Persist and retrieve contextual knowledge across AI agent sessions.
npx skills add https://github.com/Wike-CHI/acquisition-agent --skill smart-memory
Please help me install this Agent Skill. Skill: smart-memory Source: https://github.com/Wike-CHI/acquisition-agent/tree/main/skills/smart-memory Command: npx skills add https://github.com/Wike-CHI/acquisition-agent --skill smart-memory
AI agents lose context between interactions, forcing users to repeat information and breaking workflow continuity.
memory_search, memory_commit, memory_insights) for seamless integration with OpenClaw, LangChain, or custom agents.Use cases include AI sales assistants recalling past client decisions, research bots maintaining project focus, and any autonomous agent needing continuous memory.
Request the smart‑memory skill to compose your agent’s initial context by providing your agent identity and a opening message.
High-intent search queries and answers about installing and using this skill.
Persistent memory for AI agents allows them to store and retrieve contextual knowledge across sessions. You can use a local FastAPI server with Nomic embeddings to save episodic and semantic memories, preventing context loss between interactions.
Yes, CPU-only PyTorch embeddings using Nomic models support semantic retrieval for autonomous agents. This allows token-bounded prompt composition and hot working memory tracking without requiring GPU hardware.
Maintaining project focus for research bots requires continuous memory that tracks active projects, questions, and insights. Background cognition generates reflections and consolidates memories, ensuring the agent retains focus across multiple interactions.
Yes, continuous memory systems work with LangChain, OpenClaw, and custom agents through tool interfaces like memory_search, memory_commit, and memory_insights. These interfaces allow seamless integration of semantic retrieval and persistent storage.
Background cognition manages memory decay by generating reflections, consolidating memories, and decaying low-importance items. It also resolves belief conflicts automatically, ensuring the agent's persistent storage remains relevant and accurate over time.
Yes, a local FastAPI server is required to run local memory persistence for AI applications. It works alongside a Node.js adapter and CPU-only PyTorch embeddings to store episodic, semantic, belief, and goal memories.
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