ntn 0.1.2

Minimal AI coding agent powered by Claude

NTN - Coding Agent with Claude Opus 4.5

A minimal AI agent that uses Claude API to help with coding tasks in a workspace.

Features

• Docker-first file operations: All file operations run in a Docker container with Unix tools
• Web search: Search using DuckDuckGo (ddgs package)
• Web fetching: Fetch and read webpage content
• Terminal execution: Run Windows commands when needed
• Persistent container: Single container per session, auto-starts on launch
• Command denylist: Dangerous commands require user confirmation
• Colored output: Easy-to-read console with color-coded messages
• Debug logging: Incremental logging to debug/ folder (crash-resilient)
• Resume sessions: Continue previous conversations with -r flag
• Mid-turn resume: Automatically recovers from crashes mid-tool-execution
• Auto-compact: Automatically summarizes context when approaching token limit
• Auto-cleanup: Empty conversations (no user messages) are automatically deleted
• Rate limit handling: Automatically waits and retries using retry-after header
• Prompt caching: System prompt and tools are cached to reduce rate limit usage
• Streaming output: Real-time response display (always enabled)
• Extended thinking: Enable deep reasoning for complex tasks with -t flag

Installation

Install from PyPI:

pip install ntn

Or install from source:

git clone https://github.com/ntrnghia/coding-agent.git
cd ntn
pip install -e .

Setup

Set your Anthropic API key:

export ANTHROPIC_API_KEY='your-api-key-here'

Or create a .env file:

echo "ANTHROPIC_API_KEY=your-api-key-here" > .env

(Optional) Install Docker for sandbox functionality.

Usage

Run the agent:

ntn

Resume a previous session:

# Resume most recent session
ntn -r

# Resume specific session
ntn -r debug/debug_20251210_120000.txt

Enable extended thinking (better for complex reasoning):

ntn -t

Combine flags:

ntn -t -r  # Resume with extended thinking
ntn -s -r  # Resume with streaming

Alternative: Run as Python module:

python -m ntn

Input controls:

• Shift+Enter - New line (shows \)
• Enter - Submit message
• Ctrl+C - Exit the agent

Example prompts:

• "Create a new Python project with main.py and tests/"
• "Search for PyTorch distributed training docs"
• "List all Python files in this directory"
• "Run pytest on my tests"
• "Tell me what the code in D:\Downloads\some-project does" (uses Docker sandbox)

Package Structure

ntn/
├── src/ntn/
│   ├── __init__.py    # Package exports
│   ├── __main__.py    # Entry for `python -m ntn`
│   ├── agent.py       # Main agent with auto-compact and resume support
│   ├── tools.py       # Tool implementations (Terminal, Web, Docker)
│   └── cli.py         # CLI entry point
├── pyproject.toml     # Package configuration
├── LICENSE            # MIT License
└── README.md          # This file

Tools

Terminal Tool

Executes shell commands in your workspace. Dangerous commands (rm, sudo, curl, etc.) require user confirmation before execution.

Web Search Tool

Searches the web using DuckDuckGo, returns top 10 results.

Fetch Web Tool

Fetches and extracts text content from URLs.

Docker Sandbox Tool

All file operations run in a Docker container for consistent Unix environment:

• Auto-starts on launch with workspace pre-mounted
• Single persistent container per session (named agent_<timestamp>)
• Directories mounted at Unix-style paths: D:\Downloads\project → /d/downloads/project
• Read-write access to all mounted directories
• Multiple directories can be mounted dynamically
• Container persists across prompts and survives resume
• Lazy recovery: If container stops, auto-restarts on next command
• Uses python:slim image by default

Context Management

The agent uses Claude Opus 4.5 with a 200K token context window. When the context approaches the limit:

1. Auto-compact triggers: Summarizes older conversation turns
2. Preserves current task: Summary includes your current question
3. Seamless continuation: You won't notice the compaction

Debug file shows compaction events:

=== COMPACTION EVENT ===
Reason: Exceeded context (180000 tokens attempted)
Removed turns: 1-3
Summary content: [condensed conversation]

Resume Sessions

Sessions are logged incrementally to debug/debug_<timestamp>.txt. To resume:

# Resume most recent session
ntn -r

# Resume specific session  
ntn -r debug/debug_20251210_120000.txt

On resume:

• Previous conversation is displayed (including tool operations)
• Context is restored (including any compacted summaries)
• Container state is restored (mounts preserved)
• New messages append to the same debug file
• Crash recovery: If the agent crashed mid-turn, it will automatically continue from where it left off

Debug Log Format

Debug files use an incremental format for crash resilience:

=== TURN 1 ===
--- USER ---
<user message>
--- ASSISTANT ---
<JSON response>
--- TOOL_RESULT ---
<JSON tool results>
--- END_TURN ---

Each block is written immediately, so even if the agent crashes, the debug file contains all completed operations.

Output Format

The agent uses colored output for readability:

• 🟢 Green: Agent messages
• 🟡 Yellow: Tool operations (📂 List files, 📄 Read file, ✏️ Edit file, 🐳 Docker, etc.)
• 🔵 Cyan: User prompts
• 🔴 Red: Errors

Full JSON input/output is logged to debug/debug_<timestamp>.txt for debugging.

Security Notes

• Commands run without timeout (for long-running processes)
• Dangerous commands require explicit user confirmation
• Docker sandbox provides isolated environment for external directories
• All commands run in the specified workspace directory
• Never commit API keys to version control