ChatAnthropic integration

Integrate with the ChatAnthropic chat model using LangChain Python.

You can find information about Anthropic’s latest models, their costs, context windows, and supported input types in the Claude docs.

API Reference

For detailed documentation of all features and configuration options, head to the ChatAnthropic API reference.

AWS Bedrock and Google VertexAI

Note that certain Anthropic models can also be accessed via AWS Bedrock and Google VertexAI. See the ChatBedrock and ChatVertexAI integrations to use Anthropic models via these services.

For Anthropic models on AWS Bedrock with the same API as ChatAnthropic, use ChatAnthropicBedrock from langchain-aws.

Overview

Integration details

Class	Package	Serializable	JS/TS Support	Downloads	Latest Version
ChatAnthropic	langchain-anthropic	beta	✅ (npm)

Model features

Tool calling	Structured output	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
✅	✅	✅	❌	❌	✅	✅	✅	❌

Setup

To access Anthropic (Claude) models you’ll need to install the langchain-anthropic integration package and acquire a Claude API key.

Installation

pip

pip install -U langchain-anthropic

uv add langchain-anthropic

Credentials

Head to the Claude console to sign up and generate a Claude API key. Once you’ve done this set the ANTHROPIC_API_KEY environment variable:

import getpass
import os

if "ANTHROPIC_API_KEY" not in os.environ:
    os.environ["ANTHROPIC_API_KEY"] = getpass.getpass("Enter your Anthropic API key: ")

To enable automated tracing of your model calls, set your LangSmith API key:

os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"

Instantiation

Now we can instantiate our model object and generate chat completions:

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-haiku-4-5-20251001",
    # temperature=,
    # max_tokens=,
    # timeout=,
    # max_retries=,
    # ...
)

See the ChatAnthropic API reference for details on all available instantiation parameters.

Inference geography

To control where model inference runs for data residency, pass inference_geo when you create ChatAnthropic. See Anthropic documentation for supported values.

Invocation

Invoke

messages = [
    (
        "system",
        "You are a helpful translator. Translate the user sentence to French.",
    ),
    (
        "human",
        "I love programming.",
    ),
]
ai_msg = model.invoke(messages)

print(ai_msg.text)

J'adore la programmation.

Stream

stream = model.stream_events(messages, version="v3")
for token in stream.text:
    print(token, end="", flush=True)

J'aime la programmation.

To aggregate the full message from the stream:

stream = model.stream_events(messages, version="v3")
full_message = stream.output

AIMessage(content="J'aime la programmation.", id="run-b34faef0-882f-4869-a19c-ed2b856e6361")

Async

ai_msg = await model.ainvoke(messages)

# stream
stream = await model.astream_events(messages, version="v3")
async for token in stream.text:
    print(token, end="", flush=True)

# batch
await model.abatch([messages])

AIMessage(
    content="J'aime la programmation.",
    response_metadata={
        "id": "msg_01Trik66aiQ9Z1higrD5XFx3",
        "model": "claude-sonnet-4-6",
        "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,
    },
)

Learn more about supported invocation methods in our models guide.

Content blocks

When using tools, extended thinking, and other features, content from a single Anthropic AIMessage can either be a single string or a list of Anthropic content blocks.

For example, when an Anthropic model invokes a tool, the tool invocation is part of the message content (as well as being exposed in the standardized AIMessage.tool_calls):

from langchain_anthropic import ChatAnthropic
from typing_extensions import Annotated

model = ChatAnthropic(model="claude-haiku-4-5-20251001")


def get_weather(
    location: Annotated[str, ..., "Location as city and state."]
) -> str:
    """Get the weather at a location."""
    return "It's sunny."


model_with_tools = model.bind_tools([get_weather])
response = model_with_tools.invoke("Which city is hotter today: LA or NY?")
response.content

[{'text': "I'll help you compare the temperatures of Los Angeles and New York by checking their current weather. I'll retrieve the weather for both cities.",
  'type': 'text'},
 {'id': 'toolu_01CkMaXrgmsNjTso7so94RJq',
  'input': {'location': 'Los Angeles, CA'},
  'name': 'get_weather',
  'type': 'tool_use'},
 {'id': 'toolu_01SKaTBk9wHjsBTw5mrPVSQf',
  'input': {'location': 'New York, NY'},
  'name': 'get_weather',
  'type': 'tool_use'}]

Using content_blocks will render the content in LangChain’s standard format that is consistent across other model providers. Read more about content blocks.

response.content_blocks

You can also access tool calls specifically in a standard format using the tool_calls attribute:

response.tool_calls

[{'name': 'GetWeather',
  'args': {'location': 'Los Angeles, CA'},
  'id': 'toolu_01Ddzj5PkuZkrjF4tafzu54A'},
 {'name': 'GetWeather',
  'args': {'location': 'New York, NY'},
  'id': 'toolu_012kz4qHZQqD4qg8sFPeKqpP'}]

Tools

Anthropic’s tool use features allow you to define external functions that Claude can call during a conversation. This enables dynamic information retrieval, computations, and interactions with external systems.

See ChatAnthropic.bind_tools for details on how to bind tools to your model instance.

For information about Claude’s built-in tools (code execution, web browsing, files API, etc), see the Built-in tools.

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")


model_with_tools = model.bind_tools([GetWeather, GetPopulation]) 
ai_msg = model_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
ai_msg.tool_calls

[
    {
        "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",
    },
]

Strict tool use

Strict tool use requires langchain-anthropic>=1.1.0. See the Claude docs for supported models.

Anthropic supports opt-in strict schema adherence to tool calls. This guarantees that tool names and arguments are validated and correctly typed through constrained decoding.

Without strict mode, Claude can occasionally generate invalid tool inputs that break your applications:

• Type mismatches: passengers: "2" instead of passengers: 2
• Missing required fields: Omitting fields your function expects
• Invalid enum values: Values outside the allowed set
• Schema violations: Nested objects not matching expected structure

Strict tool use guarantees schema-compliant tool calls:

• Tool inputs strictly follow your input_schema
• Guaranteed field types and required fields
• Eliminate error handling for malformed inputs
• Tool name used is always from provided tools

Use strict tool use	Use standard tool calling
Building agentic workflows where reliability is critical	Simple, single-turn tool calls
Tools with many parameters or nested objects	Prototyping and experimentation
Functions that require specific types (e.g., int vs str)

To enable strict tool use, specify strict=True when calling bind_tools.

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-sonnet-4-6")

def get_weather(location: str) -> str:
    """Get the weather at a location."""
    return "It's sunny."

model_with_tools = model.bind_tools([get_weather], strict=True)  

Example: Type-safe booking system

Consider a booking system where passengers must be an integer:

from langchain_anthropic import ChatAnthropic
from typing import Literal

model = ChatAnthropic(model="claude-sonnet-4-6")

def book_flight(
    destination: str,
    departure_date: str,
    passengers: int, 
    cabin_class: Literal["economy", "business", "first"]
) -> str:
    """Book a flight to a destination.

    Args:
        destination: The destination city
        departure_date: Date in YYYY-MM-DD format
        passengers: Number of passengers (must be an integer)
        cabin_class: The cabin class for the flight
    """
    return f"Booked {passengers} passengers to {destination}"

model_with_tools = model.bind_tools(
    [book_flight],
    strict=True, 
    tool_choice="any",
)
response = model_with_tools.invoke("Book 2 passengers to Tokyo, business class, 2025-01-15")

# With strict=True, passengers is guaranteed to be int, not "2" or "two"
print(response.tool_calls[0]["args"]["passengers"])

2

Strict tool use has some JSON schema limitations to be aware of. See the Claude docs for more details.

If your tool schema uses unsupported features, you’ll receive a 400 error. In these cases, simplify the schema or use standard (non-strict) tool calling.

Input examples

For complex tools, you can provide usage examples to help Claude understand how to use them correctly. This is done by setting input_examples in the tool’s extras parameter.

from langchain_anthropic import ChatAnthropic
from langchain.tools import tool

@tool(
    extras={ 
        "input_examples": [ 
            { 
                "query": "weather report", 
                "location": "San Francisco", 
                "format": "detailed"
            }, 
            { 
                "query": "temperature", 
                "location": "New York", 
                "format": "brief"
            } 
        ] 
    } 
)
def search_weather_data(query: str, location: str, format: str = "brief") -> str:
    """Search weather database with specific query and format preferences.

    Args:
        query: The type of weather information to retrieve
        location: City or region to search
        format: Output format, either 'brief' or 'detailed'
    """
    return f"{format.title()} {query} for {location}: Data found"

model = ChatAnthropic(model="claude-sonnet-4-6")
model_with_tools = model.bind_tools([search_weather_data])

response = model_with_tools.invoke(
    "Get me a detailed weather report for Seattle"
)

The extras parameter also supports:

• defer_loading (bool): Load tool on-demand for tool search
• cache_control (dict): Enable prompt caching for the tool
• eager_input_streaming (bool): Enable fine-grained tool streaming for that tool

Fine-grained tool streaming

Anthropic supports fine-grained tool streaming, which reduces latency when streaming tool calls with large parameters.

