MineEcho

A local-first Memory OS for personal AI assistants: remember, learn, use skills, and spend fewer tokens.

中文文档

MineEcho is a source-available, local-first AI assistant framework for building private, extensible assistant workflows on top of local services and user-owned knowledge.

MineEcho is not meant to be just another chat UI. Its product loop is:

Remember user preferences and past work.
Learn from imported knowledge through Wiki++ and graph context.
Use skills and external AI apps through one routing surface.
Save context cost with TokenLess reducers and local metrics.

Use MineEcho when a normal chat UI is too forgetful, a RAG app is too passive, and an agent framework is too noisy to run as a long-term personal assistant.

Why MineEcho

What Makes It Different

• Memory-first assistant loop: MineEcho stores raw interaction memory, daily/weekly/monthly summaries, semantic recall evidence, and memory-to-knowledge alignment instead of treating every chat as disposable.
• Wiki++ knowledge system: imported sources become raw files, wiki pages, chunk indexes, graph nodes, and alignment candidates, so knowledge stays inspectable rather than becoming opaque vector chunks.
• Skill and AI app convergence: external AI apps are registered as callable skills and participate in the same trigger, routing, health, and workflow surface as local skills.
• TokenLess cost layer: long command logs, tool outputs, URL imports, and document extraction results are compressed by scenario-aware reducers before they enter memory or context.
• Local-first release posture: runtime data, provider keys, chat history, audio, and metrics live under local MineEcho state and are excluded from the release tree.

The project is designed around four baseline ideas:

• Skill routing: route user intent to focused capabilities instead of pushing every task through one general prompt.
• Memory distillation: compact interaction history into durable working memory while keeping raw transcripts optional and local.
• Wiki and graph knowledge base: organize notes, entities, and relationships into a browsable knowledge layer that can support retrieval and reasoning.
• Cost controls: keep local defaults, explicit provider configuration, and bounded memory/performance settings so experimentation does not silently become expensive.

Product Screenshots

Chat Workbench

Skills Center

Wiki++ Knowledge Base

L0-L3 Memory System

Model and Runtime Settings

Meeting Workflow

Current Capabilities

• Local console and BFF for chat, skills, memory, knowledge base, calendar/meeting helpers, and configuration.
• Skill center with JSON skill import, ZIP/.skill package import, URL install, AI app registration, trigger refresh, and routing preview APIs.
• AI app adapters that convert RAG/workflow apps into Gateway-callable skills.
• Memory-to-knowledge alignment preview, commit history, and knowledge graph refresh hooks.
• Knowledge graph neighborhood API for explaining selected nodes and one-hop relationships.
• TokenLess compression metrics with local persistence.

Core Highlights

Memory: runtime memory plus L0-L3 memory tree

MineEcho separates memory into two complementary views:

• Working / Short-term / Long-term: current session, daily interactions, cross-session profile and insights.
• L0 / L1 / L2 / L3 Memory Tree: raw memory chunks, daily summaries, weekly summaries, and monthly archives.

When a user asks a question, MineEcho recalls recent L0 chunks with keyword, semantic, local-vector, importance, and recency scoring, then reranks the top candidates with the configured embedding provider when available. L1/L2 summaries pass a local semantic gate before embedding-enhanced ranking, which reduces unrelated long-term context injection.

Wiki++ knowledge base

MineEcho's knowledge base is organized as raw source files plus AI-maintained wiki pages. Query-time retrieval uses four channels:

1. Vector search for semantic similarity.
2. BM25 for Chinese/English keyword matching.
3. Structured search over title, type, tags, and headings.
4. Graph channel search over entities and one-hop neighborhoods.

This makes it closer to a high-density, maintainable AI knowledge substrate than a simple chunk-only RAG store.

Skills and AI apps as one capability layer

Native skills, imported skills, and registered AI apps enter the same registry. AI apps are converted into Gateway-callable skills, triggers are derived from name + description, and the router scores trigger, name, description, and mode evidence before returning candidates. AI apps therefore participate in the same routing and workflow surface as local skills.

TokenLess cost controls

TokenLess ships with 15 built-in reducer rules for git, npm, cargo, docker, document extraction, and generic long output. It keeps errors, counts, head/tail context, and actionable lines instead of blindly truncating output. Based on the current rule structure, long tool/document outputs commonly fall into an estimated 20%-85% token-saving range depending on repetition and output shape; MineEcho records raw/reduced characters and estimated tokens saved locally for real workload measurement.

Quick Start

Choose one of the following paths:

• Windows desktop installer: recommended for non-developers who want to try MineEcho quickly.
• macOS DMG: recommended for macOS users who want an out-of-the-box desktop package.
• Source startup: recommended for developers who want to inspect, modify, or run the project from source.

Option 1: Windows Desktop Installer

Download the desktop packages from Baidu Netdisk and choose the Windows installer:

• Link: https://pan.baidu.com/s/1oERH-NdpTRwobfp1F8keEQ?pwd=j6kd
• Extraction code: j6kd

Steps:

1. Download the installer package from the link above.
2. Run MineEcho Setup 1.0.0.exe on Windows 10 or Windows 11.
3. Follow the installer prompts.
4. Launch MineEcho from the desktop shortcut or Start menu.
5. Complete the first-run setup wizard and configure your model provider API key in Settings.

Notes:

• The Windows installer already includes the MineEcho desktop app and bundled local runtime dependencies required by the packaged version.
• Real model provider API keys are not bundled. Configure your own key locally after first launch.
• Runtime data, chat history, local skills, knowledge files, and provider settings are stored on the user's machine.

Option 2: macOS DMG

Download the desktop packages from Baidu Netdisk and choose the macOS DMG package:

• Link: https://pan.baidu.com/s/1oERH-NdpTRwobfp1F8keEQ?pwd=j6kd
• Extraction code: j6kd

Steps:

1. Download the DMG package from the link above.
2. Open the DMG and drag MineEcho into Applications.
3. Launch MineEcho from Applications or Launchpad.
4. Complete the first-run setup wizard and configure your model provider API key in Settings.

Notes:

• The macOS DMG already includes the MineEcho desktop app and bundled local runtime dependencies required by the packaged version.
• The current macOS package is ad-hoc signed and not Apple Developer ID notarized. On first launch, if macOS blocks the app, open System Settings -> Privacy & Security and click Open Anyway, or right-click MineEcho and choose Open.
• Real model provider API keys are not bundled. Configure your own key locally after first launch.
• Runtime data, chat history, local skills, knowledge files, and provider settings are stored on the user's machine.

Option 3: Source Startup

This is the full path for first-time users who want to run MineEcho from source.

1. Prepare your machine

Install:

• Git
• Node.js 22 or later
• npm, usually bundled with Node.js

Check versions:

git --version
node -v
npm -v

If node -v is lower than 22, upgrade Node.js first.

2. Clone the repository

git clone https://github.com/Health-Yang/MineEcho.git
cd MineEcho

3. Install dependencies

npm run install:apps

This installs dependencies for BFF, Console, and the vendored OpenClaw Gateway runtime under vendor/openclaw-gateway. MineEcho ships the OpenClaw Gateway runtime source in this repository, so users do not need to install OpenClaw separately.

4. Create local environment files

cp apps/bff/.env.example apps/bff/.env
# Optional, only when you need to override Console defaults:
# cp apps/console/.env.example apps/console/.env

MineEcho does not ship real model API keys. You can start without keys first, then configure model providers in the Console settings page.

5. Start development services

npm run dev

The development URLs are:

• Console: http://127.0.0.1:5175/
• BFF: http://127.0.0.1:3085/

The checked-in Vite dev c