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ReasoningBank with AgentDB

Store, retrieve, and reason over adaptive patterns with AgentDB.

75|17|Updated Jan 11, 2026
Search Tags:#reasoning#agentdb#claude-code#adaptive-learning#memory-distillation#trajectory-tracking#multidomain-learning
One-click install
npx skills add https://github.com/smith-horn/skillsmith --skill reasoningbank-with-agentdb-smith-horn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ReasoningBank with AgentDB
Source: https://github.com/smith-horn/skillsmith/tree/main/.claude/skills/reasoningbank-agentdb
Command: npx skills add https://github.com/smith-horn/skillsmith --skill reasoningbank-with-agentdb-smith-horn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

ReasoningBank with AgentDB enables autonomous agents to learn from experiences by storing, retrieving, and reasoning over adaptive patterns with a fast backend. This solves the challenge of scalable memory, pattern-based decision-making, and continual improvement across tasks.

Core Features & Use Cases

  • Trajectory Tracking: record action sequences and outcomes to inform future decisions.
  • Verdict Judgment & Memory Distillation: evaluate similarities and distill insights into reusable patterns across domains.
  • Multi-Domain Learning: transfer learned patterns across domains to improve cross-task performance.
  • Claude Code integration: seamless MCP server integration for Claude Code workflows.

Quick Start

Use the following commands to initialize, start the MCP server, and register with Claude Code: npx agentdb@latest init ./.agentdb/reasoningbank.db --dimension 1536 npx agentdb@latest mcp claude mcp add agentdb npx agentdb@latest mcp

Frequently Asked Questions about ReasoningBank with AgentDB

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

FAQPage Schema
How do agents learn from past experiences and store adaptive patterns?▼

Agents learn from past experiences by recording action trajectories and outcomes, then distilling these into reusable patterns using memory-driven backend storage. This mechanism enables pattern-based decision-making and continual improvement across tasks by retrieving relevant adaptive patterns during future operations.

How do I set up an agent memory database and connect it to Claude Code?▼

To set up an agent memory database and connect it to Claude Code, initialize the database with a specified vector dimension, start the MCP server, and register the server with Claude Code using command-line integration. This requires Node.js 18+ and AgentDB v1.0.7+.

Can I transfer learned patterns across multiple domains for cross-task performance?▼

Yes, you can transfer learned patterns across multiple domains to improve cross-task performance. Multi-domain learning evaluates similarities through verdict judgments and distills insights into reusable patterns, allowing autonomous agents to apply knowledge gained in one domain to different tasks.

What is trajectory tracking for autonomous agents and when do I need it?▼

Trajectory tracking for autonomous agents is the process of recording action sequences and their corresponding outcomes to inform future decisions. You need it when your agents must evaluate past actions, apply memory distillation, and improve decision-making through adaptive learning.

Do I need Node.js and AgentDB to enable memory-driven learning for agents?▼

Yes, you need Node.js 18+ and AgentDB v1.0.7+ to enable memory-driven learning for agents. AgentDB serves as the high-performance backend for storing and retrieving adaptive patterns, while Node.js provides the runtime environment for the MCP server and Claude Code integration.

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