Rock Agent (Experimental)

RockAgent is the core Agent implementation in the ROCK framework, directly inheriting from the Agent abstract base class. It provides complete Agent lifecycle management, including environment initialization, ModelService integration, command execution, and more.

Using sandbox.agent.install() and sandbox.agent.run(prompt), you can install and run Agents in the Sandbox environment provided by Rock.

Core Concepts

The core workflow of RockAgent is divided into two phases:

1. install(config): Initialize the Agent environment, including deploying the working directory, setting environment variables, initializing the runtime environment, etc.
2. run(prompt): Execute the Agent task, replace placeholders, and start the Agent process

Quick Start

Claude Code Example

run_cmd: "claude -p ${prompt}"

runtime_env_config:
  type: node
  custom_install_cmd: "npm install -g @anthropic-ai/claude-code"

env:
  ANTHROPIC_BASE_URL: ""
  ANTHROPIC_API_KEY: ""

IFlowCli Example

run_cmd: "iflow -p ${prompt} --yolo"           # ${prompt} is required

runtime_env_config:
  type: node
  custom_install_cmd: "npm i -g @iflow-ai/iflow-cli@latest"

env:                                            # Environment variables
  IFLOW_API_KEY: "xxxxxxx"
  IFLOW_BASE_URL: "xxxxxxx"
  IFLOW_MODEL_NAME: "xxxxxxx"

LangGraph Agent Example

working_dir: "."                                # Upload local current directory containing langgraph_agent.py to sandbox

run_cmd: "python langgraph_agent.py ${prompt}"  # Run local script

runtime_env_config:
  type: python
  pip:                                          # Install pip dependencies
    - langchain==1.2.3
    - langchain-openai==1.1.7
    - langgraph==1.0.6

env:
  OPENAI_API_KEY: xxxxxxx

Configuration Details

Basic Configuration

agent_type: "default"                           # Agent type identifier (default: "default")
agent_name: "demo-agent"                        # Agent instance name (default: random uuid)
version: "1.0.0"                                # Version identifier (default: "default")
instance_id: "instance-001"                     # Instance ID (default: "instance-id-<random-uuid>")
agent_installed_dir: "/tmp/installed_agent"     # Agent installation directory (default: "/tmp/installed_agent")
agent_session: "my-session"                     # Bash session identifier (default: "agent-session-<random-uuid>")
env:                                            # Environment variables (default: {})
  OPENAI_API_KEY: "xxxxxxx"

Working Directory Configuration

working_dir: "./my_project"                     # Local directory to upload to sandbox (default: None, no upload)
project_path: "/testbed"                        # Working directory in sandbox for cd (default: None)
use_deploy_working_dir_as_fallback: true        # Whether to fall back to deploy.working_dir when project_path is empty (default: true)

Execution Configuration

run_cmd: "python main.py --prompt ${prompt}"    # Agent execution command, must contain ${prompt} (default: None)

# Timeout configuration
agent_install_timeout: 600                      # Installation timeout in seconds (default: 600)
agent_run_timeout: 1800                         # Run timeout in seconds (default: 1800)
agent_run_check_interval: 30                    # Check interval in seconds (default: 30)

Initialization Hooks

pre_init_cmds:                                  # Commands executed before initialization (default: read from env_vars)
  - command: "apt update && apt install -y git"
    timeout_seconds: 300                        # Command timeout in seconds (default: 300)
  - command: "cp ${working_dir}/config.json /root/.config/config.json"
    timeout_seconds: 60

post_init_cmds:                                 # Commands executed after initialization (default: [])
  - command: "echo 'Installation complete'"
    timeout_seconds: 30

Notes:

• pre_init_cmds and post_init_cmds do not inherit the Agent's env environment variables
• Typically used for installation operations and configuration file movement
• Common command examples:
  • apt update && apt install -y git wget tar
  • cp ${working_dir}/config.json /root/.config/config.json

RuntimeEnv Configuration

runtime_env_config:                             # Refer to RuntimeEnv documentation for details
  type: "python"                                # Runtime type: python / node (default: "python")
  version: "3.11"                               # Version number
  pip:                                          # Python dependency package list
    - package1==1.0.0
    - package2==2.0.0
  custom_install_cmd: "git clone https://github.com/SWE-agent/SWE-agent.git && cd SWE-agent && pip install -e ."

