LangChain v1 migration guide ↗

Original Documentation

Documentation Index#
Fetch the complete documentation index at: https://docs.langchain.com/llms.txt Use this file to discover all available pages before exploring further.

This guide outlines the major changes between LangChain v1 and previous versions.

Simplified package#

The langchain package namespace has been significantly reduced in v1 to focus on essential building blocks for agents. The streamlined package makes it easier to discover and use the core functionality.

Namespace#

Module	What’s available	Notes
`langchain.agents`	`create_agent`, `AgentState`	Core agent creation functionality
`langchain.messages`	Message types, content blocks, `trim_messages`	Re-exported from `langchain-core`
`langchain.tools`	`@tool`, `BaseTool`, injection helpers	Re-exported from `langchain-core`
`langchain.chat_models`	`init_chat_model`, `BaseChatModel`	Unified model initialization
`langchain.embeddings`	`init_embeddings`, `Embeddings`	Embedding models

`langchain-classic`#

If you were using any of the following from the langchain package, you’ll need to install langchain-classic and update your imports:

Legacy chains (LLMChain, ConversationChain, etc.)
Retrievers (e.g. MultiQueryRetriever or anything from the previous langchain.retrievers module)
The indexing API
The hub module (for managing prompts programmatically)
Embeddings modules (e.g. CacheBackedEmbeddings and community embeddings)
langchain-community re-exports

Other deprecated functionality

# Chains
from langchain_classic.chains import LLMChain

# Retrievers
from langchain_classic.retrievers import ...

# Indexing
from langchain_classic.indexes import ...

# Hub
from langchain_classic import hub

# Chains
from langchain_classic.chains import LLMChain

# Retrievers
from langchain.retrievers import ...

# Indexing
from langchain.indexes import ...

# Hub
from langchain import hub

Install with:

pip install langchain-classic

uv add langchain-classic

Migrate to `create_agent`#

Prior to v1.0, we recommended using langgraph.prebuilt.create_react_agent to build agents. Now, we recommend you use langchain.agents.create_agent to build agents.

The table below outlines what functionality has changed from create_react_agent to create_agent:

Section	TL;DR - What’s changed
Import path	Package moved from `langgraph.prebuilt` to `langchain.agents`
Prompts	Parameter renamed to `system_prompt`, dynamic prompts use middleware
Pre-model hook	Replaced by middleware with `before_model` method
Post-model hook	Replaced by middleware with `after_model` method
Custom state	`TypedDict` only, can be defined via `state_schema` or middleware
Model	Dynamic selection via middleware, pre-bound models not supported
Tools	Tool error handling moved to middleware with `wrap_tool_call`
Structured output	prompted output removed, use `ToolStrategy`/`ProviderStrategy`
Streaming node name	Node name changed from `"agent"` to `"model"`
Runtime context	Dependency injection via `context` argument instead of `config["configurable"]`
Namespace	Streamlined to focus on agent building blocks, legacy code moved to `langchain-classic`

Import path#

The import path for the agent prebuilt has changed from langgraph.prebuilt to langchain.agents. The name of the function has changed from create_react_agent to create_agent:

from langgraph.prebuilt import create_react_agent # [!code --]
from langchain.agents import create_agent # [!code ++]

For more information, see Agents.

Prompts#

Static prompt rename#

The prompt parameter has been renamed to system_prompt:

from langchain.agents import create_agent

agent = create_agent(
    model="claude-sonnet-4-6",
    tools=[check_weather],
    system_prompt="You are a helpful assistant"  # [!code highlight]
)

from langgraph.prebuilt import create_react_agent

agent = create_react_agent(
    model="claude-sonnet-4-6",
    tools=[check_weather],
    prompt="You are a helpful assistant"  # [!code highlight]
)

`SystemMessage` to string#

If using SystemMessage objects in the system prompt, extract the string content:

from langchain.agents import create_agent

agent = create_agent(
    model="claude-sonnet-4-6",
    tools=[check_weather],
    system_prompt="You are a helpful assistant"  # [!code highlight]
)

from langchain.messages import SystemMessage
from langgraph.prebuilt import create_react_agent

agent = create_react_agent(
    model="claude-sonnet-4-6",
    tools=[check_weather],
    prompt=SystemMessage(content="You are a helpful assistant")  # [!code highlight]
)

Dynamic prompts#

Dynamic prompts are a core context engineering pattern— they adapt what you tell the model based on the current conversation state. To do this, use the @dynamic_prompt decorator:

from dataclasses import dataclass

from langchain.agents import create_agent
from langchain.agents.middleware import dynamic_prompt, ModelRequest
from langgraph.runtime import Runtime


