What should I verify first?

Verify the MCP server command, permission scope, and rollback path in a non-primary host configuration first.. Keep credentials out of the first run, record expected output, define cleanup, and use public sample data before real workloads.

What is the main risk?

May increase setup, validation, or first-run risk for the user.. Use upstream documentation and a sandbox validation path before adoption.

MCP Tool Integration · Public

superlocalmemory

Q: What is superlocalmemory?

superlocalmemory is a Doramagic Project Pack for MCP setup guidance, host configuration checks, tool permission boundaries, recovery steps, and acceptance checks. It links the upstream repository, source-backed manual, prompt preview, AI context, and risk boundaries so users can evaluate the project before installation.

MCP tool integration project for safely connecting external tools, services, or data sources to an AI host.

MCPTool callingHost configurationPermission boundariesAcceptance checks

Best fitDevelopers who need Claude, Cursor, Codex, or another MCP-capable AI host to call external tools safely.

Check whether this project matches your task before installing it.

What it can doMCP setup guidance, host configuration checks, tool permission boundaries, recovery steps, and acceptance checks

Review the portable capability path.

Before continuingVerify in a sandbox

Do not treat a preview pack as a proven local install.

GitHub snapshot160 stars

21 forks · 5 contributors

Doramagic.ai Last verification date: 2026-05-31 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.

Official first step Read manual preview Source repository

Publication status · 2026-05-31

What is superlocalmemory?

superlocalmemory helps connect external tools, services, or data sources to AI hosts that support MCP.
Best fit: Developers who need Claude, Cursor, Codex, or another MCP-capable AI host to call external tools safely.
Not for: Not for users who cannot change host configuration, grant tool permissions, or isolate network, file, and credential access.
Capability added to an AI workflow: MCP setup guidance, host configuration checks, tool permission boundaries, recovery steps, and acceptance checks
First safe verification step: Verify the MCP server command, permission scope, and rollback path in a non-primary host configuration first.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: May increase setup, validation, or first-run risk for the user.
Evidence base: https://github.com/qualixar/superlocalmemory, https://github.com/qualixar/superlocalmemory#readme, Human Manual, Pitfall Log

Quick decision

Use this section to decide whether the project is worth a deeper read.

Best forDevelopers who need Claude, Cursor, Codex, or another MCP-capable AI host to call external tools safely.

Match the project to your task before installing it.

CapabilityMCP setup guidance, host configuration checks, tool permission boundaries, recovery steps, and acceptance checks

MCP tool integration project for safely connecting external tools, services, or data sources to an AI host.

Repositoryqualixar/superlocalmemory

160 stars · 21 forks

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.

Home

Related topics: System Architecture, Modes Explained (A/B/C), Installation

Source: https://github.com/qualixar/superlocalmemory / Human Manual

Installation

Migration from V2

System Architecture

Related topics: Home, Retrieval Pipeline, Modes Explained (A/B/C)

Source: https://github.com/qualixar/superlocalmemory / Human Manual

Modes Explained (A/B/C)

Related topics: Home, System Architecture

Source: https://github.com/qualixar/superlocalmemory / Human Manual

Sources: https://github.com/qualixar/superlocalmemory, Human Manual, Project Pack evidence, and downstream validation signals.

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.

Stars160 stars

Forks21 forks

Contributors5 contributors

Licenseunknown

Community Discussion Evidence

12 source-linked items

Review these external discussions before using superlocalmemory with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

01
Cognitive consolidation and trace issues on Linux/Docker setup
github / github_issue
02
GitHub issue body — qualixar SLM_HOST feature request
github / github_issue
03
RFC: Multi-Scope Memory — personal/global/shared scopes with scope-aware
github / github_issue
04
Feature Request: Support configurable local embedding endpoints (e.g., O
github / github_issue
05
slm remember xxx wait for a long time but seems no response
github / github_issue
06
SLM_DATA_DIR
github / github_issue
07
`api_key` silently dropped for Mode B LLM config
github / github_issue
08
Can't clone on Windows- repo contains bin
github / github_issue
09
v2.8.0 — Memory Lifecycle, Behavioral Learning, Enterprise Compliance
github / github_release
10
SuperLocalMemory v2.7.4
github / github_release
11
v2.7.0 — Your AI Learns You
github / github_release
12
v2.6.0 — Security Hardening & Performance
github / github_release

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.

Try the prompt first

Test the workflow without installing the upstream project.

preview

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify

pip install -U superlocalmemory

Official start command · https://github.com/qualixar/superlocalmemory#readme · verified: yes

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections · Human Manual