Middleware

Alpha Notice: These docs cover the v1-alpha release. Content is incomplete and subject to change.For the latest stable version, see the v0 LangChain Python or LangChain JavaScript docs.

Middleware provides a way to more tightly control what happens inside the agent. The core agent loop involves calling a model, letting it choose tools to execute, and then finishing when it calls no more tools.

Middleware provides control over what happens before and after those steps. Each middleware can add in three different types of modifiers:

beforeModel: Runs before model execution. Can update state or jump to a different node (model, tools, end)
modifyModelRequest: Runs before model execution, to prepare the model request object. Can only modify the current model request object (no permanent state updates) and cannot jump to a different node.
afterModel: Runs after model execution, before tools are executed. Can update state or jump to a different node (model, tools, __end__)

In addition to that, each middleware can define the following static properties:

name: The name of the middleware (required)
tools: The tools that the middleware makes available to the agent (optional)
stateSchema: The schema of the state that the middleware requires (optional)
contextSchema: The schema of the context that the middleware requires (optional)

An agent can contain multiple middleware. Each middleware does not need to implement all three of beforeModel, modifyModelRequest, afterModel.

Using in an agent

import {
  createAgent,
  summarizationMiddleware,
  humanInTheLoopMiddleware,
} from "langchain";

const agent = createAgent({
  // ...
  middleware: [summarizationMiddleware, humanInTheLoopMiddleware],
  // ...
});

Middleware is highly flexible and replaces some other functionality in the agent. As such, when middleware are used, there are some restrictions on the arguments used to create the agent:

model must be either a string or a BaseChatModel. Will error if a function is passed. If you want to dynamically control the model, use AgentMiddleware.modifyModelRequest
prompt must be either a string or None. Will error if a function is passed. If you want to dynamically control the prompt, use AgentMiddleware.modifyModelRequest
preModelHook must not be provided. Use AgentMiddleware.beforeModel instead.
postModelHook must not be provided. Use AgentMiddleware.afterModel instead.

Built-in middleware

LangChain provides several built in middleware to use off-the-shelf

Summarization
Human-in-the-loop
Anthropic prompt caching
Dynamic system prompt

Summarization

The summarizationMiddleware automatically manages conversation history by summarizing older messages when token limits are approached. This middleware monitors the total token count of messages and creates concise summaries to preserve context while staying within model limits. Key features:

Automatic token counting and threshold monitoring
Intelligent message partitioning that preserves AI/Tool message pairs
Customizable summary prompts and token limits

Use Cases:

Long-running conversations that exceed token limits
Multi-turn dialogues with extensive context

import { createAgent, summarizationMiddleware } from "langchain";

const agent = createAgent({
  model: "openai:gpt-4o",
  tools: [weatherTool, calculatorTool],
  middleware: [
    summarizationMiddleware({
      model: "openai:gpt-4o-mini",
      maxTokensBeforeSummary: 4000, // Trigger summarization at 4000 tokens
      messagesToKeep: 20, // Keep last 20 messages after summary
      summaryPrompt: "Custom prompt for summarization...", // Optional
    }),
  ],
});

Configuration options:

model: Language model to use for generating summaries (required)
maxTokensBeforeSummary: Token threshold that triggers summarization
messagesToKeep: Number of recent messages to preserve (default: 20)
tokenCounter: Custom function for counting tokens (defaults to character-based approximation)
summaryPrompt: Custom prompt template for summary generation
summaryPrefix: Prefix added to system messages containing summaries (default: ”## Previous conversation summary:”)

The middleware ensures tool call integrity by:

Never splitting AI messages from their corresponding tool responses
Preserving the most recent messages for continuity
Including previous summaries in new summarization cycles

Human-in-the-loop

The HumanInTheLoopMiddleware enables human oversight and intervention for tool calls made by the agents. Please see human-in-the-loop documentation for more details. This middleware intercepts tool executions and allows human operators to approve, modify, reject, or manually respond to tool calls before they execute.

Anthropic prompt caching

AnthropicPromptCachingMiddleware is a middleware that enables you to enable Anthropic’s native prompt caching. Prompt caching enables optimal API usage by allowing resuming from specific prefixes in your prompts. This is particularly useful for tasks with repetitive prompts or prompts with redundant information.

Learn more about Anthropic Prompt Caching (strategies, limitations, etc.) here.

When using prompt caching, you’ll likely want to use a checkpointer to store conversation history across invocations.

import { createAgent, HumanMessage, anthropicPromptCachingMiddleware } from "langchain";

const LONG_PROMPT = `
Please be a helpful assistant.

