Store and retrieve long-term facts and event logs for AI agents.
npx skills add https://github.com/openbotx/openbotx --skill memory-openbotx
Please help me install this Agent Skill. Skill: memory Source: https://github.com/openbotx/openbotx/tree/main/openbotx/skills/memory Command: npx skills add https://github.com/openbotx/openbotx --skill memory-openbotx
Provides a persistent, searchable memory layer so AI agents can retain and recall long-term facts, user preferences, and project context across conversations, eliminating the need to repeat context or manually reintroduce prior details.
Use the memory skill to save the user's preference I prefer dark mode and then retrieve it when asked what the user's preferred theme is.
High-intent search queries and answers about installing and using this skill.
To store and recall long-term context, use a two-layer memory system with MEMORY.md for always-loaded facts and HISTORY.md for an append-only event log. This lets conversational agents persist user preferences and retrieve past context instantly without manual reintroduction.
Tracking user preferences in AI assistants is best handled by saving specific details to a persistent memory file. You can save facts like a preferred UI theme immediately and retrieve them later through a searchable recall function during future interactions.
Searchable memory works by using a search function to locate past events or facts across both long-term fact files and append-only history logs. It retrieves matching context from the stored history, allowing project agents to recall milestones and decisions.
Yes, you can automatically summarize older chat sessions through automatic consolidation. This process summarizes older sessions into an append-only history log while extracting long-term facts into a dedicated memory file for future reference.
No, this memory approach eliminates the need for manual context reintroduction. By persisting important facts immediately and maintaining an archival event log, the agent retains long-term context and user preferences automatically across all future conversations.
You need persistent memory for a conversational assistant when recalling past events, summarizing history, and retrieving project context is required. It is essential for tracking user preferences, capturing project decisions, and eliminating repetitive context setup.
PaparusiStore, retrieve, and delete long-term memory with semantic embeddings.
5unnykum4rManage persistent facts in MEMORY.md and searchable event logs in HISTORY.md.
PrettiFlowMaintain long-term facts in MEMORY.md and grep-searchable events in HISTORY.md.
roy2392Retrieve long-term memory via vector search and recursive graph traversal on SQLite.
