Build hierarchical GAM memory systems for LLM agents.
npx skills add https://github.com/Aradotso/ai-agent-skills --skill gam-agentic-memory
Please help me install this Agent Skill. Skill: gam-agentic-memory Source: https://github.com/Aradotso/ai-agent-skills/tree/main/skills/gam-agentic-memory Command: npx skills add https://github.com/Aradotso/ai-agent-skills --skill gam-agentic-memory
GAM enables developers to build structured, hierarchical memory for LLM agents, improving recall, context management, and long-term task continuity across sessions.
Install GAM, initialize a memory workspace, and start adding text, video, or trajectory data to begin building memory.
High-intent search queries and answers about installing and using this skill.
Build hierarchical memory for LLM agents by chunking text, video, and trajectory data into structured summaries. This approach organizes content hierarchically, improving recall and task continuity across multi-step research and planning sessions.
Agent trajectory tracking records the sequential steps and decisions an LLM agent makes over time. Structuring this trajectory into hierarchical memory enables long-term task continuity and better context management across sessions.
Yes, manage video memory through accessible Python SDK, CLI, REST API, and Web interfaces. These interfaces support chunking and summarizing long video data into structured hierarchical memory for agent recall.
Set up a memory workspace by installing GAM and initializing the environment. Once initialized, start adding text, video, or trajectory data to begin building structured hierarchical memory with chunking, summaries, and taxonomy.
Yes, hierarchical agent memory works for long-document processing and knowledge retention. By applying chunking and summaries to text documents, it structures content into a taxonomy that improves recall and long-term context management.
AradotsoOrganize and retrieve multi-modal memories across text, video, and agent trajectories.
zilliztechSearch and retrieve relevant memories from past sessions.
DNYoussefCapture decision trajectories and extract patterns to optimize agent learning.
BoraPerusicAnalyze and manage agent memory for persistent cross-session context.
