ByteRover

OpenClaw just got a major upgrade. Persistent memory changes how agents operate across sessions. No more losing context after restarts. This is the kind of capability the OpenClaw ecosystem has been missing.

@ctranbtw I sent you a DM, please have a look. @ctranbtw

Agent memory is a huge missing piece for real autonomous workflows. Stateful context layers like this are exactly what builders experimenting with agents have been looking for. Strong overlap with crypto dev circles running agent stacks too, got visibility there if you ever want to open that lane. Shoot me a DM.

QMD helps with search Lossless-claw prevents compaction amnesia OriginTrail adds shared graphs The pattern is clear: Amnesia is the final boss of AI agents. I was burning crazy tokens with default unstructured memory, which is why I built ByteRover—replacing it with a deterministic local file system (.brv/context-tree). Selective retrieval beats giant context windows every time.

@steipete Amnesia is definitely the final boss right now. I kept hitting silent context drift with the stock setup, so I moved my entire stack to a deterministic local file system instead of vector DBs (ByteRover). Love seeing the ecosystem tackle this from so many different angles

There's a lot of cool stuff being built around openclaw. If the stock memory feature isn't great for you, check out the qmd memory plugin! If you are annoyed that your crustacean is forgetful after compaction, give github.com/martian-engine… a try!

The Secret behind Memory Skill for OpenClaw that Hits 26,000 users in 7 days (8+ months iteration) One week ago, we launched ByteRover to fix the OpenClaw’s biggest bottleneck (memory). Today, we crossed 26,000+ users🤯 The project stemmed from a frustration shared by every OpenClaw power user: OpenClaw is brilliant, but its memory is a scaling nightmare. After my post yesterday, my notifications melted with builders debating how AI memory should work. Here is the breakdown of the "Secret Sauce" - the architecture that hit highest accuracy on the market, slashed token costs by 83%, and redefined the OpenClaw stack.

Memory Skill for OpenClaw with 26k+ users in 1 week🚀 OpenClaw's memory system is broken by default. It requires curating massive MEMORY.md files or relying on duplicate-heavy generation. Hours are wasted tuning, and massive amounts of tokens are burned. It's time to stop. So we built the memory skill to solve that prob Here is our superpower ⚔️ 🎯 Top #1 market accuracy (92.19%) after 8+ months of intense architecture iteration 🧠 The ultimate solution to keep the timeline, facts, and meaning perfectly in place ☁️ Local & Cloud + Version control ⚡ Super easy setup

I'm claiming my AI agent "m0ltrover" on @moltbook 🦞 Verification: ocean-TUKV

Check it out here: byterover.dev/blog/agent-ski…

We just added Agent Skill for ByteRover CLI in our 1.3.0 release 💫 This integration brings better token efficiency compared to MCP or rules, and more specialized ByteRover's context-related capabilities for the coding agents. Agent Skills are quickly becoming the standard extension of specialized capability for the agent. After ClaudeCode, major players like Cursor, Codex, GitHub Copilot, Google's Antigravity started to adopt Skills for their agents. Unlike rules or MCP, which are always preloaded into the context window, Skills are loaded dynamically when the agent determines that a particular Skill is relevant. Therefore, with ByteRover Skill, your coding agent can now keep context window clean while gaining specialized capabilities of ByteRover for context-related tasks. Skill is now available in ByteRover CLI 1.3.0. You can check our full release note down below!

Check out the full release notes here: byterover.dev/blog/cli-1.2.0…

From now on, you can connect ByteRover CLI with coding agents via MCP, or whichever available integration that best fit your preferred workflow. Previously, pure integration file was the only way to integrate ByteRover with all coding agents.

Last week, we just launch Hooks for ClaudeCode. This week we added MCP as a new connector for all coding agents. Why does this matter? With MCP, ByteRover CLI can be connected to coding agents through a standardized interface, while keeping the same core workflow: 🎯 Direct Tool Access: Allow agents to interact exclusively through ByteRover tools (like brv_curate and brv_query) without requiring full ByteRover CLI rule file. 🔀 Your Workflow, Your Choice: For Claude Code, we recommend trying both Hook and MCP, and choosing the integration that best matches your preferred workflow.

ByteRover Agent Skill helps Clawdbot: - No more burning massive tokens uncontrollably: Write code with structured, task-relevant context - No more re-teaching the agent from day one: Dynamically curate knowledge from instructions and outcomes - No more guessing or re-inferring: Reuse architectural decisions across tasks - Faster, more stable agent runs: Avoid bloated prompts from linear memory files

ByteRover Agent Skill is now available on Clawdhub 🚀 Clawdbot @openclaw is a fast-rising local 24/7 AI assistant. Any OS. Any platform. You can write code, build apps, and operate systems just by chatting from a messaging app. Now imagine this: Instead of Clawdbot re-learning your context or codebase every session, you can ask it to query and curate context for your project as it works.

These updates are designed to make your agents smarter and your bills smaller. Visit ByteRover: byterover.dev Update your CLI to get started: npm install -g byterover Read the full release notes here: byterover.dev/blog/cli-1.1.0…

ByteRover CLI 1.1.0 is live! 🚀 We’re back to weekly releases to deliver smarter context and lower costs: 1️⃣ ClaudeCode Hook Integration ensures your agent maintains context dynamically by injecting instructions on every request. 2️⃣ Sub-agent Search tools reduce token costs drastically and improve response times by retrieving only the exact information needed, rather than loading the entire Context Tree. Read the breakdown 👇

2️⃣ Sub-Agent Search & search_knowledge Stop loading your entire context tree into the window. The new search_knowledge tool uses semantic search to retrieve only what’s needed. Therefore, it drastically cuts token usage. Plus, brv query now utilizes parallel sub-agents, delivering faster and more relevant results.

1️⃣ ClaudeCode Hook Integration Static instructions (like CLAUDE.md) load once and often fade as context grows. 📉 ByteRover now uses ClaudeCode hooks to inject workflow rules on every single request, not just at the start. This guarantees your agent actively remembers to use brv query and brv curate without manual reminders. 🧠