feat(integrations): add litellm integration

sentry_sdk/integrations/__init__.py

@@ -97,6 +97,7 @@ def iter_default_integrations(with_auto_enabling_integrations):
     "sentry_sdk.integrations.langchain.LangchainIntegration",
     "sentry_sdk.integrations.langgraph.LanggraphIntegration",
     "sentry_sdk.integrations.litestar.LitestarIntegration",
+    "sentry_sdk.integrations.litellm.LiteLLMIntegration",
     "sentry_sdk.integrations.loguru.LoguruIntegration",
     "sentry_sdk.integrations.openai.OpenAIIntegration",
     "sentry_sdk.integrations.pymongo.PyMongoIntegration",

sentry_sdk/integrations/litellm.py

@@ -0,0 +1,281 @@
+from typing import TYPE_CHECKING
+
+import sentry_sdk
+from sentry_sdk import consts
+from sentry_sdk.ai.monitoring import record_token_usage
+from sentry_sdk.ai.utils import get_start_span_function, set_data_normalized
+from sentry_sdk.consts import SPANDATA
+from sentry_sdk.integrations import DidNotEnable, Integration
+from sentry_sdk.scope import should_send_default_pii
+from sentry_sdk.utils import event_from_exception
+
+if TYPE_CHECKING:
+    from typing import Any, Dict
+    from datetime import datetime
+
+try:
+    import litellm
+except ImportError:
+    raise DidNotEnable("LiteLLM not installed")
+
+
+def _get_provider_from_model(model):
+    # type: (str) -> str
+    """Extract provider name from model string using LiteLLM's logic"""
+    if not model:
+        return "unknown"
+
+    # Common provider prefixes/patterns
+    if model.startswith("gpt-") or model.startswith("o1-") or "openai/" in model:
+        return "openai"
+    elif model.startswith("claude-") or "anthropic/" in model:
+        return "anthropic"
+    elif (
+        model.startswith("gemini-")
+        or "google/" in model
+        or model.startswith("vertex_ai/")
+    ):
+        return "google"
+    elif "cohere/" in model or model.startswith("command-"):
+        return "cohere"
+    elif "azure/" in model:
+        return "azure"
+    elif "bedrock/" in model:
+        return "bedrock"
+    elif "ollama/" in model:
+        return "ollama"
+    else:
+        # Try to use LiteLLM's internal provider detection if available
+        try:
+            if hasattr(litellm, "get_llm_provider"):
+                provider_info = litellm.get_llm_provider(model)
+                if isinstance(provider_info, tuple) and len(provider_info) > 1:
+                    return provider_info[1] or "unknown"
+            return "unknown"
+        except Exception:
+            return "unknown"
+
+
+def _input_callback(
+    kwargs,  # type: Dict[str, Any]
+):
+    # type: (...) -> None
+    """Handle the start of a request."""
+    integration = sentry_sdk.get_client().get_integration(LiteLLMIntegration)
+
+    if integration is None:
+        return
+
+    # Get key parameters
+    model = kwargs.get("model", "")
+    messages = kwargs.get("messages", [])
+    operation = "chat" if messages else "embeddings"
+
+    # Start a new span/transaction
+    span = get_start_span_function()(
+        op=(
+            consts.OP.GEN_AI_CHAT
+            if operation == "chat"
+            else consts.OP.GEN_AI_EMBEDDINGS
+        ),
+        name=f"{operation} {model}",
+        origin=LiteLLMIntegration.origin,
+    )
+    span.__enter__()
+
+    # Store span for later
+    kwargs["_sentry_span"] = span
+
+    # Set basic data
+    set_data_normalized(span, SPANDATA.GEN_AI_SYSTEM, "litellm")
+    set_data_normalized(span, SPANDATA.GEN_AI_OPERATION_NAME, operation)
+    set_data_normalized(
+        span, "gen_ai.litellm.provider", _get_provider_from_model(model)
+    )
+
+    # Record messages if allowed
+    if messages and should_send_default_pii() and integration.include_prompts:
+        set_data_normalized(
+            span, SPANDATA.GEN_AI_REQUEST_MESSAGES, messages, unpack=False
+        )
+
+    # Record other parameters
+    params = {
+        "model": SPANDATA.GEN_AI_REQUEST_MODEL,
+        "stream": SPANDATA.GEN_AI_RESPONSE_STREAMING,
+        "max_tokens": SPANDATA.GEN_AI_REQUEST_MAX_TOKENS,
+        "presence_penalty": SPANDATA.GEN_AI_REQUEST_PRESENCE_PENALTY,
+        "frequency_penalty": SPANDATA.GEN_AI_REQUEST_FREQUENCY_PENALTY,
+        "temperature": SPANDATA.GEN_AI_REQUEST_TEMPERATURE,
+        "top_p": SPANDATA.GEN_AI_REQUEST_TOP_P,
+    }
+    for key, attribute in params.items():
+        value = kwargs.get(key)
+        if value is not None:
+            set_data_normalized(span, attribute, value)
+
+    # Record LiteLLM-specific parameters
+    litellm_params = {
+        "api_base": kwargs.get("api_base"),
+        "api_version": kwargs.get("api_version"),
+        "custom_llm_provider": kwargs.get("custom_llm_provider"),
+    }
+    for key, value in litellm_params.items():
+        if value is not None:
+            set_data_normalized(span, f"gen_ai.litellm.{key}", value)
+
+
+def _success_callback(
+    kwargs,  # type: Dict[str, Any]
+    completion_response,  # type: Any
+    start_time,  # type: datetime
+    end_time,  # type: datetime
+):
+    # type: (...) -> None
+    """Handle successful completion."""
+
+    span = kwargs.get("_sentry_span")
+    if span is None:
+        return
+
