Which integrations are available for this server?

Provides comprehensive tools for interacting with Logseq graphs, enabling AI agents to search for and manage pages and blocks, execute Datalog queries, track tasks and deadlines, and manage journal entries through the Logseq HTTP API.

How do I use Logseq AI?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Logseq AI List all my overdue tasks" 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.

Logseq AI

AI-powered interactions with Logseq through two interfaces:

MCP Server - Enables external AI tools (Windsurf, Claude Desktop) to interact with your Logseq graph
Lain - Embedded AI assistant plugin within the Logseq UI

"No matter where you go, everyone's connected." - Serial Experiments Lain

Prerequisites

Node.js 18+
Logseq desktop app with HTTP API server enabled (Settings → Features → HTTP APIs server)

Setup

# Install dependencies pnpm install # Build all packages pnpm build

Quick Start

Using the Core Library

import { LogseqClient, LogseqOperations } from "@logseq-ai/core"; // Create client and operations const client = new LogseqClient({ baseUrl: "http://localhost:12315", token: "your-api-token" // optional }); const ops = new LogseqOperations(client); // Search for pages const results = await ops.search("meeting notes"); // Get page content as text const content = await ops.getPageContent("My Notes"); // Create a new block const block = await ops.createBlock({ pageName: "My Notes", content: "New idea from AI!" }); // Create multiple blocks with hierarchy (more efficient for structured content) const result = await ops.createBlocks("My Notes", [ { content: "## Project Overview", children: [ { content: "Goal: Build a new feature" }, { content: "Timeline: Q1 2025" }, ] }, { content: "## Tasks", children: [ { content: "Design the API" }, { content: "Implement backend" }, { content: "Write tests" }, ] } ]); console.log(`Created ${result.created} blocks`); // Run a Datalog query to find TODOs const todos = await ops.query(` [:find (pull ?b [*]) :where [?b :block/marker "TODO"]] `);

Task Management

import { LogseqClient, LogseqOperations } from "@logseq-ai/core"; const client = new LogseqClient(); const ops = new LogseqOperations(client); // Get all active tasks const tasks = await ops.getTasks(); // Get tasks from a specific page const projectTasks = await ops.getTasks({ pageName: "Project A" }); // Create a new task with priority and deadline const task = await ops.createTask({ pageName: "Project A", content: "Review pull request", priority: "A", deadline: "2024-12-15" }); // Mark task as in progress await ops.markTask(task.uuid, "DOING"); // Complete the task await ops.markTask(task.uuid, "DONE"); // Set/change priority await ops.setTaskPriority(task.uuid, "B"); // Set scheduled date await ops.setTaskScheduled({ uuid: task.uuid, date: "2024-12-10" }); // Remove deadline await ops.setTaskDeadline({ uuid: task.uuid, date: null });

Task Analytics

// Get overdue tasks const overdue = await ops.getOverdueTasks(); console.log(`You have ${overdue.length} overdue tasks`); // Get tasks due in the next 7 days const dueSoon = await ops.getTasksDueSoon({ days: 7 }); // Get task statistics const stats = await ops.getTaskStats(); console.log(`Total: ${stats.total}, Overdue: ${stats.overdue}`); console.log(`By status:`, stats.byMarker); // Search tasks by keyword const reviewTasks = await ops.searchTasks({ query: "review", markers: ["TODO"] });

Journal Operations

// Get or create today's journal const today = await ops.getToday(); console.log(`Today's journal: ${today.page.name}`); // Quick capture to today's journal await ops.appendToToday("Meeting notes: discussed Q1 roadmap"); // Get recent journal entries const journals = await ops.getRecentJournals({ days: 7, includeContent: true }); journals.forEach(j => console.log(`${j.date}: ${j.content.substring(0, 50)}...`));

Page Links & Backlinks

// Find pages related to a topic const links = await ops.findRelatedPages("Project A"); console.log("Pages linking to Project A:", links.backlinks); console.log("Pages Project A links to:", links.forwardLinks); // Find blocks referencing a specific block const backlinks = await ops.getBlockBacklinks("block-uuid-123"); console.log(`Found ${backlinks.backlinks.length} references`);

Error Handling

import { LogseqOperations, LogseqApiError, LogseqNotFoundError, isLogseqError } from "@logseq-ai/core"; try { await ops.getPageContent("Nonexistent Page"); } catch (error) { if (error instanceof LogseqNotFoundError) { console.log(`Page not found: ${error.identifier}`); } else if (isLogseqError(error)) { console.log(`Logseq error: ${error.toDetailedString()}`); } }

Packages

@logseq-ai/core

Shared library for Logseq API interactions. Used by both the MCP server and plugin.