<Lots more context ...>
`;

const agent = createAgent({
  model: "anthropic:claude-sonnet-4-latest",
  prompt: LONG_PROMPT,
  middleware: [anthropicPromptCachingMiddleware({ ttl: "5m" })],
});

// cache store
await agent.invoke({
  messages: [HumanMessage("Hi, my name is Bob")]
});

// cache hit, system prompt is cached
const result = await agent.invoke({
  messages: [HumanMessage("What's my name?")]
});

Dynamic system prompt

A system prompt can be dynamically set right before each model invocation using the dynamicSystemPromptMiddleware middleware. This middleware is particularly useful when the prompt depends on the current agent state or runtime context. For example, you can adjust the system prompt based on the user’s expertise level:

import { z } from "zod";
import { createAgent, dynamicSystemPromptMiddleware } from "langchain";

const contextSchema = z.object({
    userRole: z.enum(["expert", "beginner"]),
});

const agent = createAgent({
    model: "openai:gpt-4o",
    tools: [...],
    contextSchema,
    middleware: [
        dynamicSystemPromptMiddleware<z.infer<typeof contextSchema>>((state, runtime) => {
            const userRole = runtime.context.userRole || "user";
            const basePrompt = "You are a helpful assistant.";

            if (userRole === "expert") {
                return `${basePrompt} Provide detailed technical responses.`;
            } else if (userRole === "beginner") {
                return `${basePrompt} Explain concepts simply and avoid jargon.`;
            }
            return basePrompt;
        }),
    ],
});

// The system prompt will be set dynamically based on context
const result = await agent.invoke(
    { messages: [{ role: "user", content: "Explain async programming" }] },
    { context: { userRole: "expert" } }
);

Alternatively, you can adjust the system prompt based on the conversation length:

const agent = createAgent({
  model: "openai:gpt-4o",
  tools: [searchTool],
  middleware: [
    dynamicSystemPromptMiddleware((state) => {
      const messageCount = state.messages.length;

      if (messageCount > 10) {
        return "You are in an extended conversation. Be more concise.";
      }
      return "You are a helpful assistant.";
    }),
  ],
});

Custom Middleware

Middleware for agents are subclasses of AgentMiddleware, which implement one or more of its hooks. AgentMiddleware currently provides three different ways to modify the core agent loop:

beforeModel: runs before the model is run. Can update state or exit early with a jump.
modifyModelRequest: runs before the model is run. Cannot update state or exit early with a jump.
afterModel: runs after the model is run. Can update state or exit early with a jump.

In order to exit early, you can add a jump_to key to the state update with one of the following values:

"model": Jump to the model node
"tools": Jump to the tools node
"end": Jump to the end node

If this is specified, all subsequent middleware will not run. Learn more about exiting early in the agent jumps section.

`beforeModel`

Runs before the model is run. Can modify state by returning a new state object or state update. Signature:

import { createMiddleware, AIMessage } from "langchain";

const myMiddleware = createMiddleware({
  name: "MyMiddleware",
  beforeModel: (state) => {
    if (state.messages.length > 50) {
      return {
        messages: [
          new AIMessage("I'm sorry, the conversation has been terminated."),
        ],
        jumpTo: "end",
      };
    }
    return state;
  },
});

`modifyModelRequest`

Runs before the model has run, but after all the beforeModel calls. These functions cannot modify permanent state or exit early. Rather, they are intended to modify calls to the model in a stateless way. If you want to modify calls to the model in a stateful way, you will need to use beforeModel Modifies the model request. The model request has several key properties:

model (BaseChatModel): the model to use. Note: this needs to the base chat model, not a string.
systemPrompt (string): the system prompt to use. Will get prepended to messages
messages (list of messages): the message list. Should not include system prompt.
toolChoice: Can be one of:
- "auto": means the model can pick between generating a message or calling one or more tools.
- "none": means the model will not call any tool and instead generates a message.
- "required": means the model must call one or more tools.
- { type: "function", function: { name: string } }: The model will use the specified function.
tools (list of strings): the tool names to use for this model call
responseFormat (ResponseFormat): the response format to use for structured output

Signature:

import { createMiddleware, AIMessage } from "langchain";

const myMiddleware = createMiddleware({
  name: "MyMiddleware",
  modifyModelRequest: (request) => {
    // Select specific tools by name for this model call
    return {
      ...request,
      tools: ["weatherTool", "calculatorTool"], // Array of tool names
    };
  },
});

`afterModel`

Runs after the model is run. Can modify state by returning a new state object or state update. Signature:

import { createMiddleware } from "langchain";

const myMiddleware = createMiddleware({
  name: "MyMiddleware",
  afterModel: (state) => {
    // ...
  },
});

New state keys

Middleware can extend the agent’s state with custom properties, enabling rich data flow between middleware components and ensuring type safety throughout the agent execution.

