feat(langchain): middleware support in create_agent (#32828)

## Overview

Adding new `AgentMiddleware` primitive that supports `before_model`,
`after_model`, and `prepare_model_request` hooks.

This is very exciting! It makes our `create_agent` prebuilt much more
extensible + capable. Still in alpha and subject to change.

This is different than the initial
[implementation](https://github.com/langchain-ai/langgraph/tree/nc/25aug/agent)
in that it:
* Fills in gaps w/ missing features, for ex -- new structured output,
optionality of tools + system prompt, sync and async model requests,
provider builtin tools
* Exposes private state extensions for middleware, enabling things like
model call tracking, etc
* Middleware can register tools
* Uses a `TypedDict` for `AgentState` -- dataclass subclassing is tricky
w/ required values + required decorators
* Addition of `model_settings` to `ModelRequest` so that we can pass
through things to bind (like cache kwargs for anthropic middleware)

## TODOs

### top prio
- [x] add middleware support to existing agent
- [x] top prio middlewares
  - [x] summarization node
  - [x] HITL
  - [x] prompt caching
 
other ones
- [x] model call limits
- [x] tool calling limits
- [ ] usage (requires output state)

### secondary prio
- [x] improve typing for state updates from middleware (not working
right now w/ simple `AgentUpdate` and `AgentJump`, at least in Python)
- [ ] add support for public state (input / output modifications via
pregel channel mods) -- to be tackled in another PR
- [x] testing!

### docs
See https://github.com/langchain-ai/docs/pull/390
- [x] high level docs about middleware
- [x] summarization node
- [x] HITL
- [x] prompt caching

## open questions

Lots of open questions right now, many of them inlined as comments for
the short term, will catalog some more significant ones here.

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

libs/langchain_v1/langchain/agents/middleware/__init__.py

@@ -0,0 +1,15 @@
+"""Middleware plugins for agents."""
+
+from .human_in_the_loop import HumanInTheLoopMiddleware
+from .prompt_caching import AnthropicPromptCachingMiddleware
+from .summarization import SummarizationMiddleware
+from .types import AgentMiddleware, AgentState, ModelRequest
+
+__all__ = [
+    "AgentMiddleware",
+    "AgentState",
+    "AnthropicPromptCachingMiddleware",
+    "HumanInTheLoopMiddleware",
+    "ModelRequest",
+    "SummarizationMiddleware",
+]

libs/langchain_v1/langchain/agents/middleware/_utils.py

@@ -0,0 +1,11 @@
+"""Utility functions for middleware."""
+
+from typing import Any
+
+
+def _generate_correction_tool_messages(content: str, tool_calls: list) -> list[dict[str, Any]]:
+    """Generate tool messages for model behavior correction."""
+    return [
+        {"role": "tool", "content": content, "tool_call_id": tool_call["id"]}
+        for tool_call in tool_calls
+    ]

libs/langchain_v1/langchain/agents/middleware/human_in_the_loop.py

@@ -0,0 +1,128 @@
+"""Human in the loop middleware."""
+
+from typing import Any
+
+from langgraph.prebuilt.interrupt import (
+    ActionRequest,
+    HumanInterrupt,
+    HumanInterruptConfig,
+    HumanResponse,
+)
+from langgraph.types import interrupt
+
+from langchain.agents.middleware._utils import _generate_correction_tool_messages
+from langchain.agents.middleware.types import AgentMiddleware, AgentState
+
+ToolInterruptConfig = dict[str, HumanInterruptConfig]
+
+
+class HumanInTheLoopMiddleware(AgentMiddleware):
+    """Human in the loop middleware."""
+
+    def __init__(
+        self,
+        tool_configs: ToolInterruptConfig,
+        message_prefix: str = "Tool execution requires approval",
+    ) -> None:
+        """Initialize the human in the loop middleware.
+
+        Args:
+            tool_configs: The tool interrupt configs to use for the middleware.
+            message_prefix: The message prefix to use when constructing interrupt content.
+        """
+        super().__init__()
+        self.tool_configs = tool_configs
+        self.message_prefix = message_prefix
+
+    def after_model(self, state: AgentState) -> dict[str, Any] | None:
+        """Trigger HITL flows for relevant tool calls after an AIMessage."""
+        messages = state["messages"]
+        if not messages:
+            return None
+
+        last_message = messages[-1]
+
+        if not hasattr(last_message, "tool_calls") or not last_message.tool_calls:
+            return None
+
+        # Separate tool calls that need interrupts from those that don't
+        interrupt_tool_calls = []
+        auto_approved_tool_calls = []
+
+        for tool_call in last_message.tool_calls:
+            tool_name = tool_call["name"]
+            if tool_name in self.tool_configs:
+                interrupt_tool_calls.append(tool_call)
+            else:
+                auto_approved_tool_calls.append(tool_call)
+
+        # If no interrupts needed, return early
+        if not interrupt_tool_calls:
+            return None
+
+        approved_tool_calls = auto_approved_tool_calls.copy()
+
+        # Right now, we do not support multiple tool calls with interrupts
+        if len(interrupt_tool_calls) > 1:
+            tool_names = [t["name"] for t in interrupt_tool_calls]
+            msg = f"Called the following tools which require interrupts: {tool_names}\n\nYou may only call ONE tool that requires an interrupt at a time"
+            return {
+                "messages": _generate_correction_tool_messages(msg, last_message.tool_calls),
+                "jump_to": "model",
+            }
+
+        # Right now, we do not support interrupting a tool call if other tool calls exist
+        if auto_approved_tool_calls:
+            tool_names = [t["name"] for t in interrupt_tool_calls]
+            msg = f"Called the following tools which require interrupts: {tool_names}. You also called other tools that do not require interrupts. If you call a tool that requires and interrupt, you may ONLY call that tool."
+            return {
+                "messages": _generate_correction_tool_messages(msg, last_message.tool_calls),
+                "jump_to": "model",
+            }
+
+        # Only one tool call will need interrupts
+        tool_call = interrupt_tool_calls[0]
+        tool_name = tool_call["name"]
+        tool_args = tool_call["args"]
+        description = f"{self.message_prefix}\n\nTool: {tool_name}\nArgs: {tool_args}"
+        tool_config = self.tool_configs[tool_name]
+
+        request: HumanInterrupt = {
+            "action_request": ActionRequest(
+                action=tool_name,
+                args=tool_args,
+            ),
+            "config": tool_config,
+            "description": description,
+        }
+
+        responses: list[HumanResponse] = interrupt([request])
+        response = responses[0]
+
+        if response["type"] == "accept":
+            approved_tool_calls.append(tool_call)
+        elif response["type"] == "edit":
+            edited: ActionRequest = response["args"]  # type: ignore[assignment]
+            new_tool_call = {
+                "type": "tool_call",
+                "name": tool_call["name"],
+                "args": edited["args"],
+                "id": tool_call["id"],
+            }
+            approved_tool_calls.append(new_tool_call)
+        elif response["type"] == "ignore":
+            return {"jump_to": "__end__"}
+        elif response["type"] == "response":
+            tool_message = {
+                "role": "tool",
+                "tool_call_id": tool_call["id"],
+                "content": response["args"],
+            }
+            return {"messages": [tool_message], "jump_to": "model"}
+        else:
+            msg = f"Unknown response type: {response['type']}"
+            raise ValueError(msg)
+
+        last_message.tool_calls = approved_tool_calls
+
+        return {"messages": [last_message]}

libs/langchain_v1/langchain/agents/middleware/prompt_caching.py

@@ -0,0 +1,57 @@
+"""Anthropic prompt caching middleware."""
+
+from typing import Literal
+
+from langchain.agents.middleware.types import AgentMiddleware, AgentState, ModelRequest
+
+
+class AnthropicPromptCachingMiddleware(AgentMiddleware):
+    """Prompt Caching Middleware - Optimizes API usage by caching conversation prefixes for Anthropic models.
+
+    Learn more about anthropic prompt caching [here](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching).
+    """
+
+    def __init__(
+        self,
+        type: Literal["ephemeral"] = "ephemeral",
+        ttl: Literal["5m", "1h"] = "5m",
+        min_messages_to_cache: int = 0,
+    ) -> None:
+        """Initialize the middleware with cache control settings.
+
+        Args:
+            type: The type of cache to use, only "ephemeral" is supported.
+            ttl: The time to live for the cache, only "5m" and "1h" are supported.
+            min_messages_to_cache: The minimum number of messages until the cache is used, default is 0.
+        """
+        self.type = type
+        self.ttl = ttl
+        self.min_messages_to_cache = min_messages_to_cache
+
+    def modify_model_request(self, request: ModelRequest, state: AgentState) -> ModelRequest:  # noqa: ARG002
+        """Modify the model request to add cache control blocks."""
+        try:
+            from langchain_anthropic import ChatAnthropic
+        except ImportError:
+            msg = (
+                "AnthropicPromptCachingMiddleware caching middleware only supports Anthropic models."
+                "Please install langchain-anthropic."
+            )
+            raise ValueError(msg)
+
+        if not isinstance(request.model, ChatAnthropic):
+            msg = (
+                "AnthropicPromptCachingMiddleware caching middleware only supports Anthropic models, "
+                f"not instances of {type(request.model)}"
+            )
+            raise ValueError(msg)
+
+        messages_count = (
+            len(request.messages) + 1 if request.system_prompt else len(request.messages)
+        )
+        if messages_count < self.min_messages_to_cache:
+            return request
+
+        request.model_settings["cache_control"] = {"type": self.type, "ttl": self.ttl}
+
+        return request

