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ReasoningBank with AgentDB

Integrate ReasoningBank with AgentDB for adaptive-learning storage and retrieval.

Updated Jan 31, 2026
Search Tags:#reasoning#agentdb#ReasoningBank#adaptive-learning#pattern-recognition#memory-distillation#trajectory-tracking
One-click install
npx skills add https://github.com/thewoolleyman/home-tech-infrastructure --skill reasoningbank-with-agentdb-thewoolleyman
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ReasoningBank with AgentDB
Source: https://github.com/thewoolleyman/home-tech-infrastructure/tree/main/.claude/skills/reasoningbank-agentdb
Command: npx skills add https://github.com/thewoolleyman/home-tech-infrastructure --skill reasoningbank-with-agentdb-thewoolleyman

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a scalable framework to enable adaptive learning in AI agents by integrating ReasoningBank with AgentDB, delivering fast memory and reasoning capabilities.

Core Features & Use Cases

  • Trajectory tracking: capture sequences of actions and outcomes to improve agent policies.
  • Verdict judgment: evaluate past trajectories to determine likely success and guide decisions.
  • Memory distillation: synthesize large experiences into high-level patterns for reusable knowledge.
  • Pattern recognition: retrieve and reason about similar experiences to accelerate learning.
  • Real-world use case: deploy with autonomous agents requiring fast memory-backed reasoning for decision-making under uncertainty.

Quick Start

To begin, initialize ReasoningBank with AgentDB and connect to Claude Flow:

  • Initialize database: npx agentdb@latest init ./.agentdb/reasoningbank.db --dimension 1536
  • Start MCP server for Claude Code integration: npx agentdb@latest mcp, claude mcp add agentdb npx agentdb@latest mcp
  • Migrate from legacy ReasoningBank: npx agentdb@latest migrate --source .swarm/memory.db, npx agentdb@latest stats ./.agentdb/reasoningbank.db

Frequently Asked Questions about ReasoningBank with AgentDB

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

FAQPage Schema
How do I build self-improving agents with adaptive-learning memory?▼

You can build self-improving agents by integrating ReasoningBank with AgentDB to enable scalable adaptive-learning storage and retrieval, supporting trajectory tracking, verdict judgment, and memory distillation for optimized decision-making.

How do I migrate from legacy ReasoningBank to a faster vector backend?▼

To migrate from legacy ReasoningBank, run npx agentdb@latest migrate --source .swarm/memory.db to transfer your existing memory database into the AgentDB high-performance vector backend seamlessly.

Can I use AgentDB with Claude Code for pattern recognition and memory retrieval?▼

Yes, you can use AgentDB with Claude Code by starting the MCP server with npx agentdb@latest mcp and adding it via claude mcp add agentdb npx agentdb@latest mcp to enable fast pattern search and memory retrieval.

What is memory distillation and how does it accelerate intelligent agent learning?▼

Memory distillation is the process of synthesizing large experiences into high-level patterns for reusable knowledge, allowing intelligent agents to retrieve and reason about similar experiences to accelerate learning.

Does AgentDB support trajectory tracking for autonomous agents under uncertainty?▼

AgentDB supports trajectory tracking by capturing sequences of actions and outcomes to improve agent policies, making it suitable for autonomous agents requiring fast memory-backed reasoning for decision-making under uncertainty.

What are the limitations of using AgentDB for experience replay across domains?▼

AgentDB requires initializing a database with a specific vector dimension, such as 1536, via npx agentdb@latest init, meaning your experience replay across domains must conform to this dimensional constraint for pattern recognition.

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