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langchain/libs/partners/anthropic/langchain_anthropic/chat_models.py
Mason Daugherty 57ff48e62e docs(anthropic): clean up docstrings (#34317) 
migration to docs
2025-12-12 11:30:34 -05:00

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"""Anthropic chat models."""
from __future__ import annotations
import copy
import datetime
import json
import re
import warnings
from collections.abc import AsyncIterator, Callable, Iterator, Mapping, Sequence
from functools import cached_property
from operator import itemgetter
from typing import Any, Final, Literal, cast
import anthropic
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.exceptions import OutputParserException
from langchain_core.language_models import (
LanguageModelInput,
ModelProfile,
ModelProfileRegistry,
)
from langchain_core.language_models.chat_models import BaseChatModel, LangSmithParams
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
HumanMessage,
SystemMessage,
ToolCall,
ToolMessage,
is_data_content_block,
)
from langchain_core.messages import content as types
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,
JsonOutputParser,
PydanticOutputParser,
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_json_schema,
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, Self, TypedDict
from langchain_anthropic._client_utils import (
_get_default_async_httpx_client,
_get_default_httpx_client,
)
from langchain_anthropic._compat import _convert_from_v1_to_anthropic
from langchain_anthropic.data._profiles import _PROFILES
from langchain_anthropic.output_parsers import extract_tool_calls
_message_type_lookups = {
"human": "user",
"ai": "assistant",
"AIMessageChunk": "assistant",
"HumanMessageChunk": "user",
}
_MODEL_PROFILES = cast(ModelProfileRegistry, _PROFILES)
def _get_default_model_profile(model_name: str) -> ModelProfile:
"""Get the default profile for a model.
Args:
model_name: The model identifier.
Returns:
The model profile dictionary, or an empty dict if not found.
"""
default = _MODEL_PROFILES.get(model_name)
if default:
return default.copy()
return {}
_FALLBACK_MAX_OUTPUT_TOKENS: Final[int] = 4096
class AnthropicTool(TypedDict):
"""Anthropic tool definition for custom (user-defined) tools.
Custom tools use `name` and `input_schema` fields to define the tool's
interface. These are converted from LangChain tool formats (functions, Pydantic
models, `BaseTool` objects) via `convert_to_anthropic_tool`.
"""
name: str
input_schema: dict[str, Any]
description: NotRequired[str]
strict: NotRequired[bool]
cache_control: NotRequired[dict[str, str]]
defer_loading: NotRequired[bool]
input_examples: NotRequired[list[dict[str, Any]]]
allowed_callers: NotRequired[list[str]]
# ---------------------------------------------------------------------------
# Built-in Tool Support
# ---------------------------------------------------------------------------
# When Anthropic releases new built-in tools, two places may need updating:
#
# 1. _TOOL_TYPE_TO_BETA (below) - Add mapping if the tool requires a beta header.
# Not all tools need this; only add if the API requires a beta header.
#
# 2. _is_builtin_tool() - Add the tool type prefix to _BUILTIN_TOOL_PREFIXES.
# This ensures the tool dict is passed through to the API unchanged (instead
# of being converted via convert_to_anthropic_tool, which may fail).
# ---------------------------------------------------------------------------
_TOOL_TYPE_TO_BETA: dict[str, str] = {
"web_fetch_20250910": "web-fetch-2025-09-10",
"code_execution_20250522": "code-execution-2025-05-22",
"code_execution_20250825": "code-execution-2025-08-25",
"mcp_toolset": "mcp-client-2025-11-20",
"memory_20250818": "context-management-2025-06-27",
"computer_20250124": "computer-use-2025-01-24",
"computer_20251124": "computer-use-2025-11-24",
"tool_search_tool_regex_20251119": "advanced-tool-use-2025-11-20",
"tool_search_tool_bm25_20251119": "advanced-tool-use-2025-11-20",
}
"""Mapping of tool type to required beta header.
Some tool types require specific beta headers to be enabled.
"""
_BUILTIN_TOOL_PREFIXES = [
"text_editor_",
"computer_",
"bash_",
"web_search_",
"web_fetch_",
"code_execution_",
"mcp_toolset",
"memory_",
"tool_search_",
]
_ANTHROPIC_EXTRA_FIELDS: set[str] = {
"allowed_callers",
"cache_control",
"defer_loading",
"input_examples",
}
"""Valid Anthropic-specific extra fields"""
def _is_builtin_tool(tool: Any) -> bool:
"""Check if a tool is a built-in (server-side) Anthropic tool.
`tool` must be a `dict` and have a `type` key starting with one of the known
built-in tool prefixes.
[Claude docs](https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview)
"""
if not isinstance(tool, dict):
return False
tool_type = tool.get("type")
if not tool_type or not isinstance(tool_type, str):
return False
return any(tool_type.startswith(prefix) for prefix in _BUILTIN_TOOL_PREFIXES)
def _format_image(url: str) -> dict:
"""Convert part["image_url"]["url"] strings (OpenAI format) to 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,
}
msg = (
"Malformed url parameter."
" Must be either an image URL (https://example.com/image.jpg)"
" or base64 encoded string (data:image/png;base64,'/9j/4AAQSk'...)"
