feat(config): Implement new config system and resolve refactoring errors Introduced config-manager.js and new utilities (resolveEnvVariable, findProjectRoot). Removed old global CONFIG object from utils.js. Updated .taskmasterconfig, mcp.json, and .env.example. Added generateComplexityAnalysisPrompt to ui.js. Removed unused updateSubtaskById from task-manager.js. Resolved SyntaxError and ReferenceError issues across commands.js, ui.js, task-manager.js, and ai-services.js by replacing CONFIG references with config-manager getters (getDebugFlag, getProjectName, getDefaultSubtasks, isApiKeySet). Refactored 'models' command to use getConfig/writeConfig. Simplified version checking. This stabilizes the codebase after initial Task 61 refactoring, fixing CLI errors and enabling subsequent work on Subtasks 61.34 and 61.35.
This commit is contained in:
Eyal Toledano
@@ -8,9 +8,18 @@
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import { Anthropic } from '@anthropic-ai/sdk';
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import OpenAI from 'openai';
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import dotenv from 'dotenv';
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import { CONFIG, log, sanitizePrompt, isSilentMode } from './utils.js';
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import { log, sanitizePrompt, isSilentMode } from './utils.js';
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import { startLoadingIndicator, stopLoadingIndicator } from './ui.js';
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import chalk from 'chalk';
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import {
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getMainModelId,
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getMainMaxTokens,
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getMainTemperature,
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getDebugFlag,
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getResearchModelId,
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getResearchMaxTokens,
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getResearchTemperature
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} from './config-manager.js';
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// Load environment variables
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dotenv.config();
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@@ -218,7 +227,7 @@ Important: Your response must be valid JSON only, with no additional explanation
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prdContent,
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prdPath,
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numTasks,
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modelConfig?.maxTokens || CONFIG.maxTokens,
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modelConfig?.maxTokens || getMainMaxTokens(null),
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systemPrompt,
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{ reportProgress, mcpLog, session },
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aiClient || anthropic,
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@@ -254,7 +263,7 @@ Important: Your response must be valid JSON only, with no additional explanation
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);
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} else {
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console.error(chalk.red(userMessage));
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if (CONFIG.debug) {
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if (getDebugFlag(null)) {
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log('debug', 'Full error:', error);
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}
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throw new Error(userMessage);
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@@ -287,54 +296,46 @@ async function handleStreamingRequest(
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aiClient = null,
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modelConfig = null
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) {
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// Determine output format based on mcpLog presence
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const outputFormat = mcpLog ? 'json' : 'text';
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// Create custom reporter that checks for MCP log and silent mode
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const report = (message, level = 'info') => {
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if (mcpLog) {
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if (mcpLog && typeof mcpLog[level] === 'function') {
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mcpLog[level](message);
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} else if (!isSilentMode() && outputFormat === 'text') {
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// Only log to console if not in silent mode and outputFormat is 'text'
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} else if (!isSilentMode()) {
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log(level, message);
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}
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};
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// Only show loading indicators for text output (CLI)
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let loadingIndicator = null;
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if (outputFormat === 'text' && !isSilentMode()) {
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loadingIndicator = startLoadingIndicator('Generating tasks from PRD...');
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let loadingIndicator;
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if (!isSilentMode() && !mcpLog) {
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loadingIndicator = startLoadingIndicator('Claude is thinking...');
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}
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if (reportProgress) {
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await reportProgress({ progress: 0 });
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}
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let responseText = '';
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let streamingInterval = null;
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let textContent = '';
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let finalResponse = null;
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let claudeOverloaded = false;
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try {
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// Use streaming for handling large responses
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const stream = await (aiClient || anthropic).messages.create({
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model:
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modelConfig?.model || session?.env?.ANTHROPIC_MODEL || CONFIG.model,
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max_tokens:
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modelConfig?.maxTokens || session?.env?.MAX_TOKENS || maxTokens,
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temperature:
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modelConfig?.temperature ||
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session?.env?.TEMPERATURE ||
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CONFIG.temperature,
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const modelToUse = modelConfig?.modelId || getMainModelId(null);
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const temperatureToUse =
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modelConfig?.temperature || getMainTemperature(null);
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const clientToUse = aiClient || anthropic;
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report(`Using model: ${modelToUse} with temp: ${temperatureToUse}`);
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const stream = await clientToUse.messages.stream({
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model: modelToUse,
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max_tokens: maxTokens,
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temperature: temperatureToUse,
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system: systemPrompt,
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messages: [
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{
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role: 'user',
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content: `Here's the Product Requirements Document (PRD) to break down into ${numTasks} tasks:\n\n${prdContent}`
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}
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],
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stream: true
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]
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});
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// Update loading indicator to show streaming progress - only for text output
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if (outputFormat === 'text' && !isSilentMode()) {
