auth_contexts:
servers = await global_mcp_server_manager.get_allowed_mcp_servers(
- user_api_key_auth=auth_context
+ user_api_key_auth=auth_context,
)
allowed_server_ids_set.update(servers)
- allowed_server_ids = list(allowed_server_ids_set)
+ allowed_server_ids = global_mcp_server_manager.filter_server_ids_by_ip(
+ list(allowed_server_ids_set), _rest_client_ip
+ )
list_tools_result = []
error_message = None
# If server_id is specified, only query that specific server
if server_id:
- if server_id not in allowed_server_ids_set:
+ if server_id not in allowed_server_ids:
raise HTTPException(
status_code=403,
detail={
@@ -164,7 +220,9 @@ async def list_tool_rest_api(
"message": f"The key is not allowed to access server {server_id}",
},
)
- server = global_mcp_server_manager.get_mcp_server_by_id(server_id)
+ server = global_mcp_server_manager.get_mcp_server_by_id(
+ server_id
+ )
if server is None:
return {
"tools": [],
@@ -261,7 +319,14 @@ async def call_tool_rest_api(
from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import (
MCPRequestHandler,
)
- from litellm.proxy.proxy_server import add_litellm_data_to_request, proxy_config
+ from litellm.proxy.common_request_processing import (
+ ProxyBaseLLMRequestProcessing,
+ )
+ from litellm.proxy.proxy_server import (
+ general_settings,
+ proxy_config,
+ proxy_logging_obj,
+ )
try:
data = await request.json()
@@ -289,28 +354,22 @@ async def call_tool_rest_api(
tool_arguments = data.get("arguments")
- data = await add_litellm_data_to_request(
- data=data,
- request=request,
- user_api_key_dict=user_api_key_dict,
- proxy_config=proxy_config,
+ proxy_base_llm_response_processor = ProxyBaseLLMRequestProcessing(data=data)
+ data, logging_obj = (
+ await proxy_base_llm_response_processor.common_processing_pre_call_logic(
+ request=request,
+ user_api_key_dict=user_api_key_dict,
+ proxy_config=proxy_config,
+ route_type=CallTypes.call_mcp_tool.value,
+ proxy_logging_obj=proxy_logging_obj,
+ general_settings=general_settings,
+ )
)
- # FIX: Extract MCP auth headers from request
- # The UI sends bearer token in x-mcp-auth header and server-specific headers,
- # but they weren't being extracted and passed to call_mcp_tool.
- # This fix ensures auth headers are properly extracted from the HTTP request
- # and passed through to the MCP server for authentication.
- headers = request.headers
- raw_headers_from_request = dict(headers)
- mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers(
- headers
- )
- mcp_server_auth_headers = (
- MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers)
+ # Extract MCP auth headers from request and add to data dict
+ mcp_auth_header, mcp_server_auth_headers, raw_headers_from_request = (
+ _extract_mcp_headers_from_request(request, MCPRequestHandler)
)
-
- # Add extracted headers to data dict to pass to call_mcp_tool
if mcp_auth_header:
data["mcp_auth_header"] = mcp_auth_header
if mcp_server_auth_headers:
@@ -325,14 +384,21 @@ async def call_tool_rest_api(
# Get all auth contexts
auth_contexts = await build_effective_auth_contexts(user_api_key_dict)
- # Collect allowed server IDs from all contexts
+ # Collect allowed server IDs from all contexts, then apply IP filtering
+ _rest_client_ip = IPAddressUtils.get_mcp_client_ip(request)
allowed_server_ids_set = set()
for auth_context in auth_contexts:
servers = await global_mcp_server_manager.get_allowed_mcp_servers(
- user_api_key_auth=auth_context
+ user_api_key_auth=auth_context,
)
allowed_server_ids_set.update(servers)
+ allowed_server_ids_set = set(
+ global_mcp_server_manager.filter_server_ids_by_ip(
+ list(allowed_server_ids_set), _rest_client_ip
+ )
+ )
+
# Check if the specified server_id is allowed
if server_id not in allowed_server_ids_set:
raise HTTPException(
diff --git a/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py b/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py
new file mode 100644
index 00000000000..e5cb6a0098d
--- /dev/null
+++ b/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py
@@ -0,0 +1,250 @@
+"""
+Semantic MCP Tool Filtering using semantic-router
+
+Filters MCP tools semantically for /chat/completions and /responses endpoints.
