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Add cross-tool behavioral memory that learns user preferences and context.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.helinakdogan/agent-magnet" MCP server from askskill: Run: claude mcp add 'io-github-helinakdogan-agent-magnet' -- npx -y agent-magnet
Connect Agent Magnet to Claude, Cursor, and Codex, and remember my coding preferences: use type hints in Python, write short docstrings, and prefer testable code. Explain how to configure it and what information it will remember.
Provides setup instructions and explains how the tool reuses those preferences across AI coding environments.
I discuss the same product requirements across different AI tools. Use Agent Magnet to store project background, target users, prioritization rules, and terminology preferences, and explain how this context can be brought into future conversations automatically.
Shows an example context structure to store and explains how future chats can automatically inherit it.
Help me create research preference memory with Agent Magnet: I prefer abstracts first, sources from the last three years, and bullet-point outputs, and I want this to stay consistent across AI assistants. List the setup steps and expected results.
Outlines a research preference setup and explains how multiple AI assistants can share a consistent response style and retrieval behavior.
Provide local-first shared memory, auditing, and sync across AI clients.
Provide persistent, explainable, MCP-native memory for AI agents.
Manage AI memory, context, and multi-agent workflows at enterprise scale.
Give agents local memory, recall, search, context, and graph traversal tools.
Build a self-evolving memory graph for coding agents with semantic search.
Give Claude Code persistent, searchable local memory and knowledge management.