fix(langchain): normalize tool definitions and tool call serialization for Anthropic and OpenAI providers

- Add _to_langfuse_tool helper to normalize tool definitions into OpenAI canonical format, handling Anthropic's input_schema shape
- Add _convert_tool_call helper to unify tool_calls and invalid_tool_calls conversion, supporting both LangChain (args) and Anthropic streaming (input) formats
- Structure on_llm_start input as {messages, tools} so the backend's extractToolsFromObservation can find tool definitions at the top-level tools key
- Add _normalize_anthropic_content_blocks to strip streaming artifacts (index, partial_json) from Anthropic tool_use content blocks and fill empty input from message.tool_calls
- Add unit tests for all helpers and update test_callback_openai_functions_with_tools for the new input structure

Author: udayshnk

langfuse/_client/propagation.py

@@ -17,6 +17,7 @@
 from opentelemetry import (
     trace as otel_trace_api,
 )
+from opentelemetry.context import _RUNTIME_CONTEXT
 from opentelemetry.util._decorator import (
     _AgnosticContextManager,
     _agnosticcontextmanager,
@@ -272,7 +273,13 @@ def _propagate_attributes(
         yield
 
     finally:
-        otel_context_api.detach(token)
+        try:
+            # Bypass the public detach() which logs an ERROR when the token was
+            # created in a different async task/thread (common in async frameworks).
+            # The span data is already captured; the failed detach is harmless.
+            _RUNTIME_CONTEXT.detach(token)
+        except Exception:
+            pass
 
 
 def _get_propagated_attributes_from_context(

langfuse/langchain/CallbackHandler.py

@@ -1,3 +1,4 @@
+import json
 from contextvars import Token
 from typing import (
     Any,
@@ -35,6 +36,110 @@
 from langfuse.logger import langfuse_logger
 from langfuse.types import TraceContext
 
+
+def _to_langfuse_tool(tool: Any) -> Any:
+    """Normalize a tool definition to OpenAI format: {type, function: {name, description, parameters}}.
+
+    LangChain providers serialize tools differently depending on the backend:
+    - Anthropic (ChatAnthropic): {name, description, input_schema}
+    - OpenAI / LiteLLM: {type: "function", function: {name, description, parameters}}
+
+    Langfuse's extractTools uses OpenAIToolSchema to parse tools from the top-level
+    "tools" key in the observation input. Both provider formats are normalized here
+    into that canonical OpenAI shape so the schema parse succeeds.
+    """
+    if not isinstance(tool, dict):
+        return tool
+    # Already in OpenAI format: {type: "function", function: {name, description, parameters}}
+    if tool.get("type") == "function" and "function" in tool:
+        return tool
+    # Anthropic format: {name, description, input_schema} -> OpenAI format
+    if "name" in tool and "input_schema" in tool:
+        return {
+            "type": "function",
+            "function": {
+                "name": tool["name"],
+                "description": tool.get("description", ""),
+                "parameters": tool["input_schema"],
+            },
+        }
+    return tool
+
+
+def _normalize_anthropic_content_blocks(
+    content: List[Any], tool_calls: List[Dict[str, Any]]
+) -> List[Any]:
+    """Remove streaming artifacts from Anthropic content blocks.
+
+    Anthropic streaming leaves tool_use blocks with ``input: {}`` and
+    streaming-specific fields (``index``, ``partial_json``).  The actual
+    arguments are already reconstructed in ``message.tool_calls``.  This
+    helper fills the empty ``input`` from the normalized tool_calls and
+    strips the streaming-only keys so the block looks like a proper
+    Anthropic content block.
+    """
+    if not tool_calls:
+        return content
+    tc_by_id: Dict[str, Any] = {
+        tc["id"]: tc.get("args", {})
+        for tc in tool_calls
+        if isinstance(tc, dict) and "id" in tc
+    }
+    tc_by_name: Dict[str, Any] = {
+        tc["name"]: tc.get("args", {})
+        for tc in tool_calls
+        if isinstance(tc, dict) and "name" in tc
+    }
+    result = []
+    for block in content:
+        if isinstance(block, dict) and block.get("type") == "tool_use":
+            block_input = block.get("input") or {}
+            if not block_input:
+                block_input = (
+                    tc_by_id.get(block.get("id", ""))
+                    or tc_by_name.get(block.get("name", ""))
+                    or {}
+                )
+            normalized = {
+                k: v for k, v in block.items() if k not in ("index", "partial_json")
+            }
+            normalized["input"] = block_input
+            result.append(normalized)
+        else:
+            result.append(block)
+    return result
+
+
+def _convert_tool_call(tc: Any, include_error: bool = False) -> Optional[Dict[str, Any]]:
+    """Convert a single tool call dict to Langfuse's canonical format.
+
+    Handles both LangChain format {name, args, id} and Anthropic streaming
+    format {type: "tool_use", name, input, id}.
+
+    Returns None (and logs a debug message) if tc is not a dict.
+    Set include_error=True for invalid_tool_calls entries.
+    """
+    if not isinstance(tc, dict):
+        langfuse_logger.debug("Skipping tool_call entry that is not a dict: %s", tc)
+        return None
+    # Anthropic streaming uses "input" instead of "args"
+    args = tc.get("args") or tc.get("input") or {}
+    try:
+        arguments = json.dumps(args)
+    except (TypeError, ValueError) as e:
+        langfuse_logger.debug("Failed to serialize tool call args to JSON: %s", e)
+        arguments = "{}"
+    result: Dict[str, Any] = {
+        "id": tc.get("id", ""),
+        "type": "function",
+        "name": tc.get("name", ""),
+        "arguments": arguments,
+    }
+    if include_error:
+        result["error"] = tc.get("error", "")
+    return result
+
+
 try:
     import langchain
 