libs/langchain_v1/langchain/agents/middleware/summarization.py

@@ -0,0 +1,248 @@
+"""Summarization middleware."""
+
+import uuid
+from collections.abc import Callable, Iterable
+from typing import Any, cast
+
+from langchain_core.messages import (
+    AIMessage,
+    AnyMessage,
+    MessageLikeRepresentation,
+    RemoveMessage,
+    ToolMessage,
+)
+from langchain_core.messages.human import HumanMessage
+from langchain_core.messages.utils import count_tokens_approximately, trim_messages
+from langgraph.graph.message import (
+    REMOVE_ALL_MESSAGES,
+)
+
+from langchain.agents.middleware.types import AgentMiddleware, AgentState
+from langchain.chat_models import BaseChatModel, init_chat_model
+
+TokenCounter = Callable[[Iterable[MessageLikeRepresentation]], int]
+
+DEFAULT_SUMMARY_PROMPT = """<role>
+Context Extraction Assistant
+</role>
+
+<primary_objective>
+Your sole objective in this task is to extract the highest quality/most relevant context from the conversation history below.
+</primary_objective>
+
+<objective_information>
+You're nearing the total number of input tokens you can accept, so you must extract the highest quality/most relevant pieces of information from your conversation history.
+This context will then overwrite the conversation history presented below. Because of this, ensure the context you extract is only the most important information to your overall goal.
+</objective_information>
+
+<instructions>
+The conversation history below will be replaced with the context you extract in this step. Because of this, you must do your very best to extract and record all of the most important context from the conversation history.
+You want to ensure that you don't repeat any actions you've already completed, so the context you extract from the conversation history should be focused on the most important information to your overall goal.
+</instructions>
+
+The user will message you with the full message history you'll be extracting context from, to then replace. Carefully read over it all, and think deeply about what information is most important to your overall goal that should be saved:
+
+With all of this in mind, please carefully read over the entire conversation history, and extract the most important and relevant context to replace it so that you can free up space in the conversation history.
+Respond ONLY with the extracted context. Do not include any additional information, or text before or after the extracted context.
+
+<messages>
+Messages to summarize:
+{messages}
+</messages>"""
+
+SUMMARY_PREFIX = "## Previous conversation summary:"
+
+_DEFAULT_MESSAGES_TO_KEEP = 20
+_DEFAULT_TRIM_TOKEN_LIMIT = 4000
+_DEFAULT_FALLBACK_MESSAGE_COUNT = 15
+_SEARCH_RANGE_FOR_TOOL_PAIRS = 5
+
+
+class SummarizationMiddleware(AgentMiddleware):
+    """Middleware that summarizes conversation history when token limits are approached.
+
+    This middleware monitors message token counts and automatically summarizes older
+    messages when a threshold is reached, preserving recent messages and maintaining
+    context continuity by ensuring AI/Tool message pairs remain together.
+    """
+
+    def __init__(
+        self,
+        model: str | BaseChatModel,
+        max_tokens_before_summary: int | None = None,
+        messages_to_keep: int = _DEFAULT_MESSAGES_TO_KEEP,
+        token_counter: TokenCounter = count_tokens_approximately,
+        summary_prompt: str = DEFAULT_SUMMARY_PROMPT,
+        summary_prefix: str = SUMMARY_PREFIX,
+    ) -> None:
+        """Initialize the summarization middleware.
+
+        Args:
+            model: The language model to use for generating summaries.
+            max_tokens_before_summary: Token threshold to trigger summarization.
+                If None, summarization is disabled.
+            messages_to_keep: Number of recent messages to preserve after summarization.
+            token_counter: Function to count tokens in messages.
+            summary_prompt: Prompt template for generating summaries.
+            summary_prefix: Prefix added to system message when including summary.
+        """
+        super().__init__()
+
+        if isinstance(model, str):
+            model = init_chat_model(model)
+
+        self.model = model
+        self.max_tokens_before_summary = max_tokens_before_summary
+        self.messages_to_keep = messages_to_keep
+        self.token_counter = token_counter
+        self.summary_prompt = summary_prompt
+        self.summary_prefix = summary_prefix
+
+    def before_model(self, state: AgentState) -> dict[str, Any] | None:
+        """Process messages before model invocation, potentially triggering summarization."""
+        messages = state["messages"]
+        self._ensure_message_ids(messages)
+
+        total_tokens = self.token_counter(messages)
+        if (
+            self.max_tokens_before_summary is not None
+            and total_tokens < self.max_tokens_before_summary
+        ):
+            return None
+
+        cutoff_index = self._find_safe_cutoff(messages)
+
+        if cutoff_index <= 0:
+            return None
+
+        messages_to_summarize, preserved_messages = self._partition_messages(messages, cutoff_index)
+
+        summary = self._create_summary(messages_to_summarize)
+        new_messages = self._build_new_messages(summary)
+
+        return {
+            "messages": [
+                RemoveMessage(id=REMOVE_ALL_MESSAGES),
+                *new_messages,
+                *preserved_messages,
+            ]
+        }
+
+    def _build_new_messages(self, summary: str) -> list[HumanMessage]:
+        return [
+            HumanMessage(content=f"Here is a summary of the conversation to date:\n\n{summary}")
+        ]
+
+    def _ensure_message_ids(self, messages: list[AnyMessage]) -> None:
+        """Ensure all messages have unique IDs for the add_messages reducer."""
+        for msg in messages:
+            if msg.id is None:
+                msg.id = str(uuid.uuid4())
+
+    def _partition_messages(
+        self,
+        conversation_messages: list[AnyMessage],
+        cutoff_index: int,
+    ) -> tuple[list[AnyMessage], list[AnyMessage]]:
+        """Partition messages into those to summarize and those to preserve."""
+        messages_to_summarize = conversation_messages[:cutoff_index]
+        preserved_messages = conversation_messages[cutoff_index:]
+
+        return messages_to_summarize, preserved_messages
+
+    def _find_safe_cutoff(self, messages: list[AnyMessage]) -> int:
+        """Find safe cutoff point that preserves AI/Tool message pairs.
+
+        Returns the index where messages can be safely cut without separating
+        related AI and Tool messages. Returns 0 if no safe cutoff is found.
+        """
+        if len(messages) <= self.messages_to_keep:
+            return 0
+
+        target_cutoff = len(messages) - self.messages_to_keep
+
+        for i in range(target_cutoff, -1, -1):
+            if self._is_safe_cutoff_point(messages, i):
+                return i
+
+        return 0
+
+    def _is_safe_cutoff_point(self, messages: list[AnyMessage], cutoff_index: int) -> bool:
+        """Check if cutting at index would separate AI/Tool message pairs."""
+        if cutoff_index >= len(messages):
+            return True
+
+        search_start = max(0, cutoff_index - _SEARCH_RANGE_FOR_TOOL_PAIRS)
+        search_end = min(len(messages), cutoff_index + _SEARCH_RANGE_FOR_TOOL_PAIRS)
+
+        for i in range(search_start, search_end):
+            if not self._has_tool_calls(messages[i]):
+                continue
+
+            tool_call_ids = self._extract_tool_call_ids(cast("AIMessage", messages[i]))
+            if self._cutoff_separates_tool_pair(messages, i, cutoff_index, tool_call_ids):
+                return False
+
+        return True
+
+    def _has_tool_calls(self, message: AnyMessage) -> bool:
+        """Check if message is an AI message with tool calls."""
+        return (
+            isinstance(message, AIMessage) and hasattr(message, "tool_calls") and message.tool_calls  # type: ignore[return-value]
+        )
+
+    def _extract_tool_call_ids(self, ai_message: AIMessage) -> set[str]:
+        """Extract tool call IDs from an AI message."""
+        tool_call_ids = set()
+        for tc in ai_message.tool_calls:
+            call_id = tc.get("id") if isinstance(tc, dict) else getattr(tc, "id", None)
+            if call_id is not None:
+                tool_call_ids.add(call_id)
+        return tool_call_ids
+
+    def _cutoff_separates_tool_pair(
+        self,
+        messages: list[AnyMessage],
+        ai_message_index: int,
+        cutoff_index: int,
+        tool_call_ids: set[str],
+    ) -> bool:
+        """Check if cutoff separates an AI message from its corresponding tool messages."""
+        for j in range(ai_message_index + 1, len(messages)):
+            message = messages[j]
+            if isinstance(message, ToolMessage) and message.tool_call_id in tool_call_ids:
+                ai_before_cutoff = ai_message_index < cutoff_index
+                tool_before_cutoff = j < cutoff_index
+                if ai_before_cutoff != tool_before_cutoff:
+                    return True
+        return False
+
+    def _create_summary(self, messages_to_summarize: list[AnyMessage]) -> str:
+        """Generate summary for the given messages."""
+        if not messages_to_summarize:
+            return "No previous conversation history."
+
+        trimmed_messages = self._trim_messages_for_summary(messages_to_summarize)
+        if not trimmed_messages:
+            return "Previous conversation was too long to summarize."
+
+        try:
+            response = self.model.invoke(self.summary_prompt.format(messages=trimmed_messages))
+            return cast("str", response.content).strip()
+        except Exception as e:  # noqa: BLE001
+            return f"Error generating summary: {e!s}"
+
+    def _trim_messages_for_summary(self, messages: list[AnyMessage]) -> list[AnyMessage]:
+        """Trim messages to fit within summary generation limits."""
+        try:
+            return trim_messages(
+                messages,
+                max_tokens=_DEFAULT_TRIM_TOKEN_LIMIT,
+                token_counter=self.token_counter,
+                start_on="human",
+                strategy="last",
+                allow_partial=True,
+                include_system=True,
+            )
+        except Exception:  # noqa: BLE001
+            return messages[-_DEFAULT_FALLBACK_MESSAGE_COUNT:]

libs/langchain_v1/langchain/agents/middleware/types.py

@@ -0,0 +1,78 @@
+"""Types for middleware and agents."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Annotated, Any, Generic, Literal, cast
+
+# needed as top level import for pydantic schema generation on AgentState
+from langchain_core.messages import AnyMessage  # noqa: TC002
+from langgraph.channels.ephemeral_value import EphemeralValue
+from langgraph.graph.message import Messages, add_messages
+from typing_extensions import NotRequired, Required, TypedDict, TypeVar
+
+if TYPE_CHECKING:
+    from langchain_core.language_models.chat_models import BaseChatModel
+    from langchain_core.tools import BaseTool
+
+    from langchain.agents.structured_output import ResponseFormat
+
+JumpTo = Literal["tools", "model", "__end__"]
+"""Destination to jump to when a middleware node returns."""
+
+ResponseT = TypeVar("ResponseT")
+
+
+@dataclass
+class ModelRequest:
+    """Model request information for the agent."""
+
+    model: BaseChatModel
+    system_prompt: str | None
+    messages: list[AnyMessage]  # excluding system prompt
+    tool_choice: Any | None
+    tools: list[BaseTool]
+    response_format: ResponseFormat | None
+    model_settings: dict[str, Any] = field(default_factory=dict)
+
+
+class AgentState(TypedDict, Generic[ResponseT]):
+    """State schema for the agent."""
+
+    messages: Required[Annotated[list[AnyMessage], add_messages]]
+    model_request: NotRequired[Annotated[ModelRequest | None, EphemeralValue]]
+    jump_to: NotRequired[Annotated[JumpTo | None, EphemeralValue]]
+    response: NotRequired[ResponseT]
+
+
+class PublicAgentState(TypedDict, Generic[ResponseT]):
+    """Input / output schema for the agent."""
+
+    messages: Required[Messages]
+    response: NotRequired[ResponseT]
+
+
+StateT = TypeVar("StateT", bound=AgentState)
+
+
+class AgentMiddleware(Generic[StateT]):
+    """Base middleware class for an agent.
+
+    Subclass this and implement any of the defined methods to customize agent behavior between steps in the main agent loop.
+    """
+
+    state_schema: type[StateT] = cast("type[StateT]", AgentState)
+    """The schema for state passed to the middleware nodes."""
+
+    tools: list[BaseTool]
+    """Additional tools registered by the middleware."""
+
+    def before_model(self, state: StateT) -> dict[str, Any] | None:
+        """Logic to run before the model is called."""
+
+    def modify_model_request(self, request: ModelRequest, state: StateT) -> ModelRequest:  # noqa: ARG002
+        """Logic to modify request kwargs before the model is called."""
+        return request
+
+    def after_model(self, state: StateT) -> dict[str, Any] | None:
+        """Logic to run after the model is called."""

