fix(compaction): chain any input filter already set on the run config

The image scrub for text-only models also sets call_model_input_filter; replacing it dropped the scrub.

strix/config/models.py

@@ -905,12 +905,6 @@ def model_supports_reasoning(model_name: str) -> bool:
     return bool(entry and entry.get("supports_reasoning"))
 
 
-def model_supports_images(model_name: str) -> bool:
-    """Return whether the model accepts image input. Assume yes until proven otherwise."""
-    entry = _catalog_entry(model_name)
-    return entry is None or bool(entry.get("supports_vision"))
-
-
 def _bare_openai_name(model_name: str) -> str:
     name = model_name.strip().lower()
     for prefix in ("litellm/", "any-llm/", "openai/"):

strix/core/execution.py

@@ -4,9 +4,11 @@ from __future__ import annotations
 
 import asyncio
 import contextlib
+import inspect
 import logging
 import uuid
 from collections.abc import Callable
+from dataclasses import replace
 from functools import cache
 from typing import TYPE_CHECKING, Any, cast
 
@@ -34,7 +36,12 @@ from strix.core.sessions import (
     strip_all_images_from_session,
 )
 from strix.llm import request_log
-from strix.llm.compaction import is_context_overflow, maybe_compact
+from strix.llm.compaction import (
+    CompactionNeededError,
+    is_context_overflow,
+    maybe_compact,
+    needs_compaction,
+)
 
 
 if TYPE_CHECKING:
@@ -44,6 +51,7 @@ if TYPE_CHECKING:
     from agents.lifecycle import RunHooks
     from agents.memory import Session, SQLiteSession
     from agents.result import RunResultBase
+    from agents.run_config import CallModelData, ModelInputData
 
     from strix.core.agents import AgentCoordinator, Status
 
@@ -120,6 +128,51 @@ async def _compact_session(
     )
 
 
+async def _session_needs_compaction(agent: Any, session: Session, run_config: RunConfig) -> bool:
+    model = _run_config_model(run_config)
+    if model is None:
+        return False
+    return await asyncio.to_thread(
+        needs_compaction,
+        model,
+        _agent_instructions(agent),
+        _agent_tools_text(agent),
+        list(await session.get_items()),
+    )
+
+
+def _with_compaction_check(run_config: RunConfig) -> RunConfig:
+    """Stop the run before a model request that would overrun the context window so
+    the cycle can compact and resume: the SDK builds each request from its in-memory
+    history, so compacting the session mid-run would not shrink it."""
+    model = _run_config_model(run_config)
+    if model is None:
+        return run_config
+    inner = run_config.call_model_input_filter
+    first_call = True
+
+    async def _check(data: CallModelData[Any]) -> ModelInputData:
+        nonlocal first_call
+        model_data = data.model_data
+        if inner is not None:
+            result = inner(data)
+            model_data = await result if inspect.isawaitable(result) else result
+        # Skip the first call: the cycle compacts right before the run, and the run's
+        # new input is only persisted after this filter, so stopping here would lose it.
+        if not first_call and await asyncio.to_thread(
+            needs_compaction,
+            model,
+            model_data.instructions or "",
+            _agent_tools_text(data.agent),
+            model_data.input,
+        ):
+            raise CompactionNeededError(model)
+        first_call = False
+        return model_data
+
+    return replace(run_config, call_model_input_filter=_check)
+
+
 _MAX_TRANSIENT_MODEL_RETRIES = 5
 _TRANSIENT_MODEL_RETRY_BASE_DELAY_S = 2.0
 _TRANSIENT_MODEL_RETRY_MAX_DELAY_S = 90.0
@@ -725,6 +778,7 @@ async def _run_cycle(  # noqa: PLR0912, PLR0915
     image_strips = 0
     compactions = 0
     model_retries = 0
+    check_context = session is not None
     request_log.set_retry_attempt(0)
     while True:
         stream: Any = None
@@ -747,7 +801,7 @@ async def _run_cycle(  # noqa: PLR0912, PLR0915
             stream = Runner.run_streamed(
                 agent,
                 input=input_data,
-                run_config=run_config,
+                run_config=_with_compaction_check(run_config) if check_context else run_config,
                 context=context,
                 max_turns=max_turns,
                 session=session,
@@ -824,6 +878,26 @@ async def _run_cycle(  # noqa: PLR0912, PLR0915
                     )
                     input_data = []
                     continue
+            if session is not None and isinstance(exc, CompactionNeededError):
+                # Rerun without the check if compaction can't bring the session under
+                # the budget; a real overflow then falls to the capped recovery below.
+                try:
+                    compacted = await _compact_session(agent, session, run_config, force=True)
+                    check_context = compacted and not await _session_needs_compaction(
+                        agent, session, run_config
+                    )
+                except Exception:
+                    logger.exception("context budget compaction failed for %s", agent_id)
+                    check_context = False
+                logger.info(
+                    "%s hit the context budget; %s",
+                    agent_id,
+                    "compacted and resuming"
+                    if check_context
+                    else "rerunning without the budget check",
+                )
+                input_data = []
+                continue
             if (
                 compactions < _MAX_COMPACTIONS_PER_CYCLE
                 and session is not None

