import copy
import json
import re
import warnings
from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
from functools import cached_property
from operator import itemgetter
from typing import (
Any,
Callable,
Literal,
Optional,
Union,
cast,
)
import anthropic
from langchain_core._api import beta, deprecated
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.exceptions import OutputParserException
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import (
BaseChatModel,
LangSmithParams,
agenerate_from_stream,
generate_from_stream,
)
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
HumanMessage,
SystemMessage,
ToolCall,
ToolMessage,
is_data_content_block,
)
from langchain_core.messages.ai import InputTokenDetails, UsageMetadata
from langchain_core.messages.tool import tool_call_chunk as create_tool_call_chunk
from langchain_core.output_parsers import (
JsonOutputKeyToolsParser,
PydanticToolsParser,
)
from langchain_core.output_parsers.base import OutputParserLike
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.runnables import (
Runnable,
RunnableMap,
RunnablePassthrough,
)
from langchain_core.tools import BaseTool
from langchain_core.utils import (
from_env,
get_pydantic_field_names,
secret_from_env,
)
from langchain_core.utils.function_calling import convert_to_openai_tool
from langchain_core.utils.pydantic import is_basemodel_subclass
from langchain_core.utils.utils import _build_model_kwargs
from pydantic import (
BaseModel,
ConfigDict,
Field,
SecretStr,
model_validator,
)
from typing_extensions import NotRequired, TypedDict
from langchain_anthropic.output_parsers import extract_tool_calls
_message_type_lookups = {
"human": "user",
"ai": "assistant",
"AIMessageChunk": "assistant",
"HumanMessageChunk": "user",
}
def _is_builtin_tool(tool: Any) -> bool:
if not isinstance(tool, dict):
return False
tool_type = tool.get("type")
if not tool_type or not isinstance(tool_type, str):
return False
_builtin_tool_prefixes = [
"text_editor_",
"computer_",
"bash_",
"web_search_",
"code_execution_",
]
return any(tool_type.startswith(prefix) for prefix in _builtin_tool_prefixes)
def _format_image(url: str) -> dict:
"""
Converts part["image_url"]["url"] strings (OpenAI format)
to the correct Anthropic format:
{
"type": "base64",
"media_type": "image/jpeg",
"data": "/9j/4AAQSkZJRg...",
}
Or
{
"type": "url",
"url": "https://example.com/image.jpg",
}
"""
# Base64 encoded image
base64_regex = r"^data:(?P<media_type>image/.+);base64,(?P<data>.+)$"
base64_match = re.match(base64_regex, url)
if base64_match:
return {
"type": "base64",
"media_type": base64_match.group("media_type"),
"data": base64_match.group("data"),
}
# Url
url_regex = r"^https?://.*$"
url_match = re.match(url_regex, url)
if url_match:
return {
"type": "url",
"url": url,
}
raise ValueError(
"Malformed url parameter."
" Must be either an image URL (https://example.com/image.jpg)"
" or base64 encoded string (data:image/png;base64,'/9j/4AAQSk'...)"
)
def _merge_messages(
messages: Sequence[BaseMessage],
) -> list[Union[SystemMessage, AIMessage, HumanMessage]]:
"""Merge runs of human/tool messages into single human messages with content blocks.""" # noqa: E501
merged: list = []
for curr in messages:
if isinstance(curr, ToolMessage):
if (
isinstance(curr.content, list)
and curr.content
and all(
isinstance(block, dict) and block.get("type") == "tool_result"
for block in curr.content
)
):
curr = HumanMessage(curr.content) # type: ignore[misc]
else:
curr = HumanMessage( # type: ignore[misc]
[
{
"type": "tool_result",
"content": curr.content,
"tool_use_id": curr.tool_call_id,
"is_error": curr.status == "error",
}
]
)
last = merged[-1] if merged else None
if any(
all(isinstance(m, c) for m in (curr, last))
for c in (SystemMessage, HumanMessage)
):
if isinstance(cast(BaseMessage, last).content, str):
new_content: list = [
{"type": "text", "text": cast(BaseMessage, last).content}
]
else:
new_content = copy.copy(cast(list, cast(BaseMessage, last).content))
if isinstance(curr.content, str):
new_content.append({"type": "text", "text": curr.content})
else:
new_content.extend(curr.content)
merged[-1] = curr.model_copy(update={"content": new_content})
else:
merged.append(curr)
return merged
def _format_data_content_block(block: dict) -> dict:
"""Format standard data content block to format expected by Anthropic."""
if block["type"] == "image":
if block["source_type"] == "url":
if block["url"].startswith("data:"):
# Data URI
formatted_block = {
"type": "image",
"source": _format_image(block["url"]),
}
else:
formatted_block = {
"type": "image",
"source": {"type": "url", "url": block["url"]},
}
elif block["source_type"] == "base64":
formatted_block = {
"type": "image",
"source": {
"type": "base64",
"media_type": block["mime_type"],
"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 "
"content blocks."