libs/langchain_v1/langchain/agents/middleware_agent.py

@@ -0,0 +1,554 @@
+"""Middleware agent implementation."""
+
+import itertools
+from collections.abc import Callable, Sequence
+from typing import Any, Union
+
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.messages import AIMessage, AnyMessage, SystemMessage, ToolMessage
+from langchain_core.runnables import Runnable
+from langchain_core.tools import BaseTool
+from langgraph.constants import END, START
+from langgraph.graph.state import StateGraph
+from langgraph.typing import ContextT
+from typing_extensions import TypedDict, TypeVar
+
+from langchain.agents.middleware.types import (
+    AgentMiddleware,
+    AgentState,
+    JumpTo,
+    ModelRequest,
+    PublicAgentState,
+)
+
+# Import structured output classes from the old implementation
+from langchain.agents.structured_output import (
+    MultipleStructuredOutputsError,
+    OutputToolBinding,
+    ProviderStrategy,
+    ProviderStrategyBinding,
+    ResponseFormat,
+    StructuredOutputValidationError,
+    ToolStrategy,
+)
+from langchain.agents.tool_node import ToolNode
+from langchain.chat_models import init_chat_model
+
+STRUCTURED_OUTPUT_ERROR_TEMPLATE = "Error: {error}\n Please fix your mistakes."
+
+
+def _merge_state_schemas(schemas: list[type]) -> type:
+    """Merge multiple TypedDict schemas into a single schema with all fields."""
+    if not schemas:
+        return AgentState
+
+    all_annotations = {}
+
+    for schema in schemas:
+        all_annotations.update(schema.__annotations__)
+
+    return TypedDict("MergedState", all_annotations)  # type: ignore[operator]
+
+
+def _filter_state_for_schema(state: dict[str, Any], schema: type) -> dict[str, Any]:
+    """Filter state to only include fields defined in the given schema."""
+    if not hasattr(schema, "__annotations__"):
+        return state
+
+    schema_fields = set(schema.__annotations__.keys())
+    return {k: v for k, v in state.items() if k in schema_fields}
+
+
+def _supports_native_structured_output(model: Union[str, BaseChatModel]) -> bool:
+    """Check if a model supports native structured output."""
+    model_name: str | None = None
+    if isinstance(model, str):
+        model_name = model
+    elif isinstance(model, BaseChatModel):
+        model_name = getattr(model, "model_name", None)
+
+    return (
+        "grok" in model_name.lower()
+        or any(part in model_name for part in ["gpt-5", "gpt-4.1", "gpt-oss", "o3-pro", "o3-mini"])
+        if model_name
+        else False
+    )
+
+
+def _handle_structured_output_error(
+    exception: Exception,
+    response_format: ResponseFormat,
+) -> tuple[bool, str]:
+    """Handle structured output error. Returns (should_retry, retry_tool_message)."""
+    if not isinstance(response_format, ToolStrategy):
+        return False, ""
+
+    handle_errors = response_format.handle_errors
+
+    if handle_errors is False:
+        return False, ""
+    if handle_errors is True:
+        return True, STRUCTURED_OUTPUT_ERROR_TEMPLATE.format(error=str(exception))
+    if isinstance(handle_errors, str):
+        return True, handle_errors
+    if isinstance(handle_errors, type) and issubclass(handle_errors, Exception):
+        if isinstance(exception, handle_errors):
+            return True, STRUCTURED_OUTPUT_ERROR_TEMPLATE.format(error=str(exception))
+        return False, ""
+    if isinstance(handle_errors, tuple):
+        if any(isinstance(exception, exc_type) for exc_type in handle_errors):
+            return True, STRUCTURED_OUTPUT_ERROR_TEMPLATE.format(error=str(exception))
+        return False, ""
+    if callable(handle_errors):
+        # type narrowing not working appropriately w/ callable check, can fix later
+        return True, handle_errors(exception)  # type: ignore[return-value,call-arg]
+    return False, ""
+
+
+ResponseT = TypeVar("ResponseT")
+
+
+def create_agent(  # noqa: PLR0915
+    *,
+    model: str | BaseChatModel,
+    tools: Sequence[BaseTool | Callable | dict[str, Any]] | ToolNode | None = None,
+    system_prompt: str | None = None,
+    middleware: Sequence[AgentMiddleware] = (),
+    response_format: ResponseFormat[ResponseT] | type[ResponseT] | None = None,
+    context_schema: type[ContextT] | None = None,
+) -> StateGraph[
+    AgentState[ResponseT], ContextT, PublicAgentState[ResponseT], PublicAgentState[ResponseT]
+]:
+    """Create a middleware agent graph."""
+    # init chat model
+    if isinstance(model, str):
+        model = init_chat_model(model)
+
+    # Handle tools being None or empty
+    if tools is None:
+        tools = []
+
+    # Setup structured output
+    structured_output_tools: dict[str, OutputToolBinding] = {}
+    native_output_binding: ProviderStrategyBinding | None = None
+
+    if response_format is not None:
+        if not isinstance(response_format, (ToolStrategy, ProviderStrategy)):
+            # Auto-detect strategy based on model capabilities
+            if _supports_native_structured_output(model):
+                response_format = ProviderStrategy(schema=response_format)
+            else:
+                response_format = ToolStrategy(schema=response_format)
+
+        if isinstance(response_format, ToolStrategy):
+            # Setup tools strategy for structured output
+            for response_schema in response_format.schema_specs:
+                structured_tool_info = OutputToolBinding.from_schema_spec(response_schema)
+                structured_output_tools[structured_tool_info.tool.name] = structured_tool_info
+        elif isinstance(response_format, ProviderStrategy):
+            # Setup native strategy
+            native_output_binding = ProviderStrategyBinding.from_schema_spec(
+                response_format.schema_spec
+            )
+    middleware_tools = [t for m in middleware for t in getattr(m, "tools", [])]
+
+    # Setup tools
+    tool_node: ToolNode | None = None
+    if isinstance(tools, list):
+        # Extract builtin provider tools (dict format)
+        builtin_tools = [t for t in tools if isinstance(t, dict)]
+        regular_tools = [t for t in tools if not isinstance(t, dict)]
+
+        # Add structured output tools to regular tools
+        structured_tools = [info.tool for info in structured_output_tools.values()]
+        all_tools = middleware_tools + regular_tools + structured_tools
+
+        # Only create ToolNode if we have tools
+        tool_node = ToolNode(tools=all_tools) if all_tools else None
+        default_tools = regular_tools + builtin_tools + structured_tools + middleware_tools
+    elif isinstance(tools, ToolNode):
+        # tools is ToolNode or None
+        tool_node = tools
+        if tool_node:
+            default_tools = list(tool_node.tools_by_name.values()) + middleware_tools
+            # Update tool node to know about tools provided by middleware
+            all_tools = list(tool_node.tools_by_name.values()) + middleware_tools
+            tool_node = ToolNode(all_tools)
+            # Add structured output tools
+            for info in structured_output_tools.values():
+                default_tools.append(info.tool)
+    else:
+        default_tools = (
+            list(structured_output_tools.values()) if structured_output_tools else []
+        ) + middleware_tools
+
+    # validate middleware
+    assert len({m.__class__.__name__ for m in middleware}) == len(middleware), (  # noqa: S101
+        "Please remove duplicate middleware instances."
+    )
+    middleware_w_before = [
+        m for m in middleware if m.__class__.before_model is not AgentMiddleware.before_model
+    ]
+    middleware_w_modify_model_request = [
+        m
+        for m in middleware
+        if m.__class__.modify_model_request is not AgentMiddleware.modify_model_request
+    ]
+    middleware_w_after = [
+        m for m in middleware if m.__class__.after_model is not AgentMiddleware.after_model
+    ]
+
+    # Collect all middleware state schemas and create merged schema
+    merged_state_schema: type[AgentState] = _merge_state_schemas(
+        [m.state_schema for m in middleware]
+    )
+
+    # create graph, add nodes
+    graph = StateGraph(
+        merged_state_schema,
+        input_schema=PublicAgentState,
+        output_schema=PublicAgentState,
+        context_schema=context_schema,
+    )
+
+    def _prepare_model_request(state: dict[str, Any]) -> tuple[ModelRequest, list[AnyMessage]]:
+        """Prepare model request and messages."""
+        request = state.get("model_request") or ModelRequest(
+            model=model,
+            tools=default_tools,
+            system_prompt=system_prompt,
+            response_format=response_format,
+            messages=state["messages"],
+            tool_choice=None,
+        )
+
+        # prepare messages
+        messages = request.messages
+        if request.system_prompt:
+            messages = [SystemMessage(request.system_prompt), *messages]
+
+        return request, messages
+
+    def _handle_model_output(state: dict[str, Any], output: AIMessage) -> dict[str, Any]:
+        """Handle model output including structured responses."""
+        # Handle structured output with native strategy
+        if isinstance(response_format, ProviderStrategy):
+            if not output.tool_calls and native_output_binding:
+                structured_response = native_output_binding.parse(output)
+                return {"messages": [output], "response": structured_response}
+            if state.get("response") is not None:
+                return {"messages": [output], "response": None}
+            return {"messages": [output]}
+
+        # Handle structured output with tools strategy
+        if (
+            isinstance(response_format, ToolStrategy)
+            and isinstance(output, AIMessage)
+            and output.tool_calls
+        ):
+            structured_tool_calls = [
+                tc for tc in output.tool_calls if tc["name"] in structured_output_tools
+            ]
+
+            if structured_tool_calls:
+                exception: Exception | None = None
+                if len(structured_tool_calls) > 1:
+                    # Handle multiple structured outputs error
+                    tool_names = [tc["name"] for tc in structured_tool_calls]
+                    exception = MultipleStructuredOutputsError(tool_names)
+                    should_retry, error_message = _handle_structured_output_error(
+                        exception, response_format
+                    )
+                    if not should_retry:
+                        raise exception
+
+                    # Add error messages and retry
+                    tool_messages = [
+                        ToolMessage(
+                            content=error_message,
+                            tool_call_id=tc["id"],
+                            name=tc["name"],
+                        )
+                        for tc in structured_tool_calls
+                    ]