@dataclass
class Context:  # [!code highlight]
    user_role: str = "user"

@dynamic_prompt  # [!code highlight]
def dynamic_prompt(request: ModelRequest) -> str:  # [!code highlight]
    user_role = request.runtime.context.user_role
    base_prompt = "You are a helpful assistant."

    if user_role == "expert":
        prompt = (
            f"{base_prompt} Provide detailed technical responses."
        )
    elif user_role == "beginner":
        prompt = (
            f"{base_prompt} Explain concepts simply and avoid jargon."
        )
    else:
        prompt = base_prompt

    return prompt  # [!code highlight]

agent = create_agent(
    model="gpt-4.1",
    tools=tools,
    middleware=[dynamic_prompt],  # [!code highlight]
    context_schema=Context
)

# Use with context
agent.invoke(
    {"messages": [{"role": "user", "content": "Explain async programming"}]},
    context=Context(user_role="expert")
)

from dataclasses import dataclass

from langgraph.prebuilt import create_react_agent, AgentState
from langgraph.runtime import get_runtime

@dataclass
class Context:
    user_role: str

def dynamic_prompt(state: AgentState) -> str:
    runtime = get_runtime(Context)  # [!code highlight]
    user_role = runtime.context.user_role
    base_prompt = "You are a helpful assistant."

    if user_role == "expert":
        return f"{base_prompt} Provide detailed technical responses."
    elif user_role == "beginner":
        return f"{base_prompt} Explain concepts simply and avoid jargon."
    return base_prompt

agent = create_react_agent(
    model="gpt-4.1",
    tools=tools,
    prompt=dynamic_prompt,
    context_schema=Context
)

# Use with context
agent.invoke(
    {"messages": [{"role": "user", "content": "Explain async programming"}]},
    context=Context(user_role="expert")
)

Pre-model hook#

Pre-model hooks are now implemented as middleware with the before_model method. This new pattern is more extensible–you can define multiple middlewares to run before the model is called, reusing common patterns across different agents.

Common use cases include:

Summarizing conversation history
Trimming messages
Input guardrails, like PII redaction

v1 now has summarization middleware as a built in option:

from langchain.agents import create_agent
from langchain.agents.middleware import SummarizationMiddleware

agent = create_agent(
    model="claude-sonnet-4-6",
    tools=tools,
    middleware=[
        SummarizationMiddleware(  # [!code highlight]
            model="claude-sonnet-4-6",  # [!code highlight]
            trigger={"tokens": 1000}  # [!code highlight]
        )  # [!code highlight]
    ]  # [!code highlight]
)

from langgraph.prebuilt import create_react_agent, AgentState

def custom_summarization_function(state: AgentState):
    """Custom logic for message summarization."""
    ...

agent = create_react_agent(
    model="claude-sonnet-4-6",
    tools=tools,
    pre_model_hook=custom_summarization_function
)

Post-model hook#

Post-model hooks are now implemented as middleware with the after_model method. This new pattern is more extensible–you can define multiple middlewares to run after the model is called, reusing common patterns across different agents.

Common use cases include:

Human in the loop
Output guardrails

v1 has a built in middleware for human in the loop approval for tool calls:

from langchain.agents import create_agent
from langchain.agents.middleware import HumanInTheLoopMiddleware

agent = create_agent(
    model="claude-sonnet-4-6",
    tools=[read_email, send_email],
    middleware=[
        HumanInTheLoopMiddleware(
            interrupt_on={
                "send_email": {
                    "description": "Please review this email before sending",
                    "allowed_decisions": ["approve", "reject"]
                }
            }
        )
    ]
)

from langgraph.prebuilt import create_react_agent
from langgraph.prebuilt import AgentState

def custom_human_in_the_loop_hook(state: AgentState):
    """Custom logic for human in the loop approval."""
    ...

agent = create_react_agent(
    model="claude-sonnet-4-6",
    tools=[read_email, send_email],
    post_model_hook=custom_human_in_the_loop_hook
)

Custom state#

Custom state extends the default agent state with additional fields. You can define custom state in two ways:

Via state_schema on create_agent - Best for state used in tools
Via middleware - Best for state managed by specific middleware hooks and tools attached to said middleware

Defining custom state via middleware is preferred over defining it via state_schema on create_agent because it allows you to keep state extensions conceptually scoped to the relevant middleware and tools.

state_schema is still supported for backwards compatibility on create_agent.