+"""
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
+
+from litellm._logging import verbose_logger
+
+if TYPE_CHECKING:
+ from semantic_router.routers import SemanticRouter
+
+ from litellm.router import Router
+
+
+class SemanticMCPToolFilter:
+ """Filters MCP tools using semantic similarity to reduce context window size."""
+
+ def __init__(
+ self,
+ embedding_model: str,
+ litellm_router_instance: "Router",
+ top_k: int = 10,
+ similarity_threshold: float = 0.3,
+ enabled: bool = True,
+ ):
+ """
+ Initialize the semantic tool filter.
+
+ Args:
+ embedding_model: Model to use for embeddings (e.g., "text-embedding-3-small")
+ litellm_router_instance: Router instance for embedding generation
+ top_k: Maximum number of tools to return
+ similarity_threshold: Minimum similarity score for filtering
+ enabled: Whether filtering is enabled
+ """
+ self.enabled = enabled
+ self.top_k = top_k
+ self.similarity_threshold = similarity_threshold
+ self.embedding_model = embedding_model
+ self.router_instance = litellm_router_instance
+ self.tool_router: Optional["SemanticRouter"] = None
+ self._tool_map: Dict[str, Any] = {} # MCPTool objects or OpenAI function dicts
+
+ async def build_router_from_mcp_registry(self) -> None:
+ """Build semantic router from all MCP tools in the registry (no auth checks)."""
+ from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
+ global_mcp_server_manager,
+ )
+
+ try:
+ # Get all servers from registry without auth checks
+ registry = global_mcp_server_manager.get_registry()
+ if not registry:
+ verbose_logger.warning("MCP registry is empty")
+ self.tool_router = None
+ return
+
+ # Fetch tools from all servers in parallel
+ all_tools = []
+ for server_id, server in registry.items():
+ try:
+ tools = await global_mcp_server_manager.get_tools_for_server(server_id)
+ all_tools.extend(tools)
+ except Exception as e:
+ verbose_logger.warning(f"Failed to fetch tools from server {server_id}: {e}")
+ continue
+
+ if not all_tools:
+ verbose_logger.warning("No MCP tools found in registry")
+ self.tool_router = None
+ return
+
+ verbose_logger.info(f"Fetched {len(all_tools)} tools from {len(registry)} MCP servers")
+ self._build_router(all_tools)
+
+ except Exception as e:
+ verbose_logger.error(f"Failed to build router from MCP registry: {e}")
+ self.tool_router = None
+ raise
+
+ def _extract_tool_info(self, tool) -> tuple[str, str]:
+ """Extract name and description from MCP tool or OpenAI function dict."""
+ name: str
+ description: str
+
+ if isinstance(tool, dict):
+ # OpenAI function format
+ name = tool.get("name", "")
+ description = tool.get("description", name)
+ else:
+ # MCPTool object
+ name = str(tool.name)
+ description = str(tool.description) if tool.description else str(tool.name)
+
+ return name, description
+
+ def _build_router(self, tools: List) -> None:
+ """Build semantic router with tools (MCPTool objects or OpenAI function dicts)."""
+ from semantic_router.routers import SemanticRouter
+ from semantic_router.routers.base import Route
+
+ from litellm.router_strategy.auto_router.litellm_encoder import (
+ LiteLLMRouterEncoder,
+ )
+
+ if not tools:
+ self.tool_router = None
+ return
+
+ try:
+ # Convert tools to routes
+ routes = []
+ self._tool_map = {}
+
+ for tool in tools:
+ name, description = self._extract_tool_info(tool)
+ self._tool_map[name] = tool
+
+ routes.append(
+ Route(
+ name=name,
+ description=description,
+ utterances=[description],
+ score_threshold=self.similarity_threshold,
+ )
+ )
+
+ self.tool_router = SemanticRouter(
+ routes=routes,
+ encoder=LiteLLMRouterEncoder(
+ litellm_router_instance=self.router_instance,
+ model_name=self.embedding_model,
+ score_threshold=self.similarity_threshold,
+ ),
+ auto_sync="local",
+ )
+
+ verbose_logger.info(
+ f"Built semantic router with {len(routes)} tools"
+ )
+
+ except Exception as e:
+ verbose_logger.error(f"Failed to build semantic router: {e}")
+ self.tool_router = None
+ raise
+
+ async def filter_tools(
+ self,
+ query: str,
+ available_tools: List[Any],
+ top_k: Optional[int] = None,
+ ) -> List[Any]:
+ """
+ Filter tools semantically based on query.