)
raise ValueError(
msg,
)
def _merge_messages(
messages: Sequence[BaseMessage],
) -> list[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 "url" in block:
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 "base64" in block or block.get("source_type") == "base64":
formatted_block = {
"type": "image",
"source": {
"type": "base64",
"media_type": block["mime_type"],
"data": block.get("base64") or block.get("data", ""),
},
}
elif "file_id" in block:
formatted_block = {
"type": "image",
"source": {
"type": "file",
"file_id": block["file_id"],
},
}
elif block.get("source_type") == "id":
formatted_block = {
"type": "image",
"source": {
"type": "file",
"file_id": block["id"],
},
}
else:
msg = (
"Anthropic only supports 'url', 'base64', or 'id' keys for image "
"content blocks."
)
raise ValueError(
msg,
)
elif block["type"] == "file":
if "url" in block:
formatted_block = {
"type": "document",
"source": {
"type": "url",
"url": block["url"],
},
}
elif "base64" in block or block.get("source_type") == "base64":
formatted_block = {
"type": "document",
"source": {
"type": "base64",
"media_type": block.get("mime_type") or "application/pdf",
"data": block.get("base64") or block.get("data", ""),
},
}
elif block.get("source_type") == "text":
formatted_block = {
"type": "document",
"source": {
"type": "text",
"media_type": block.get("mime_type") or "text/plain",
"data": block["text"],
},
}
elif "file_id" in block:
formatted_block = {
"type": "document",
"source": {
"type": "file",
"file_id": block["file_id"],
},
}
elif block.get("source_type") == "id":
formatted_block = {
"type": "document",
"source": {
"type": "file",
"file_id": block["id"],
},
}
else:
msg = (
"Anthropic only supports 'url', 'base64', or 'id' keys for file "
"content blocks."
)
raise ValueError(msg)
elif block["type"] == "text-plain":
formatted_block = {
"type": "document",
"source": {
"type": "text",
"media_type": block.get("mime_type") or "text/plain",
"data": block["text"],
},
}
else:
msg = f"Block of type {block['type']} is not supported."
raise ValueError(msg)
if formatted_block:
for key in ["cache_control", "citations", "title", "context"]:
if key in block:
formatted_block[key] = block[key]
elif (metadata := block.get("extras")) and key in metadata:
formatted_block[key] = metadata[key]
elif (metadata := block.get("metadata")) and key in metadata:
# Backward compat
formatted_block[key] = metadata[key]
return formatted_block
def _format_messages(
messages: Sequence[BaseMessage],
) -> tuple[str | list[dict] | None, list[dict]]:
"""Format messages for Anthropic's API."""
system: 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:
msg = "Received multiple non-consecutive system messages."
raise ValueError(msg)
if 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: str | list
if not isinstance(message.content, str):
# parse as dict
if not isinstance(message.content, list):
msg = "Anthropic message content must be str or list of dicts"
raise ValueError(
msg,
)
# 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:
msg = "Dict content block must have a type key"
raise ValueError(msg)
if 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])
and not block.get("caller")
):
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:
if tool_input := block.get("input"):
args = tool_input
elif "partial_json" in block:
try:
args = json.loads(block["partial_json"] or "{}")
except json.JSONDecodeError:
args = {}
else:
args = {}
tool_use_block = _AnthropicToolUse(
type="tool_use",
name=block["name"],
input=args,
id=block["id"],
)
if caller := block.get("caller"):
tool_use_block["caller"] = caller
content.append(tool_use_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():
formatted_block = {
k: v
for k, v in block.items()
if k in ("type", "text", "cache_control", "citations")
}
# Clean up citations to remove null file_id fields
if formatted_block.get("citations"):
cleaned_citations = []
for citation in formatted_block["citations"]:
cleaned_citation = {
k: v
for k, v in citation.items()
if not (k == "file_id" and v is None)
}
cleaned_citations.append(cleaned_citation)
formatted_block["citations"] = cleaned_citations
content.append(formatted_block)
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"
and isinstance(block.get("content"), list)
and any(
isinstance(item, dict)
and item.get("type") == "tool_reference"
for item in block["content"]
)
):
# Tool search results with tool_reference blocks
content.append(
{
k: v
for k, v in block.items()
if k
in (
"type",
"content",
"tool_use_id",
"cache_control",
)
},
)
elif block["type"] == "tool_result":
# Regular tool results that need content formatting
tool_content = _format_messages(
[HumanMessage(block["content"])],
)[1][0]["content"]
content.append({**block, "content": tool_content})
elif block["type"] in (
"code_execution_tool_result",
"bash_code_execution_tool_result",
"text_editor_code_execution_tool_result",
"mcp_tool_result",
"web_search_tool_result",
"web_fetch_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",
"retrieved_at", # for web_fetch_tool_result
)
},
)
else:
content.append(block)
else:
msg = (
f"Content blocks must be str or dict, instead was: "
f"{type(block)}"
)
raise ValueError(
msg,
)
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),
)
if not content and role == "assistant" and _i < len(merged_messages) - 1:
# anthropic.BadRequestError: Error code: 400: all messages must have
# non-empty content except for the optional final assistant message
continue
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, stacklevel=2)
raise e
raise
class ChatAnthropic(BaseChatModel):
"""Anthropic (Claude) chat models.