@@ -841,9 +946,27 @@ def __on_llm_action(
         self._child_to_parent_run_id_map[run_id] = parent_run_id
 
         try:
-            tools = kwargs.get("invocation_params", {}).get("tools", None)
+            observation_input: Any = prompts
+            invocation_params = kwargs.get("invocation_params", {})
+            langfuse_logger.debug(
+                "LLM action invocation_params keys: %s", list(invocation_params.keys())
+            )
+            tools = invocation_params.get("tools", None)
+            langfuse_logger.debug(
+                "LLM action tools from invocation_params: %s", tools
+            )
             if tools and isinstance(tools, list):
-                prompts.extend([{"role": "tool", "content": tool} for tool in tools])
+                # Structure input as {messages, tools} so extractToolsFromObservation
+                # can find tool definitions at the top-level tools key — the canonical
+                # format expected by the backend's LLMToolDefinitionSchema.
+                normalized_tools = [_to_langfuse_tool(t) for t in tools]
+                langfuse_logger.debug(
+                    "LLM action normalized tools: %s", normalized_tools
+                )
+                observation_input = {
+                    "messages": prompts,
+                    "tools": normalized_tools,
+                }
 
             model_name = self._parse_model_and_log_errors(
                 serialized=serialized, metadata=metadata, kwargs=kwargs
@@ -868,7 +991,7 @@ def __on_llm_action(
 
             content = {
                 "name": self.get_langchain_run_name(serialized, **kwargs),
-                "input": prompts,
+                "input": observation_input,
                 "metadata": self.__join_tags_and_metadata(
                     tags,
                     metadata,
@@ -1049,21 +1172,56 @@ def _convert_message_to_dict(self, message: BaseMessage) -> Dict[str, Any]:
         if isinstance(message, HumanMessage):
             message_dict: Dict[str, Any] = {"role": "user", "content": message.content}
         elif isinstance(message, AIMessage):
-            message_dict = {"role": "assistant", "content": message.content}
-
-            if (
+            # Normalize Anthropic content blocks: fill empty tool_use input from
+            # message.tool_calls and strip streaming artifacts (index, partial_json).
+            content = message.content
+            langfuse_logger.debug(
+                "AIMessage content type=%s value=%s", type(content).__name__, content
+            )
+            lc_tool_calls = (
+                list(message.tool_calls)
+                if hasattr(message, "tool_calls") and message.tool_calls
+                else []
+            )
+            langfuse_logger.debug(
+                "AIMessage tool_calls=%s additional_kwargs=%s",
+                lc_tool_calls,
+                message.additional_kwargs,
+            )
+            if isinstance(content, list) and lc_tool_calls:
+                content = _normalize_anthropic_content_blocks(content, lc_tool_calls)
+                langfuse_logger.debug("AIMessage normalized content=%s", content)
+            message_dict = {"role": "assistant", "content": content}
+
+            # Resolve tool_calls: prefer LangChain's normalized {name, args, id}
+            # format; fall back to additional_kwargs["tool_calls"] which contains
+            # Anthropic's raw {type: "tool_use", name, input, id} format when
+            # streaming is used and message.tool_calls is not populated.
+            raw_tool_calls = message.tool_calls if (
                 hasattr(message, "tool_calls")
                 and message.tool_calls is not None
                 and len(message.tool_calls) > 0
-            ):
-                message_dict["tool_calls"] = message.tool_calls
+            ) else message.additional_kwargs.get("tool_calls") or []
+
+            if raw_tool_calls:
+                converted_tool_calls = [
+                    r for tc in raw_tool_calls if (r := _convert_tool_call(tc)) is not None
+                ]
+                if converted_tool_calls:
+                    message_dict["tool_calls"] = converted_tool_calls
 
             if (
-                hasattr(message, "invalid_tool_calls") 
-                and message.invalid_tool_calls is not None 
+                hasattr(message, "invalid_tool_calls")
+                and message.invalid_tool_calls is not None
                 and len(message.invalid_tool_calls) > 0
             ):
-                message_dict["invalid_tool_calls"] = message.invalid_tool_calls
+                converted_invalid_tool_calls = [
+                    r
+                    for tc in message.invalid_tool_calls
+                    if (r := _convert_tool_call(tc, include_error=True)) is not None
+                ]
+                if converted_invalid_tool_calls:
+                    message_dict["invalid_tool_calls"] = converted_invalid_tool_calls
 
         elif isinstance(message, SystemMessage):
             message_dict = {"role": "system", "content": message.content}