+                    return {"messages": [output, *tool_messages]}
+
+                # Handle single structured output
+                tool_call = structured_tool_calls[0]
+                try:
+                    structured_tool_binding = structured_output_tools[tool_call["name"]]
+                    structured_response = structured_tool_binding.parse(tool_call["args"])
+
+                    tool_message_content = (
+                        response_format.tool_message_content
+                        if response_format.tool_message_content
+                        else f"Returning structured response: {structured_response}"
+                    )
+
+                    return {
+                        "messages": [
+                            output,
+                            ToolMessage(
+                                content=tool_message_content,
+                                tool_call_id=tool_call["id"],
+                                name=tool_call["name"],
+                            ),
+                        ],
+                        "response": structured_response,
+                    }
+                except Exception as exc:  # noqa: BLE001
+                    exception = StructuredOutputValidationError(tool_call["name"], exc)
+                    should_retry, error_message = _handle_structured_output_error(
+                        exception, response_format
+                    )
+                    if not should_retry:
+                        raise exception
+
+                    return {
+                        "messages": [
+                            output,
+                            ToolMessage(
+                                content=error_message,
+                                tool_call_id=tool_call["id"],
+                                name=tool_call["name"],
+                            ),
+                        ],
+                    }
+
+        # Standard response handling
+        if state.get("response") is not None:
+            return {"messages": [output], "response": None}
+        return {"messages": [output]}
+
+    def _get_bound_model(request: ModelRequest) -> Runnable:
+        """Get the model with appropriate tool bindings."""
+        if isinstance(response_format, ProviderStrategy):
+            # Use native structured output
+            kwargs = response_format.to_model_kwargs()
+            return request.model.bind_tools(
+                request.tools, strict=True, **kwargs, **request.model_settings
+            )
+        if isinstance(response_format, ToolStrategy):
+            tool_choice = "any" if structured_output_tools else request.tool_choice
+            return request.model.bind_tools(
+                request.tools, tool_choice=tool_choice, **request.model_settings
+            )
+        # Standard model binding
+        if request.tools:
+            return request.model.bind_tools(
+                request.tools, tool_choice=request.tool_choice, **request.model_settings
+            )
+        return request.model.bind(**request.model_settings)
+
+    def model_request(state: dict[str, Any]) -> dict[str, Any]:
+        """Sync model request handler with sequential middleware processing."""
+        # Start with the base model request
+        request, messages = _prepare_model_request(state)
+
+        # Apply modify_model_request middleware in sequence
+        for m in middleware_w_modify_model_request:
+            # Filter state to only include fields defined in this middleware's schema
+            filtered_state = _filter_state_for_schema(state, m.state_schema)
+            request = m.modify_model_request(request, filtered_state)
+
+        # Get the bound model with the final request
+        model_ = _get_bound_model(request)
+        output = model_.invoke(messages)
+        return _handle_model_output(state, output)
+
+    async def amodel_request(state: dict[str, Any]) -> dict[str, Any]:
+        """Async model request handler with sequential middleware processing."""
+        # Start with the base model request
+        request, messages = _prepare_model_request(state)
+
+        # Apply modify_model_request middleware in sequence
+        for m in middleware_w_modify_model_request:
+            # Filter state to only include fields defined in this middleware's schema
+            filtered_state = _filter_state_for_schema(state, m.state_schema)
+            request = m.modify_model_request(request, filtered_state)
+
+        # Get the bound model with the final request
+        model_ = _get_bound_model(request)
+        output = await model_.ainvoke(messages)
+        return _handle_model_output(state, output)
+
+    # Use sync or async based on model capabilities
+    from langgraph._internal._runnable import RunnableCallable
+
+    graph.add_node("model_request", RunnableCallable(model_request, amodel_request))
+
+    # Only add tools node if we have tools
+    if tool_node is not None:
+        graph.add_node("tools", tool_node)
+
+    # Add middleware nodes
+    for m in middleware:
+        if m.__class__.before_model is not AgentMiddleware.before_model:
+            graph.add_node(
+                f"{m.__class__.__name__}.before_model",
+                m.before_model,
+                input_schema=m.state_schema,
+            )
+
+        if m.__class__.after_model is not AgentMiddleware.after_model:
+            graph.add_node(
+                f"{m.__class__.__name__}.after_model",
+                m.after_model,
+                input_schema=m.state_schema,
+            )
+
+    # add start edge
+    first_node = (
+        f"{middleware_w_before[0].__class__.__name__}.before_model"
+        if middleware_w_before
+        else "model_request"
+    )
+    last_node = (
+        f"{middleware_w_after[0].__class__.__name__}.after_model"
+        if middleware_w_after
+        else "model_request"
+    )
+    graph.add_edge(START, first_node)
+
+    # add conditional edges only if tools exist
+    if tool_node is not None:
+        graph.add_conditional_edges(
+            "tools",
+            _make_tools_to_model_edge(tool_node, first_node),
+            [first_node, END],
+        )
+        graph.add_conditional_edges(
+            last_node,
+            _make_model_to_tools_edge(first_node, structured_output_tools),
+            [first_node, "tools", END],
+        )
+    elif last_node == "model_request":
+        # If no tools, just go to END from model
+        graph.add_edge(last_node, END)
+    else:
+        # If after_model, then need to check for jump_to
+        _add_middleware_edge(
+            graph,
+            f"{middleware_w_after[0].__class__.__name__}.after_model",
+            END,
+            first_node,
+            tools_available=tool_node is not None,
+        )
+
+    # Add middleware edges (same as before)
+    if middleware_w_before:
+        for m1, m2 in itertools.pairwise(middleware_w_before):
+            _add_middleware_edge(
+                graph,
+                f"{m1.__class__.__name__}.before_model",
+                f"{m2.__class__.__name__}.before_model",
+                first_node,
+                tools_available=tool_node is not None,
+            )
+        # Go directly to model_request after the last before_model
+        _add_middleware_edge(
+            graph,
+            f"{middleware_w_before[-1].__class__.__name__}.before_model",
+            "model_request",
+            first_node,
+            tools_available=tool_node is not None,
+        )
+
+    if middleware_w_after:
+        graph.add_edge("model_request", f"{middleware_w_after[-1].__class__.__name__}.after_model")
+        for idx in range(len(middleware_w_after) - 1, 0, -1):
+            m1 = middleware_w_after[idx]
+            m2 = middleware_w_after[idx - 1]
+            _add_middleware_edge(
+                graph,
+                f"{m1.__class__.__name__}.after_model",
+                f"{m2.__class__.__name__}.after_model",
+                first_node,
+                tools_available=tool_node is not None,
+            )
+
+    return graph
+
+
+def _resolve_jump(jump_to: JumpTo | None, first_node: str) -> str | None:
+    if jump_to == "model":
+        return first_node
+    if jump_to:
+        return jump_to
+    return None
+
+
+def _make_model_to_tools_edge(
+    first_node: str, structured_output_tools: dict[str, OutputToolBinding]
+) -> Callable[[AgentState], str | None]:
+    def model_to_tools(state: AgentState) -> str | None:
+        if jump_to := state.get("jump_to"):
+            return _resolve_jump(jump_to, first_node)
+
+        message = state["messages"][-1]
+
+        # Check if this is a ToolMessage from structured output - if so, end
+        if isinstance(message, ToolMessage) and message.name in structured_output_tools:
+            return END
+
+        # Check for tool calls
+        if isinstance(message, AIMessage) and message.tool_calls:
+            # If all tool calls are for structured output, don't go to tools
+            non_structured_calls = [
+                tc for tc in message.tool_calls if tc["name"] not in structured_output_tools
+            ]
+            if non_structured_calls:
+                return "tools"
+
+        return END
+
+    return model_to_tools
+
+
+def _make_tools_to_model_edge(
+    tool_node: ToolNode, next_node: str
+) -> Callable[[AgentState], str | None]:
+    def tools_to_model(state: AgentState) -> str | None:
+        ai_message = [m for m in state["messages"] if isinstance(m, AIMessage)][-1]
+        if all(
+            tool_node.tools_by_name[c["name"]].return_direct
+            for c in ai_message.tool_calls
+            if c["name"] in tool_node.tools_by_name
+        ):
+            return END
+
+        return next_node
+
+    return tools_to_model
+
+
+def _add_middleware_edge(
+    graph: StateGraph[AgentState, ContextT, PublicAgentState, PublicAgentState],
+    name: str,
+    default_destination: str,
+    model_destination: str,
+    tools_available: bool,  # noqa: FBT001
+) -> None:
+    """Add an edge to the graph for a middleware node.
+
+    Args:
+        graph: The graph to add the edge to.
+        method: The method to call for the middleware node.
+        name: The name of the middleware node.
+        default_destination: The default destination for the edge.
+        model_destination: The destination for the edge to the model.
+        tools_available: Whether tools are available for the edge to potentially route to.
+    """
+
+    def jump_edge(state: AgentState) -> str:
+        return _resolve_jump(state.get("jump_to"), model_destination) or default_destination
+
+    destinations = [default_destination]
+    if default_destination != END:
+        destinations.append(END)
+    if tools_available:
+        destinations.append("tools")
+    if name != model_destination:
+        destinations.append(model_destination)
+
+    graph.add_conditional_edges(name, jump_edge, destinations)

