Octobrain

Name: Octobrain
Author: muvon

作者：muvon版本 0.15.1i★ –i在你的电脑上运行

注册名称io.github.Muvon/octobrain

安装

概览

作者提供的说明（英文原文）

Persistent memory for AI assistants with semantic search and knowledge graph relationships.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 搜索与 AI 记忆

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
注册中心没有列出需要填写的密钥。

你需要

npm已安装 Node.js（用于 npx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

下载作者的 .mcpb 文件

链接指向作者自己的文件；我们不托管也不重新打包它。打开文件后，Claude Desktop 会进行安装。

在终端中运行这条命令

claude mcp add octobrain -- npx -y @muvon/octobrain mcp

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "octobrain": {
      "command": "npx",
      "args": [
        "-y",
        "@muvon/octobrain",
        "mcp"
      ]
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "octobrain": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@muvon/octobrain",
        "mcp"
      ]
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 muvon 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 7254 个服务器的 0%
发布到注册中心的时间比此分类中其他 7254 个服务器的 90% 更新
基础检查结果等于或优于此分类中其他 7254 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Octobrain 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.15.1）于3天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js, MCPB。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者：upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT今天更新

安装

Codebase Memory

作者：DeusData

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

在你的电脑上运行★ 48,148MIT20天前更新

安装

Exa

作者：exa-labs

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

在云端运行★ 5,213MIT上个月更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

Octobrain

Persistent memory for AI assistants — store insights, decisions, and knowledge that survives across conversations.

_MCP Registry: mcp-name: io.github.Muvon/octobrain_

Why Octobrain?
Quick Start
Installation
Usage
Features
Benchmarks
Configuration
Memory Types
MCP Integration
Storage Locations
Contributing
License

Octobrain gives your AI assistant a long-term memory. Store code insights, architecture decisions, bug fixes, and knowledge — then retrieve them with semantic search in future sessions. Works as a CLI tool or as an MCP server for integration with Claude Desktop and other AI tools.

Why Octobrain?

AI assistants start every conversation with zero context. You explain your project, your preferences, your decisions — every single time. Octobrain breaks that cycle:

Persistent memory — Insights survive across sessions, not just within them
Semantic search — Find memories by meaning, not exact keywords
Auto-linking — Related memories connect automatically (Zettelkasten-style)
Knowledge indexing — Ingest docs, articles, and files for retrieval
MCP integration — Works with Claude Desktop and other MCP-compatible tools

Quick Start

# Install from crates.io

cargo install octobrain

Store your first memory

octobrain memory memorize --title "API Design Pattern" \ --content "Use REST for CRUD, GraphQL for complex queries" \ --memory-type architecture --tags "api,design"

Search memories

octobrain memory remember "how should I design APIs"

Start MCP server for Claude Desktop integration

octobrain mcp

Installation

From crates.io (Recommended)

cargo install octobrain

From Source

# Clone and build

git clone https://github.com/muvon/octobrain.git cd octobrain cargo build --release

Binary location

./target/release/octobrain --help

Docker

docker pull ghcr.io/muvon/octobrain:latest

Homebrew (macOS/Linux)

brew install muvon/tap/octobrain

Feature Flags

Octobrain supports multiple embedding providers:

Flag	Description	API Key Required
`fastembed`	Local embeddings via FastEmbed	No
`huggingface`	Local embeddings via HuggingFace	No
(default)	Both `fastembed` + `huggingface`	No
(no features)	API-based: Voyage, OpenAI, Google, Jina	Yes

# Build with local embeddings (default, no API keys needed)

cargo build --release

Build with API-based embeddings only

cargo build --no-default-features --release

For API-based embeddings, set the appropriate environment variable:

VOYAGE_API_KEY for Voyage AI
OPENAI_API_KEY for OpenAI
GOOGLE_API_KEY for Google
JINA_API_KEY for Jina

Usage

Memory Management

Store and retrieve insights, decisions, and context:

All memory subcommands accept global flags:

--scope — Override project scope (default: auto-detected from Git remote)
--role — Filter by role (e.g. "developer", "reviewer")

# Store a memory

octobrain memory memorize --title "API Design" \ --content "Use REST for CRUD, GraphQL for complex queries" \ --memory-type architecture --tags "api,design"

Search memories (semantic search)

octobrain memory remember "api design patterns"

Multi-query search for broader coverage

octobrain memory remember "authentication" "security" "jwt"

