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mnlt/wellread

by Various

Collective research memory for AI agents

M

MCP

mnlt/wellread

Added 1 June 2026

Overview

Wellread is a TypeScript library that gives AI agents a shared, persistent memory for research context. It stores and retrieves structured knowledge across sessions, enabling agents to build on past findings without repeating work.

Best for

Best for
Developers building multi-agent systems that need persistent research memory

Use cases

  • Persisting research context across multiple agent conversations
  • Building a shared knowledge base for collaborative AI agents
  • Reducing redundant queries by caching prior research results

How to use

Install

npx wellread

Tested with

Claude Code, Cursor, Windsurf, Gemini CLI, VS Code, OpenCode

Notes

Wellread is a TypeScript library that gives AI agents a shared, persistent memory for research context. It stores and retrieves structured knowledge across sessions, enabling agents to build on past findings without repeating work.

8 stars on GitHub. Last updated 2026-04-19. Licensed AGPL-3.0.

Use cases

  • Persisting research context across multiple agent conversations
  • Building a shared knowledge base for collaborative AI agents
  • Reducing redundant queries by caching prior research results

Pros

  • Lightweight TypeScript library with minimal dependencies
  • Designed specifically for agent-to-agent memory sharing
  • Open source with a permissive license

Cons

  • Limited community adoption with only 8 GitHub stars
  • No documented support for non-TypeScript environments
  • Unclear integration complexity with existing agent frameworks

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Lightweight TypeScript library with minimal dependencies
  • Designed specifically for agent-to-agent memory sharing
  • Open source with a permissive license

Cons

  • Limited community adoption with only 8 GitHub stars
  • No documented support for non-TypeScript environments
  • Unclear integration complexity with existing agent frameworks
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