See the [LangChain docs for `ChatAnthropic`](https://docs.langchain.com/oss/python/integrations/chat/anthropic)
for tutorials, feature walkthroughs, and examples.
See the [Claude Platform docs](https://platform.claude.com/docs/en/about-claude/models/overview)
for a list of the latest models, their capabilities, and pricing.
Example:
```python
# pip install -U langchain-anthropic
# export ANTHROPIC_API_KEY="your-api-key"
from langchain_anthropic import ChatAnthropic
model = ChatAnthropic(
model="claude-sonnet-4-5-20250929",
# temperature=,
# max_tokens=,
# timeout=,
# max_retries=,
# base_url="...",
# Refer to API reference for full list of parameters
)
```
Note:
Any param which is not explicitly supported will be passed directly to
[`Anthropic.messages.create(...)`](https://platform.claude.com/docs/en/api/python/messages/create)
each time to the model is invoked.
"""
model_config = ConfigDict(
populate_by_name=True,
)
model: str = Field(alias="model_name")
"""Model name to use."""
max_tokens: int | None = Field(default=None, alias="max_tokens_to_sample")
"""Denotes the number of tokens to predict per generation.
If not specified, this is set dynamically using the model's `max_output_tokens`
from its model profile.
See docs on [model profiles](https://docs.langchain.com/oss/python/langchain/models#model-profiles)
for more information.
"""
temperature: float | None = None
"""A non-negative float that tunes the degree of randomness in generation."""
top_k: int | None = None
"""Number of most likely tokens to consider at each step."""
top_p: float | None = None
"""Total probability mass of tokens to consider at each step."""
default_request_timeout: float | None = Field(None, alias="timeout")
"""Timeout for requests to Claude 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 Claude API."""
stop_sequences: list[str] | None = Field(None, alias="stop")
"""Default stop sequences."""
anthropic_api_url: str | None = 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`.
"""
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."""
anthropic_proxy: str | None = Field(
default_factory=from_env("ANTHROPIC_PROXY", default=None)
)
"""Proxy to use for the Anthropic clients, will be used for every API call.
If not provided, will attempt to read from the `ANTHROPIC_PROXY` environment
variable.
"""
default_headers: Mapping[str, str] | None = None
"""Headers to pass to the Anthropic clients, will be used for every API call."""
betas: list[str] | None = None
"""List of beta features to enable. If specified, invocations will be routed
through `client.beta.messages.create`.
Example: `#!python betas=["token-efficient-tools-2025-02-19"]`
"""
# Can also be passed in w/ model_kwargs, but having it as a param makes better devx
#
# Precedence order:
# 1. Call-time kwargs (e.g., llm.invoke(..., betas=[...]))
# 2. model_kwargs (e.g., ChatAnthropic(model_kwargs={"betas": [...]}))
# 3. Direct parameter (e.g., ChatAnthropic(betas=[...]))
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: dict[str, Any] | None = Field(default=None)
"""Parameters for Claude reasoning,
e.g., `#!python {"type": "enabled", "budget_tokens": 10_000}`
"""
effort: Literal["high", "medium", "low"] | None = None
"""Control how many tokens Claude uses when responding.
This parameter will be merged into the `output_config` parameter when making
API calls.
Example: `effort="medium"`
!!! note
Setting `effort` to `'high'` produces exactly the same behavior as omitting the
parameter altogether.
!!! note "Model Support"
This feature is currently only supported by Claude Opus 4.5.
!!! note "Automatic beta header"
The required `effort-2025-11-24` beta header is
automatically appended to the request when using `effort`, so you
don't need to manually specify it in the `betas` parameter.
"""
mcp_servers: list[dict[str, Any]] | None = None
"""List of MCP servers to use for the request.
Example: `#!python mcp_servers=[{"type": "url", "url": "https://mcp.example.com/mcp",
"name": "example-mcp"}]`
"""
context_management: dict[str, Any] | None = None
"""Configuration for
[context management](https://platform.claude.com/docs/en/build-with-claude/context-editing).
"""
reuse_last_container: bool | None = None
"""Automatically reuse container from most recent response (code execution).
When using the built-in
[code execution tool](https://docs.langchain.com/oss/python/integrations/chat/anthropic#code-execution),
model responses will include container metadata. Set `reuse_last_container=True`
to automatically reuse the container from the most recent response for subsequent
invocations.
"""
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "anthropic-chat"
@property
def lc_secrets(self) -> dict[str, str]:
"""Return a mapping of secret keys to environment variables."""
return {
"anthropic_api_key": "ANTHROPIC_API_KEY",
"mcp_servers": "ANTHROPIC_MCP_SERVERS",
}
@classmethod
def is_lc_serializable(cls) -> bool:
"""Whether the class is serializable in langchain."""
return True
@classmethod
def get_lc_namespace(cls) -> list[str]:
"""Get the namespace of the LangChain object.