Get a memory by ID

octobrain memory get

Get recent memories

octobrain memory recent --limit 20

Filter by type

octobrain memory by-type architecture --limit 10

Filter by tags

octobrain memory by-tags "api,security"

Find memories related to files

octobrain memory for-files "src/main.rs,src/lib.rs"

Update a memory

octobrain memory update --title "New Title" --add-tags "new-tag"

Delete a memory

octobrain memory forget --memory-id

Get memories relevant to the current Git commit

octobrain memory current-commit

Show memory statistics

octobrain memory stats

Clean up old/low-importance memories

octobrain memory cleanup

Compact tables and refresh indices (run when searches slow down)

octobrain memory maintenance

⚠️ Delete ALL memory data

octobrain memory clear-all --yes

Memory Consolidation

Close a goal and fold all its contributing memories into a consolidated summary:

# Consolidate a goal (Achieves-link sources → Consolidated state, importance dampened)

octobrain memory consolidate --summary "Final summary"

Sleep consolidation: auto-cluster recent similar memories

octobrain memory sleep-consolidate --threshold 0.85 --min-size 3

Memory Relationships

Connect related memories for context-rich retrieval:

# Create a relationship between memories

octobrain memory relate \ --relationship-type "depends_on" \ --description "Source requires target to function"

View relationships for a memory

octobrain memory relationships

Find related memories through relationships

octobrain memory related

Auto-link similar memories (Zettelkasten-style)

octobrain memory auto-link

Explore memory graph

octobrain memory graph --depth 2

Knowledge Base

Index and search web content, docs, and files:

# Index a URL

octobrain knowledge index https://docs.rs/tokio/latest/tokio/

Index a local file (.txt, .md, .pdf, .docx, .html)

octobrain knowledge index ./docs/architecture.md

Search knowledge base

octobrain knowledge search "how to handle async tasks"

Search within a specific source (auto-indexes if outdated)

octobrain knowledge search "spawn blocking" --source https://docs.rs/tokio/

Read full content of a URL or local file

octobrain knowledge read https://docs.rs/tokio/latest/tokio/

Search indexed content by regex pattern

octobrain knowledge match "spawn_blocking|block_in_place"

Store raw text content

octobrain knowledge store "meeting-notes" --content "Discussion points..."

List indexed sources

octobrain knowledge list --limit 20

Show statistics

octobrain knowledge stats

Delete a source

octobrain knowledge delete https://example.com/docs

Delete stored content by key

octobrain knowledge delete-stored "meeting-notes"

Knowledge Boxes

Import and sync git-backed knowledge bundles scoped to your projects:

# Import a remote git repo as a knowledge box

octobrain box import https://github.com/org/docs-repo.git

Import at global scope (visible in every project)

octobrain box import https://github.com/org/docs-repo.git --global

Pull and re-index all subscribed boxes + local .box/ directories

(the MCP server syncs each session's repos on first contact; agents run `knowledge` `sync` after editing .box/)

octobrain box sync

List subscribed boxes

octobrain box list

Remove a box

octobrain box remove github.com/org/docs-repo

MCP Server

Run as an MCP server for integration with Claude Desktop and other AI tools:

# Start with stdio transport (for Claude Desktop)

octobrain mcp

Start with HTTP transport (for web-based tools)

octobrain mcp --bind 0.0.0.0:12345

Available MCP Tools:

Tool	Description
`memorize`	Store memories with metadata; optional `related_to` for inline relationships
`remember`	Semantic search with filters; returns 1-hop graph neighbors
`forget`	Delete memories (requires confirmation)
`knowledge`	Unified tool: `search`, `store`, `delete`, `read`, `match`, `sync` via `command` field; `sync` re-indexes knowledge boxes after `.box/` edits

See MCP Integration for Claude Desktop setup.

Features

Semantic Search — Find memories by meaning using vector embeddings, not exact keyword matches
Hybrid Search — Combines BM25 full-text search with vector similarity for better results
Reranking Support — Pluggable cross-encoder reranking stage (provider-dependent)
Auto-Linking — Automatically connects semantically similar memories (Zettelkasten-style)
Temporal Decay — Ebbinghaus forgetting curve for importance management
Knowledge Indexing — Ingest URLs, PDFs, docs for retrieval
Project Scoping — Isolate memories per Git project or share across projects
Role Filtering — Tag memories by role (developer, reviewer, etc.)
Query Expansion (HyDE-lite) — Pseudo-relevance feedback for +10-30% recall on long-tail queries
MCP Protocol — Full MCP 2026-07-28 compliance for AI tool integration

Benchmarks

Retrieval quality of octobrain's knowledge system on standard BEIR datasets — nDCG@10, fully local, no LLM judge, using the harness's default embedder bge-small-en-v1.5 (384-dim, 33M params). Each corpus passage is indexed through octobrain's real retrieval path and scored against the official qrels (metrics reproduce pytrec_eval).

