deepagent-runner 0.1.2

A terminal application for running DeepAgent in a workspace directory

DeepAgent Runner 🤖

A cross-platform terminal application that runs a DeepAgent in your workspace directory to help you write code, fix bugs, and execute shell commands.

Features

• 🗂️ Workspace Sandbox: Agent operates only within your selected directory
• 🐚 Cross-platform Shell Execution: Prefers Linux-style shells (bash), falls back to PowerShell on Windows
• 🤖 DeepAgent Integration: Full planning, filesystem tools, and subagent capabilities
• 🎨 Rich Terminal UI: Colorful, intuitive command-line interface with Markdown rendering
• 🔒 Human-in-the-loop: Approve/edit/reject sensitive operations before execution
• 💬 Interactive REPL: Natural conversation with multi-turn context
• 💾 Session Management: Persistent sessions with metadata, resume across restarts, multiple concurrent sessions

Prerequisites

• Python 3.11 or higher
• OpenAI API key

Installation

Quick Install (from PyPI - Recommended)

Nếu package đã được publish lên PyPI:

# 1. Cài đặt trực tiếp từ PyPI (không cần source code)
python3.11 install deepagent-runner

# Hoặc với uv (nhanh hơn)
uv pip install deepagent-runner

# Với optional dependencies (web research)
python3.11 install "deepagent-runner[tavily]"

Bước tiếp theo: Sau khi cài đặt, bạn cần:

1. Tạo file .env với API keys (xem phần Configuration)
2. Chạy deepagent-runner check để kiểm tra cấu hình
3. Bắt đầu sử dụng: deepagent-runner --workspace /path/to/project

Install from Git Repository

Nếu chưa publish lên PyPI, có thể cài từ Git:

# Cài trực tiếp từ Git repo (không cần clone)
pip install git+https://github.com/yourusername/CodeAgent.git

# Hoặc với uv
uv pip install git+https://github.com/yourusername/CodeAgent.git

Install from Source (Development)

Nếu muốn develop hoặc modify code:

# Clone repository
git clone <repository-url>
cd CodeAgent

# Create virtual environment
uv venv
source .venv/bin/activate  # On Linux/macOS
# .venv\Scripts\activate   # On Windows

# First install deepagents (required dependency)
uv add deepagents

# Then install the package in editable mode
uv pip install -e .

# Optional: Install tavily-python for web research (research-agent subagent)
uv pip install tavily-python

Hoặc với pip:

# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate  # On Linux/macOS
# .venv\Scripts\activate   # On Windows

# Install deepagents first (required dependency)
pip install deepagents

# Then install the package
pip install -e .

# Optional: Install tavily-python for web research (research-agent subagent)
pip install tavily-python

Development installation

# After activating venv
pip install -e ".[dev]"

# Or with tavily support
pip install -e ".[dev,tavily]"

Note: Always activate your virtual environment before running commands. See INSTALL.md for detailed installation instructions.

Uninstall

Để gỡ bỏ package:

# Gỡ package
python3.11 -m pip uninstall deepagent-runner

# Hoặc với uv
uv pip uninstall deepagent-runner

Khi được hỏi xác nhận, nhấn y hoặc Enter.

Kiểm tra đã gỡ chưa:

# Kiểm tra package còn tồn tại không
python3.11 -m pip show deepagent-runner 

# Hoặc kiểm tra trong danh sách đã cài
python3.11 -m pip list | grep deepagent-runner

Nếu không thấy output, package đã được gỡ thành công.

Lưu ý:

• Gỡ package không xóa file .env hoặc các cấu hình khác
• Nếu cài trong virtual environment, bạn có thể đơn giản deactivate hoặc xóa venv
• Nếu cài global (không dùng venv), cần chạy pip uninstall với quyền phù hợp

Configuration

Sau khi cài đặt từ PyPI, bạn cần tạo file .env để cấu hình API keys.

Tạo file .env

Cách 1: Tạo trong workspace directory (khuyến nghị)

Khi bạn chạy deepagent-runner --workspace /path/to/project, tool sẽ tự động tìm file .env trong thư mục workspace:

# Tạo file .env trong workspace của bạn
cd /path/to/your/workspace
nano .env  # hoặc vim, code, etc.

Cách 2: Tạo trong home directory

Tool cũng sẽ tìm file .env trong home directory (~/.env hoặc %USERPROFILE%\.env trên Windows):

# Linux/macOS
nano ~/.env

# Windows
notepad %USERPROFILE%\.env

Nội dung file .env

Tạo file .env với nội dung sau:

# Required: OpenAI API key
OPENAI_API_KEY=sk-your-openai-api-key-here

# Optional: Model to use (default: openai:gpt-4.1-mini)
OPENAI_MODEL=openai:gpt-4.1-mini

# Optional: For web research capabilities
TAVILY_API_KEY=your-tavily-key-here

# Optional: For image analysis (read_image tool)
VISION_MODEL=openai:gpt-4o-mini

Lấy API Keys

• OpenAI API Key: Đăng ký tại https://platform.openai.com/api-keys
• Tavily API Key (optional): Đăng ký tại https://www.tavily.com/
• Vision Model: Sử dụng model có vision capability như openai:gpt-4.1-mini hoặc openai:gpt-4o-mini

Kiểm tra cấu hình

Sau khi tạo file .env, kiểm tra cấu hình:

deepagent-runner check

Bạn sẽ thấy thông tin về API keys đã được cấu hình.