Returns:
`["langchain", "chat_models", "anthropic"]`
"""
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: list[str] | None = 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=params.get("model", 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 set_default_max_tokens(cls, values: dict[str, Any]) -> Any:
"""Set default `max_tokens` from model profile with fallback."""
if values.get("max_tokens") is None:
model = values.get("model") or values.get("model_name")
profile = _get_default_model_profile(model) if model else {}
values["max_tokens"] = profile.get(
"max_output_tokens", _FALLBACK_MAX_OUTPUT_TOKENS
)
return values
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: dict) -> Any:
"""Build model kwargs."""
all_required_field_names = get_pydantic_field_names(cls)
return _build_model_kwargs(values, all_required_field_names)
@model_validator(mode="after")
def _set_model_profile(self) -> Self:
"""Set model profile if not overridden."""
if self.profile is None:
self.profile = _get_default_model_profile(self.model)
return self
@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:
client_params = self._client_params
http_client_params = {"base_url": client_params["base_url"]}
if "timeout" in client_params:
http_client_params["timeout"] = client_params["timeout"]
if self.anthropic_proxy:
http_client_params["anthropic_proxy"] = self.anthropic_proxy
http_client = _get_default_httpx_client(**http_client_params)
params = {
**client_params,
"http_client": http_client,
}
return anthropic.Client(**params)
@cached_property
def _async_client(self) -> anthropic.AsyncClient:
client_params = self._client_params
http_client_params = {"base_url": client_params["base_url"]}
if "timeout" in client_params:
http_client_params["timeout"] = client_params["timeout"]
if self.anthropic_proxy:
http_client_params["anthropic_proxy"] = self.anthropic_proxy
http_client = _get_default_async_httpx_client(**http_client_params)
params = {
**client_params,
"http_client": http_client,
}
return anthropic.AsyncClient(**params)
def _get_request_payload(
self,
input_: LanguageModelInput,
*,
stop: list[str] | None = None,
**kwargs: dict,
) -> dict:
"""Get the request payload for the Anthropic API."""
messages = self._convert_input(input_).to_messages()
for idx, message in enumerate(messages):
# Translate v1 content
if (
isinstance(message, AIMessage)
and message.response_metadata.get("output_version") == "v1"
):
tcs: list[types.ToolCall] = [
{
"type": "tool_call",
"name": tool_call["name"],
"args": tool_call["args"],
"id": tool_call.get("id"),
}
for tool_call in message.tool_calls
]
messages[idx] = message.model_copy(
update={
"content": _convert_from_v1_to_anthropic(
cast(list[types.ContentBlock], message.content),
tcs,
message.response_metadata.get("model_provider"),
)
}
)
system, formatted_messages = _format_messages(messages)
# If cache_control is provided in kwargs, add it to last message
# and content block.
if "cache_control" in kwargs and formatted_messages:
if isinstance(formatted_messages[-1]["content"], list):
formatted_messages[-1]["content"][-1]["cache_control"] = kwargs.pop(
"cache_control"
)
elif isinstance(formatted_messages[-1]["content"], str):
formatted_messages[-1]["content"] = [
{
"type": "text",
"text": formatted_messages[-1]["content"],
"cache_control": kwargs.pop("cache_control"),
}
]
else:
pass
# If cache_control remains in kwargs, it would be passed as a top-level param
# to the API, but Anthropic expects it nested within a message
_ = kwargs.pop("cache_control", None)
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,
"context_management": self.context_management,
"mcp_servers": self.mcp_servers,
"system": system,
**self.model_kwargs,
**kwargs,
}
if self.thinking is not None:
payload["thinking"] = self.thinking
# Handle output_config and effort parameter
# Priority: self.effort > payload output_config
output_config = payload.get("output_config", {})
output_config = output_config.copy() if isinstance(output_config, dict) else {}
if self.effort:
output_config["effort"] = self.effort
if output_config:
payload["output_config"] = output_config
# Auto-append required beta for effort
if "effort" in output_config:
required_beta = "effort-2025-11-24"
if payload["betas"]:
# Merge with existing betas
if required_beta not in payload["betas"]:
payload["betas"] = [*payload["betas"], required_beta]
else:
payload["betas"] = [required_beta]
if "response_format" in payload:
# response_format present when using agents.create_agent's ProviderStrategy
# ---
# ProviderStrategy converts to OpenAI-style format, which passes kwargs to
# ChatAnthropic, ending up in our payload
response_format = payload.pop("response_format")
if (
isinstance(response_format, dict)
and response_format.get("type") == "json_schema"
and "schema" in response_format.get("json_schema", {})
):
response_format = cast(dict, response_format["json_schema"]["schema"])
# Convert OpenAI-style response_format to Anthropic's output_format
payload["output_format"] = _convert_to_anthropic_output_format(
response_format
)
if "output_format" in payload:
# Native structured output requires the structured outputs beta
if payload["betas"]:
if "structured-outputs-2025-11-13" not in payload["betas"]:
# Merge with existing betas
payload["betas"] = [
*payload["betas"],
"structured-outputs-2025-11-13",
]
else:
payload["betas"] = ["structured-outputs-2025-11-13"]
if self.reuse_last_container:
# Check for most recent AIMessage with container set in response_metadata
# and set as a top-level param on the request
for message in reversed(messages):
if (
isinstance(message, AIMessage)