strix/llm/compaction.py

@@ -15,7 +15,11 @@ from typing import TYPE_CHECKING, Any
 
 from agents.model_settings import ModelSettings
 from agents.models.interface import ModelTracing
-from openai.types.responses import ResponseOutputMessage, ResponseOutputText
+from openai.types.responses import (
+    ResponseOutputMessage,
+    ResponseOutputText,
+    ResponseReasoningItem,
+)
 
 from strix.config import load_settings
 from strix.config.models import StrixProvider
@@ -160,22 +164,22 @@ def _content_text(content: Any) -> str:
     return ""
 
 
-def _truncate(text: str, limit: int) -> str:
-    return text if len(text) <= limit else f"{text[:limit]}\n[truncated]"
+def _truncate(text: str, limit: int | None) -> str:
+    return text if limit is None or len(text) <= limit else f"{text[:limit]}\n[truncated]"
 
 
-def _serialize_item(item: Any) -> str:
+def _serialize_item(item: Any, max_chars: int | None = _TOOL_OUTPUT_MAX_CHARS) -> str:
     if not isinstance(item, dict):
         return str(item)
     item_type = item.get("type")
     role = item.get("role")
     if item_type == "function_call":
-        args = _truncate(str(item.get("arguments", "")), _TOOL_OUTPUT_MAX_CHARS)
+        args = _truncate(str(item.get("arguments", "")), max_chars)
         return f"[tool_call {item.get('name', '?')}] {args}"
     if item_type == "function_call_output":
         output = item.get("output")
         text = output if isinstance(output, str) else _content_text(output)
-        return f"[tool_result] {_truncate(text, _TOOL_OUTPUT_MAX_CHARS)}"
+        return f"[tool_result] {_truncate(text, max_chars)}"
     if item_type == "reasoning":
         return ""
     if role or item_type == "message":
@@ -183,8 +187,30 @@ def _serialize_item(item: Any) -> str:
     return ""
 
 
-def _serialize_items(items: list[Any]) -> str:
-    return "\n".join(s for s in (_serialize_item(item) for item in items) if s)
+def _serialize_items(items: list[Any], max_chars: int | None = _TOOL_OUTPUT_MAX_CHARS) -> str:
+    return "\n".join(s for s in (_serialize_item(item, max_chars) for item in items) if s)
+
+
+class CompactionNeededError(Exception):
+    """The next model request would overrun the model's usable context window."""
+
+
+def _context_budget(model: str) -> int:
+    context = load_settings().context
+    reserve = max(context.compact_buffer_tokens, output_limit(model))
+    return max(context.keep_tokens, context_window(model) - reserve)
+
+
+def _request_tokens(model: str, instructions: str, tools_text: str, items: list[Any]) -> int:
+    text = "\n".join((instructions, tools_text, _serialize_items(items, max_chars=None)))
+    return count_tokens(model, text)
+
+
+def needs_compaction(model: str, instructions: str, tools_text: str, items: list[Any]) -> bool:
+    """Whether a request carrying ``items`` would overrun ``model``'s context budget."""
+    if not load_settings().context.auto_compact:
+        return False
+    return _request_tokens(model, instructions, tools_text, items) > _context_budget(model)
 
 
 def _is_tool_call(item: Any) -> bool:
@@ -288,15 +314,22 @@ def _checkpoint_item(summary: str) -> dict[str, Any]:
 