)
elif block["type"] == "file":
if block["source_type"] == "url":
formatted_block = {
"type": "document",
"source": {
"type": "url",
"url": block["url"],
},
}
elif block["source_type"] == "base64":
formatted_block = {
"type": "document",
"source": {
"type": "base64",
"media_type": block.get("mime_type") or "application/pdf",
"data": block["data"],
},
}
elif block["source_type"] == "text":
formatted_block = {
"type": "document",
"source": {
"type": "text",
"media_type": block.get("mime_type") or "text/plain",
"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.")
if formatted_block:
for key in ["cache_control", "citations", "title", "context"]:
if key in block:
formatted_block[key] = block[key]
elif (metadata := block.get("metadata")) and key in metadata:
formatted_block[key] = metadata[key]
return formatted_block
def _format_messages(
messages: Sequence[BaseMessage],
) -> tuple[Union[str, list[dict], None], list[dict]]:
"""Format messages for anthropic."""
"""
[
{
"role": _message_type_lookups[m.type],
"content": [_AnthropicMessageContent(text=m.content).model_dump()],
}
for m in messages
]
"""
system: Union[str, list[dict], None] = None
formatted_messages: list[dict] = []
merged_messages = _merge_messages(messages)
for i, message in enumerate(merged_messages):
if message.type == "system":
if system is not None:
raise ValueError("Received multiple non-consecutive system messages.")
elif isinstance(message.content, list):
system = [
(
block
if isinstance(block, dict)
else {"type": "text", "text": block}
)
for block in message.content
]
else:
system = message.content
continue
role = _message_type_lookups[message.type]
content: Union[str, list]
if not isinstance(message.content, str):
# parse as dict
assert isinstance(
message.content, list
), "Anthropic message content must be str or list of dicts"
# populate content
content = []
for block in message.content:
if isinstance(block, str):
content.append({"type": "text", "text": block})
elif isinstance(block, dict):
if "type" not in block:
raise ValueError("Dict content block must have a type key")
elif block["type"] == "image_url":
# convert format
source = _format_image(block["image_url"]["url"])
content.append({"type": "image", "source": source})
elif is_data_content_block(block):
content.append(_format_data_content_block(block))
elif block["type"] == "tool_use":
# If a tool_call with the same id as a tool_use content block
# exists, the tool_call is preferred.
if isinstance(message, AIMessage) and block["id"] in [
tc["id"] for tc in message.tool_calls
]:
overlapping = [
tc
for tc in message.tool_calls
if tc["id"] == block["id"]
]
content.extend(
_lc_tool_calls_to_anthropic_tool_use_blocks(overlapping)
)
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
# accepted.
# https://github.com/anthropics/anthropic-sdk-python/issues/461
if text.strip():
content.append(
{
k: v
for k, v in block.items()
if k in ("type", "text", "cache_control")
}
)
elif block["type"] == "thinking":
content.append(
{
k: v
for k, v in block.items()
if k
in ("type", "thinking", "cache_control", "signature")
}
)
elif block["type"] == "redacted_thinking":
content.append(
{
k: v
for k, v in block.items()
if k in ("type", "cache_control", "data")
}
)
elif block["type"] == "tool_result":
tool_content = _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:
raise ValueError(
f"Content blocks must be str or dict, instead was: "
f"{type(block)}"
)
else:
content = message.content
# Ensure all tool_calls have a tool_use content block
if isinstance(message, AIMessage) and message.tool_calls:
content = content or []
content = (
[{"type": "text", "text": message.content}]
if isinstance(content, str) and content
else content
)
tool_use_ids = [
cast(dict, block)["id"]
for block in content
if cast(dict, block)["type"] == "tool_use"
]
missing_tool_calls = [
tc for tc in message.tool_calls if tc["id"] not in tool_use_ids
]
cast(list, content).extend(
_lc_tool_calls_to_anthropic_tool_use_blocks(missing_tool_calls)
)
formatted_messages.append({"role": role, "content": content})
return system, formatted_messages
def _handle_anthropic_bad_request(e: anthropic.BadRequestError) -> None:
"""Handle Anthropic BadRequestError."""
if ("messages: at least one message is required") in e.message:
message = "Received only system message(s). "
warnings.warn(message)
raise e
else:
raise
[docs]
class ChatAnthropic(BaseChatModel):
"""Anthropic chat models.
See https://docs.anthropic.com/en/docs/models-overview for a list of the latest models.
Setup:
Install ``langchain-anthropic`` and set environment variable ``ANTHROPIC_API_KEY``.
.. code-block:: bash
pip install -U langchain-anthropic
export ANTHROPIC_API_KEY="your-api-key"
Key init args — completion params:
model: str
Name of Anthropic model to use. E.g. "claude-3-sonnet-20240229".
temperature: float
Sampling temperature. Ranges from 0.0 to 1.0.
max_tokens: int
Max number of tokens to generate.
Key init args — client params:
timeout: Optional[float]
Timeout for requests.
max_retries: int
Max number of retries if a request fails.
api_key: Optional[str]
Anthropic API key. If not passed in will be read from env var ANTHROPIC_API_KEY.
base_url: Optional[str]
Base URL for API requests. Only specify if using a proxy or service
emulator.