libs/langchain_v1/langchain/agents/react_agent.py

@@ -45,6 +45,7 @@ from langgraph.typing import ContextT, StateT
 from pydantic import BaseModel
 from typing_extensions import NotRequired, TypedDict, TypeVar
 
+from langchain.agents.middleware_agent import create_agent as create_middleware_agent
 from langchain.agents.structured_output import (
     MultipleStructuredOutputsError,
     OutputToolBinding,
@@ -66,6 +67,7 @@ if TYPE_CHECKING:
     from langchain.agents._internal._typing import (
         SyncOrAsync,
     )
+    from langchain.agents.types import AgentMiddleware
 
 StructuredResponseT = TypeVar("StructuredResponseT", default=None)
 
@@ -908,6 +910,7 @@ def create_agent(  # noqa: D417
     ],
     tools: Union[Sequence[Union[BaseTool, Callable, dict[str, Any]]], ToolNode],
     *,
+    middleware: Sequence[AgentMiddleware] = (),
     prompt: Prompt | None = None,
     response_format: Union[
         ToolStrategy[StructuredResponseT],
@@ -1114,6 +1117,29 @@ def create_agent(  # noqa: D417
             print(chunk)
         ```
     """
+    if middleware:
+        assert isinstance(model, str | BaseChatModel)  # noqa: S101
+        assert isinstance(prompt, str | None)  # noqa: S101
+        assert not isinstance(response_format, tuple)  # noqa: S101
+        assert pre_model_hook is None  # noqa: S101
+        assert post_model_hook is None  # noqa: S101
+        assert state_schema is None  # noqa: S101
+        return create_middleware_agent(  # type: ignore[return-value]
+            model=model,
+            tools=tools,
+            system_prompt=prompt,
+            middleware=middleware,
+            response_format=response_format,
+            context_schema=context_schema,
+        ).compile(
+            checkpointer=checkpointer,
+            store=store,
+            name=name,
+            interrupt_after=interrupt_after,
+            interrupt_before=interrupt_before,
+            debug=debug,
+        )
+
     # Handle deprecated config_schema parameter
     if (config_schema := deprecated_kwargs.pop("config_schema", MISSING)) is not MISSING:
         warn(

libs/langchain_v1/pyproject.toml

@@ -9,12 +9,12 @@ requires-python = ">=3.10"
 dependencies = [
     "langchain-core<2.0.0,>=0.3.75",
     "langchain-text-splitters<1.0.0,>=0.3.11",
-    "langgraph>=0.6.0",
+    "langgraph>=0.6.7",
     "pydantic>=2.7.4",
 ]
 
 name = "langchain"
-version = "1.0.0a3"
+version = "1.0.0a4"
 description = "Building applications with LLMs through composability"
 readme = "README.md"
 

libs/langchain_v1/tests/unit_tests/agents/__snapshots__/test_middleware_agent.ambr

@@ -0,0 +1,533 @@
+# serializer version: 1
+# name: test_create_agent_diagram
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	__end__([<p>__end__</p>]):::last
+  	__start__ --> model_request;
+  	model_request --> __end__;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---
+# name: test_create_agent_diagram.1
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	NoopOne_before_model(NoopOne.before_model)
+  	__end__([<p>__end__</p>]):::last
+  	NoopOne_before_model -.-> __end__;
+  	NoopOne_before_model -.-> model_request;
+  	__start__ --> NoopOne_before_model;
+  	model_request --> __end__;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---
+# name: test_create_agent_diagram.10
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	NoopTen_before_model(NoopTen.before_model)
+  	NoopTen_after_model(NoopTen.after_model)
+  	__end__([<p>__end__</p>]):::last
+  	NoopTen_after_model -.-> NoopTen_before_model;
+  	NoopTen_after_model -.-> __end__;
+  	NoopTen_before_model -.-> __end__;
+  	NoopTen_before_model -.-> model_request;
+  	__start__ --> NoopTen_before_model;
+  	model_request --> NoopTen_after_model;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---
+# name: test_create_agent_diagram.11
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	NoopTen_before_model(NoopTen.before_model)
+  	NoopTen_after_model(NoopTen.after_model)
+  	NoopEleven_before_model(NoopEleven.before_model)
+  	NoopEleven_after_model(NoopEleven.after_model)
+  	__end__([<p>__end__</p>]):::last
+  	NoopEleven_after_model -.-> NoopTen_after_model;
+  	NoopEleven_after_model -.-> NoopTen_before_model;
+  	NoopEleven_after_model -.-> __end__;
+  	NoopEleven_before_model -.-> NoopTen_before_model;
+  	NoopEleven_before_model -.-> __end__;
+  	NoopEleven_before_model -.-> model_request;
+  	NoopTen_after_model -.-> NoopTen_before_model;
+  	NoopTen_after_model -.-> __end__;
+  	NoopTen_before_model -.-> NoopEleven_before_model;
+  	NoopTen_before_model -.-> __end__;
+  	__start__ --> NoopTen_before_model;
+  	model_request --> NoopEleven_after_model;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---
+# name: test_create_agent_diagram.2
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	NoopOne_before_model(NoopOne.before_model)
+  	NoopTwo_before_model(NoopTwo.before_model)
+  	__end__([<p>__end__</p>]):::last
+  	NoopOne_before_model -.-> NoopTwo_before_model;
+  	NoopOne_before_model -.-> __end__;
+  	NoopTwo_before_model -.-> NoopOne_before_model;
+  	NoopTwo_before_model -.-> __end__;
+  	NoopTwo_before_model -.-> model_request;
+  	__start__ --> NoopOne_before_model;
+  	model_request --> __end__;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---
+# name: test_create_agent_diagram.3
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	NoopOne_before_model(NoopOne.before_model)
+  	NoopTwo_before_model(NoopTwo.before_model)
+  	NoopThree_before_model(NoopThree.before_model)
+  	__end__([<p>__end__</p>]):::last
+  	NoopOne_before_model -.-> NoopTwo_before_model;
+  	NoopOne_before_model -.-> __end__;
+  	NoopThree_before_model -.-> NoopOne_before_model;
+  	NoopThree_before_model -.-> __end__;
+  	NoopThree_before_model -.-> model_request;
+  	NoopTwo_before_model -.-> NoopOne_before_model;
+  	NoopTwo_before_model -.-> NoopThree_before_model;
+  	NoopTwo_before_model -.-> __end__;
+  	__start__ --> NoopOne_before_model;
+  	model_request --> __end__;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---
+# name: test_create_agent_diagram.4
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	NoopFour_after_model(NoopFour.after_model)
+  	__end__([<p>__end__</p>]):::last
+  	NoopFour_after_model -.-> __end__;
+  	NoopFour_after_model -.-> model_request;
+  	__start__ --> model_request;
+  	model_request --> NoopFour_after_model;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---
+# name: test_create_agent_diagram.5
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	NoopFour_after_model(NoopFour.after_model)
+  	NoopFive_after_model(NoopFive.after_model)
+  	__end__([<p>__end__</p>]):::last
+  	NoopFive_after_model -.-> NoopFour_after_model;
+  	NoopFive_after_model -.-> __end__;
+  	NoopFive_after_model -.-> model_request;
+  	NoopFour_after_model -.-> __end__;
+  	NoopFour_after_model -.-> model_request;
+  	__start__ --> model_request;
+  	model_request --> NoopFive_after_model;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---
+# name: test_create_agent_diagram.6
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	NoopFour_after_model(NoopFour.after_model)
+  	NoopFive_after_model(NoopFive.after_model)
+  	NoopSix_after_model(NoopSix.after_model)
+  	__end__([<p>__end__</p>]):::last
+  	NoopFive_after_model -.-> NoopFour_after_model;
+  	NoopFive_after_model -.-> __end__;
+  	NoopFive_after_model -.-> model_request;
+  	NoopFour_after_model -.-> __end__;
+  	NoopFour_after_model -.-> model_request;
+  	NoopSix_after_model -.-> NoopFive_after_model;
+  	NoopSix_after_model -.-> __end__;
+  	NoopSix_after_model -.-> model_request;
+  	__start__ --> model_request;
+  	model_request --> NoopSix_after_model;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---
+# name: test_create_agent_diagram.7
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	NoopSeven_before_model(NoopSeven.before_model)
+  	NoopSeven_after_model(NoopSeven.after_model)
+  	__end__([<p>__end__</p>]):::last
+  	NoopSeven_after_model -.-> NoopSeven_before_model;
+  	NoopSeven_after_model -.-> __end__;
+  	NoopSeven_before_model -.-> __end__;
+  	NoopSeven_before_model -.-> model_request;
+  	__start__ --> NoopSeven_before_model;
+  	model_request --> NoopSeven_after_model;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---
+# name: test_create_agent_diagram.8
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	NoopSeven_before_model(NoopSeven.before_model)
+  	NoopSeven_after_model(NoopSeven.after_model)
+  	NoopEight_before_model(NoopEight.before_model)
+  	NoopEight_after_model(NoopEight.after_model)
+  	__end__([<p>__end__</p>]):::last
+  	NoopEight_after_model -.-> NoopSeven_after_model;
+  	NoopEight_after_model -.-> NoopSeven_before_model;
+  	NoopEight_after_model -.-> __end__;
+  	NoopEight_before_model -.-> NoopSeven_before_model;
+  	NoopEight_before_model -.-> __end__;
+  	NoopEight_before_model -.-> model_request;
+  	NoopSeven_after_model -.-> NoopSeven_before_model;
+  	NoopSeven_after_model -.-> __end__;
+  	NoopSeven_before_model -.-> NoopEight_before_model;
+  	NoopSeven_before_model -.-> __end__;
+  	__start__ --> NoopSeven_before_model;
+  	model_request --> NoopEight_after_model;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---
+# name: test_create_agent_diagram.9
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	NoopSeven_before_model(NoopSeven.before_model)
+  	NoopSeven_after_model(NoopSeven.after_model)
+  	NoopEight_before_model(NoopEight.before_model)
+  	NoopEight_after_model(NoopEight.after_model)
+  	NoopNine_before_model(NoopNine.before_model)
+  	NoopNine_after_model(NoopNine.after_model)
+  	__end__([<p>__end__</p>]):::last
+  	NoopEight_after_model -.-> NoopSeven_after_model;
+  	NoopEight_after_model -.-> NoopSeven_before_model;
+  	NoopEight_after_model -.-> __end__;
+  	NoopEight_before_model -.-> NoopNine_before_model;
+  	NoopEight_before_model -.-> NoopSeven_before_model;
+  	NoopEight_before_model -.-> __end__;
+  	NoopNine_after_model -.-> NoopEight_after_model;
+  	NoopNine_after_model -.-> NoopSeven_before_model;
+  	NoopNine_after_model -.-> __end__;
+  	NoopNine_before_model -.-> NoopSeven_before_model;
+  	NoopNine_before_model -.-> __end__;
+  	NoopNine_before_model -.-> model_request;
+  	NoopSeven_after_model -.-> NoopSeven_before_model;
+  	NoopSeven_after_model -.-> __end__;
+  	NoopSeven_before_model -.-> NoopEight_before_model;
+  	NoopSeven_before_model -.-> __end__;
+  	__start__ --> NoopSeven_before_model;
+  	model_request --> NoopNine_after_model;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---
+# name: test_create_agent_jump[memory]
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	tools(tools)
+  	NoopSeven_before_model(NoopSeven.before_model)
+  	NoopSeven_after_model(NoopSeven.after_model)
+  	NoopEight_before_model(NoopEight.before_model)
+  	NoopEight_after_model(NoopEight.after_model)
+  	__end__([<p>__end__</p>]):::last
+  	NoopEight_after_model -.-> NoopSeven_after_model;
+  	NoopEight_after_model -.-> NoopSeven_before_model;
+  	NoopEight_after_model -.-> __end__;
+  	NoopEight_after_model -.-> tools;
+  	NoopEight_before_model -.-> NoopSeven_before_model;
+  	NoopEight_before_model -.-> __end__;
+  	NoopEight_before_model -.-> model_request;
+  	NoopEight_before_model -.-> tools;
+  	NoopSeven_after_model -.-> NoopSeven_before_model;
+  	NoopSeven_after_model -.-> __end__;
+  	NoopSeven_after_model -.-> tools;
+  	NoopSeven_before_model -.-> NoopEight_before_model;
+  	NoopSeven_before_model -.-> __end__;
+  	NoopSeven_before_model -.-> tools;
+  	__start__ --> NoopSeven_before_model;
+  	model_request --> NoopEight_after_model;
+  	tools -.-> NoopSeven_before_model;
+  	tools -.-> __end__;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---
+# name: test_create_agent_jump[postgres]
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	tools(tools)
+  	NoopSeven_before_model(NoopSeven.before_model)
+  	NoopSeven_after_model(NoopSeven.after_model)
+  	NoopEight_before_model(NoopEight.before_model)
+  	NoopEight_after_model(NoopEight.after_model)
+  	__end__([<p>__end__</p>]):::last
+  	NoopEight_after_model -.-> NoopSeven_after_model;
+  	NoopEight_after_model -.-> NoopSeven_before_model;
+  	NoopEight_after_model -.-> __end__;
+  	NoopEight_after_model -.-> tools;
+  	NoopEight_before_model -.-> NoopSeven_before_model;
+  	NoopEight_before_model -.-> __end__;
+  	NoopEight_before_model -.-> model_request;
+  	NoopEight_before_model -.-> tools;
+  	NoopSeven_after_model -.-> NoopSeven_before_model;
+  	NoopSeven_after_model -.-> __end__;
+  	NoopSeven_after_model -.-> tools;
+  	NoopSeven_before_model -.-> NoopEight_before_model;
+  	NoopSeven_before_model -.-> __end__;
+  	NoopSeven_before_model -.-> tools;
+  	__start__ --> NoopSeven_before_model;
+  	model_request --> NoopEight_after_model;
+  	tools -.-> NoopSeven_before_model;
+  	tools -.-> __end__;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---
+# name: test_create_agent_jump[postgres_pipe]
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	tools(tools)
+  	NoopSeven_before_model(NoopSeven.before_model)
+  	NoopSeven_after_model(NoopSeven.after_model)
+  	NoopEight_before_model(NoopEight.before_model)
+  	NoopEight_after_model(NoopEight.after_model)
+  	__end__([<p>__end__</p>]):::last
+  	NoopEight_after_model -.-> NoopSeven_after_model;
+  	NoopEight_after_model -.-> NoopSeven_before_model;
+  	NoopEight_after_model -.-> __end__;
+  	NoopEight_after_model -.-> tools;
+  	NoopEight_before_model -.-> NoopSeven_before_model;
+  	NoopEight_before_model -.-> __end__;
+  	NoopEight_before_model -.-> model_request;
+  	NoopEight_before_model -.-> tools;
+  	NoopSeven_after_model -.-> NoopSeven_before_model;
+  	NoopSeven_after_model -.-> __end__;
+  	NoopSeven_after_model -.-> tools;
+  	NoopSeven_before_model -.-> NoopEight_before_model;
+  	NoopSeven_before_model -.-> __end__;
+  	NoopSeven_before_model -.-> tools;
+  	__start__ --> NoopSeven_before_model;
+  	model_request --> NoopEight_after_model;
+  	tools -.-> NoopSeven_before_model;
+  	tools -.-> __end__;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---
+# name: test_create_agent_jump[postgres_pool]
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	tools(tools)
+  	NoopSeven_before_model(NoopSeven.before_model)
+  	NoopSeven_after_model(NoopSeven.after_model)
+  	NoopEight_before_model(NoopEight.before_model)
+  	NoopEight_after_model(NoopEight.after_model)
+  	__end__([<p>__end__</p>]):::last
+  	NoopEight_after_model -.-> NoopSeven_after_model;
+  	NoopEight_after_model -.-> NoopSeven_before_model;
+  	NoopEight_after_model -.-> __end__;
+  	NoopEight_after_model -.-> tools;
+  	NoopEight_before_model -.-> NoopSeven_before_model;
+  	NoopEight_before_model -.-> __end__;
+  	NoopEight_before_model -.-> model_request;
+  	NoopEight_before_model -.-> tools;
+  	NoopSeven_after_model -.-> NoopSeven_before_model;
+  	NoopSeven_after_model -.-> __end__;
+  	NoopSeven_after_model -.-> tools;
+  	NoopSeven_before_model -.-> NoopEight_before_model;
+  	NoopSeven_before_model -.-> __end__;
+  	NoopSeven_before_model -.-> tools;
+  	__start__ --> NoopSeven_before_model;
+  	model_request --> NoopEight_after_model;
+  	tools -.-> NoopSeven_before_model;
+  	tools -.-> __end__;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---
+# name: test_create_agent_jump[sqlite]
+  '''
+  ---
+  config:
+    flowchart:
+      curve: linear
+  ---
+  graph TD;
+  	__start__([<p>__start__</p>]):::first
+  	model_request(model_request)
+  	tools(tools)
+  	NoopSeven_before_model(NoopSeven.before_model)
+  	NoopSeven_after_model(NoopSeven.after_model)
+  	NoopEight_before_model(NoopEight.before_model)
+  	NoopEight_after_model(NoopEight.after_model)
+  	__end__([<p>__end__</p>]):::last
+  	NoopEight_after_model -.-> NoopSeven_after_model;
+  	NoopEight_after_model -.-> NoopSeven_before_model;
+  	NoopEight_after_model -.-> __end__;
+  	NoopEight_after_model -.-> tools;
+  	NoopEight_before_model -.-> NoopSeven_before_model;
+  	NoopEight_before_model -.-> __end__;
+  	NoopEight_before_model -.-> model_request;
+  	NoopEight_before_model -.-> tools;
+  	NoopSeven_after_model -.-> NoopSeven_before_model;
+  	NoopSeven_after_model -.-> __end__;
+  	NoopSeven_after_model -.-> tools;
+  	NoopSeven_before_model -.-> NoopEight_before_model;
+  	NoopSeven_before_model -.-> __end__;
+  	NoopSeven_before_model -.-> tools;
+  	__start__ --> NoopSeven_before_model;
+  	model_request --> NoopEight_after_model;
+  	tools -.-> NoopSeven_before_model;
+  	tools -.-> __end__;
+  	classDef default fill:#f2f0ff,line-height:1.2
+  	classDef first fill-opacity:0
+  	classDef last fill:#bfb6fc
+  
+  '''
+# ---

