Octobrain

Octobrain

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

[Crates.io] [License] [Rust] [CI] [Coverage]

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.iocargo install octobrain
# Store your first memoryoctobrain memory memorize --title "API Design Pattern" \  --content "Use REST for CRUD, GraphQL for complex queries" \  --memory-type architecture --tags "api,design"
# Search memoriesoctobrain memory remember "how should I design APIs"
# Start MCP server for Claude Desktop integrationoctobrain mcp

Installation

From crates.io (Recommended)

cargo install octobrain

From Source

# Clone and buildgit clone https://github.com/muvon/octobrain.gitcd octobraincargo 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:

# Build with local embeddings (default, no API keys needed)cargo build --release
# Build with API-based embeddings onlycargo 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 <string> — Override project scope (default: auto-detected from Git remote)
• --role <string> — Filter by role (e.g. "developer", "reviewer")

# Store a memoryoctobrain 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 coverageoctobrain memory remember "authentication" "security" "jwt"
# Get a memory by IDoctobrain memory get <id>
# Get recent memoriesoctobrain memory recent --limit 20
# Filter by typeoctobrain memory by-type architecture --limit 10
# Filter by tagsoctobrain memory by-tags "api,security"
# Find memories related to filesoctobrain memory for-files "src/main.rs,src/lib.rs"
# Update a memoryoctobrain memory update <id> --title "New Title" --add-tags "new-tag"
# Delete a memoryoctobrain memory forget --memory-id <id>
# Get memories relevant to the current Git commitoctobrain memory current-commit
# Show memory statisticsoctobrain memory stats
# Clean up old/low-importance memoriesoctobrain memory cleanup
# Compact tables and refresh indices (run when searches slow down)octobrain memory maintenance
# ⚠️ Delete ALL memory dataoctobrain 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 <goal-id> --summary "Final summary"
# Sleep consolidation: auto-cluster recent similar memoriesoctobrain memory sleep-consolidate --threshold 0.85 --min-size 3

Memory Relationships

Connect related memories for context-rich retrieval:

# Create a relationship between memoriesoctobrain memory relate <source-id> <target-id> \  --relationship-type "depends_on" \  --description "Source requires target to function"
# View relationships for a memoryoctobrain memory relationships <memory-id>
# Find related memories through relationshipsoctobrain memory related <memory-id>
# Auto-link similar memories (Zettelkasten-style)octobrain memory auto-link <memory-id>
# Explore memory graphoctobrain memory graph <memory-id> --depth 2

Knowledge Base

Index and search web content, docs, and files:

# Index a URLoctobrain 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 baseoctobrain 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 fileoctobrain knowledge read https://docs.rs/tokio/latest/tokio/
# Search indexed content by regex patternoctobrain knowledge match "spawn_blocking|block_in_place"
# Store raw text contentoctobrain knowledge store "meeting-notes" --content "Discussion points..."
# List indexed sourcesoctobrain knowledge list --limit 20
# Show statisticsoctobrain knowledge stats
# Delete a sourceoctobrain knowledge delete https://example.com/docs
# Delete stored content by keyoctobrain 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 boxoctobrain 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 boxesoctobrain box list
# Remove a boxoctobrain 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:

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).

• 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

Embedding Providers

[embedding]# Local models (no API key, runs on CPU, model auto-downloaded on first use)model = "fastembed:Qdrant/all-MiniLM-L6-v2-onnx"                       # Default: 384-dim quantized, ~125MB residentmodel = "fastembed:nomic-ai/nomic-embed-text-v1.5"                      # 768-dim, 8192-ctx, ~550MB weights, highest qualitymodel = "fastembed:BAAI/bge-small-en-v1.5"                              # 384-dim, ~62 MTEB, fast + good qualitymodel = "fastembed:sentence-transformers/all-MiniLM-L6-v2-quantized"  # Smallest (~22MB), fastestmodel = "fastembed:BAAI/bge-base-en-v1.5"                              # Larger (~440MB), higher qualitymodel = "fastembed:intfloat/multilingual-e5-small"                     # Multilingual
# Cloud providers (require API keys, generally higher quality)model = "voyage:voyage-3.5-lite"          # VOYAGE_API_KEYmodel = "openai:text-embedding-3-small"   # OPENAI_API_KEYmodel = "google:text-embedding-004"       # GOOGLE_API_KEYmodel = "jina:jina-embeddings-v3"         # JINA_API_KEY

Full Configuration

See config-templates/default.toml for all available options with documentation.

Memory Types

Organize memories by category for better filtering:

MCP Integration

Claude Desktop Setup

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{  "mcpServers": {    "octobrain": {      "command": "/path/to/octobrain",      "args": ["mcp"]    }  }}

Restart Claude Desktop. Octobrain tools will be available in your conversations.

HTTP Transport

For web-based integrations:

octobrain mcp --bind 0.0.0.0:12345

The server exposes endpoints at /mcp for MCP protocol communication.

Storage Locations

Data is stored in platform-specific directories:

Project-specific memories are isolated by normalized Git remote URL (e.g., github.com/org/repo).

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Run cargo fmt --all to format code
4. Run cargo clippy and fix all warnings
5. Run cargo test --no-default-features
6. Submit a pull request

Development Setup

# Clone and buildgit clone https://github.com/muvon/octobrain.gitcd octobraincargo build --no-default-features
# Run testscargo test --no-default-features
# Run clippycargo clippy --no-default-features

License

Apache-2.0 — see LICENSE for details.

Credits

Developed by Muvon Un Limited.