State extension

Middleware can define additional state properties that persist throughout the agent’s execution. These properties become part of the agent’s state and are available to all hooks for said middleware. When a middleware defines required state properties through its stateSchema, these properties must be provided when invoking the agent:

import { createMiddleware, createAgent, HumanMessage } from "langchain";
import { z } from "zod";

// Middleware with custom state requirements
const authMiddleware = createMiddleware({
  name: "AuthMiddleware",
  stateSchema: z.object({
    userId: z.string(), // Required
    userRole: z.string().default("user"), // Optional with default
  }),
  beforeModel: (state) => {
    // Access custom state properties
    console.log(`User ${state.userId} with role ${state.userRole}`);
    return;
  },
});

const agent = createAgent({
  model: "openai:gpt-4o",
  tools: [],
  middleware: [authMiddleware] as const,
});

// TypeScript enforces required state properties
const result = await agent.invoke({
  messages: [new HumanMessage("Hello")],
  userId: "user-123", // Required by middleware
  // userRole is optional due to default value
});

Context extension

This is currently only available in JavaScript.

Context properties are configuration values passed through the runnable config. Unlike state, context is read-only and typically used for configuration that doesn’t change during execution. Middleware can define context requirements that must be satisfied through the agent’s configuration:

import { z } from "zod";
import { createMiddleware, HumanMessage } from "langchain";

const rateLimitMiddleware = createMiddleware({
  name: "RateLimitMiddleware",
  contextSchema: z.object({
    maxRequestsPerMinute: z.number(),
    apiKey: z.string(),
  }),
  beforeModel: async (state, runtime) => {
    // Access context through runtime
    const { maxRequestsPerMinute, apiKey } = runtime.context;

    // Implement rate limiting logic
    const allowed = await checkRateLimit(apiKey, maxRequestsPerMinute);
    if (!allowed) {
      return { jumpTo: "END" };
    }

    return state;
  },
});

// Context is provided through config
await agent.invoke(
  { messages: [new HumanMessage("Process data")] },
  {
    context: {
      maxRequestsPerMinute: 60,
      apiKey: "api-key-123",
    },
  }
);

Combining multiple middleware

When using multiple middleware, their state and context schemas are merged. All required properties from all middleware must be satisfied:

import { createMiddleware, createAgent, HumanMessage } from "langchain";
import { z } from "zod";

const middleware1 = createMiddleware({
  name: "Middleware1",
  stateSchema: z.object({
    prop1: z.string(),
    sharedProp: z.number(),
  }),
});

const middleware2 = createMiddleware({
  name: "Middleware2",
  stateSchema: z.object({
    prop2: z.boolean(),
    sharedProp: z.number(), // Same property name must have compatible types
  }),
});

const agent = createAgent({
  model: "openai:gpt-4o",
  tools: [],
  middleware: [middleware1, middleware2] as const,
});

// Must provide all required properties
const result = await agent.invoke({
  messages: [new HumanMessage("Hello")],
  prop1: "value1", // Required by middleware1
  prop2: true, // Required by middleware2
  sharedProp: 42, // Required by both
});

Agent-level context schema

Agents can also define their own context requirements that combine with middleware requirements:

import { createAgent, HumanMessage } from "langchain";
import { z } from "zod";

const agent = createAgent({
  model: "openai:gpt-4o",
  tools: [],
  contextSchema: z.object({
    environment: z.enum(["development", "production"]),
  }),
  middleware: [rateLimitMiddleware] as const,
});

// Must satisfy both agent and middleware context requirements
await agent.invoke(
  { messages: [new HumanMessage("Deploy application")] },
  {
    context: {
      environment: "production", // Required by agent
      maxRequestsPerMinute: 60, // Required by middleware
      apiKey: "api-key-123", // Required by middleware
    },
  }
);

Best practices

Use State for Dynamic Data: Properties that change during execution (user session, accumulated data)
Use Context for Configuration: Static configuration values (API keys, feature flags, limits)
Provide Defaults When Possible: Use .default() in Zod schemas to make properties optional
Document Requirements: Clearly document what state and context properties your middleware requires
Type Safety: Leverage TypeScript’s type checking to catch missing properties at compile time

The type system ensures all required properties are provided, preventing runtime errors:

// TypeScript error: Property 'userId' is missing
await agent.invoke({
  messages: [new HumanMessage("Hello")],
  // userId is required but not provided
});

// TypeScript error: Type 'number' is not assignable to type 'string'
await agent.invoke({
  messages: [new HumanMessage("Hello")],
  userId: 123, // Wrong type
});

Middleware execution order

You can provide multiple middlewares. They are executed in the following logic: beforeModel: Are run in the order they are passed in. If an earlier middleware exits early, then following middleware are not run modifyModelRequest: Are run in the order they are passed in. afterModel: Are run in the reverse order that they are passed in. If an earlier middleware exits early, then following middleware are not run

Agent jumps

In order to exit early, you can add a jumpTo key to the state update with one of the following values:

