crbro-memory
A local MCP server that gives AI assistants persistent long-term memory using a biological neural architecture with cortex, synapses, and hippocampus.
README
🧠 CRBRO — Persistent Neural Memory for AI
CRBRO is a local MCP (Model Context Protocol) server that gives your AI assistant persistent long-term memory across sessions. It uses a biological neural architecture — cortex, synapses, hippocampus — to store, connect, and retrieve knowledge automatically.

Free and open source (MIT). All 15 tools included — no license, no account, no tiers.
⭐ If CRBRO gives your AI a memory worth keeping, a star on GitHub is the best way to support it.
Features
- 🧬 Biological Architecture — Knowledge organized as neurons (cortex), connections (synapses), and session memory (hippocampus)
- 🔍 Hybrid Search — Powered by Orama for fast BM25 + fuzzy text search
- 🔥 Heat Scores — Automatic relevance tracking based on frequency, recency, and connectivity
- 🗺️ Global Map — Cluster detection and cross-domain bridge identification
- ⛏️ Knowledge Miner — Optionally scans your local
.md/.txtnotes and feeds them into the brain - 🔒 Fully Local — Runs on Node.js alone: no Python, no Docker, no databases, no external services. Your memory never leaves your machine
- 💾 File-Based — All data stored as readable JSON files in
~/.crbro/— inspectable, diffable, and versionable with git - 🔌 MCP Native — Works with Claude Desktop, Claude Code, Cursor, Windsurf, and any MCP-compatible client
Quick Start
1. Initialize
npx crbro-memory init
2. Add to your MCP config
Register CRBRO at the user level, not per-project. Your brain lives in
~/.crbro/and is shared across every folder — but if you register the server inside a single project, other folders won't have the tools and it will look like the memory is gone. User-level registration makes it available everywhere, which is the whole point.
Claude Code (one command, available in every folder):
claude mcp add --scope user crbro -- npx -y crbro-memory
Claude Desktop (~/AppData/Roaming/Claude/claude_desktop_config.json):
{
"mcpServers": {
"crbro": {
"command": "npx",
"args": ["-y", "crbro-memory"]
}
}
}
Cursor (~/.cursor/mcp.json — the one in your home folder, not a project's .cursor/):
{
"mcpServers": {
"crbro": {
"command": "npx",
"args": ["-y", "crbro-memory"]
}
}
}
3. Start using it
Your AI will now have access to 15 memory tools. Start any session with crbro_boot.
Tools
| Tool | Description |
|---|---|
crbro_boot |
Boot the brain at session start — loads hot topics and context |
crbro_status |
Brain status — neurons, synapses, sessions count |
crbro_learn |
Store a fact, decision, pattern, or preference |
crbro_neuron |
Read a specific neuron (topic) with all its knowledge |
crbro_neurons |
List neurons with optional filters (domain, type, heat) |
crbro_recall |
Search the brain using hybrid text search |
crbro_connect |
Create or strengthen a connection between neurons |
crbro_connections |
Get all connections for a neuron |
crbro_session_log |
Log a session summary |
crbro_sessions |
List recent sessions |
crbro_context |
Read/update active working context |
crbro_hot_topics |
Get the most active topics by heat score |
crbro_global_map |
View the neural network — clusters and cross-domain bridges |
crbro_maintenance |
Full brain maintenance — archive, prune, rebuild |
crbro_consolidate |
End-of-session consolidation |
Architecture
~/.crbro/
├── manifest.json ← Brain metadata
├── cortex/ ← One JSON per neuron (topic)
│ ├── project_octochat.json
│ └── tech_firebase.json
├── synapses/ ← One JSON per connection
│ └── syn_octochat__firebase.json
├── hippocampus/ ← One JSON per session
│ └── session_2026-05-06.json
├── prefrontal/ ← Working memory
│ ├── active_context.json
│ ├── hot_topics.json
│ └── global_map.json
├── archives/ ← Cold neurons
└── .search/ ← Orama search index
└── orama.index.json
Heat Score Algorithm
Each neuron has a heat score (0.0 - 1.0) calculated from:
- Frequency (35%) — How often the neuron is accessed
- Recency (40%) — When it was last accessed (today = 1.0, >3 months = 0.05)
- Connectivity (25%) — How many synapses connect to it
Knowledge Miner
The miner is an optional, fully local helper that scans a directory for .md and .txt files (notes, docs, journals) and extracts knowledge into the brain — so CRBRO can learn from what you already wrote, not just from conversations. It never touches the network and never leaves your machine.
npx crbro-memory mine [dir] # One-shot scan of a directory
npx crbro-memory setup-miner # Install a scheduled auto-scan (OS task scheduler)
npx crbro-memory miner-status # Check the auto-miner status
npx crbro-memory remove-miner # Remove the scheduled task
Naming note: "miner" here means knowledge mining — extracting facts from your own text files. Nothing to do with cryptocurrency.
CLI Commands
npx crbro-memory # Start MCP server (stdio)
npx crbro-memory init # Initialize brain + detect IDEs
npx crbro-memory status # Show brain status
npx crbro-memory --help # Help
License
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。