and (container := message.response_metadata.get("container"))
and isinstance(container, dict)
and (container_id := container.get("id"))
):
payload["container"] = container_id
break
# Check if any tools have strict mode enabled
if "tools" in payload and isinstance(payload["tools"], list):
has_strict_tool = any(
isinstance(tool, dict) and tool.get("strict") is True
for tool in payload["tools"]
)
if has_strict_tool:
# Strict tool use requires the structured outputs beta
if payload["betas"]:
if "structured-outputs-2025-11-13" not in payload["betas"]:
# Merge with existing betas
payload["betas"] = [
*payload["betas"],
"structured-outputs-2025-11-13",
]
else:
payload["betas"] = ["structured-outputs-2025-11-13"]
# Auto-append required betas for specific tool types and input_examples
has_input_examples = False
for tool in payload["tools"]:
if isinstance(tool, dict):
tool_type = tool.get("type")
if tool_type and tool_type in _TOOL_TYPE_TO_BETA:
required_beta = _TOOL_TYPE_TO_BETA[tool_type]
if payload["betas"]:
if required_beta not in payload["betas"]:
payload["betas"] = [
*payload["betas"],
required_beta,
]
else:
payload["betas"] = [required_beta]
# Check for input_examples
if tool.get("input_examples"):
has_input_examples = True
# Auto-append header for input_examples
if has_input_examples:
required_beta = "advanced-tool-use-2025-11-20"
if payload["betas"]:
if required_beta not in payload["betas"]:
payload["betas"] = [*payload["betas"], required_beta]
else:
payload["betas"] = [required_beta]
# Auto-append required beta for mcp_servers
if payload.get("mcp_servers"):
required_beta = "mcp-client-2025-11-20"
if payload["betas"]:
# Append to existing betas if not already present
if required_beta not in payload["betas"]:
payload["betas"] = [*payload["betas"], required_beta]
else:
payload["betas"] = [required_beta]
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)
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)
return await self._async_client.messages.create(**payload)
def _stream(
self,
messages: list[BaseMessage],
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
*,
stream_usage: bool | None = 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: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
*,
stream_usage: bool | None = 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:
"""Format the output from the Anthropic API to LC."""
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):
if "citations" in block and block["citations"] is None:
block.pop("citations")
if "caller" in block and block["caller"] is None:
block.pop("caller")
if (
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 (
(container := llm_output.get("container"))
and isinstance(container, dict)
and (expires_at := container.get("expires_at"))
and isinstance(expires_at, datetime.datetime)
):
# TODO: dump all `data` with `mode="json"`
llm_output["container"]["expires_at"] = expires_at.isoformat()
response_metadata = {"model_provider": "anthropic"}
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"], response_metadata=response_metadata
)
elif any(block["type"] == "tool_use" for block in content):
tool_calls = extract_tool_calls(content)
msg = AIMessage(
content=content,
tool_calls=tool_calls,
response_metadata=response_metadata,
)
else:
msg = AIMessage(content=content, response_metadata=response_metadata)
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: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
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: list[str] | None = None,
run_manager: AsyncCallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
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: dict | type,
formatted_tool: AnthropicTool,
) -> Runnable[LanguageModelInput, BaseMessage]:
thinking_admonition = (
"You are attempting to use structured output via forced tool calling, "
"which is not guaranteed when `thinking` is enabled. This method will "
"raise an OutputParserException if tool calls are not generated. Consider "
"disabling `thinking` or adjust your prompt to ensure the tool is called."
)
warnings.warn(thinking_admonition, stacklevel=2)
llm = self.bind_tools(
[schema],
# We don't specify tool_choice here since the API will reject attempts to
# force tool calls when thinking=true
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
def bind_tools(
self,
tools: Sequence[Mapping[str, Any] | type | Callable | BaseTool],
*,
tool_choice: dict[str, str] | str | None = None,
parallel_tool_calls: bool | None = None,
strict: bool | None = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, AIMessage]:
r"""Bind tool-like objects to `ChatAnthropic`.
Args:
tools: A list of tool definitions to bind to this chat model.
Supports Anthropic format tool schemas and any tool definition handled
by [`convert_to_openai_tool`][langchain_core.utils.function_calling.convert_to_openai_tool].
tool_choice: Which tool to require the model to call. Options are:
- Name of the tool as a string or as dict `{"type": "tool", "name": "<<tool_name>>"}`: calls corresponding tool
- `'auto'`, `{"type: "auto"}`, or `None`: automatically selects a tool (including no tool)
- `'any'` or `{"type: "any"}`: force at least one tool to be called
parallel_tool_calls: Set to `False` to disable parallel tool use.
Defaults to `None` (no specification, which allows parallel tool use).
!!! version-added "Added in `langchain-anthropic` 0.3.2"
strict: If `True`, Claude's schema adherence is applied to tool calls.
See the [docs](https://docs.langchain.com/oss/python/integrations/chat/anthropic#strict-tool-use) for more info.
kwargs: Any additional parameters are passed directly to `bind`.
