devduck 1.4.0

🦆 Self-adapting agent - one file.

🦆 DevDuck

Self-modifying AI agent that hot-reloads its own code—builds itself as it runs.

One Python file that adapts to your environment, fixes itself, and expands capabilities at runtime.

Learn more: https://duck.nyc

🎬 See It In Action

Feature	What You'll See	Demo
🔥 Hot-Reload	Agent detects code changes and restarts instantly	Watch
🌐 Web UI	Clean web interface with real-time streaming	Watch
🛠️ Dynamic Tools	Save .py file in ./tools/ → use instantly	Watch
🌊 TCP Streaming	Connect via netcat, apps, or other agents	Watch
🔗 Zenoh P2P	Auto-discover & coordinate multiple DevDucks	Multi-terminal magic ✨
🔌 IPC & Tray	macOS menu bar + Unix socket IPC
💬 Ambient Overlay	Floating AI input with glassmorphism UI	Watch

---

Quick Start

# Install & run
pipx install devduck && devduck

# With speech-to-speech capabilities (optional)
pipx install "devduck[speech]" && devduck

# One-shot query
devduck "create a REST API with FastAPI"

# Python API
python -c "import devduck; devduck('analyze this code')"

Requirements: Python 3.10-3.13, AWS credentials (or Ollama/Anthropic/GitHub/MLX)

Optional extras:

• devduck[speech] - Real-time speech-to-speech conversations (Nova Sonic, OpenAI Realtime, Gemini Live)

---

Core Capabilities

Feature	What It Does	How to Use
🔥 Hot-Reload	Auto-restarts on code changes	Edit __init__.py → saves → auto-restart
🛠️ Runtime Tools	Add/remove tools without restart	manage_tools(action="add", ...)
📦 Dynamic Loading	Install packages and load tools on-the-fly	install_tools(action="install_and_load", package="...")
🧠 Auto-RAG	Remembers conversations via Knowledge Base	Set DEVDUCK_KNOWLEDGE_BASE_ID
🌊 Multi-Protocol	TCP, WebSocket, MCP, IPC servers	Auto-starts on ports 9999, 8080, 8000
🔗 Zenoh P2P	Auto-discover & coordinate with other DevDucks	zenoh_peer(action="broadcast", message="...")
🔌 MCP Client	Connect to external MCP servers	Set MCP_SERVERS env var
💾 State Time-Travel	Save/restore agent state	state_manager(action="export")
📝 Self-Improvement	Updates own system prompt	system_prompt(action="add_context", ...)
☁️ AWS Deploy	One-command serverless	agentcore_config(auto_launch=True)
🎤 Speech-to-Speech	Real-time voice conversations	pip install devduck[speech]

---

Developer Setup

git clone [e-mail]:cagataycali/devduck.git
cd devduck
python3.13 -m venv .venv
source .venv/bin/activate

# Basic install
.venv/bin/pip3.13 install -e .

# With speech capabilities
.venv/bin/pip3.13 install -e ".[speech]"

devduck

---

Architecture

graph TB
    A[User Input] -->|CLI/TCP/WS/MCP/IPC| B[DevDuck Core]
    B -->|Auto RAG| C[Knowledge Base]
    C -.->|Context Retrieval| B
    B -->|Tool Calls| D[40+ Built-in Tools]
    D --> E[shell/editor/calculator]
    D --> F[GitHub/AgentCore]
    D --> G[TCP/WebSocket/MCP/IPC]
    D --> H[tray/ambient/cursor/clipboard]
    B -->|Hot-reload| I[./tools/*.py + __init__.py]
    I -.->|Load Instantly| D
    B -->|Runtime| K[manage_tools/install_tools]
    K -.->|Expand| D
    B <-->|Zenoh P2P| L[Other DevDucks]
    L -.->|Auto-discover| B
    B -->|Response| J[User Output]
    J -.->|Store Memory| C
    
    style B fill:#e1f5ff
    style C fill:#d4edda
    style I fill:#fff3cd
    style K fill:#ffe6cc
    style L fill:#f0e6ff

Self-adapting loop: Query → RAG → Tools → Response → Memory → Hot-reload/Runtime-load → Repeat

---

Model Setup

DevDuck auto-detects providers based on credentials:

Priority: Bedrock → Anthropic → OpenAI → GitHub → Gemini → Cohere → Writer → Mistral → LiteLLM → LlamaAPI → MLX → Ollama

Provider	API Key	Auto-Detected
Bedrock	AWS credentials	✅ If boto3 auth succeeds
Anthropic	ANTHROPIC_API_KEY	✅ If key present
OpenAI	OPENAI_API_KEY	✅ If key present
GitHub	GITHUB_TOKEN or PAT_TOKEN	✅ If key present
Gemini	GOOGLE_API_KEY or GEMINI_API_KEY	✅ If key present
Cohere	COHERE_API_KEY	✅ If key present
Writer	WRITER_API_KEY	✅ If key present
Mistral	MISTRAL_API_KEY	✅ If key present
LiteLLM	LITELLM_API_KEY	✅ If key present
LlamaAPI	LLAMAAPI_API_KEY	✅ If key present
MLX	No key needed	✅ On Apple Silicon (M1/M2/M3)
Ollama	No key needed	✅ Fallback if nothing else found

Just set your API key - DevDuck handles the rest:

export ANTHROPIC_API_KEY=sk-ant-...
devduck  # Auto-uses Anthropic

export OPENAI_API_KEY=sk-...
devduck  # Auto-uses OpenAI

export GOOGLE_API_KEY=...
devduck  # Auto-uses Gemini

Manual override:

export MODEL_PROVIDER=bedrock
export STRANDS_MODEL_ID=us.anthropic.claude-sonnet-4-20250514-v1:0
devduck

---

Tool Management

Runtime Tool Management

Add, remove, or reload tools while agent is running:

# List all loaded tools
manage_tools(action="list")

# Add tools from a package at runtime
manage_tools(action="add", package="strands_fun_tools", tool_names="cursor,clipboard,bluetooth")

# Remove tools you don't need
manage_tools(action="remove", tool_names="cursor,clipboard")

# Reload specific tools after editing
manage_tools(action="reload", tool_names="shell,editor")

# Reload all tools (restarts agent)
manage_tools(action="reload")

# Load custom tool from file
manage_tools(action="add", tool_path="./my_custom_tool.py")

Dynamic Package Installation

Install Python packages and load their tools at runtime:

# Discover available tools before loading
install_tools(action="list_available", package="strands-fun-tools", module="strands_fun_tools")

# Install package and load all tools
install_tools(action="install_and_load", package="strands-agents-tools", module="strands_tools")

# Install and load specific tools only
install_tools(
    action="install_and_load",
    package="strands-fun-tools", 
    module="strands_fun_tools",
    tool_names=["clipboard", "cursor", "bluetooth"]
)

# Load tools from already installed package
install_tools(action="load", module="strands_tools", tool_names=["shell", "calculator"])

# List currently loaded tools
install_tools(action="list_loaded")

Static Tool Configuration

Format: package1:tool1,tool2;package2:tool3,tool4

# Minimal (shell + editor only)
export DEVDUCK_TOOLS="strands_tools:shell,editor"

# Dev essentials
export DEVDUCK_TOOLS="strands_tools:shell,editor,file_read,file_write,calculator"

# Full stack + GitHub
export DEVDUCK_TOOLS="devduck.tools:tcp,websocket,mcp_server,use_github;strands_tools:shell,editor,file_read"

devduck

Hot-Reload Tools from Directory

Create ./tools/weather.py:

from strands import tool
import requests

@tool
def weather(city: str) -> str:
    """Get weather for a city."""
    r = requests.get(f"https://wttr.in/{city}?format=%C+%t")
    return r.text

Enable directory auto-loading:

export DEVDUCK_LOAD_TOOLS_FROM_DIR=true
devduck
# Save weather.py → use instantly (no restart needed)

Default: Directory loading is OFF. Use manage_tools() or install_tools() for explicit control.