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(
model="claude-3-sonnet-20240229",
temperature=0,
max_tokens=1024,
timeout=None,
max_retries=2,
# api_key="...",
# base_url="...",
# other params...
)
**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
ChatAnthropic(..., extra_headers={}).invoke(...)
# results in underlying API call of:
anthropic.Anthropic(..).messages.create(..., extra_headers={})
# which is also equivalent to:
ChatAnthropic(...).invoke(..., extra_headers={})
Invoke:
.. code-block:: python
messages = [
("system", "You are a helpful translator. Translate the user sentence to French."),
("human", "I love programming."),
]
llm.invoke(messages)
.. code-block:: python
AIMessage(content="J'aime la programmation.", response_metadata={'id': 'msg_01Trik66aiQ9Z1higrD5XFx3', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 25, 'output_tokens': 11}}, id='run-5886ac5f-3c2e-49f5-8a44-b1e92808c929-0', usage_metadata={'input_tokens': 25, 'output_tokens': 11, 'total_tokens': 36})
Stream:
.. code-block:: python
for chunk in llm.stream(messages):
print(chunk.text(), end="")
.. code-block:: python
AIMessageChunk(content='J', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
AIMessageChunk(content="'", id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
AIMessageChunk(content='a', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
AIMessageChunk(content='ime', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
AIMessageChunk(content=' la', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
AIMessageChunk(content=' programm', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
AIMessageChunk(content='ation', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
AIMessageChunk(content='.', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
.. code-block:: python
stream = llm.stream(messages)
full = next(stream)
for chunk in stream:
full += chunk
full
.. code-block:: python
AIMessageChunk(content="J'aime la programmation.", id='run-b34faef0-882f-4869-a19c-ed2b856e6361')
Async:
.. code-block:: python
await llm.ainvoke(messages)
# stream:
# async for chunk in (await llm.astream(messages))
# batch:
# await llm.abatch([messages])
.. code-block:: python
AIMessage(content="J'aime la programmation.", response_metadata={'id': 'msg_01Trik66aiQ9Z1higrD5XFx3', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 25, 'output_tokens': 11}}, id='run-5886ac5f-3c2e-49f5-8a44-b1e92808c929-0', usage_metadata={'input_tokens': 25, 'output_tokens': 11, 'total_tokens': 36})
Tool calling:
.. code-block:: python
from pydantic import BaseModel, Field
class GetWeather(BaseModel):
'''Get the current weather in a given location'''
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
class GetPopulation(BaseModel):
'''Get the current population in a given location'''
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
ai_msg = llm_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
ai_msg.tool_calls
.. code-block:: python
[{'name': 'GetWeather',
'args': {'location': 'Los Angeles, CA'},
'id': 'toolu_01KzpPEAgzura7hpBqwHbWdo'},
{'name': 'GetWeather',
'args': {'location': 'New York, NY'},
'id': 'toolu_01JtgbVGVJbiSwtZk3Uycezx'},
{'name': 'GetPopulation',
'args': {'location': 'Los Angeles, CA'},
'id': 'toolu_01429aygngesudV9nTbCKGuw'},
{'name': 'GetPopulation',
'args': {'location': 'New York, NY'},
'id': 'toolu_01JPktyd44tVMeBcPPnFSEJG'}]
See ``ChatAnthropic.bind_tools()`` method for more.
Structured output:
.. code-block:: python
from typing import Optional
from pydantic import BaseModel, Field
class Joke(BaseModel):
'''Joke to tell user.'''
setup: str = Field(description="The setup of the joke")
punchline: str = Field(description="The punchline to the joke")
rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")
structured_llm = llm.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about cats")
.. code-block:: python
Joke(setup='Why was the cat sitting on the computer?', punchline='To keep an eye on the mouse!', rating=None)
See ``ChatAnthropic.with_structured_output()`` for more.
Image input:
See `multimodal guides <https://python.langchain.com/docs/how_to/multimodal_inputs/>`_
for more detail.
.. code-block:: python
import base64
import httpx
from langchain_anthropic import ChatAnthropic
from langchain_core.messages import HumanMessage
image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
llm = ChatAnthropic(model="claude-3-5-sonnet-latest")
message = HumanMessage(
content=[
{
"type": "text",
"text": "Can you highlight the differences between these two images?",
},
{
"type": "image",
"source_type": "base64",
"data": image_data,
"mime_type": "image/jpeg",
},
{
"type": "image",
"source_type": "url",
"url": image_url,
},
],
)
ai_msg = llm.invoke([message])
ai_msg.content
.. code-block:: python
"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.
.. code-block:: python
from base64 import b64encode
from langchain_anthropic import ChatAnthropic
from langchain_core.messages import HumanMessage
import requests
url = "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
data = b64encode(requests.get(url).content).decode()
llm = ChatAnthropic(model="claude-3-5-sonnet-latest")
ai_msg = llm.invoke(
[
HumanMessage(
[
"Summarize this document.",
{
"type": "file",
"source_type": "base64",
"mime_type": "application/pdf",
"data": data,
},
]
)
]
)
ai_msg.content
.. code-block:: python
"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>`_
feature, which will output the step-by-step reasoning process that led to its
final answer.