 def _extract_text(response: ModelResponse) -> str:
     parts: list[str] = []
+    reasoning: list[str] = []
     for item in response.output:
-        if not isinstance(item, ResponseOutputMessage):
-            continue
-        parts.extend(
-            chunk.text
-            for chunk in item.content
-            if isinstance(chunk, ResponseOutputText) and chunk.text
-        )
-    return "".join(parts)
+        if isinstance(item, ResponseOutputMessage):
+            parts.extend(
+                chunk.text
+                for chunk in item.content
+                if isinstance(chunk, ResponseOutputText) and chunk.text
+            )
+        elif isinstance(item, ResponseReasoningItem):
+            reasoning.extend(summary.text for summary in item.summary if summary.text)
+    if parts or not reasoning:
+        return "".join(parts)
+    # Reasoning models can spend the whole output budget thinking and return no
+    # message; their reasoning is a degraded but usable summary.
+    logger.warning("compaction summary had no output text; using reasoning text")
+    return "\n".join(reasoning)
 
 
 async def _summarize(model: str, prompt: str, max_tokens: int) -> str | None:
@@ -358,11 +391,8 @@ async def maybe_compact(
     if len(items) < _MIN_ITEMS_TO_COMPACT:
         return False
 
-    window = context_window(model)
-    reserve = max(context.compact_buffer_tokens, output_limit(model))
-    budget = max(context.keep_tokens, window - reserve)
-    used = count_tokens(model, "\n".join((instructions, tools_text, _serialize_items(items))))
-    if not force and used <= budget:
+    used = _request_tokens(model, instructions, tools_text, items)
+    if not force and used <= _context_budget(model):
         return False
 
     split = _select_split(model, items, context.keep_tokens)

tests/test_compaction.py

@@ -6,6 +6,8 @@ from types import SimpleNamespace
 from typing import TYPE_CHECKING, Any
 
 import pytest
+from agents.extensions.models.litellm_model import InternalChatCompletionMessage
+from agents.models.chatcmpl_converter import Converter
 from litellm.exceptions import BadRequestError, ContextWindowExceededError, RateLimitError
 from openai.types.responses import ResponseOutputMessage, ResponseOutputText
 
@@ -136,7 +138,9 @@ def test_select_split_handles_parallel_calls(monkeypatch: pytest.MonkeyPatch) ->
     assert not _has_orphan_tool_output(items[split:])
 
 
-def _patch_budget(monkeypatch: pytest.MonkeyPatch, *, keep_tokens: int, window: int) -> None:
+def _patch_budget(
+    monkeypatch: pytest.MonkeyPatch, *, keep_tokens: int, window: int
+) -> ContextSettings:
     monkeypatch.setattr(compaction, "count_tokens", lambda _m, t: len(t))
     monkeypatch.setattr(compaction, "context_window", lambda _m: window)
     monkeypatch.setattr(compaction, "output_limit", lambda _m: 0)
@@ -150,6 +154,7 @@ def _patch_budget(monkeypatch: pytest.MonkeyPatch, *, keep_tokens: int, window:
         llm=SimpleNamespace(api_key=None, api_base=None, timeout=1, extra_headers=None),
     )
     monkeypatch.setattr(compaction, "load_settings", lambda: settings)
+    return context
 
 
 def _model_response(text: str) -> Any:
@@ -178,6 +183,20 @@ def _patch_summary(
     monkeypatch.setattr(compaction, "StrixProvider", FakeProvider)
 
 
+def test_needs_compaction_counts_full_tool_outputs(monkeypatch: pytest.MonkeyPatch) -> None:
+    _patch_budget(monkeypatch, keep_tokens=50, window=10_000)
+    items = [_call("c1"), _output("c1", "x" * 20_000)]
+
+    assert compaction.needs_compaction("m", "", "", items) is True
+    assert compaction.needs_compaction("m", "", "", _turns(2)) is False
+
+
+def test_needs_compaction_respects_auto_compact_off(monkeypatch: pytest.MonkeyPatch) -> None:
+    _patch_budget(monkeypatch, keep_tokens=50, window=10_000).auto_compact = False
+
+    assert compaction.needs_compaction("m", "", "", [_output("c1", "x" * 20_000)]) is False
+
+
 @pytest.mark.asyncio
 async def test_maybe_compact_noop_when_within_budget(monkeypatch: pytest.MonkeyPatch) -> None:
     _patch_budget(monkeypatch, keep_tokens=50, window=1_000_000)
@@ -242,6 +261,29 @@ async def test_summarize_routes_through_provider_with_settings(
     assert settings.max_tokens == 64
 
