# Middleware samples

This folder contains focused middleware samples for `Agent`, chat clients, tools, sessions, and runtime context behavior.

## Files

| File | Description |
|------|-------------|
| [`agent_and_run_level_middleware.py`](./agent_and_run_level_middleware.py) | Demonstrates combining agent-level and run-level middleware. |
| [`agent_loop_middleware_refinement.py`](./agent_loop_middleware_refinement.py) | Demonstrates `AgentLoopMiddleware` with a `should_continue` predicate: a completion-marker refinement loop with feedback tracking and `fresh_context`. |
| [`agent_loop_middleware_todos.py`](./agent_loop_middleware_todos.py) | Demonstrates `AgentLoopMiddleware` with a `should_continue` predicate built from a `TodoProvider` via `todos_remaining`, so the agent keeps working while open todos remain. |
| [`agent_loop_middleware_judge.py`](./agent_loop_middleware_judge.py) | Demonstrates `AgentLoopMiddleware.with_judge`: a ChatClient judge re-runs the agent until it decides the original request was answered, with `criteria` shared between the agent and the judge. |
| [`agent_loop_middleware_report.py`](./agent_loop_middleware_report.py) | Demonstrates composing two `AgentLoopMiddleware` on one agent: an inner `todos_remaining` loop that drafts a report todo-by-todo, wrapped by an outer report-style `with_judge` loop that re-runs it until an editor chat client judges the report publication-ready. |
| [`atr_validation_middleware.py`](./atr_validation_middleware.py) | Demonstrates deterministic validation at the tool-execution boundary: a `FunctionMiddleware` that inspects the validated tool arguments and raises `MiddlewareTermination` before the tool runs when they match an attack rule. Loads the open, MIT-licensed Agent Threat Rules ruleset and runs the real engine locally (`pip install pyatr`), with a built-in deny-list fallback when it is not installed. |
| [`chat_middleware.py`](./chat_middleware.py) | Shows class-based and function-based chat middleware that can observe, modify, and override model calls. |
| [`class_based_middleware.py`](./class_based_middleware.py) | Shows class-based agent and function middleware. |
| [`decorator_middleware.py`](./decorator_middleware.py) | Demonstrates middleware registration with decorators. |
| [`exception_handling_with_middleware.py`](./exception_handling_with_middleware.py) | Shows how middleware can handle failures and recover cleanly. |
| [`function_based_middleware.py`](./function_based_middleware.py) | Shows function-based agent and function middleware. |
| [`middleware_termination.py`](./middleware_termination.py) | Demonstrates stopping a middleware pipeline early. |
| [`message_injection_middleware.py`](./message_injection_middleware.py) | Demonstrates `MessageInjectionMiddleware` with a real Foundry chat client: enqueueing a follow-up message into the active session while a long-running async tool is awaiting. |
| [`override_result_with_middleware.py`](./override_result_with_middleware.py) | Shows how middleware can replace regular and streaming results, then post-process the final response. |
| [`runtime_context_delegation.py`](./runtime_context_delegation.py) | Demonstrates delegating arguments with runtime context data. |
| [`session_behavior_middleware.py`](./session_behavior_middleware.py) | Shows how middleware interacts with session-backed runs. |
| [`shared_state_middleware.py`](./shared_state_middleware.py) | Demonstrates sharing mutable state across middleware invocations. |
| [`usage_tracking_middleware.py`](./usage_tracking_middleware.py) | Demonstrates one chat middleware function that tracks per-call usage in non-streaming and streaming tool-loop runs. |

## Running the usage tracking sample

The new usage tracking sample uses `OpenAIChatClient`, so set the usual OpenAI responses environment variables first:

```bash
export OPENAI_API_KEY="your-openai-api-key"
export OPENAI_CHAT_MODEL="gpt-4.1-mini"
```

Then run:

```bash
uv run samples/02-agents/middleware/usage_tracking_middleware.py
```

The sample forces a tool call so you can see middleware output for each inner model call in both non-streaming and streaming modes.

## Security Considerations

`AgentLoopMiddleware.with_judge` (used by `agent_loop_middleware_judge.py` and
`agent_loop_middleware_report.py`) is an explicit opt-in to sending the original request and the
agent's latest response to a second, external judge chat client on every iteration. A compromised
or malicious judge endpoint could exfiltrate that data, or return a manipulated verdict/gap
analysis that gets fed back into the loop as feedback — a form of indirect prompt injection. Only
configure a judge client that points at a service you trust as much as the primary model.