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let streamingInterval = null;
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if (!isSilentMode() && process.stdout.isTTY) {
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let dotCount = 0;
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const readline = await import('readline');
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streamingInterval = setInterval(() => {
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@@ -346,64 +347,76 @@ async function handleStreamingRequest(
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}, 500);
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}
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// Process the stream
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for await (const chunk of stream) {
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if (chunk.type === 'content_block_delta' && chunk.delta.text) {
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responseText += chunk.delta.text;
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textContent += chunk.delta.text;
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}
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if (reportProgress) {
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await reportProgress({
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progress: (responseText.length / maxTokens) * 100
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progress: (textContent.length / maxTokens) * 100
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});
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}
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if (mcpLog) {
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mcpLog.info(`Progress: ${(responseText.length / maxTokens) * 100}%`);
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mcpLog.info(`Progress: ${(textContent.length / maxTokens) * 100}%`);
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}
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}
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if (streamingInterval) clearInterval(streamingInterval);
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// Only call stopLoadingIndicator if we started one
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if (loadingIndicator && outputFormat === 'text' && !isSilentMode()) {
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stopLoadingIndicator(loadingIndicator);
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if (loadingIndicator) {
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stopLoadingIndicator(
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loadingIndicator,
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'Claude processing finished',
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true
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);
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loadingIndicator = null;
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}
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report(
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`Completed streaming response from ${aiClient ? 'provided' : 'default'} AI client!`,
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'info'
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);
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// Pass options to processClaudeResponse
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return processClaudeResponse(
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responseText,
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finalResponse = processClaudeResponse(
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textContent,
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numTasks,
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0,
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prdContent,
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prdPath,
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{ reportProgress, mcpLog, session }
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);
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if (claudeOverloaded) {
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report('Claude is overloaded, falling back to Perplexity', 'warn');
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const perplexityClient = getPerplexityClient();
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finalResponse = await handleStreamingRequest(
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prdContent,
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prdPath,
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numTasks,
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maxTokens,
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systemPrompt,
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{ reportProgress, mcpLog, session },
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perplexityClient,
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modelConfig
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);
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}
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return finalResponse;
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} catch (error) {
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if (streamingInterval) clearInterval(streamingInterval);
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// Only call stopLoadingIndicator if we started one
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if (loadingIndicator && outputFormat === 'text' && !isSilentMode()) {
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stopLoadingIndicator(loadingIndicator);
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if (loadingIndicator) {
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stopLoadingIndicator(loadingIndicator, 'Claude stream failed', false);
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loadingIndicator = null;
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}
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// Get user-friendly error message
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if (error.error?.type === 'overloaded_error') {
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claudeOverloaded = true;
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}
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const userMessage = handleClaudeError(error);
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report(`Error: ${userMessage}`, 'error');
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report(userMessage, 'error');
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// Only show console error for text output (CLI)
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if (outputFormat === 'text' && !isSilentMode()) {
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console.error(chalk.red(userMessage));
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throw error;
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} finally {
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if (loadingIndicator) {
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const success = !!finalResponse;
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const message = success
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? 'Claude stream finished'
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: 'Claude stream ended';
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stopLoadingIndicator(loadingIndicator, message, success);
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}
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if (CONFIG.debug && outputFormat === 'text' && !isSilentMode()) {
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log('debug', 'Full error:', error);
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}
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throw new Error(userMessage);
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}
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}
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@@ -528,18 +541,27 @@ async function generateSubtasks(
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additionalContext = '',
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{ reportProgress, mcpLog, session } = {}
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) {
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log('info', `Generating ${numSubtasks} subtasks for Task ${task.id}...`);
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const report = (message, level = 'info') => {
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if (mcpLog && typeof mcpLog[level] === 'function') {
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mcpLog[level](message);
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} else if (!isSilentMode()) {
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log(level, message);
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}
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};
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let loadingIndicator;
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if (!isSilentMode() && !mcpLog) {
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loadingIndicator = startLoadingIndicator(
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'Claude is generating subtasks...'
