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Provide dual-memory retrieval, storage, and maintenance for long-context AI applications.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "hipocampo" yet — see the docs or source repo.
Use hipocampo to design a long-term memory workflow for my AI assistant. It should support conversation memory storage, semantic retrieval, user profile updates, plus periodic deduplication and health checks. Recommend the tool call sequence.
A memory architecture and tool sequence for an AI assistant covering save, retrieval, profiling, and maintenance.
Use hipocampo to inspect the current memory system state, report health and stats, and if issues are found, suggest the order for auto-repair, maintenance, or tune actions.
A health overview, key metrics, and recommended repair and optimization actions when issues appear.
Using hipocampo's Sparse Selective Caching and progressive retrieval, help me optimize retrieval quality and response speed for knowledge-base QA, and explain how to tune it.
Optimization guidance for caching, retrieval stages, and parameter tuning to balance recall and performance.
Give AI agents bilingual memory, semantic search, and profile management.
Store and search AI conversation history for cross-session recall of reasoning.
Lightweight vector memory for AI agents to store, search, and delete memories.
Provide self-hosted long-term memory and hierarchical recall for AI agents.
Give AI persistent, synced memory with full-text and semantic hybrid search.
Persist long-term AI memory with semantic retrieval and knowledge graph context.