Octobrain

Octobrain

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:

# 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:

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).

• 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

| [search.reranker] | enabled | `