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https://github.com/usestrix/strix.git
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0xallam
1404864097
Re-architects the agent loop to support interactive (chat-like) mode where text-only responses pause execution and wait for user input, while tool-call responses continue looping autonomously. - Add `interactive` flag to LLMConfig (default False, no regression) - Add configurable `waiting_timeout` to AgentState (0 = disabled) - _process_iteration returns None for text-only → agent_loop pauses - Conditional system prompt: interactive allows natural text responses - Skip <meta>Continue the task.</meta> injection in interactive mode - Sub-agents inherit interactive from parent (300s auto-resume timeout) - Root interactive agents wait indefinitely for user input (timeout=0) - TUI sets interactive=True; CLI unchanged (non_interactive=True)
34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
from strix.config import Config
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from strix.config.config import resolve_llm_config
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from strix.llm.utils import resolve_strix_model
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class LLMConfig:
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def __init__(
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self,
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model_name: str | None = None,
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enable_prompt_caching: bool = True,
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skills: list[str] | None = None,
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timeout: int | None = None,
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scan_mode: str = "deep",
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interactive: bool = False,
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):
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resolved_model, self.api_key, self.api_base = resolve_llm_config()
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self.model_name = model_name or resolved_model
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if not self.model_name:
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raise ValueError("STRIX_LLM environment variable must be set and not empty")
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api_model, canonical = resolve_strix_model(self.model_name)
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self.litellm_model: str = api_model or self.model_name
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self.canonical_model: str = canonical or self.model_name
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self.enable_prompt_caching = enable_prompt_caching
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self.skills = skills or []
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self.timeout = timeout or int(Config.get("llm_timeout") or "300")
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self.scan_mode = scan_mode if scan_mode in ["quick", "standard", "deep"] else "deep"
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self.interactive = interactive
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