Example:
```python
from langchain_anthropic import ChatAnthropic
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 GetPrice(BaseModel):
'''Get the price of a specific product.'''
product: str = Field(..., description="The product to look up.")
model = ChatAnthropic(model="claude-sonnet-4-5-20250929", temperature=0)
model_with_tools = model.bind_tools([GetWeather, GetPrice])
model_with_tools.invoke(
"What is the weather like in San Francisco",
)
# -> AIMessage(
# content=[
# {'text': '<thinking>\nBased on the user\'s question, the relevant function to call is GetWeather, which requires the "location" parameter.\n\nThe user has directly specified the location as "San Francisco". Since San Francisco is a well known city, I can reasonably infer they mean San Francisco, CA without needing the state specified.\n\nAll the required parameters are provided, so I can proceed with the API call.\n</thinking>', 'type': 'text'},
# {'text': None, 'type': 'tool_use', 'id': 'toolu_01SCgExKzQ7eqSkMHfygvYuu', 'name': 'GetWeather', 'input': {'location': 'San Francisco, CA'}}
# ],
# response_metadata={'id': 'msg_01GM3zQtoFv8jGQMW7abLnhi', 'model': 'claude-sonnet-4-5-20250929', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 487, 'output_tokens': 145}},
# id='run-87b1331e-9251-4a68-acef-f0a018b639cc-0'
# )
```
""" # noqa: E501
# Allows built-in tools either by their:
# - Raw `dict` format
# - Extracting extras["provider_tool_definition"] if provided on a BaseTool
formatted_tools = [
tool
if _is_builtin_tool(tool)
else convert_to_anthropic_tool(tool, strict=strict)
for tool in tools
]
if not tool_choice:
pass
elif isinstance(tool_choice, dict):
kwargs["tool_choice"] = tool_choice
elif isinstance(tool_choice, str) and tool_choice in ("any", "auto"):
kwargs["tool_choice"] = {"type": tool_choice}
elif isinstance(tool_choice, str):
kwargs["tool_choice"] = {"type": "tool", "name": tool_choice}
else:
msg = (
f"Unrecognized 'tool_choice' type {tool_choice=}. Expected dict, "
f"str, or None."
)
raise ValueError(
msg,
)
if parallel_tool_calls is not None:
disable_parallel_tool_use = not parallel_tool_calls
if "tool_choice" in kwargs:
kwargs["tool_choice"]["disable_parallel_tool_use"] = (
disable_parallel_tool_use
)
else:
kwargs["tool_choice"] = {
"type": "auto",
"disable_parallel_tool_use": disable_parallel_tool_use,
}
return self.bind(tools=formatted_tools, **kwargs)
def with_structured_output(
self,
schema: dict | type,
*,
include_raw: bool = False,
method: Literal["function_calling", "json_schema"] = "function_calling",
**kwargs: Any,
) -> Runnable[LanguageModelInput, dict | BaseModel]:
"""Model wrapper that returns outputs formatted to match the given schema.
See the [LangChain docs](https://docs.langchain.com/oss/python/integrations/chat/anthropic#structured-output)
for more details and examples.
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 `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'`.
method: The structured output method to use. Options are:
- `'function_calling'` (default): Use forced tool calling to get
structured output.
- `'json_schema'`: Use Claude's dedicated
[structured output](https://platform.claude.com/docs/en/build-with-claude/structured-outputs)
feature.
kwargs: Additional keyword arguments are ignored.
Returns:
A `Runnable` that takes same inputs as a
`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'`: `BaseException | None`
Example:
```python hl_lines="13"
from langchain_anthropic import ChatAnthropic
from pydantic import BaseModel, Field
model = ChatAnthropic(model="claude-sonnet-4-5")
class Movie(BaseModel):
\"\"\"A movie with details.\"\"\"
title: str = Field(..., description="The title of the movie")
year: int = Field(..., description="The year the movie was released")
director: str = Field(..., description="The director of the movie")
rating: float = Field(..., description="The movie's rating out of 10")
model_with_structure = model.with_structured_output(Movie, method="json_schema")
response = model_with_structure.invoke("Provide details about the movie Inception")
print(response)
# -> Movie(title="Inception", year=2010, director="Christopher Nolan", rating=8.8)
```
""" # noqa: E501
if method == "json_mode":
warning_message = (
"Unrecognized structured output method 'json_mode'. Defaulting to "
"'json_schema' method."
)
warnings.warn(warning_message, stacklevel=2)
method = "json_schema"
if method == "function_calling":
formatted_tool = cast(AnthropicTool, convert_to_anthropic_tool(schema))
# The result of convert_to_anthropic_tool for 'method=function_calling' will
# always be an AnthropicTool
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, # Force tool call
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,
)
elif method == "json_schema":
llm = self.bind(
output_format=_convert_to_anthropic_output_format(schema),
ls_structured_output_format={
"kwargs": {"method": "json_schema"},
"schema": convert_to_openai_tool(schema),
},
)
if isinstance(schema, type) and is_basemodel_subclass(schema):
output_parser = PydanticOutputParser(pydantic_object=schema)
else:
output_parser = JsonOutputParser()
else:
error_message = (
f"Unrecognized structured output method '{method}'. "
f"Expected 'function_calling' or 'json_schema'."
)
raise ValueError(error_message)
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
return llm | output_parser
def get_num_tokens_from_messages(
self,
messages: list[BaseMessage],
tools: Sequence[dict[str, Any] | type | Callable | BaseTool] | None = None,
**kwargs: Any,
) -> int:
"""Count tokens in a sequence of input messages.
This uses Anthropic's official [token counting API](https://platform.claude.com/docs/en/build-with-claude/token-counting).
Args:
messages: The message inputs to tokenize.
tools: If provided, sequence of `dict`, `BaseModel`, function, or `BaseTool`
objects to be converted to tool schemas.
kwargs: Additional keyword arguments are passed to the Anthropic
`messages.count_tokens` method.