 
+@pytest.mark.asyncio
+async def test_summarize_falls_back_to_reasoning_when_no_output_text(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    _patch_budget(monkeypatch, keep_tokens=30, window=4_000)
+    message = InternalChatCompletionMessage(
+        role="assistant", content=None, reasoning_content="REASONED SUMMARY"
+    )
+    response = SimpleNamespace(output=Converter.message_to_output_items(message))
+
+    class FakeModel:
+        async def get_response(self, **_kwargs: Any) -> Any:
+            return response
+
+    class FakeProvider:
+        def get_model(self, _model_name: str | None) -> Any:
+            return FakeModel()
+
+    monkeypatch.setattr(compaction, "StrixProvider", FakeProvider)
+
+    assert await compaction._summarize("m", "p", 64) == "REASONED SUMMARY"
+
+
 def test_fit_to_tokens_truncates_oversized_text(monkeypatch: pytest.MonkeyPatch) -> None:
     monkeypatch.setattr(compaction, "count_tokens", lambda _m, t: len(t))
     text = "x" * 10_000

tests/test_execution.py

@@ -9,9 +9,11 @@ from typing import Any, cast
 from unittest.mock import MagicMock
 
 import pytest
+from agents import RunConfig
 from agents.exceptions import MaxTurnsExceeded
 from agents.items import MessageOutputItem
 from agents.memory import SQLiteSession
+from agents.run_config import CallModelData, ModelInputData
 from agents.tool_context import ToolContext
 from openai.types.responses import ResponseOutputMessage, ResponseOutputRefusal, ResponseOutputText
 
@@ -22,6 +24,7 @@ from strix.core.execution import (
     notify_parent_on_terminal,
 )
 from strix.core.sessions import seed_initial_input
+from strix.llm.compaction import CompactionNeededError
 from strix.tools.agents_graph.tools import agent_finish, stop_agent
 from strix.tools.finish.tool import finish_scan
 
@@ -1531,3 +1534,146 @@ async def test_autonomous_nudge_does_not_offer_the_user() -> None:
     )
 
