ByteRover CLI
Overview
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:
- 🌐 Web dashboard for curating and querying context (
brv webui)
- 🖥️ Interactive TUI with REPL interface (React/Ink)
- 🧠 Context tree and knowledge storage management
- 🔀 Git-like version control for the context tree (branch, commit, merge, push/pull)
- 🤖 20 LLM providers (Anthropic, OpenAI, Google, Groq, Mistral, xAI, DeepSeek, and more)
- 🛠️ 24 built-in agent tools (code exec, file ops, knowledge search, memory management)
- 🔄 Cloud sync with push/pull
- 👀 Review workflow for curate operations (approve/reject pending changes)
- 🔌 MCP (Model Context Protocol) integration
- 📦 Hub and connectors ecosystem for skills and bundles
- 🤝 Works with 22+ AI coding agents (Cursor, Claude Code, Windsurf, Cline, and more)
- 🏢 Enterprise proxy support
Benchmark Results
All benchmarks are run using the production byterover-cli codebase in this repository - no separate research prototype.
We evaluate on two long-term conversational memory benchmarks:
- LoCoMo - ultra-long conversations (~20K tokens, 35 sessions) testing single-hop, multi-hop, temporal, and open-domain retrieval.
- LongMemEval-S - large-scale benchmark (23,867 docs, ~48 sessions per question) testing 6 memory abilities including knowledge update, temporal reasoning, and multi-session synthesis.
LoCoMo - 96.1% overall accuracy (1,982 questions, 272 docs).
LongMemEval-S - 92.8% overall accuracy (500 questions, 23,867 docs).
All metrics are LLM-as-Judge accuracy (%). See the paper, or self-hosted PDF for full details.
Quick Start
Shell Script (macOS & Linux)
No Node.js required - everything is bundled.
curl -fsSL https://byterover.dev/install.sh | sh
Supported platforms: macOS ARM64, macOS x64 (Intel), Linux x64, Linux ARM64.
npm (All Platforms)
Requires Node.js >= 20.
npm install -g byterover-cli
Verify
brv --version
First Run
cd your/project
brv
The REPL auto-configures on first run - no setup needed. Type / to discover all available commands:
/curate "Auth uses JWT with 24h expiry" @src/middleware/auth.ts
/query How is authentication implemented?
ByteRover Cloud
ByteRover Cloud is a hosted platform for teams to sync, share, and manage context knowledge across projects and machines.
Everything works locally by default - Cloud adds collaboration and persistence without changing your workflow.
Sign in from the dashboard, or run brv login with an API key.
- 🔄 Team context sync — push and pull shared knowledge across teammates
- 📂 Shared spaces — organize context across multiple projects and teams
- 💻 Multi-machine access — sync your context tree across devices with cloud backup
- 💻 Multi-machine access — sync your context tree across devices
- 🧠 Built-in hosted LLM — start immediately with limited free usage
- 👥 Team management — manage members, spaces, and permissions via the web app
- 📊 Usage analytics — track seat allocation and monthly credit consumption
- 🔒 SOC 2 Type II certified infrastructure with privacy mode
Most users only need brv webui. The commands below are for advanced users and automation. Run brv --help for the full, up-to-date reference.
Core Workflow
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
Sync (Legacy)
brv push # Legacy — migrate or snapshot context to cloud
brv pull # Legacy — restore context from cloud snapshot
Use brv vc push / brv vc pull for version-controlled sync going forward.