libs/langchain_v1/tests/unit_tests/agents/test_middleware_agent.py

@@ -0,0 +1,712 @@
+import pytest
+from typing import Any
+from unittest.mock import patch
+
+from syrupy.assertion import SnapshotAssertion
+
+from langchain_core.language_models import BaseChatModel
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.messages import (
+    AIMessage,
+    HumanMessage,
+    RemoveMessage,
+    ToolCall,
+    ToolMessage,
+)
+from langchain_core.tools import tool
+
+from langchain.agents.middleware_agent import create_agent
+from langchain.agents.middleware.human_in_the_loop import HumanInTheLoopMiddleware
+from langchain.agents.middleware.prompt_caching import AnthropicPromptCachingMiddleware
+from langchain.agents.middleware.summarization import SummarizationMiddleware
+from langchain.agents.middleware.types import AgentMiddleware, ModelRequest
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.constants import END
+from langgraph.graph.message import REMOVE_ALL_MESSAGES
+from langgraph.prebuilt.interrupt import ActionRequest, HumanInterruptConfig
+
+from .messages import _AnyIdHumanMessage, _AnyIdToolMessage
+from .model import FakeToolCallingModel
+
+
+def test_create_agent_diagram(
+    snapshot: SnapshotAssertion,
+):
+    class NoopOne(AgentMiddleware):
+        def before_model(self, state):
+            pass
+
+    class NoopTwo(AgentMiddleware):
+        def before_model(self, state):
+            pass
+
+    class NoopThree(AgentMiddleware):
+        def before_model(self, state):
+            pass
+
+    class NoopFour(AgentMiddleware):
+        def after_model(self, state):
+            pass
+
+    class NoopFive(AgentMiddleware):
+        def after_model(self, state):
+            pass
+
+    class NoopSix(AgentMiddleware):
+        def after_model(self, state):
+            pass
+
+    class NoopSeven(AgentMiddleware):
+        def before_model(self, state):
+            pass
+
+        def after_model(self, state):
+            pass
+
+    class NoopEight(AgentMiddleware):
+        def before_model(self, state):
+            pass
+
+        def after_model(self, state):
+            pass
+
+    class NoopNine(AgentMiddleware):
+        def before_model(self, state):
+            pass
+
+        def after_model(self, state):
+            pass
+
+    class NoopTen(AgentMiddleware):
+        def before_model(self, state):
+            pass
+
+        def modify_model_request(self, request, state):
+            pass
+
+        def after_model(self, state):
+            pass
+
+    class NoopEleven(AgentMiddleware):
+        def before_model(self, state):
+            pass
+
+        def modify_model_request(self, request, state):
+            pass
+
+        def after_model(self, state):
+            pass
+
+    agent_zero = create_agent(
+        model=FakeToolCallingModel(),
+        tools=[],
+        system_prompt="You are a helpful assistant.",
+    )
+
+    assert agent_zero.compile().get_graph().draw_mermaid() == snapshot
+
+    agent_one = create_agent(
+        model=FakeToolCallingModel(),
+        tools=[],
+        system_prompt="You are a helpful assistant.",
+        middleware=[NoopOne()],
+    )
+
+    assert agent_one.compile().get_graph().draw_mermaid() == snapshot
+
+    agent_two = create_agent(
+        model=FakeToolCallingModel(),
+        tools=[],
+        system_prompt="You are a helpful assistant.",
+        middleware=[NoopOne(), NoopTwo()],
+    )
+
+    assert agent_two.compile().get_graph().draw_mermaid() == snapshot
+
+    agent_three = create_agent(
+        model=FakeToolCallingModel(),
+        tools=[],
+        system_prompt="You are a helpful assistant.",
+        middleware=[NoopOne(), NoopTwo(), NoopThree()],
+    )
+
+    assert agent_three.compile().get_graph().draw_mermaid() == snapshot
+
+    agent_four = create_agent(
+        model=FakeToolCallingModel(),
+        tools=[],
+        system_prompt="You are a helpful assistant.",
+        middleware=[NoopFour()],
+    )
+
+    assert agent_four.compile().get_graph().draw_mermaid() == snapshot
+
+    agent_five = create_agent(
+        model=FakeToolCallingModel(),
+        tools=[],
+        system_prompt="You are a helpful assistant.",
+        middleware=[NoopFour(), NoopFive()],
+    )
+
+    assert agent_five.compile().get_graph().draw_mermaid() == snapshot
+
+    agent_six = create_agent(
+        model=FakeToolCallingModel(),
+        tools=[],
+        system_prompt="You are a helpful assistant.",
+        middleware=[NoopFour(), NoopFive(), NoopSix()],
+    )
+
+    assert agent_six.compile().get_graph().draw_mermaid() == snapshot
+
+    agent_seven = create_agent(
+        model=FakeToolCallingModel(),
+        tools=[],
+        system_prompt="You are a helpful assistant.",
+        middleware=[NoopSeven()],
+    )
+
+    assert agent_seven.compile().get_graph().draw_mermaid() == snapshot
+
+    agent_eight = create_agent(
+        model=FakeToolCallingModel(),
+        tools=[],
+        system_prompt="You are a helpful assistant.",
+        middleware=[NoopSeven(), NoopEight()],
+    )
+
+    assert agent_eight.compile().get_graph().draw_mermaid() == snapshot
+
+    agent_nine = create_agent(
+        model=FakeToolCallingModel(),
+        tools=[],
+        system_prompt="You are a helpful assistant.",
+        middleware=[NoopSeven(), NoopEight(), NoopNine()],
+    )
+
+    assert agent_nine.compile().get_graph().draw_mermaid() == snapshot
+
+    agent_ten = create_agent(
+        model=FakeToolCallingModel(),
+        tools=[],
+        system_prompt="You are a helpful assistant.",
+        middleware=[NoopTen()],
+    )
+
+    assert agent_ten.compile().get_graph().draw_mermaid() == snapshot
+
+    agent_eleven = create_agent(
+        model=FakeToolCallingModel(),
+        tools=[],
+        system_prompt="You are a helpful assistant.",
+        middleware=[NoopTen(), NoopEleven()],
+    )
+
+    assert agent_eleven.compile().get_graph().draw_mermaid() == snapshot
+
+
+def test_create_agent_invoke(
+    snapshot: SnapshotAssertion,
+    sync_checkpointer: BaseCheckpointSaver,
+):
+    calls = []
+
+    class NoopSeven(AgentMiddleware):
+        def before_model(self, state):
+            calls.append("NoopSeven.before_model")
+
+        def modify_model_request(self, request, state):
+            calls.append("NoopSeven.modify_model_request")
+            return request
+
+        def after_model(self, state):
+            calls.append("NoopSeven.after_model")
+
+    class NoopEight(AgentMiddleware):
+        def before_model(self, state):
+            calls.append("NoopEight.before_model")
+
+        def modify_model_request(self, request, state):
+            calls.append("NoopEight.modify_model_request")
+            return request
+
+        def after_model(self, state):
+            calls.append("NoopEight.after_model")
+
+    @tool
+    def my_tool(input: str) -> str:
+        """A great tool"""
+        calls.append("my_tool")
+        return input.upper()
+
+    agent_one = create_agent(
+        model=FakeToolCallingModel(
+            tool_calls=[
+                [
+                    {"args": {"input": "yo"}, "id": "1", "name": "my_tool"},
+                ],
+                [],
+            ]
+        ),
+        tools=[my_tool],
+        system_prompt="You are a helpful assistant.",
+        middleware=[NoopSeven(), NoopEight()],
+    ).compile(checkpointer=sync_checkpointer)
+
+    thread1 = {"configurable": {"thread_id": "1"}}
+    assert agent_one.invoke({"messages": ["hello"]}, thread1) == {
+        "messages": [
+            _AnyIdHumanMessage(content="hello"),
+            AIMessage(
+                content="You are a helpful assistant.-hello",
+                additional_kwargs={},
+                response_metadata={},
+                id="0",
+                tool_calls=[
+                    {
+                        "name": "my_tool",
+                        "args": {"input": "yo"},
+                        "id": "1",
+                        "type": "tool_call",
+                    }
+                ],
+            ),
+            _AnyIdToolMessage(content="YO", name="my_tool", tool_call_id="1"),
+            AIMessage(
+                content="You are a helpful assistant.-hello-You are a helpful assistant.-hello-YO",
+                additional_kwargs={},
+                response_metadata={},
+                id="1",
+            ),
+        ],
+    }
+    assert calls == [
+        "NoopSeven.before_model",
+        "NoopEight.before_model",
+        "NoopSeven.modify_model_request",
+        "NoopEight.modify_model_request",
+        "NoopEight.after_model",
+        "NoopSeven.after_model",
+        "my_tool",
+        "NoopSeven.before_model",
+        "NoopEight.before_model",
+        "NoopSeven.modify_model_request",
+        "NoopEight.modify_model_request",
+        "NoopEight.after_model",
+        "NoopSeven.after_model",
+    ]
+
+
+def test_create_agent_jump(
+    snapshot: SnapshotAssertion,
+    sync_checkpointer: BaseCheckpointSaver,
+):
+    calls = []
+
+    class NoopSeven(AgentMiddleware):
+        def before_model(self, state):
+            calls.append("NoopSeven.before_model")
+
+        def modify_model_request(self, request, state):
+            calls.append("NoopSeven.modify_model_request")
+            return request
+
+        def after_model(self, state):
+            calls.append("NoopSeven.after_model")
+
+    class NoopEight(AgentMiddleware):
+        def before_model(self, state) -> dict[str, Any]:
+            calls.append("NoopEight.before_model")
+            return {"jump_to": END}
+
+        def modify_model_request(self, request, state) -> ModelRequest:
+            calls.append("NoopEight.modify_model_request")
+            return request
+
+        def after_model(self, state):
+            calls.append("NoopEight.after_model")
+
+    @tool
+    def my_tool(input: str) -> str:
+        """A great tool"""
+        calls.append("my_tool")
+        return input.upper()
+
+    agent_one = create_agent(
+        model=FakeToolCallingModel(
+            tool_calls=[[ToolCall(id="1", name="my_tool", args={"input": "yo"})]],
+        ),
+        tools=[my_tool],
+        system_prompt="You are a helpful assistant.",
+        middleware=[NoopSeven(), NoopEight()],
+    ).compile(checkpointer=sync_checkpointer)
+
+    if isinstance(sync_checkpointer, InMemorySaver):
+        assert agent_one.get_graph().draw_mermaid() == snapshot
+
+    thread1 = {"configurable": {"thread_id": "1"}}
+    assert agent_one.invoke({"messages": []}, thread1) == {"messages": []}
+    assert calls == ["NoopSeven.before_model", "NoopEight.before_model"]
+
+
+# Tests for HumanInTheLoopMiddleware
+def test_human_in_the_loop_middleware_initialization() -> None:
+    """Test HumanInTheLoopMiddleware initialization."""
+    tool_configs = {
+        "test_tool": HumanInterruptConfig(
+            allow_ignore=True, allow_respond=True, allow_edit=True, allow_accept=True
+        )
+    }
+
+    middleware = HumanInTheLoopMiddleware(tool_configs=tool_configs, message_prefix="Custom prefix")
+
+    assert middleware.tool_configs == tool_configs
+    assert middleware.message_prefix == "Custom prefix"
+
+
+def test_human_in_the_loop_middleware_no_interrupts_needed() -> None:
+    """Test HumanInTheLoopMiddleware when no interrupts are needed."""
+    tool_configs = {
+        "test_tool": HumanInterruptConfig(
+            allow_ignore=True, allow_respond=True, allow_edit=True, allow_accept=True
+        )
+    }
+
+    middleware = HumanInTheLoopMiddleware(tool_configs=tool_configs)
+
+    # Test with no messages
+    state: dict[str, Any] = {"messages": []}
+    result = middleware.after_model(state)
+    assert result is None
+
+    # Test with message but no tool calls
+    state = {"messages": [HumanMessage(content="Hello"), AIMessage(content="Hi there")]}
+    result = middleware.after_model(state)
+    assert result is None
+
+    # Test with tool calls that don't require interrupts
