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feat: Enhance Devlog Manager with AI context and decision tracking
- Refactored DevlogEntry and DevlogNote interfaces to include AIContext and DevlogContext for richer context management. - Implemented methods in DevlogManager for updating AI context, adding decisions, and retrieving AI-optimized context. - Updated createDevlog method to accept additional parameters for business and technical context, acceptance criteria, and initial insights. - Added new tools for updating AI context, adding decisions, and retrieving context in the server index. - Created a test script to validate the new AI context features, including creating devlogs, updating context, and recording decisions. - Updated pnpm-lock.yaml to link to the new types package.
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‎demo-ai-context.js‎

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#!/usr/bin/env node
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/**
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* Demo: Document-Based Context Persistence for AI Agents
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*
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* This shows how AI agents can maintain persistent context across sessions,
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* similar to how experienced developers maintain mental models of projects.
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*
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* Run: node demo-ai-context.js
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*/
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// Simple types for demonstration
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const DevlogTypes = {
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feature: "feature",
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bugfix: "bugfix",
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task: "task"
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};
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const DevlogStatus = {
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todo: "todo",
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inProgress: "in-progress",
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done: "done"
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};
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class DocumentBasedContext {
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/**
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* Create a development task with rich context
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*/
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createTaskDocument(taskData) {
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const now = new Date().toISOString();
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const id = `task-${Date.now()}`;
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return {
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// Basic task info
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id,
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title: taskData.title,
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type: "feature",
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description: taskData.description,
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status: "todo",
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priority: "medium",
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createdAt: now,
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updatedAt: now,
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// Rich business & technical context
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context: {
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businessContext: taskData.businessContext,
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technicalContext: taskData.technicalContext,
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acceptanceCriteria: taskData.acceptanceCriteria,
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decisions: [],
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risks: [],
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dependencies: []
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},
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// AI-specific persistent memory
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aiMemory: {
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currentUnderstanding: `New task: ${taskData.title}. Goal: ${taskData.businessContext}`,
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keyInsights: taskData.initialInsights || [],
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openQuestions: [],
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relatedPatterns: taskData.relatedPatterns || [],
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suggestedNextSteps: [
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"Analyze requirements",
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"Design solution",
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"Identify risks"
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],
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lastAIUpdate: now,
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memoryVersion: 1
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}
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};
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}
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/**
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* AI agent updates its understanding as work progresses
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*/
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updateAIMemory(document, update) {
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const now = new Date().toISOString();
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// Accumulate insights over time
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if (update.newInsights) {
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document.aiMemory.keyInsights.push(...update.newInsights);
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}
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// Track questions and answers
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if (update.questionsAnswered) {
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document.aiMemory.openQuestions = document.aiMemory.openQuestions.filter(
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q => !update.questionsAnswered.some(answered => q.includes(answered))
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);
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}
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if (update.newQuestions) {
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document.aiMemory.openQuestions.push(...update.newQuestions);
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}
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// Update current understanding
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if (update.progressSummary) {
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document.aiMemory.currentUnderstanding = update.progressSummary;
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}
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// Update next steps
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if (update.nextSteps) {
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document.aiMemory.suggestedNextSteps = update.nextSteps;
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}
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// Track patterns across projects
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if (update.patternsDiscovered) {
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document.aiMemory.relatedPatterns.push(...update.patternsDiscovered);
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}
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document.aiMemory.lastAIUpdate = now;
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document.aiMemory.memoryVersion += 1;
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document.updatedAt = now;
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return document;
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}
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/**
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* Record important decisions with rationale
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*/
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recordDecision(document, decision) {
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const now = new Date().toISOString();
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const decisionRecord = {
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id: `decision-${Date.now()}`,
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timestamp: now,
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decision: decision.decision,
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rationale: decision.rationale,
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alternatives: decision.alternatives || [],
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madeBy: decision.madeBy
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};
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document.context.decisions.push(decisionRecord);
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// Add to AI memory as insight
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document.aiMemory.keyInsights.push(
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`Decision: ${decision.decision}. Why: ${decision.rationale}`
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);
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document.aiMemory.lastAIUpdate = now;
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document.aiMemory.memoryVersion += 1;
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return document;
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}
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/**
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* Generate AI-optimized context for feeding to language models
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*/
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getAIContextSummary(document) {
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return `
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# Development Task: ${document.title}
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## Current Status: ${document.status}
