Vector Retrieval and RAG · Public

local-memory-mcp

Vector retrieval project for checking embedding storage, query semantics, RAG integration, data boundaries, and rollback.

Vector databaseRAGEmbeddingsSemantic searchData boundaries

Best fitDevelopers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.

Check whether this project matches your task before installing it.

What it can doVector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance

Review the portable capability path.

Before continuingVerify in a sandbox

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

GitHub snapshot1 stars

1 forks · 4 contributors

Doramagic.ai Last verification date: 2026-07-25 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.

Official first step Read manual preview Source repository

Publication status · 2026-07-25

What is local-memory-mcp?

local-memory-mcp is a vector database, retrieval, or RAG storage component for AI applications.
Best fit: Developers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.
Not for: Not for one-off model API calls or environments that cannot isolate indexed data, credentials, and persistence paths.
Capability added to an AI workflow: Vector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance
First safe verification step: Verify create, query, delete, and rollback with a small public text sample before using real data.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: Developers may fail before the first successful local run: feat: full-local natural language codebase search (codebase_search tool)
Evidence base: https://github.com/vheins/local-memory-mcp, https://github.com/vheins/local-memory-mcp#readme, Human Manual, Pitfall Log

Quick decision

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

Best forDevelopers connecting knowledge bases, documents, or app data to semantic retrieval or RAG workflows.

Match the project to your task before installing it.

CapabilityVector database setup checks, embedding model boundaries, collection management, query acceptance, and deletion guidance

Vector retrieval project for checking embedding storage, query semantics, RAG integration, data boundaries, and rollback.

Repositoryvheins/local-memory-mcp

1 stars · 1 forks

What it can do

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

Overview and System Architecture

Related topics: Codebase Index and Source Code Search, Memory Management, Semantic Search, and Knowledge Graph, Dashboard UI, Performance, and Operations

Source: https://github.com/vheins/local-memory-mcp / Human Manual

Codebase Index and Source Code Search

Related topics: Overview and System Architecture, Memory Management, Semantic Search, and Knowledge Graph, Dashboard UI, Performance, and Operations

Source: https://github.com/vheins/local-memory-mcp / Human Manual

Memory Management, Semantic Search, and Knowledge Graph

Related topics: Overview and System Architecture, Codebase Index and Source Code Search, Dashboard UI, Performance, and Operations

Source: https://github.com/vheins/local-memory-mcp / Human Manual

Dashboard UI, Performance, and Operations

Related topics: Overview and System Architecture, Codebase Index and Source Code Search, Memory Management, Semantic Search, and Knowledge Graph

Source: https://github.com/vheins/local-memory-mcp / Human Manual

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

Sources: https://github.com/vheins/local-memory-mcp, 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.

Stars1 stars

Forks1 forks

Contributors4 contributors

Licenseunknown

Community Discussion Evidence

12 source-linked items

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

01
feat: unified neural network graph for memory, codebase, and task visual
github / github_issue
02
fix: nested .gitignore not respected during file discovery
github / github_issue
03
fix: double vector computation in StandardEntity.checkConflicts
github / github_issue
04
perf: StubVectorStore upsert stores zero-filled vectors — redundant comp
github / github_issue
05
refactor: normalizeToolArguments duplicated between router.ts and tools/
github / github_issue
06
perf: coding_standards uses LIKE %term% without FTS5 — full table scan o
github / github_issue
07
perf: RealVectorStore ONNX model blocks first request — lazy init timeou
github / github_issue
08
perf: synchronous lstatSync + low concurrency bottlenecks in indexing pi
github / github_issue
09
perf: tree-sitter grammar WASMs eager-loaded at startup — 30MB+ waste
github / github_issue
10
perf: missing composite indexes on (owner, repo) — full table scans on f
github / github_issue
11
perf: dashboard export loads entire DB into memory — OOM risk
github / github_issue
12
perf: write lock contention on memory-acknowledge and hit-count tools
github / github_issue

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

npm install -g @vheins/local-memory-mcp

Official start command · https://github.com/vheins/local-memory-mcp#readme · verified: yes

Human Manual

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

8+ sections · Human Manual

local-memory-mcp Manual

A lightweight MCP server enabling persistent AI memory with semantic search, powered by SQLite. Designed for local-first workflows with fast retrieval, domain-based context isolation, and zero external database dependencies.

Open the full manual

https://github.com/vheins/local-memory-mcp Project Manual
Table of Contents
Overview and System Architecture
Related Pages
Purpose and Scope
High-Level Architecture
Component Breakdown
Storage and Migration Layer

Overview and System Architecture

Related topics: Codebase Index and Source Code Search, Memory Management, Semantic Search, and Knowledge Graph, Dashboard UI, Performance, and Operations

Source: https://github.com/vheins/local-memory-mcp / Human Manual

Codebase Index and Source Code Search

Related topics: Overview and System Architecture, Memory Management, Semantic Search, and Knowledge Graph, Dashboard UI, Performance, and Operations

Source: https://github.com/vheins/local-memory-mcp / Human Manual

Memory Management, Semantic Search, and Knowledge Graph

Related topics: Overview and System Architecture, Codebase Index and Source Code Search, Dashboard UI, Performance, and Operations

Source: https://github.com/vheins/local-memory-mcp / Human Manual

Dashboard UI, Performance, and Operations

Related topics: Overview and System Architecture, Codebase Index and Source Code Search, Memory Management, Semantic Search, and Knowledge Graph

Source: https://github.com/vheins/local-memory-mcp / Human Manual

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

AI Context Pack and portable assets

After deciding to continue, take the project context into your own AI host.

Complete pack plus user-owned assets

These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.

Download complete pack Read Human Manual

BundleComplete Project Pack AssetAI Context Pack AssetBoundary & Risk Card AssetHuman Manual AssetPitfall Log AssetPrompt Preview AssetQuick Start EvidenceREPO_INSPECTION.json

Preflight checks

Treat this page as a planning asset, not proof that your local environment is ready.

The manual is generated from source-linked project files and Doramagic validation signals.
Community evidence warnings stay visible instead of being converted into marketing claims.
This English page is indexable because the locale quality gate passed and explicit English index approval is enabled.
Use the upstream repository as the final authority for installation commands, license, and version-specific behavior.

Pitfall Log and verification risks

Doramagic surfaces high-risk items before users treat a candidate capability as verified.

medium

Installation risk requires verification

Developers may fail before the first successful local run: feat: full-local natural language codebase search (codebase_search tool)

medium

Installation risk requires verification

Developers may fail before the first successful local run: feat: tambahkan monorepo workspace seperti turbo repo

medium

Installation risk requires verification

Developers may fail before the first successful local run: perf: tree-sitter grammar WASMs eager-loaded at startup — 30MB+ waste

medium

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

medium

Configuration risk requires verification

May increase setup, validation, or first-run risk for the user.