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campfirein/byterover-cli

4,737TypeScriptAudience · developerComplexity · 3/5LicenseSetup · easy

TLDR

A command-line tool that gives AI coding assistants like Claude Code and Cursor a persistent memory layer so they can remember your codebase knowledge across sessions.

Mindmap

mindmap
  root((ByteRover CLI))
    What it does
      AI persistent memory
      Cross-session context
      Team knowledge sync
    Features
      Context tree storage
      Git-style versioning
      Cloud sync
      Web dashboard
    Integrations
      Claude Code
      Cursor
      MCP server
      20+ AI providers
    Setup
      npm install
      Shell script option
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Code map

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Things people build with this

USE CASE 1

Give Claude Code or Cursor memory about your project that persists between sessions instead of starting fresh each time

USE CASE 2

Share a synchronized knowledge tree across your whole team so every developer's AI assistant starts from the same base

USE CASE 3

Browse and edit your stored AI context through a web dashboard without using the terminal

USE CASE 4

Connect ByteRover as an MCP server so any AI tool that supports the Model Context Protocol can query your project notes

Tech stack

TypeScriptNode.jsMCP

Getting it running

Difficulty · easy Time to first run · 5min

Requires an API key for one of the 20+ supported AI providers to start a chat session.

Free for personal and most commercial use, check the full Elastic 2.0 license terms if you plan to resell or offer it as a hosted service.

In plain English

ByteRover CLI (the command brv) is a tool that gives AI coding assistants a persistent memory layer. When you use AI tools like Claude Code, Cursor, or similar coding assistants, they normally start fresh with each session and have no memory of what they learned about your project before. ByteRover addresses this by letting you store and retrieve structured knowledge about your codebase, which the AI can then use across sessions. You run brv inside any project directory to start an interactive chat session powered by a language model of your choice. The tool can read and write files, run code, and save notes about your project to a searchable knowledge store called a context tree. In later sessions, the AI can query this stored knowledge to pick up where it left off, rather than rediscovering context from scratch. The knowledge store supports version control that mirrors the concepts in git: you can branch, commit, push, pull, and merge your stored context just as you would source code. A cloud sync option lets teams share the same context tree across machines and teammates, so everyone's AI assistant works from the same base of project knowledge. A web dashboard is included for browsing and editing stored context without using the command line. The CLI supports more than 20 language model providers including Anthropic, OpenAI, Google, Groq, and others, so you are not tied to a single AI service. It also works as an MCP (Model Context Protocol) server, which is a standard way for AI tools to communicate with external data sources. The project includes benchmark results on two long-term memory evaluation datasets, reporting above 92% accuracy. It installs via npm or a standalone shell script on macOS and Linux. The Elastic 2.0 license allows personal and most commercial use, check the full terms if you plan to redistribute the software or offer it as a hosted service.

Copy-paste prompts

Prompt 1
Set up ByteRover CLI in my project and show me how to store a note about our API authentication pattern so Claude Code recalls it next session
Prompt 2
Help me configure ByteRover cloud sync so my whole team shares the same context tree when working with Cursor on our monorepo
Prompt 3
Show me the ByteRover branch and merge commands so I can save a snapshot of my context tree before starting a big refactor
Prompt 4
Set up ByteRover as an MCP server and connect it to Claude Desktop so I can query stored project notes from any chat session
Prompt 5
Walk me through committing a decision to ByteRover's knowledge store after we decide on an architecture pattern so the AI never asks again
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