Overview

Control and customize agent execution at every step

Middleware provides a way to more tightly control what happens inside the agent. Middleware is useful for the following:

• Tracking agent behavior with logging, analytics, and debugging.
• Transforming prompts, tool selection, and output formatting.
• Adding retries, fallbacks, and early termination logic.
• Applying rate limits, guardrails, and PII detection.

Add middleware by passing them to create_agent:

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

agent = create_agent(
    model="gpt-4.1",
    tools=[...],
    middleware=[
        SummarizationMiddleware(...),
        HumanInTheLoopMiddleware(...)
    ],
)

The agent loop

The core agent loop involves calling a model, letting it choose tools to execute, and then finishing when it calls no more tools:

Middleware exposes hooks before and after each of those steps:

Additional resources

Built-in middleware

Explore built-in middleware for common use cases.

Custom middleware

Build your own middleware with hooks and decorators.

Middleware API reference

Complete API reference for middleware.

Testing agents

Test your agents with LangSmith.

---

Edit this page on GitHub or file an issue.

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