About This Site
Local-first shared memory and AI agent for multiple AI tools Memmy is a local-first personal memory hub and AI agent that gives multiple AI tools, including Claude Code, Codex, Cursor, OpenClaw, and Hermes, access to shared long-term memory. It scans authorized AI conversation histories, organizes preferences, decisions, project context, and experiences into searchable local memory, and injects relevant context into future tasks. Memmy also acts as an independent agent that can organize materials, merge plans, continue unfinished work, and connect with tools, channels, and external agents through its desktop app, CLI, API, and integrations.
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Key Features AI
Core Features
Shared long-term memory across Cursor, Claude Code, Codex, OpenClaw, Hermes, and other AI tools
Local scanning, organization, deduplication, search, and retrieval of AI conversation history
Relevant memory injection instead of sending complete conversation histories
Standalone AI agent for task execution, plan merging, material organization, and unfinished-work continuation
Desktop app with workbench, memory panel, tool connections, settings, and desktop pet mode
CLI and HTTP API for external agents, scripts, and other products
Support for multi-session tasks, streaming output, attachments, voice input, slash commands, and scheduled tasks
Integrations with Telegram, WeChat, Feishu, DingTalk, GitHub, Notion, Slack, MCP servers, and custom tools
Support for OpenAI-compatible, Anthropic, Gemini, DeepSeek, Ollama, and other providers
Local SQLite memory storage and local-first privacy controls
Advantages
Creates one shared memory layer for multiple AI tools
Local-first architecture keeps memory stored on the user's device
Users can review, delete, control, or revoke memory access
Supports both account-based usage and bring-your-own-key mode
Combines memory management with direct agent task execution
Provides desktop, CLI, API, and messaging-channel access
Open-source and supports extensive integrations
Supports multiple model providers, including local Ollama models
缺点：Requires explicit setup and authorization for conversation-history scanning
缺点：Desktop use is limited to macOS and Windows, while CLI use requires Node.js 22 or later
缺点：The feature set may require technical configuration for APIs, MCP, integrations, and providers
缺点：No specific paid package prices are provided in the supplied content
缺点：Memory quality depends on the conversations scanned and the selected model provider
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30-Day Click Trend
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09-07
09-22
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