EverOS
ActiveDescription
EverOS is a platform for building, evaluating, and integrating long-term memory for self-evolving agents, enabling AI agents to continuously accumulate experience and optimize themselves.
Key Features
- Markdown as source of truth — all memory persists as .md files, editable with Obsidian/Git and version-controlled
- Lightweight three-piece storage — Markdown (truth) + SQLite (state/queue) + LanceDB (vector+BM25+scalar), no heavy dependencies
- Hybrid retrieval — BM25 + vector (HNSW/IVF-PQ) + scalar filtering in a single LanceDB query
- Cascade index sync — edit .md → file watcher → entry-level diff → LanceDB sync, sub-second
- Dual-track memory — user-track (Episodes/Profiles) + agent-track (Cases/Skills)
- Multi-modal support — inline text + small images/audio, large media via S3/OSS references
Use Cases
💡 Build long-term memory systems for AI agents to accumulate experience and user preferences across sessions
💡 Store and retrieve structured memory as Markdown files in personal knowledge management
💡 Provide context-enhanced responses for chat assistants based on conversation history
💡 Build self-evolving agents that continuously optimize decisions through Cases/Skills memory
💡 Maintain independent long-term memory spaces per user in multi-user scenarios
Strengths & Limitations
✅ Strengths
- • Actively maintained, recent updates
- • High community interest (12.7k stars)
- • Permissive open-source license (Apache-2.0)
- • Responsive to issues, low backlog
Tags
Categories
Quick Start
Install with uv pip install everos, run everos init to generate .env config, edit API keys, then everos server start. Add memory via /api/v1/memory/add and retrieve with search endpoints. Optional everos[multimodal] for image/audio/PDF support.