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https://github.com/willmiao/ComfyUI-Lora-Manager.git
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cf898da193
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
24 lines
705 B
Python
24 lines
705 B
Python
"""Agent-powered skill system for LoRA Manager.
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This package provides the orchestration layer for LLM/agent-powered features.
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Skills define *what* to do (prompt template). The :class:`AgentService`
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handles *how* (LLM calls, context gathering, validation, progress).
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"""
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from __future__ import annotations
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from .skill_definition import SkillDefinition, SkillPermissions
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from .skill_registry import SkillRegistry
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from .agent_service import AgentService, AgentProgressReporter, SkillResult
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from .post_processor import PostProcessor
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__all__ = [
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"AgentProgressReporter",
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"AgentService",
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"PostProcessor",
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"SkillDefinition",
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"SkillPermissions",
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"SkillRegistry",
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"SkillResult",
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]
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