+    ai_message = AIMessage(
+        content="I'll help you",
+        tool_calls=[{"name": "other_tool", "args": {"input": "test"}, "id": "1"}],
+    )
+    state = {"messages": [HumanMessage(content="Hello"), ai_message]}
+    result = middleware.after_model(state)
+    assert result is None
+
+
+def test_human_in_the_loop_middleware_interrupt_responses() -> None:
+    """Test HumanInTheLoopMiddleware with different interrupt response types."""
+    tool_configs = {
+        "test_tool": HumanInterruptConfig(
+            allow_ignore=True, allow_respond=True, allow_edit=True, allow_accept=True
+        )
+    }
+
+    middleware = HumanInTheLoopMiddleware(tool_configs=tool_configs)
+
+    ai_message = AIMessage(
+        content="I'll help you",
+        tool_calls=[{"name": "test_tool", "args": {"input": "test"}, "id": "1"}],
+    )
+    state = {"messages": [HumanMessage(content="Hello"), ai_message]}
+
+    # Test accept response
+    def mock_accept(requests):
+        return [{"type": "accept", "args": None}]
+
+    with patch("langchain.agents.middleware.human_in_the_loop.interrupt", side_effect=mock_accept):
+        result = middleware.after_model(state)
+        assert result is not None
+        assert result["messages"][0] == ai_message
+        assert result["messages"][0].tool_calls == ai_message.tool_calls
+
+    # Test edit response
+    def mock_edit(requests):
+        return [
+            {"type": "edit", "args": ActionRequest(action="test_tool", args={"input": "edited"})}
+        ]
+
+    with patch("langchain.agents.middleware.human_in_the_loop.interrupt", side_effect=mock_edit):
+        result = middleware.after_model(state)
+        assert result is not None
+        assert result["messages"][0].tool_calls[0]["args"] == {"input": "edited"}
+
+    # Test ignore response
+    def mock_ignore(requests):
+        return [{"type": "ignore", "args": None}]
+
+    with patch("langchain.agents.middleware.human_in_the_loop.interrupt", side_effect=mock_ignore):
+        result = middleware.after_model(state)
+        assert result is not None
+        assert result["jump_to"] == "__end__"
+
+    # Test response type
+    def mock_response(requests):
+        return [{"type": "response", "args": "Custom response"}]
+
+    with patch(
+        "langchain.agents.middleware.human_in_the_loop.interrupt", side_effect=mock_response
+    ):
+        result = middleware.after_model(state)
+        assert result is not None
+        assert result["jump_to"] == "model"
+        assert result["messages"][0]["role"] == "tool"
+        assert result["messages"][0]["content"] == "Custom response"
+
+    # Test unknown response type
+    def mock_unknown(requests):
+        return [{"type": "unknown", "args": None}]
+
+    with patch("langchain.agents.middleware.human_in_the_loop.interrupt", side_effect=mock_unknown):
+        with pytest.raises(ValueError, match="Unknown response type: unknown"):
+            middleware.after_model(state)
+
+
+# Tests for AnthropicPromptCachingMiddleware
+def test_anthropic_prompt_caching_middleware_initialization() -> None:
+    """Test AnthropicPromptCachingMiddleware initialization."""
+    # Test with custom values
+    middleware = AnthropicPromptCachingMiddleware(
+        type="ephemeral", ttl="1h", min_messages_to_cache=5
+    )
+    assert middleware.type == "ephemeral"
+    assert middleware.ttl == "1h"
+    assert middleware.min_messages_to_cache == 5
+
+    # Test with default values
+    middleware = AnthropicPromptCachingMiddleware()
+    assert middleware.type == "ephemeral"
+    assert middleware.ttl == "5m"
+    assert middleware.min_messages_to_cache == 0
+
+
+# Tests for SummarizationMiddleware
+def test_summarization_middleware_initialization() -> None:
+    """Test SummarizationMiddleware initialization."""
+    model = FakeToolCallingModel()
+    middleware = SummarizationMiddleware(
+        model=model,
+        max_tokens_before_summary=1000,
+        messages_to_keep=10,
+        summary_prompt="Custom prompt: {messages}",
+        summary_prefix="Custom prefix:",
+    )
+
+    assert middleware.model == model
+    assert middleware.max_tokens_before_summary == 1000
+    assert middleware.messages_to_keep == 10
+    assert middleware.summary_prompt == "Custom prompt: {messages}"
+    assert middleware.summary_prefix == "Custom prefix:"
+
+    # Test with string model
+    with patch(
+        "langchain.agents.middleware.summarization.init_chat_model",
+        return_value=FakeToolCallingModel(),
+    ):
+        middleware = SummarizationMiddleware(model="fake-model")
+        assert isinstance(middleware.model, FakeToolCallingModel)
+
+
+def test_summarization_middleware_no_summarization_cases() -> None:
+    """Test SummarizationMiddleware when summarization is not needed or disabled."""
+    model = FakeToolCallingModel()
+    middleware = SummarizationMiddleware(model=model, max_tokens_before_summary=1000)
+
+    # Test when summarization is disabled
+    middleware_disabled = SummarizationMiddleware(model=model, max_tokens_before_summary=None)
+    state = {"messages": [HumanMessage(content="Hello"), AIMessage(content="Hi")]}
+    result = middleware_disabled.before_model(state)
+    assert result is None
+
+    # Test when token count is below threshold
+    def mock_token_counter(messages):
+        return 500  # Below threshold
+
+    middleware.token_counter = mock_token_counter
+    result = middleware.before_model(state)
+    assert result is None
+
+
+def test_summarization_middleware_helper_methods() -> None:
+    """Test SummarizationMiddleware helper methods."""
+    model = FakeToolCallingModel()
+    middleware = SummarizationMiddleware(model=model, max_tokens_before_summary=1000)
+
+    # Test message ID assignment
+    messages = [HumanMessage(content="Hello"), AIMessage(content="Hi")]
+    middleware._ensure_message_ids(messages)
+    for msg in messages:
+        assert msg.id is not None
+
+    # Test message partitioning
+    messages = [
+        HumanMessage(content="1"),
+        HumanMessage(content="2"),
+        HumanMessage(content="3"),
+        HumanMessage(content="4"),
+        HumanMessage(content="5"),
+    ]
+    to_summarize, preserved = middleware._partition_messages(messages, 2)
+    assert len(to_summarize) == 2
+    assert len(preserved) == 3
+    assert to_summarize == messages[:2]
+    assert preserved == messages[2:]
+
+    # Test summary message building
+    summary = "This is a test summary"
+    new_messages = middleware._build_new_messages(summary)
+    assert len(new_messages) == 1
+    assert isinstance(new_messages[0], HumanMessage)
+    assert "Here is a summary of the conversation to date:" in new_messages[0].content
+    assert summary in new_messages[0].content
+
+    # Test tool call detection
+    ai_message_no_tools = AIMessage(content="Hello")
+    assert not middleware._has_tool_calls(ai_message_no_tools)
+
+    ai_message_with_tools = AIMessage(
+        content="Hello", tool_calls=[{"name": "test", "args": {}, "id": "1"}]
+    )
+    assert middleware._has_tool_calls(ai_message_with_tools)
+
+    human_message = HumanMessage(content="Hello")
+    assert not middleware._has_tool_calls(human_message)
+
+
+def test_summarization_middleware_tool_call_safety() -> None:
+    """Test SummarizationMiddleware tool call safety logic."""
+    model = FakeToolCallingModel()
+    middleware = SummarizationMiddleware(
+        model=model, max_tokens_before_summary=1000, messages_to_keep=3
+    )
+
+    # Test safe cutoff point detection with tool calls
+    messages = [
+        HumanMessage(content="1"),
+        AIMessage(content="2", tool_calls=[{"name": "test", "args": {}, "id": "1"}]),
+        ToolMessage(content="3", tool_call_id="1"),
+        HumanMessage(content="4"),
+    ]
+
+    # Safe cutoff (doesn't separate AI/Tool pair)
+    is_safe = middleware._is_safe_cutoff_point(messages, 0)
+    assert is_safe is True
+
+    # Unsafe cutoff (separates AI/Tool pair)
+    is_safe = middleware._is_safe_cutoff_point(messages, 2)
+    assert is_safe is False
+
+    # Test tool call ID extraction
+    ids = middleware._extract_tool_call_ids(messages[1])
+    assert ids == {"1"}
+
+
+def test_summarization_middleware_summary_creation() -> None:
+    """Test SummarizationMiddleware summary creation."""
+
+    class MockModel(BaseChatModel):
+        def invoke(self, prompt):
+            from langchain_core.messages import AIMessage
+
+            return AIMessage(content="Generated summary")
+
+        def _generate(self, messages, **kwargs):
+            from langchain_core.outputs import ChatResult, ChatGeneration
+            from langchain_core.messages import AIMessage
+
+            return ChatResult(generations=[ChatGeneration(message=AIMessage(content="Summary"))])
+
+        @property
+        def _llm_type(self):
+            return "mock"
+
+    middleware = SummarizationMiddleware(model=MockModel(), max_tokens_before_summary=1000)
+
+    # Test normal summary creation
+    messages = [HumanMessage(content="Hello"), AIMessage(content="Hi")]
+    summary = middleware._create_summary(messages)
+    assert summary == "Generated summary"
+
+    # Test empty messages
+    summary = middleware._create_summary([])
+    assert summary == "No previous conversation history."
+
+    # Test error handling
+    class ErrorModel(BaseChatModel):
+        def invoke(self, prompt):
+            raise Exception("Model error")
+
+        def _generate(self, messages, **kwargs):
+            from langchain_core.outputs import ChatResult, ChatGeneration
+            from langchain_core.messages import AIMessage
+
+            return ChatResult(generations=[ChatGeneration(message=AIMessage(content="Summary"))])
+
+        @property
+        def _llm_type(self):
+            return "mock"
+
+    middleware_error = SummarizationMiddleware(model=ErrorModel(), max_tokens_before_summary=1000)
+    summary = middleware_error._create_summary(messages)
+    assert "Error generating summary: Model error" in summary
+
+
+def test_summarization_middleware_full_workflow() -> None:
+    """Test SummarizationMiddleware complete summarization workflow."""
+
+    class MockModel(BaseChatModel):
+        def invoke(self, prompt):
+            from langchain_core.messages import AIMessage
+
+            return AIMessage(content="Generated summary")
+
+        def _generate(self, messages, **kwargs):
+            from langchain_core.outputs import ChatResult, ChatGeneration
+            from langchain_core.messages import AIMessage
+
+            return ChatResult(generations=[ChatGeneration(message=AIMessage(content="Summary"))])
+
+        @property
+        def _llm_type(self):
+            return "mock"
+
+    middleware = SummarizationMiddleware(
+        model=MockModel(), max_tokens_before_summary=1000, messages_to_keep=2
+    )
+
+    # Mock high token count to trigger summarization
+    def mock_token_counter(messages):
+        return 1500  # Above threshold
+
+    middleware.token_counter = mock_token_counter
+
+    messages = [
+        HumanMessage(content="1"),
+        HumanMessage(content="2"),
+        HumanMessage(content="3"),
+        HumanMessage(content="4"),
+        HumanMessage(content="5"),
+    ]
+
+    state = {"messages": messages}
+    result = middleware.before_model(state)
+
+    assert result is not None
+    assert "messages" in result
+    assert len(result["messages"]) > 0
+
+    # Should have RemoveMessage for cleanup
+    assert isinstance(result["messages"][0], RemoveMessage)
+    assert result["messages"][0].id == REMOVE_ALL_MESSAGES
+
+    # Should have summary message
+    summary_message = None
+    for msg in result["messages"]:
+        if isinstance(msg, HumanMessage) and "summary of the conversation" in msg.content:
+            summary_message = msg
+            break
+
+    assert summary_message is not None
+    assert "Generated summary" in summary_message.content

