lossless-memory vs supermemory
Lossless long-term memory for a personal AI: verbatim JSONL logs, time-first search over SQLite FTS5 (sqlite-vec as last resort) and a tiny 'where are we now' index injected every turn. — versus — Memory and context engine for AI: fact extraction, user profiles, contradiction handling and forgetting, hybrid RAG + memory search, connectors, agent plugins and a one-binary local mode.
Same job — long-term memory for an assistant — opposite stance: supermemory extracts facts, builds profiles and forgets; lossless-memory never summarizes and returns the original lines with timestamps.
| lossless-memory | supermemory | |
|---|---|---|
| Stars | 143 | 31k |
| Forks | 13 | 2.7k |
| Language | Python | TypeScript |
| License | MIT | MIT |
| Last activity | 18 days ago | 3 days ago |
| Topics | memory | memory, rag |
| Curated connections | 3 | 5 |
lossless-memory — the curator's take
Choose it for one person, one assistant, one machine, when 'what exactly did we say last Tuesday' matters more than distilled facts — it never summarizes, and time phrases narrow the range before anything is ranked. Not for multi-user products, agent fleets or benchmark-chasing: no server, no published evals. If you want extracted facts, profiles and forgetting, supermemory is the opposite philosophy.
supermemory — the curator's take
The most complete memory product on the map: fact extraction with temporal updates and forgetting, ~50ms user profiles, RAG and memory in one query, connectors for Drive, Gmail, Notion and GitHub, and plugins for Claude Code, Codex, Cursor, OpenCode and Hermes. `npx supermemory local` runs the same Memory API on your machine with local embeddings, so prototyping does not need their cloud; connectors are not in the local feature list, so check before planning around them. Treat the '#1 on every benchmark' banner as vendor-reported. Skip it if you want memory you can read and edit as files (acontext, okf-agent-memory) or only need session recall for one coding agent.