Rather than buffering entire parameter values before transmission, fine-grained streaming sends parameter data as it becomes available. This can reduce the initial delay from 15 seconds to around 3 seconds for large tool parameters.

Fine-grained streaming may return invalid or partial JSON inputs, especially if the response reaches max_tokens before completing. Implement appropriate error handling for incomplete JSON data.

To enable fine-grained tool streaming for a tool that should stream tool parameters incrementally, set extras={"eager_input_streaming": True} on the tool. That value is passed through to the Anthropic API on the tool definition.

from langchain_anthropic import ChatAnthropic
from langchain.tools import tool

model = ChatAnthropic(model="claude-sonnet-4-6")

@tool(extras={"eager_input_streaming": True})
def write_document(title: str, content: str) -> str:
    """Write a document with the given title and content."""
    return f"Document '{title}' written successfully"

model_with_tools = model.bind_tools([write_document])

# Stream tool calls with reduced latency
for chunk in model_with_tools.stream(
    "Write a detailed technical document about the benefits of streaming APIs"
):
    print(chunk.content)

The streaming data arrives as input_json_delta blocks in chunk.content. You can accumulate these to build the complete tool arguments:

import json

from langchain_anthropic import ChatAnthropic
from langchain.tools import tool

model = ChatAnthropic(model="claude-sonnet-4-6")

@tool(extras={"eager_input_streaming": True})
def write_document(title: str, content: str) -> str:
    """Write a document with the given title and content."""
    return f"Document '{title}' written successfully"

model_with_tools = model.bind_tools([write_document])

accumulated_json = ""

for chunk in model_with_tools.stream("Write a document about AI"):
    for block in chunk.content:
        if isinstance(block, dict) and block.get("type") == "input_json_delta":
            accumulated_json += block.get("partial_json", "")
            try:
                # Try to parse accumulated JSON
                parsed = json.loads(accumulated_json)
                print(f"Complete args: {parsed}")
            except json.JSONDecodeError:
                # JSON is still incomplete, continue accumulating
                pass

Complete args: {'title': 'Artificial Intelligence: An Overview', 'content': '# Artificial Intelligence: An Overview...

Programmatic tool calling

Programmatic tool calling requires langchain-anthropic>=1.3.0. See the Claude docs for supported models.

Tools can be configured to be callable from Claude’s code execution environment, reducing latency and token consumption in contexts involving large data processing or multi-tool workflows.

Refer to Claude’s programmatic tool calling guide for details. To use this feature:

• Include the code execution built-in tool in your set of tools
• Specify extras={"allowed_callers": ["code_execution_20250825"]} on tools you wish to call programmatically

See below for a full example with create_agent.

You can specify reuse_last_container on initialization to automatically reuse code execution containers from previous model responses.

from langchain.agents import create_agent
from langchain.tools import tool
from langchain_anthropic import ChatAnthropic


@tool(extras={"allowed_callers": ["code_execution_20250825"]}) 
def get_weather(location: str) -> str:
    """Get the weather at a location."""
    return "It's sunny."

tools = [
    {"type": "code_execution_20250825", "name": "code_execution"}, 
    get_weather,
]

model = ChatAnthropic(
    model="claude-sonnet-4-6",
    reuse_last_container=True,  
)

agent = create_agent(model, tools=tools)

input_query = {
    "role": "user",
    "content": "What's the weather in Boston?",
}

result = agent.invoke({"messages": [input_query]})

Multimodal

Claude supports image and PDF inputs as content blocks, both in Anthropic’s native format (see docs for vision and PDF support) as well as LangChain’s standard format.

Supported input methods

Method	Image	PDF
Base64 inline data	✅	✅
HTTP/HTTPS URLs	✅	✅
Files API	✅	✅

The Files API can also be used to upload files to a container for use with Claude’s built-in code-execution tools. See the code execution section for details.

Image input

Provide image inputs along with text using a HumanMessage with list content format.

URL

from langchain_anthropic import ChatAnthropic
from langchain.messages import HumanMessage

model = ChatAnthropic(model="claude-sonnet-4-6")

message = HumanMessage(
    content=[
        {"type": "text", "text": "Describe the image at the URL."},
        {
            "type": "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",
        },
    ]
)
response = model.invoke([message])

import base64
import httpx
from langchain_anthropic import ChatAnthropic
from langchain.messages import HumanMessage

model = ChatAnthropic(model="claude-sonnet-4-6")

image_url = "https://picsum.photos/id/237/200/300"
image_data = base64.b64encode(httpx.get(image_url, follow_redirects=True).content).decode("utf-8")

message = HumanMessage(
    content=[
        {"type": "text", "text": "Describe the image."},
        { 
            "type": "image", 
            "base64": image_data, 
            "mime_type": "image/jpeg", 
        }, 
    ]
)
response = model.invoke([message])

import anthropic
from langchain_anthropic import ChatAnthropic
from langchain.messages import HumanMessage

client = anthropic.Anthropic()
file = client.beta.files.upload(
    file=("image.png", open("/path/to/image.png", "rb"), "image/png"),
)

model = ChatAnthropic(
    model="claude-sonnet-4-6",
    betas=["files-api-2025-04-14"], 
)

message = HumanMessage(
    content=[
        {"type": "text", "text": "Describe this image."},
        {
            "type": "image",
            "file_id": file.id, 
        },
    ]
)
response = model.invoke([message])

PDF input

Provide PDF file inputs along with text.

URL

from langchain_anthropic import ChatAnthropic
from langchain.messages import HumanMessage

model = ChatAnthropic(model="claude-sonnet-4-6")

message = HumanMessage(
    content=[
        {"type": "text", "text": "Summarize this document."},
        {
            "type": "file",
            "url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf",
            "mime_type": "application/pdf",
        },
    ]
)
response = model.invoke([message])

import base64
import httpx
from langchain_anthropic import ChatAnthropic
from langchain.messages import HumanMessage

model = ChatAnthropic(model="claude-sonnet-4-6")

pdf_url = "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
pdf_data = base64.b64encode(httpx.get(pdf_url).content).decode("utf-8")

message = HumanMessage(
    content=[
        {"type": "text", "text": "Summarize this document."},
        {
            "type": "file",
            "base64": pdf_data, 
            "mime_type": "application/pdf", 
        },
    ]
)
response = model.invoke([message])

import anthropic
from langchain_anthropic import ChatAnthropic
from langchain.messages import HumanMessage

client = anthropic.Anthropic()
file = client.beta.files.upload(
    file=("document.pdf", open("/path/to/document.pdf", "rb"), "application/pdf"),
)

model = ChatAnthropic(
    model="claude-sonnet-4-6",
    betas=["files-api-2025-04-14"], 
)

message = HumanMessage(
    content=[
        {"type": "text", "text": "Summarize this document."},
        {
            "type": "file",
            "file_id": file.id, 
        },
    ]
)
response = model.invoke([message])

Extended thinking

Some Claude models support an extended thinking feature, which will output the step-by-step reasoning process that led to its final answer.

See compatible models in the Claude documentation.

To use extended thinking, specify the thinking parameter when initializing ChatAnthropic. If needed, it can also be passed in as a parameter during invocation.

For Claude Sonnet and earlier models, you will need to specify a token budget. For Claude Opus 4.6+, you can use adaptive thinking which automatically determines the budget.

With token budget

import json
from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-sonnet-4-6",
    max_tokens=5000,
    thinking={"type": "enabled", "budget_tokens": 2000}, 
)

response = model.invoke("What is the cube root of 50.653?")
print(json.dumps(response.content_blocks, indent=2))

import json
from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-opus-4-8",
    max_tokens=5000,
    thinking={"type": "adaptive"}, 
)

response = model.invoke("What is the cube root of 50.653?")
print(json.dumps(response.content_blocks, indent=2))

import json
from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-sonnet-4-6")

response = model.invoke(
    "What is the cube root of 50.653?",
    max_tokens=5000,
    thinking={"type": "enabled", "budget_tokens": 2000} 
)
print(json.dumps(response.content_blocks, indent=2))

[
  {
    "type": "reasoning",
    "reasoning": "To find the cube root of 50.653, I need to find the value of $x$ such that $x^3 = 50.653$.\n\nI can try to estimate this first. \n$3^3 = 27$\n$4^3 = 64$\n\nSo the cube root of 50.653 will be somewhere between 3 and 4, but closer to 4.\n\nLet me try to compute this more precisely. I can use the cube root function:\n\ncube root of 50.653 = 50.653^(1/3)\n\nLet me calculate this:\n50.653^(1/3) \u2248 3.6998\n\nLet me verify:\n3.6998^3 \u2248 50.6533\n\nThat's very close to 50.653, so I'm confident that the cube root of 50.653 is approximately 3.6998.\n\nActually, let me compute this more precisely:\n50.653^(1/3) \u2248 3.69981\n\nLet me verify once more:\n3.69981^3 \u2248 50.652998\n\nThat's extremely close to 50.653, so I'll say that the cube root of 50.653 is approximately 3.69981.",
    "extras": {"signature": "ErUBCkYIBxgCIkB0UjV..."}
  },
  {
    "type": "text",
    "text": "The cube root of 50.653 is approximately 3.6998.\n\nTo verify: 3.6998\u00b3 = 50.6530, which is very close to our original number.",
  }
]

The Claude Messages API handles thinking differently across Claude Sonnet 3.7 and Claude 4 models.

