[P2] Integrate Claude OS Architecture: Memory Systems, Skills Library & Real-Time Kanban

[Ash-Blanc]

Problem

DSPy-based AI agents lack persistent memory systems that allow them to:

• Learn from codebase analysis across multiple sessions
• Share reusable workflows and patterns via a skills library
• Track task progress and dependencies visually
• Automatically recall relevant context without explicit prompting

This limits the effectiveness of AI agents in complex, long-running projects where context retention and pattern discovery are critical.

Solution

Integrate Claude OS architecture into dspy-compounding-engineering to provide:

1. Persistent Memory System (Knowledge Base Manager)

• SQLite-backed vector store with semantic search
• Automatic context loading at session start
• Project-specific memory bases for different concerns (decisions, patterns, architecture)
• Pattern recognition and insight extraction from session history

2. Skills Library & Skill Management

• Local skills templates organized by domain (agents, workflows, compounding patterns)
• Community skills repository integration
• One-command skill installation: /dspy-skills install pattern-name
• Custom skill creation framework for project-specific workflows

3. Real-Time Kanban Board (for Spec-Driven Development)

• Auto-sync task/spec tracking from project files
• Visual progress monitoring for agent-executed specifications
• Real-time file watching with 3-second sync latency
• Task dependency visualization and risk level tracking

4. Hybrid Indexing System

• Phase 1: Fast structural indexing with tree-sitter (30 seconds for 10k files)
• Phase 2: Selective semantic indexing (top 20% important files in background)
• Enables immediate productivity without waiting for full embeddings

5. Session Management & Insights

• Parse Claude/DSPy session history automatically
• Extract patterns, decisions, and blockers
• Track tool usage and code changes across sessions
• Analytics dashboard for agent performance

Implementation Areas

1. Backend (app/core/)

   • memory_manager.py - Knowledge base operations
   • skill_manager.py - Skills CRUD and discovery
   • session_parser.py - Session history analysis
   • insight_extractor.py - Pattern extraction
   • Hybrid indexing components

2. API Endpoints (mcp_server/)

   • /api/memories/* - Memory CRUD and search
   • /api/skills/* - Skills management
   • /api/specs/* - Kanban spec tracking
   • /api/sessions/* - Session analytics

3. Frontend (frontend/)

   • Skills Library browser with install UI
   • Kanban board for real-time progress tracking
   • Memory search and insights dashboard
   • Session replay and pattern visualization

4. CLI Commands

   • /dspy-search [query] - Search memories
   • /dspy-remember [content] - Quick save
   • /dspy-skills [action] - Manage skills
   • /dspy-session [action] - Manage sessions

Benefits

✅ Knowledge Compounding - Agents learn from past work, improving over time
✅ Faster Onboarding - New agents start with project context pre-loaded
✅ Better Workflows - Skills library enables proven patterns and best practices
✅ Visual Transparency - Kanban shows exactly what agents are working on
✅ Intelligent Context - Automatic context injection based on relevance
✅ Scalable - Hybrid indexing handles 10k+ file projects efficiently

Reference Implementation

See: https://github.com/brobertsaz/claude-os for complete implementation reference

• Memory system architecture
• Skills library structure
• Real-time Kanban sync patterns
• Hybrid indexing strategy
• Session parsing approach

Acceptance Criteria

• Memory system stores and retrieves agent decisions with semantic search
• Skills can be installed from local templates and community repos
• Kanban board auto-syncs from spec files with <6s latency
• Hybrid indexing completes large projects in <30 seconds
• Session parser extracts insights automatically
• CLI commands available for all core features
• Documentation covers setup and usage for each subsystem