+
+ Args:
+ query: User query to match against tools
+ available_tools: Full list of available MCP tools
+ top_k: Override default top_k (optional)
+
+ Returns:
+ Filtered and ordered list of tools (up to top_k)
+ """
+ # Early returns for cases where we can't/shouldn't filter
+ if not self.enabled:
+ return available_tools
+
+ if not available_tools:
+ return available_tools
+
+ if not query or not query.strip():
+ return available_tools
+
+ # Router should be built on startup - if not, something went wrong
+ if self.tool_router is None:
+ verbose_logger.warning("Router not initialized - was build_router_from_mcp_registry() called on startup?")
+ return available_tools
+
+ # Run semantic filtering
+ try:
+ limit = top_k or self.top_k
+ matches = self.tool_router(text=query, limit=limit)
+ matched_tool_names = self._extract_tool_names_from_matches(matches)
+
+ if not matched_tool_names:
+ return available_tools
+
+ return self._get_tools_by_names(matched_tool_names, available_tools)
+
+ except Exception as e:
+ verbose_logger.error(f"Semantic tool filter failed: {e}", exc_info=True)
+ return available_tools
+
+ def _extract_tool_names_from_matches(self, matches) -> List[str]:
+ """Extract tool names from semantic router match results."""
+ if not matches:
+ return []
+
+ # Handle single match
+ if hasattr(matches, "name") and matches.name:
+ return [matches.name]
+
+ # Handle list of matches
+ if isinstance(matches, list):
+ return [m.name for m in matches if hasattr(m, "name") and m.name]
+
+ return []
+
+ def _get_tools_by_names(
+ self, tool_names: List[str], available_tools: List[Any]
+ ) -> List[Any]:
+ """Get tools from available_tools by their names, preserving order."""
+ # Match tools from available_tools (preserves format - dict or MCPTool)
+ matched_tools = []
+ for tool in available_tools:
+ tool_name, _ = self._extract_tool_info(tool)
+ if tool_name in tool_names:
+ matched_tools.append(tool)
+
+ # Reorder to match semantic router's ordering
+ tool_map = {self._extract_tool_info(t)[0]: t for t in matched_tools}
+ return [tool_map[name] for name in tool_names if name in tool_map]
+
+ def extract_user_query(self, messages: List[Dict[str, Any]]) -> str:
+ """
+ Extract user query from messages for /chat/completions or /responses.
+
+ Args:
+ messages: List of message dictionaries (from 'messages' or 'input' field)
+
+ Returns:
+ Extracted query string
+ """
+ for msg in reversed(messages):
+ if msg.get("role") == "user":
+ content = msg.get("content", "")
+
+ if isinstance(content, str):
+ return content
+
+ if isinstance(content, list):
+ texts = [
+ block.get("text", "") if isinstance(block, dict) else str(block)
+ for block in content
+ if isinstance(block, (dict, str))
+ ]
+ return " ".join(texts)
+
+ return ""
diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py
index 6d54c3871e5..890c4ae8fb2 100644
--- a/litellm/proxy/_experimental/mcp_server/server.py
+++ b/litellm/proxy/_experimental/mcp_server/server.py
@@ -5,12 +5,24 @@
import asyncio
import contextlib
-from datetime import datetime
import traceback
import uuid
-from typing import Any, AsyncIterator, Dict, List, Optional, Tuple, Union, cast, Callable
+from datetime import datetime
+from typing import (
+ Any,
+ AsyncIterator,
+ Callable,
+ Dict,
+ List,
+ Optional,
+ Tuple,
+ Union,
+ cast,
+)
+
from fastapi import FastAPI, HTTPException
from pydantic import AnyUrl, ConfigDict
+from starlette.requests import Request as StarletteRequest
from starlette.types import Receive, Scope, Send
from litellm._logging import verbose_logger
@@ -25,6 +37,7 @@
LITELLM_MCP_SERVER_VERSION,
)
from litellm.proxy._types import UserAPIKeyAuth
+from litellm.proxy.auth.ip_address_utils import IPAddressUtils
from litellm.types.mcp import MCPAuth
from litellm.types.mcp_server.mcp_server_manager import MCPInfo, MCPServer
from litellm.types.utils import CallTypes, StandardLoggingMCPToolCall
@@ -213,6 +226,7 @@ async def list_tools() -> List[MCPTool]:
mcp_server_auth_headers,
oauth2_headers,
raw_headers,
+ _client_ip,
) = get_auth_context()
verbose_logger.debug(
f"MCP list_tools - User API Key Auth from context: {user_api_key_auth}"
@@ -276,6 +290,7 @@ async def mcp_server_tool_call(
mcp_server_auth_headers,
oauth2_headers,
raw_headers,
+ _client_ip,
) = get_auth_context()
verbose_logger.debug(
@@ -288,7 +303,7 @@ async def mcp_server_tool_call(
host_token = getattr(host_ctx.meta, 'progressToken', None)
if host_token and hasattr(host_ctx, 'session') and host_ctx.session:
host_session = host_ctx.session
-
+
async def forward_progress(progress: float, total: float | None):
"""Forward progress notifications from external MCP to Host"""
try:
@@ -300,7 +315,7 @@ async def forward_progress(progress: float, total: float | None):
verbose_logger.debug(f"Forwarded progress {progress}/{total} to Host")
except Exception as e:
verbose_logger.error(f"Failed to forward progress to Host: {e}")
-
+
host_progress_callback = forward_progress
verbose_logger.debug(f"Host progressToken captured: {host_token[:8]}...")