Usage

Important: Make sure your virtual environment is activated before running commands:

source .venv/bin/activate  # On Linux/macOS
# .venv\Scripts\activate   # On Windows

Basic usage

Run in current directory:

deepagent-runner --workspace .

Specify workspace:

deepagent-runner --workspace /path/to/project

Example Session

$ deepagent-runner --workspace my-project

Welcome to DeepAgent Runner! 🤖

You: List all Python files in src/

Agent: Found 5 Python files:
- src/main.py
- src/utils.py
...

You: Run the tests

⚠️ Agent wants to execute: pytest tests/
Decision (approve/edit/reject): approve

✓ All tests passed!

You: /exit
Goodbye! 👋

REPL Commands

• /help - Show help and examples
• /workspace - Display workspace info
• /config - Show configuration (including session info)
• /sessions - List all saved sessions
• /rename <name> - Rename current session for easy identification
• /clear - Clear conversation history and create new session
• /exit - Exit session

Advanced options

deepagent-runner \
  --workspace /path/to/project \
  --model openai:gpt-4.1-mini \
  --max-runtime 600 \
  --log-file agent.log \
  --verbose

For detailed examples and workflows, see USAGE.md.

Session Management

DeepAgent Runner supports persistent sessions that save automatically. Each session maintains its own conversation history and metadata.

List All Sessions

# List all saved sessions
deepagent-runner sessions

# Filter by workspace
deepagent-runner sessions --workspace /path/to/project

Resume a Session

# Resume a specific session
deepagent-runner --session abc12345

# Or with workspace (if different from session's workspace)
deepagent-runner --workspace /path/to/project --session abc12345

Session Commands in REPL

While in a session, you can manage it:

You: /sessions

Sessions
ID          Name              Workspace    Messages    Last Used
abc123...   Current ←         my-project   5           2025-12-18 10:30
def456...   Bug Fix            my-project   12          2025-12-18 09:15

You: /rename Feature: User Profiles
✓ Session renamed to: Feature: User Profiles

You: /config

Configuration
  Model: openai:gpt-4.1-mini
  Session ID: abc123...
  Workspace: /path/to/project
  Message Count: 5
  Session Name: Feature: User Profiles

Session Storage

Sessions are stored at: ~/.deepagent/sessions/

• sessions.db - Session metadata (name, workspace, model, timestamps)
• checkpoints.db - Conversation state (if using persistent checkpointer)

Example: Multiple Sessions

# Terminal 1: Frontend work
$ deepagent-runner --workspace frontend
You: /rename Frontend Work
You: Update the navbar component

# Terminal 2: Backend work (concurrent)
$ deepagent-runner --workspace backend
You: /rename Backend API
You: Add authentication endpoint

# Both sessions run independently!

For complete session management guide, see SESSION_MANAGEMENT.md.

Check system configuration

# Make sure venv is activated first!
deepagent-runner check

Show version

# Make sure venv is activated first!
deepagent-runner version

Troubleshooting: If you get command not found: deepagent-runner, make sure:

1. Virtual environment is activated: source .venv/bin/activate
2. Package is installed: pip install -e . or uv pip install -e .

See INSTALL.md for more troubleshooting tips.

CLI Options

Option	Short	Description	Default
--workspace	-w	Path to workspace directory	Current directory (interactive)
--model	-m	Model identifier (e.g., openai:gpt-4.1-mini)	From OPENAI_MODEL or openai:gpt-4.1-mini
--session	-s	Resume an existing session by ID	None (creates new session)
--max-runtime		Max command execution time (seconds)	300
--log-file		Path to log file	None
--verbose	-v	Enable verbose output	False

Additional Commands

Command	Description
deepagent-runner sessions	List all saved sessions
deepagent-runner sessions --workspace <path>	Filter sessions by workspace
deepagent-runner check	Check system configuration and API keys
deepagent-runner version	Show version information

Documentation

• USAGE.md - Complete usage guide with examples
• INSTALL.md - Installation instructions
• SESSION_MANAGEMENT.md - Complete guide to session management

What Can the Agent Do?

Code Analysis & Navigation:

• List and search files
• Read and analyze code structure
• Find patterns, TODOs, bugs

Code Modification:

• Write new files
• Edit existing files (with approval)
• Refactor code
• Add documentation

Testing & Execution:

• Run test suites
• Execute build scripts
• Run linters and formatters
• Any shell command (with approval)

Project Management:

• Plan multi-step tasks
• Track progress with todos
• Delegate to subagents

All within the safety of workspace sandboxing! 🔒

License

MIT