开源MCP工具

Install

bun install -g @gmickel/gno

Installation

Install GNO

Requires Bun >= 1.0.0.

bun install -g @gmickel/gno

macOS: Vector search requires Homebrew SQLite:

brew install sqlite3

Verify everything works:

gno doctor

Windows: current validated target is windows-x64, with a packaged desktop beta zip now published on GitHub Releases. See docs/WINDOWS.md for support scope and validation notes.

Keep an index fresh continuously without opening the Web UI:

gno daemon            # foreground (Ctrl+C to stop)
gno daemon --detach   # background (macOS/Linux); use --status / --stop to manage

gno daemon runs the watch/sync/embed loop headless. --detach self-spawns a detached child and exits 0; gno daemon --status and gno daemon --stop give you lifecycle control without nohup, launchd, or systemd units.

See also: docs/DAEMON.md

Quick Start

gno init ~/notes --name notes    # Point at your docs
gno index                        # Build search index
gno daemon --detach              # Keep index fresh in the background (macOS/Linux)
gno query "auth best practices"  # Hybrid search
gno ask "summarize the API" --answer  # AI answer with citations

Manage the detached process with gno daemon --status and gno daemon --stop.

---

~/.config/gno/index.yml

models: activePreset: remote-gpu presets: - id: remote-gpu name: Remote GPU Server embed: "http://192.168.1.100:8081/v1/embeddings#qwen3-embedding-0.6b" rerank: "http://192.168.1.100:8082/v1/completions#reranker" expand: "http://192.168.1.100:8083/v1/chat/completions#gno-expand" gen: "http://192.168.1.100:8083/v1/chat/completions#qwen3-4b" ```

Works with llama-server, Ollama, LocalAI, vLLM, or any OpenAI-compatible server.

Configuration: Model Setup

Remote/BYOM guides:

• Bring Your Own Models
• Per-Collection Models

---

SDK

Embed GNO directly in another Bun or TypeScript app. No CLI subprocesses. No local server required.

Install:

bun add @gmickel/gno

Minimal client:

import { createDefaultConfig, createGnoClient } from "@gmickel/gno";

const config = createDefaultConfig();
config.collections = [
  {
    name: "notes",
    path: "/Users/me/notes",
    pattern: "**/*",
    include: [],
    exclude: [],
  },
];

const client = await createGnoClient({
  config,
  dbPath: "/tmp/gno-sdk.sqlite",
});

await client.index({ noEmbed: true });

const results = await client.query("JWT token flow", {
  noExpand: true,
  noRerank: true,
});

console.log(results.results[0]?.uri);
await client.close();

More SDK examples:

import { createGnoClient } from "@gmickel/gno";

const client = await createGnoClient({
  configPath: "/Users/me/.config/gno/index.yml",
});

// Fast exact search
const bm25 = await client.search("DEC-0054", {
  collection: "work-docs",
});

// Semantic code lookup
const semantic = await client.vsearch("retry failed jobs with backoff", {
  collection: "gno-code",
});

// Hybrid retrieval with explicit intent
const hybrid = await client.query("token refresh", {
  collection: "work-docs",
  intent: "JWT refresh token rotation in our auth stack",
  candidateLimit: 12,
});

// Fetch content directly
const doc = await client.get("gno://work-docs/auth/refresh.md");
const bundle = await client.multiGet(["gno-code/**/*.ts"], { maxBytes: 25000 });

// Indexing / embedding
await client.update({ collection: "work-docs" });
await client.embed({ collection: "gno-code" });

await client.close();

Core SDK surface:

• createGnoClient({ config | configPath, dbPath? })
• search, vsearch, query, ask
• get, multiGet, list, status
• update, embed, index
• close

Full guide: SDK docs

---

Common CLI Recipes

```bash

REST API

Programmatic access to all GNO features via HTTP.

```bash

Agent Integration

Give your local LLM agents a long-term memory. GNO integrates as a Claude Code skill or MCP server, allowing agents to search, read, and cite your local files.

Search in the way that fits the question

gno search "DEC-0054" # exact keyword / identifier gno vsearch "retry failed jobs with backoff" # natural-language semantic lookup gno query "JWT refresh token rotation" --explain # hybrid retrieval with score traces