libs/langchain_v1/uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.13' and platform_python_implementation == 'PyPy'",
@@ -131,7 +131,7 @@ wheels = [
 
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-version = "0.64.0"
+version = "0.66.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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+sdist = { url = "https://files.pythonhosted.org/packages/fa/50/daa51c035e6a941f7b8034705796c7643443a85f5381cb41a797757fc6d3/anthropic-0.66.0.tar.gz", hash = "sha256:5aa8b18da57dc27d83fc1d82c9fb860977e5adfae3e0c215d7ab2ebd70afb9cb", size = 436933, upload-time = "2025-09-03T14:55:40.879Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/00/6a/d4ec7de9cc88b9a39c74dab1db259203b29b17fc564ecd1f92991678bd1e/anthropic-0.66.0-py3-none-any.whl", hash = "sha256:67b8cd4486f3cdd09211598dc5325cc8e4e349c106a03041231d551603551c06", size = 308035, upload-time = "2025-09-03T14:55:39.109Z" },
 ]
 
 [[package]]
@@ -266,30 +266,30 @@ wheels = [
 
 [[package]]
 name = "boto3"
-version = "1.40.18"
+version = "1.40.25"
 source = { registry = "https://pypi.org/simple" }
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+sdist = { url = "https://files.pythonhosted.org/packages/2c/36/de7e622fd7907faec3823eaee7299b55130f577a4ba609717a290e9f3897/boto3-1.40.25.tar.gz", hash = "sha256:debfa4b2c67492d53629a52c999d71cddc31041a8b62ca1a8b1fb60fb0712ee1", size = 111534, upload-time = "2025-09-05T19:23:21.942Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/c7/9a/6b280f01f5ec7e812ac8be9803bf52868b190e15c500bee3319d9d68eb34/boto3-1.40.25-py3-none-any.whl", hash = "sha256:d39bc3deb6780d910f00580837b720132055b0604769fd978780865ed3c019ea", size = 139325, upload-time = "2025-09-05T19:23:20.551Z" },
 ]
 
 [[package]]
 name = "botocore"
-version = "1.40.18"
+version = "1.40.25"
 source = { registry = "https://pypi.org/simple" }
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