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ReasoningBank with AgentDB

Store and retrieve agent experiences and trajectories via AgentDB vector database.

1|Updated Jul 26, 2025
Search Tags:#AI learning#vector database#memory#adaptive learning#reasoning#pattern recognition#agent intelligence
One-click install
npx skills add https://github.com/jcolano/claude-flow --skill reasoningbank-with-agentdb-jcolano
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ReasoningBank with AgentDB
Source: https://github.com/jcolano/claude-flow/tree/main/.claude/skills/reasoningbank-agentdb
Command: npx skills add https://github.com/jcolano/claude-flow --skill reasoningbank-with-agentdb-jcolano

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires Node.js 18+, AgentDB v1.0.7+.

What problem does it solve?

Building self-learning AI agents that can effectively learn from experience, judge outcomes, and distill memories often involves slow, inefficient data storage and retrieval. This Skill integrates ReasoningBank's adaptive learning with AgentDB's high-performance vector database, enabling 150x faster pattern retrieval and sub-millisecond memory access.

Core Features & Use Cases

  • High-Performance Backend: Leverages AgentDB for 150x faster pattern retrieval and 500x faster batch operations, ensuring rapid learning and decision-making.
  • Trajectory Tracking & Verdict Judgment: Records agent execution paths and outcomes, then judges their success based on similarity to past successful patterns.
  • Memory Distillation & Pattern Recognition: Consolidates similar experiences into high-level patterns and uses reasoning modules (PatternMatcher, ContextSynthesizer) to generate rich context.
  • Use Case: Develop an AI agent that optimizes database queries. The agent tracks its query optimization attempts (trajectories), judges their success, and distills successful approaches into reusable patterns, continuously improving its ability to optimize queries over time.

Quick Start

Initialize AgentDB for ReasoningBank, then store a successful experience of optimizing database queries. 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 I enable adaptive learning for autonomous agents with fast pattern retrieval?▼

Adaptive learning for agents uses vector databases to store and retrieve experience patterns at high speed. ReasoningBank with AgentDB achieves this by leveraging AgentDB's vector database for 150x faster pattern retrieval, enabling agents to learn from past trajectories and distill successful approaches into reusable decision patterns in real-time.

What's the best way to track agent trajectories and judge decision outcomes?▼

Trajectory tracking records agent execution paths and their outcomes; judgment compares new decisions against past successful patterns stored in a vector database. ReasoningBank automates this by storing trajectories in AgentDB, then using reasoning modules to match current decisions against similar past successes and distill patterns.

Can I use AgentDB for memory distillation and pattern-based decision making in Node.js?▼

Yes. ReasoningBank with AgentDB requires Node.js 18+ and AgentDB v1.0.7+. It consolidates similar experiences into high-level patterns and uses PatternMatcher and ContextSynthesizer modules to generate rich context, enabling pattern-based decisions with sub-millisecond memory access.

Why is vector database performance critical for self-learning agents?▼

Self-learning agents must retrieve relevant past experiences quickly to make real-time decisions. AgentDB's 500x faster batch operations and sub-millisecond memory access eliminate slow retrieval bottlenecks, allowing agents to judge outcomes and adapt behavior continuously without degrading performance.

Do I need embedding and retrieval APIs to use ReasoningBank?▼

Yes. ReasoningBank requires embedding and retrieval APIs alongside Node.js 18+ and AgentDB v1.0.7+ to store experiences and trajectories as vectors, then retrieve similar patterns for adaptive learning and memory distillation in autonomous agents.

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