@bunny-agent/runner-cli

Bunny Agent Runner CLI - A lightweight, local command-line interface for running AI agents in your terminal.

Like gemini-cli, claude-code, or codex-cli, this tool runs directly on your local filesystem. It has two subcommands, one per output protocol: run streams AI SDK UI messages to stdout for a single task, and acp serves as a long-lived Agent Client Protocol agent over stdio for editors like Zed and JetBrains.

🎯 Key Features

• 🔌 Choose Different Runners: Switch between Claude, Codex, Gemini, Copilot with --runner flag
• 🚀 Local Execution: Runs directly on your filesystem, no sandbox required
• 💨 Lightweight: No manager dependency, minimal overhead
• 📡 Streaming: Real-time AI SDK UI streaming (run) or ACP JSON-RPC over stdio (acp)

📐 Architecture

runner-cli
  ├─ run  → @bunny-agent/runner-harness → runner-claude / runner-codex / ...
  └─ acp  → @bunny-agent/server-acp     → runner-harness → same runners

Dependencies:
✅ @bunny-agent/runner-harness (dispatches to every runner)
✅ @bunny-agent/server-acp (the `acp` subcommand's ACP agent + stdio transport)
❌ NO @bunny-agent/manager
❌ NO @bunny-agent/sandbox-*

Difference from manager-cli:

• runner-cli: Local filesystem, no isolation, lightweight, direct runner usage
• manager-cli: Sandboxed execution, uses manager + sandbox adapters + runner

Installation

# Global install (recommended if you want the `bunny-agent` command)
npm install -g @bunny-agent/runner-cli@latest

# Or add to a project
npm install @bunny-agent/runner-cli@latest

Usage

bunny-agent run [options] -- "<user input>"

Without installing globally, you can also run it via npx:

npx -y @bunny-agent/runner-cli@latest run -- "Create a hello world script"

Basic Examples

# Using Claude (default)
bunny-agent run -- "Create a hello world script"

# Explicitly choose Claude
bunny-agent run --runner claude -- "Create a hello world script"

# Using Codex
bunny-agent run --runner codex -- "Build a REST API with Express"

# Using Gemini
bunny-agent run --runner gemini -- "Build a REST API with Express"

# Using GitHub Copilot
bunny-agent run --runner copilot -- "Refactor this code"

# With custom system prompt
bunny-agent run --runner claude --system-prompt "You are a coding assistant" -- "Build a REST API with Express"

Options

--allowed-tools limits built-in runner tools. Custom tools are provided through the SDK streamText({ tools }) API, not directly through runner-cli.

Output Format

bunny-agent run always outputs AI SDK data stream (SSE) format, one-shot — it runs the task and exits.

bunny-agent run -- "Calculate 2+2"

Output:

data: {"type":"start","messageId":"msg_123"}
data: {"type":"text-delta","id":"text_1","delta":"The answer is 4."}
data: [DONE]

For the ACP protocol instead, use bunny-agent acp — see below. The two are separate subcommands, not a flag on run: ACP is a long-lived session (initialize → session/new → possibly many session/prompt calls from the connected editor), not a single request/response.

bunny-agent acp

Serves BunnyAgent as an Agent Client Protocol agent over stdio — the transport editors use when they spawn an agent binary directly, e.g. Zed's "custom agent" setup or JetBrains' AI Assistant agent integration. The process stays alive handling JSON-RPC over stdin/stdout until the connecting client disconnects; it does not take a task argument like run does.

bunny-agent acp --runner claude
bunny-agent acp --runner pi --model gemini-2.0-flash --cwd ./my-project

These flags set the session default. ACP's session/new has no native runner/model field, so a connecting client can still override per-session through the protocol's _meta extension, namespaced under "bunny-agent":

{ "cwd": "/workspace", "mcpServers": [],
  "_meta": { "bunny-agent": { "runner": "pi", "model": "gemini-2.0-flash" } } }

Point an ACP client at the CLI itself as the agent command (exact configuration is client-specific — see each editor's ACP docs):

{ "command": "bunny-agent", "args": ["acp", "--runner", "claude"] }

For the HTTP equivalent (POST /api/coding/acp, used by the daemon instead of a spawned subprocess), see apps/daemon/README.md. Both are backed by @bunny-agent/server-acp and support every runner this CLI does.

Environment Variables

Advanced Examples

Specify Working Directory

bunny-agent run --cwd ./my-project -- "Fix the bug in main.ts"

Combined Options

bunny-agent run \
  -m claude-sonnet-4-20250514 \
  --system-prompt "You are a helpful coding assistant" \
  --max-turns 10 \
  -- "Build a REST API"

Architecture

The CLI is designed to:

1. Execute in a specific working directory
2. Load settings from .claude/settings.json and CLAUDE.md in the project
3. Stream AI SDK UI messages directly to stdout
4. Output AI SDK data stream (SSE) format

🐳 Docker Image Build

Build Docker images with agent templates baked in:

# Build image
bunny-agent image build --name vikadata/bunny-agent-seo --tag 0.1.0 --template ./templates/seo-agent

# Build and push
bunny-agent image build --name vikadata/bunny-agent-seo --tag 0.1.0 --template ./templates/seo-agent --push

# Without template
bunny-agent image build --name vikadata/bunny-agent --tag 0.1.0

Image Build Options

Related Documentation

• Skills — Where skills live and how they are loaded (local and Docker)
• Claude Agent SDK
• AI SDK UI Stream Protocol
• Agent Client Protocol — the protocol bunny-agent acp speaks
• Runner maturity / output protocols — how run and acp compare