except Exception as e:
@@ -387,6 +402,7 @@ async def list_prompts() -> List[Prompt]:
mcp_server_auth_headers,
oauth2_headers,
raw_headers,
+ _client_ip,
) = get_auth_context()
verbose_logger.debug(
f"MCP list_prompts - User API Key Auth from context: {user_api_key_auth}"
@@ -440,6 +456,7 @@ async def get_prompt(
mcp_server_auth_headers,
oauth2_headers,
raw_headers,
+ _client_ip,
) = get_auth_context()
verbose_logger.debug(
@@ -467,6 +484,7 @@ async def list_resources() -> List[Resource]:
mcp_server_auth_headers,
oauth2_headers,
raw_headers,
+ _client_ip,
) = get_auth_context()
verbose_logger.debug(
f"MCP list_resources - User API Key Auth from context: {user_api_key_auth}"
@@ -505,6 +523,7 @@ async def list_resource_templates() -> List[ResourceTemplate]:
mcp_server_auth_headers,
oauth2_headers,
raw_headers,
+ _client_ip,
) = get_auth_context()
verbose_logger.debug(
f"MCP list_resource_templates - User API Key Auth from context: {user_api_key_auth}"
@@ -544,6 +563,7 @@ async def read_resource(url: AnyUrl) -> list[ReadResourceContents]:
mcp_server_auth_headers,
oauth2_headers,
raw_headers,
+ _client_ip,
) = get_auth_context()
read_resource_result = await mcp_read_resource(
@@ -702,13 +722,57 @@ def filter_tools_by_allowed_tools(
return tools_to_return
+ def _get_client_ip_from_context() -> Optional[str]:
+ """
+ Extract client_ip from auth context.
+ Returns None if context not set (caller should handle this as "no IP filtering").
+ """
+ try:
+ auth_user = auth_context_var.get()
+ if auth_user and isinstance(auth_user, MCPAuthenticatedUser):
+ return auth_user.client_ip
+ except Exception:
+ pass
+ return None
+
async def _get_allowed_mcp_servers(
user_api_key_auth: Optional[UserAPIKeyAuth],
mcp_servers: Optional[List[str]],
+ client_ip: Optional[str] = None,
) -> List[MCPServer]:
- """Return allowed MCP servers for a request after applying filters."""
+ """Return allowed MCP servers for a request after applying filters.
+
+ Args:
+ user_api_key_auth: The authenticated user's API key info.
+ mcp_servers: Optional list of server names to filter to.
+ client_ip: Client IP for IP-based access control. If None, falls back to
+ auth context. Pass explicitly from request handlers for safety.
+ Note: If client_ip is None and auth context is not set, IP filtering is skipped.
+ This is intentional for internal callers but may indicate a bug if called
+ from a request handler without proper context setup.
+ """
+ # Use explicit client_ip if provided, otherwise try auth context
+ if client_ip is None:
+ client_ip = _get_client_ip_from_context()
+ if client_ip is None:
+ verbose_logger.debug(
+ "MCP _get_allowed_mcp_servers called without client_ip and no auth context. "
+ "IP filtering will be skipped. This is expected for internal calls."