Key exports:

LogseqClient - Low-level HTTP client for Logseq API
LogseqOperations - High-level operations with error handling
Error classes: LogseqError, LogseqApiError, LogseqConnectionError, LogseqNotFoundError, LogseqValidationError
Type definitions: Page, Block, SearchResult, etc.

@logseq-ai/mcp-server

MCP server that exposes Logseq operations as tools for AI clients.

Features:

34 tools for comprehensive Logseq interaction
Input validation with clear error messages (powered by Zod)
Proper error handling for Logseq API errors

# Build pnpm --filter @logseq-ai/mcp-server build # Run LOGSEQ_API_TOKEN=your-token pnpm --filter @logseq-ai/mcp-server start # Test pnpm --filter @logseq-ai/mcp-server test

Configure in Windsurf/Claude Desktop

Add to your MCP configuration:

{ "mcpServers": { "logseq": { "command": "node", "args": ["/path/to/logseq-ai/packages/mcp-server/dist/index.js"], "env": { "LOGSEQ_API_URL": "http://localhost:12315", "LOGSEQ_API_TOKEN": "your-token" } } } }

Available Tools

Page & Block Operations

Tool	Description
`search_logseq`	Search for pages and blocks
`get_page`	Get page content as plain text
`get_pages`	Get multiple pages in a batch (more efficient)
`get_page_with_context`	Get page with backlinks and forward links
`list_pages`	List all pages in the graph
`create_page`	Create a new page (optionally with blocks)
`delete_page`	Delete a page
`create_block`	Create a single block
`create_blocks`	Create multiple blocks with hierarchy
`update_block`	Update a block's content
`delete_block`	Delete a block
`query_logseq`	Run Datalog queries
`update_page_properties`	Update properties on an existing page

Graph Discovery

Tool	Description
`get_current_graph`	Get current graph info
`get_graph_stats`	Get graph statistics (pages by type, orphans, etc.)
`find_missing_pages`	Find referenced pages that don't exist
`find_orphan_pages`	Find pages with no incoming links
`find_pages_by_properties`	Find pages by property values
`find_related_pages`	Find backlinks and forward links
`get_block_backlinks`	Find blocks referencing a block

Journal Operations

Tool	Description
`get_today`	Get today's journal page
`append_to_today`	Add content to today's journal
`get_recent_journals`	Get recent journal entries

Task Management

Tool	Description
`get_tasks`	Get TODO/DOING tasks
`create_task`	Create a new task
`mark_task`	Change task status
`mark_tasks`	Change multiple tasks' status (batch)
`search_tasks`	Search tasks by keyword
`get_overdue_tasks`	Get tasks past deadline
`get_tasks_due_soon`	Get tasks due within N days
`get_task_stats`	Get task statistics
`set_task_priority`	Set task priority (A/B/C)
`set_task_deadline`	Set task deadline
`set_task_scheduled`	Set task scheduled date

Example: Creating Structured Content

When creating pages with multiple sections, use create_blocks for efficiency:

User: Create a page about Python with sections for Overview, Features, and Links AI uses: 1. create_page("Python", "type:: #Technology\ntags:: #Programming") 2. create_blocks("Python", [ { content: "## Overview", children: [ { content: "Python is a high-level programming language." } ] }, { content: "## Features", children: [ { content: "Dynamic typing" }, { content: "Garbage collection" }, { content: "Large standard library" } ] }, { content: "## Links", children: [ { content: "[Official Site](https://python.org)" } ] } ])

logseq-lain

Lain - AI assistant plugin for Logseq.

# Build pnpm --filter logseq-lain build # Development (watch mode) pnpm dev:plugin

Install in Logseq

Enable Developer Mode in Logseq (Settings → Advanced → Developer mode)
Go to Plugins → Load unpacked plugin
Select the packages/logseq-plugin directory
Use /lain ask, /lain summarize, /lain expand slash commands

Architecture

See doc/architecture.md for detailed architecture documentation.

Development

# Build everything pnpm build # Run all tests (188 tests) pnpm test # Type check all packages pnpm typecheck # Lint pnpm lint

License

MIT

Logseq AI

Logseq AI

Prerequisites

Setup

Quick Start

Using the Core Library

Task Management

Task Analytics

Journal Operations

Page Links & Backlinks

Error Handling

Packages

@logseq-ai/core

@logseq-ai/mcp-server

Configure in Windsurf/Claude Desktop

Available Tools

Example: Creating Structured Content

logseq-lain

Install in Logseq

Architecture

Development

License

Resources

New MCP Servers

Latest Blog Posts

MCP directory API