Refer to the Claude docs for more info.

Effort

Certain Claude models support an effort feature, which controls how many tokens Claude uses when responding. This is useful for balancing response quality against latency and cost.

Model support

Effort is generally available on Claude Opus 4.6 and Claude Opus 4.5. The max effort level is only supported on Claude Opus 4.6. The xhigh effort level is supported on Claude Opus 4.7. Anthropic may add or adjust model support over time—use the Claude effort documentation as the source of truth.

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-opus-4-5-20251101",
    effort="medium",  # Options: "max", "xhigh", "high", "medium", "low"
)

response = model.invoke("Analyze the trade-offs between microservices and monolithic architectures")

Setting effort to "high" produces exactly the same behavior as omitting the parameter altogether.

See the Claude documentation for detail on when to use different effort levels and to see supported models.

Task budgets

Claude Opus 4.7 and later support task budgets, an advisory token target for an agentic loop (thinking, tool calls, tool results, and final output). The model sees a running countdown and uses it to prioritize work and finish gracefully. Unlike max_tokens, task budgets are not a hard cap.

Task budgets require langchain-anthropic>=1.4.1 and are currently in beta.

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-opus-4-8",
    output_config={  
        "effort": "high",  
        "task_budget": {"type": "tokens", "total": 128_000},  
    },  
)

Citations

Anthropic supports a citations feature that lets Claude attach context to its answers based on source documents supplied by the user.

When document or search_result content blocks with "citations": {"enabled": True} are included in a query, Claude may generate citations in its response.

Simple example

In this example we pass a plain text document. In the background, Claude automatically chunks the input text into sentences, which are used when generating citations.

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-haiku-4-5-20251001")

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 = model.invoke(messages)
response.content

[{'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'}]

In tool results (agentic RAG)

Claude supports a search_result content block representing citable results from queries against a knowledge base or other custom source. These content blocks can be passed to claude both top-line (as in the above example) and within a tool result. This allows Claude to cite elements of its response using the result of a tool call.

To pass search results in response to tool calls, define a tool that returns a list of search_result content blocks in Anthropic’s native format. For example:

def retrieval_tool(query: str) -> list[dict]:
    """Access my knowledge base."""

    # Run a search (e.g., with a LangChain vector store)
    results = vector_store.similarity_search(query=query, k=2)

    # Package results into search_result blocks
    return [
        {
            "type": "search_result",
            # Customize fields as desired, using document metadata or otherwise
            "title": "My Document Title",
            "source": "Source description or provenance",
            "citations": {"enabled": True},
            "content": [{"type": "text", "text": doc.page_content}],
        }
        for doc in results
    ]

End to end example with LangGraph

Here we demonstrate an end-to-end example in which we populate a LangChain vector store with sample documents and equip Claude with a tool that queries those documents.

The tool here takes a search query and a category string literal, but any valid tool signature can be used.

This example requires langchain-openai and numpy to be installed:

pip install langchain-openai numpy

from typing import Literal

from langchain.chat_models import init_chat_model
from langchain.embeddings import init_embeddings
from langchain_core.documents import Document
from langchain_core.vectorstores import InMemoryVectorStore
from langgraph.checkpoint.memory import InMemorySaver
from langchain.agents import create_agent


# Set up vector store
# Ensure you set your OPENAI_API_KEY environment variable
embeddings = init_embeddings("openai:text-embedding-3-small")
vector_store = InMemoryVectorStore(embeddings)

document_1 = Document(
    id="1",
    page_content=(
        "To request vacation days, submit a leave request form through the "
        "HR portal. Approval will be sent by email."
    ),
    metadata={
        "category": "HR Policy",
        "doc_title": "Leave Policy",
        "provenance": "Leave Policy - page 1",
    },
)
document_2 = Document(
    id="2",
    page_content="Managers will review vacation requests within 3 business days.",
    metadata={
        "category": "HR Policy",
        "doc_title": "Leave Policy",
        "provenance": "Leave Policy - page 2",
    },
)
document_3 = Document(
    id="3",
    page_content=(
        "Employees with over 6 months tenure are eligible for 20 paid vacation days "
        "per year."
    ),
    metadata={
        "category": "Benefits Policy",
        "doc_title": "Benefits Guide 2025",
        "provenance": "Benefits Policy - page 1",
    },
)

documents = [document_1, document_2, document_3]
vector_store.add_documents(documents=documents)


# Define tool
async def retrieval_tool(
    query: str, category: Literal["HR Policy", "Benefits Policy"]
) -> list[dict]:
    """Access my knowledge base."""

    def _filter_function(doc: Document) -> bool:
        return doc.metadata.get("category") == category

    results = vector_store.similarity_search(
        query=query, k=2, filter=_filter_function
    )

    return [
        {
            "type": "search_result",
            "title": doc.metadata["doc_title"],
            "source": doc.metadata["provenance"],
            "citations": {"enabled": True},
            "content": [{"type": "text", "text": doc.page_content}],
        }
        for doc in results
    ]



# Create agent
model = init_chat_model("claude-haiku-4-5-20251001")

checkpointer = InMemorySaver()
agent = create_agent(model, [retrieval_tool], checkpointer=checkpointer)


# Invoke on a query
config = {"configurable": {"thread_id": "session_1"}}

input_message = {
    "role": "user",
    "content": "How do I request vacation days?",
}
stream = await agent.astream_events(
    {"messages": [input_message]},
    config,
    version="v3",
)
async for snapshot in stream.values:
    snapshot["messages"][-1].pretty_print()

Using with text splitters

Anthropic also lets you specify your own splits using custom document types. LangChain text splitters can be used to generate meaningful splits for this purpose. See the below example, where we split the LangChain README.md (a markdown document) and pass it to Claude as context:

This example requires langchain-text-splitters to be installed:

pip install langchain-text-splitters

import requests
from langchain_anthropic import ChatAnthropic
from langchain_text_splitters import MarkdownTextSplitter


def format_to_anthropic_documents(documents: list[str]):
    return {
        "type": "document",
        "source": {
            "type": "content",
            "content": [{"type": "text", "text": document} for document in documents],
        },
        "citations": {"enabled": True},
    }


# Pull readme
get_response = requests.get(
    "https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md"
)
readme = get_response.text

# Split into chunks
splitter = MarkdownTextSplitter(
    chunk_overlap=0,
    chunk_size=50,
)
documents = splitter.split_text(readme)

# Construct message
message = {
    "role": "user",
    "content": [
        format_to_anthropic_documents(documents),
        {"type": "text", "text": "Give me a link to LangChain's tutorials."},
    ],
}

# Query model
model = ChatAnthropic(model="claude-haiku-4-5-20251001")
response = model.invoke([message])

Prompt caching

Anthropic supports caching of elements of your prompts, including messages, tool definitions, tool results, images and documents. This allows you to reuse large documents, instructions, few-shot documents, and other data to reduce latency and costs.

There are two ways to enable prompt caching on direct model calls:

• Automatic caching: Pass cache_control at invocation time (model.invoke(..., cache_control=...)). This is provider/API-level caching: the Anthropic API applies the cache breakpoint to the last cacheable block and moves it forward as conversations grow.
• Explicit cache breakpoints: Place cache_control directly on individual content blocks for fine-grained, direct breakpoint control over exactly what gets cached.

Only certain Claude models support prompt caching. See the Claude documentation for details.

For LangChain agents, prefer AnthropicPromptCachingMiddleware when you want LangChain to optimize stable system prompt and tool content. The middleware is a LangChain agent/harness optimization and is not the same as the invocation-level cache_control shown below, which mirrors the Anthropic API behavior.

Automatic caching

Automatic caching requires langchain-anthropic>=1.4.0.