Defining state via `state_schema`#

Use the state_schema parameter when your custom state needs to be accessed by tools:

from langchain.tools import tool, ToolRuntime
from langchain.agents import create_agent, AgentState  # [!code highlight]


# Define custom state extending AgentState
class CustomState(AgentState):
    user_name: str

@tool  # [!code highlight]
def greet(
    runtime: ToolRuntime[None, CustomState]
) -> str:
    """Use this to greet the user by name."""
    user_name = runtime.state.get("user_name", "Unknown")  # [!code highlight]
    return f"Hello {user_name}!"

agent = create_agent(  # [!code highlight]
    model="claude-sonnet-4-6",
    tools=[greet],
    state_schema=CustomState  # [!code highlight]
)

from typing import Annotated
from langgraph.prebuilt import InjectedState, create_react_agent
from langgraph.prebuilt.chat_agent_executor import AgentState

class CustomState(AgentState):
    user_name: str

def greet(
    state: Annotated[CustomState, InjectedState]
) -> str:
    """Use this to greet the user by name."""
    user_name = state["user_name"]
    return f"Hello {user_name}!"

agent = create_react_agent(
    model="claude-sonnet-4-6",
    tools=[greet],
    state_schema=CustomState
)

Defining state via middleware#

Middleware can also define custom state by setting the state_schema attribute. This helps to keep state extensions conceptually scoped to the relevant middleware and tools.

from langchain.agents.middleware import AgentState, AgentMiddleware
from typing_extensions import NotRequired
from typing import Any

class CustomState(AgentState):
    model_call_count: NotRequired[int]

class CallCounterMiddleware(AgentMiddleware[CustomState]):
    state_schema = CustomState  # [!code highlight]

    def before_model(self, state: CustomState, runtime) -> dict[str, Any] | None:
        count = state.get("model_call_count", 0)
        if count > 10:
            return {"jump_to": "end"}
        return None

    def after_model(self, state: CustomState, runtime) -> dict[str, Any] | None:
        return {"model_call_count": state.get("model_call_count", 0) + 1}

agent = create_agent(
    model="claude-sonnet-4-6",
    tools=[...],
    middleware=[CallCounterMiddleware()]  # [!code highlight]
)

See the middleware documentation for more details on defining custom state via middleware.

State type restrictions#

create_agent only supports TypedDict for state schemas. Pydantic models and dataclasses are no longer supported.

from langchain.agents import AgentState, create_agent

# AgentState is a TypedDict
class CustomAgentState(AgentState):  # [!code highlight]
    user_id: str

agent = create_agent(
    model="claude-sonnet-4-6",
    tools=tools,
    state_schema=CustomAgentState  # [!code highlight]
)

from typing_extensions import Annotated

from pydantic import BaseModel
from langgraph.graph import StateGraph
from langgraph.graph.messages import add_messages
from langchain.messages import AnyMessage


class AgentState(BaseModel):  # [!code highlight]
    messages: Annotated[list[AnyMessage], add_messages]
    user_id: str

agent = create_react_agent(
    model="claude-sonnet-4-6",
    tools=tools,
    state_schema=AgentState
)

Simply inherit from langchain.agents.AgentState instead of BaseModel or decorating with dataclass. If you need to perform validation, handle it in middleware hooks instead.

Model#

Dynamic model selection allows you to choose different models based on runtime context (e.g., task complexity, cost constraints, or user preferences). create_react_agent released in v0.6 of langgraph-prebuilt supported dynamic model and tool selection via a callable passed to the model parameter.

This functionality has been ported to the middleware interface in v1.

Dynamic model selection#

from langchain.agents import create_agent
from langchain.agents.middleware import (
    AgentMiddleware, ModelRequest
)
from langchain.agents.middleware.types import ModelResponse
from langchain_openai import ChatOpenAI
from typing import Callable

basic_model = ChatOpenAI(model="gpt-5-nano")
advanced_model = ChatOpenAI(model="gpt-5")

class DynamicModelMiddleware(AgentMiddleware):

    def wrap_model_call(self, request: ModelRequest, handler: Callable[[ModelRequest], ModelResponse]) -> ModelResponse:
        if len(request.state.messages) > self.messages_threshold:
            model = advanced_model
        else:
            model = basic_model
        return handler(request.override(model=model))

    def __init__(self, messages_threshold: int) -> None:
        self.messages_threshold = messages_threshold

agent = create_agent(
    model=basic_model,
    tools=tools,
    middleware=[DynamicModelMiddleware(messages_threshold=10)]
)

from langgraph.prebuilt import create_react_agent, AgentState
from langchain_openai import ChatOpenAI

basic_model = ChatOpenAI(model="gpt-5-nano")
advanced_model = ChatOpenAI(model="gpt-5")

def select_model(state: AgentState) -> BaseChatModel:
    # use a more advanced model for longer conversations
    if len(state.messages) > 10:
        return advanced_model
    return basic_model

agent = create_react_agent(
    model=select_model,
    tools=tools,
)

