Update Anthropic instrumentation

agentops/instrumentation/__init__.py

@@ -58,7 +58,7 @@ def get_instance(self) -> BaseInstrumentor:
         provider_import_name="openai",
     ),
     InstrumentorLoader(
-        module_name="opentelemetry.instrumentation.anthropic",
+        module_name="agentops.instrumentation.anthropic",
         class_name="AnthropicInstrumentor",
         provider_import_name="anthropic",
     ),

agentops/instrumentation/anthropic/__init__.py

@@ -0,0 +1,38 @@
+"""Anthropic API instrumentation.
+
+This module provides instrumentation for the Anthropic API,
+including chat completions, streaming, and event handling.
+"""
+
+import logging
+from typing import Collection
+
+def get_version() -> str:
+    """Get the version of the Anthropic SDK, or 'unknown' if not found
+
+    Attempts to retrieve the installed version of the Anthropic SDK using importlib.metadata.
+    Falls back to 'unknown' if the version cannot be determined.
+
+    Returns:
+        The version string of the Anthropic SDK or 'unknown'
+    """
+    try:
+        from importlib.metadata import version
+        return version("anthropic")
+    except ImportError:
+        logger.debug("Could not find Anthropic SDK version")
+        return "unknown"
+
+LIBRARY_NAME = "anthropic"
+LIBRARY_VERSION: str = get_version()
+
+logger = logging.getLogger(__name__)
+
+# Import after defining constants to avoid circular imports
+from agentops.instrumentation.anthropic.instrumentor import AnthropicInstrumentor  # noqa: E402
+
+__all__ = [
+    "LIBRARY_NAME",
+    "LIBRARY_VERSION", 
+    "AnthropicInstrumentor",
+] 

agentops/instrumentation/anthropic/attributes/__init__.py

@@ -0,0 +1,20 @@
+"""Attribute extraction for Anthropic API instrumentation."""
+
+from agentops.instrumentation.anthropic.attributes.common import get_common_instrumentation_attributes
+from agentops.instrumentation.anthropic.attributes.message import get_message_attributes, get_completion_attributes
+from agentops.instrumentation.anthropic.attributes.tools import (
+    extract_tool_definitions,
+    extract_tool_use_blocks,
+    extract_tool_results,
+    get_tool_attributes
+)
+
+__all__ = [
+    "get_common_instrumentation_attributes",
+    "get_message_attributes",
+    "get_completion_attributes",
+    "extract_tool_definitions",
+    "extract_tool_use_blocks",
+    "extract_tool_results",
+    "get_tool_attributes",
+] 

agentops/instrumentation/anthropic/attributes/common.py

@@ -0,0 +1,62 @@
+"""Common attribute extraction for Anthropic instrumentation."""
+
+from typing import Dict, Any
+
+from agentops.logging import logger
+from agentops.semconv import InstrumentationAttributes, SpanAttributes
+from agentops.instrumentation.common.attributes import AttributeMap, get_common_attributes
+from agentops.instrumentation.anthropic import LIBRARY_NAME, LIBRARY_VERSION
+
+def get_common_instrumentation_attributes() -> AttributeMap:
+    """Get common instrumentation attributes for the Anthropic instrumentation.
+
+    This combines the generic AgentOps attributes with Anthropic specific library attributes.
+
+    Returns:
+        Dictionary of common instrumentation attributes
+    """
+    attributes = get_common_attributes()
+    attributes.update({
+        InstrumentationAttributes.LIBRARY_NAME: LIBRARY_NAME,
+        InstrumentationAttributes.LIBRARY_VERSION: LIBRARY_VERSION,
+    })
+    return attributes
+
+
+def extract_request_attributes(kwargs: Dict[str, Any]) -> AttributeMap:
+    """Extract all request attributes from kwargs.
+
+    This consolidated function extracts all relevant attributes from the request
+    kwargs, including model, system prompt, messages, max_tokens, temperature,
+    and other parameters. It replaces the individual extraction functions with
+    a single comprehensive approach.
+
+    Args:
+        kwargs: Request keyword arguments
+
+    Returns:
+        Dictionary of extracted request attributes
+    """
+    attributes = {}
+
+    # Extract model
+    if 'model' in kwargs:
+        attributes[SpanAttributes.LLM_REQUEST_MODEL] = kwargs["model"]
+
+    # Extract max_tokens
+    if 'max_tokens' in kwargs:
+        attributes[SpanAttributes.LLM_REQUEST_MAX_TOKENS] = kwargs["max_tokens"]
+
+    # Extract temperature
+    if 'temperature' in kwargs:
+        attributes[SpanAttributes.LLM_REQUEST_TEMPERATURE] = kwargs["temperature"]
+
+    # Extract top_p
+    if "top_p" in kwargs:
+        attributes[SpanAttributes.LLM_REQUEST_TOP_P] = kwargs["top_p"]
+
+    # Extract streaming
+    if "stream" in kwargs:
+        attributes[SpanAttributes.LLM_REQUEST_STREAMING] = kwargs["stream"]
+
+    return attributes