Pass cache_control as an invocation parameter to automatically cache all content up to and including the last cacheable block. On subsequent requests with the same prefix, cached content is reused automatically. The cache breakpoint moves forward as conversations grow, so you don’t need to manage individual cache_control markers.

from langchain_anthropic import ChatAnthropic
import requests

model = ChatAnthropic(model="claude-sonnet-4-5")

# Pull LangChain readme
get_response = requests.get(
    "https://raw.githubusercontent.com/langchain-ai/langchain/b476fdb54aa6e6f5f0b24a68c2f4a94e43b369f9/README.md"
)
readme = get_response.text

messages = [
    {
        "role": "system",
        "content": [
            {
                "type": "text",
                "text": "You are a technology expert.",
            },
            {
                "type": "text",
                "text": f"{readme}"
            },
        ],
    },
    {
        "role": "user",
        "content": "What's LangChain, according to its README?",
    },
]

response = model.invoke(
    messages,
    cache_control={"type": "ephemeral"},  
)

usage = response.usage_metadata["input_token_details"]
print(f"Usage:\n{usage}")

For 1-hour caching, specify the ttl field:

response = model.invoke(
    messages,
    cache_control={"type": "ephemeral", "ttl": "1h"},  
)

Explicit cache breakpoints

For fine-grained control, mark individual content blocks with cache_control. This is useful when you need to cache different sections that change at different frequencies.

Messages

import requests
from langchain_anthropic import ChatAnthropic


model = ChatAnthropic(model="claude-sonnet-4-5")

# Pull LangChain readme
get_response = requests.get(
    "https://raw.githubusercontent.com/langchain-ai/langchain/b476fdb54aa6e6f5f0b24a68c2f4a94e43b369f9/README.md"
)
readme = get_response.text

messages = [
    {
        "role": "system",
        "content": [
            {
                "type": "text",
                "text": "You are a technology expert.",
            },
            {
                "type": "text",
                "text": f"{readme}",
                "cache_control": {"type": "ephemeral"},  
            },
        ],
    },
    {
        "role": "user",
        "content": "What's LangChain, according to its README?",
    },
]

response_1 = model.invoke(messages)
response_2 = model.invoke(messages)

usage_1 = response_1.usage_metadata["input_token_details"]
usage_2 = response_2.usage_metadata["input_token_details"]

print(f"First invocation:\n{usage_1}")
print(f"\nSecond:\n{usage_2}")

First invocation:
{'cache_read': 0, 'cache_creation': 0, 'ephemeral_5m_input_tokens': 1569, 'ephemeral_1h_input_tokens': 0}

Second:
{'cache_read': 1569, 'cache_creation': 0, 'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}

1-hour caching

The cache lifetime is 5 minutes by default. For longer caching, specify "ttl": "1h" in the cache_control field. 1-hour cache writes cost 2x the base input token price (vs 1.25x for 5-minute).

Example

model = ChatAnthropic(model="claude-sonnet-4-6")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": f"{long_text}",
                "cache_control": {"type": "ephemeral", "ttl": "1h"}, 
            },
        ],
    }
]

Details of cached token counts will be included on the InputTokenDetails of response’s usage_metadata:

response = model.invoke(messages)
response.usage_metadata

{
    "input_tokens": 1500,
    "output_tokens": 200,
    "total_tokens": 1700,
    "input_token_details": {
        "cache_read": 0,
        "cache_creation": 1000,
        "ephemeral_1h_input_tokens": 750,
        "ephemeral_5m_input_tokens": 250,
    }
}

Caching tools

import requests

from langchain_anthropic import ChatAnthropic
from langchain.tools import tool


# For demonstration purposes, we artificially expand the
# tool description using the LangChain README text.
get_response = requests.get(
    "https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md"
)
readme = get_response.text
description = (
    "Get the weather at a location. "
    f"By the way, check out this readme: {readme}"
)


@tool(description=description, extras={"cache_control": {"type": "ephemeral"}})  
def get_weather(location: str) -> str:
    return "It's sunny."


model = ChatAnthropic(model="claude-sonnet-4-6")
model_with_tools = model.bind_tools([get_weather])
query = "What's the weather in San Francisco?"

response_1 = model_with_tools.invoke(query)
response_2 = model_with_tools.invoke(query)

usage_1 = response_1.usage_metadata["input_token_details"]
usage_2 = response_2.usage_metadata["input_token_details"]

print(f"First invocation:\n{usage_1}")
print(f"\nSecond:\n{usage_2}")

First invocation:
{'cache_read': 0, 'cache_creation': 1809}

Second:
{'cache_read': 1809, 'cache_creation': 0}

Incremental caching in conversational applications

Prompt caching can be used in multi-turn conversations to maintain context from earlier messages without redundant processing.

We can enable incremental caching by marking the final message with cache_control. Claude will automatically use the longest previously-cached prefix for follow-up messages.

Below, we implement a simple chatbot that incorporates this feature. We follow the LangChain chatbot tutorial, but add a custom reducer that automatically marks the last content block in each user message with cache_control:

Chatbot with incremental prompt caching

import requests
from langchain_anthropic import ChatAnthropic
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import START, StateGraph, add_messages
from typing_extensions import Annotated, TypedDict


model = ChatAnthropic(model="claude-sonnet-4-6")

# Pull LangChain readme
get_response = requests.get(
    "https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md"
)
readme = get_response.text


def messages_reducer(left: list, right: list) -> list:
    # Update last user message
    for i in range(len(right) - 1, -1, -1):
        if right[i].type == "human":
            right[i].content[-1]["cache_control"] = {"type": "ephemeral"}
            break

    return add_messages(left, right)


class State(TypedDict):
    messages: Annotated[list, messages_reducer]


workflow = StateGraph(state_schema=State)


# Define the function that calls the model
def call_model(state: State):
    response = model.invoke(state["messages"])
    return {"messages": [response]}


# Define the (single) node in the graph
workflow.add_edge(START, "model")
workflow.add_node("model", call_model)

# Add memory
memory = MemorySaver()
app = workflow.compile(checkpointer=memory)

from langchain.messages import HumanMessage

config = {"configurable": {"thread_id": "abc123"}}

query = "Hi! I'm Bob."

input_message = HumanMessage([{"type": "text", "text": query}])
output = app.invoke({"messages": [input_message]}, config)
output["messages"][-1].pretty_print()
print(f"\n{output['messages'][-1].usage_metadata['input_token_details']}")

================================== Ai Message ==================================

Hello, Bob! It's nice to meet you. How are you doing today? Is there something I can help you with?

{'cache_read': 0, 'cache_creation': 0, 'ephemeral_5m_input_tokens': 0, 'ephemeral_1h_input_tokens': 0}

query = f"Check out this readme: {readme}"

input_message = HumanMessage([{"type": "text", "text": query}])
output = app.invoke({"messages": [input_message]}, config)
output["messages"][-1].pretty_print()
print(f"\n{output['messages'][-1].usage_metadata['input_token_details']}")

================================== Ai Message ==================================

I can see you've shared the README from the LangChain GitHub repository. This is the documentation for LangChain, which is a popular framework for building applications powered by Large Language Models (LLMs). Here's a summary of what the README contains:

LangChain is:
- A framework for developing LLM-powered applications
- Helps chain together components and integrations to simplify AI application development
- Provides a standard interface for models, embeddings, vector stores, etc.

Key features/benefits:
- Real-time data augmentation (connect LLMs to diverse data sources)
- Model interoperability (swap models easily as needed)
- Large ecosystem of integrations

The LangChain ecosystem includes:
- LangSmith - For evaluations and observability
- LangGraph - For building complex agents with customizable architecture
- LangSmith - For deployment and scaling of agents

The README also mentions installation instructions (`pip install -U langchain`) and links to various resources including tutorials, how-to guides, conceptual guides, and API references.

Is there anything specific about LangChain you'd like to know more about, Bob?

{'cache_read': 0, 'cache_creation': 1846, 'ephemeral_5m_input_tokens': 1846, 'ephemeral_1h_input_tokens': 0}

query = "What was my name again?"

input_message = HumanMessage([{"type": "text", "text": query}])
output = app.invoke({"messages": [input_message]}, config)
output["messages"][-1].pretty_print()
print(f"\n{output['messages'][-1].usage_metadata['input_token_details']}")

================================== Ai Message ==================================

Your name is Bob. You introduced yourself at the beginning of our conversation.

{'cache_read': 1846, 'cache_creation': 278, 'ephemeral_5m_input_tokens': 278, 'ephemeral_1h_input_tokens': 0}

In the LangSmith trace, toggling “raw output” will show exactly what messages are sent to the chat model, including cache_control keys.

Token counting

You can count tokens in messages before sending them to the model using get_num_tokens_from_messages(). This uses Anthropic’s official token counting API.

Message token counting

from langchain_anthropic import ChatAnthropic
from langchain.messages import HumanMessage, SystemMessage

model = ChatAnthropic(model="claude-sonnet-4-6")

messages = [
    SystemMessage(content="You are a scientist"),
    HumanMessage(content="Hello, Claude"),
]

token_count = model.get_num_tokens_from_messages(messages)
print(token_count)

14

Tool token counting

You can also count tokens when using tools:

from langchain.tools import tool

@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?"),
]

token_count = model.get_num_tokens_from_messages(messages, tools=[get_weather])
print(token_count)

586

Context management

Anthropic supports context management features that automatically manage the model’s context window to optimize performance and cost.