Pre-bound models#

To better support structured output, create_agent no longer accepts pre-bound models with tools or configuration:

# No longer supported
model_with_tools = ChatOpenAI().bind_tools([some_tool])
agent = create_agent(model_with_tools, tools=[])

# Use instead
agent = create_agent("gpt-4.1-mini", tools=[some_tool])

Dynamic model functions can return pre-bound models if structured output is not used.

Tools#

The tools argument to create_agent accepts a list of:

LangChain BaseTool instances (functions decorated with @tool)
Callable objects (functions) with proper type hints and a docstring
dict that represents a built-in provider tools

The argument will no longer accept ToolNode instances.

from langchain.agents import create_agent

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

from langgraph.prebuilt import create_react_agent, ToolNode


agent = create_react_agent(
    model="claude-sonnet-4-6",
    tools=ToolNode([check_weather, search_web]) # [!code highlight]
)

Handling tool errors#

You can now configure the handling of tool errors with middleware implementing the wrap_tool_call method.

from langchain.agents import create_agent
from langchain.agents.middleware import wrap_tool_call
from langchain.messages import ToolMessage


@wrap_tool_call
def handle_tool_errors(request, handler):
    """Handle tool execution errors with custom messages."""
    try:
        return handler(request)
    except Exception as e:
        # Only handle errors that occur during tool execution due to invalid inputs
        # that pass schema validation but fail at runtime (e.g., invalid SQL syntax).
        # Do NOT handle:
        # - Network failures (use tool retry middleware instead)
        # - Incorrect tool implementation errors (should bubble up)
        # - Schema mismatch errors (already auto-handled by the framework)
        #
        # Return a custom error message to the model
        return ToolMessage(
            content=f"Tool error: Please check your input and try again. ({str(e)})",
            tool_call_id=request.tool_call["id"]
        )

agent = create_agent(
    model="claude-sonnet-4-6",
    tools=[check_weather, search_web],
    middleware=[handle_tool_errors]
)

from langgraph.prebuilt import create_react_agent, ToolNode
from langchain.messages import ToolMessage


def handle_tool_error(error: Exception) -> str:
    """Custom error handler function."""
    return f"Tool error: Please check your input and try again. ({str(error)})"

agent = create_react_agent(
    model="claude-sonnet-4-6",
    tools=ToolNode(
        [check_weather, search_web],
        handle_tool_errors=handle_tool_error  # [!code highlight]
    )
)

Structured output#

Node changes#

Structured output used to be generated in a separate node from the main agent. This is no longer the case. We generate structured output in the main loop, reducing cost and latency.

Tool and provider strategies#

In v1, there are two new structured output strategies:

ToolStrategy uses artificial tool calling to generate structured output

ProviderStrategy uses provider-native structured output generation

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


class OutputSchema(BaseModel):
    summary: str
    sentiment: str

# Using ToolStrategy
agent = create_agent(
    model="gpt-4.1-mini",
    tools=tools,
    # explicitly using tool strategy
    response_format=ToolStrategy(OutputSchema)  # [!code highlight]
)

from langgraph.prebuilt import create_react_agent
from pydantic import BaseModel

class OutputSchema(BaseModel):
    summary: str
    sentiment: str

agent = create_react_agent(
    model="gpt-4.1-mini",
    tools=tools,
    # using tool strategy by default with no option for provider strategy
    response_format=OutputSchema  # [!code highlight]
)

# OR

agent = create_react_agent(
    model="gpt-4.1-mini",
    tools=tools,
    # using a custom prompt to instruct the model to generate the output schema
    response_format=("please generate ...", OutputSchema)  # [!code highlight]
)

Prompted output removed#

Prompted output is no longer supported via the response_format argument. Compared to strategies like artificial tool calling and provider native structured output, prompted output has not proven to be particularly reliable.

Streaming node name rename#

When streaming events from agents, the node name has changed from "agent" to "model" to better reflect the node’s purpose.

