mirror of https://github.com/aliasrobotics/cai.git
fix: count LLM responses not outer-loop turns for auto-compact interval
CAI_SUPPORT_INTERVAL previously counted outer while-loop iterations
(one per user input). In agentic/continue mode the agent makes many
tool-call rounds per single user input, so the interval never fired
unless the user typed N separate messages.
Fix: count assistant messages in message_history as a proxy for LLM
API calls. This fires after every N responses from the main model
regardless of how many came from a single outer iteration.
- Startup banner updated: 'every N LLM responses' instead of 'turns'
- Countdown shows [{current}/{threshold}] response counts
- Fire condition: llm_call_count >= support_interval (fires as soon
as threshold is reached; resets naturally when history is cleared
after compact)
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giveen
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@ -491,7 +491,7 @@ def run_cai_cli(
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if _sc_model_startup and _sc_interval_startup:
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try:
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console.print(
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f"[bold cyan]🗜 Auto-compact enabled: every {int(_sc_interval_startup)} turns "
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f"[bold cyan]🗜 Auto-compact enabled: every {int(_sc_interval_startup)} LLM responses "
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f"using {_sc_model_startup}[/bold cyan]"
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)
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except ValueError:
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@ -1735,50 +1735,58 @@ def run_cai_cli(
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_last_user_input = user_input if isinstance(user_input, str) else ""
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# Auto-compact: when CAI_SUPPORT_MODEL + CAI_SUPPORT_INTERVAL are both set,
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# compact the conversation every N turns using the support model so the
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# main model's context window is kept small. After compaction the
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# summary is injected into the agent's system prompt and the local
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# agent reference is refreshed so the loop uses the reloaded instance.
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# compact the conversation every N LLM *responses* (assistant messages in
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# history) using the support model. Counting assistant messages rather
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# than outer-loop turns means agentic sessions — where the agent makes
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# many tool-call rounds per single user input — are handled correctly.
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_support_model = os.getenv("CAI_SUPPORT_MODEL")
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_support_interval_raw = os.getenv("CAI_SUPPORT_INTERVAL")
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if _support_model and _support_interval_raw:
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try:
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_support_interval = int(_support_interval_raw)
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if _support_interval > 0:
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_turns_until = _support_interval - (turn_count % _support_interval)
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if _turns_until != _support_interval: # don't show when just compacted
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console.print(
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f"[dim cyan] ↻ auto-compact in {_turns_until} turn(s) "
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f"[turn {turn_count}/{_support_interval}×{turn_count // _support_interval + 1}][/dim cyan]"
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)
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if turn_count % _support_interval == 0:
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from cai.repl.commands.compact import COMPACT_COMMAND_INSTANCE
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console.print(
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f"\n[bold yellow]⟳ Auto-compact: turn {turn_count} "
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f"(every {_support_interval} turns) — "
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f"summarising with {_support_model}[/bold yellow]"
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)
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COMPACT_COMMAND_INSTANCE._perform_compaction(
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model_override=_support_model
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)
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# Re-sync the local agent reference so the loop continues
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# with the freshly reloaded agent (history cleared, memory
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# summary already injected into its system prompt).
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from cai.sdk.agents.simple_agent_manager import AGENT_MANAGER as _AM
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_reloaded = _AM.get_active_agent()
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if _reloaded is not None:
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agent = _reloaded
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# Queue the last user task to be replayed on the next
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# iteration so the agent continues without human input.
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_post_compact_input = (
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_last_user_input
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if _last_user_input.strip()
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else "Continue the current task."
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)
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console.print(
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"[bold green]✓ Memory summary applied to agent system prompt — "
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"context window reset — continuing task[/bold green]\n"
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)
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# Count assistant messages as a proxy for LLM API calls.
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_history = getattr(getattr(agent, 'model', None), 'message_history', [])
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_llm_call_count = sum(
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1 for m in _history
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if (m.get("role") if isinstance(m, dict) else getattr(m, "role", None))
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== "assistant"
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)
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if _llm_call_count > 0:
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_calls_until = max(0, _support_interval - _llm_call_count)
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if _calls_until > 0:
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console.print(
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f"[dim cyan] ↻ auto-compact in {_calls_until} LLM response(s) "
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f"[{_llm_call_count}/{_support_interval}][/dim cyan]"
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)
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if _llm_call_count >= _support_interval:
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from cai.repl.commands.compact import COMPACT_COMMAND_INSTANCE
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console.print(
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f"\n[bold yellow]⟳ Auto-compact: {_llm_call_count} LLM responses "
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f"(threshold {_support_interval}) — "
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f"summarising with {_support_model}[/bold yellow]"
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)
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COMPACT_COMMAND_INSTANCE._perform_compaction(
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model_override=_support_model
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)
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# Re-sync the local agent reference so the loop continues
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# with the freshly reloaded agent (history cleared, memory
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# summary already injected into its system prompt).
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from cai.sdk.agents.simple_agent_manager import AGENT_MANAGER as _AM
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_reloaded = _AM.get_active_agent()
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if _reloaded is not None:
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agent = _reloaded
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# Queue the last user task to be replayed on the next
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# iteration so the agent continues without human input.
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_post_compact_input = (
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_last_user_input
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if _last_user_input.strip()
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else "Continue the current task."
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)
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console.print(
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"[bold green]✓ Memory summary applied to agent system prompt — "
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"context window reset — continuing task[/bold green]\n"
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)
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except (ValueError, Exception) as _e:
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# Always show auto-compact errors so they are never silently lost.
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console.print(f"[red]Auto-compact error: {_e}[/red]")
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