Agent Memory

Name: Agent Memory
Author: xChuCx

by xChuCxversion 0.6.1i★ –iRuns on your computer

Registry nameio.github.xChuCx/agent-memory

Install

Overview

Description from the maker (original English)

Git-native project memory for AI coding agents: Markdown source of truth, reviewable, secret-safe.

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

npmNode.js installed (for the npx 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": {
    "agent-memory": {
      "command": "npx",
      "args": [
        "-y",
        "@xchucx/agent-memory"
      ]
    }
  }
}

Run this in your terminal

claude mcp add agent-memory -- npx -y @xchucx/agent-memory

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "agent-memory": {
      "command": "npx",
      "args": [
        "-y",
        "@xchucx/agent-memory"
      ]
    }
  }
}

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": {
    "agent-memory": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@xchucx/agent-memory"
      ]
    }
  }
}

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 xChuCx, 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 7234 servers in this category
Published to the registry more recently than 47% of the other 7234 servers in this category
Basic checks equal to or better than 100% of the other 7234 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 Agent Memory 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 (0.6.1) reached the registry 44 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js 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.

agent-memory

Local, git-native project memory for AI coding agents. One MCP call in, structured memory updates out — current task state, decisions, conventions, pitfalls, per-module facts. Branch-aware. Secret-safe. Byte-preserving. No cloud, no vector DB — Markdown is the source of truth and git is the sync. Three MCP tools + a full CLI.

Why it's different: memory is plain Markdown committed to your repo, so you can read and git diff it; durable changes stage for human review (review --diff → apply) instead of landing silently; and secrets/PII are scanned out before anything is written. See ROADMAP.md for where this is headed (system-level / multi-repo memory).

Demo

An agent records a durable decision; it stages for review; you see the exact diff, apply it, and a later `fetch` surfaces it — local, git-native, reviewable, secret-safe. The clip is reproducible: docs/demo/demo.sh is the runnable flow and docs/demo/demo.tape renders the gif with vhs — see docs/demo/.

How it compares

Capability	AGENTS.md / CLAUDE.md	Vendor memory (e.g. Claude)	Vector / DB memory (mem0, Zep)	agent-memory
Plain-text, git-versioned source of truth	✓ flat file	✗ vendor-managed	✗ DB / cloud	✓ Markdown in your repo
Structured, section-level updates	✗	✗	~	✓
Human review gate (see the diff first)	✗ free edit	✗	✗	✓ stage → `review --diff` → apply
Vendor-neutral (MCP — any agent)	~ broad convention	✗ one vendor	~ varies	✓ Claude · Cursor · Codex · Gemini
Secret / PII scan on write	✗	✗	~ varies	✓
Team merge for concurrent edits	✗ text conflicts	✗	✗	✓ section merge driver
Runs fully local (no cloud)	✓	✗	~ varies	✓
Verifiable Task Protocol (VTP-1) & Machine Receipts	✗	✗	✗	✓ 5-phase cryptographic lifecycle + Clause B disjoint seat

These are general characterizations and the tools evolve fast — see something inaccurate? Open an issue and I'll fix the row. agent-memory is complementary to instruction files like AGENTS.md/CLAUDE.md (it even installs one): those say *how to behave*; agent-memory is the *durable, searchable, reviewed knowledge* behind it.

Status

Release 0.6.1 — the Swarm Audit Hardening & Metamorphic Rebase release: bridges durable memory with verifiable autonomous execution and strict fail-closed security invariants:

Astra Pro Audit Hardening (AM-001..AM-008) — Category-enforced approval routing (DecideRoutingWithCategory), whole-file pre-state CAS (proposal.PreHashes), polarity/negation qualifier preservation in Jaccard dedup (criticalQualifiers), idempotent pure read-only fetch_context, and canonical 3-way merge deletion semantics.
Metamorphic Rebase & Drift Detection (`internal/memory/rebase.go`) — Detects whole-file pre-hash drift during rebase, preventing silent clobber when adjacent sections are concurrently edited, verified by metamorphic composition tests.
Control-Plane Manifest Isolation — Enforces fail-closed protection of .agent-memory/meta/manifest.yaml against direct modification, symlink bypass, or path traversal attacks.
RFC 8785 (JCS) Determinism (`internal/vtp`) — Strict JSON Canonicalization Scheme compliance passing all 26 official RFC 8785 Appendix B test vectors, ECMAScript float formatting (1e+21), lone surrogate rejection, and recursive UTF-8 validation.
Fail-Closed Cross-Process SQLite NonceStore (`internal/memory/nonce.go`) — Persistent repo-scoped store using the withDB pattern to eliminate Windows file descriptor locks, providing zero-drift atomic validation and auto-sweeping expired nonces.
Two-Phase Staging & Compound Rollback (`internal/memory/staging.go`) — Strict path traversal sanitization (ValidateStagingID, agentfs.ValidateMemoryPath), symlink containment, and atomic multi-file rollback reporting RollbackIncomplete compound errors on partial failure.
VTP-1 Strict Assertion Identification & Dual-Oracle Settlement (`internal/vtp`) — Mandatory identifiable AssertionResults matching declared assertion IDs, verifier key binding validation, and Workpool/0 Clause B disjoint seat enforcement.

It builds on 0.6.0 (anti-ornamental SAR-008 grounding nonces, SAR-007 byte preservation), 0.5.4 (VTP-1 initial protocol engine), 0.5.0 (federation, referenced landscape stores), and 0.4 (section merge driver).