To use it, specify the `thinking` parameter when initializing `ChatAnthropic`.
It can also be passed in as a kwarg during invocation.
You will need to specify a token budget to use this feature. See usage example:
.. code-block:: python
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(
model="claude-3-7-sonnet-latest",
max_tokens=5000,
thinking={"type": "enabled", "budget_tokens": 2000},
)
response = llm.invoke("What is the cube root of 50.653?")
response.content
.. code-block:: python
[{'signature': '...', 'thinking': "To find the cube root of 50.653...", 'type': 'thinking'}, {'text': 'The cube root of 50.653 is ...', 'type': 'text'}]
Citations:
Anthropic supports a
`citations <https://docs.anthropic.com/en/docs/build-with-claude/citations>`_
feature that lets Claude attach context to its answers based on source
documents supplied by the user. When
`document content blocks <https://docs.anthropic.com/en/docs/build-with-claude/citations#document-types>`_
with ``"citations": {"enabled": True}`` are included in a query, Claude may
generate citations in its response.
.. code-block:: python
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-3-5-haiku-latest")
messages = [
{
"role": "user",
"content": [
{
"type": "document",
"source": {
"type": "text",
"media_type": "text/plain",
"data": "The grass is green. The sky is blue.",
},
"title": "My Document",
"context": "This is a trustworthy document.",
"citations": {"enabled": True},
},
{"type": "text", "text": "What color is the grass and sky?"},
],
}
]
response = llm.invoke(messages)
response.content
.. code-block:: python
[{'text': 'Based on the document, ', 'type': 'text'},
{'text': 'the grass is green',
'type': 'text',
'citations': [{'type': 'char_location',
'cited_text': 'The grass is green. ',
'document_index': 0,
'document_title': 'My Document',
'start_char_index': 0,
'end_char_index': 20}]},
{'text': ', and ', 'type': 'text'},
{'text': 'the sky is blue',
'type': 'text',
'citations': [{'type': 'char_location',
'cited_text': 'The sky is blue.',
'document_index': 0,
'document_title': 'My Document',
'start_char_index': 20,
'end_char_index': 36}]},
{'text': '.', 'type': 'text'}]
Token usage:
.. code-block:: python
ai_msg = llm.invoke(messages)
ai_msg.usage_metadata
.. code-block:: python
{'input_tokens': 25, 'output_tokens': 11, 'total_tokens': 36}
Message chunks containing token usage will be included during streaming by
default:
.. code-block:: python
stream = llm.stream(messages)
full = next(stream)
for chunk in stream:
full += chunk
full.usage_metadata
.. code-block:: python
{'input_tokens': 25, 'output_tokens': 11, 'total_tokens': 36}
These can be disabled by setting ``stream_usage=False`` in the stream method,
or by setting ``stream_usage=False`` when initializing ChatAnthropic.
Prompt caching:
See LangChain `docs <https://python.langchain.com/docs/integrations/chat/anthropic/#built-in-tools>`_
for more detail.
.. code-block:: python
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-3-7-sonnet-20250219")
messages = [
{
"role": "system",
"content": [
{
"type": "text",
"text": "Below is some long context:",
},
{
"type": "text",
"text": f"{long_text}",
"cache_control": {"type": "ephemeral"},
},
],
},
{
"role": "user",
"content": "What's that about?",
},
]
response = llm.invoke(messages)
response.usage_metadata["input_token_details"]
.. code-block:: python
{'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.
.. code-block:: python
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
llm = ChatAnthropic(
model="claude-3-7-sonnet-20250219",
temperature=0,
model_kwargs={
"extra_headers": {
"anthropic-beta": "token-efficient-tools-2025-02-19"
}
}
)
@tool
def get_weather(location: str) -> str:
\"\"\"Get the weather at a location.\"\"\"
return "It's sunny."
llm_with_tools = llm.bind_tools([get_weather])
response = llm_with_tools.invoke(
"What's the weather in San Francisco?"
)
print(response.tool_calls)
print(f'Total tokens: {response.usage_metadata["total_tokens"]}')
.. code-block:: none
[{'name': 'get_weather', 'args': {'location': 'San Francisco'}, 'id': 'toolu_01HLjQMSb1nWmgevQUtEyz17', 'type': 'tool_call'}]
Total tokens: 408
Built-in tools:
See LangChain `docs <https://python.langchain.com/docs/integrations/chat/anthropic/>`_
for more detail.
.. dropdown:: Web search
.. code-block:: python
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-3-5-sonnet-latest")
tool = {"type": "web_search_20250305", "name": "web_search", "max_uses": 3}
llm_with_tools = llm.bind_tools([tool])
response = llm_with_tools.invoke(
"How do I update a web app to TypeScript 5.5?"
)
.. 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
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-3-7-sonnet-20250219")
tool = {"type": "text_editor_20250124", "name": "str_replace_editor"}
llm_with_tools = llm.bind_tools([tool])
response = llm_with_tools.invoke(
"There's a syntax error in my primes.py file. Can you help me fix it?"
)
print(response.text())
response.tool_calls
.. code-block:: none
I'd be happy to help you fix the syntax error in your primes.py file. First, let's look at the current content of the file to identify the error.