???+ example "Basic usage"
```python
from langchain_anthropic import ChatAnthropic
from langchain_core.messages import HumanMessage, SystemMessage
model = ChatAnthropic(model="claude-sonnet-4-5-20250929")
messages = [
SystemMessage(content="You are a scientist"),
HumanMessage(content="Hello, Claude"),
]
model.get_num_tokens_from_messages(messages)
```
```txt
14
```
??? example "Pass tool schemas"
```python
from langchain_anthropic import ChatAnthropic
from langchain_core.messages import HumanMessage
from langchain_core.tools import tool
model = ChatAnthropic(model="claude-sonnet-4-5-20250929")
@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?"),
]
model.get_num_tokens_from_messages(messages, tools=[get_weather])
```
```txt
403
```
""" # noqa: D214
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]
if self.context_management is not None:
kwargs["context_management"] = self.context_management
if self.betas is not None:
beta_response = self._client.beta.messages.count_tokens(
betas=self.betas,
model=self.model,
messages=formatted_messages, # type: ignore[arg-type]
**kwargs,
)
return beta_response.input_tokens
response = self._client.messages.count_tokens(
model=self.model,
messages=formatted_messages, # type: ignore[arg-type]
**kwargs,
)
return response.input_tokens
def convert_to_anthropic_tool(
tool: Mapping[str, Any] | type | Callable | BaseTool,
*,
strict: bool | None = None,
) -> AnthropicTool:
"""Convert a tool-like object to an Anthropic tool definition.
Args:
tool: A tool-like object to convert. Can be an Anthropic tool dict,
a Pydantic model, a function, or a `BaseTool`.
strict: If `True`, enables strict schema adherence for the tool.
!!! note
Requires Claude Sonnet 4.5 or Opus 4.1.
Returns:
`AnthropicTool` for custom/user-defined tools
"""
if (
isinstance(tool, BaseTool)
and hasattr(tool, "extras")
and isinstance(tool.extras, dict)
and "provider_tool_definition" in tool.extras
):
# Pass through built-in tool definitions
return tool.extras["provider_tool_definition"] # type: ignore[return-value]
if isinstance(tool, dict) and all(
k in tool for k in ("name", "description", "input_schema")
):
# Anthropic tool format
anthropic_formatted = AnthropicTool(tool) # type: ignore[misc]
else:
oai_formatted = convert_to_openai_tool(tool, strict=strict)["function"]
anthropic_formatted = AnthropicTool(
name=oai_formatted["name"],
input_schema=oai_formatted["parameters"],
)
if "description" in oai_formatted:
anthropic_formatted["description"] = oai_formatted["description"]
if "strict" in oai_formatted and isinstance(strict, bool):
anthropic_formatted["strict"] = oai_formatted["strict"]
# Select params from tool.extras
if (
isinstance(tool, BaseTool)
and hasattr(tool, "extras")
and isinstance(tool.extras, dict)
):
for key, value in tool.extras.items():
if key in _ANTHROPIC_EXTRA_FIELDS:
# all are populated top-level
anthropic_formatted[key] = value # type: ignore[literal-required]
return anthropic_formatted
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
caller: NotRequired[dict[str, Any]]
def _lc_tool_calls_to_anthropic_tool_use_blocks(
tool_calls: list[ToolCall],
) -> list[_AnthropicToolUse]:
return [
_AnthropicToolUse(
type="tool_use",
name=tool_call["name"],
input=tool_call["args"],
id=cast("str", tool_call["id"]),
)
for tool_call in tool_calls
]
def _convert_to_anthropic_output_format(schema: dict | type) -> dict[str, Any]:
"""Convert JSON schema, Pydantic model, or `TypedDict` into Claude `output_format`.
See Claude docs on [structured outputs](https://platform.claude.com/docs/en/build-with-claude/structured-outputs).
"""
from anthropic import transform_schema
is_pydantic_class = isinstance(schema, type) and is_basemodel_subclass(schema)
if is_pydantic_class or isinstance(schema, dict):
json_schema = transform_schema(schema)
else:
# TypedDict
json_schema = transform_schema(convert_to_json_schema(schema))
return {"type": "json_schema", "schema": json_schema}
def _make_message_chunk_from_anthropic_event(
event: anthropic.types.RawMessageStreamEvent,
*,
stream_usage: bool = True,
coerce_content_to_string: bool,
block_start_event: anthropic.types.RawMessageStreamEvent | None = None,
) -> tuple[AIMessageChunk | None, anthropic.types.RawMessageStreamEvent | None]:
"""Convert Anthropic streaming event to `AIMessageChunk`.
Args:
event: Raw streaming event from Anthropic SDK
stream_usage: Whether to include usage metadata in the output chunks.
coerce_content_to_string: Whether to convert structured content to plain
text strings.
When `True`, only text content is preserved; when `False`, structured
content like tool calls and citations are maintained.
block_start_event: Previous content block start event, used for tracking
tool use blocks and maintaining context across related events.
