Ag2 Middleware

Middleware

When to use

Middleware is for cross-cutting behaviour that should apply consistently across many runs without changing the agent, model client, or tools themselves. Common use cases:

• Logging, tracing, timing
• Retry on transient failures
• Trim history before it reaches the model
• Cap or estimate token usage
• Rewrite tool arguments / results
• Enforce policies before a tool runs
• Audit human-input requests

Four hooks

BaseMiddleware exposes four async hooks. Implement only the ones you need:

| Hook | Wraps | Use for | |---|---|---| | on_turn(call_next, event, context) → ModelResponse | The whole agent turn | Total latency, request/response inspection, turn-level policies | | on_llm_call(call_next, events, context) → ModelResponse | Each LLM API call | Retry, logging, history trim, request mutation, caching | | on_tool_execution(call_next, event, context) → ToolResultType | Each tool invocation | Validate args, redact results, fallback on failure, access control | | on_human_input(call_next, event, context) → HumanMessage | Each context.input() | Audit, rewrite prompts, automated short-circuit, rate limit |

Each instance is created once per turn and can hold per-turn state on self. The same instance can implement multiple hooks.

Built-in middleware

Importable from ag2.middleware:

| Middleware | Purpose | Constructor | |---|---|---| | LoggingMiddleware | Logs turn start/end, each LLM call, each tool execution | no args | | RetryMiddleware | Retries failed LLM calls | max_retries=N, retry_on=ExceptionClass | | HistoryLimiter | Cap event count before LLM call | max_events=N | | TokenLimiter | Char-based token-budget cap before LLM call | max_tokens=N, chars_per_token=4 | | TelemetryMiddleware | OpenTelemetry GenAI spans (see ag2-telemetry) | see telemetry skill |

Registration — agent-level

Apply to every turn:

from ag2 import Agent
from ag2.config import OpenAIConfig
from ag2.middleware import LoggingMiddleware, RetryMiddleware

agent = Agent(
    "assistant",
    config=OpenAIConfig(model="gpt-4o-mini"),
    middleware=[
        LoggingMiddleware(),
        RetryMiddleware(max_retries=2),
    ],
)

Registration — call-level

Add temporary middleware for one turn. Both agent.ask(...) and reply.ask(...) accept it:

from ag2.middleware import TokenLimiter

reply = await agent.ask("Summarise the latest messages.", middleware=[LoggingMiddleware()])
next_turn = await reply.ask("Now answer in one paragraph.", middleware=[TokenLimiter(max_tokens=4000)])

Call-level middleware is appended after the agent's middleware list.

Ordering

Middleware runs in registration order, like nested with blocks. Registering [A, B, C] enters A → B → C and unwinds C → B → A:

enter A
  enter B
    enter C
      
    exit C
  exit B
exit A

This matters when you mix logging, mutation, retry. If RetryMiddleware should retry mutated requests, mutation goes inside retry; if you want each retry attempt logged separately, logging goes inside retry.

Writing your own

Subclass BaseMiddleware, implement the hooks you need:

import logging
from collections.abc import Sequence
from ag2 import Agent, Context
from ag2.config import OpenAIConfig
from ag2.events import BaseEvent, ModelResponse, ToolCallEvent
from ag2.middleware import BaseMiddleware, LLMCall, Middleware, ToolExecution

class AuditMiddleware(BaseMiddleware):
    def __init__(self, event: BaseEvent, context: Context, logger: logging.Logger) -> None:
        super().__init__(event, context)
        self.logger = logger

    async def on_llm_call(self, call_next: LLMCall, events: Sequence[BaseEvent], context: Context) -> ModelResponse:
        self.logger.info("Calling model with %d events", len(events))
        response = await call_next(events, context)
        self.logger.info("Model returned: %s", response)
        return response

    async def on_tool_execution(self, call_next: ToolExecution, event: ToolCallEvent, context: Context):
        self.logger.info("Executing tool: %s", event.name)
        return await call_next(event, context)

agent = Agent(
    "assistant",
    config=OpenAIConfig(model="gpt-4o-mini"),
    middleware=[
        Middleware(AuditMiddleware, logger=logging.getLogger("ag2.audit")),
    ],
)

If your middleware needs constructor args beyond event and context, wrap with Middleware(YourClass, ...) when registering. Zero-config middleware can be passed bare (middleware=[LoggingMiddleware()]).

Tool-scoped vs agent-scoped

For behaviour that applies to one tool only (validation, redaction for that tool's output, approval gates), use tool middleware instead — middleware=[hook] on @tool, @agent.tool, or Toolkit. See ag2-add-custom-tool for the syntax. The approval_required() built-in (see ag2-hitl) is a tool middleware.

Agent middleware runs outside tool middleware: BaseMiddleware.on_tool_execution() sees the full execution including tool-scoped hooks.

Picking the right hook

• on_turn → behaviour about the whole request/response lifecycle.
• on_llm_call → behaviour about what goes into / comes out of the model.
• on_tool_execution → tool safety / auditing / result shaping across many tools.
• Tool-scoped middleware (not BaseMiddleware) → behaviour for a single tool's definition.
• on_human_input → intercept HITL requests/responses.

Going deeper

• references/builtin_middleware.md — every built-in's params, common-case recipes, when each fits.
• website/docs/user-guide/middleware.mdx — full reference, ordering examples, custom-middleware guidelines.
• website/docs/user-guide/tools/tool_middleware.mdx — per-tool hooks (different mental model — plain async callables, not BaseMiddleware).
• For OpenTelemetry instrumentation specifically, see ag2-telemetry.

Common pitfalls

• Forgetting Middleware(...) for constructor args — middleware=[AuditMiddleware] (no wrapper) only works if the class needs only event and context. Otherwise wrap: middleware=[Middleware(AuditMiddleware, logger=...)].
• Mutation order surprises — middleware runs in registration order. If middleware A trims history and middleware B logs it, register [A, B] so B sees the trimmed view.
• Per-call middleware doesn't replace agent middleware — it's appended. Agent middleware still runs.
• One big middleware doing five things — keep hooks focused. Logging + retry + mutation + policy in one class is hard to reason about and order. Split into multiple instances.
• on_tool_execution branching on event.name for a single tool — that's a smell; use tool-scoped middleware for one-tool behaviour and reserve on_tool_execution for cross-cutting policies.
• Putting OpenTelemetry instrumentation in custom code — there's a TelemetryMiddleware for that; see ag2-telemetry.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: ag2ai
• Source: ag2ai/ag2-skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.