[{'name': 'str_replace_editor',
'args': {'command': 'view', 'path': '/repo/primes.py'},
'id': 'toolu_01VdNgt1YV7kGfj9LFLm6HyQ',
'type': 'tool_call'}]
Response metadata
.. code-block:: python
ai_msg = llm.invoke(messages)
ai_msg.response_metadata
.. code-block:: python
{'id': 'msg_013xU6FHEGEq76aP4RgFerVT',
'model': 'claude-3-sonnet-20240229',
'stop_reason': 'end_turn',
'stop_sequence': None,
'usage': {'input_tokens': 25, 'output_tokens': 11}}
""" # noqa: E501
model_config = ConfigDict(
populate_by_name=True,
)
model: str = Field(alias="model_name")
"""Model name to use."""
max_tokens: int = Field(default=1024, alias="max_tokens_to_sample")
"""Denotes the number of tokens to predict per generation."""
temperature: Optional[float] = None
"""A non-negative float that tunes the degree of randomness in generation."""
top_k: Optional[int] = None
"""Number of most likely tokens to consider at each step."""
top_p: Optional[float] = None
"""Total probability mass of tokens to consider at each step."""
default_request_timeout: Optional[float] = Field(None, alias="timeout")
"""Timeout for requests to Anthropic Completion API."""
# sdk default = 2: https://github.com/anthropics/anthropic-sdk-python?tab=readme-ov-file#retries
max_retries: int = 2
"""Number of retries allowed for requests sent to the Anthropic Completion API."""
stop_sequences: Optional[list[str]] = Field(None, alias="stop")
"""Default stop sequences."""
anthropic_api_url: Optional[str] = Field(
alias="base_url",
default_factory=from_env(
["ANTHROPIC_API_URL", "ANTHROPIC_BASE_URL"],
default="https://api.anthropic.com",
),
)
"""Base URL for API requests. Only specify if using a proxy or service emulator.
If a value isn't passed in, will attempt to read the value first from
ANTHROPIC_API_URL and if that is not set, ANTHROPIC_BASE_URL.
If neither are set, the default value of 'https://api.anthropic.com' will
be used.
"""
anthropic_api_key: SecretStr = Field(
alias="api_key",
default_factory=secret_from_env("ANTHROPIC_API_KEY", default=""),
)
"""Automatically read from env var `ANTHROPIC_API_KEY` if not provided."""
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
"""Whether to use streaming or not."""
stream_usage: bool = True
"""Whether to include usage metadata in streaming output. If True, additional
message chunks will be generated during the stream including usage metadata.
"""
thinking: Optional[dict[str, Any]] = Field(default=None)
"""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."""
return "anthropic-chat"
@property
def lc_secrets(self) -> dict[str, str]:
return {
"anthropic_api_key": "ANTHROPIC_API_KEY",
"mcp_servers": "ANTHROPIC_MCP_SERVERS",
}
@classmethod
def is_lc_serializable(cls) -> bool:
return True
@classmethod
def get_lc_namespace(cls) -> list[str]:
"""Get the namespace of the langchain object."""
return ["langchain", "chat_models", "anthropic"]
@property
def _identifying_params(self) -> dict[str, Any]:
"""Get the identifying parameters."""
return {
"model": self.model,
"max_tokens": self.max_tokens,
"temperature": self.temperature,
"top_k": self.top_k,
"top_p": self.top_p,
"model_kwargs": self.model_kwargs,
"streaming": self.streaming,
"max_retries": self.max_retries,
"default_request_timeout": self.default_request_timeout,
"thinking": self.thinking,
}
def _get_ls_params(
self, stop: Optional[list[str]] = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = self._get_invocation_params(stop=stop, **kwargs)
ls_params = LangSmithParams(
ls_provider="anthropic",
ls_model_name=self.model,
ls_model_type="chat",
ls_temperature=params.get("temperature", self.temperature),
)
if ls_max_tokens := params.get("max_tokens", self.max_tokens):
ls_params["ls_max_tokens"] = ls_max_tokens
if ls_stop := stop or params.get("stop", None):
ls_params["ls_stop"] = ls_stop
return ls_params
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: dict) -> Any:
all_required_field_names = get_pydantic_field_names(cls)
values = _build_model_kwargs(values, all_required_field_names)
return values
@cached_property
def _client_params(self) -> dict[str, Any]:
