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  1. Home
  2. 🧠 Knowledge & Memory
  3. Memkin
Memkin logo
Health: ActiveRecent health check succeeded.Last checked 9/9/2026, 12:32:03 PM

Memkin

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View Repository5 GitHub StarsTotal stargazers on GitHub for the source repository (5 stars).Visit Website
memoryknowledge-graphragproductivity

Local-first MCP memory system that builds a private knowledge graph from Feishu and AI coding sessions.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
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We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag β€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "andrelyl-memkin": {
      "command": "npx",
      "args": [
        "-y",
        "memkin"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives🧠 More in Knowledge & Memory

Overview

AndreLYL/memkin MCP server collects Feishu/Lark data and AI coding sessions, extracts structured signals, and stores them in a local knowledge graph. It exposes memory through MCP using stdio or Streamable HTTP, with tools for recall, reports, troubleshooting, and session context. Data remains on the local machine by default through PGLite, with optional Ollama embeddings and local PostgreSQL support. Reach for it when an agent needs cross-session context from workplace conversations, decisions, tasks, and coding history.

Use cases

β€’Recall decisions and tasks from prior Feishu conversations
β€’Generate a daily report from messages, meetings, email, and calendar entries
β€’Prepare for a meeting with a person using historical interactions
β€’Troubleshoot a recurring issue from extracted playbooks
β€’Restore project context across coding sessions

Key features

β€’Feishu/Lark data collection
β€’AI coding session ingestion
β€’Local entity knowledge graph
β€’Hybrid full-text and vector search
β€’MCP read/write access
β€’Obsidian bidirectional synchronization

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Memkin.

Extracted Tool Capabilities
Feishu/Lark data collection
AI coding session ingestion
Local entity knowledge graph
Hybrid full-text and vector search
MCP read/write access
Obsidian bidirectional synchronization

How Memkin works

What AndreLYL/memkin MCP server does

AndreLYL/memkin MCP server turns information from supported workplace and coding sources into searchable personal memory. It can ingest Feishu/Lark private messages, group chats, email, calendars, documents, tasks, and message search, along with sessions from Claude Code, Codex, and Hermes/OpenClaw. Extracted material is represented as entities, decisions, tasks, discoveries, knowledge, and relationships, then associated with people, projects, and tools.

The resulting store is a private knowledge graph kept on the user’s machine. Agents can query the graph and write memory back through MCP. Responses can include references to the source messages, allowing an agent or user to trace an answer back to collected material.

How it works

Collection supports incremental updates and historical backfills. Content passes through chunking, rule-based and LLM-based noise filtering, structured signal extraction, scoring, and privacy redaction before storage. Signals retain links to their original messages, while identity resolution can associate the same person across platforms.

Search combines Chinese-capable PostgreSQL full-text search with vector retrieval and reciprocal-rank fusion. The default embedded database is PGLite, so the basic storage path does not require an external database. An optional self-managed local PostgreSQL engine and optional Ollama embeddings are also documented.

The MCP interface supports stdio and Streamable HTTP transports. The project describes 36 available tools, with 15 high-intent tools exposed by default. Examples include recall for combining conversations and follow-up tasks, daily_report for aggregating daily activity, prep_for_person for preparing around an interaction, troubleshoot for ordered diagnostic guidance, and get_session_context for project decisions, tasks, and recent timeline information.

Setup and configuration

AndreLYL/memkin MCP server runs on macOS, Linux, and Windows, with Node.js 18 or newer required. The quick trial command is npx memkin start; when configuration is missing, it opens a setup flow and then starts the service and Web UI. A separate installation script can install Node when needed, install the npm package globally, launch setup, register the background service, and write MCP configuration for detected clients.

The memkin install command can configure supported clients, including Claude Code, Claude Desktop, Cursor, Codex, Windsurf, and Hermes/OpenClaw. It also supports a dry-run mode and selecting a specific agent. Historical Claude Code data can be extracted with memkin extract --source claude-code, and optional Claude Code hooks can provide automatic recall. Configuration includes an LLM API key; the exact provider and environment variable names are not specified in the supplied material.

Tools and capabilities

  • Collect Feishu/Lark sources incrementally or through historical backfill.
  • Import Claude Code, Codex, and Hermes/OpenClaw sessions.
  • Search memory using combined full-text and vector retrieval.
  • Maintain entities, relationships, decisions, tasks, and source references.
  • Run a background daemon with scheduled collection, run history, and alerts.
  • Export to an Obsidian Markdown vault and import edits back into memory.
  • Inspect data through a dashboard, timeline, graph view, search interface, CLI, REST API, or MCP.

Limitations and notes

The roadmap lists additional sources such as DingTalk, WeCom, WeChat chat records, and local documents as future work, so they should not be treated as currently supported inputs. Web UI memory editing is also listed as planned; the current interface is described as read-only for editing purposes. The system can use an LLM API key for extraction, while Ollama embeddings are optional. Although MCP supports read and write operations, the supplied material does not specify the complete individual tool schema or all configuration fields.

Read the full README β†’View source on GitHub β†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks β€” not a rating.

GitHub stars
5
Stargazers on the source repository.
npm downloads
112
Package downloads in the last 30 days.
Last commit
3d ago
Most recent push to the default branch.
Directory activity
6 views
Config copies, upvotes, and views on AllMCPs.

Reviews

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Frequently Asked Questions about Memkin

Run `npx memkin start` for a temporary setup and launch, or use the repository's installation script to install the package and register the background service.

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Technical Specs & Signals

Category🧠Knowledge & Memory
PricingBring your own API key (usage-based cost)
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
AuthAPI key
LicenseApache-2.0
ClientsClaude Desktop, Cursor, Windsurf
Last updatedSep 7, 2026
10/10 checks healthy over the last 31d
Views6
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars5
GitHub Star CountTotal stargazers on GitHub representing community popularity (5 stars).
Last commit3d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Sep 7, 2026
npm downloads112/mo
Monthly npm DownloadsAverage monthly package installs recorded from npm registry statistics.
56Quality signal: Good Β· 56/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools24/30
Adoption & activity7/15
Community engagement1/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 15d ago via OSV.dev Β· memkin (npm)

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