+    integration = sentry_sdk.get_client().get_integration(LiteLLMIntegration)
+    if integration is None:
+        return
+
+    try:
+        # Record model information
+        if hasattr(completion_response, "model"):
+            set_data_normalized(
+                span, SPANDATA.GEN_AI_RESPONSE_MODEL, completion_response.model
+            )
+
+        # Record response content if allowed
+        if should_send_default_pii() and integration.include_prompts:
+            if hasattr(completion_response, "choices"):
+                response_messages = []
+                for choice in completion_response.choices:
+                    if hasattr(choice, "message"):
+                        if hasattr(choice.message, "model_dump"):
+                            response_messages.append(choice.message.model_dump())
+                        elif hasattr(choice.message, "dict"):
+                            response_messages.append(choice.message.dict())
+                        else:
+                            # Fallback for basic message objects
+                            msg = {}
+                            if hasattr(choice.message, "role"):
+                                msg["role"] = choice.message.role
+                            if hasattr(choice.message, "content"):
+                                msg["content"] = choice.message.content
+                            if hasattr(choice.message, "tool_calls"):
+                                msg["tool_calls"] = choice.message.tool_calls
+                            response_messages.append(msg)
+
+                if response_messages:
+                    set_data_normalized(
+                        span, SPANDATA.GEN_AI_RESPONSE_TEXT, response_messages
+                    )
+
+        # Record token usage
+        if hasattr(completion_response, "usage"):
+            usage = completion_response.usage
+            record_token_usage(
+                span,
+                input_tokens=getattr(usage, "prompt_tokens", None),
+                output_tokens=getattr(usage, "completion_tokens", None),
+                total_tokens=getattr(usage, "total_tokens", None),
+            )
+
+    finally:
+        # Always finish the span and clean up
+        span.__exit__(None, None, None)
+
+
+def _failure_callback(
+    kwargs,  # type: Dict[str, Any]
+    exception,  # type: Exception
+    start_time,  # type: datetime
+    end_time,  # type: datetime
+):
+    # type: (...) -> None
+    """Handle request failure."""
+    span = kwargs.get("_sentry_span")
+
+    try:
+        # Capture the exception
+        event, hint = event_from_exception(
+            exception,
+            client_options=sentry_sdk.get_client().options,
+            mechanism={"type": "litellm", "handled": False},
+        )
+        sentry_sdk.capture_event(event, hint=hint)
+    finally:
+        # Always finish the span and clean up
+        span.__exit__(None, None, None)
+
+
+class LiteLLMIntegration(Integration):
+    """
+    LiteLLM integration for Sentry.
+
+    This integration automatically captures LiteLLM API calls and sends them to Sentry
+    for monitoring and error tracking. It supports all 100+ LLM providers that LiteLLM
+    supports, including OpenAI, Anthropic, Google, Cohere, and many others.
+
+    Features:
+    - Automatic exception capture for all LiteLLM calls
+    - Token usage tracking across all providers
+    - Provider detection and attribution
+    - Input/output message capture (configurable)
+    - Streaming response support
+    - Cost tracking integration
+
+    Usage:
+
+    ```python
+    import litellm
+    import sentry_sdk
+
+    # Initialize Sentry with the LiteLLM integration
+    sentry_sdk.init(
+        dsn="your-dsn",
+        integrations=[
+            sentry_sdk.integrations.LiteLLMIntegration(
+                include_prompts=True  # Set to False to exclude message content
+            )
+        ]
+    )
+
+    # All LiteLLM calls will now be monitored
+    response = litellm.completion(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "user", "content": "Hello!"}]
+    )
+    ```
+
+    Configuration:
+    - include_prompts (bool): Whether to include prompts and responses in spans.
+      Defaults to True. Set to False to exclude potentially sensitive data.
+    """
+
+    identifier = "litellm"
+    origin = f"auto.ai.{identifier}"
+
+    def __init__(self, include_prompts=True):
+        # type: (LiteLLMIntegration, bool) -> None
+        self.include_prompts = include_prompts
+
+    @staticmethod
+    def setup_once():
+        # type: () -> None
+        """Set up LiteLLM callbacks for monitoring."""
+        litellm.input_callback = litellm.input_callback or []
+        if _input_callback not in litellm.input_callback:
+            litellm.input_callback.append(_input_callback)
+
+        litellm.success_callback = litellm.success_callback or []
+        if _success_callback not in litellm.success_callback:
+            litellm.success_callback.append(_success_callback)
+
+        litellm.failure_callback = litellm.failure_callback or []
+        if _failure_callback not in litellm.failure_callback:
+            litellm.failure_callback.append(_failure_callback)