ReasoningBank with AgentDB

ReasoningBank with AgentDB is a skill for Claude Code from natea/fitfinder. It costs 56 tokens per session (2,645 once invoked), scanned A, a copy of ReasoningBank with AgentDB, MIT.

A ReasoningBank learning system backed by AgentDB, a database for storing and finding vector-based memories. It tracks task paths, judges results, distills memories, and recognizes patterns.

In plain words
What is it for?
Use it to build self-learning agents, experience replay, and adaptive decision-making with AgentDB storage.
Why use it?
It gives agents a persistent store for experience so they can retrieve useful past patterns and improve future decisions.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: mentions Claude Code.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is npx agentdb@latest init ./.agentdb/reasoningbank.db --dimension 1536.

Good fit Use it to build self-learning agents, experience replay, and adaptive decision-making with AgentDB storage.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/natea/fitfinder
agentmods
npx agentmods add skills/natea/fitfinder/reasoningbank-agentdb

Made for: Claude Code.

Wrote this? Show the measurements

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agentmods badge for ReasoningBank with AgentDB

README.md
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agentmods 80×15 button for ReasoningBank with AgentDB

Your own site · 80×15
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Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,645 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00056 $0.02645
Opus 5 $0.00028 $0.01323
Sonnet 5 $0.00011 $0.00529
Haiku 4.5 $0.00006 $0.00265

Measured 7d ago against content hash d60a616ad0bb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

ReasoningBank with AgentDB scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to ReasoningBank with AgentDB — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.claude/skills/reasoningbank-agentdb/SKILL.md · 447 lines

How it starts

The opening of the file, as written. The whole thing — 447 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ReasoningBank with AgentDB

What This Skill Does

Provides ReasoningBank adaptive learning patterns using AgentDB's high-performance backend (150x-12,500x faster). Enables agents to learn from experiences, judge outcomes, distill memories, and improve decision-making over time with 100% backward compatibility.

Performance: 150x faster pattern retrieval, 500x faster batch operations, <1ms memory access.

Prerequisites

  • Node.js 18+
  • AgentDB v1.0.7+ (via agentic-flow)
  • Understanding of reinforcement learning concepts (optional)

Quick Start with CLI

Initialize ReasoningBank Database

# Initialize AgentDB for ReasoningBank
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

# Automatic migration with validation
npx agentdb@latest migrate --source .swarm/memory.db

# Verify migration
npx agentdb@latest stats ./.agentdb/reasoningbank.db

Quick Start with API

import { createAgentDBAdapter, computeEmbedding } from 'agentic-flow/reasoningbank';

// Initialize ReasoningBank with AgentDB
const rb = await createAgentDBAdapter({
  dbPath: '.agentdb/reasoningbank.db',
  enableLearning: true,      // Enable learning plugins
  enableReasoning: true,      // Enable reasoning agents
  cacheSize: 1000,            // 1000 pattern cache
});

// Store successful experience
const query = "How to optimize database queries?";
const embedding = await computeEmbedding(query);

await rb.insertPattern({
  id: '',
  type: 'experience',
  domain: 'database-optimization',
  pattern_data: JSON.stringify({
    embedding,
    pattern: {
      query,
      approach: 'indexing + query optimization',
      outcome: 'success',
      metrics: { latency_reduction: 0.85 }
    }
  }),
  confidence: 0.95,
  usage_count: 1,
  success_count: 1,
  created_at: Date.now(),
  last_used: Date.now(),
});

// Retrieve similar experiences with reasoning
const result = await rb.retrieveWithReasoning(embedding, {
  domain: 'database-optimization',
  k: 5,
  useMMR: true,              // Diverse results
  synthesizeContext: true,    // Rich context synthesis
});

console.log('Memories:', result.memories);
console.log('Context:', result.context);
console.log('Patterns:', result.patterns);

Read the full file on GitHub · 447 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 447 lines · 56 tokens per session scan A d60a616ad0bb

Subscribe to this mod's changes

ReasoningBank with AgentDB is a skill published in the GitHub repository natea/fitfinder (4 stars, last pushed 10mo ago), licensed MIT. It adds 56 tokens to every session and 2,645 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ReasoningBank with AgentDB, differing in 0 lines, and is treated as a copy.

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