Local LLM Hub for Obsidian

Your company's security policy blocks cloud APIs. But you refuse to give up AI-powered note automation.

Local LLM Hub brings the full power of Gemini Helper's workflow automation, RAG, MCP integration, and agent skills to a completely local environment. Ollama, LM Studio, vLLM, or AnythingLLM — your data never leaves your machine.

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Why Local?

Every byte stays on your machine. No API keys sent to the cloud. No vault contents uploaded anywhere. This isn't a privacy "option" — it's the architecture.

What	Where it stays
Chat history	Markdown files in your vault
RAG index	Local embeddings in workspace folder
LLM requests	localhost only (Ollama / LM Studio / vLLM / AnythingLLM)
MCP servers	Local child processes via stdio
Encrypted files	Encrypted/decrypted locally
Edit history	In-memory (cleared on restart)

If you use Gemini Helper at home but need something for work — this is it. Same workflow engine, same UX, zero cloud dependency.

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Workflow Automation — The Core Feature

Describe what you want in plain language. The AI builds the workflow. No YAML knowledge required.

Create Workflows & Skills with AI

1. Open the Workflow tab → select + New (AI)
2. Describe: "Convert the current page into an infographic and save it"
3. Check "Create as agent skill" if you want to create an agent skill instead of a standalone workflow
4. Click Generate — done

Don't have a powerful local model? Click Copy Prompt, paste into Claude/GPT/Gemini, paste the response back, and click Apply.

Modify with AI

Load any workflow, click AI Modify, describe the change. Reference execution history to debug failures.

Visual Node Editor

23 node types across 12 categories:

Category	Nodes
Variables	variable, set
Control	if, while
LLM	command
Data	http, json
Notes	note, note-read, note-search, note-list, folder-list, open
Files	file-explorer, file-save
Prompts	prompt-file, prompt-selection, dialog
Composition	workflow (sub-workflows)
RAG	rag-sync
Script	script (sandboxed JavaScript)
External	obsidian-command
Utility	sleep

Event Triggers & Hotkeys

• Event triggers — auto-run workflows on file create / modify / delete / rename / open
• Hotkey support — assign keyboard shortcuts to any named workflow
• Execution history — review past runs with step-by-step details

See WORKFLOW_NODES.md for the complete node reference.

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AI Chat

Streaming chat with your local LLM. Thinking display, file attachments, @ mentions for vault notes, multiple sessions.

Vault Tools (Function Calling)

Models with function calling support (Qwen, Llama 3.1+, Mistral) can directly interact with your vault:

read_note · create_note · update_note · rename_note · create_folder · search_notes · list_notes · list_folders · get_active_note · propose_edit · execute_javascript

Three modes — All, No Search, Off — selectable from the input area.

MCP Servers

Connect local MCP servers to extend the AI with external tools. MCP tools are merged with vault tools and routed via function calling — all running as local child processes.

RAG (Local Embeddings)

Index your vault with a local embedding model (e.g. nomic-embed-text). Relevant notes are automatically included as context. Everything computed and stored locally.

Agent Skills

Inject reusable instructions into the system prompt via SKILL.md files. Activate per conversation. Skills can also expose workflows that the AI can invoke as tools during chat.

Create skills the same way as workflows — select + New (AI), check "Create as agent skill", and describe what you want. The AI generates both the SKILL.md instructions and the workflow.

See SKILLS.md for details.

Slash Commands & Compact History

• Custom prompt templates triggered by /
• /compact to compress long conversations while preserving context

File Encryption

Password-protect sensitive notes. Encrypted files are invisible to AI chat tools but accessible to workflows with password prompt — ideal for storing API keys or credentials.

Edit History

Automatic tracking of AI-made changes with diff view and one-click restore.

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Setup

Requirements

• Ollama, LM Studio, vLLM, or AnythingLLM
• A chat model (e.g. ollama pull qwen3.5:4b)
• For RAG: an embedding model (e.g. ollama pull nomic-embed-text)

Quick Start

1. Install and start your LLM server
2. Open plugin settings → select framework (Ollama / LM Studio / vLLM / AnythingLLM)
3. Set the server URL (defaults pre-filled)
4. Fetch and select your chat model
5. Click Verify connection

RAG Setup

1. Enable RAG in settings
2. Fetch and select the embedding model
3. Configure target folders (optional — defaults to entire vault)
4. Click Sync to build the index

MCP Server Setup

1. Settings → MCP servers → Add server
2. Configure: name, command (e.g. npx), arguments, optional env vars
3. Toggle on — connects automatically via stdio

Workspace Settings

Supported Frameworks

Framework	Chat Endpoint	Streaming	Thinking	Function Calling
Ollama	/api/chat (native)	Real-time	message.thinking field	tools parameter
LM Studio (OpenAI compatible)	/v1/chat/completions	SSE	<think> tags	tools parameter
vLLM	/v1/chat/completions	SSE	<think> tags	tools parameter
AnythingLLM	/v1/openai/chat/completions	SSE	<think> tags	tools parameter

Using Cloud LLMs (OpenAI, Gemini, etc.)

The "LM Studio (OpenAI compatible)" framework works with any OpenAI-compatible API endpoint, including cloud services:

Service	Base URL	API Key
OpenAI	https://api.openai.com	Your OpenAI API key
Google Gemini	https://generativelanguage.googleapis.com/v1beta/openai	Your Gemini API key

RAG with cloud LLMs: Cloud LLMs cannot use local embedding models directly. To use RAG, configure the Embedding server URL in RAG settings to point to a local Ollama instance (e.g. http://localhost:11434) and select an embedding model like nomic-embed-text.

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Installation

BRAT (Recommended)

1. Install BRAT plugin
2. Open BRAT settings → "Add Beta plugin"
3. Enter: https://github.com/takeshy/obsidian-local-llm-hub
4. Enable the plugin in Community plugins settings

Manual

1. Download main.js, manifest.json, styles.css from releases
2. Create local-llm-hub folder in .obsidian/plugins/
3. Copy files and enable in Obsidian settings

From Source

git clone https://github.com/takeshy/obsidian-local-llm-hub
cd obsidian-local-llm-hub
npm install
npm run build

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Gemini Helper との関係 / Relationship to Gemini Helper

This plugin is the local-only sibling of obsidian-gemini-helper. Same workflow engine, same UX patterns, but designed for environments where cloud APIs are not an option.

	Gemini Helper	Local LLM Hub
LLM Backend	Google Gemini API / CLI	Ollama / LM Studio / vLLM / AnythingLLM / OpenAI-compatible APIs
Data destination	Google servers	localhost only
Workflow engine	✅	✅ (same architecture)
RAG	Google File Search	Local embeddings
MCP	✅	✅ (stdio only)
Agent Skills	✅	✅
Image generation	✅ (Gemini)	—
Web search	✅ (Google)	—
Cost	Free / Pay-per-use	Free forever (your hardware)

Choose Gemini Helper when you want cutting-edge cloud models. Choose Local LLM Hub when privacy is non-negotiable.