Repository History
5 repositories tagged with Agent Memory
ai-memory: Long-Term Memory Solution for AI Coding Agents
ai-memory is a robust solution providing long-term memory for AI coding agents, enabling seamless handoffs between different agent vendors and machines. It ensures that project knowledge, failed approaches, and open questions persist, facilitating collaborative development and continuous progress across various tools and teams. Built in Rust, this open-source project offers a reliable and transparent way to manage agent memory.
Magic Context: Unbounded, Self-Managing Memory for AI Coding Agents
Magic Context is a powerful GitHub repository that provides unbounded, self-managing memory for AI coding agents. Acting as the 'hippocampus' for agents, it ensures continuous learning and recall across sessions without disruptive context compaction. This tool, part of CortexKit, allows agents to build lasting project knowledge and maintain context efficiently.
deja-vu: Retroactive Memory for AI Coding Agents
deja-vu is a powerful local-first tool that provides retroactive memory for AI coding agents, indexing past coding sessions from various agents, even those from before installation. This Go binary allows agents to recall relevant information without needing an LLM or embeddings by default. It enhances agent performance by providing context at the point of action, preventing repeated mistakes and improving efficiency.
OpenViking: A Self-Evolving Context Database for AI Agents
OpenViking is an open-source context database designed for AI agents, unifying agent memory, knowledge RAG, and skills into a virtual filesystem. It allows agents to browse their context deterministically using familiar commands like `ls` and `tree`. This innovative approach aims to enhance agent performance and reduce token spend by loading content in tiered layers.
Agent-Memory: Persistent Memory for AI Coding Agents
Agent-Memory provides persistent memory for AI coding agents, ensuring they remember past interactions and learned patterns across sessions. Built on the iii engine, it eliminates the need for re-explaining context, significantly improving agent efficiency and reducing token usage. This solution integrates seamlessly with various agents, offering a robust memory management system.