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

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.

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

# 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

cargo install octobrain

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

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

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

Store and retrieve insights, decisions, and context:

# 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 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>

Connect related memories for context-rich retrieval:

# 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

Index and search web content, docs, and files:

# Index a URL
octobrain knowledge index https://docs.rs/tokio/latest/tokio/

# 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/

# 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

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.

• 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 — Optional cross-encoder reranking for 20-35% accuracy improvement
• 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.)
• MCP Protocol — Full MCP 2025-11-25 compliance for AI tool integration

Configuration is stored in ~/.local/share/octobrain/config.toml. All options have sensible defaults.

[embedding]
# Local embeddings (no API key needed)
model = "voyage:voyage-3.5-lite"  # Fast, accurate
model = "openai:text-embedding-3-small"  # OpenAI
model = "google:text-embedding-004"  # Google
model = "jina:jina-embeddings-v2"  # Jina

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

Organize memories by category for better filtering:

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.

For web-based integrations:

octobrain mcp --bind 0.0.0.0:12345

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

Data is stored in platform-specific directories:

Project-specific memories are isolated by Git remote URL hash.

Contributions are welcome! Please:

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

# Clone and build
git clone https://github.com/muvon/octobrain.git
cd octobrain
cargo build --no-default-features

# Run tests
cargo test --no-default-features

# Run clippy
cargo clippy --no-default-features

Apache-2.0 — see LICENSE for details.

Developed by Muvon Un Limited.