client_params: dict[str, Any] = {
"api_key": self.anthropic_api_key.get_secret_value(),
"base_url": self.anthropic_api_url,
"max_retries": self.max_retries,
"default_headers": (self.default_headers or None),
}
# value <= 0 indicates the param should be ignored. None is a meaningful value
# for Anthropic client and treated differently than not specifying the param at
# all.
if self.default_request_timeout is None or self.default_request_timeout > 0:
client_params["timeout"] = self.default_request_timeout
return client_params
@cached_property
def _client(self) -> anthropic.Client:
return anthropic.Client(**self._client_params)
@cached_property
def _async_client(self) -> anthropic.AsyncClient:
return anthropic.AsyncClient(**self._client_params)
def _get_request_payload(
self,
input_: LanguageModelInput,
*,
stop: Optional[list[str]] = None,
**kwargs: dict,
) -> dict:
messages = self._convert_input(input_).to_messages()
system, formatted_messages = _format_messages(messages)
payload = {
"model": self.model,
"max_tokens": self.max_tokens,
"messages": formatted_messages,
"temperature": self.temperature,
"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,
}
if self.thinking is not None:
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],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
*,
stream_usage: Optional[bool] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
if stream_usage is None:
stream_usage = self.stream_usage
kwargs["stream"] = True
payload = self._get_request_payload(messages, stop=stop, **kwargs)
try:
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, 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)
if run_manager and isinstance(msg.content, str):
run_manager.on_llm_new_token(msg.content, chunk=chunk)
yield chunk
except anthropic.BadRequestError as e:
_handle_anthropic_bad_request(e)
async def _astream(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
*,
stream_usage: Optional[bool] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
if stream_usage is None:
stream_usage = self.stream_usage
kwargs["stream"] = True
payload = self._get_request_payload(messages, stop=stop, **kwargs)
try:
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, 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)
if run_manager and isinstance(msg.content, str):
await run_manager.on_llm_new_token(msg.content, chunk=chunk)
yield chunk
except anthropic.BadRequestError as e:
_handle_anthropic_bad_request(e)
def _format_output(self, data: Any, **kwargs: Any) -> ChatResult:
data_dict = data.model_dump()
content = data_dict["content"]
# Remove citations if they are None - introduced in anthropic sdk 0.45
for block in content:
if (
isinstance(block, dict)
and "citations" in block
and block["citations"] is None
):
block.pop("citations")
if (
isinstance(block, dict)
and block.get("type") == "thinking"
and "text" in block
and block["text"] is None
):
block.pop("text")
llm_output = {
k: v for k, v in data_dict.items() if k not in ("content", "role", "type")
}
if "model" in llm_output and "model_name" not in llm_output:
llm_output["model_name"] = llm_output["model"]
if (
len(content) == 1
and content[0]["type"] == "text"
and not content[0].get("citations")
):
msg = AIMessage(content=content[0]["text"])
elif any(block["type"] == "tool_use" for block in content):
tool_calls = extract_tool_calls(content)
msg = AIMessage(
content=content,
tool_calls=tool_calls,
)
else:
msg = AIMessage(content=content)
msg.usage_metadata = _create_usage_metadata(data.usage)
return ChatResult(
generations=[ChatGeneration(message=msg)],
llm_output=llm_output,
)
def _generate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
stream_iter = self._stream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return generate_from_stream(stream_iter)
payload = self._get_request_payload(messages, stop=stop, **kwargs)
try:
data = self._create(payload)
except anthropic.BadRequestError as e:
_handle_anthropic_bad_request(e)
return self._format_output(data, **kwargs)
async def _agenerate(
self,
messages: list[BaseMessage],
stop: Optional[list[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
stream_iter = self._astream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return await agenerate_from_stream(stream_iter)
payload = self._get_request_payload(messages, stop=stop, **kwargs)
try:
data = await self._acreate(payload)
except anthropic.BadRequestError as e:
_handle_anthropic_bad_request(e)
return self._format_output(data, **kwargs)
def _get_llm_for_structured_output_when_thinking_is_enabled(
self,
schema: Union[dict, type],
formatted_tool: AnthropicTool,
) -> Runnable[LanguageModelInput, BaseMessage]:
thinking_admonition = (
"Anthropic structured output relies on forced tool calling, "
"which is not supported when `thinking` is enabled. This method will raise "
"langchain_core.exceptions.OutputParserException if tool calls are not "
"generated. Consider disabling `thinking` or adjust your prompt to ensure "
"the tool is called."
)
warnings.warn(thinking_admonition)
llm = self.bind_tools(
[schema],
ls_structured_output_format={
"kwargs": {"method": "function_calling"},
"schema": formatted_tool,
},
)
def _raise_if_no_tool_calls(message: AIMessage) -> AIMessage:
if not message.tool_calls:
raise OutputParserException(thinking_admonition)
return message
return llm | _raise_if_no_tool_calls
[docs]
def with_structured_output(
self,
schema: Union[dict, type],
*,
include_raw: bool = False,
**kwargs: Any,
) -> Runnable[LanguageModelInput, Union[dict, BaseModel]]:
"""Model wrapper that returns outputs formatted to match the given schema.
Args:
schema: The output schema. Can be passed in as:
- an Anthropic tool schema,
- an OpenAI function/tool schema,
- a JSON Schema,
- a TypedDict class,
- or a Pydantic class.
If ``schema`` is a Pydantic class then the model output will be a
Pydantic instance of that class, and the model-generated fields will be
validated by the Pydantic class. Otherwise the model output will be a
dict and will not be validated. See :meth:`~langchain_core.utils.function_calling.convert_to_openai_tool`
for more on how to properly specify types and descriptions of
schema fields when specifying a Pydantic or TypedDict class.
include_raw:
If False then only the parsed structured output is returned. If
an error occurs during model output parsing it will be raised. If True
then both the raw model response (a BaseMessage) and the parsed model
response will be returned. If an error occurs during output parsing it
will be caught and returned as well. The final output is always a dict
with keys "raw", "parsed", and "parsing_error".
kwargs: Additional keyword arguments are ignored.