See the Claude documentation for more details and configuration options.

Clearing tool uses

Clear tool results from the context to reduce token usage while preserving the conversation flow.

Context management is supported since langchain-anthropic>=0.3.21

You must specify the context-management-2025-06-27 beta header to apply context management to your model calls.

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-sonnet-4-6",
    betas=["context-management-2025-06-27"], 
    context_management={"edits": [{"type": "clear_tool_uses_20250919"}]}, 
)
model_with_tools = model.bind_tools([{"type": "web_search_20260209", "name": "web_search"}])
response = model_with_tools.invoke("Search for recent developments in AI")

Automatic compaction

Claude Opus 4.6 and later support automatic server-side compaction, which intelligently condenses conversation history when the context window approaches its limit. This allows for longer conversations without manual context management.

Automatic compaction requirements:

• Claude Opus 4.6 or later
• langchain-anthropic>=1.3.0
• compact-2026-01-12 beta header

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-opus-4-8",
    betas=["compact-2026-01-12"], 
    max_tokens=4096,
    context_management={ 
        "edits": [ 
            { 
                "type": "compact_20260112", 
                "trigger": {"type": "input_tokens", "value": 50000}, 
            } 
        ] 
    }, 
)

See Anthropic’s docs for details on trigger configuration.

When a compaction event is triggered, ChatAnthropic will return compaction blocks representing the state of the prompt. These should be retained in the message history that is passed back to the model in multi-turn applications.

Structured output

Structured output requires langchain-anthropic>=1.1.0. See the Claude docs for supported models.

Anthropic supports a native structured output feature, which guarantees that its responses adhere to a given schema.

You can access this feature in individual model calls, or by specifying the response format of a LangChain agent. See below for examples.

Individual model calls

Use the with_structured_output method to generate a structured model response. Specify method="json_schema" to enable Anthropic’s native structured output feature; otherwise the method defaults to using function calling.

from langchain_anthropic import ChatAnthropic
from pydantic import BaseModel, Field

model = ChatAnthropic(model="claude-sonnet-4-6")

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")
response

Movie(title='Inception', year=2010, director='Christopher Nolan', rating=8.8)

Agent response format

Specify response_format with ProviderStrategy to engage Anthropic’s structured output feature when generating its final response.

from langchain.agents import create_agent
from langchain.agents.structured_output import ProviderStrategy
from pydantic import BaseModel

class Weather(BaseModel):
    temperature: float
    condition: str

def weather_tool(location: str) -> str:
    """Get the weather at a location."""
    return "Sunny and 75 degrees F."

agent = create_agent(
    model="anthropic:claude-sonnet-4-5",
    tools=[weather_tool],
    response_format=ProviderStrategy(Weather),  
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "What's the weather in SF?"}]
})

result["structured_response"]

Weather(temperature=75.0, condition='Sunny')

Built-in tools

Anthropic supports a variety of built-in client and server-side tools.

Server-side tools (e.g., web search) are passed to the model and executed by Anthropic. Client-side tools (e.g., bash tool) require you to implement the callback execution logic in your application and return results to the model.

In either case, you make tools accessible to your chat model by using bind_tools on the model instance.

Importantly, client-side tools require you to implement the execution logic. See the relevant sections below for examples.

Middleware vs tools

For client-side tools (e.g. bash, text editor, memory), you may opt to use middleware, which provide production-ready implementations that contain built-in execution, state management, and security policies.

Use middleware when you want a turnkey solution; use tools (documented below) when you need custom execution logic or want to use bind_tools directly.

Beta tools

If binding a beta tool to your chat model, LangChain will automatically add the required beta header for you.

Bash tool

Claude supports a client-side bash tool that allows it to execute shell commands in a persistent bash session. This enables system operations, script execution, and command-line automation.

Important: You must provide the execution environment

LangChain handles the API integration (sending/receiving tool calls), but you are responsible for:

• Setting up a sandboxed computing environment (Docker, VM, etc.)
• Implementing command execution and output capture
• Passing results back to Claude in an agent loop

See the Claude bash tool docs for implementation guidance.

Requirements:

• Claude 4 models or Claude Sonnet 3.7

Anthropic type

import subprocess

from anthropic.types.beta import BetaToolBash20250124Param  
from langchain_anthropic import ChatAnthropic
from langchain.messages import HumanMessage, ToolMessage
from langchain.tools import tool

tool_spec = BetaToolBash20250124Param(  
    name="bash",  
    type="bash_20250124",  
)  


@tool(extras={"provider_tool_definition": tool_spec})  
def bash(*, command: str, restart: bool = False, **kw):
    """Execute a bash command."""
    if restart:
        return "Bash session restarted"
    try:
        result = subprocess.run(
            command,
            shell=True,
            capture_output=True,
            text=True,
            timeout=30,
        )
        return result.stdout + result.stderr
    except Exception as e:
        return f"Error: {e}"


model = ChatAnthropic(model="claude-sonnet-4-6")
model_with_bash = model.bind_tools([bash]) 

# Initial request
messages = [HumanMessage("List all files in the current directory")]
response = model_with_bash.invoke(messages)
print(response.content_blocks)

# Tool execution loop
while response.tool_calls:
    # Execute each tool call
    tool_messages = []
    for tool_call in response.tool_calls:
        result = bash.invoke(tool_call)
        tool_messages.append(result)

    # Continue conversation with tool results
    messages = [*messages, response, *tool_messages]
    response = model_with_bash.invoke(messages)
    print(response.content_blocks)

create_agent

import subprocess

from anthropic.types.beta import BetaToolBash20250124Param 
from langchain.agents import create_agent
from langchain_anthropic import ChatAnthropic
from langchain.tools import tool

tool_spec = BetaToolBash20250124Param( 
    name="bash", 
    type="bash_20250124", 
) 


@tool(extras={"provider_tool_definition": tool_spec}) 
def bash(*, command: str, restart: bool = False, **kw):
    """Execute a bash command."""
    if restart:
        return "Bash session restarted"
    result = subprocess.run(
        command,
        shell=True,
        capture_output=True,
        text=True,
    )
    return result.stdout + result.stderr


agent = create_agent(
    model=ChatAnthropic(model="claude-sonnet-4-6"),
    tools=[bash],  
)

result = agent.invoke({"messages": [{"role": "user", "content": "List files"}]})

for message in result["messages"]:
    message.pretty_print()

Dict

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-sonnet-4-6")

bash_tool = { 
    "type": "bash_20250124", 
    "name": "bash", 
} 

model_with_bash = model.bind_tools([bash_tool]) 
response = model_with_bash.invoke(
    "List all Python files in the current directory"
)
# You must handle execution of the bash command in response.tool_calls via a tool execution loop

Using create_agent handles the tool execution loop automatically.

response.tool_calls will contain the bash command Claude wants to execute. You must run this command in your environment and pass the result back.

[{'type': 'text',
'text': "I'll list the Python files in the current directory for you."},
{'type': 'tool_call',
'name': 'bash',
'args': {'command': 'ls -la *.py'},
'id': 'toolu_01ABC123...'}]

The bash tool supports two parameters:

• command (required): The bash command to execute
• restart (optional): Set to true to restart the bash session

For a “batteries-included” implementation, consider using ClaudeBashToolMiddleware which provides persistent sessions, Docker isolation, output redaction, and startup/shutdown commands out of the box.

Code execution

Claude can use a server-side code execution tool to execute code in a sandboxed environment.

Anthropic’s 2025-08-25 code execution tools are supported since langchain-anthropic>=1.0.3.

The legacy 2025-05-22 tool is supported since langchain-anthropic>=0.3.14.

The code sandbox does not have internet access, thus you may only use packages that are pre-installed in the environment. See the Claude docs for more info.

