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Code Issues Packages Projects Releases Wiki Activity

anthropic: support for code execution, MCP connector, files API features (#31340)

Browse Source 
Support for the new [batch of beta
features](https://www.anthropic.com/news/agent-capabilities-api)
released yesterday:

- [Code
execution](https://docs.anthropic.com/en/docs/agents-and-tools/tool-use/code-execution-tool)
- [MCP
connector](https://docs.anthropic.com/en/docs/agents-and-tools/mcp-connector)
- [Files
API](https://docs.anthropic.com/en/docs/build-with-claude/files)

Also verified support for [prompt cache
TTL](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching#1-hour-cache-duration-beta).
This commit is contained in:
ccurme
2025-05-27 12:45:45 -04:00
committed by GitHub
parent 1ebcbf1d11
commit 580986b260
11 changed files with 824 additions and 41 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
libs/core/langchain_core/language_models/chat_models.py
View File

@@ -129,6 +129,7 @@ def _format_for_tracing(messages: list[BaseMessage]) -> list[BaseMessage]:
isinstance(block, dict)
and block.get("type") == "image"
and is_data_content_block(block)
and block.get("source_type") != "id"
):
if message_to_trace is message:
message_to_trace = message.model_copy()

2
libs/langchain/tests/unit_tests/chat_models/test_base.py
View File

@@ -193,6 +193,7 @@ def test_configurable_with_default() -> None:
"name": None,
"disable_streaming": False,
"model": "claude-3-sonnet-20240229",
"mcp_servers": None,
"max_tokens": 1024,
"temperature": None,
"thinking": None,
@@ -203,6 +204,7 @@ def test_configurable_with_default() -> None:
"stop_sequences": None,
"anthropic_api_url": "https://api.anthropic.com",
"anthropic_api_key": SecretStr("bar"),
"betas": None,
"default_headers": None,
"model_kwargs": {},
"streaming": False,

