Store and retrieve conversation memories using a configurable SimpleMemory node.
npx skills add https://github.com/trohitg/MachinaOS --skill memory-skill
Please help me install this Agent Skill. Skill: memory-skill Source: https://github.com/trohitg/MachinaOS/tree/main/server/skills/assistant/memory-skill Command: npx skills add https://github.com/trohitg/MachinaOS --skill memory-skill
Memory management helps maintain context across multiple interactions by storing relevant user information and preferences in a durable, searchable format.
Connect the SimpleMemory node and start saving notes to remember important details across conversations.
High-intent search queries and answers about installing and using this skill.
To maintain long-term memory across sessions, you can store and retrieve conversation memories using a markdown-based format and an optional configurable SimpleMemory node for durable semantic search.
The best way to save notes and search past conversations semantically is to apply the memory-management workflow, which stores user information in a searchable vector database format for later retrieval.
No, a vector database is not required to function. The tool supports short-term memory natively via markdown files, while long-term semantic search uses an optional configurable SimpleMemory node.
To clear conversation history and reset memory within an interactive chat, you simply apply the clear memory command from the memory-management workflow to erase stored notes.
Yes, you can view recent conversation history in markdown format. The memory-management workflow includes a view history feature that outputs recent interactive chat interactions directly as markdown.
SobansaudCreate and load conversations by ID with role-based message storage.
whiteboardevClassify user queries to retrieve memories via recent, keyword, or semantic search.
zeenie-aiSave, retrieve, and clear timestamped markdown conversation history with semantic search.
Ivanblancoinusual-2106Index and query documents by semantic similarity using an in-memory vector database.