Runtime context#

When you invoke an agent, it’s often the case that you want to pass two types of data:

Dynamic state that changes throughout the conversation (e.g., message history)
Static context that doesn’t change during the conversation (e.g., user metadata)

In v1, static context is supported by setting the context parameter to invoke and stream.

from dataclasses import dataclass

from langchain.agents import create_agent


@dataclass
class Context:
    user_id: str
    session_id: str

agent = create_agent(
    model=model,
    tools=tools,
    context_schema=Context  # [!code highlight]
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "Hello"}]},
    context=Context(user_id="123", session_id="abc")  # [!code highlight]
)

from langgraph.prebuilt import create_react_agent


agent = create_react_agent(model, tools)

# Pass context via configurable
result = agent.invoke(
    {"messages": [{"role": "user", "content": "Hello"}]},
    config={  # [!code highlight]
        "configurable": {  # [!code highlight]
            "user_id": "123",  # [!code highlight]
            "session_id": "abc"  # [!code highlight]
        }  # [!code highlight]
    }  # [!code highlight]
)

The old config["configurable"] pattern still works for backward compatibility, but using the new context parameter is recommended for new applications or applications migrating to v1.

Standard content#

In v1, messages gain provider-agnostic standard content blocks. Access them via message.content_blocks for a consistent, typed view across providers. The existing message.content field remains unchanged for strings or provider-native structures.

What changed#

New content_blocks property on messages for normalized content
Standardized block shapes, documented in Messages
Optional serialization of standard blocks into content via LC_OUTPUT_VERSION=v1 or output_version="v1"

Read standardized content#

from langchain.chat_models import init_chat_model

model = init_chat_model("gpt-5-nano")
response = model.invoke("Explain AI")

for block in response.content_blocks:
    if block["type"] == "reasoning":
        print(block.get("reasoning"))
    elif block["type"] == "text":
        print(block.get("text"))

# Provider-native formats vary; you needed per-provider handling
response = model.invoke("Explain AI")
for item in response.content:
    if item.get("type") == "reasoning":
        ...  # OpenAI-style reasoning
    elif item.get("type") == "thinking":
        ...  # Anthropic-style thinking
    elif item.get("type") == "text":
        ...  # Text

Create multimodal messages#

from langchain.messages import HumanMessage

message = HumanMessage(content_blocks=[
    {"type": "text", "text": "Describe this image."},
    {"type": "image", "url": "https://example.com/image.jpg"},
])
res = model.invoke([message])

from langchain.messages import HumanMessage

message = HumanMessage(content=[
    # Provider-native structure
    {"type": "text", "text": "Describe this image."},
    {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}},
])
res = model.invoke([message])

Example block shapes#

# Text block
text_block = {
    "type": "text",
    "text": "Hello world",
}

# Image block
image_block = {
    "type": "image",
    "url": "https://example.com/image.png",
    "mime_type": "image/png",
}

See the content blocks reference for more details.

Serialize standard content#

Standard content blocks are not serialized into the content attribute by default. If you need to access standard content blocks in the content attribute (e.g., when sending messages to a client), you can opt-in to serializing them into content.

export LC_OUTPUT_VERSION=v1

from langchain.chat_models import init_chat_model

model = init_chat_model(
    "gpt-5-nano",
    output_version="v1",
)

Learn more: Messages, Standard content blocks, and Multimodal.

Simplified package#

Namespace#

Module	What’s available	Notes
`langchain.agents`	`create_agent`, `AgentState`	Core agent creation functionality
`langchain.messages`	Message types, content blocks, `trim_messages`	Re-exported from `langchain-core`
`langchain.tools`	`@tool`, `BaseTool`, injection helpers	Re-exported from `langchain-core`
`langchain.chat_models`	`init_chat_model`, `BaseChatModel`	Unified model initialization
`langchain.embeddings`	`init_embeddings`, `Embeddings`	Embedding models

`langchain-classic`#

If you were using any of the following from the langchain package, you’ll need to install langchain-classic and update your imports:

Legacy chains (LLMChain, ConversationChain, etc.)
Retrievers (e.g. MultiQueryRetriever or anything from the previous langchain.retrievers module)
The indexing API
The hub module (for managing prompts programmatically)
Embeddings modules (e.g. CacheBackedEmbeddings and community embeddings)
langchain-community re-exports

Other deprecated functionality

# Chains
from langchain_classic.chains import LLMChain

# Retrievers
from langchain_classic.retrievers import ...

# Indexing
from langchain_classic.indexes import ...

# Hub
from langchain_classic import hub

# Chains
from langchain_classic.chains import LLMChain

# Retrievers
from langchain.retrievers import ...

# Indexing
from langchain.indexes import ...

# Hub
from langchain import hub

Installation:

uv pip install langchain-classic

Breaking changes#

Dropped Python 3.9 support#

All LangChain packages now require Python 3.10 or higher. Python 3.9 reaches end of life in October 2025.

