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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
This commit is contained in:
Will Miao
196
static/js/managers/AgentManager.js
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196
static/js/managers/AgentManager.js
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/**
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* AgentManager — WebSocket listener for agent skill progress events.
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*
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* Connects to the generic WebSocket endpoint and filters for
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* `type: "agent_progress"` messages. Dispatches progress and completion
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* events to registered callbacks.
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*/
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class AgentManager {
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constructor() {
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this.websocket = null;
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this.progressCallbacks = [];
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this.completeCallbacks = [];
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this.errorCallbacks = [];
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this.connected = false;
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}
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/**
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* Connect to the WebSocket endpoint for agent progress events.
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* Safe to call multiple times — won't reconnect if already connected.
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*/
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connect() {
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if (this.connected && this.websocket?.readyState === WebSocket.OPEN) {
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return;
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}
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const wsProtocol = window.location.protocol === 'https:' ? 'wss://' : 'ws://';
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try {
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this.websocket = new WebSocket(
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`${wsProtocol}${window.location.host}/ws/fetch-progress`
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);
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} catch (e) {
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console.error('AgentManager: Failed to create WebSocket:', e);
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return;
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}
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this.websocket.onopen = () => {
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this.connected = true;
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console.debug('AgentManager: WebSocket connected');
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};
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this.websocket.onmessage = (event) => {
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try {
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const data = JSON.parse(event.data);
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if (data.type !== 'agent_progress') return;
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this._dispatch(data);
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} catch (e) {
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// Not JSON or wrong format — ignore
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}
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};
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this.websocket.onerror = (error) => {
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console.error('AgentManager: WebSocket error:', error);
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this.connected = false;
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};
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this.websocket.onclose = () => {
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this.connected = false;
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console.debug('AgentManager: WebSocket closed');
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};
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}
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/**
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* Dispatch a parsed agent event to the appropriate callbacks.
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* @param {Object} data - The parsed WebSocket message
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*/
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_dispatch(data) {
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const { status, skill } = data;
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if (status === 'error') {
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this.errorCallbacks.forEach((cb) => {
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try {
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cb(data);
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} catch (e) {
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console.error('AgentManager error callback failed:', e);
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}
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});
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return;
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}
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if (status === 'completed') {
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this.completeCallbacks.forEach((cb) => {
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try {
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cb(data);
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} catch (e) {
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console.error('AgentManager complete callback failed:', e);
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}
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});
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return;
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}
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// started, processing — general progress
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this.progressCallbacks.forEach((cb) => {
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try {
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cb(data);
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} catch (e) {
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console.error('AgentManager progress callback failed:', e);
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}
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});
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}
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/**
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* Register a callback for progress events (started, processing).
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* @param {Function} callback - Receives the event data
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*/
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onProgress(callback) {
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this.progressCallbacks.push(callback);
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}
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/**
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* Register a callback for completion events.
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* @param {Function} callback - Receives the event data
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*/
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onComplete(callback) {
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this.completeCallbacks.push(callback);
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}
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/**
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* Register a callback for error events.
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* @param {Function} callback - Receives the event data
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*/
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onError(callback) {
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this.errorCallbacks.push(callback);
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}
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/**
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* Clear all registered callbacks.
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*/
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clearCallbacks() {
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this.progressCallbacks = [];
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this.completeCallbacks = [];
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this.errorCallbacks = [];
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}
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/**
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* Execute an agent skill on the provided model paths.
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*
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* @param {string} skillName - The skill to execute
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* @param {string[]} modelPaths - Model file paths to process
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* @returns {Promise<Object>} The response JSON
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*/
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async executeSkill(skillName, modelPaths) {
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const response = await fetch(
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`/api/lm/agent/execute/${encodeURIComponent(skillName)}`,
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{
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({ model_paths: modelPaths }),
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}
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);
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if (!response.ok) {
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const errorData = await response.json().catch(() => ({}));
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throw new Error(
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errorData.error || `HTTP ${response.status}: ${response.statusText}`
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);
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}
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return response.json();
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}
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/**
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* Check if the LLM provider is configured.
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*
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* Returns true when both an API key and a model name are set.
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*
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* @returns {Promise<boolean>}
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*/
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async isLlmConfigured() {
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try {
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const response = await fetch('/api/lm/settings');
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if (!response.ok) return false;
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const data = await response.json();
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const provider = data.settings?.llm_provider;
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const hasModel = !!data.settings?.llm_model;
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const hasKey = !!data.settings?.llm_api_key;
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return hasModel && (hasKey || provider === 'ollama');
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} catch {
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return false;
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}
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}
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/**
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* Get the list of available agent skills.
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*
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* @returns {Promise<Array>}
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*/
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async listSkills() {
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const response = await fetch('/api/lm/agent/skills');
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if (!response.ok) return [];
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const data = await response.json();
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return data.skills || [];
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}
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}
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// Export as singleton
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export const agentManager = new AgentManager();
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