/add-conversation-memory

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🚨 EXECUTION NOTICE FOR CLAUDE

When you invoke this command via SlashCommand, the system returns THESE INSTRUCTIONS below.

YOU are the executor. This is NOT an autonomous subprocess.

• ✅ The phases below are YOUR execution checklist
• ✅ YOU must run each phase immediately using tools (Bash, Read, Write, Edit, TodoWrite)
• ✅ Complete ALL phases before considering this command done
• ❌ DON't wait for "the command to complete" - YOU complete it by executing the phases
• ❌ DON't treat this as status output - it IS your instruction set

Immediately after SlashCommand returns, start executing Phase 0, then Phase 1, etc.

See @CLAUDE.md section "SlashCommand Execution - YOU Are The Executor" for detailed explanation.

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Available Skills

This commands has access to the following skills from the mem0 plugin:

• memory-design-patterns: Best practices for memory architecture design including user vs agent vs session memory patterns, vector vs graph memory tradeoffs, retention strategies, and performance optimization. Use when designing memory systems, architecting AI memory layers, choosing memory types, planning retention strategies, or when user mentions memory architecture, user memory, agent memory, session memory, memory patterns, vector storage, graph memory, or Mem0 architecture.
• memory-optimization: Performance optimization patterns for Mem0 memory operations including query optimization, caching strategies, embedding efficiency, database tuning, batch operations, and cost reduction for both Platform and OSS deployments. Use when optimizing memory performance, reducing costs, improving query speed, implementing caching, tuning database performance, analyzing bottlenecks, or when user mentions memory optimization, performance tuning, cost reduction, slow queries, caching, or Mem0 optimization.
• supabase-integration: Complete Supabase setup for Mem0 OSS including PostgreSQL schema with pgvector for embeddings, memory_relationships tables for graph memory, RLS policies for user/tenant isolation, performance indexes, connection pooling, and backup/migration strategies. Use when setting up Mem0 with Supabase, configuring OSS memory backend, implementing memory persistence, migrating from Platform to OSS, or when user mentions Mem0 Supabase, memory database, pgvector for Mem0, memory isolation, or Mem0 backup.

To use a skill:

!{skill skill-name}

Use skills when you need:

• Domain-specific templates and examples
• Validation scripts and automation
• Best practices and patterns
• Configuration generators

Skills provide pre-built resources to accelerate your work.

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Security Requirements

CRITICAL: All generated files must follow security rules:

@docs/security/SECURITY-RULES.md

Key requirements:

• Never hardcode API keys or secrets
• Use placeholders: your_service_key_here
• Protect .env files with .gitignore
• Create .env.example with placeholders only
• Document key acquisition for users

Arguments: $ARGUMENTS

Goal: Integrate conversation memory tracking into existing application, automatically storing and retrieving conversation context.

Core Principles:

• Detect existing AI framework (Vercel AI SDK, LangChain, etc.)
• Add memory middleware/wrapper
• Minimal code changes required
• Automatic context retrieval

Phase 1: Framework Detection Goal: Identify existing AI framework

Actions:

• Search for AI framework imports and usage:
  • Vercel AI SDK (streamText, generateText, useChat)
  • LangChain (ConversationalChain, ChatOpenAI)
  • CrewAI (Crew, Agent)
  • OpenAI Agents SDK (Agent, query)
• Locate AI route handlers or chat functions
• Check Mem0 is already initialized

Phase 2: Integration Planning Goal: Determine where to add memory hooks

Actions:

• Identify chat entry points
• Find message handling logic
• Determine user/session identification method
• Plan memory retrieval and storage points

Phase 3: Implementation Goal: Add conversation memory integration

Actions:

Launch the mem0-integrator agent to add conversation memory.

Provide the agent with:

• Framework: [Detected from Phase 1]
• Integration points: [Identified in Phase 2]
• Requirements:
  • Add memory retrieval before AI generation
  • Add memory storage after AI response
  • Include user_id and session_id tracking
  • Handle conversation context automatically
  • Add error handling for memory operations
  • Generate framework-specific integration code
• Expected output: Complete integration with conversation memory

Phase 4: Verification Goal: Test conversation memory works

Actions:

• Test memory is stored after conversations
• Test memory is retrieved in next conversation
• Verify context improves AI responses
• Check user isolation works correctly

Phase 5: Summary Goal: Show what was integrated

Actions:

• Display integration results:
  • Framework: [Name]
  • Files modified: [List]
  • Memory hooks added: [List]
• Show usage:
  • How conversation memory is stored
  • How context is retrieved automatically
  • How to customize memory behavior
• Provide next steps:
  • Test with multi-turn conversations
  • Customize memory filtering if needed
  • Use /mem0:add-user-memory for preferences
  • Use /mem0:configure for advanced settings