Version Control
brv vc init # Initialize version control for context tree
brv vc status # Show version control status
brv vc add # Stage files for the next commit
brv vc commit # Save staged changes as a commit
brv vc log # Show commit history
brv vc branch # List, create, or delete branches
brv vc checkout # Switch branches
brv vc merge # Merge a branch into the current branch
brv vc clone # Clone a ByteRover space repository
brv vc push # Push commits to ByteRover cloud
brv vc pull # Pull commits from ByteRover cloud
brv vc fetch # Fetch refs from ByteRover cloud
brv vc remote # Show current remote origin
brv vc remote add # Add a named remote
brv vc remote set-url # Update a remote URL
brv vc config # Get or set commit author
brv vc reset # Unstage files or undo commits
Providers & Models
brv providers list # List available LLM providers
brv providers connect # Connect to an LLM provider
brv providers switch # Switch active provider
brv providers disconnect # Disconnect a provider
brv model list # List available models
brv model switch # Switch active model
Hub & Connectors
brv hub list # List available hub packages
brv hub install # Install a hub package
brv hub registry add # Add a custom registry
brv hub registry list # List registries
brv hub registry remove # Remove a registry
brv connectors list # List connectors
brv connectors install # Install a connector
Spaces (Deprecated)
brv space list # Deprecated — use web dashboard
brv space switch # Deprecated — use brv vc clone
Settings
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:
- Linux:
$XDG_DATA_HOME/brv/settings.json (defaults to ~/.local/share/brv/settings.json)
- macOS:
~/Library/Application Support/brv/settings.json
- Windows:
%LOCALAPPDATA%/brv/settings.json
Set 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.
Other
brv mcp # Start MCP server
brv login # Authenticate to ByteRover
brv logout # Disconnect and clear credentials
brv locations # List registered projects
brv restart # Restart daemon
brv debug # Debug mode
Run brv --help for the full command reference.
ByteRover CLI supports 20 LLM providers out of the box. Connect and switch providers from the dashboard, or use brv providers connect / brv providers switch.
| Provider | Description |
|---|
| Anthropic | Claude models |
| OpenAI | GPT models |
| Google | Gemini models |
| Groq | Fast inference |
| Mistral | Mistral models |
| xAI | Grok models |
| Cerebras | Fast inference |
| Cohere | Command models |
| DeepInfra | Open-source model hosting |
| DeepSeek | DeepSeek V3 and R1 reasoning models |
| OpenRouter | Multi-provider gateway |
| Perplexity | Search-augmented models |
| TogetherAI | Open-source model hosting |
| Vercel | AI SDK provider |
| Minimax | Minimax models |
| Moonshot | Kimi models |
| GLM | GLM models |
| GLM Coding Plan | GLM models on Z.AI Coding Plan subscription |
| OpenAI-Compatible | Any OpenAI-compatible API |
| ByteRover | ByteRover's hosted models |
Worktrees and Knowledge Sources
Vocabulary
- Worktree link — a subdirectory pointer to a parent project (
brv worktree)
- Source — a read-only reference to another project's knowledge (
brv source)
- Origin — where an indexed search result came from (
local vs shared)
ByteRover can run from a linked subdirectory without creating a nested .brv/.
projectRoot: the directory that owns .brv/config.jsonworktreeRoot: the linked worktree directory, or projectRoot when unlinkedclientCwd: the shell cwd where you ran brv
When you run brv query or brv curate from a linked worktree, implicit defaults use worktreeRoot so scope stays stable even if clientCwd drifts deeper into the package. Explicit relative paths that you pass yourself, such as brv curate -f ./src/auth.ts, still resolve from clientCwd to match normal shell behavior.
Use brv worktree add from the project root to register a subdirectory (or sibling) as a worktree. This creates a .brv pointer file in the target directory that redirects to the parent project — the same pattern git uses for git worktree. Use brv worktree remove to unregister, and brv worktree list to inspect. To search another project's knowledge from here, use brv source add <path> (with brv source list / brv source remove to inspect or detach).
Documentation
Visit docs.byterover.dev for full guides on setup, integrations, and advanced usage.
Contributing
We welcome contributions! See our Contributing Guide for development setup, coding standards, and the PR workflow.
Community & Support
ByteRover CLI is built and maintained by the ByteRover team.
- Join our Discord to share projects, ask questions, or just say hi
- Report issues on GitHub
- If you enjoy ByteRover CLI, please give us a star on GitHub — it helps a lot!
- Follow @kevinnguyendn on X
Contributors
Star History
License
Elastic License 2.0. See LICENSE for full terms.