Node Runtime Example:

runtime_env_config:
  type: "node"
  version: "22.18.0"
  npm_registry: "https://registry.npmmirror.com"
  custom_install_cmd: "npm i -g some-package"

Automatic Operations:

• Install corresponding runtime based on type (Python or Node.js)
• Install pip dependencies (if configured)
• Execute custom_install_cmd custom installation command (if configured)
• Support npm_registry configuration for Node.js npm mirror source

ModelService Configuration

model_service_config:                           # Refer to ModelService documentation for details
  enabled: true                                 # Enable ModelService (default: false)

Automatic Operations:

• Installation phase: Install ModelService (install only, do not start)
• Run phase: Start ModelService + watch_agent monitoring process

Notes: You need to set the model request URL to the ModelService URL. For example, if the ModelService provides an OpenAI-compatible URL at http://127.0.0.1:8080/v1/chat/completions, you typically need to set the Agent's LLM request URL to http://127.0.0.1:8080/v1/.

API Reference

install(config)

Initialize the Agent environment.

Execution Flow:

1. If working_dir is configured, deploy to sandbox
2. Set up bash session and configure env environment variables
3. Execute pre_init_cmds
4. Initialize RuntimeEnv and ModelService in parallel (if enabled)
5. Execute post_init_cmds

Parameters:

• config: Agent configuration file, supports two input methods:
  • String path: YAML configuration file path, default value is "rock_agent_config.yaml"
  • RockAgentConfig object: Directly pass a RockAgentConfig instance

run(prompt)

Execute the Agent task.

Execution Flow:

1. Replace placeholders and prepare Agent run command
2. Start the agent process
3. If ModelService is enabled, start watch_agent
4. Wait for task completion and return results

Advanced Usage

Difference and Interaction between working_dir and project_path

Configuration	Function	Interaction Method
working_dir	Local directory uploaded to sandbox	Calls deploy.deploy_working_dir() to upload, after upload deploy.working_dir becomes the path in sandbox
${working_dir}	Placeholder in commands	Replaced by deploy.format() with the value of deploy.working_dir, replaced in init_cmds and run_cmd in the configuration
project_path	Working directory in sandbox	Used for cd project_path before running, when not set it enters the deploy.working_dir working directory
use_deploy_working_dir_as_fallback	Whether to fall back to deploy.working_dir when project_path is not set at runtime	Default is true, when set to false it will not enter working_dir even if project_path is not set

Usage Recommendations:

• Use working_dir to upload local project code to sandbox
• Use project_path to specify the working directory in sandbox (e.g., /testbed)
• Set use_deploy_working_dir_as_fallback: false scenario: Need to perform local file mounting, but want to run Agent in the image's default working directory

Placeholder Usage

Rock Agent supports replacing the following placeholders in the configuration file:

• ${prompt}: Required in run_cmd, will be replaced with the prompt passed to run(prompt)
• ${working_dir}: Optional, will be replaced with the actual working directory path in sandbox, also supported in init_cmds and run_cmd

Example:

run_cmd: "python ${working_dir}/main.py --prompt ${prompt}"

use_deploy_working_dir_as_fallback Explanation

When project_path is not set:

• true (default): Before running Agent, it will automatically cd to deploy.working_dir
• false: Before running Agent, it will not automatically switch directories, staying in the current directory

Applicable Scenarios:

• true: Most scenarios, where you want Agent to run in the uploaded code directory
• false: Need to mount local files, but want to run Agent in the image's default working directory (e.g., /app, /testbed)

Complete Configuration Example

# ========== Basic Configuration ==========
agent_type: "default"
agent_name: "demo-agent"
version: "1.0.0"
instance_id: "instance-001"
agent_installed_dir: "/tmp/installed_agent"
agent_session: "my-session"
env:
  OPENAI_API_KEY: "xxxxxxx"

# ========== Working Directory Configuration ==========
working_dir: "./my_project"
project_path: "/testbed"
use_deploy_working_dir_as_fallback: true

# ========== Run Configuration ==========
run_cmd: "python ${working_dir}/main.py --prompt ${prompt}"

# Timeout configuration
agent_install_timeout: 600
agent_run_timeout: 1800
agent_run_check_interval: 30

# ========== Initialization Commands ==========
pre_init_cmds:
  - command: "apt update && apt install -y git"
    timeout_seconds: 300
  - command: "cp ${working_dir}/config.json /root/.config/config.json"
    timeout_seconds: 60

post_init_cmds:
  - command: "echo 'Installation complete'"
    timeout_seconds: 30

# ========== Runtime Environment Configuration ==========
runtime_env_config:
  type: "python"
  version: "3.11"
  pip:
    - langchain==1.2.3
    - langchain-openai==1.1.7

# ========== ModelService Integration ==========
model_service_config:
  enabled: true

Usage Examples

Using YAML Configuration File (Recommended)

# prepare a rock_agent_config.yaml
await sandbox.agent.install(config="rock_agent_config.yaml")
await sandbox.agent.run(prompt="hello")