+ )
+
allowed_mcp_server_ids = (
- await global_mcp_server_manager.get_allowed_mcp_servers(user_api_key_auth)
+ await global_mcp_server_manager.get_allowed_mcp_servers(
+ user_api_key_auth
+ )
+ )
+ allowed_mcp_server_ids = (
+ global_mcp_server_manager.filter_server_ids_by_ip(
+ allowed_mcp_server_ids, client_ip
+ )
+ )
+ verbose_logger.debug(
+ "MCP IP filter: client_ip=%s, allowed_server_ids=%s",
+ client_ip, allowed_mcp_server_ids,
)
allowed_mcp_servers: List[MCPServer] = []
for allowed_mcp_server_id in allowed_mcp_server_ids:
@@ -1715,7 +1779,7 @@ async def _handle_managed_mcp_tool(
oauth2_headers: Optional[Dict[str, str]] = None,
raw_headers: Optional[Dict[str, str]] = None,
litellm_logging_obj: Optional[Any] = None,
- host_progress_callback: Optional[Callable] = None,
+ host_progress_callback: Optional[Callable] = None,
) -> CallToolResult:
"""Handle tool execution for managed server tools"""
# Import here to avoid circular import
@@ -1840,6 +1904,43 @@ async def extract_mcp_auth_context(scope, path):
raw_headers,
)
+ def _strip_stale_mcp_session_header(
+ scope: Scope,
+ mgr: "StreamableHTTPSessionManager",
+ ) -> None:
+ """
+ Strip stale ``mcp-session-id`` headers so the session manager
+ creates a fresh session instead of returning 404 "Session not found".
+
+ When clients like VSCode reconnect after a reload they may resend a
+ session id that has already been cleaned up. Rather than letting the
+ SDK return a 404 error loop, we detect the stale id and remove the
+ header so a brand-new session is created transparently.
+
+ Fixes https://github.com/BerriAI/litellm/issues/20292
+ """
+ _mcp_session_header = b"mcp-session-id"
+ _session_id: Optional[str] = None
+ for header_name, header_value in scope.get("headers", []):
+ if header_name == _mcp_session_header:
+ _session_id = header_value.decode("utf-8", errors="replace")
+ break
+
+ if _session_id is None:
+ return
+
+ known_sessions = getattr(mgr, "_server_instances", None)
+ if known_sessions is not None and _session_id not in known_sessions:
+ verbose_logger.warning(
+ "MCP session ID '%s' not found in active sessions. "
+ "Stripping stale header to force new session creation.",
+ _session_id,
+ )
+ scope["headers"] = [
+ (k, v) for k, v in scope["headers"]
+ if k != _mcp_session_header
+ ]
+
async def handle_streamable_http_mcp(
scope: Scope, receive: Receive, send: Send
) -> None:
@@ -1854,6 +1955,10 @@ async def handle_streamable_http_mcp(
oauth2_headers,
raw_headers,
) = await extract_mcp_auth_context(scope, path)
+
+ # Extract client IP for MCP access control
+ _client_ip = IPAddressUtils.get_mcp_client_ip(StarletteRequest(scope))
+
verbose_logger.debug(
f"MCP request mcp_servers (header/path): {mcp_servers}"
)
@@ -1862,11 +1967,11 @@ async def handle_streamable_http_mcp(
)
# https://datatracker.ietf.org/doc/html/rfc9728#name-www-authenticate-response
for server_name in mcp_servers or []:
- server = global_mcp_server_manager.get_mcp_server_by_name(server_name)
+ server = global_mcp_server_manager.get_mcp_server_by_name(
+ server_name, client_ip=_client_ip
+ )
if server and server.auth_type == MCPAuth.oauth2 and not oauth2_headers:
- from starlette.requests import Request
-
- request = Request(scope)
+ request = StarletteRequest(scope)
base_url = str(request.base_url).rstrip("/")
authorization_uri = (
@@ -1888,6 +1993,7 @@ async def handle_streamable_http_mcp(
mcp_server_auth_headers=mcp_server_auth_headers,
oauth2_headers=oauth2_headers,
raw_headers=raw_headers,
+ client_ip=_client_ip,
)
# Ensure session managers are initialized
@@ -1896,13 +2002,16 @@ async def handle_streamable_http_mcp(
# Give it a moment to start up
await asyncio.sleep(0.1)
+ _strip_stale_mcp_session_header(scope, session_manager)
+
await session_manager.handle_request(scope, receive, send)
+ except HTTPException:
+ # Re-raise HTTP exceptions to preserve status codes and details
+ raise
except Exception as e:
- raise e
