memory-patterns

name	memory-patterns
description	mem0 integration patterns — when to use memory, hook-based auto-capture, scoping strategy, and practical examples per agent type

Load this skill when working with persistent memory or setting up memory automation.

Overview

mem0 provides persistent memory across sessions through an MCP server. Memories are shared across all agents (user_id scope by default) and survive context compaction, session restarts, and even project switches.

When to Use Memory

Proactive Memory Usage (Recommended)

Store information that should persist across sessions:

• Architectural decisions with rationale (prevents re-explaining choices)
• User preferences discovered during conversation (code style, naming conventions)
• Project-specific patterns (test structure, deployment process)
• Effective solutions to recurring problems
• Performance observations (which models work best for what tasks)

Reactive Memory Usage

Search memories when:

• User asks "what did we decide about X?"
• Starting work on a familiar project
• You notice recurring questions/patterns
• Before proposing architectural changes (check for prior decisions)

Hook-Based Auto-Capture

Use hooks to automatically capture memories at key points. Reference implementations available in .claude/hooks/examples/ (copy and customize).

PostToolUse Pattern - Capture Code Style

After Edit/Write operations, extract code style preferences:

PostToolUse:
  - matcher: "Edit|Write"
    hooks:
      - type: command
        command: "$CLAUDE_PROJECT_DIR/.claude/hooks/examples/capture-code-style.sh"

What it captures:

• Indentation preferences (tabs vs spaces)
• Import organization patterns
• Naming conventions observed
• Testing patterns

PreCompact Pattern - Preserve Critical Context

Before context compaction, save important information that would be lost:

PreCompact:
  - hooks:
      - type: command
        command: "$CLAUDE_PROJECT_DIR/.claude/hooks/examples/preserve-context.sh"

What it captures:

• Key architectural decisions from current session
• Unresolved issues flagged for follow-up
• Performance insights discovered
• User preferences expressed

SessionEnd Pattern - Session Summary

At session end, create searchable summary:

SessionEnd:
  - hooks:
      - type: command
        command: "$CLAUDE_PROJECT_DIR/.claude/hooks/examples/session-summary.sh"

What it captures:

• Tasks completed
• Decisions made
• Problems solved
• Patterns observed

Stop Pattern - Conversational Insights

After each agent response, optionally extract insights:

Stop:
  - hooks:
      - type: command
        command: "$CLAUDE_PROJECT_DIR/.claude/hooks/examples/conversation-insights.sh"

Use sparingly - fires frequently. Best for:

• High-value architectural discussions
• User feedback on agent performance
• Explicit preference statements

Memory Scoping Strategy

Default scope: user_id (shared across all agents)

# Add memory (visible to all agents)
mem0_add_memory(
    text="User prefers functional components over class components in React",
    user_id="default"  # Shared across agents
)

# Search memories (all agents can find this)
mem0_search_memories(
    query="React component preferences"
)

When to use agent_id scope:

• Agent-specific performance data
• Agent-specific learned behaviors
• When explicitly isolating agent memories

# Agent-specific memory
mem0_add_memory(
    text="@llm-engineer: qwen3:4b performs better than llama3.3:8b for structured output on this project",
    agent_id="llm-engineer"
)

Memory Hygiene

When to delete:

• Outdated architectural decisions (mark old, store new)
• Obsolete preferences (user changed their mind)
• Project-specific memories when project ends

# Clean up outdated memories
mem0_delete_all_memories(user_id="default")  # Nuclear option
mem0_delete_memory(memory_id="specific_id")  # Surgical removal

When to update:

• Refining existing preferences
• Correcting misunderstood patterns

mem0_update_memory(
    memory_id="abc123",
    text="Updated: User prefers 88-char line length (not 80)"
)

Agent-Specific Patterns

@architect

Store: Architectural decisions, design trade-offs, refactoring strategies Search: Before proposing major structural changes Example: "We chose SQLAlchemy over raw SQL for maintainability despite performance cost"

@llm-engineer

Store: Model performance observations, effective prompt patterns, provider quirks Search: Before choosing models or designing prompts Example: "Small models (4B) outperform 8B on structured output with constrained generation"

@prompt-engineer

Store: Successful prompt templates, few-shot examples that worked Search: When designing new prompts for similar tasks Example: "Chain-of-thought prompting reduces hallucination for small models on this task"

@security-reviewer

Store: Security patterns found in codebase, vulnerabilities discovered, fixes applied Search: Before auditing similar code Example: "This project uses cryptography library for password hashing, not bcrypt"

@github-ops

Store: PR merge patterns, team reviewer preferences, CI/CD quirks Search: Before creating PRs or releases Example: "Team prefers squash merges, always tag @reviewer-name for API changes"

@frontend-dev

Store: UI/UX decisions, user feedback, component patterns Search: Before implementing similar UI features Example: "User prefers spinner over progress bar for indeterminate operations"

Anti-Patterns

❌ Don't store ephemeral state (current file contents, temporary variables) ❌ Don't duplicate information already in AGENTS.md or project docs ❌ Don't store too frequently (creates noise, use PreCompact instead) ❌ Don't forget to search before storing similar information ✅ Do store decisions with context (why, not just what) ✅ Do use descriptive memory text (searchable, specific) ✅ Do review memories periodically (delete outdated information)