memory-boost

Name: memory-boost
Author: arnaugonzalez

by arnaugonzalezversion 0.2.0i★ –iRuns on your computer

Registry nameio.github.arnaugonzalez/memory-boost

Install

Overview

Description from the maker (original English)

Markdown memory for coding agents that audits itself: drift reports and session digests.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "memory-boost": {
      "command": "uvx",
      "args": [
        "memory-boost",
        "serve"
      ]
    }
  }
}

Run this in your terminal

claude mcp add memory-boost -- uvx memory-boost serve

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "memory-boost": {
      "command": "uvx",
      "args": [
        "memory-boost",
        "serve"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "memory-boost": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "memory-boost",
        "serve"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account arnaugonzalez, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7233 servers in this category
Published to the registry more recently than 67% of the other 7233 servers in this category
Basic checks equal to or better than 100% of the other 7233 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is memory-boost safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.2.0) reached the registry 19 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 3 days ago

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 44 days ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

memory-boost

Memory for coding agents that reviews itself.

Try it · What it answers · Connect your agent · How it works · vs. alternatives

Your agents write everything down. Nobody reads it back.

Every memory tool for coding agents helps them remember *more*. None of them mention that half of what they remember expired in July. memory-boost keeps a plain-markdown memory your agents read and write over MCP, and then does the unglamorous part: it audits that memory and your own sessions, and tells you what drifted. Politely, with numbers.

Think of it as a code review for what your agents remember and a fitness tracker for how you work with them. Mostly the fitness tracker: mine told me my most-failed command was cd. Sixty-nine times.

What it answers

You wonder…	memory-boost answers
Which decisions quietly expired while we were busy shipping?	`drift`: review_by 2026-07-08, 78 days ago
Which "active" projects actually died months ago?	`drift`: last session 161 days ago; still listed as live
We fixed this exact bug in another repo. How?	`lessons`: the fix follows you to every project with the same stack
Why does my agent keep re-reading the same file?	`mine`: one file, read 18 times
Is my agent spawning 20 subagents behind my back?	`mine`: yes, and in which session
Where was I before `/compact` ate my context?	The session-start hook puts your checkpoint back

Using an AI agent? Point Claude Code, Codex, Cursor or Gemini CLI at this repo: AGENTS.md tells it how to install memory-boost, seed a wiki and run the reports for you.

Try it in 30 seconds

No install needed; the wiki lands in ~/.local/share/memory-boost:

uvx memory-boost==0.2.0 init --example   # sample wiki with five kinds of rot + four synthetic transcripts

uvx memory-boost==0.2.0 drift # what in that memory is stale, orphaned or never written down uvx memory-boost==0.2.0 mine # now on your real ~/.claude/projects: aggregates only

To keep it in Claude Code, install the plugin. It brings the MCP server, the session-start brief and the skills in one step; it only needs uv on your PATH.

/plugin marketplace add arnaugonzalez/memory-boost

/plugin install memory-boost@memory-boost

Other agents, or the CLI on its own: uv tool install memory-boost==0.2.0 (or pipx install memory-boost==0.2.0). Or let your agent do it: see Install from your agent.

Install from your agent

Paste this into Claude Code, Codex, Cline or Cursor:

Install memory-boost 0.2.0 by following

https://github.com/arnaugonzalez/memory-boost/blob/main/llms-install.md : install the CLI with

uv tool install memory-boost==0.2.0, run memory-boost init, register memory-boost serve

as a stdio MCP server named memory, check that it answers with nine memory_* tools, then run

memory-boost drift and show me the output. Ask me before writing outside the memory home

(~/.local/share/memory-boost) or editing any global agent config.

The agent-oriented steps (stdio and serve --http, per harness, with checks) are in llms-install.md.

Example: back on a project after two months

You come back to acme-api and the agent still trusts everything it wrote down. drift says which of it no longer holds: a Postgres pin whose review date passed in June, a superseded decision that points nowhere, projects that went silent. page shows why the pin was made, so you can decide in a minute. lessons brings over what pixel-notes learned about retrying jobs, because both projects run background jobs. Bundled example data (memory-boost init --example), recorded with vhs from docs/demo.tape.

On a real machine

Five weeks of the author's own daily use: 29 decisions, 24 projects, 657 Claude Code transcripts. Output as produced; only project and file names are replaced.

$ memory-boost drift

Drift report — 2026-09-24

29 decisions · 24 projects · 204 log entries · 33 open checkpoints · 20 findings

decision-expired (9)

deploy-via-bastion — review_by 2026-07-08 (78 days ago); revisit or mark historic
bot-guardrails — review_by 2026-07-08 (78 days ago); revisit or mark historic …

project-no-decisions (2)

thesis-solver — 20 sessions logged, 0 decisions tagged 'thesis-solver': the learning lives only in transcripts

project-silent (5)

mood-api — last session 2026-04-16 (161 days ago); still listed as live …

$ memory-boost mine

Session digest — 2026-09-24

sessions 80 with ≥20 turns tools Bash 5956 · Edit 860 · Read 692 · Write 493 · Agent 203 · ToolSearch 138 bash 5956 calls, 159 failed (2.7%); most failed: cd (69), sleep (12), ssh (9), pkill (8), ls (7) bash/edit 4.4x; extreme: crm-wt-f11 107.5x, sandbox 70.0x, sandbox 54.0x reads 692 calls, 131 re-reads of a file already read (19%); top: …/apply_email.py ×18 discipline 62 long sessions (≥60 turns): 47 never checkpointed, 29 never saved subagents 203 spawned in 44 sessions; max in one session: 20

Every line is a habit to change, a decision to revisit, or a rule worth writing down. None of it needed a new tool: the data was already on disk. It needed a reader.