Returns:
A Runnable that takes same inputs as a :class:`~langchain_core.language_models.chat.BaseChatModel`.
If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs
an instance of ``schema`` (i.e., a Pydantic object).
Otherwise, if ``include_raw`` is False then Runnable outputs a dict.
If ``include_raw`` is True, then Runnable outputs a dict with keys:
- ``"raw"``: BaseMessage
- ``"parsed"``: None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
- ``"parsing_error"``: Optional[BaseException]
Example: Pydantic schema (include_raw=False):
.. code-block:: python
from langchain_anthropic import ChatAnthropic
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> AnswerWithJustification(
# answer='They weigh the same',
# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )
Example: Pydantic schema (include_raw=True):
.. code-block:: python
from langchain_anthropic import ChatAnthropic
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
# 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
# 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
# 'parsing_error': None
# }
Example: Dict schema (include_raw=False):
.. code-block:: python
from langchain_anthropic import ChatAnthropic
schema = {
"name": "AnswerWithJustification",
"description": "An answer to the user question along with justification for the answer.",
"input_schema": {
"type": "object",
"properties": {
"answer": {"type": "string"},
"justification": {"type": "string"},
},
"required": ["answer", "justification"]
}
}
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0)
structured_llm = llm.with_structured_output(schema)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
# 'answer': 'They weigh the same',
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }
.. versionchanged:: 0.1.22
Added support for TypedDict class as `schema`.
""" # noqa: E501
formatted_tool = convert_to_anthropic_tool(schema)
tool_name = formatted_tool["name"]
if self.thinking is not None and self.thinking.get("type") == "enabled":
llm = self._get_llm_for_structured_output_when_thinking_is_enabled(
schema, formatted_tool
)
else:
llm = self.bind_tools(
[schema],
tool_choice=tool_name,
ls_structured_output_format={
"kwargs": {"method": "function_calling"},
"schema": formatted_tool,
},
)
if isinstance(schema, type) and is_basemodel_subclass(schema):
output_parser: OutputParserLike = PydanticToolsParser(
tools=[schema], first_tool_only=True
)
else:
output_parser = JsonOutputKeyToolsParser(
key_name=tool_name, first_tool_only=True
)
if include_raw:
parser_assign = RunnablePassthrough.assign(
parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
)
parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
parser_with_fallback = parser_assign.with_fallbacks(
[parser_none], exception_key="parsing_error"
)
return RunnableMap(raw=llm) | parser_with_fallback
else:
return llm | output_parser
[docs]
@beta()
def get_num_tokens_from_messages(
self,
messages: list[BaseMessage],
tools: Optional[
Sequence[Union[dict[str, Any], type, Callable, BaseTool]]
] = None,
**kwargs: Any,
) -> int:
"""Count tokens in a sequence of input messages.
Args:
messages: The message inputs to tokenize.
tools: If provided, sequence of dict, BaseModel, function, or BaseTools
to be converted to tool schemas.
Basic usage:
.. code-block:: python
from langchain_anthropic import ChatAnthropic
from langchain_core.messages import HumanMessage, SystemMessage
llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")
messages = [
SystemMessage(content="You are a scientist"),
HumanMessage(content="Hello, Claude"),
]
llm.get_num_tokens_from_messages(messages)
.. code-block:: none
14
Pass tool schemas:
.. code-block:: python
from langchain_anthropic import ChatAnthropic
from langchain_core.messages import HumanMessage
from langchain_core.tools import tool
llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")
@tool(parse_docstring=True)
def get_weather(location: str) -> str:
\"\"\"Get the current weather in a given location
Args:
location: The city and state, e.g. San Francisco, CA
\"\"\"
return "Sunny"
messages = [
HumanMessage(content="What's the weather like in San Francisco?"),
]
llm.get_num_tokens_from_messages(messages, tools=[get_weather])
.. code-block:: none
403
.. versionchanged:: 0.3.0
Uses Anthropic's token counting API to count tokens in messages. See:
https://docs.anthropic.com/en/docs/build-with-claude/token-counting
"""
formatted_system, formatted_messages = _format_messages(messages)
if isinstance(formatted_system, str):
kwargs["system"] = formatted_system
if tools:
kwargs["tools"] = [convert_to_anthropic_tool(tool) for tool in tools]
response = self._client.beta.messages.count_tokens(
betas=["token-counting-2024-11-01"],
model=self.model,
messages=formatted_messages, # type: ignore[arg-type]
**kwargs,
)
return response.input_tokens
def _tools_in_params(params: dict) -> bool:
return (
"tools" in params
or ("extra_body" in params and params["extra_body"].get("tools"))
or "mcp_servers" in params
)
def _thinking_in_params(params: dict) -> bool:
return params.get("thinking", {}).get("type") == "enabled"
def _documents_in_params(params: dict) -> bool:
for message in params.get("messages", []):
if isinstance(message.get("content"), list):
for block in message["content"]:
if (
isinstance(block, dict)
and block.get("type") == "document"
and block.get("citations", {}).get("enabled")
):
return True
return False
class _AnthropicToolUse(TypedDict):
type: Literal["tool_use"]
name: str
input: dict
id: str
def _lc_tool_calls_to_anthropic_tool_use_blocks(
tool_calls: list[ToolCall],
) -> list[_AnthropicToolUse]:
blocks = []
for tool_call in tool_calls:
blocks.append(
_AnthropicToolUse(
type="tool_use",
name=tool_call["name"],
input=tool_call["args"],
id=cast(str, tool_call["id"]),
)
)
return blocks
def _make_message_chunk_from_anthropic_event(
event: anthropic.types.RawMessageStreamEvent,
*,
stream_usage: bool = True,
coerce_content_to_string: bool,
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
we return None.