301
libs/partners/anthropic/langchain_anthropic/chat_models.py
View File

@@ -1,4 +1,5 @@
import copy
import json
import re
import warnings
from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
@@ -100,6 +101,7 @@ def _is_builtin_tool(tool: Any) -> bool:
"computer_",
"bash_",
"web_search_",
"code_execution_",
]
return any(tool_type.startswith(prefix) for prefix in _builtin_tool_prefixes)
@@ -219,6 +221,14 @@ def _format_data_content_block(block: dict) -> dict:
"data": block["data"],
},
}
elif block["source_type"] == "id":
formatted_block = {
"type": "image",
"source": {
"type": "file",
"file_id": block["id"],
},
}
else:
raise ValueError(
"Anthropic only supports 'url' and 'base64' source_type for image "
@@ -252,6 +262,14 @@ def _format_data_content_block(block: dict) -> dict:
"data": block["text"],
},
}
elif block["source_type"] == "id":
formatted_block = {
"type": "document",
"source": {
"type": "file",
"file_id": block["id"],
},
}
else:
raise ValueError(f"Block of type {block['type']} is not supported.")
@@ -340,6 +358,29 @@ def _format_messages(
else:
block.pop("text", None)
content.append(block)
elif block["type"] in ("server_tool_use", "mcp_tool_use"):
formatted_block = {
k: v
for k, v in block.items()
if k
in (
"type",
"id",
"input",
"name",
"server_name", # for mcp_tool_use
"cache_control",
)
}
# Attempt to parse streamed output
if block.get("input") == {} and "partial_json" in block:
try:
input_ = json.loads(block["partial_json"])
if input_:
formatted_block["input"] = input_
except json.JSONDecodeError:
pass
content.append(formatted_block)
elif block["type"] == "text":
text = block.get("text", "")
# Only add non-empty strings for now as empty ones are not
@@ -375,6 +416,25 @@ def _format_messages(
[HumanMessage(block["content"])]
)[1][0]["content"]
content.append({**block, **{"content": tool_content}})
elif block["type"] in (
"code_execution_tool_result",
"mcp_tool_result",
"web_search_tool_result",
):
content.append(
{
k: v
for k, v in block.items()
if k
in (
"type",
"content",
"tool_use_id",
"is_error", # for mcp_tool_result
"cache_control",
)
}
)
else:
content.append(block)
else:
@@ -472,20 +532,21 @@ class ChatAnthropic(BaseChatModel):
**NOTE**: Any param which is not explicitly supported will be passed directly to the
``anthropic.Anthropic.messages.create(...)`` API every time to the model is
invoked. For example:
.. code-block:: python
from langchain_anthropic import ChatAnthropic
import anthropic
.. code-block:: python
ChatAnthropic(..., extra_headers={}).invoke(...)
from langchain_anthropic import ChatAnthropic
import anthropic
# results in underlying API call of:
ChatAnthropic(..., extra_headers={}).invoke(...)
anthropic.Anthropic(..).messages.create(..., extra_headers={})
# results in underlying API call of:
# which is also equivalent to:
anthropic.Anthropic(..).messages.create(..., extra_headers={})
ChatAnthropic(...).invoke(..., extra_headers={})
# which is also equivalent to:
ChatAnthropic(...).invoke(..., extra_headers={})
Invoke:
.. code-block:: python
@@ -645,6 +706,35 @@ class ChatAnthropic(BaseChatModel):
"After examining both images carefully, I can see that they are actually identical."
.. dropdown:: Files API
You can also pass in files that are managed through Anthropic's
`Files API <https://docs.anthropic.com/en/docs/build-with-claude/files>`_:
.. code-block:: python
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(
model="claude-sonnet-4-20250514",
betas=["files-api-2025-04-14"],
)
input_message = {
"role": "user",
"content": [
{
"type": "text",
"text": "Describe this document.",
},
{
"type": "image",
"source_type": "id",
"id": "file_abc123...",
},
],
}
llm.invoke([input_message])
PDF input:
See `multimodal guides <https://python.langchain.com/docs/how_to/multimodal_inputs/>`_
for more detail.
@@ -681,6 +771,35 @@ class ChatAnthropic(BaseChatModel):
"This appears to be a simple document..."
.. dropdown:: Files API
You can also pass in files that are managed through Anthropic's
`Files API <https://docs.anthropic.com/en/docs/build-with-claude/files>`_:
.. code-block:: python
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(
model="claude-sonnet-4-20250514",
betas=["files-api-2025-04-14"],
)
input_message = {
"role": "user",
"content": [
{
"type": "text",
"text": "Describe this document.",
},
{
"type": "file",
"source_type": "id",
"id": "file_abc123...",
},
],
}
llm.invoke([input_message])
Extended thinking:
Claude 3.7 Sonnet supports an
`extended thinking <https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking>`_
@@ -797,7 +916,7 @@ class ChatAnthropic(BaseChatModel):
or by setting ``stream_usage=False`` when initializing ChatAnthropic.