What it is

A wiki you own. projects/*.md, decisions/*.md, concepts/*.md, log.md. Plain markdown; edit by hand, keep it in git. SQLite FTS5 index as a disposable cache. Nine MCP tools for any harness: memory_brief, memory_recall, memory_page, memory_save, memory_checkpoint, memory_resume, memory_drift, memory_lesson, memory_shelf. memory_shelf (CLI: memory-boost shelf "idea") appends a dated one-line idea to concepts/idea-shelf.md without the agent reading the page back.
Freshness as data. Decisions carry status: and review_by:. Search results are tagged current / unclassified / review / historic / superseded, current first.
Session checkpoints that survive /compact, with one line per *other* active session on the same project so parallel agents don't collide.
`drift` reads the wiki and reports what expired, what went silent, what was never written down and what was left half-done. The current project's top findings go into every session brief.
`mine` reads your local Claude Code transcripts and reports tool usage, failed commands, re-reads, subagent fan-out and checkpoint discipline. Aggregates only: no prompt, command or file content is stored or printed, so the digest is safe to paste anywhere.
`lessons`: concepts/ pages with applies_when: [tags]. A project page that lists matching tags: gets those lessons in its brief, so what worked in one repo reaches the next. The /retro skill writes them at session end.

Connect your harness

MCP server (all harnesses): command memory-boost, args ["serve"].

To share one server between several sessions, run it over streamable HTTP (stateless, JSON responses) and point each harness at http://127.0.0.1:8765/mcp:

memory-boost serve --http                      # --host 127.0.0.1 --port 8765 are the defaults

claude mcp add --transport http memory -s user http://127.0.0.1:8765/mcp

It binds to loopback by default and has no authentication: keep it there.

Harness	MCP registration	Context at session start
Claude Code (plugin)	`/plugin install memory-boost@memory-boost`	Included: hook, MCP server, skills
Claude Code (manual)	`claude mcp add memory -s user -- memory-boost serve`	Hook below (startup, resume, compact); `mine` reads its transcripts
Codex CLI	`codex mcp add memory -- memory-boost serve`	`AGENTS.md`: "call `memory_brief` first"
Cursor / VS Code / Gemini CLI	stdio entry in their `mcp.json` / `settings.json`	Same line in rules (hook adapters: roadmap)

Manual Claude Code hook (~/.claude/settings.json), not needed with the plugin:

{ "hooks": { "SessionStart": [ { "hooks": [ { "type": "command", "command": "memory-boost hook session-start" } ] } ] } }

On startup/resume it injects ~1k tokens: catalog, the project's context and last sessions, its drift, lessons from other projects, your open checkpoint. On compact only this session's checkpoint. It never blocks session start: on any error it prints nothing.

Skills: /memory-boost (router: when to run what), /save-session, /restore-session, /retro. The plugin installs them as /memory-boost:retro and so on; without it, copy skills/* into ~/.claude/skills/. The first session after installing the plugin takes a few extra seconds while uv fetches memory-boost==0.2.0 and its dependencies from PyPI (the only network access the plugin makes); later sessions start in about 0.2 s.

CLI contract for agents: data on stdout, errors on stderr with a fix: line, --json on every reading command (with a version field), exit 0 = trust stdout, 1 = not found, 2 = bad input. Reports end with a next: line naming the exact command to run.

How it works

Source: docs/architecture.mmd

The project is resolved from the working directory name (~/work/acme-api → projects/acme-api.md); map other names in aliases.json. Under 1,900 lines of Python, one dependency (mcp), no embeddings unless you opt into semantic recall, no daemon unless you run serve --http.

Semantic recall (optional)

Off by default: memory_recall is full-text only and the base install stays one dependency. To also match paraphrases and other languages, run a local embedding model:

pip install "memory-boost[semantic]==0.2.0"   # adds sqlite-vec

ollama pull embeddinggemma export MEMORY_BOOST_SEMANTIC=1 memory-boost index # embeds the wiki once; later runs embed only changes

The first index of a large wiki on CPU can take minutes: as a rough guide, about 15 minutes for some 850 documents on a laptop without a GPU (one measurement, not a benchmark). memory-boost index and recall show embedding N/M chunks… on stderr while it runs; later runs re-

Only the beginning is shown; read the rest in the repo

memory-boost

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

What it answers

Try it in 30 seconds

Install from your agent

Example: back on a project after two months

On a real machine

Drift report — 2026-09-24

decision-expired (9)

project-no-decisions (2)

project-silent (5)

Session digest — 2026-09-24

What it is

Connect your harness

How it works

Semantic recall (optional)