verbose_logger.exception(f"Error handling MCP request: {e}")
- # Instead of re-raising, try to send a graceful error response
+ # Try to send a graceful error response for non-HTTP exceptions
try:
- # Send a proper HTTP error response instead of letting the exception bubble up
from starlette.responses import JSONResponse
from starlette.status import HTTP_500_INTERNAL_SERVER_ERROR
@@ -1930,6 +2039,10 @@ async def handle_sse_mcp(scope: Scope, receive: Receive, send: Send) -> None:
oauth2_headers,
raw_headers,
) = await extract_mcp_auth_context(scope, path)
+
+ # Extract client IP for MCP access control
+ _sse_client_ip = IPAddressUtils.get_mcp_client_ip(StarletteRequest(scope))
+
verbose_logger.debug(
f"MCP request mcp_servers (header/path): {mcp_servers}"
)
@@ -1943,6 +2056,7 @@ async def handle_sse_mcp(scope: Scope, receive: Receive, send: Send) -> None:
mcp_server_auth_headers=mcp_server_auth_headers,
oauth2_headers=oauth2_headers,
raw_headers=raw_headers,
+ client_ip=_sse_client_ip,
)
if not _SESSION_MANAGERS_INITIALIZED:
@@ -2006,6 +2120,7 @@ def set_auth_context(
mcp_server_auth_headers: Optional[Dict[str, Dict[str, str]]] = None,
oauth2_headers: Optional[Dict[str, str]] = None,
raw_headers: Optional[Dict[str, str]] = None,
+ client_ip: Optional[str] = None,
) -> None:
"""
Set the UserAPIKeyAuth in the auth context variable.
@@ -2015,6 +2130,7 @@ def set_auth_context(
mcp_auth_header: MCP auth header to be passed to the MCP server (deprecated)
mcp_servers: Optional list of server names and access groups to filter by
mcp_server_auth_headers: Optional dict of server-specific auth headers {server_alias: auth_value}
+ client_ip: Client IP address for MCP access control
"""
auth_user = MCPAuthenticatedUser(
user_api_key_auth=user_api_key_auth,
@@ -2023,6 +2139,7 @@ def set_auth_context(
mcp_server_auth_headers=mcp_server_auth_headers,
oauth2_headers=oauth2_headers,
raw_headers=raw_headers,
+ client_ip=client_ip,
)
auth_context_var.set(auth_user)
@@ -2034,14 +2151,15 @@ def get_auth_context() -> (
Optional[Dict[str, Dict[str, str]]],
Optional[Dict[str, str]],
Optional[Dict[str, str]],
+ Optional[str],
]
):
"""
Get the UserAPIKeyAuth from the auth context variable.
Returns:
- Tuple[Optional[UserAPIKeyAuth], Optional[str], Optional[List[str]], Optional[Dict[str, str]]]:
- UserAPIKeyAuth object, MCP auth header (deprecated), MCP servers (can include access groups), and server-specific auth headers
+ Tuple containing: UserAPIKeyAuth, MCP auth header (deprecated),
+ MCP servers, server-specific auth headers, OAuth2 headers, raw headers, client IP
"""
auth_user = auth_context_var.get()
if auth_user and isinstance(auth_user, MCPAuthenticatedUser):
@@ -2052,8 +2170,9 @@ def get_auth_context() -> (
auth_user.mcp_server_auth_headers,
auth_user.oauth2_headers,
auth_user.raw_headers,
+ auth_user.client_ip,
)
- return None, None, None, None, None, None
+ return None, None, None, None, None, None, None
########################################################
############ End of Auth Context Functions #############
diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html
index a8bd30680ab..e40a85b4ddb 100644
--- a/litellm/proxy/_experimental/out/404.html
+++ b/litellm/proxy/_experimental/out/404.html
@@ -1 +1 @@
-404: This page could not be found.LiteLLM Dashboard
404
This page could not be found.
\ No newline at end of file
+404: This page could not be found.LiteLLM Dashboard
404
This page could not be found.
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1059-26bdac09bbb12a4b.js b/litellm/proxy/_experimental/out/_next/static/chunks/1059-26bdac09bbb12a4b.js
deleted file mode 100644
index cd4f5e4e326..00000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/1059-26bdac09bbb12a4b.js
+++ /dev/null
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deleted file mode 100644
index 8bdb738f169..00000000000
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+++ /dev/null
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