Prompt caching:
See LangChain `docs <https://python.langchain.com/docs/integrations/chat/anthropic/>`_
See LangChain `docs <https://python.langchain.com/docs/integrations/chat/anthropic/#built-in-tools>`_
for more detail.
.. code-block:: python
@@ -834,6 +953,24 @@ class ChatAnthropic(BaseChatModel):
{'cache_read': 0, 'cache_creation': 1458}
.. dropdown:: Extended caching
The cache lifetime is 5 minutes by default. If this is too short, you can
apply one hour caching by enabling the ``"extended-cache-ttl-2025-04-11"``
beta header:
.. code-block:: python
llm = ChatAnthropic(
model="claude-3-7-sonnet-20250219",
betas=["extended-cache-ttl-2025-04-11"],
)
and specifying ``"cache_control": {"type": "ephemeral", "ttl": "1h"}``.
See `Claude documentation <https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching#1-hour-cache-duration-beta>`_
for detail.
Token-efficient tool use (beta):
See LangChain `docs <https://python.langchain.com/docs/integrations/chat/anthropic/>`_
for more detail.
@@ -875,7 +1012,7 @@ class ChatAnthropic(BaseChatModel):
See LangChain `docs <https://python.langchain.com/docs/integrations/chat/anthropic/>`_
for more detail.
Web search:
.. dropdown:: Web search
.. code-block:: python
@@ -890,7 +1027,53 @@ class ChatAnthropic(BaseChatModel):
"How do I update a web app to TypeScript 5.5?"
)
Text editor:
.. dropdown:: Code execution
.. code-block:: python
llm = ChatAnthropic(
model="claude-sonnet-4-20250514",
betas=["code-execution-2025-05-22"],
)
tool = {"type": "code_execution_20250522", "name": "code_execution"}
llm_with_tools = llm.bind_tools([tool])
response = llm_with_tools.invoke(
"Calculate the mean and standard deviation of [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
)
.. dropdown:: Remote MCP
.. code-block:: python
from langchain_anthropic import ChatAnthropic
mcp_servers = [
{
"type": "url",
"url": "https://mcp.deepwiki.com/mcp",
"name": "deepwiki",
"tool_configuration": { # optional configuration
"enabled": True,
"allowed_tools": ["ask_question"],
},
"authorization_token": "PLACEHOLDER", # optional authorization
}
]
llm = ChatAnthropic(
model="claude-sonnet-4-20250514",
betas=["mcp-client-2025-04-04"],
mcp_servers=mcp_servers,
)
response = llm.invoke(
"What transport protocols does the 2025-03-26 version of the MCP "
"spec (modelcontextprotocol/modelcontextprotocol) support?"
)
.. dropdown:: Text editor
.. code-block:: python
@@ -986,6 +1169,13 @@ class ChatAnthropic(BaseChatModel):
default_headers: Optional[Mapping[str, str]] = None
"""Headers to pass to the Anthropic clients, will be used for every API call."""
betas: Optional[list[str]] = None
"""List of beta features to enable. If specified, invocations will be routed
through client.beta.messages.create.
Example: ``betas=["mcp-client-2025-04-04"]``
"""
model_kwargs: dict[str, Any] = Field(default_factory=dict)
streaming: bool = False
@@ -1000,6 +1190,13 @@ class ChatAnthropic(BaseChatModel):
"""Parameters for Claude reasoning,
e.g., ``{"type": "enabled", "budget_tokens": 10_000}``"""
mcp_servers: Optional[list[dict[str, Any]]] = None
"""List of MCP servers to use for the request.
Example: ``mcp_servers=[{"type": "url", "url": "https://mcp.example.com/mcp",
"name": "example-mcp"}]``
"""
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
@@ -1007,7 +1204,10 @@ class ChatAnthropic(BaseChatModel):
@property
def lc_secrets(self) -> dict[str, str]:
return {"anthropic_api_key": "ANTHROPIC_API_KEY"}
return {
"anthropic_api_key": "ANTHROPIC_API_KEY",
"mcp_servers": "ANTHROPIC_MCP_SERVERS",
}
@classmethod
def is_lc_serializable(cls) -> bool:
@@ -1099,6 +1299,8 @@ class ChatAnthropic(BaseChatModel):
"top_k": self.top_k,
"top_p": self.top_p,
"stop_sequences": stop or self.stop_sequences,
"betas": self.betas,
"mcp_servers": self.mcp_servers,
"system": system,
**self.model_kwargs,
**kwargs,
@@ -1107,6 +1309,18 @@ class ChatAnthropic(BaseChatModel):
payload["thinking"] = self.thinking
return {k: v for k, v in payload.items() if v is not None}
def _create(self, payload: dict) -> Any:
if "betas" in payload:
return self._client.beta.messages.create(**payload)
else:
return self._client.messages.create(**payload)
async def _acreate(self, payload: dict) -> Any:
if "betas" in payload:
return await self._async_client.beta.messages.create(**payload)
else:
return await self._async_client.messages.create(**payload)
def _stream(
self,
messages: list[BaseMessage],
@@ -1121,17 +1335,19 @@ class ChatAnthropic(BaseChatModel):
kwargs["stream"] = True
payload = self._get_request_payload(messages, stop=stop, **kwargs)
try:
stream = self._client.messages.create(**payload)
stream = self._create(payload)
coerce_content_to_string = (
