File-based memory for OpenClaw with >92% retrieval accuracy
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.
Over the last few months, we’ve watched developers try to scale autonomous agents (like OpenClaw and local Ollama setups) and hit a massive brick wall: Agent Amnesia.
An agent solves a bug or writes a script, and then immediately forgets the context. To fix this, teams are dumping entire codebases into giant vector databases or blindly prepending massive context windows, resulting in insane API token bills and VRAM crashes.
We got tired of these manual workarounds. So we built Memory Skill for OpenClaw.
It is a deterministic, file-based memory system (.brv/context-tree) that lives directly in your local environment.
How it works: 🧠 Selective Retrieval: Instead of blindly injecting everything, ByteRover actively curates decisions and feeds the agent exactly what it needs to know. 📉 Cuts Token Burn: Our users are seeing token usage drop by ~40-70% because the prompts stay noise-free. 📂 Local & Portable: Your memory is version-controlled via Git, preventing silent context drift. What Git did for code, we are doing for AI context.
We’ve seen 26k+ downloads from OpenClaw power users in the last week, hitting a 92.19% retrieval accuracy on the LoCoMo benchmark.
I would love the community’s feedback on our architecture. Drop any questions below I’ll be here all day answering them! 👇
About ByteRover Memory System for OpenClaw on Product Hunt
“File-based memory for OpenClaw with >92% retrieval accuracy ”
ByteRover Memory System for OpenClaw launched on Product Hunt on March 15th, 2026 and earned 146 upvotes and 22 comments, placing #8 on the daily leaderboard. 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.
On the analytics side, ByteRover Memory System for OpenClaw competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how ByteRover Memory System for OpenClaw performed against the three products that launched closest to it on the same day.
Who hunted ByteRover Memory System for OpenClaw ?
ByteRover Memory System for OpenClaw was hunted by Chi Nguyen. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of ByteRover Memory System for OpenClaw including community comment highlights and product details, visit the product overview.
Chi Nguyen
Hey Product Hunt! 👋
Andy here, founder of ByteRover.
Over the last few months, we’ve watched developers try to scale autonomous agents (like OpenClaw and local Ollama setups) and hit a massive brick wall: Agent Amnesia.
An agent solves a bug or writes a script, and then immediately forgets the context. To fix this, teams are dumping entire codebases into giant vector databases or blindly prepending massive context windows, resulting in insane API token bills and VRAM crashes.
We got tired of these manual workarounds. So we built Memory Skill for OpenClaw.
It is a deterministic, file-based memory system (.brv/context-tree) that lives directly in your local environment.
How it works:
🧠 Selective Retrieval: Instead of blindly injecting everything, ByteRover actively curates decisions and feeds the agent exactly what it needs to know.
📉 Cuts Token Burn: Our users are seeing token usage drop by ~40-70% because the prompts stay noise-free.
📂 Local & Portable: Your memory is version-controlled via Git, preventing silent context drift. What Git did for code, we are doing for AI context.
We’ve seen 26k+ downloads from OpenClaw power users in the last week, hitting a 92.19% retrieval accuracy on the LoCoMo benchmark.
I would love the community’s feedback on our architecture. Drop any questions below I’ll be here all day answering them! 👇