---

Speech-to-Speech (Optional)

Install speech capabilities:

pip install "devduck[speech]"

Real-time voice conversations with multiple providers:

# Start speech session with Nova Sonic (AWS Bedrock)
speech_to_speech(action="start", provider="novasonic")

# Start with OpenAI Realtime API
speech_to_speech(action="start", provider="openai")

# Start with Gemini Live
speech_to_speech(action="start", provider="gemini_live")

# Custom voice and settings
speech_to_speech(
    action="start",
    provider="novasonic",
    model_settings={
        "provider_config": {"audio": {"voice": "matthew"}},
        "client_config": {"region": "us-east-1"}
    }
)

# Stop session
speech_to_speech(action="stop", session_id="speech_20250126_140000")

# Check status
speech_to_speech(action="status")

# List conversation histories
speech_to_speech(action="list_history")

# List available audio devices
speech_to_speech(action="list_audio_devices")

Supported Providers:

• Nova Sonic (AWS Bedrock): 11 voices (English, French, Italian, German, Spanish)
• OpenAI Realtime API: GPT-4o Realtime models
• Gemini Live: Native audio streaming

Environment Variables:

• OPENAI_API_KEY - For OpenAI Realtime
• GOOGLE_API_KEY or GEMINI_API_KEY - For Gemini Live
• AWS credentials - For Nova Sonic (boto3 default credential chain)

Features:

• Background execution (parent agent stays responsive)
• Tool inheritance from parent agent
• Conversation history saved automatically
• Natural interruption with VAD
• Custom audio device selection

---

MCP Integration

As MCP Server (Expose DevDuck)

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "devduck": {
      "command": "uvx",
      "args": ["devduck", "--mcp"]
    }
  }
}

Or start HTTP MCP server:

mcp_server(action="start", port=8000, stateless=True)
# Connect at: http://localhost:8000/mcp

Modes: --mcp (stdio for Claude Desktop) | http (background server) | stateless=True (multi-node)

As MCP Client (Load External Servers)

Expand capabilities by loading tools from external MCP servers:

export MCP_SERVERS='{
  "mcpServers": {
    "strands-docs": {"command": "uvx", "args": ["strands-agents-mcp-server"]},
    "remote": {"url": "https://api.example.com/mcp", "headers": {"Auth": "Bearer token"}},
    "custom": {"command": "python", "args": ["my_server.py"]}
  }
}'
devduck

Supported transports: stdio (command/args/env) | HTTP (url/headers) | SSE (url with /sse path)

Tool prefixing: Each server's tools get prefixed (e.g., strands-docs_search_docs)

---

Zenoh Peer-to-Peer Networking

Auto-discover and coordinate multiple DevDuck instances across terminals or networks.

How It Works

1. Each DevDuck joins a Zenoh peer network
2. Multicast scouting (224.0.0.224:7446) auto-discovers peers on local network
3. Peers exchange heartbeats to maintain presence awareness
4. Commands can be broadcast to ALL peers or sent to specific peers
5. Responses stream back in real-time

Quick Start

# Terminal 1: Start DevDuck (Zenoh enabled by default)
devduck
# 🦆 ✓ Zenoh peer: hostname-abc123

# Terminal 2: Start another DevDuck
devduck
# 🦆 ✓ Zenoh peer: hostname-def456
# Auto-discovers Terminal 1!

# Terminal 1: See discovered peers
🦆 zenoh_peer(action="list_peers")

# Terminal 1: Broadcast to ALL DevDucks
🦆 zenoh_peer(action="broadcast", message="git status")
# Both terminals execute and stream responses!

# Send to specific peer
🦆 zenoh_peer(action="send", peer_id="hostname-def456", message="what files are here?")

Cross-Network Connections

Connect DevDuck instances across different networks:

# Machine A (office): Listen for remote connections
export ZENOH_LISTEN="tcp/0.0.0.0:7447"
devduck

# Machine B (home): Connect to office
export ZENOH_CONNECT="tcp/office.example.com:7447"
devduck

# Now they can communicate!
🦆 zenoh_peer(action="broadcast", message="sync all repos")

Use Cases

Scenario	Command	Description
Multi-terminal ops	broadcast "git pull && npm install"	Run on all instances
Distributed tasks	broadcast "analyze ./src"	Parallel analysis
Peer monitoring	list_peers	See all active DevDucks
Direct messaging	send peer_id="..." message="..."	Task specific instance
Cross-network	Set ZENOH_CONNECT	Connect home ↔ office