     assert "wait_for_user" not in items[0]["content"]
+
+
+@pytest.mark.asyncio
+async def test_compaction_check_stops_run_after_first_call_when_over_budget(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    monkeypatch.setattr(execution, "needs_compaction", lambda *_args: True)
+    guard: Any = execution._with_compaction_check(RunConfig(model="m")).call_model_input_filter
+    data = CallModelData(
+        model_data=ModelInputData(input=[{"role": "user", "content": "hi"}], instructions="sys"),
+        agent=MagicMock(tools=[]),
+        context=None,
+    )
+
+    assert await guard(data) is data.model_data
+    with pytest.raises(CompactionNeededError):
+        await guard(data)
+
+
+@pytest.mark.asyncio
+async def test_compaction_check_chains_an_existing_input_filter(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    seen: list[Any] = []
+    monkeypatch.setattr(execution, "needs_compaction", lambda *args: seen.append(args[3]))
+    filtered = ModelInputData(input=[{"role": "user", "content": "scrubbed"}], instructions="sys")
+    run_config = RunConfig(model="m", call_model_input_filter=lambda _data: filtered)
+    check: Any = execution._with_compaction_check(run_config).call_model_input_filter
+    data = CallModelData(
+        model_data=ModelInputData(input=[], instructions="sys"),
+        agent=MagicMock(tools=[]),
+        context=None,
+    )
+
+    assert await check(data) is filtered
+    assert await check(data) is filtered
+    assert seen == [filtered.input]
+
+
+class _BudgetStream:
+    def __init__(self, exc: BaseException | None) -> None:
+        self.run_loop_exception = exc
+        self.new_items: list[Any] = []
+
+    async def stream_events(self) -> Any:
+        for event in _NO_STREAM_EVENTS:
+            yield event
+
+
+@pytest.mark.asyncio
+async def test_run_cycle_compacts_and_resumes_on_every_budget_stop(
+    tmp_path: Any, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    stops = execution._MAX_COMPACTIONS_PER_CYCLE + 1
+    streams = [_BudgetStream(CompactionNeededError("m")) for _ in range(stops)]
+    streams.append(_BudgetStream(None))
+    runs: list[dict[str, Any]] = []
+
+    def _run_streamed(*_args: Any, **kwargs: Any) -> _BudgetStream:
+        runs.append(kwargs)
+        return streams[len(runs) - 1]
+
+    forced: list[bool] = []
+
+    async def _compact(*_args: Any, force: bool) -> bool:
+        forced.append(force)
+        return True
+
+    monkeypatch.setattr("strix.core.execution.Runner.run_streamed", _run_streamed)
+    monkeypatch.setattr(execution, "_compact_session", _compact)
+    monkeypatch.setattr(execution, "needs_compaction", lambda *_args: False)
+    coordinator = AgentCoordinator()
+    await coordinator.register("root", "strix", parent_id=None)
+    session = SQLiteSession("root", tmp_path / "agents.db")
+
+    result = await execution._run_cycle(
+        MagicMock(),
+        coordinator,
+        "root",
+        input_data="task",
+        run_config=RunConfig(model="m"),
+        context={},
+        max_turns=5,
+        session=session,
+        interactive=True,
+        event_sink=None,
+        hooks=None,
+    )
+
+    assert cast("Any", result) is streams[-1]
+    assert len(runs) == stops + 1
+    assert forced.count(True) == stops
+    assert all(run["input"] == [] for run in runs[1:])
+    assert all(run["run_config"].call_model_input_filter is not None for run in runs)
+    session.close()
+
+
+@pytest.mark.parametrize(
+    ("compacted", "still_over"),
+    [(False, False), (True, True)],
+    ids=["compaction_failed", "still_over_budget"],
+)
+@pytest.mark.asyncio
+async def test_run_cycle_reruns_unchecked_when_compaction_cannot_fit(
+    tmp_path: Any, monkeypatch: pytest.MonkeyPatch, compacted: bool, still_over: bool
+) -> None:
+    streams = [_BudgetStream(CompactionNeededError("m")), _BudgetStream(None)]
+    runs: list[dict[str, Any]] = []
+
+    def _run_streamed(*_args: Any, **kwargs: Any) -> _BudgetStream:
+        runs.append(kwargs)
+        return streams[len(runs) - 1]
+
+    async def _compact(*_args: Any, **_kwargs: Any) -> bool:
+        return compacted
+
+    monkeypatch.setattr("strix.core.execution.Runner.run_streamed", _run_streamed)
+    monkeypatch.setattr(execution, "_compact_session", _compact)
+    monkeypatch.setattr(execution, "needs_compaction", lambda *_args: still_over)
+    coordinator = AgentCoordinator()
+    await coordinator.register("root", "strix", parent_id=None)
+    session = SQLiteSession("root", tmp_path / "agents.db")
+
+    result = await execution._run_cycle(
+        MagicMock(),
+        coordinator,
+        "root",
+        input_data="task",
+        run_config=RunConfig(model="m"),
+        context={},
+        max_turns=5,
+        session=session,
+        interactive=True,
+        event_sink=None,
+        hooks=None,
+    )
+
+    assert cast("Any", result) is streams[-1]
+    assert len(runs) == 2
+    assert runs[0]["run_config"].call_model_input_filter is not None
+    assert runs[1]["run_config"].call_model_input_filter is None
+    assert runs[1]["input"] == []
+    session.close()

tests/test_models.py

@@ -16,7 +16,6 @@ from strix.config.models import (
     _NonStreamingModel,
     _TurnGuardModel,
     configure_sdk_model_defaults,
-    model_supports_images,
     request_timeout_extra_args,
     resolve_api_type,
     routes_through_litellm,
@@ -203,11 +202,3 @@ def test_configure_sdk_api_route_follows_the_given_model(
     models.configure_sdk_api_route("my-private-model", settings)
 
     assert routes == ["responses", "chat_completions"]
-
-
-def test_image_support_follows_the_catalog(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setitem(litellm.model_cost, "acme-text", {})
-    monkeypatch.setitem(litellm.model_cost, "acme-vision", {"supports_vision": True})
-    assert model_supports_images("acme-text") is False
-    assert model_supports_images("litellm/openai/acme-vision") is True
-    assert model_supports_images("acme-unknown-model") is True