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Give AI agents persistent local memory with searchable long-term context.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "agentrecall" yet — see the docs or source repo.
Use agentrecall to store this long-term memory with useful keywords: the user prefers Chinese replies, likes table summaries, and the project codename is Aurora.
The tool saves the preference data into local memory for later keyword or semantic retrieval.
Search agentrecall for past memories related to "reasons for pricing strategy changes," prioritizing the closest semantic matches and including relevant keywords.
It returns relevant past records so you can quickly recover prior conclusions and context.
Configure agentrecall as the memory layer for a local AI assistant, storing all memories in a single SQLite file and enabling hybrid keyword plus semantic search.
You get a lightweight, persistent local memory setup that is easy to deploy and maintain.
Give AI coding agents persistent memory across sessions for people, decisions, and context.
Give AI agents persistent memory across sessions with automatic context retrieval.
Query all memory stores at once and get a ranked, token-budgeted briefing.
Give AI agents persistent project memory with searchable code and decisions.
Give LLMs persistent local memory and semantic recall across sessions.
Give AI coding agents persistent local shared memory across agents.