Anthropic type

from anthropic.types.beta import BetaCodeExecutionTool20250825Param 
from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-sonnet-4-6",
    # (Optional) Enable the param below to automatically
    # pass back in container IDs from previous response
    reuse_last_container=True,
)

code_tool = BetaCodeExecutionTool20250825Param( 
    name="code_execution", 
    type="code_execution_20250825", 
) 
model_with_tools = model.bind_tools([code_tool]) 

response = model_with_tools.invoke(
    "Calculate the mean and standard deviation of [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
)

create_agent

from anthropic.types.beta import BetaCodeExecutionTool20250825Param 
from langchain.agents import create_agent
from langchain_anthropic import ChatAnthropic

code_tool = BetaCodeExecutionTool20250825Param( 
    name="code_execution", 
    type="code_execution_20250825", 
) 

agent = create_agent(
    model=ChatAnthropic(model="claude-sonnet-4-6"),
    tools=[code_tool],  
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "Calculate mean and std of [1,2,3,4,5]"}]
})

for message in result["messages"]:
    message.pretty_print()

Dict

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-sonnet-4-6",
)

code_tool = {"type": "code_execution_20250825", "name": "code_execution"} 
model_with_tools = model.bind_tools([code_tool])

response = model_with_tools.invoke(
    "Calculate the mean and standard deviation of [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
)

Use with Files API

Using the Files API, Claude can write code to access files for data analysis and other purposes. See example below:

import anthropic
from anthropic.types.beta import BetaCodeExecutionTool20250825Param 
from langchain_anthropic import ChatAnthropic


client = anthropic.Anthropic()
file = client.beta.files.upload(
    file=open("/path/to/sample_data.csv", "rb")
)
file_id = file.id


# Run inference
model = ChatAnthropic(
    model="claude-sonnet-4-6",
)

code_tool = BetaCodeExecutionTool20250825Param( 
    name="code_execution", 
    type="code_execution_20250825", 
) 
model_with_tools = model.bind_tools([code_tool])

input_message = {
    "role": "user",
    "content": [
        {
            "type": "text",
            "text": "Please plot these data and tell me what you see.",
        },
        {
            "type": "container_upload",
            "file_id": file_id,
        },
    ]
}
response = model_with_tools.invoke([input_message])

Note that Claude may generate files as part of its code execution. You can access these files using the Files API:

# Take all file outputs for demonstration purposes
file_ids = []
for block in response.content:
    if block["type"] == "bash_code_execution_tool_result":
        file_ids.extend(
            content["file_id"]
            for content in block.get("content", {}).get("content", [])
            if "file_id" in content
        )

for i, file_id in enumerate(file_ids):
    file_content = client.beta.files.download(file_id)
    file_content.write_to_file(f"/path/to/file_{i}.png")

Available tool versions:

• code_execution_20250522 (legacy)
• code_execution_20250825 (recommended)

Computer use

Claude supports client-side computer use capabilities, allowing it to interact with desktop environments through screenshots, mouse control, and keyboard input.

Important: You must provide the execution environment

LangChain handles the API integration (sending/receiving tool calls), but you are responsible for:

• Setting up a sandboxed computing environment (Linux VM, Docker container, etc.)
• Implementing a virtual display (e.g., Xvfb)
• Executing Claude’s tool calls (screenshot, mouse clicks, keyboard input)
• Passing results back to Claude in an agent loop

Anthropic provides a reference implementation to help you get started.

Requirements:

• Claude Opus 4.5, Claude 4, or Claude Sonnet 3.7

Anthropic type

import base64
from typing import Literal

from anthropic.types.beta import BetaToolComputerUse20250124Param 
from langchain_anthropic import ChatAnthropic
from langchain.messages import HumanMessage, ToolMessage
from langchain.tools import tool

DISPLAY_WIDTH = 1024
DISPLAY_HEIGHT = 768

tool_spec = BetaToolComputerUse20250124Param( 
    name="computer", 
    type="computer_20250124", 
    display_width_px=DISPLAY_WIDTH, 
    display_height_px=DISPLAY_HEIGHT, 
    display_number=1, 
) 

@tool(extras={"provider_tool_definition": tool_spec}) 
def computer(
    *,
    action: Literal[
        "key", "type", "mouse_move", "left_click", "left_click_drag",
        "right_click", "middle_click", "double_click", "screenshot",
        "cursor_position", "scroll"
    ],
    coordinate: list[int] | None = None,
    text: str | None = None,
    **kw
):
    """Control the computer display."""
    if action == "screenshot":
        # Take screenshot and return base64-encoded image
        # Implementation depends on your display setup (e.g., Xvfb, pyautogui)
        return {"type": "image", "data": "base64_screenshot_data..."}
    elif action == "left_click" and coordinate:
        # Execute click at coordinate
        return f"Clicked at {coordinate}"
    elif action == "type" and text:
        # Type text
        return f"Typed: {text}"
    # ... implement other actions
    return f"Executed {action}"

model = ChatAnthropic(model="claude-sonnet-4-6")
model_with_computer = model.bind_tools([computer]) 

# Initial request
messages = [HumanMessage("Take a screenshot to see what's on the screen")]
response = model_with_computer.invoke(messages)
print(response.content_blocks)

# Tool execution loop
while response.tool_calls:
    tool_messages = []
    for tool_call in response.tool_calls:
        result = computer.invoke(tool_call)
        tool_messages.append(
            ToolMessage(content=str(result), tool_call_id=tool_call["id"])
        )

    messages = [*messages, response, *tool_messages]
    response = model_with_computer.invoke(messages)
    print(response.content_blocks)

create_agent

from typing import Literal

from anthropic.types.beta import BetaToolComputerUse20250124Param 
from langchain.agents import create_agent
from langchain_anthropic import ChatAnthropic
from langchain.tools import tool

tool_spec = BetaToolComputerUse20250124Param( 
    name="computer", 
    type="computer_20250124", 
    display_width_px=1024, 
    display_height_px=768, 
) 


@tool(extras={"provider_tool_definition": tool_spec}) 
def computer(
    *,
    action: Literal[
        "key", "type", "mouse_move", "left_click", "left_click_drag",
        "right_click", "middle_click", "double_click", "screenshot",
        "cursor_position", "scroll"
    ],
    coordinate: list[int] | None = None,
    text: str | None = None,
    **kw
):
    """Control the computer display."""
    if action == "screenshot":
        return {"type": "image", "data": "base64_screenshot_data..."}
    elif action == "left_click" and coordinate:
        return f"Clicked at {coordinate}"
    elif action == "type" and text:
        return f"Typed: {text}"
    return f"Executed {action}"


agent = create_agent(
    model=ChatAnthropic(model="claude-sonnet-4-6"),
    tools=[computer],  
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "Take a screenshot"}]
})

for message in result["messages"]:
    message.pretty_print()

Dict

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-sonnet-4-6")

computer_tool = {
    "type": "computer_20250124",
    "name": "computer",
    "display_width_px": 1024,
    "display_height_px": 768,
    "display_number": 1,
}

model_with_computer = model.bind_tools([computer_tool]) 
response = model_with_computer.invoke(
    "Take a screenshot to see what's on the screen"
)
# You must handle execution of the computer actions in response.tool_calls via a tool execution loop

Using create_agent handles the tool execution loop automatically.

response.tool_calls will contain the computer action Claude wants to perform. You must execute this action in your environment and pass the result back.

[{'type': 'text',
'text': "I'll take a screenshot to see what's currently on the screen."},
{'type': 'tool_call',
'name': 'computer',
'args': {'action': 'screenshot'},
'id': 'toolu_01RNsqAE7dDZujELtacNeYv9'}]

Available tool versions:

• computer_20250124 (for Claude 4 and Claude Sonnet 3.7)
• computer_20251124 (for Claude Opus 4.5)

Remote MCP

Claude can use a server-side MCP connector tool for model-generated calls to remote MCP servers.

Remote MCP is supported since langchain-anthropic>=0.3.14

Anthropic type

from anthropic.types.beta import BetaMCPToolsetParam 
from langchain_anthropic import ChatAnthropic

mcp_servers = [
    {
        "type": "url",
        "url": "https://docs.langchain.com/mcp",
        "name": "LangChain Docs",
    }
]

model = ChatAnthropic(
    model="claude-sonnet-4-6",
    mcp_servers=mcp_servers, 
)

mcp_tool = BetaMCPToolsetParam( 
    type="mcp_toolset", 
    mcp_server_name="LangChain Docs", 
) 

response = model.invoke(
    "What are LangChain content blocks?",
    tools=[mcp_tool], 
)

create_agent

from anthropic.types.beta import BetaMCPToolsetParam 
from langchain.agents import create_agent
from langchain_anthropic import ChatAnthropic

mcp_servers = [
    {
        "type": "url",
        "url": "https://docs.langchain.com/mcp",
        "name": "LangChain Docs",
    }
]

mcp_tool = BetaMCPToolsetParam( 
    type="mcp_toolset", 
    mcp_server_name="LangChain Docs", 
) 

agent = create_agent(
    model=ChatAnthropic(
        model="claude-sonnet-4-6",
        mcp_servers=mcp_servers,  
    ),
    tools=[mcp_tool],  
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "What are LangChain content blocks?"}]
})

for message in result["messages"]:
    message.pretty_print()

Dict

from langchain_anthropic import ChatAnthropic

mcp_servers = [
    {
        "type": "url",
        "url": "https://docs.langchain.com/mcp",
        "name": "LangChain Docs",
        # "tool_configuration": {  # optional configuration
        #     "enabled": True,
        #     "allowed_tools": ["ask_question"],
        # },
        # "authorization_token": "PLACEHOLDER",  # optional authorization if needed
    }
]

model = ChatAnthropic(
    model="claude-sonnet-4-6",
    mcp_servers=mcp_servers, 
)

response = model.invoke(
    "What are LangChain content blocks?",
    tools=[{"type": "mcp_toolset", "mcp_server_name": "LangChain Docs"}], 
)
response.content_blocks

Text editor

Claude supports a client-side text editor tool can be used to view and modify text local files. See the text editor tool documentation for details.

