ReasoningBank with AgentDB

ReasoningBank with AgentDB is a skill for Claude Code, Codex from ayushcodes10/echo-mem. It costs 56 tokens per session (2,645 once invoked), scanned A, a copy of ReasoningBank with AgentDB, Apache-2.0.

An adaptive learning system that stores agent experiences in AgentDB, a database designed for retrieving related information. It tracks task paths, judges results, distils useful memories, and recognises patterns.

In plain words
What is it for?
Use it to record and retrieve experience, evaluate outcomes, migrate existing learning data, and build agents that improve their decisions.
Why use it?
It provides a persistent place for agents to reuse lessons and improve decisions instead of starting from scratch each time.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents); mentions Codex.

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 record and retrieve experience, evaluate outcomes, migrate existing learning data, and build agents that improve their decisions.

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/ayushcodes10/echo-mem
agentmods
npx agentmods add skills/ayushcodes10/echo-mem/reasoningbank-agentdb

Made for: Claude Code, Codex.

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
<a href="https://agentmods.dev/skills/ayushcodes10/echo-mem/reasoningbank-agentdb"><img src="https://agentmods.dev/badge/skills/ayushcodes10/echo-mem/reasoningbank-agentdb.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
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 5.5 $0.00022 $0.01058
Sonnet 5.5 $0.00011 $0.00529
Haiku 4.5 $0.00006 $0.00265

Measured 1mo ago against content hash b11b5f5b61f1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 1mo 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 — 4 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.

.agents/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 Codex integration
npx agentdb@latest mcp
Codex 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. 1mo ago Changed b11b5f5b61f1
  2. 1mo 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 ayushcodes10/echo-mem (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 56 tokens to every session and 2,645 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to ReasoningBank with AgentDB, differing in 4 lines, and is treated as a copy.

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