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## Business Goal
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${document.context.businessContext}
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## Technical Context
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${document.context.technicalContext}
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## Current AI Understanding
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${document.aiMemory.currentUnderstanding}
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## Key Insights Learned
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${document.aiMemory.keyInsights.map(insight => `• ${insight}`).join('\n')}
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## Open Questions
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${document.aiMemory.openQuestions.map(q => `• ${q}`).join('\n')}
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## Related Patterns from Other Projects
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${document.aiMemory.relatedPatterns.map(pattern => `• ${pattern}`).join('\n')}
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## Acceptance Criteria
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${document.context.acceptanceCriteria.map(criteria => `• ${criteria}`).join('\n')}
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## Decisions Made
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${document.context.decisions.map(d =>
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`• ${d.decision} (by ${d.madeBy}): ${d.rationale}`
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).join('\n')}
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## Suggested Next Steps
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${document.aiMemory.suggestedNextSteps.map(step => `• ${step}`).join('\n')}
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---
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Memory Version: ${document.aiMemory.memoryVersion} | Last Updated: ${document.aiMemory.lastAIUpdate}
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`.trim();
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}
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/**
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* Demonstrate AI workflow with persistent context
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*/
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demonstrateAIWorkflow() {
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console.log("🤖 Demonstrating Document-Based AI Context Persistence\n");
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// 1. AI agent starts new feature
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console.log("📝 Step 1: AI Agent creates new feature task...");
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let taskDoc = this.createTaskDocument({
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title: "Add user authentication system",
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description: "Implement JWT-based auth with role-based access control",
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businessContext: "Secure API endpoints for customer portal launch. Need admin vs user access levels.",
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technicalContext: "Node.js/Express + PostgreSQL. Consider JWT middleware vs passport.js integration.",
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acceptanceCriteria: [
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"Users can register and login",
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"JWT tokens issued with roles",
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"API endpoints protected by role",
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"Password reset functionality",
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"Token refresh mechanism"
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],
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initialInsights: [
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"Similar JWT pattern used in billing service",
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"Need GDPR compliance consideration"
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],
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relatedPatterns: [
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"JWT refresh pattern from billing-service",
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"RBAC middleware from admin-dashboard"
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]
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});
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console.log(`✅ Created task document: ${taskDoc.id}\n`);
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// 2. AI agent makes progress
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console.log("🔄 Step 2: AI Agent updates understanding after analysis...");
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taskDoc = this.updateAIMemory(taskDoc, {
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progressSummary: "Analyzed user table schema. Designed JWT structure. Started auth middleware implementation.",
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newInsights: [
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"User table already has 'role' column - perfect for RBAC",
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"Need to add 'last_login' and 'refresh_token' columns",
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"Custom middleware simpler than passport-jwt for our use case"
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],
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newQuestions: [
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"Should we implement login rate limiting?",
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"How long should refresh tokens be valid?",
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"Do admin users need 2FA?"
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],
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nextSteps: [
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"Implement JWT generation and validation",
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"Create role-based route protection middleware",
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"Add database migrations for user table",
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"Build password reset flow"
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]
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});
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console.log("✅ Updated AI memory with new insights and questions\n");
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// 3. AI agent makes technical decision
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console.log("🎯 Step 3: AI Agent makes technical decision...");
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taskDoc = this.recordDecision(taskDoc, {
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decision: "Use bcrypt with 12 rounds for password hashing",
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rationale: "Balances security with performance. 12 rounds gives strong security while keeping response times under 100ms on our servers.",
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alternatives: [
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"argon2 (newer but complex setup)",
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"scrypt (good but bcrypt more adopted)"
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],
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madeBy: "ai-agent"
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});
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console.log("✅ Recorded decision with rationale\n");
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// 4. Generate context for next AI session
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console.log("📋 Step 4: Generate AI context for next session...");
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const contextSummary = this.getAIContextSummary(taskDoc);
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console.log("=" .repeat(60));
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console.log("AI CONTEXT SUMMARY (for next session)");
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console.log("=" .repeat(60));
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console.log(contextSummary);
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console.log("=" .repeat(60));
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console.log(`\n🎉 Demo complete! The AI agent now has persistent context across sessions.`);
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console.log(`📊 Memory version: ${taskDoc.aiMemory.memoryVersion}`);
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console.log(`💡 Total insights: ${taskDoc.aiMemory.keyInsights.length}`);
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console.log(`❓ Open questions: ${taskDoc.aiMemory.openQuestions.length}`);
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console.log(`🔗 Related patterns: ${taskDoc.aiMemory.relatedPatterns.length}`);
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return taskDoc;
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}
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}
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// Run the demonstration
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if (require.main === module) {
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const demo = new DocumentBasedContext();
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demo.demonstrateAIWorkflow();
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}
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module.exports = { DocumentBasedContext };

‎packages/mcp-server/package.json‎

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"author": "",
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"license": "MIT",
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"dependencies": {
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"@modelcontextprotocol/sdk": "^1.0.0"
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"@modelcontextprotocol/sdk": "^1.0.0",
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"@devlog/types": "workspace:*"
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},
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"devDependencies": {
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"@types/node": "^20.0.0",

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