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);
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}
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const model = getMainModelId(null);
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const maxTokens = getMainMaxTokens(null);
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const temperature = getMainTemperature(null);
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try {
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log(
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'info',
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`Generating ${numSubtasks} subtasks for task ${task.id}: ${task.title}`
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);
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const loadingIndicator = startLoadingIndicator(
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`Generating subtasks for task ${task.id}...`
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);
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let streamingInterval = null;
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let responseText = '';
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const systemPrompt = `You are an AI assistant helping with task breakdown for software development.
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You need to break down a high-level task into ${numSubtasks} specific subtasks that can be implemented one by one.
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@@ -585,72 +607,62 @@ Return exactly ${numSubtasks} subtasks with the following JSON structure:
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Note on dependencies: Subtasks can depend on other subtasks with lower IDs. Use an empty array if there are no dependencies.`;
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try {
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// Update loading indicator to show streaming progress
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// Only create interval if not silent and stdout is a TTY
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if (!isSilentMode() && process.stdout.isTTY) {
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let dotCount = 0;
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const readline = await import('readline');
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streamingInterval = setInterval(() => {
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readline.cursorTo(process.stdout, 0);
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process.stdout.write(
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`Generating subtasks for task ${task.id}${'.'.repeat(dotCount)}`
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);
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dotCount = (dotCount + 1) % 4;
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}, 500);
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}
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// TODO: MOVE THIS TO THE STREAM REQUEST FUNCTION (DRY)
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// Use streaming API call
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const stream = await anthropic.messages.create({
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model: session?.env?.ANTHROPIC_MODEL || CONFIG.model,
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max_tokens: session?.env?.MAX_TOKENS || CONFIG.maxTokens,
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temperature: session?.env?.TEMPERATURE || CONFIG.temperature,
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system: systemPrompt,
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messages: [
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{
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role: 'user',
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content: userPrompt
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}
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],
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stream: true
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});
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// Process the stream
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for await (const chunk of stream) {
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if (chunk.type === 'content_block_delta' && chunk.delta.text) {
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responseText += chunk.delta.text;
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const stream = await anthropic.messages.create({
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model: model,
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max_tokens: maxTokens,
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temperature: temperature,
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system: systemPrompt,
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messages: [
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{
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role: 'user',
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content: userPrompt
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}
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if (reportProgress) {
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await reportProgress({
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progress: (responseText.length / CONFIG.maxTokens) * 100
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});
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}
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if (mcpLog) {
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mcpLog.info(
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`Progress: ${(responseText.length / CONFIG.maxTokens) * 100}%`
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);
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}
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}
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],
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stream: true
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});
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if (streamingInterval) clearInterval(streamingInterval);
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stopLoadingIndicator(loadingIndicator);
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let responseText = '';
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let streamingInterval = null;
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log('info', `Completed generating subtasks for task ${task.id}`);
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return parseSubtasksFromText(
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responseText,
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nextSubtaskId,
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numSubtasks,
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task.id
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);
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} catch (error) {
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if (streamingInterval) clearInterval(streamingInterval);
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stopLoadingIndicator(loadingIndicator);
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throw error;
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if (!isSilentMode() && process.stdout.isTTY) {
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let dotCount = 0;
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const readline = await import('readline');
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streamingInterval = setInterval(() => {
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readline.cursorTo(process.stdout, 0);
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process.stdout.write(
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`Generating subtasks for task ${task.id}${'.'.repeat(dotCount)}`
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);
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dotCount = (dotCount + 1) % 4;
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}, 500);
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}
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for await (const chunk of stream) {
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if (chunk.type === 'content_block_delta' && chunk.delta.text) {
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responseText += chunk.delta.text;
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}
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if (reportProgress) {
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await reportProgress({
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progress: (responseText.length / maxTokens) * 100
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});
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}
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if (mcpLog) {
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mcpLog.info(`Progress: ${(responseText.length / maxTokens) * 100}%`);
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}
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}
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if (streamingInterval) clearInterval(streamingInterval);
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if (loadingIndicator) stopLoadingIndicator(loadingIndicator);
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log('info', `Completed generating subtasks for task ${task.id}`);
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return parseSubtasksFromText(
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responseText,
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nextSubtaskId,
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numSubtasks,
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task.id
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);
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} catch (error) {
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if (loadingIndicator) stopLoadingIndicator(loadingIndicator);
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log('error', `Error generating subtasks: ${error.message}`);
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throw error;
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}
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