Actions

# Start Zenoh networking (auto-starts by default)
zenoh_peer(action="start")

# Stop Zenoh
zenoh_peer(action="stop")

# Check status and peer count
zenoh_peer(action="status")

# List all discovered peers
zenoh_peer(action="list_peers")

# Broadcast to ALL peers (waits for responses)
zenoh_peer(action="broadcast", message="your command", wait_time=60)

# Send to specific peer
zenoh_peer(action="send", peer_id="hostname-abc123", message="your command", wait_time=120)

# Start with remote connection
zenoh_peer(action="start", connect="tcp/remote.example.com:7447")

# Start listening for remote connections
zenoh_peer(action="start", listen="tcp/0.0.0.0:7447")

Environment Variables

Variable	Default	Description
DEVDUCK_ENABLE_ZENOH	true	Auto-start Zenoh on launch
ZENOH_CONNECT	-	Remote endpoint(s) to connect to
ZENOH_LISTEN	-	Endpoint(s) to listen on for remote connections

Features

• Zero Config: Just start multiple DevDucks - they find each other
• Real-time Streaming: Responses stream as they're generated
• Peer Awareness: Dynamic context injection shows connected peers
• Cross-Network: Connect instances across different networks
• Fault Tolerant: Peers auto-detect disconnections (15s timeout)

---

Advanced Features

State Management (Time-Travel)

Save and restore agent state for reproducibility:

# Export current state
state_manager(action="export", metadata={"note": "before refactor"})

# List saved states
state_manager(action="list")

# Load and display state
state_manager(action="load", state_file="~/.devduck/states/devduck_20250118_150000.pkl")

# Resume from state (ephemeral - doesn't mutate parent)
state_manager(
    action="resume", 
    state_file="~/.devduck/states/devduck_20250118_150000.pkl",
    query="continue the analysis from where we left off"
)

# Modify state metadata
state_manager(
    action="modify",
    state_file="path/to/state.pkl",
    metadata={"tags": ["important", "refactor"]}
)

# Delete state
state_manager(action="delete", state_file="path/to/state.pkl")

States saved to: ~/.devduck/states/

System Prompt Management

Self-improvement - agent updates its own system prompt:

# View current system prompt
system_prompt(action="view")

# Add new context (appends to prompt)
system_prompt(action="add_context", context="New learning: Always use FastAPI for APIs")

# Update entire prompt
system_prompt(action="update", prompt="You are a specialized DevOps agent...")

# Sync to GitHub (persist across deployments)
system_prompt(
    action="update",
    prompt="Updated system prompt with new learnings...",
    repository="cagataycali/devduck"
)

# Reset to default
system_prompt(action="reset")

Pattern: Learn → Add context → Sync to GitHub → Persist forever

Knowledge Base (Auto-RAG)

Automatic memory across sessions:

export DEVDUCK_KNOWLEDGE_BASE_ID=your_kb_id
devduck

How it works:

1. Before each query: Retrieves relevant context from KB
2. After each response: Stores conversation for future reference
3. No manual tool calls needed - fully automatic

Manual storage:

store_in_kb(
    content="Important information to remember...",
    title="Project Context",
    knowledge_base_id="optional-kb-id"
)

Sub-Agent Creation

Delegate tasks to specialized agents via GitHub Actions:

# Create sub-agent with specific model and tools
create_subagent(
    repository="owner/repo",
    workflow_id="agent.yml",
    task="Analyze this dataset and provide insights",
    model="us.anthropic.claude-sonnet-4-20250514-v1:0",
    provider="bedrock",
    max_tokens=60000,
    tools="file_read,python_repl,calculator,http_request"
)

# Custom system prompt for specialized behavior
create_subagent(
    repository="owner/repo",
    workflow_id="agent.yml",
    task="Review code and suggest improvements",
    tools="file_read,editor,shell",
    system_prompt="You are a senior code reviewer focused on best practices"
)

# Check sub-agent status
create_subagent(action="status", repository="owner/repo", workflow_id="agent.yml", run_id="12345")

# List recent runs
create_subagent(action="list", repository="owner/repo", workflow_id="agent.yml")

---

📋 All Built-in Tools (40 total)

DevDuck Core (19 tools)