Dataset	octobrain vector	octobrain hybrid	BM25¹	bge-small-en-v1.5²
SciFact (5.2K docs, 300 q)	0.722	0.742	0.665	0.713
NFCorpus (3.6K docs, 323 q)	0.341	0.363	0.325	0.343

vector = dense-only retrieval; reproduces the embedder's published BEIR numbers (validates the harness).
hybrid = BM25 + vector fused with Reciprocal Rank Fusion (k=60) — octobrain's default. Adds +2 nDCG@10 over the bare embedding and beats classic BM25 on both datasets.

¹ Canonical BM25 from the BEIR paper (Anserini/Lucene, k1=0.9 b=0.4). ² From the bge-small-en-v1.5 model card (MTEB).

Scope: this measures the ranking layer (embedding + BM25 fusion + reranking). BEIR passages are pre-chunked, so octobrain's chunking strategy is not exercised here.

⚠️ Reranker status: The cross-encoder reranker is currently a no-op with fastembed models (byte-identical results to hybrid-only). Rerank is excluded from the numbers above pending investigation. See benches/README.md for details.

Reproduce (downloads the datasets, builds a release binary, runs fully offline):

cd benches && bash scripts/run_retrieval.sh

Configuration

Configuration is stored in ~/.local/share/octobrain/config.toml. The template written on first run defines every option with sensible defaults, but loading is strict — a field you delete by hand causes startup to fail rather than falling back to a default.

Override the config location with OCTOBRAIN_CONFIG_PATH=/path/to/config.toml.

Key Settings

Section	Option	Default	Description
`[embedding]`	`model`	`fastembed:Qdrant/all-MiniLM-L6-v2-onnx`	Embedding model (provider:model format). Default is a local fastembed model — no API key, runs on CPU.
`[search]`	`similarity_threshold`	`0.3`	Minimum relevance (0.0-1.0)
`[search.hybrid]`	`enabled`	`true`	Enable BM25 + vector fusion

| [search.reranker] | enabled | `

只显示了开头部分；其余内容请在仓库中阅读

Octobrain

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“搜索与 AI 记忆”中的其他服务器比较i

常见问题

替代选择

Context7

Codebase Memory

Exa

作者的 README

Octobrain

Table of Contents

Why Octobrain?

Quick Start

Store your first memory

Search memories

Start MCP server for Claude Desktop integration

Installation

From crates.io (Recommended)

From Source

Binary location

Docker

Homebrew (macOS/Linux)

Feature Flags

Build with API-based embeddings only

Usage

Memory Management

Search memories (semantic search)

Multi-query search for broader coverage

Get a memory by ID

Get recent memories

Filter by type

Filter by tags

Find memories related to files

Update a memory

Delete a memory

Get memories relevant to the current Git commit

Show memory statistics

Clean up old/low-importance memories

Compact tables and refresh indices (run when searches slow down)

⚠️ Delete ALL memory data

Memory Consolidation

Sleep consolidation: auto-cluster recent similar memories

Memory Relationships

View relationships for a memory

Find related memories through relationships

Auto-link similar memories (Zettelkasten-style)

Explore memory graph

Knowledge Base

Index a local file (.txt, .md, .pdf, .docx, .html)

Search knowledge base

Search within a specific source (auto-indexes if outdated)

Read full content of a URL or local file

Search indexed content by regex pattern

Store raw text content

List indexed sources

Show statistics

Delete a source

Delete stored content by key

Knowledge Boxes

Import at global scope (visible in every project)

Pull and re-index all subscribed boxes + local .box/ directories

(the MCP server syncs each session's repos on first contact; agents run knowledge sync after editing .box/)

List subscribed boxes

Remove a box

MCP Server

Start with HTTP transport (for web-based tools)

Features

Benchmarks

(the MCP server syncs each session's repos on first contact; agents run `knowledge` `sync` after editing .box/)