Byterover
File-based memory for agents with >92% retrieval accuracy
1.2K followers
File-based memory for agents with >92% retrieval accuracy
1.2K followers
ByteRover is a fully local, file-based memory layer for agents with market-best 92.2% retrieval accuracy, that supports cloud portability, and built-in version control. From OpenClaw to Claude Code to Cursor to whatever's next, your own memory travels with you, not trapped in one tool. ByteRover gives your agents stateful memory that keep your context's timeline, facts, and meaning perfectly in place.
This is the 5th launch from Byterover. View more
ByteRover Memory System for OpenClaw
Launched this week
Give OpenClaw agents stateful memory that keep your context's timeline, facts, and meaning perfectly in place. ByteRover is a memory layer that gets 26k+ downloads from OpenClaw power users within one week, and delivers a market-best 92.19% retrieval accuracy, local-to-cloud portability, and built-in version control.
Free
Launch Team
100% agree the default memory setup can get noisy fast. The win is separating short-term daily logs from curated long-term memory + good retrieval. Less token burn, better continuity, fewer hallucinated “memories”.
Goodl luck today! Question: How does ByteRover achieve 92%+ retrieval accuracy with file-based memory. Are you using embedding indexes with semantic ranking, or a hybrid approach combining structured metadata and vector search?
Byterover
The idea of a free, local version with no friction (no accounts required) really motivates me to try our the CLI.
"Interesting approach with local file-based memory. How does this compare to cloud-based memory layers for non-developer users?"
Roster
I've been using ByteRover for a while and really love the ease of set up, work smoothly from my IDE. Can't wait to get my hand on the OpenClaw version.
What does this do
AutonomyAI
Congrats, love this!