byterover-cli-memory-layer

Manage persistent project memory for AI coding agents via the ByteRover CLI.

4|Updated May 16, 2026
One-click install
npx skills add https://github.com/reason-machines/devtools-skills --skill byterover-cli-memory-layer-reason-machines
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: byterover-cli-memory-layer
Source: https://github.com/reason-machines/devtools-skills/tree/main/skills/byterover-cli-memory-layer
Command: npx skills add https://github.com/reason-machines/devtools-skills --skill byterover-cli-memory-layer-reason-machines

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires byterover-cli.

What problem does it solve? AI coding agents lose all project knowledge between sessions, forcing developers to repeatedly re-explain architecture decisions, coding patterns, and configuration details. This Skill provides a persistent, version-controlled memory layer so agents can store and retrieve project context across sessions, machines, and teammates. ## Core Features & Use Cases - Knowledge Curation and Querying: Record design decisions, business logic, and configuration patterns with brv curate, then retrieve them with semantic search via brv query. - Version Control for Context: Branch, commit, merge, push, and pull the context tree with Git-like commands (brv vc), enabling team collaboration on shared project knowledge. - MCP and Multi-Agent Integration: Expose memory tools to Claude Desktop and 22+ AI coding agents through the Model Context Protocol server (brv mcp), with support for 20+ LLM providers. - Use Case: A team curates API design decisions and database configuration into the context tree, pushes it to ByteRover Cloud, and a newly onboarded developer clones the shared space and immediately queries setup instructions and endpoint conventions. ## Quick Start Install ByteRover CLI with npm install -g byterover-cli, then ask the agent to initialize ByteRover in the current project and curate a key architectural decision into the context tree.

Frequently Asked Questions about byterover-cli-memory-layer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I give my AI coding agent persistent memory across sessions?▼

Install ByteRover CLI and run brv in your project to create a local context tree. Use brv curate to store decisions and patterns, and brv query to retrieve them in later sessions, so knowledge persists beyond a single conversation.

How to share project context with my team using ByteRover?▼

Initialize version control with brv vc init, then commit context changes with brv vc add and brv vc commit. Push to ByteRover Cloud with brv vc push, and teammates run brv vc pull or brv vc clone to receive the shared context tree.

Does ByteRover work with Claude Desktop and other AI agents?▼

Yes, ByteRover implements the Model Context Protocol. Run brv mcp to start a JSON-RPC server and register it in the client's MCP configuration, allowing Claude Desktop and other MCP-compatible agents to call curate and query tools directly.

Which LLM providers does ByteRover CLI support?▼

ByteRover supports over 20 providers including Anthropic, OpenAI, Google, Groq, Mistral, DeepSeek, and OpenRouter. Connect one with brv providers connect or by setting environment variables like ANTHROPIC_API_KEY, then switch models with brv model switch.

Why are my ByteRover curate operations not appearing?▼

Curate operations are pending by default because the review workflow is enabled. Run brv review pending to list them and brv review approve with the operation ID, or set reviewEnabled to false in .brv/config.json to auto-approve.

Can ByteRover handle monorepos with multiple packages?▼

Yes, use brv worktree add to link package subdirectories to a single parent context tree, avoiding nested .brv directories. You can also add read-only sources with brv source add to query another project's knowledge without duplicating it.