Project-Specific Context Memory - AI Should Remember Project History

Feature Requests
1

Feature request for product/service

Cursor IDE

Describe the request

Project-Specific Context Memory - AI Should Remember Project History

Problem Statement

Cursor AI currently lacks the ability to maintain project-specific context, learnings, and domain knowledge across development sessions. This leads to repetitive mistakes, forgotten decisions, and the need to re-explain established patterns and business logic.

Current Limitations

  • No Project Memory: Cursor AI forgets project-specific decisions and learnings
  • Context Expiration: Session-based context limits long-term understanding
  • Repeated Mistakes: AI suggests previously rejected approaches
  • Lost Domain Knowledge: Business logic and domain expertise not preserved
  • Inconsistent Solutions: Different approaches for similar problems across sessions
  • Development Loops: AI gets stuck in repetitive solution attempts

Real-World Impact Example: Piano Application

Session 1: “We learned that staff notation should use MusicXML API, not SVG”
Session 2: “Let’s fix this staff notation issue” → AI suggests SVG approach
Session 3: “We established that measures need total beats = time signature numerator”
Session 4: “Fix this measure calculation” → AI suggests incorrect beat counting
Session 5: “We decided on this specific audio library for piano sounds”
Session 6: “Add piano sound” → AI suggests different audio library

Result: Developer constantly reverts AI suggestions and re-explains established patterns.

Proposed Solution

Core Features

1. Project Knowledge Base

  • Persistent Memory: Store project-specific learnings across sessions
  • Decision History: Track all technical and architectural decisions
  • Domain Knowledge: Maintain business logic and domain expertise
  • Pattern Recognition: Learn from repeated corrections and preferences

2. Contextual Learning System

  • Learning from Corrections: When developer reverts AI suggestions, learn why
  • Pattern Storage: Store successful approaches for future reference
  • Anti-Patterns: Remember approaches that were rejected and why
  • Domain-Specific Rules: Maintain business logic and constraints

3. Intelligent Memory Management

  • Relevance Scoring: Prioritize most relevant learnings for current context
  • Memory Consolidation: Merge similar learnings and avoid redundancy
  • Forgetting Mechanism: Remove outdated or irrelevant information
  • Context Switching: Adapt to different parts of the same project

User Experience Examples

Before (Current State)

Session 1: “We learned that staff notation should use MusicXML API”
Session 2: “Fix this staff notation issue” → AI suggests SVG approach
Session 3: “Add piano sound” → AI suggests HTML5 Audio
Session 4: “Fix measure calculation” → AI suggests incorrect beat counting

After (Proposed State)

Session 1: “We learned that staff notation should use MusicXML API”
AI: “Learning stored: Staff notation → MusicXML API (Performance reasons)”
Session 2: “Fix this staff notation issue”
AI: “Based on our previous learning, I’ll use MusicXML API approach”
Session 3: “Add piano sound”
AI: “I’ll use Web Audio API as we established it provides better latency”
Session 4: “Fix measure calculation”
AI: “Applying our business rule: measure_total_beats = time_signature_numerator”

Benefits

For Developers

  • Consistent Solutions: AI remembers and applies established patterns
  • Reduced Repetition: No need to re-explain decisions
  • Faster Development: Build on previous learnings
  • Domain Expertise: AI maintains business logic understanding

For Projects

  • Knowledge Preservation: Project expertise doesn’t get lost
  • Consistency: Uniform approach across development sessions
  • Efficiency: Build on established patterns
  • Quality: Avoid previously identified anti-patterns

Success Metrics

  • Learning Retention: 90% of corrections remembered
  • Consistency: 95% of suggestions follow established patterns
  • Efficiency: 50% reduction in re-explaining decisions
  • Quality: 80% reduction in repeated mistakes

Priority

High - This addresses a fundamental limitation in long-term project development.

Labels

  • feature-request
  • memory
  • context
  • learning
  • high-priority