See CHANGELOG.md for the full changelist.

Document	Purpose
ROADMAP.md	Where the project is going, principles, and non-goals.
CHANGELOG.md	Per-release feature list and known limitations.
Design Doc v0.4.1	Canonical design this binary implements.
Implementation Plan	Historical MVP build log (M0–M8); see ROADMAP for what's next.
Retrieval eval	Offline recall/MRR/nDCG benchmark of `fetch` (method + numbers).
Patterns	Reusable design patterns documented per subsystem.
Spikes	Pre-M1 spike outcomes (byte-preserving engine, MCP SDK, flock, FTS5).

Quick start

Install — download a prebuilt binary (recommended): grab the archive for your OS/arch from the latest release, extract it, and put agent-memory on your PATH. No toolchain needed.

# npx (no Go, no manual download): fetches the verified release binary on

first run and caches it — also usable straight from an MCP client config.

npx -y @xchucx/agent-memory --help

Go toolchain alternative (Go 1.25+)

go install github.com/xChuCx/agent-memory/cmd/agent-memory@latest

from source

go build -o agent-memory ./cmd/agent-memory

Homebrew, Scoop, and winget packages are planned. agent-memory is also listed on the MCP Registry.

Then, inside the repo you want to give a memory:

# Scaffold .agent-memory/ in a repo

agent-memory init --name my-project

Install the Claude Code skill + register the project MCP server

(writes .claude/skills/agent-memory/SKILL.md and merges .mcp.json)

agent-memory install claude

Verify (prints the release tag, the go-install version, or dev+vcs locally)

agent-memory version

Read context

agent-memory fetch # bootstrap pack agent-memory fetch "auth" # FTS query

Start MCP server (your agent spawns this automatically once configured)

agent-memory mcp

install claude registers the MCP server for you: it merges a project-scoped .mcp.json at the repo root that runs agent-memory mcp --root ${CLAUDE_PROJECT_DIR:-.}. Claude Code expands CLAUDE_PROJECT_DIR to the repo at spawn, so the server always serves this repo — the config is portable across clones and (by Claude Code's scope precedence, local > project > user) overrides any stray user-scoped server. Commit .mcp.json so your team shares it.

⚠️ Do not register a single user-scoped server with a hardcoded root

(claude mcp add -s user agent-memory -- agent-memory mcp --root /some/repo):

it serves *every* project from that one repo, so memory you write in project B

silently lands in project A. Per-project registration (what install writes)

is the correct model; agent-memory doctor flags a mis-rooted registration.

The server resolves its repo from --root, then $CLAUDE_PROJECT_DIR, then the working directory. Other runtimes (Cursor, Gemini CLI, anything reading AGENTS.md) use the same server — install their adapter (see below).

Adopt on an existing project

init scaffolds empty memory. To seed it from a real codebase, let your coding agent do the analysis — that's the whole point. After init + install + registering the MCP server (above), restart the agent so the memory.* tools load, then paste the prompt below.

What happens: the agent reads the repo and calls memory.propose_update. Working notes and pitfalls apply immediately; durable categories (conventions, decisions, modules) stage for your review — inspect each with agent-memory review --diff and land it with agent-memory apply (or reject). Nothing durable is written without your approval.

You now have agent-memory MCP tools (memory.fetch_context,

memory.propose_update, memory.status) backed by this repository's .agent-memory/ store. Bootstrap the project's memory from the codebase.

Call memory.fetch_context with an empty query to see the current (mostly empty) state and the conventions/decisions/pitfalls/modules layout.

Analyze THIS repository — read the build files, CI config, entry points, and the main packages/modules. Identify:

build / test / run / lint commands and the toolchain;
conventions: code style, branching, commit rules, review practices;
architecture: the major modules/components and what each is for;
durable decisions: notable choices and WHY (only ones that are real and stable — not speculation);
pitfalls: footguns, sharp edges, "don't do X because Y" you can infer from the code, tests, or docs.

Persist what you found via memory.propose_update, choosing the intent per kind:

update_conventions → conventions.md (build/test/style/workflow)
refresh_module → modules/ .md (one per major component)
record_decision → decisions.md (Date / Status / Confidence + sources; type ∈ file|test|user, NOT external)
add_pitfall → pitfalls.md
update_shared → local/current.shared.md (a short "current state / where things stand" summary)

Rules:

Cite provenance: pass sources as file references you actually read (e.g. {"type":"file","ref":"internal/auth/session.go"}). Use confidence=confirmed for facts from code, inferred for deductions.
Every section needs a unique " " anchor; keep entries concise — this is working knowledge, not a wiki. Decisions need Date, Status (active|superseded|deprecated|proposed), and Confidence fields.
NEVER put secrets, tokens, or credentials in memory (the server will reject them anyway).
Work in a few focused passes (conventions + architecture first, then modules, then decisions/pitfalls). Report what you proposed and what staged for review.

No MCP s

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

Agent Memory

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

agent-memory

Demo

How it compares

Status

Quick start

first run and caches it — also usable straight from an MCP client config.

Go toolchain alternative (Go 1.25+)

from source

Install the Claude Code skill + register the project MCP server

(writes .claude/skills/agent-memory/SKILL.md and merges .mcp.json)

Verify (prints the release tag, the go-install version, or dev+vcs locally)

Read context

Start MCP server (your agent spawns this automatically once configured)

Adopt on an existing project