AI Skill Hub 强烈推荐:byterover-cli MCP工具 是一款优质的AI工具。已获得 4.8k 颗 GitHub Star,AI 综合评分 8.2 分,在同类工具中表现稳健。如果你正在寻找可靠的AI工具解决方案,这是一个值得深入了解的选择。
byterover-cli MCP工具 是一款基于 TypeScript 开发的开源工具,专注于 MCP协议、AI代理、内存管理 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
byterover-cli MCP工具 是一款基于 TypeScript 开发的开源工具,专注于 MCP协议、AI代理、内存管理 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
# 方式一:npm 全局安装 npm install -g byterover-cli # 方式二:npx 直接运行(无需安装) npx byterover-cli --help # 方式三:项目依赖安装 npm install byterover-cli # 方式四:从源码运行 git clone https://github.com/campfirein/byterover-cli cd byterover-cli npm install npm start
# 命令行使用
byterover-cli --help
# 基本用法
byterover-cli [options] <input>
# Node.js 代码中使用
const byterover_cli = require('byterover-cli');
const result = await byterover_cli.run(options);
console.log(result);
# byterover-cli 配置说明 # 查看配置选项 byterover-cli --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量(覆盖配置文件) export BYTEROVER_CLI_CONFIG="/path/to/config.yml"
<img src="./assets/images/logo/byterover-logo.svg" alt="ByteRover Logo" width="280" />
<p align="center"> <em>Interactive REPL CLI for AI-powered context memory</em> </p>
<p align="center"> <a href="LICENSE"><img src="https://img.shields.io/badge/License-Elastic%202.0-blue.svg" alt="License" /></a> <a href="https://npmjs.org/package/byterover-cli"><img src="https://img.shields.io/npm/v/byterover-cli.svg" alt="Version" /></a> <a href="https://npmjs.org/package/byterover-cli"><img src="https://img.shields.io/npm/dw/byterover-cli.svg" alt="Downloads" /></a> <a href="https://docs.byterover.dev"><img src="https://img.shields.io/badge/Docs-Documentation-green.svg" alt="Documentation" /></a> <a href="https://discord.com/invite/UMRrpNjh5W"><img src="https://img.shields.io/badge/Discord-Join%20Community-7289da" alt="Discord" /></a> </p>
</div>
ByteRover CLI (brv) gives AI coding agents persistent, structured memory. It lets developers curate project knowledge into a context tree, sync it to the cloud, and share it across tools and teammates.
Run brv in any project directory to start an interactive REPL powered by your choice of LLM. The agent understands your codebase through an agentic map, can read and write files, execute code, and store knowledge for future sessions.
📄 Read the paper for the full technical details.
Or download our self-hosted PDF version of the paper here.
Key Features:
brv webui)brv ships with operational defaults that suit most users. The brv settings command group lets you inspect and override these values.
brv settings # List all settings with current and default values
brv settings list # Alias for `brv settings`
brv settings get <key> # Show current and default for one key
brv settings set <key> <value> # Update one key (restart required)
brv settings reset <key> # Restore one key to its default (restart required)
Available keys:
| Key | Default | What it controls |
|---|---|---|
agentPool.maxSize | 10 | Maximum concurrent active projects (one agent process per project) |
agentPool.maxConcurrentTasksPerProject | 5 | Parallel brv curate / brv query tasks within a single project |
llm.iterationBudgetMs | 600000 | Wall-clock budget for the agentic loop on one task, in milliseconds |
llm.requestTimeoutMs | 120000 | Wall-clock budget for one direct LLM HTTP request, in milliseconds |
taskHistory.maxEntries | 1000 | Number of task records brv query-log view retains per project |
Raise llm.iterationBudgetMs when a single brv curate or brv query on a slow local LLM (Ollama on CPU, heavy quantization, cold model load) routinely hits the default 10-minute cap on legitimate work. Lower it on cloud providers when you want a stuck task to surface as an error faster instead of waiting out the full budget. The cap is enforced per task, not per request.
llm.requestTimeoutMs bounds one HTTP request to the LLM. A frozen Ollama or LM Studio connection aborts at this boundary and the retry layer treats it as a transient error rather than wasting the full iteration budget. The setting must satisfy llm.requestTimeoutMs <= llm.iterationBudgetMs; the daemon rejects writes that violate the rule and falls back to defaults for both keys when the file violates it at startup. Suggested presets:
| Profile | llm.requestTimeoutMs | llm.iterationBudgetMs |
|---|---|---|
| Cloud | 120000 (2 min) | 600000 (10 min) |
| Local LLM, fast GPU | 300000 (5 min) | 1200000 (20 min) |
| Local LLM, CPU / heavy quantization | 900000 (15 min) | 3600000 (60 min) |
Settings persist in settings.json under the brv global data directory:
$XDG_DATA_HOME/brv/settings.json (defaults to ~/.local/share/brv/settings.json)~/Library/Application Support/brv/settings.json%LOCALAPPDATA%/brv/settings.jsonSet the BRV_DATA_DIR environment variable to override the directory.
Changes apply after brv restart. The daemon reads the file once at startup and does not observe file changes during runtime. This keeps every running agent on the same configuration for the lifetime of the daemon.
Example session:
$ brv settings list
KEY CURRENT DEFAULT RESTART?
agentPool.maxSize 10 10 yes
agentPool.maxConcurrentTasksPerProject 5 5 yes
taskHistory.maxEntries 1000 1000 yes
$ brv settings set agentPool.maxSize 20
Setting saved. Run `brv restart` to apply.
$ brv settings get agentPool.maxSize
20 (default: 10)
Every command supports --format json for scripting. Validation errors (unknown key, value out of range) exit with code 1 and a message naming the key.
brv # Start interactive REPL
brv webui # Open the ByteRover dashboard (primary UI)
brv status # Show project and daemon status
brv curate # Add context to knowledge storage
brv curate view # View curate history
brv query # Query context tree and knowledge
brv review pending # List pending review operations
brv review approve # Approve curate operations
brv review reject # Reject curate operations
高星项目,MCP生态重要工具。提供AI代理内存管理能力,架构清晰,TypeScript开发,活跃维护,生产就绪度高。
该工具使用 NOASSERTION 协议,商用场景请仔细阅读协议条款,必要时咨询法律意见。
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建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。
总体来看,byterover-cli MCP工具 是一款质量优秀的AI工具,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。
| 原始名称 | byterover-cli |
| 原始描述 | 开源MCP工具:ByteRover CLI (brv) - The portable memory layer for autonomous coding agents (f。⭐4.8k · TypeScript |
| Topics | MCP协议AI代理内存管理自主编程CLI工具 |
| GitHub | https://github.com/campfirein/byterover-cli |
| License | NOASSERTION |
| 语言 | TypeScript |
收录时间:2026-05-18 · 更新时间:2026-05-19 · License:NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。