Updated return type for chat models#

The return type signature for chat model invocation has been fixed from BaseMessage to AIMessage. Custom chat models implementing bind_tools should update their return signature:

def bind_tools(
        ...
    ) -> Runnable[LanguageModelInput, AIMessage]:

def bind_tools(
        ...
    ) -> Runnable[LanguageModelInput, BaseMessage]:

Default message format for OpenAI responses API#

When interacting with the Responses API, langchain-openai now defaults to storing response items in message content. To restore previous behavior, set the LC_OUTPUT_VERSION environment variable to v0, or specify output_version="v0" when instantiating ChatOpenAI.

# Enforce previous behavior with output_version flag
model = ChatOpenAI(model="gpt-4.1-mini", output_version="v0")

Default `max_tokens` in `langchain-anthropic`#

The max_tokens parameter in langchain-anthropic now defaults to higher values based on the model chosen, rather than the previous default of 1024. If you relied on the old default, explicitly set max_tokens=1024.

Legacy code moved to `langchain-classic`#

Existing functionality outside the focus of standard interfaces and agents has been moved to the langchain-classic package. See the Simplified namespace section for details on what’s available in the core langchain package and what moved to langchain-classic.

Removal of deprecated APIs#

Methods, functions, and other objects that were already deprecated and slated for removal in 1.0 have been deleted. Check the deprecation notices from previous versions for replacement APIs.

Text property#

Use of the .text() method on message objects should drop the parentheses, as it is now a property:

# Property access
text = response.text

# Deprecated method call
text = response.text()

Existing usage patterns (i.e., .text()) will continue to function but now emit a warning. The method form will be removed in v2.

`example` parameter removed from `AIMessage`#

The example parameter has been removed from AIMessage objects. We recommend migrating to use additional_kwargs for passing extra metadata as needed.

Minor changes#

AIMessageChunk objects now include a chunk_position attribute with position 'last' to indicate the final chunk in a stream. This allows for clearer handling of streamed messages. If the chunk is not the final one, chunk_position will be None.
LanguageModelOutputVar is now typed to AIMessage instead of BaseMessage.
The logic for merging message chunks (AIMessageChunk.add) has been updated with more sophisticated selection handling for the final id for the merged chunk. It prioritizes provider-assigned IDs over LangChain-generated IDs.
We now open files with utf-8 encoding by default.
Standard tests now use multimodal content blocks.

Archived docs#

Old docs are archived for reference:

Edit this page on GitHub or file an issue.

Connect these docs to Claude, VSCode, and more via MCP for real-time answers.

Link last verified June 7, 2026. View original ↗

Source: LangChain Docs

Link last verified: 2026-03-04

LangChain v1 migration guide ↗

Original Documentation

Documentation Index#

Simplified package#

Namespace#

langchain-classic#

Migrate to create_agent#

Import path#

Prompts#

Static prompt rename#

SystemMessage to string#

Dynamic prompts#

Pre-model hook#

Post-model hook#

Custom state#

Defining state via state_schema#

Defining state via middleware#

State type restrictions#

Model#

Dynamic model selection#

Pre-bound models#

Tools#

Handling tool errors#

Structured output#

Node changes#

Tool and provider strategies#

Prompted output removed#

Streaming node name rename#

Runtime context#

Standard content#

What changed#

Read standardized content#

Create multimodal messages#

Example block shapes#

Serialize standard content#

Simplified package#

Namespace#

langchain-classic#

Breaking changes#

Dropped Python 3.9 support#

Updated return type for chat models#

Default message format for OpenAI responses API#

Default max_tokens in langchain-anthropic#

Legacy code moved to langchain-classic#

Removal of deprecated APIs#

Text property#

example parameter removed from AIMessage#

Minor changes#

Archived docs#

`langchain-classic`#

Migrate to `create_agent`#

`SystemMessage` to string#

Defining state via `state_schema`#

`langchain-classic`#

Default `max_tokens` in `langchain-anthropic`#

Legacy code moved to `langchain-classic`#

`example` parameter removed from `AIMessage`#