Octobrain
Persistent memory for AI assistants with semantic search and knowledge graph relationships.
- Transport
- Not stated
- Package
- —
- Registry id
- io.github.Muvon/octobrain
No install snippet on purpose. A working MCP config is a command, its arguments and an environment block — the last two are where API keys live, so this catalogue never stores them and cannot publish them. Follow the link above for the authors' own instructions.
Persistent memory for AI assistants — store insights, decisions, and knowledge that survives across conversations.
MCP Registry: mcp-name: io.github.Muvon/octobrain
Table of Contents
- 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_KEYfor Voyage AIOPENAI_API_KEYfor OpenAIGOOGLE_API_KEYfor GoogleJINA_API_KEYfor Jina
Usage
Memory Management
Store and retrieve insights, decisions, and context:
All memory subcommands accept global flags:
--scope <string>— Override project scope (default: auto-detected from Git remote)--role <string>— Filter by role (e.g. "developer", "reviewer")
From the project's README.