* Python: Add AgentLoopMiddleware for re-running agents in a loop
Add `AgentLoopMiddleware`, an `AgentMiddleware` that re-runs the wrapped
agent in a loop. A single configurable class covers three common patterns,
each with a convenience classmethod factory:
- Ralph loop (`.ralph(...)`): no exit criteria, with feedback tracking
(`record_feedback`/`progress`), progress injection (`inject_progress`),
optional fresh context per iteration (`fresh_context`), and an early-stop
completion signal (`is_complete`).
- Predicate (`.with_predicate(...)`): loop while a `should_continue` callable
returns True (e.g. paired with `todos_remaining`/`background_tasks_running`).
- Judge (`.with_judge(...)`): a second chat client decides whether the original
request was answered, using a `JudgeVerdict` structured-output response.
The loop also auto-resolves pending function-approval / user-input requests via
an `on_approval_request` callable (bounded by `max_approval_rounds`), and the
next iteration's input is controlled by `next_message`. Supports both streaming
and non-streaming runs.
Exports `AgentLoopMiddleware`, `JudgeVerdict`, `todos_remaining`, and
`background_tasks_running`. Adds tests, a sample, and docs.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Refine AgentLoopMiddleware API and sample
- with_judge: add criteria list with {{criteria}} templating into judge
instructions plus an agent-side instruction; add fresh_context, additional
judge feedback relay; default judge max_iterations.
- should_continue is now required and positional; supports (bool, str|None)
feedback tuples surfaced to next_message/record_feedback via feedback kwarg.
- Judge forwards full multi-modal request and response messages.
- Default max_iterations=10 (explicit None = unbounded); removed is_complete and
Ralph terminology; ShouldContinueResult is a real TypeAlias.
- Sample: stream all loops, print iteration counts via injected user-block
boundaries (robust to function calling), <role>: content formatting, per-method
expected output, and a looping todo sample.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Fix CI checks for AgentLoopMiddleware
- Resolve pyright errors in _loop.py: drop the always-true final_result None
check (the while loop always assigns it) and cast finish_reason to the
AgentResponse constructor's expected type.
- Apply pyupgrade --py310-plus: import TypeAlias from typing.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Resolve mypy/pyright disagreement on finish_reason
pyright infers AgentResponse.finish_reason as including str and rejects the
direct assignment, while mypy considers a cast redundant. Drop the cast and
suppress only pyright with a targeted reportArgumentType ignore, satisfying
both type checkers.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Add todo+judge AgentLoopMiddleware sample
Add a second AgentLoopMiddleware sample that composes two criteria in one
should_continue predicate: a TodoProvider check (evaluated first) and a
report-style judge chat client (evaluated once todos are complete) that grades
the assembled report against shared requirements. Register it in the middleware
samples README.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Python: Compose todo+judge loops as two middleware
Rework the todo+judge sample to compose two AgentLoopMiddleware on the agent
itself (middleware=[judge_loop, todo_loop]) instead of a single hand-written
predicate. The inner todos_remaining loop drafts the report todo-by-todo and the
outer with_judge loop re-runs it until an editor chat client judges the report
publication-ready, reusing the built-in helpers.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Reset session for fresh_context loops via snapshot/restore
AgentLoopMiddleware.fresh_context previously only reset context.messages,
so with an attached session each iteration still reloaded the local
transcript or re-threaded the service-side conversation id and the model
saw the accumulated history. Snapshot the session once before the loop
(via to_dict) and restore it (from_dict + field copy) between iterations,
so every pass starts from the pre-loop baseline. The final iteration's
pass is persisted (no restore after the terminating iteration), so a
subsequent agent.run continues from there.
Removed the obsolete warning, updated docstrings and core AGENTS.md, and
added tests: a snapshot/restore round-trip, a session-reset
streaming x fresh_context x inject_progress x store matrix across multiple
runs and loop iterations, and response_format parsing across the loop.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* Updated samples and docstrings
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Nick Brady