feedback-memory MCP server

Self-Learning Agent Setup

A small, practical pattern for making an AI agent remember your business and learn from every correction you give it. No framework required: a folder of brand files, your existing database, and a feedback table.

I run a one-person education business in Korea this way. Agents (Claude Code, Codex) draft posts and replies. I approve, edit or reject them from my phone. Every decision goes into a database, and the next draft reads that history before it writes a word.

This repo has the minimal schema and the prompts. The full walkthrough is a free 23-page field guide (link at the bottom).

Try it in one minute

In your browser, no install: https://ssap-pa.github.io/self-learning-agent-setup/ (Postgres + pgvector via PGlite and the all-MiniLM-L6-v2 embedding model via Transformers.js, all running in the tab, no API key).

Locally:

git clone https://github.com/ssap-pa/self-learning-agent-setup
cd self-learning-agent-setup/demo
npm install
npm run demo

It starts an in-memory Postgres (PGlite + pgvector), feeds in three days of feedback for a made-up candle shop (one edit, one reject, one plain approval), then prints what the agent would read before its next drafts. No database or API key needed. npm test runs the tests the same way.

Day 3  new comment: "How long does shipping to Canada take for the lavender set?"
       before drafting, the agent reads:
         Feedback on similar past tasks (most similar first):
         - task: "Do you ship to Canada? Love the lavender candle" -> edited to: "We do! Canada usually takes 5-7 days." (why: don't promise speed, give the real shipping time)

Day 3  new comment: "Could you gift wrap a candle for a birthday?"
       before drafting, the agent reads:
         Feedback on similar past tasks (most similar first):
         - task: "Can you gift wrap the vanilla set for my mom?" -> rejected (why: we don't gift wrap, offer the gift note instead)

Day 3  new comment: "What wax do you use?"
       before drafting, the agent reads:
         (nothing yet: no feedback on anything similar)

The demo embeds text by hashing words, so "similar" here means shared words. Run EMBED=openai OPENAIAPIKEY=... npm run demo to use real embeddings, or point Loop at your own Postgres / Supabase with pg.

Use it in Claude Code, Cursor or Claude Desktop (MCP server)

feedback-memory is a small MCP server built on the same loop. Your agent calls recallcorrections before it drafts and recorddecision after you approve, edit or reject. Everything lives in a local Postgres (PGlite + pgvector) in ~/.feedback-memory, so there's nothing to host.

Claude Code, as a plugin (the easiest way). Your standing rules load when a session starts (and again after /clear or compaction), and the closest past corrections are added to each prompt by a hook, so they apply even when Claude doesn't call a tool:

/plugin marketplace add https://github.com/ssap-pa/self-learning-agent-setup.git
/plugin install feedback-memory@ssap-pa

(The short form /plugin marketplace add ssap-pa/self-learning-agent-setup works too if you have an SSH key set up for GitHub.)

In a real run with the plugin, Claude made no tool calls and still answered a new "how long does shipping to Canada take?" comment with "Thanks for asking. Shipping to Canada usually takes 5-7 business days for the lavender set.": the shipping time from a past edit (reason: don't promise speed) and no exclamation marks, from a stored rule.

Shortened. The full README is on GitHub.