"model": Jump to the model node
"tools": Jump to the tools node
"end": Jump to the end node

If this is specified, all subsequent middleware will not run. If you jump to model node, all beforeModel middleware will run. It’s forbidden to jump to model from an existing beforeModel middleware. Example usage:

import { createMiddleware } from "langchain";

const middleware = createMiddleware({
  name: "MyMiddleware",
  afterModel: (state) => {
    // ...
    return {
      messages: [
        /* ... */
      ],
      jumpTo: "model",
    };
  },
});

Examples

Dynamically selecting tools

In many applications, you may have a large set of tools, but only a small subset is relevant for a specific request. To optimize performance and accuracy, it’s best to expose only the tools that are needed for each request. Doing so provides several benefits:

Shorter prompts – reducing unnecessary complexity.
Improved accuracy – the model chooses from fewer options.
Permission control – can select tools based on user permissions.

Use middleware to dynamically select which tools are available at runtime based on context.

from langchain.agents import create_agent
from langchain.agents.middleware import AgentState, ModelRequest, modify_model_request

@modify_model_request
def tool_selector(state: AgentState, request: ModelRequest) -> ModelRequest:
    """Middleware to select relevant tools based on state/context."""
    # Select a small, relevant subset of tools based on state/context
    request.tools = ["relevant_tool_1", "relevant_tool_2"] 
    return request

agent = create_agent(
    ...,
    tools=all_tools,  # All available tools need to be registered upfront
    # Middleware can be used to select a smaller subset that's relevant for the given
    # run.
    middleware=[tool_selector], 
)

Show Extended example: Select tools based on runtime context

This example shows how to select between GitHub and GitLab tools based on the user’s provider.

Expandable

import { z } from "zod";
import { createAgent, createMiddleware, tool, HumanMessage } from "langchain";

const githubCreateIssue = tool(
  async ({ repo, title }) => ({
    url: `https://github.com/${repo}/issues/1`,
    title,
  }),
  {
    name: "github_create_issue",
    description: "Create an issue in a GitHub repository",
    schema: z.object({ repo: z.string(), title: z.string() }),
  }
);

const gitlabCreateIssue = tool(
  async ({ project, title }) => ({
    url: `https://gitlab.com/${project}/-/issues/1`,
    title,
  }),
  {
    name: "gitlab_create_issue",
    description: "Create an issue in a GitLab project",
    schema: z.object({ project: z.string(), title: z.string() }),
  }
);

const allTools = [githubCreateIssue, gitlabCreateIssue];

const toolSelector = createMiddleware({
  name: "toolSelector",
  contextSchema: z.object({ provider: z.enum(["github", "gitlab"]) }),
  modifyModelRequest: (request, _state, runtime) => {
    const provider = runtime.context.provider;
    const toolName = provider === "gitlab" ? "gitlab_create_issue" : "github_create_issue";
    return { ...request, tools: [toolName] };
  },
});

const agent = createAgent({
  model: "openai:gpt-4o",
  tools: allTools,
  middleware: [toolSelector],
});

// Invoke with GitHub context
await agent.invoke(
  {
    messages: [
      new HumanMessage("Open an issue titled 'Bug: where are the cats' in the repository `its-a-cats-game`"),
    ],
  },
  {
    context: { provider: "github" },
  }
);

Key points:

Register all tools with the agent upfront
Use middleware to select the relevant subset per request
Define required context properties using contextSchema
Use context for configuration that doesn’t change during execution
Use state for values that change during the agent run

Get started

Core components

Advanced usage

Use in production

Using in an agent

Built-in middleware

Summarization

Human-in-the-loop

Anthropic prompt caching

Dynamic system prompt

Custom Middleware

`beforeModel`

`modifyModelRequest`

`afterModel`

New state keys

State extension

Context extension

Combining multiple middleware

Agent-level context schema

Best practices

Middleware execution order

Agent jumps

Examples

Dynamically selecting tools

Get started

Core components

Advanced usage

Use in production

​Using in an agent

​Built-in middleware

​Summarization

​Human-in-the-loop

​Anthropic prompt caching

​Dynamic system prompt

​Custom Middleware

​beforeModel

​modifyModelRequest

​afterModel

​New state keys

​State extension

​Context extension

​Combining multiple middleware

​Agent-level context schema

​Best practices

​Middleware execution order

​Agent jumps

​Examples

​Dynamically selecting tools

Using in an agent

Built-in middleware

Summarization

Human-in-the-loop

Anthropic prompt caching

Dynamic system prompt

Custom Middleware

`beforeModel`

`modifyModelRequest`

`afterModel`

New state keys

State extension

Context extension

Combining multiple middleware

Agent-level context schema

Best practices

Middleware execution order

Agent jumps

Examples

Dynamically selecting tools