not _tools_in_params(payload)
and not _documents_in_params(payload)
and not _thinking_in_params(payload)
)
block_start_event = None
for event in stream:
msg = _make_message_chunk_from_anthropic_event(
msg, block_start_event = _make_message_chunk_from_anthropic_event(
event,
stream_usage=stream_usage,
coerce_content_to_string=coerce_content_to_string,
block_start_event=block_start_event,
)
if msg is not None:
chunk = ChatGenerationChunk(message=msg)
@@ -1155,17 +1371,19 @@ class ChatAnthropic(BaseChatModel):
kwargs["stream"] = True
payload = self._get_request_payload(messages, stop=stop, **kwargs)
try:
stream = await self._async_client.messages.create(**payload)
stream = await self._acreate(payload)
coerce_content_to_string = (
not _tools_in_params(payload)
and not _documents_in_params(payload)
and not _thinking_in_params(payload)
)
block_start_event = None
async for event in stream:
msg = _make_message_chunk_from_anthropic_event(
msg, block_start_event = _make_message_chunk_from_anthropic_event(
event,
stream_usage=stream_usage,
coerce_content_to_string=coerce_content_to_string,
block_start_event=block_start_event,
)
if msg is not None:
chunk = ChatGenerationChunk(message=msg)
@@ -1234,7 +1452,7 @@ class ChatAnthropic(BaseChatModel):
return generate_from_stream(stream_iter)
payload = self._get_request_payload(messages, stop=stop, **kwargs)
try:
data = self._client.messages.create(**payload)
data = self._create(payload)
except anthropic.BadRequestError as e:
_handle_anthropic_bad_request(e)
return self._format_output(data, **kwargs)
@@ -1253,7 +1471,7 @@ class ChatAnthropic(BaseChatModel):
return await agenerate_from_stream(stream_iter)
payload = self._get_request_payload(messages, stop=stop, **kwargs)
try:
data = await self._async_client.messages.create(**payload)
data = await self._acreate(payload)
except anthropic.BadRequestError as e:
_handle_anthropic_bad_request(e)
return self._format_output(data, **kwargs)
@@ -1722,8 +1940,10 @@ def convert_to_anthropic_tool(
def _tools_in_params(params: dict) -> bool:
return "tools" in params or (
"extra_body" in params and params["extra_body"].get("tools")
return (
"tools" in params
or ("extra_body" in params and params["extra_body"].get("tools"))
or "mcp_servers" in params
)
@@ -1772,7 +1992,8 @@ def _make_message_chunk_from_anthropic_event(
*,
stream_usage: bool = True,
coerce_content_to_string: bool,
) -> Optional[AIMessageChunk]:
block_start_event: Optional[anthropic.types.RawMessageStreamEvent] = None,
) -> tuple[Optional[AIMessageChunk], Optional[anthropic.types.RawMessageStreamEvent]]:
"""Convert Anthropic event to AIMessageChunk.
Note that not all events will result in a message chunk. In these cases
@@ -1800,7 +2021,17 @@ def _make_message_chunk_from_anthropic_event(
elif (
event.type == "content_block_start"
and event.content_block is not None
and event.content_block.type in ("tool_use", "document", "redacted_thinking")
and event.content_block.type
in (
"tool_use",
"code_execution_tool_result",
"document",
"redacted_thinking",
"mcp_tool_use",
"mcp_tool_result",
"server_tool_use",
"web_search_tool_result",
)
):
if coerce_content_to_string:
warnings.warn("Received unexpected tool content block.")
@@ -1820,6 +2051,7 @@ def _make_message_chunk_from_anthropic_event(
content=[content_block],
tool_call_chunks=tool_call_chunks, # type: ignore
)
block_start_event = event
elif event.type == "content_block_delta":
if event.delta.type in ("text_delta", "citations_delta"):
if coerce_content_to_string and hasattr(event.delta, "text"):
@@ -1849,16 +2081,23 @@ def _make_message_chunk_from_anthropic_event(
elif event.delta.type == "input_json_delta":
content_block = event.delta.model_dump()
content_block["index"] = event.index
content_block["type"] = "tool_use"
tool_call_chunk = create_tool_call_chunk(
index=event.index,
id=None,
name=None,
args=event.delta.partial_json,
)
if (
(block_start_event is not None)
and hasattr(block_start_event, "content_block")
and (block_start_event.content_block.type == "tool_use")
):
tool_call_chunk = create_tool_call_chunk(
index=event.index,
id=None,
name=None,
args=event.delta.partial_json,
)
tool_call_chunks = [tool_call_chunk]
else:
tool_call_chunks = []
message_chunk = AIMessageChunk(
content=[content_block],
tool_call_chunks=[tool_call_chunk], # type: ignore
tool_call_chunks=tool_call_chunks, # type: ignore
)
elif event.type == "message_delta" and stream_usage:
usage_metadata = UsageMetadata(
@@ -1877,7 +2116,7 @@ def _make_message_chunk_from_anthropic_event(
else:
pass
return message_chunk
return message_chunk, block_start_event
@deprecated(since="0.1.0", removal="1.0.0", alternative="ChatAnthropic")

