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https://github.com/willmiao/ComfyUI-Lora-Manager.git
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feat(agent): add LLM-powered metadata enrichment system with AgentCLI and PostProcessor
Introduce an agent skill framework for LLM-driven metadata enrichment: - AgentCLI (py/agent_cli/): in-process wrappers around internal services using standard relative imports, eliminating the need for sys.path hacks - LLMService: centralized BYOK (bring-your-own-key) LLM client supporting OpenAI, Ollama, and custom OpenAI-compatible endpoints - PostProcessor: deterministic engine that applies LLM output via AgentCLI (replaces old handler.py + _BASE_MODEL_ALIASES approach) - SkillRegistry: filesystem-based skill discovery (skill.yaml + prompt.md) - AgentService: orchestrates skill execution with WebSocket progress - Frontend AgentManager: WebSocket listeners, skill execution, config UI - Context menu entries (single + bulk) for "Enrich Metadata (Agent)" - Settings UI for AI Provider configuration (BYOK) - Full i18n support across 9 locales Bug fixes found during review: - aiohttp.web.json_response: status_code= -> status= - settings_modal cancelEditApiKey: wrong argument position - AgentManager.isLlmConfigured: allow Ollama without API key - PostProcessor._merge_tags: lowercase all tags to match TagUpdateService
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@@ -35,6 +35,9 @@ class BaseModelMetadata:
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metadata_source: Optional[str] = None # Last provider that supplied metadata
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last_checked_at: float = 0 # Last checked timestamp
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hash_status: str = "completed" # Hash calculation status: pending | calculating | completed | failed
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trainedWords: List[str] = field(
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default_factory=list
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) # Trigger words / activation prompts (source-agnostic)
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_unknown_fields: Dict[str, Any] = field(
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default_factory=dict, repr=False, compare=False
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) # Store unknown fields
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@@ -47,6 +50,9 @@ class BaseModelMetadata:
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if self.tags is None:
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self.tags = []
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if self.trainedWords is None:
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self.trainedWords = []
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@classmethod
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def from_dict(cls, data: Dict) -> "BaseModelMetadata":
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"""Create instance from dictionary"""
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