Returns:
Tuple with
- `AIMessageChunk`: Converted message chunk with appropriate content and
metadata, or `None` if the event doesn't produce a chunk
- `RawMessageStreamEvent`: Updated `block_start_event` for tracking content
blocks across sequential events, or `None` if not applicable
Note:
Not all Anthropic events result in message chunks. Events like internal
state changes return `None` for the message chunk while potentially
updating the `block_start_event` for context tracking.
"""
message_chunk: AIMessageChunk | None = None
# Reference: Anthropic SDK streaming implementation
# 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:
# Capture model name, but don't include usage_metadata yet
# as it will be properly reported in message_delta with complete info
if hasattr(event.message, "model"):
response_metadata: dict[str, Any] = {"model_name": event.message.model}
else:
response_metadata = {}
message_chunk = AIMessageChunk(
content="" if coerce_content_to_string else [],
response_metadata=response_metadata,
)
elif (
event.type == "content_block_start"
and event.content_block is not None
and (
"tool_result" in event.content_block.type
or "tool_use" in event.content_block.type
or "document" in event.content_block.type
or "redacted_thinking" in event.content_block.type
)
):
if coerce_content_to_string:
warnings.warn("Received unexpected tool content block.", stacklevel=2)
content_block = event.content_block.model_dump()
if "caller" in content_block and content_block["caller"] is None:
content_block.pop("caller")
content_block["index"] = event.index
if event.content_block.type == "tool_use":
if (
parsed_args := getattr(event.content_block, "input", None)
) and isinstance(parsed_args, dict):
# In some cases parsed args are represented in start event, with no
# following input_json_delta events
args = json.dumps(parsed_args)
else:
args = ""
tool_call_chunk = create_tool_call_chunk(
index=event.index,
id=event.content_block.id,
name=event.content_block.name,
args=args,
)
tool_call_chunks = [tool_call_chunk]
else:
tool_call_chunks = []
message_chunk = AIMessageChunk(
content=[content_block],
tool_call_chunks=tool_call_chunks,
)
block_start_event = event
# Process incremental content updates
elif event.type == "content_block_delta":
# Text and citation deltas (incremental text content)
if event.delta.type in ("text_delta", "citations_delta"):
if coerce_content_to_string and hasattr(event.delta, "text"):
text = getattr(event.delta, "text", "")
message_chunk = AIMessageChunk(content=text)
else:
content_block = event.delta.model_dump()
content_block["index"] = event.index
# All citation deltas are part of a text block
content_block["type"] = "text"
if "citation" in content_block:
# Assign citations to a list if present
content_block["citations"] = [content_block.pop("citation")]
message_chunk = AIMessageChunk(content=[content_block])
# Reasoning
elif event.delta.type in {"thinking_delta", "signature_delta"}:
content_block = event.delta.model_dump()
content_block["index"] = event.index
content_block["type"] = "thinking"
message_chunk = AIMessageChunk(content=[content_block])
# Tool input JSON (streaming tool arguments)
elif event.delta.type == "input_json_delta":
content_block = event.delta.model_dump()
content_block["index"] = event.index
start_event_block = (
getattr(block_start_event, "content_block", None)
if block_start_event
else None
)
if (
start_event_block is not None
and getattr(start_event_block, "type", None) == "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,
)
# Process final usage metadata and completion info
elif event.type == "message_delta" and stream_usage:
usage_metadata = _create_usage_metadata(event.usage)
response_metadata = {
"stop_reason": event.delta.stop_reason,
"stop_sequence": event.delta.stop_sequence,
}
if context_management := getattr(event, "context_management", None):
response_metadata["context_management"] = context_management.model_dump()
message_delta = getattr(event, "delta", None)
if message_delta and (container := getattr(message_delta, "container", None)):
response_metadata["container"] = container.model_dump(mode="json")
message_chunk = AIMessageChunk(
content="" if coerce_content_to_string else [],
usage_metadata=usage_metadata,
response_metadata=response_metadata,
)
if message_chunk.response_metadata.get("stop_reason"):
# Mark final Anthropic stream chunk
message_chunk.chunk_position = "last"
# Unhandled event types (e.g., `content_block_stop`, `ping` events)
# https://platform.claude.com/docs/en/build-with-claude/streaming#other-events
else:
pass
if message_chunk:
message_chunk.response_metadata["model_provider"] = "anthropic"
return message_chunk, block_start_event
def _create_usage_metadata(anthropic_usage: BaseModel) -> UsageMetadata:
"""Create LangChain `UsageMetadata` from Anthropic `Usage` data.
Note:
Anthropic's `input_tokens` excludes cached tokens, so we manually add
`cache_read` and `cache_creation` tokens to get the true total.
"""
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 cache TTL information if provided (5-minute and 1-hour ephemeral cache)
cache_creation = getattr(anthropic_usage, "cache_creation", None)
# Currently just copying over the 5m and 1h keys, but if more are added in the
# future we'll need to expand this tuple
cache_creation_keys = ("ephemeral_5m_input_tokens", "ephemeral_1h_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)
# Calculate total input tokens: Anthropic's `input_tokens` excludes cached tokens,
# so we need to add them back to get the true total input token count
input_tokens = (
(getattr(anthropic_usage, "input_tokens", 0) or 0) # Base input tokens
+ (input_token_details["cache_read"] or 0) # Tokens read from cache
+ (input_token_details["cache_creation"] or 0) # Tokens used to create cache
)
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},
),
)
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