Anthropic type

from typing import Literal

from anthropic.types.beta import BetaToolTextEditor20250728Param 
from langchain_anthropic import ChatAnthropic
from langchain.messages import HumanMessage, ToolMessage
from langchain.tools import tool

tool_spec = BetaToolTextEditor20250728Param( 
    name="str_replace_based_edit_tool", 
    type="text_editor_20250728", 
) 

# Simple in-memory file storage for demonstration
files: dict[str, str] = {
    "/workspace/primes.py": "def is_prime(n):\n    if n < 2\n        return False\n    return True"
}

@tool(extras={"provider_tool_definition": tool_spec}) 
def str_replace_based_edit_tool(
    *,
    command: Literal["view", "create", "str_replace", "insert", "undo_edit"],
    path: str,
    file_text: str | None = None,
    old_str: str | None = None,
    new_str: str | None = None,
    insert_line: int | None = None,
    view_range: list[int] | None = None,
    **kw
):
    """View and edit text files."""
    if command == "view":
        if path not in files:
            return f"Error: File {path} not found"
        content = files[path]
        if view_range:
            lines = content.splitlines()
            start, end = view_range[0] - 1, view_range[1]
            return "\n".join(lines[start:end])
        return content
    elif command == "create":
        files[path] = file_text or ""
        return f"Created {path}"
    elif command == "str_replace" and old_str is not None:
        if path not in files:
            return f"Error: File {path} not found"
        files[path] = files[path].replace(old_str, new_str or "", 1)
        return f"Replaced in {path}"
    # ... implement other commands
    return f"Executed {command} on {path}"

model = ChatAnthropic(model="claude-sonnet-4-6")
model_with_tools = model.bind_tools([str_replace_based_edit_tool]) 

# Initial request
messages = [HumanMessage("There's a syntax error in my primes.py file. Can you fix it?")]
response = model_with_tools.invoke(messages)
print(response.content_blocks)

# Tool execution loop
while response.tool_calls:
    tool_messages = []
    for tool_call in response.tool_calls:
        result = str_replace_based_edit_tool.invoke(tool_call)
        tool_messages.append(
            ToolMessage(content=result, tool_call_id=tool_call["id"])
        )

    messages = [*messages, response, *tool_messages]
    response = model_with_tools.invoke(messages)
    print(response.content_blocks)

create_agent

from typing import Literal

from anthropic.types.beta import BetaToolTextEditor20250728Param 
from langchain.agents import create_agent
from langchain_anthropic import ChatAnthropic
from langchain.tools import tool

# Simple in-memory file storage
files: dict[str, str] = {
    "/workspace/primes.py": "def is_prime(n):\n    if n < 2\n        return False\n    return True"
}

tool_spec = BetaToolTextEditor20250728Param( 
    name="str_replace_based_edit_tool", 
    type="text_editor_20250728", 
) 


@tool(extras={"provider_tool_definition": tool_spec}) 
def str_replace_based_edit_tool(
    *,
    command: Literal["view", "create", "str_replace", "insert", "undo_edit"],
    path: str,
    file_text: str | None = None,
    old_str: str | None = None,
    new_str: str | None = None,
    **kw
):
    """View and edit text files."""
    if command == "view":
        return files.get(path, f"Error: File {path} not found")
    elif command == "create":
        files[path] = file_text or ""
        return f"Created {path}"
    elif command == "str_replace" and old_str is not None:
        if path not in files:
            return f"Error: File {path} not found"
        files[path] = files[path].replace(old_str, new_str or "", 1)
        return f"Replaced in {path}"
    return f"Executed {command} on {path}"


agent = create_agent(
    model=ChatAnthropic(model="claude-sonnet-4-6"),
    tools=[str_replace_based_edit_tool],  
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "Fix the syntax error in /workspace/primes.py"}]
})

for message in result["messages"]:
    message.pretty_print()

Dict

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-sonnet-4-6")

editor_tool = {"type": "text_editor_20250728", "name": "str_replace_based_edit_tool"} 

model_with_tools = model.bind_tools([editor_tool]) 

response = model_with_tools.invoke(
    "There's a syntax error in my primes.py file. Can you help me fix it?"
)
# You must handle execution of the text editor commands in response.tool_calls via a tool execution loop

Using create_agent handles the tool execution loop automatically.

[{'name': 'str_replace_based_edit_tool',
'args': {'command': 'view', 'path': '/root'},
'id': 'toolu_011BG5RbqnfBYkD8qQonS9k9',
'type': 'tool_call'}]

Available tool versions:

• text_editor_20250124 (legacy)
• text_editor_20250728 (recommended)

For a “batteries-included” implementation, consider using StateClaudeTextEditorMiddleware or FilesystemClaudeTextEditorMiddleware which provide LangGraph state integration or filesystem persistence, path validation, and other features.

Web fetching

Claude can use a server-side web fetching tool to retrieve full content from specified web pages and PDF documents and ground its responses with citations.

Anthropic type

from anthropic.types.beta import BetaWebFetchTool20250910Param 
from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-haiku-4-5-20251001")

fetch_tool = BetaWebFetchTool20250910Param( 
    name="web_fetch", 
    type="web_fetch_20250910", 
    max_uses=3, 
) 

model_with_tools = model.bind_tools([fetch_tool]) 

response = model_with_tools.invoke(
    "Please analyze the content at https://docs.langchain.com/"
)

create_agent

from anthropic.types.beta import BetaWebFetchTool20250910Param 
from langchain.agents import create_agent
from langchain_anthropic import ChatAnthropic

fetch_tool = BetaWebFetchTool20250910Param( 
    name="web_fetch", 
    type="web_fetch_20250910", 
    max_uses=3, 
) 

agent = create_agent(
    model=ChatAnthropic(model="claude-haiku-4-5-20251001"),
    tools=[fetch_tool],  
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "Analyze https://docs.langchain.com/"}]
})

for message in result["messages"]:
    message.pretty_print()

Dict

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-haiku-4-5-20251001")

fetch_tool = {"type": "web_fetch_20250910", "name": "web_fetch", "max_uses": 3} 

model_with_tools = model.bind_tools([fetch_tool]) 

response = model_with_tools.invoke(
    "Please analyze the content at https://docs.langchain.com/"
)

Web search

Claude can use a server-side web search tool to run searches and ground its responses with citations.

Web search tool is supported since langchain-anthropic>=0.3.13

Anthropic type

from anthropic.types import WebSearchTool20260209Param 
from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-sonnet-4-6")

search_tool = WebSearchTool20260209Param( 
    name="web_search", 
    type="web_search_20260209", 
    max_uses=3, 
) 

model_with_tools = model.bind_tools([search_tool]) 

response = model_with_tools.invoke("How do I update a web app to TypeScript 5.5?")

create_agent

from anthropic.types import WebSearchTool20260209Param 
from langchain.agents import create_agent
from langchain_anthropic import ChatAnthropic

search_tool = WebSearchTool20260209Param( 
    name="web_search", 
    type="web_search_20260209", 
    max_uses=3, 
) 

agent = create_agent(
    model=ChatAnthropic(model="claude-sonnet-4-6"),
    tools=[search_tool],  
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "How do I update a web app to TypeScript 5.5?"}]
})

for message in result["messages"]:
    message.pretty_print()

Dict

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-sonnet-4-6")

search_tool = {"type": "web_search_20260209", "name": "web_search", "max_uses": 3} 

model_with_tools = model.bind_tools([search_tool]) 

response = model_with_tools.invoke("How do I update a web app to TypeScript 5.5?")

Memory tool

Claude supports a memory tool for client-side storage and retrieval of context across conversational threads. See the memory tool documentation for details.