2
libs/partners/anthropic/pyproject.toml
View File

@@ -7,7 +7,7 @@ authors = []
license = { text = "MIT" }
requires-python = ">=3.9"
dependencies = [
"anthropic<1,>=0.51.0",
"anthropic<1,>=0.52.0",
"langchain-core<1.0.0,>=0.3.59",
"pydantic<3.0.0,>=2.7.4",
]

204
libs/partners/anthropic/tests/integration_tests/test_chat_models.py
View File

@@ -1,6 +1,7 @@
"""Test ChatAnthropic chat model."""
import json
import os
from base64 import b64encode
from typing import Optional
@@ -863,3 +864,206 @@ def test_image_tool_calling() -> None:
]
llm = ChatAnthropic(model="claude-3-5-sonnet-latest")
llm.bind_tools([color_picker]).invoke(messages)
# TODO: set up VCR
def test_web_search() -> None:
pytest.skip()
llm = ChatAnthropic(model="claude-3-5-sonnet-latest")
tool = {"type": "web_search_20250305", "name": "web_search", "max_uses": 1}
llm_with_tools = llm.bind_tools([tool])
input_message = {
"role": "user",
"content": [
{
"type": "text",
"text": "How do I update a web app to TypeScript 5.5?",
}
],
}
response = llm_with_tools.invoke([input_message])
block_types = {block["type"] for block in response.content}
assert block_types == {"text", "server_tool_use", "web_search_tool_result"}
# Test streaming
full: Optional[BaseMessageChunk] = None
for chunk in llm_with_tools.stream([input_message]):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
assert isinstance(full.content, list)
block_types = {block["type"] for block in full.content} # type: ignore[index]
assert block_types == {"text", "server_tool_use", "web_search_tool_result"}
# Test we can pass back in
next_message = {
"role": "user",
"content": "Please repeat the last search, but focus on sources from 2024.",
}
_ = llm_with_tools.invoke(
[input_message, full, next_message],
)
def test_code_execution() -> None:
pytest.skip()
llm = ChatAnthropic(
model="claude-sonnet-4-20250514",
betas=["code-execution-2025-05-22"],
)
tool = {"type": "code_execution_20250522", "name": "code_execution"}
llm_with_tools = llm.bind_tools([tool])
input_message = {
"role": "user",
"content": [
{
"type": "text",
"text": (
"Calculate the mean and standard deviation of "
"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
),
}
],
}
response = llm_with_tools.invoke([input_message])
block_types = {block["type"] for block in response.content}
assert block_types == {"text", "server_tool_use", "code_execution_tool_result"}
# Test streaming
full: Optional[BaseMessageChunk] = None
for chunk in llm_with_tools.stream([input_message]):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
assert isinstance(full.content, list)
block_types = {block["type"] for block in full.content} # type: ignore[index]
assert block_types == {"text", "server_tool_use", "code_execution_tool_result"}
# Test we can pass back in
next_message = {
"role": "user",
"content": "Please add more comments to the code.",
}
_ = llm_with_tools.invoke(
[input_message, full, next_message],
)
def test_remote_mcp() -> None:
pytest.skip()
mcp_servers = [
{
"type": "url",
"url": "https://mcp.deepwiki.com/mcp",
"name": "deepwiki",
"tool_configuration": {"enabled": True, "allowed_tools": ["ask_question"]},
"authorization_token": "PLACEHOLDER",
}
]
llm = ChatAnthropic(
model="claude-sonnet-4-20250514",
betas=["mcp-client-2025-04-04"],
mcp_servers=mcp_servers,
)
input_message = {
"role": "user",
"content": [
{
"type": "text",
"text": (
"What transport protocols does the 2025-03-26 version of the MCP "
"spec (modelcontextprotocol/modelcontextprotocol) support?"
),
}
],
}
response = llm.invoke([input_message])
block_types = {block["type"] for block in response.content}
assert block_types == {"text", "mcp_tool_use", "mcp_tool_result"}
# Test streaming
full: Optional[BaseMessageChunk] = None
for chunk in llm.stream([input_message]):
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
assert isinstance(full.content, list)
block_types = {block["type"] for block in full.content}
assert block_types == {"text", "mcp_tool_use", "mcp_tool_result"}
# Test we can pass back in
next_message = {
"role": "user",
"content": "Please query the same tool again, but add 'please' to your query.",
}
_ = llm.invoke(
[input_message, full, next_message],
)
@pytest.mark.parametrize("block_format", ["anthropic", "standard"])
def test_files_api_image(block_format: str) -> None:
image_file_id = os.getenv("ANTHROPIC_FILES_API_IMAGE_ID")
if not image_file_id:
pytest.skip()
llm = ChatAnthropic(
model="claude-sonnet-4-20250514",
betas=["files-api-2025-04-14"],
)
if block_format == "anthropic":
block = {
"type": "image",
"source": {
"type": "file",
"file_id": image_file_id,
},
}
else:
# standard block format
block = {
"type": "image",
"source_type": "id",
"id": image_file_id,
}
input_message = {
"role": "user",
"content": [
{"type": "text", "text": "Describe this image."},
block,
],
}
_ = llm.invoke([input_message])
@pytest.mark.parametrize("block_format", ["anthropic", "standard"])
def test_files_api_pdf(block_format: str) -> None:
pdf_file_id = os.getenv("ANTHROPIC_FILES_API_PDF_ID")
if not pdf_file_id:
pytest.skip()
llm = ChatAnthropic(
model="claude-sonnet-4-20250514",
betas=["files-api-2025-04-14"],
)
if block_format == "anthropic":
block = {"type": "document", "source": {"type": "file", "file_id": pdf_file_id}}
else:
# standard block format
block = {
"type": "file",
"source_type": "id",
"id": pdf_file_id,
}
input_message = {
"role": "user",
"content": [
{"type": "text", "text": "Describe this document."},
block,
],
}
_ = llm.invoke([input_message])

