Chetna: Rust-based Memory System for AI Agents
What it is
Chetna is a standalone memory system written in Rust that provides AI agents with persistent, searchable, and context-aware memory capabilities. It combines semantic search using vector embeddings with hybrid keyword search and includes features for namespace isolation, importance scoring, and LLM-based consolidation.
The problem it solves
AI agents typically lack persistent, efficient, and semantically searchable memory, limiting their ability to learn from past interactions and maintain context across sessions.
What you could use it for
- Providing long-term memory for autonomous AI agents
- Enabling semantic search over agent interaction history
- Managing multi-tenant agent sessions with isolated memory spaces
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.