"""
message_chunk: Optional[AIMessageChunk] = None
# See https://github.com/anthropics/anthropic-sdk-python/blob/main/src/anthropic/lib/streaming/_messages.py # noqa: E501
if event.type == "message_start" and stream_usage:
usage_metadata = _create_usage_metadata(event.message.usage)
# We pick up a cumulative count of output_tokens at the end of the stream,
# so here we zero out to avoid double counting.
usage_metadata["total_tokens"] = (
usage_metadata["total_tokens"] - usage_metadata["output_tokens"]
)
usage_metadata["output_tokens"] = 0
if hasattr(event.message, "model"):
response_metadata = {"model_name": event.message.model}
else:
response_metadata = {}
message_chunk = AIMessageChunk(
content="" if coerce_content_to_string else [],
usage_metadata=usage_metadata,
response_metadata=response_metadata,
)
elif (
event.type == "content_block_start"
and event.content_block is not None
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.")
content_block = event.content_block.model_dump()
content_block["index"] = event.index
if event.content_block.type == "tool_use":
tool_call_chunk = create_tool_call_chunk(
index=event.index,
id=event.content_block.id,
name=event.content_block.name,
args="",
)
tool_call_chunks = [tool_call_chunk]
else:
tool_call_chunks = []
message_chunk = AIMessageChunk(
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"):
text = event.delta.text
message_chunk = AIMessageChunk(content=text)
else:
content_block = event.delta.model_dump()
content_block["index"] = event.index
content_block["type"] = "text"
if "citation" in content_block:
content_block["citations"] = [content_block.pop("citation")]
message_chunk = AIMessageChunk(content=[content_block])
elif event.delta.type == "thinking_delta":
content_block = event.delta.model_dump()
if "text" in content_block and content_block["text"] is None:
content_block.pop("text")
content_block["index"] = event.index
content_block["type"] = "thinking"
message_chunk = AIMessageChunk(content=[content_block])
elif event.delta.type == "signature_delta":
content_block = event.delta.model_dump()
if "text" in content_block and content_block["text"] is None:
content_block.pop("text")
content_block["index"] = event.index
content_block["type"] = "thinking"
message_chunk = AIMessageChunk(content=[content_block])
elif event.delta.type == "input_json_delta":
content_block = event.delta.model_dump()
content_block["index"] = event.index
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_chunks, # type: ignore
)
elif event.type == "message_delta" and stream_usage:
usage_metadata = UsageMetadata(
input_tokens=0,
output_tokens=event.usage.output_tokens,
total_tokens=event.usage.output_tokens,
)
message_chunk = AIMessageChunk(
content="",
usage_metadata=usage_metadata,
response_metadata={
"stop_reason": event.delta.stop_reason,
"stop_sequence": event.delta.stop_sequence,
},
)
else:
pass
return message_chunk, block_start_event
[docs]
@deprecated(since="0.1.0", removal="1.0.0", alternative="ChatAnthropic")
class ChatAnthropicMessages(ChatAnthropic):
pass
def _create_usage_metadata(anthropic_usage: BaseModel) -> UsageMetadata:
input_token_details: dict = {
"cache_read": getattr(anthropic_usage, "cache_read_input_tokens", None),
"cache_creation": getattr(anthropic_usage, "cache_creation_input_tokens", None),
}
# Add (beta) cache TTL information if available
cache_creation = getattr(anthropic_usage, "cache_creation", None)
cache_creation_keys = ("ephemeral_1h_input_tokens", "ephemeral_5m_input_tokens")
if cache_creation:
if isinstance(cache_creation, BaseModel):
cache_creation = cache_creation.model_dump()
for k in cache_creation_keys:
input_token_details[k] = cache_creation.get(k)
# Anthropic input_tokens exclude cached token counts.
input_tokens = (
(getattr(anthropic_usage, "input_tokens", 0) or 0)
+ (input_token_details["cache_read"] or 0)
+ (input_token_details["cache_creation"] or 0)
)
output_tokens = getattr(anthropic_usage, "output_tokens", 0) or 0
return UsageMetadata(
input_tokens=input_tokens,
output_tokens=output_tokens,
total_tokens=input_tokens + output_tokens,
input_token_details=InputTokenDetails(
**{k: v for k, v in input_token_details.items() if v is not None},
),
)