64
libs/partners/anthropic/tests/unit_tests/test_chat_models.py
View File

@@ -2,7 +2,7 @@
import os
from typing import Any, Callable, Literal, Optional, cast
from unittest.mock import patch
from unittest.mock import MagicMock, patch
import anthropic
import pytest
@@ -10,6 +10,8 @@ from anthropic.types import Message, TextBlock, Usage
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
from langchain_core.runnables import RunnableBinding
from langchain_core.tools import BaseTool
from langchain_core.tracers.base import BaseTracer
from langchain_core.tracers.schemas import Run
from pydantic import BaseModel, Field, SecretStr
from pytest import CaptureFixture, MonkeyPatch
@@ -994,3 +996,63 @@ def test_usage_metadata_standardization() -> None:
assert result["input_tokens"] == 0
assert result["output_tokens"] == 0
assert result["total_tokens"] == 0
class FakeTracer(BaseTracer):
def __init__(self) -> None:
super().__init__()
self.chat_model_start_inputs: list = []
def _persist_run(self, run: Run) -> None:
"""Persist a run."""
pass
def on_chat_model_start(self, *args: Any, **kwargs: Any) -> Run:
self.chat_model_start_inputs.append({"args": args, "kwargs": kwargs})
return super().on_chat_model_start(*args, **kwargs)
def test_mcp_tracing() -> None:
# Test we exclude sensitive information from traces
mcp_servers = [
{
"type": "url",
"url": "https://mcp.deepwiki.com/mcp",
"name": "deepwiki",
"authorization_token": "PLACEHOLDER",
}
]
llm = ChatAnthropic(
model="claude-sonnet-4-20250514",
betas=["mcp-client-2025-04-04"],
mcp_servers=mcp_servers,
)
tracer = FakeTracer()
mock_client = MagicMock()
def mock_create(*args: Any, **kwargs: Any) -> Message:
return Message(
id="foo",
content=[TextBlock(type="text", text="bar")],
model="baz",
role="assistant",
stop_reason=None,
stop_sequence=None,
usage=Usage(input_tokens=2, output_tokens=1),
type="message",
)
mock_client.messages.create = mock_create
input_message = HumanMessage("Test query")
with patch.object(llm, "_client", mock_client):
_ = llm.invoke([input_message], config={"callbacks": [tracer]})
# Test headers are not traced
assert len(tracer.chat_model_start_inputs) == 1
assert "PLACEHOLDER" not in str(tracer.chat_model_start_inputs)
# Test headers are correctly propagated to request
payload = llm._get_request_payload([input_message])
assert payload["mcp_servers"][0]["authorization_token"] == "PLACEHOLDER"