• system_prompt - Update agent's system prompt (GitHub sync support)
• store_in_kb - Store content in Bedrock Knowledge Base
• state_manager - Save/restore agent state (time-travel)
• tcp - TCP server with real-time streaming
• websocket - WebSocket server with concurrent messaging
• ipc - Unix socket IPC server for local processes
• mcp_server - Expose as MCP server (HTTP/stdio)
• zenoh_peer - Peer-to-peer networking with auto-discovery
• install_tools - Install packages and load tools at runtime
• create_subagent - Spawn sub-agents via GitHub Actions
• use_github - GitHub GraphQL API operations
• tray - System tray app control (macOS)
• ambient - Ambient AI input overlay (macOS)
• agentcore_config - Configure & launch on Bedrock AgentCore
• agentcore_invoke - Invoke deployed AgentCore agents
• agentcore_logs - View CloudWatch logs from agents
• agentcore_agents - List/manage agent runtimes
• manage_tools - Runtime tool add/remove/reload
• view_logs - View/search/clear DevDuck logs
• speech_to_speech - Real-time speech-to-speech conversations (optional - install with pip install devduck[speech])

Strands Tools (13 tools)

• shell - Interactive shell with PTY support
• editor - File editing (view/create/replace/insert/undo)
• file_read - Multi-file reading with search modes
• file_write - Write content to files
• file_read - Read files with document mode for PDFs/CSVs
• calculator - SymPy-powered math (solve/derive/integrate)
• image_reader - Read images for Converse API
• use_agent - Nested agent with different model
• load_tool - Load custom tools from Python files
• environment - Environment variable management
• mcp_client - Connect to external MCP servers autonomously
• retrieve - Bedrock Knowledge Base retrieval
• speak - Text-to-speech (macOS say or AWS Polly)
• slack - Slack messaging and event handling

Strands Fun Tools (6 tools - macOS)

• listen - Background speech transcription (Whisper)
• cursor - Mouse & keyboard control
• clipboard - Clipboard monitoring & control
• screen_reader - OCR & UI element detection
• bluetooth - BLE scanning and GATT operations
• yolo_vision - Object detection with YOLO

Community Tools (./tools/)

• fetch_github_tool - Fetch and load tools from GitHub repos
• gist - Comprehensive GitHub Gist management (create/update/fork/star/comment)
• scraper - HTML/XML parsing with BeautifulSoup4
• add_comment - Add comments to GitHub issues/PRs
• list_issues - List GitHub repository issues
• list_pull_requests - List GitHub repository PRs

Plus: Hot-reload tools from ./tools/ directory when DEVDUCK_LOAD_TOOLS_FROM_DIR=true

---

Hot-Reload Example

# ./tools/weather.py
from strands import tool
import requests

@tool
def weather(city: str) -> str:
    """Get weather for a city."""
    r = requests.get(f"https://wttr.in/{city}?format=%C+%t")
    return r.text

Save → use instantly:

🦆 weather(city="Tokyo")
# Clear sky +15°C

No restart. No configuration. Just works.

---

Access Methods

Protocol	Endpoint	Test Command	Use Case
CLI	Terminal	devduck "query"	Interactive/one-shot
Python	Import	import devduck; devduck("query")	Script integration
TCP	localhost:9999	nc localhost 9999	Network clients
WebSocket	localhost:8080	wscat -c ws://localhost:8080	Browser/async apps
MCP	localhost:8000/mcp	Add to Claude Desktop	MCP clients
IPC	/tmp/devduck_main.sock	nc -U /tmp/devduck_main.sock	Local processes

Custom ports:

export DEVDUCK_TCP_PORT=9000 DEVDUCK_WS_PORT=8001 DEVDUCK_MCP_PORT=8002
devduck

Disable servers:

export DEVDUCK_ENABLE_TCP=false DEVDUCK_ENABLE_MCP=false
devduck

---

Configuration

Variable	Default	Description
Model
MODEL_PROVIDER	Auto	Manual override: bedrock, anthropic, openai, github, gemini, cohere, writer, mistral, litellm, llamaapi, mlx, ollama
STRANDS_MODEL_ID	Auto	Model name (e.g., claude-sonnet-4, gpt-4o, qwen3:1.7b)
Provider API Keys
ANTHROPIC_API_KEY	-	Anthropic API key (auto-detected)
OPENAI_API_KEY	-	OpenAI API key (auto-detected)
GOOGLE_API_KEY / GEMINI_API_KEY	-	Google Gemini API key (auto-detected)
GITHUB_TOKEN / PAT_TOKEN	-	GitHub token for GitHub Models (auto-detected)
COHERE_API_KEY	-	Cohere API key (auto-detected)
WRITER_API_KEY	-	Writer API key (auto-detected)
MISTRAL_API_KEY	-	Mistral API key (auto-detected)
LITELLM_API_KEY	-	LiteLLM API key (auto-detected)
LLAMAAPI_API_KEY	-	LlamaAPI key (auto-detected)
Tools
DEVDUCK_TOOLS	40 tools	Format: package1:tool1,tool2;package2:tool3
DEVDUCK_LOAD_TOOLS_FROM_DIR	false	Auto-load from ./tools/ directory
Memory
DEVDUCK_KNOWLEDGE_BASE_ID	-	Bedrock KB ID for auto-RAG
SYSTEM_PROMPT	-	Additional system prompt content
MCP
MCP_SERVERS	-	JSON config for external MCP servers
Servers
DEVDUCK_TCP_PORT	9999	TCP server port
DEVDUCK_WS_PORT	8080	WebSocket server port
DEVDUCK_MCP_PORT	8000	MCP server port
DEVDUCK_IPC_SOCKET	/tmp/devduck_main.sock	IPC socket path
DEVDUCK_ENABLE_TCP	true	Enable TCP server
DEVDUCK_ENABLE_WS	true	Enable WebSocket server
DEVDUCK_ENABLE_MCP	true	Enable MCP server
DEVDUCK_ENABLE_IPC	true	Enable IPC server
DEVDUCK_ENABLE_ZENOH	true	Enable Zenoh peer-to-peer
ZENOH_CONNECT	-	Remote Zenoh endpoint(s) to connect to
ZENOH_LISTEN	-	Zenoh endpoint(s) to listen on
Speech
BIDI_MODEL_ID	Provider default	Override bidi model (e.g., amazon.nova-2-sonic-v1:0)
Context
DEVDUCK_LOG_LINE_COUNT	50	Recent log lines in context
DEVDUCK_LAST_MESSAGE_COUNT	200	Recent messages in context

---

Troubleshooting

Ollama model not found:

# DevDuck auto-pulls models, but if it fails:
ollama pull qwen3:1.7b

Port already in use:

# Change ports
export DEVDUCK_TCP_PORT=9000
export DEVDUCK_WS_PORT=8001
devduck

Hot-reload not working:

# Ensure tools directory exists
mkdir -p ./tools

# Check file watcher logs
devduck
🦆 view_logs(action="search", pattern="watcher")

Memory/performance issues:

# Use lighter model
export STRANDS_MODEL_ID="qwen3:0.5b"

# Reduce context
export DEVDUCK_LOG_LINE_COUNT=20
export DEVDUCK_LAST_MESSAGE_COUNT=50

Speech dependencies not found:

# Install speech extras
pip install "devduck[speech]"

# Or with pipx
pipx install "devduck[speech]"

Ambient overlay not starting:

# Make sure tkinter is installed
python3 -c "import tkinter"

# Install tkinter if missing
brew install python-tk@3.13  # macOS
sudo apt-get install python3-tk  # Ubuntu/Debian
sudo dnf install python3-tkinter  # Fedora

Tray app not starting (macOS):

# Install rumps
pip install rumps

# Or reinstall devduck
pip install -e .

View logs: devduck → 🦆 view_logs()

---

GitHub Actions

Run DevDuck in CI/CD pipelines:

name: AI Code Assistant
on: 
  issues:
    types: [opened, edited]
  pull_request:
    types: [opened, edited, synchronize]

jobs:
  devduck:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      issues: write
      pull-requests: write
    steps:
      - uses: cagataycali/devduck@main
        with:
          task: "Analyze and help with this issue or PR"
          provider: "github"
          model: "gpt-4o"
          tools: "shell,file_read,file_write,use_github,calculator"
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

Sub-agent workflows:

devduck("Create a sub-agent to analyze test coverage")

---

Resources

• Strands SDK: github.com/strands-agents/sdk-python
• Documentation: strandsagents.com
• Web UI: cagataycali.github.io/devduck

---

Citation

@software{devduck2025,
  author = {Cagatay Cali},
  title = {DevDuck: Self-Modifying AI Agent with Hot-Reload and Multi-Protocol Servers},
  year = {2025},
  url = {https://github.com/cagataycali/devduck}
}

---

Apache 2.0 | Built with Strands Agents | @cagataycali