SeaStar
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@SeaStarSearch my vault and tell me what I decided about the vineyard irrigation project."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
⭐ SeaStar
A private, local-first AI agent that lives in your Obsidian vault.
Goose's power. One-click's ease. Nothing leaves your house.
A sea star has no brain and clings to its rock. SeaStar is the agent that clings to your notes — and never leaves your rock.
The problem
Right now you get to pick two of three:
Powerful local agents (Goose, Aider, custom stacks) — but configuring them is a weekend of YAML and silent failures.
One-click agents (the frontier products) — but they're cloud, proprietary, and they read your notes.
Your Obsidian vault as the brain — but wiring Obsidian + a local model + an agent loop together is left as an exercise for the reader.
SeaStar is the missing third option: the zero-config, fully-local agent that treats your Obsidian vault as first-class memory, runs on whatever local model you already have, and plugs into whatever MCP host you already use.
Related MCP server: obsidian-local-mcp
What it is
SeaStar is an MCP server that is also an MCP client — a slightly unusual beast, on purpose:
As an MCP server, it exposes one simple tool (
ask_seastar) to any host — Claude Desktop, Cursor, TRAE, whatever. That tool runs a full local agent loop.As an MCP client, it consumes other MCP servers — including the existing Obsidian ones — as its own tools.
The reasoning happens on your machine, against your model. The host is just a window.
Why it stays sovereign
SeaStar talks to any OpenAI-compatible local inference endpoint. That means it works, unchanged, with:
Runtime | Default endpoint |
Ollama |
|
LM Studio |
|
llama.cpp ( |
|
MLX ( |
|
vLLM |
|
Unsloth Studio | its OpenAI-compatible port |
Runtime-agnostic by contract. You never touch this layer again.
Install
# Zero-install, if you have uv (recommended):
uvx seastar-mcp serve
# Or install it:
pip install seastar-mcpQuickstart
# 1. Point SeaStar at your Obsidian vault
seastar init --vault "~/Documents/MyVault"
# 2. Check that it can see a local model
seastar doctor
# 3. Run it as an MCP server (stdio)
seastar serveThen add it to your MCP host of choice:
{
"mcpServers": {
"seastar": {
"command": "uvx",
"args": ["seastar-mcp", "serve"]
}
}
}Ask your host: "Ask SeaStar what I decided about the vineyard irrigation project." SeaStar searches your vault, reasons locally, and answers with citations to your own notes.
Configuration
SeaStar is zero-config by default: it auto-detects common local runtimes and finds your Obsidian
vault from Obsidian's own config. Override anything in ~/.seastar/config.toml:
# ~/.seastar/config.toml
[vault]
path = "~/Documents/MyVault"
[inference]
base_url = "http://localhost:11434/v1" # any OpenAI-compatible endpoint
model = "qwen2.5:7b-instruct"
api_key = "not-needed-locally"
[mcp_servers.obsidian]
command = "uvx"
args = ["mcp-obsidian", "~/Documents/MyVault"]How it works
┌──────────────────┐ MCP (stdio) ┌─────────────────────────────┐
│ Your MCP host │ ───────────────────────▶ │ SEASTAR │
│ (Claude/Cursor) │ ask_seastar(question) │ ┌───────────────────────┐ │
└──────────────────┘ ◀─────────────────────── │ │ agent loop │ │
│ │ plan → tool → answer │ │
│ └──────────┬────────────┘ │
│ │ │
┌────────────────────────────────────────┼─────────────┼───────────────┘
│ │ │
▼ ▼ ▼
┌────────────────┐ ┌──────────────────┐ ┌──────────────────┐
│ Local model │ │ Obsidian vault │ │ other MCP │
│ (any OpenAI- │ │ (your notes, │ │ servers you │
│ compatible) │ │ as memory) │ │ configure │
└────────────────┘ └──────────────────┘ └──────────────────┘
▲
│ everything above this line runs on YOUR machine
─────┴───────────────────────── no cloud. no telemetry. ──────────────────────Project layout
src/seastar/
cli.py # `seastar init | doctor | serve | ask`
config.py # config + runtime/vault autodetection
inference.py # runtime-agnostic OpenAI-compatible adapter
vault.py # Obsidian vault index, search, read/write, citation
tools.py # built-in tools (vault search/read/write/list)
agent.py # the agent loop (plan → tool call → answer)
mcp_server.py # exposes the agent over MCP
mcp_client.py # consumes other MCP servers as toolsRoadmap
Runtime-agnostic inference adapter
Vault search / read / write with citations
Agent loop + MCP server + MCP client
Semantic (embedding) recall over the vault
.mcpbone-click Desktop Extension"Which local models actually work with MCP" conformance table
Contributing
Issues and PRs welcome — especially conformance reports ("SeaStar worked with model X on runtime Y"). This project is designed to be maintained.
License
MIT © Roqett / Up Periscope
Built by a winemaker who bought an M4 Pro, got curious, and started a small software factory called Up Periscope. Learned in public. No drugs, no alcohol, no guns — just Python.
This server cannot be deployed
Maintenance
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