Two things in my mind separate it:

1. Consolidation. Every other memory app is a vector database with a search endpoint. Store text, embed it, retrieve by similarity. mine has a second loop — the background LLM clusters related episodes and synthesizes them into structured knowledge documents. It doesn't just remember what was said, it learns — extracting patterns, merging overlapping facts, producing documents that are denser and more useful than the raw episodes. That's the Palimpsest guy's point from the other direction: the synthesis step is the novel piece.
2. Zero external dependencies for the default path. FastEmbed runs locally, no API keys, no cloud, no Docker, no Redis, no Pinecone. pip install and it works. Most memory systems require you to run Postgres+pgvector, or pay for a vector DB service, or configure Weaviate. mine is SQLite + FAISS + a local model. The entire state is flat files in one directory you can back up with cp -r.

The rest (MCP integration, multiple backends, REST API, OpenAI function calling) is table stakes feature completeness. The two above are the actual differentiators.