Power your agent
คำสั่งของผู้เผยแพร่เอง ไม่ใช่เอนด์พอยต์ของ PlugMCP
Host a copy with our keys and logs.
npx -y @muvon/[email protected]Claude Code ไม่มีลิงก์ติดตั้งสำหรับคำสั่งแบบโลคัล ให้คัดลอกคำสั่งด้านล่างแล้วรันในเทอร์มินัล
claude mcp add io.github.muvon-octobrain -- npx -y @muvon/octobrain@0.15.2รันในเทอร์มินัล
{ "mcpServers": { "io.github.muvon-octobrain": { "command": "npx", "args": [ "-y", "@muvon/[email protected]" ] } }}claude_desktop_config.json
URL แบบรีโมตหรือคำสั่งบนเครื่องของคุณ? การติดตั้งสองแบบต่างกันอย่างไร
รันบน PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
เปิดMCP Registry: mcp-name: io.github.Muvon/octobrain
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:
# 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
docker pull ghcr.io/muvon/octobrain:latest
brew install muvon/tap/octobrain
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 AIOPENAI_API_KEY for OpenAIGOOGLE_API_KEY for GoogleJINA_API_KEY for JinaStore 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 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
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 memories
octobrain memory sleep-consolidate --threshold 0.85 --min-size 3
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/
# 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"
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
octobrain box sync
# List subscribed boxes
octobrain box list
# Remove a box
octobrain box remove github.com/org/docs-repo
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:
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).
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.mdfor details.
Reproduce (downloads the datasets, builds a release binary, runs fully offline):
cd benches && bash scripts/run_retrieval.sh
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.
[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 resident
model = "fastembed:nomic-ai/nomic-embed-text-v1.5" # 768-dim, 8192-ctx, ~550MB weights, highest quality
model = "fastembed:BAAI/bge-small-en-v1.5" # 384-dim, ~62 MTEB, fast + good quality
model = "fastembed:sentence-transformers/all-MiniLM-L6-v2-quantized" # Smallest (~22MB), fastest
model = "fastembed:BAAI/bge-base-en-v1.5" # Larger (~440MB), higher quality
model = "fastembed:intfloat/multilingual-e5-small" # Multilingual
# Cloud providers (require API keys, generally higher quality)
model = "voyage:voyage-3.5-lite" # VOYAGE_API_KEY
model = "openai:text-embedding-3-small" # OPENAI_API_KEY
model = "google:text-embedding-004" # GOOGLE_API_KEY
model = "jina:jina-embeddings-v3" # JINA_API_KEY
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 normalized Git remote URL (e.g., github.com/org/repo).
Contributions are welcome! Please:
git checkout -b feature/amazing-feature)cargo fmt --all to format codecargo clippy and fix all warningscargo test --no-default-features# 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.
Persistent memory for AI assistants with semantic search and knowledge graph relationships.
{ "mcpServers": { "io.github.muvon-octobrain": { "command": "npx", "args": [ "-y", "@muvon/[email protected]" ] } }}เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
Octobrain ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-11-13 Persistent memory for AI assistants with semantic search and knowledge graph relationships.