Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ag2ai/ag2-skills --skill ag2-middlewaregit clone --depth 1 https://github.com/ag2ai/ag2-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ag2ai/ag2-skills/ag2-middleware)<a href="https://agentmods.dev/skills/ag2ai/ag2-skills/ag2-middleware"><img src="https://agentmods.dev/badge/skills/ag2ai/ag2-skills/ag2-middleware/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ag2ai/ag2-skills/ag2-middleware"><img src="https://agentmods.dev/badge/skills/ag2ai/ag2-skills/ag2-middleware.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00128 | $0.01764 |
| Opus 5 | $0.00064 | $0.00882 |
| Sonnet 5 | $0.00026 | $0.00353 |
| Haiku 4.5 | $0.00013 | $0.00176 |
Grade A, and why
ag2-middleware scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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),
],
)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 161 lines · 128 tokens per session scan A 01a21b2f1664
ag2-middleware is a skill published in the GitHub repository ag2ai/ag2-skills (10 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 128 tokens to every session and 1,764 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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