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ByteRover CLI

Persistent structured memory layer for AI coding agents

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ByteRover CLI provides a persistent, structured memory layer for AI coding agents. It lets you curate project knowledge into a context tree that syncs to the cloud and shares across tools and teammates. Supports 20+ LLM providers with 24 built-in agent tools for code execution, file operations, and memory management. Achieves 96.1% accuracy on the LoCoMo benchmark. SOC 2 Type II certified.

ByteRover CLI (brv) is a portable memory layer for autonomous coding agents that gives AI assistants persistent, structured context across sessions and projects. Formerly known as Cipher, it provides an interactive REPL powered by configurable LLM backends where the agent understands your codebase through an agentic map, reads and writes files, executes code, and stores knowledge in a context tree for future sessions. The tool works with Cursor, Windsurf, Claude Desktop, Claude Code, Gemini CLI, Kiro, VS Code, and Roo Code through MCP integration, making it a universal memory layer that bridges multiple AI coding environments.

The core innovation is treating agent memory as a first-class, shareable artifact rather than ephemeral conversation context. Developers curate project knowledge into structured context trees that capture architectural decisions, codebase conventions, dependency relationships, and debugging history. ByteRover Cloud extends this with team synchronization, allowing shared context knowledge across projects, machines, and team members while keeping everything functional locally by default. The MCP server integration means any MCP-compatible editor automatically gains access to the accumulated project memory, enabling new coding agents to start with full historical context rather than rediscovering patterns from scratch.

Built in TypeScript with SOC 2 compliance considerations for enterprise environments, ByteRover CLI has attracted over 4,300 GitHub stars under the Elastic License 2.0. The project maintains compatibility extensions for major editors including a Zed extension and integration with the OpenClaw context engine. Performance benchmarks cite 96.1% accuracy on the LoCoMo evaluation, indicating strong retrieval quality for coding-specific memory tasks. The self-improving memory architecture means the system becomes more useful over time as agents accumulate project-specific knowledge, addressing the fundamental limitation of stateless AI coding assistants that lose context between sessions.

Pricing

Free local use with built-in LLM, paid cloud sync

Platforms

macOS, Linux, Windows — TypeScript CLI

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