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Persistent file-based memory for AI agents: inspectable, versionable, fully local.
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Persistent file-based memory for AI agents: inspectable, versionable, fully local.
Security Report
Valid MCP server (2 strong, 3 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
4 files analyzed Β· 1 issue found
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How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-octonove-crbro-memory": {
"args": [
"-y",
"crbro-memory"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
π§ CRBRO β 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 β cortex, synapses, hippocampus β to store, connect, and retrieve knowledge automatically.

Free and open source (MIT). All 16 tools included β no license, no account, no tiers.
β If CRBRO gives your AI a memory worth keeping, a star on GitHub is the best way to support it.
Features
- 𧬠Biological Architecture β Knowledge organized as neurons (cortex), connections (synapses), and session memory (hippocampus)
- π Fact-Level Search β Powered by Orama. Every fact is indexed on its own, so a topic with hundreds of facts stays as findable as one with three, and each result comes back with the exact fact that matched and the date it was recorded
- π₯ Heat Scores β Automatic relevance tracking based on frequency, recency, and connectivity
- βοΈ Correctable β Knowledge can be superseded or retracted, not just piled up. A memory that only appends keeps serving yesterday's answer with today's confidence
- πΊοΈ Global Map β Cluster detection and cross-domain bridge identification
- βοΈ Knowledge Miner β Optionally scans your local
.md/.txtnotes and feeds them into the brain - π Fully Local β Runs on Node.js alone: no Python, no Docker, no databases, no external services. Your memory never leaves your machine
- πΎ File-Based β All data stored as readable JSON files in
~/.crbro/β inspectable, diffable, and versionable with git - π MCP Native β 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 β 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 β 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 16 memory tools. Start any session with crbro_boot.
Tools
| Tool | Description |
|---|---|
crbro_boot | Boot the brain at session start β loads hot topics and context |
crbro_status | Brain status β 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 every stored fact, not just topic names β returns the fact that matched |
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 β clusters and cross-domain bridges |
crbro_revise | Mark facts as superseded or retracted when they stop being true |
crbro_maintenance | Brain maintenance β heat, pruning, integrity, index rebuild |
crbro_consolidate | End-of-session consolidation |
Architecture
~/.crbro/
βββ manifest.json β Brain metadata
βββ cortex/ β One JSON per neuron (topic)
β βββ project_octochat.json
β βββ tech_firebase.json
βββ synapses/ β One JSON per connection
β βββ syn_octochat__firebase.json
βββ hippocampus/ β One JSON per session
β βββ session_2026-05-06.json
βββ prefrontal/ β Working memory
β βββ active_context.json
β βββ hot_topics.json
β βββ global_map.json
βββ archives/ β Cold neurons (opt-in; nothing is archived unless you ask)
βββ .search/ β Orama search index
βββ chunks.index.json β one document per fact
Heat Score Algorithm
Each neuron has a heat score (0.0 - 1.0) calculated from:
- Frequency (35%) β How often the neuron is accessed
- Recency (40%) β When it was last accessed (today = 1.0, >3 months = 0.05)
- Connectivity (25%) β 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 β 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 β 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 reindex # Rebuild the search index
npx crbro-memory eval # Measure retrieval quality against your own query set
npx crbro-memory --help # Help
Measuring retrieval
eval is there so you can tell a fix from a feeling. Write
~/.crbro/.eval/queries.json as a list of questions you would actually ask,
each naming the neuron that should answer it:
[
{ "query": "how we deploy the api",
"expect_neuron": "project_octochat",
"expect_contains": "Cloud Run" }
]
Then npx crbro-memory eval reports how often the right neuron comes back
first, how often it makes the top three, and MRR β plus every miss, so you can
see what it got wrong instead of guessing.
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