fix(context): bound the compaction summary input to the model window

Head+tail-truncate the serialized history before the summary request so a
very large head cannot itself overflow the model context and abort the
recovery it is meant to perform.

strix/llm/compaction.py

@@ -28,6 +28,9 @@ logger = logging.getLogger(__name__)
 _CHECKPOINT_TAG = "<conversation-checkpoint>"
 _TOOL_OUTPUT_MAX_CHARS = 2_000
 _MIN_ITEMS_TO_COMPACT = 6
+# Floor for how much of the head we still try to summarise even on a tiny model.
+_MIN_SUMMARY_INPUT_TOKENS = 1_000
+_HEAD_TRUNCATED_MARKER = "\n\n[... older conversation omitted to fit the summary request ...]\n\n"
 
 # Substrings that identify a context-window-overflow error across providers.
 # Deliberately excludes rate-limit/throttle wording, which must not trigger
@@ -182,6 +185,40 @@ def _previous_summary(head: list[Any]) -> str | None:
     return None
 
 
+def _fit_to_tokens(model: str, text: str, max_tokens: int) -> str:
+    """Head+tail-truncate ``text`` so it fits within ``max_tokens``.
+
+    Keeps the start (objective/setup) and the end (most recent activity), which
+    matter most for continuity, and drops the middle. Prevents the summary
+    request itself from overflowing the model window on very large histories.
+    """
+    if count_tokens(model, text) <= max_tokens:
+        return text
+    # Convert the token budget to a rough character budget (chars ~= 4x tokens)
+    # split between head and tail, then confirm and tighten by real token count.
+    budget_chars = max_tokens * 4
+    head_chars = budget_chars // 2
+    tail_chars = budget_chars - head_chars
+    candidate = text[:head_chars] + _HEAD_TRUNCATED_MARKER + text[len(text) - tail_chars :]
+    while count_tokens(model, candidate) > max_tokens and (head_chars > 0 or tail_chars > 0):
+        head_chars = int(head_chars * 0.8)
+        tail_chars = int(tail_chars * 0.8)
+        candidate = text[:head_chars] + _HEAD_TRUNCATED_MARKER + text[len(text) - tail_chars :]
+    return candidate
+
+
+def _summary_input_budget(model: str, previous: str | None) -> int:
+    """Token room left for the head after instructions and the summary output."""
+    context = load_settings().context
+    overhead = count_tokens(model, _SUMMARY_INSTRUCTIONS)
+    if previous:
+        overhead += count_tokens(model, previous)
+    # Leave slack for the prompt's wrapper text ("Conversation to summarise:",
+    # the update instructions, etc.) that is not part of ``overhead``.
+    room = context_window(model) - context.summary_max_tokens - overhead - 256
+    return max(_MIN_SUMMARY_INPUT_TOKENS, room)
+
+
 def _build_summary_prompt(serialized_head: str, previous: str | None) -> str:
     previous_block = (
         f"\n\nA previous checkpoint summary follows. Update it: keep what is "
@@ -263,9 +300,13 @@ async def maybe_compact(
     if not head:
         return False
 
+    previous = _previous_summary(head)
+    serialized_head = _fit_to_tokens(
+        model, _serialize_items(head), _summary_input_budget(model, previous)
+    )
     summary = await _summarize(
         model,
-        _build_summary_prompt(_serialize_items(head), _previous_summary(head)),
+        _build_summary_prompt(serialized_head, previous),
         context.summary_max_tokens,
     )
     if summary is None: