fix(memory): reserve completion headroom for consolidation

Trigger token consolidation before prompt usage reaches the full context window so response tokens and tokenizer estimation drift still fit safely within the model budget.

Made-with: Cursor

nanobot/agent/loop.py

@@ -115,6 +115,7 @@ class AgentLoop:
             context_window_tokens=context_window_tokens,
             build_messages=self.context.build_messages,
             get_tool_definitions=self.tools.get_definitions,
+            max_completion_tokens=provider.generation.max_tokens,
         )
         self._register_default_tools()
 

nanobot/agent/memory.py

@@ -224,6 +224,8 @@ class MemoryConsolidator:
 
     _MAX_CONSOLIDATION_ROUNDS = 5
 
+    _SAFETY_BUFFER = 1024  # extra headroom for tokenizer estimation drift
+
     def __init__(
         self,
         workspace: Path,
@@ -233,12 +235,14 @@ class MemoryConsolidator:
         context_window_tokens: int,
         build_messages: Callable[..., list[dict[str, Any]]],
         get_tool_definitions: Callable[[], list[dict[str, Any]]],
+        max_completion_tokens: int = 4096,
     ):
         self.store = MemoryStore(workspace)
         self.provider = provider
         self.model = model
         self.sessions = sessions
         self.context_window_tokens = context_window_tokens
+        self.max_completion_tokens = max_completion_tokens
         self._build_messages = build_messages
         self._get_tool_definitions = get_tool_definitions
         self._locks: weakref.WeakValueDictionary[str, asyncio.Lock] = weakref.WeakValueDictionary()
@@ -300,17 +304,22 @@ class MemoryConsolidator:
         return True
 
     async def maybe_consolidate_by_tokens(self, session: Session) -> None:
-        """Loop: archive old messages until prompt fits within half the context window."""
+        """Loop: archive old messages until prompt fits within safe budget.
+
+        The budget reserves space for completion tokens and a safety buffer
+        so the LLM request never exceeds the context window.
+        """
         if not session.messages or self.context_window_tokens <= 0:
             return
 
         lock = self.get_lock(session.key)
         async with lock:
-            target = self.context_window_tokens // 2
+            budget = self.context_window_tokens - self.max_completion_tokens - self._SAFETY_BUFFER
+            target = budget // 2
             estimated, source = self.estimate_session_prompt_tokens(session)
             if estimated <= 0:
                 return
-            if estimated < self.context_window_tokens:
+            if estimated < budget:
                 logger.debug(
                     "Token consolidation idle {}: {}/{} via {}",
                     session.key,

tests/test_loop_consolidation_tokens.py

@@ -9,8 +9,10 @@ from nanobot.providers.base import LLMResponse
 
 
 def _make_loop(tmp_path, *, estimated_tokens: int, context_window_tokens: int) -> AgentLoop:
+    from nanobot.providers.base import GenerationSettings
     provider = MagicMock()
     provider.get_default_model.return_value = "test-model"
+    provider.generation = GenerationSettings(max_tokens=0)
     provider.estimate_prompt_tokens.return_value = (estimated_tokens, "test-counter")
     _response = LLMResponse(content="ok", tool_calls=[])
     provider.chat_with_retry = AsyncMock(return_value=_response)
@@ -24,6 +26,7 @@ def _make_loop(tmp_path, *, estimated_tokens: int, context_window_tokens: int) -
         context_window_tokens=context_window_tokens,
     )
     loop.tools.get_definitions = MagicMock(return_value=[])
+    loop.memory_consolidator._SAFETY_BUFFER = 0
     return loop