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Awannaphasch2016
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AgentDB Memory Patterns

Persist agent memory across sessions using AgentDB and ReasoningBank.

Updated Jan 27, 2026
Search Tags:#memory#AgentDB#ReasoningBank#AI-agents#long-term-memory#session-memory
One-click install
npx skills add https://github.com/Awannaphasch2016/agent-kernel-mcp --skill agentdb-memory-patterns-awannaphasch2016
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Memory Patterns
Source: https://github.com/Awannaphasch2016/agent-kernel-mcp/tree/main/assets/skills/agentdb-memory-patterns
Command: npx skills add https://github.com/Awannaphasch2016/agent-kernel-mcp --skill agentdb-memory-patterns-awannaphasch2016

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps AI agents preserve context across conversations by implementing session memory, long-term memory, and pattern learning using AgentDB and ReasoningBank integration.

Core Features & Use Cases

  • Session Memory: store and retrieve recent messages to maintain continuity in conversations.
  • Long-Term Memory: persist important facts and preferences across sessions.
  • Pattern Learning: capture recurring successful interactions to improve agent behavior.
  • Use Case: Build a customer support agent that remembers past issues and preferred responses to deliver personalized support.

Quick Start

Use the following commands to initialize the memory store, start MCP integration, and store/retrieve memory patterns:

  • CLI npx agentdb@latest init ./agents.db npx agentdb@latest mcp
  • API import { createAgentDBAdapter } from 'agentic-flow/reasoningbank'; const adapter = await createAgentDBAdapter({ dbPath: '.agentdb/reasoningbank.db', enableLearning: true }); // Example: store a session message // await adapter.insertPattern({ domain: 'conversation', pattern_data: JSON.stringify({ user: 'Hi', agent: 'Hello' }) });

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 long-term memory to my AI agents so they remember past sessions?▼

You can add long-term memory to AI agents by using AgentDB and ReasoningBank to persist important facts, preferences, and learned patterns across sessions. This integration ensures stateful conversational continuity.

What's the best way to store session memory for a customer support chatbot?▼

The best way to store session memory for a chatbot is using the AgentDB adapter to insert and retrieve recent messages. This maintains conversational continuity and enables personalized support based on past interactions.

Can I use AgentDB to capture recurring patterns and improve agent behavior?▼

Yes, you can use AgentDB to capture recurring successful interactions through pattern learning. By enabling the learning feature in the ReasoningBank adapter, your agent improves its behavior over time.

How do I initialize a memory store for stateful conversational systems using a CLI?▼

You initialize a memory store for stateful systems by running `npx agentdb@latest init ./agents.db` in your CLI. This sets up the local database required to persist session data.

Do I need any external dependencies to integrate long-term memory into my chat assistant?▼

No external dependencies are required to integrate long-term memory into your chat assistant. The Skill provides a clear integration workflow using a CLI and API with AgentDB and ReasoningBank.

How does pattern learning work when persisting context across sessions?▼

Pattern learning works by capturing and storing recurring successful interactions as pattern data in the ReasoningBank adapter. This historical context is retrieved later to improve agent responses.

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