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AgentDB Memory Patterns

Automate AI agent memory management with AgentDB persistent storage and ReasoningBank integration.

4.4k|580|Updated Nov 19, 2025
Search Tags:#reasoningbank#agentdb#ai-agents#persistent-memory#long-term-memory#session-memory#memory-patterns
One-click install
npx skills add https://github.com/ruvnet/ruvector --skill agentdb-memory-patterns-ruvnet
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Memory Patterns
Source: https://github.com/ruvnet/ruvector/tree/main/.claude/skills/agentdb-memory-patterns
Command: npx skills add https://github.com/ruvnet/ruvector --skill agentdb-memory-patterns-ruvnet

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides memory management patterns for AI agents using AgentDB's persistent storage and ReasoningBank integration. Enables agents to remember conversations, learn from interactions, and maintain context across sessions.

Core Features & Use Cases

  • Session Memory: store and retrieve recent interactions for a given session to preserve context.
  • Long-Term Memory: persist important facts and user preferences for future conversations.
  • Pattern Learning: capture successful interaction patterns and apply learned strategies to new tasks.
  • Integration with ReasoningBank: leverage reasoning plugins and learning templates to improve agent behavior across tasks.
  • Real-World Use Case: build chat assistants that remember user preferences, recall past conversations, and adapt responses over time.

Quick Start

Use AgentDB to initialize storage, start MCP server, and create a learning plugin. Example commands:

  • Initialize AgentDB: npx agentdb@latest init ./agents.db npx agentdb@latest init ./agents.db --dimension 768
  • Start MCP server for Claude Code: npx agentdb@latest mcp claude mcp add agentdb npx agentdb@latest mcp
  • Create a learning plugin: npx agentdb@latest create-plugin
  • Use plugin templates: npx agentdb@latest create-plugin -t decision-transformer -n my-agent

Frequently Asked Questions about AgentDB Memory Patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add persistent long-term memory to AI agents so they remember past sessions?▼

Persistent long-term memory for AI agents is achieved by storing interactions and facts in AgentDB, allowing chat assistants and task-oriented agents to recall user preferences and context across multiple sessions.

What's the best way to capture and apply learned interaction patterns for task-oriented assistants?▼

Applying learned interaction patterns uses ReasoningBank integration with learning plugin templates, capturing successful strategies from past tasks and applying them to new agent behaviors.

How do I initialize AgentDB and connect it to Claude Code for context management?▼

To initialize AgentDB, run npx agentdb@latest init, start the MCP server with npx agentdb@latest mcp, and add it to Claude Code using claude mcp add agentdb npx agentdb@latest mcp.

Can I use hierarchical memory and custom plugins for research agents requiring context persistence?▼

Hierarchical memory and custom learning plugins are supported for research agents, utilizing optional plugin templates like decision-transformers to manage context persistence and adapt reasoning strategies.

Does AgentDB memory management require external databases or dependencies?▼

AgentDB memory management requires no external dependencies, using local persistent storage files like agents.db initialized directly via npx agentdb@latest init with optional vector dimensions.

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