Anthropic’s built-in memory tool is supported since langchain-anthropic>=0.3.21

Anthropic type

from typing import Literal

from anthropic.types.beta import BetaMemoryTool20250818Param  
from langchain_anthropic import ChatAnthropic
from langchain.messages import HumanMessage, ToolMessage
from langchain.tools import tool

tool_spec = BetaMemoryTool20250818Param(  
    name="memory",  
    type="memory_20250818",  
)  

# Simple in-memory storage for demonstration purposes
memory_store: dict[str, str] = {
    "/memories/interests": "User enjoys Python programming and hiking"
}


@tool(extras={"provider_tool_definition": tool_spec})  
def memory(
    *,
    command: Literal["view", "create", "str_replace", "insert", "delete", "rename"],
    path: str,
    content: str | None = None,
    old_str: str | None = None,
    new_str: str | None = None,
    insert_line: int | None = None,
    new_path: str | None = None,
    **kw,
):
    """Manage persistent memory across conversations."""
    if command == "view":
        if path == "/memories":
            # List all memories
            return "\n".join(memory_store.keys()) or "No memories stored"
        return memory_store.get(path, f"No memory at {path}")
    elif command == "create":
        memory_store[path] = content or ""
        return f"Created memory at {path}"
    elif command == "str_replace" and old_str is not None:
        if path in memory_store:
            memory_store[path] = memory_store[path].replace(old_str, new_str or "", 1)
        return f"Updated {path}"
    elif command == "delete":
        memory_store.pop(path, None)
        return f"Deleted {path}"
    # ... implement other commands
    return f"Executed {command} on {path}"


model = ChatAnthropic(model="claude-sonnet-4-6")
model_with_tools = model.bind_tools([memory]) 

# Initial request
messages = [HumanMessage("What are my interests?")]
response = model_with_tools.invoke(messages)
print(response.content_blocks)

# Tool execution loop
while response.tool_calls:
    tool_messages = []
    for tool_call in response.tool_calls:
        result = memory.invoke(tool_call)
        tool_messages.append(ToolMessage(content=result, tool_call_id=tool_call["id"]))

    messages = [*messages, response, *tool_messages]
    response = model_with_tools.invoke(messages)
    print(response.content_blocks)

[{'type': 'text',
'text': "I'll check my memory to see what information I have about your interests."},
{'type': 'tool_call',
'name': 'memory',
'args': {'command': 'view', 'path': '/memories'},
'id': 'toolu_01XeP9sxx44rcZHFNqXSaKqh'}]

create_agent

from typing import Literal

from anthropic.types.beta import BetaMemoryTool20250818Param 
from langchain.agents import create_agent
from langchain_anthropic import ChatAnthropic
from langchain.tools import tool

# Simple in-memory storage
memory_store: dict[str, str] = {
    "/memories/interests": "User enjoys Python programming and hiking"
}

tool_spec = BetaMemoryTool20250818Param( 
    name="memory", 
    type="memory_20250818", 
) 


@tool(extras={"provider_tool_definition": tool_spec}) 
def memory(
    *,
    command: Literal["view", "create", "str_replace", "insert", "delete", "rename"],
    path: str,
    content: str | None = None,
    old_str: str | None = None,
    new_str: str | None = None,
    **kw
):
    """Manage persistent memory across conversations."""
    if command == "view":
        if path == "/memories":
            return "\n".join(memory_store.keys()) or "No memories stored"
        return memory_store.get(path, f"No memory at {path}")
    elif command == "create":
        memory_store[path] = content or ""
        return f"Created memory at {path}"
    elif command == "str_replace" and old_str is not None:
        if path in memory_store:
            memory_store[path] = memory_store[path].replace(old_str, new_str or "", 1)
        return f"Updated {path}"
    elif command == "delete":
        memory_store.pop(path, None)
        return f"Deleted {path}"
    return f"Executed {command} on {path}"


agent = create_agent(
    model=ChatAnthropic(model="claude-sonnet-4-6"),
    tools=[memory],  
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "What are my interests?"}]
})

for message in result["messages"]:
    message.pretty_print()

Using create_agent handles the tool execution loop automatically.

Dict

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-sonnet-4-6",
)
model_with_tools = model.bind_tools([{"type": "memory_20250818", "name": "memory"}]) 

response = model_with_tools.invoke("What are my interests?")
response.content_blocks
# You must handle execution of the memory commands in response.tool_calls via a tool execution loop

[{'type': 'text',
'text': "I'll check my memory to see what information I have about your interests."},
{'type': 'tool_call',
'name': 'memory',
'args': {'command': 'view', 'path': '/memories'},
'id': 'toolu_01XeP9sxx44rcZHFNqXSaKqh'}]

For a “batteries-included” implementation, consider using StateClaudeMemoryMiddleware or FilesystemClaudeMemoryMiddleware which provide LangGraph state integration or filesystem persistence, automatic system prompt injection, and other features.

Tool search

Claude supports a server-side tool search feature that enables dynamic tool discovery and loading. Instead of loading all tool definitions into the context window upfront, Claude can search your tool catalog and load only the tools it needs.

This is useful when:

• You have 10+ tools available in your system
• Tool definitions are consuming significant tokens
• You’re experiencing tool selection accuracy issues with large tool sets

There are two tool search variants:

• Regex (tool_search_tool_regex_20251119): Claude constructs regex patterns to search for tools
• BM25 (tool_search_tool_bm25_20251119): Claude uses natural language queries to search for tools

Use the extras parameter to specify defer_loading on LangChain tools:

Anthropic type

from anthropic.types.beta import BetaToolSearchToolRegex20251119Param 
from langchain_anthropic import ChatAnthropic
from langchain.tools import tool

@tool(extras={"defer_loading": True}) 
def get_weather(location: str, unit: str = "fahrenheit") -> str:
    """Get the current weather for a location.

    Args:
        location: City name
        unit: Temperature unit (celsius or fahrenheit)
    """
    return f"Weather in {location}: Sunny"

@tool(extras={"defer_loading": True}) 
def search_files(query: str) -> str:
    """Search through files in the workspace.

    Args:
        query: Search query
    """
    return f"Found files matching '{query}'"

model = ChatAnthropic(model="claude-sonnet-4-6")

tool_search = BetaToolSearchToolRegex20251119Param( 
    name="tool_search_tool_regex", 
    type="tool_search_tool_regex_20251119", 
) 

model_with_tools = model.bind_tools([
    tool_search, 
    get_weather,
    search_files,
])
response = model_with_tools.invoke("What's the weather in San Francisco?")

create_agent

from anthropic.types.beta import BetaToolSearchToolRegex20251119Param 
from langchain.agents import create_agent
from langchain_anthropic import ChatAnthropic
from langchain.tools import tool

tool_search = BetaToolSearchToolRegex20251119Param( 
    name="tool_search_tool_regex", 
    type="tool_search_tool_regex_20251119", 
) 


@tool(extras={"defer_loading": True}) 
def get_weather(location: str, unit: str = "fahrenheit") -> str:
    """Get the current weather for a location.

    Args:
        location: City name
        unit: Temperature unit (celsius or fahrenheit)
    """
    return f"Weather in {location}: Sunny"


@tool(extras={"defer_loading": True}) 
def search_files(query: str) -> str:
    """Search through files in the workspace.

    Args:
        query: Search query
    """
    return f"Found files matching '{query}'"


agent = create_agent(
    model=ChatAnthropic(model="claude-sonnet-4-6"),
    tools=[
        tool_search,  
        get_weather,
        search_files,
    ],
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]
})

for message in result["messages"]:
    message.pretty_print()

Dict

from langchain_anthropic import ChatAnthropic
from langchain.tools import tool

@tool(extras={"defer_loading": True})  
def get_weather(location: str, unit: str = "fahrenheit") -> str:
    """Get the current weather for a location.

    Args:
        location: City name
        unit: Temperature unit (celsius or fahrenheit)
    """
    return f"Weather in {location}: Sunny"

@tool(extras={"defer_loading": True})  
def search_files(query: str) -> str:
    """Search through files in the workspace.

    Args:
        query: Search query
    """
    return f"Found files matching '{query}'"

model = ChatAnthropic(model="claude-sonnet-4-6")

model_with_tools = model.bind_tools([
    {"type": "tool_search_tool_regex_20251119", "name": "tool_search_tool_regex"}, 
    get_weather,
    search_files,
])
response = model_with_tools.invoke("What's the weather in San Francisco?")

Key points:

• Tools with defer_loading: True are only loaded when Claude discovers them via search
• Keep your 3-5 most frequently used tools as non-deferred for optimal performance
• Both variants search tool names, descriptions, argument names, and argument descriptions

See the Claude documentation for more details on tool search, including usage with MCP servers and client-side implementations.

Response metadata

ai_msg = model.invoke(messages)
ai_msg.response_metadata

{
    "id": "msg_013xU6FHEGEq76aP4RgFerVT",
    "model": "claude-sonnet-4-6",
    "stop_reason": "end_turn",
    "stop_sequence": None,
    "usage": {"input_tokens": 25, "output_tokens": 11},
}

Token usage metadata

ai_msg = model.invoke(messages)
ai_msg.usage_metadata

{"input_tokens": 25, "output_tokens": 11, "total_tokens": 36}

Message chunks containing token usage will be included during streaming by default:

stream = model.stream_events(messages, version="v3")
full_message = stream.output
full_message.usage_metadata

{"input_tokens": 25, "output_tokens": 11, "total_tokens": 36}

These can be disabled by setting stream_usage=False in the stream method or when initializing ChatAnthropic.

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API reference

For detailed documentation of all features and configuration options, head to the ChatAnthropic API reference.

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