Octobrain

Persistent memory for AI assistants with semantic search and knowledge graph relationships.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet — we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON ▾

No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself — its README, its docs, or a verified owner. We haven’t found those for Octobrain, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.

Documentation Overview

Octobrain

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

MCP Registry: mcp-name: io.github.Muvon/octobrain

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

bash

# 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)

bash

cargo install octobrain

From Source

bash

# Clone and build
git clone https://github.com/muvon/octobrain.git
cd octobrain
cargo build --release

# Binary location
./target/release/octobrain --help

Docker

Terminal

docker pull ghcr.io/muvon/octobrain:latest

Homebrew (macOS/Linux)

bash

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

bash

# 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 <string> — Override project scope (default: auto-detected from Git remote)
--role <string> — Filter by role (e.g. "developer", "reviewer")

bash

# 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 <id>

# 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 <id> --title "New Title" --add-tags "new-tag"

# Delete a memory
octobrain memory forget --memory-id <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:

bash

# Consolidate a goal (Achieves-link sources → Consolidated state, importance dampened)
octobrain memory consolidate <goal-id> --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:

bash

# Create a relationship between memories
octobrain memory relate <source-id> <target-id> \
  --relationship-type "depends_on" \
  --description "Source requires target to function"

# View relationships for a memory
octobrain memory relationships <memory-id>

# Find related memories through relationships
octobrain memory related <memory-id>

# Auto-link similar memories (Zettelkasten-style)
octobrain memory auto-link <memory-id>

# Explore memory graph
octobrain memory graph <memory-id> --depth 2

Knowledge Base

Index and search web content, docs, and files:

server.ts

# 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:

server.ts

# 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
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:

bash

# 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:

Tool	Description
`memorize`	Store memories with metadata; optional `related_to` for inline relationships
`remember`	Semantic search with filters; returns 1-hop graph neighbors
`forget`	Delete memories (requires confirmation)
`knowledge`	Unified tool: `search`, `store`, `delete`, `read`, `match` via `command` field
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).

Dataset	octobrain vector	octobrain hybrid	BM25¹	bge-small-en-v1.5²
SciFact (5.2K docs, 300 q)	0.722	0.742	0.665	0.713
NFCorpus (3.6K docs, 323 q)	0.341	0.363	0.325	0.343

Read the full README →View source on GitHub →

Related MCP Servers

View all in Knowledge & Memory View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

9d ago

Most recent push to the default branch.

Directory activity

1 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Octobrain

We don't have a confirmed install command for Octobrain yet, so we don't publish a generated one — a guessed package name would point at the wrong package or none at all. Follow the project's own README or setup instructions (https://github.com/muvon/octobrain) for the current steps.

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Technical Specs & Signals

CategoryKnowledge & Memory

9/10 checks healthy over the last 46d

36Quality signal: Fair · 36/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools11/30

Adoption & activity6/15

Community engagement0/10

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More in 🧠 Knowledge & Memory →Best MCP servers for Memory & Knowledge →Alternatives to Octobrain →Install in Claude Desktop Install in Cursor Install in VS Code Setup guides for all 13 MCP clients

Octobrain

Persistent memory for AI assistants with semantic search and knowledge graph relationships.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Manual Client & Custom JSON ConfigExpand JSON ▾

Documentation Overview

Octobrain

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

MCP Registry: mcp-name: io.github.Muvon/octobrain

Why Octobrain?
Quick Start
Installation
Usage
Features
Benchmarks
Configuration
Memory Types
MCP Integration
Storage Locations
Contributing
License

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

bash

# 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)

bash

cargo install octobrain

From Source

bash

# Clone and build
git clone https://github.com/muvon/octobrain.git
cd octobrain
cargo build --release

# Binary location
./target/release/octobrain --help

Docker

Terminal

docker pull ghcr.io/muvon/octobrain:latest

Homebrew (macOS/Linux)

bash

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

bash

# 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 <string> — Override project scope (default: auto-detected from Git remote)
--role <string> — Filter by role (e.g. "developer", "reviewer")

bash

# 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 <id>

# 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 <id> --title "New Title" --add-tags "new-tag"

# Delete a memory
octobrain memory forget --memory-id <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:

bash

# Consolidate a goal (Achieves-link sources → Consolidated state, importance dampened)
octobrain memory consolidate <goal-id> --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:

bash

# Create a relationship between memories
octobrain memory relate <source-id> <target-id> \
  --relationship-type "depends_on" \
  --description "Source requires target to function"

# View relationships for a memory
octobrain memory relationships <memory-id>

# Find related memories through relationships
octobrain memory related <memory-id>

# Auto-link similar memories (Zettelkasten-style)
octobrain memory auto-link <memory-id>

# Explore memory graph
octobrain memory graph <memory-id> --depth 2

Knowledge Base

Index and search web content, docs, and files:

server.ts

# 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:

server.ts

# 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
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:

bash

# 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:

Tool	Description
`memorize`	Store memories with metadata; optional `related_to` for inline relationships
`remember`	Semantic search with filters; returns 1-hop graph neighbors
`forget`	Delete memories (requires confirmation)
`knowledge`	Unified tool: `search`, `store`, `delete`, `read`, `match` via `command` field
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

Dataset	octobrain vector	octobrain hybrid	BM25¹	bge-small-en-v1.5²
SciFact (5.2K docs, 300 q)	0.722	0.742	0.665	0.713
NFCorpus (3.6K docs, 323 q)	0.341	0.363	0.325	0.343

Read the full README →View source on GitHub →

Related MCP Servers

View all in Knowledge & Memory View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

9d ago

Most recent push to the default branch.

Directory activity

1 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Octobrain

AllMCPs Directory Badge

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Technical Specs & Signals

CategoryKnowledge & Memory

9/10 checks healthy over the last 46d

36Quality signal: Fair · 36/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools11/30

Adoption & activity6/15

Community engagement0/10

★ Featured

AllMCPs Server

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Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

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Octobrain

Quick Install

Documentation Overview

Octobrain

Table of Contents

Why Octobrain?

Quick Start

Installation

From crates.io (Recommended)

From Source

Docker

Homebrew (macOS/Linux)

Feature Flags

Usage

Memory Management

Memory Consolidation

Memory Relationships

Knowledge Base

Knowledge Boxes

MCP Server

Features

Benchmarks

Related MCP Servers

Adoption & maintenance

Reviews

Frequently Asked Questions about Octobrain

AllMCPs Directory Badge

Technical Specs & Signals

AllMCPs Server

Own this project?

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Octobrain

Quick Install

Documentation Overview

Octobrain

Table of Contents

Why Octobrain?

Quick Start

Installation

From crates.io (Recommended)

From Source

Docker

Homebrew (macOS/Linux)

Feature Flags

Usage

Memory Management

Memory Consolidation

Memory Relationships

Knowledge Base

Knowledge Boxes

MCP Server

Features

Benchmarks

Related MCP Servers

Adoption & maintenance

Reviews

Frequently Asked Questions about Octobrain

AllMCPs Directory Badge

Technical Specs & Signals

AllMCPs Server

Own this project?

Share & Embed

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