15
libs/partners/anthropic/uv.lock generated
View File

@@ -18,7 +18,7 @@ wheels = [
[[package]]
name = "anthropic"
version = "0.51.0"
version = "0.52.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "anyio" },
@@ -29,9 +29,9 @@ dependencies = [
{ name = "sniffio" },
{ name = "typing-extensions" },
]
sdist = { url = "https://files.pythonhosted.org/packages/63/4a/96f99a61ae299f9e5aa3e765d7342d95ab2e2ba5b69a3ffedb00ef779651/anthropic-0.51.0.tar.gz", hash = "sha256:6f824451277992af079554430d5b2c8ff5bc059cc2c968cdc3f06824437da201", size = 219063 }
sdist = { url = "https://files.pythonhosted.org/packages/57/fd/8a9332f5baf352c272494a9d359863a53385a208954c1a7251a524071930/anthropic-0.52.0.tar.gz", hash = "sha256:f06bc924d7eb85f8a43fe587b875ff58b410d60251b7dc5f1387b322a35bd67b", size = 229372 }
wheels = [
{ url = "https://files.pythonhosted.org/packages/8c/6e/9637122c5f007103bd5a259f4250bd8f1533dd2473227670fd10a1457b62/anthropic-0.51.0-py3-none-any.whl", hash = "sha256:b8b47d482c9aa1f81b923555cebb687c2730309a20d01be554730c8302e0f62a", size = 263957 },
{ url = "https://files.pythonhosted.org/packages/a0/43/172c0031654908bbac2a87d356fff4de1b4947a9b14b9658540b69416417/anthropic-0.52.0-py3-none-any.whl", hash = "sha256:c026daa164f0e3bde36ce9cbdd27f5f1419fff03306be1e138726f42e6a7810f", size = 286076 },
]
[[package]]
@@ -451,7 +451,7 @@ typing = [
[package.metadata]
requires-dist = [
{ name = "anthropic", specifier = ">=0.51.0,<1" },
{ name = "anthropic", specifier = ">=0.52.0,<1" },
{ name = "langchain-core", editable = "../../core" },
{ name = "pydantic", specifier = ">=2.7.4,<3.0.0" },
]
@@ -486,7 +486,7 @@ typing = [
[[package]]
name = "langchain-core"
version = "0.3.59"
version = "0.3.61"
source = { editable = "../../core" }
dependencies = [
{ name = "jsonpatch" },
@@ -501,10 +501,9 @@ dependencies = [
[package.metadata]
requires-dist = [
{ name = "jsonpatch", specifier = ">=1.33,<2.0" },
{ name = "langsmith", specifier = ">=0.1.125,<0.4" },
{ name = "langsmith", specifier = ">=0.1.126,<0.4" },
{ name = "packaging", specifier = ">=23.2,<25" },
{ name = "pydantic", marker = "python_full_version < '3.12.4'", specifier = ">=2.5.2,<3.0.0" },
{ name = "pydantic", marker = "python_full_version >= '3.12.4'", specifier = ">=2.7.4,<3.0.0" },
{ name = "pydantic", specifier = ">=2.7.4" },
{ name = "pyyaml", specifier = ">=5.3" },
{ name = "tenacity", specifier = ">=8.1.0,!=8.4.0,<10.0.0" },
{ name = "typing-extensions", specifier = ">=4.7" },