memshare: Peer-to-Peer AI Memory Sharing Tool
Solve the blank-slate AI problem for new teammates using memshare, an open peer-to-peer AI memory sharing tool built on plain JSON files.
Stock photo for illustration only, not from the actual event
- memshare enables sharing AI memory and context between teammates without starting over.
- Stores AI memory as plain JSON data files owned and controlled entirely by the user.
- Supports Model Context Protocol (MCP) to integrate smoothly with Claude, Cursor, and VS Code.
- Features strict four-tier security gates and mutual consent requirements before sharing.
Using artificial intelligence tools today usually means locking memory features inside a single account. While assistants like Claude or ChatGPT remember personal workflows and preferences, joining teammates always start from zero because their AI models lack any context regarding the existing codebase and project conventions.
To address this gap, developers introduced memshare, a peer-to-peer AI memory-sharing tool designed around mutual consent. Instead of treating memory as a proprietary product feature inside a chat platform, memshare handles AI memory as an open data type composed of plain JSON files that users fully own.
Stock photo for illustration only, not from the actual event
Getting started requires running terminal commands such as npm install -g memshare-mcp followed by memshare init. Once connected to an AI tool, capture happens naturally during normal conversations. For instance, stating a technical choice like Postgres because of JSONB support prompts the AI to trigger memory_set and store the decision privately.
When sharing project context with a teammate, users can inspect exact export payloads using memshare export --tags "project-x,architecture" --for alice --preview. Satisfied users can then generate a bundle file stored locally under ~/.memshare/bundles/bundle-a3f8c2d1.memshare.json to transmit via Slack, email, or AirDrop.
Leveraging the Model Context Protocol (MCP) gives memshare exceptional architectural flexibility by decoupling the memory store from chat applications. Even if MCP protocols change, raw memory data remains human-readable, diff-friendly, and fully accessible within local directories.
On the recipient's end, Alice can inspect bundles using memshare preview and import items selectively using memshare import. Every incoming item lands privately, ensuring that receiving operational context does not grant automatic consent to pass it along further.
Because language models can occasionally fail to invoke required tools, memshare includes statistics tracking via memshare stats. The dashboard displays metrics such as 12 total memories with 9 captured automatically and 3 added manually, accompanied by ASCII frequency charts to alert users immediately if capture stops working.
The project is fully open-source under the MIT license, operating without external servers, tracking, or mandatory sign-ups. Feedback, issue reports, and pull requests are welcomed by the creator.
Source: Dev.to
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