Migration v3 to v4

This guide will help you migrate your Droidrun projects from v3 to v4. Version 4 introduces significant changes to installation, configuration, and API design that improve developer experience and simplify usage.

Overview of Major Changes

Droidrun v4 introduces several breaking changes:

Installation method changed from pip to uv
Python version requirement increased from 3.10+ to 3.11+
Configuration system completely redesigned with DroidrunConfig
Agent API simplified with config-based initialization
LLM initialization now handled through config profiles
Result format changed from dictionary to ResultEvent object
Credential Manager feature included for secure handling of sensitive data
App Instruction Cards for improved app navigation
Custom Tools support for extending agent capabilities
Output Extraction using Pydantic models for structured data

Installation Changes

v3 Installation

pip install 'droidrun[google,anthropic,openai,deepseek,ollama,dev]'

v4 Installation

For CLI usage only:

uv tool install 'droidrun[google,anthropic,openai,deepseek,ollama,openrouter]'

For CLI + Python integration:

uv pip install 'droidrun[google,anthropic,openai,deepseek,ollama,openrouter]'

Note: droidrun now provides a --version flag — verify your installation with droidrun --version after installing.

Migration Steps

Install uv (if not already installed):

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

Uninstall v3 (optional but recommended):
```
pip uninstall droidrun
```
Install v4 using your preferred method above
Update Python version to 3.11+ if needed

Configuration System Changes

v3: No Config Files

v3 had no configuration files. All settings were passed directly to the agent.

v4: Configuration-Driven Approach

v4 uses a config.yaml file that is automatically created on first run:

# config.yaml (auto-generated)
llm_profiles:
  - name: "gemini-flash"
    provider: "GoogleGenAI"
    model: "models/gemini-2.5-flash"
    
  - name: "gpt-4o"
    provider: "OpenAI"
    model: "gpt-4o"

default_profile: "gemini-flash"
vision: false
reasoning: false
max_steps: 15

Migration Steps

Create config.yaml (automatically created on first run, or copy from config_example.yaml)
Define your LLM profiles in the config file instead of in code

Set environment variables for API keys:

export GOOGLE_API_KEY=your-api-key
export OPENAI_API_KEY=your-api-key
export ANTHROPIC_API_KEY=your-api-key

API Changes

Agent Initialization

v3 Code:

from droidrun import AdbTools, DroidAgent
from llama_index.llms.google_genai import GoogleGenAI

# Manual LLM setup
llm = GoogleGenAI(
    api_key="YOUR_GEMINI_API_KEY",
    model="gemini-2.5-flash",
)

# Manual tools loading
tools = AdbTools()

# Agent creation
agent = DroidAgent(
    goal="Open Settings and check battery level",
    llm=llm,
    tools=tools,
    vision=True,
    reasoning=False,
)

v4 Code:

from droidrun import DroidAgent
from droidrun.config_manager.config_manager import DroidrunConfig

# Config-based initialization (LLMs loaded automatically)
config = DroidrunConfig()

# Agent creation (tools loaded automatically)
agent = DroidAgent(
    goal="Open Settings and check battery level",
    config=config,
)

Key Differences

Feature	v3	v4
LLM Setup	Manual instantiation	Loaded from config profiles
Tools Loading	Manual `AdbTools()`	Automatic from config
Configuration	Constructor parameters	`DroidrunConfig` object
API Keys	In code	Environment variables

Result Object Changes

v3: Dictionary Result

result = await agent.run()
print(f"Success: {result['success']}")
if result.get('output'):
    print(f"Output: {result['output']}")

v4: ResultEvent Object

result = await agent.run()
print(f"Success: {result.success}")
print(f"Reason: {result.reason}")
print(f"Steps: {result.steps}")

Migration Steps

Update all result access patterns:

result['success'] → result.success
result.get('output') → result.reason (or appropriate attribute)
Add access to new fields: result.steps

CLI Changes

New CLI Flags

v4 introduces additional flags:

Flag	Description	Default
`--config`, `-c`	Custom config path	`config.yaml`
`--device`, `-d`	Device serial or IP	Auto-detect
`--tcp`	Use TCP instead of content provider	`false`
`--save-trajectory`	Save execution data	`none`

Breaking Changes Summary

1. Python Version

v3: Python 3.10+
v4: Python 3.11+
Action: Update your Python installation

2. Installation Tool

v3: pip install
v4: uv tool install or uv pip install
Action: Install uv and use new commands

3. Import Changes

# v3
from droidrun import AdbTools, DroidAgent
from llama_index.llms.google_genai import GoogleGenAI

# v4
from droidrun import DroidAgent
from droidrun.config_manager.config_manager import DroidrunConfig
# No need to import LLM classes directly

4. Agent Constructor

# v3: Multiple required parameters
agent = DroidAgent(
    goal="...",
    llm=llm,           # Required
    tools=tools,       # Required
    vision=True,
    reasoning=False,
)

# v4: Config-based, simplified
agent = DroidAgent(
    goal="...",
    config=config,     # Single config object
)

5. LLM Management

v3: Manually instantiate LLM objects
v4: Define profiles in config.yaml, loaded automatically
Action: Move LLM configs to config.yaml

6. Tools Loading

v3: tools = AdbTools() required
v4: Tools loaded automatically from config
Action: Remove manual AdbTools() instantiation

7. Result Access

v3: Dictionary with string keys
v4: Object with attributes
Action: Update result access patterns

Step-by-Step Migration Guide

Step 1: Update Environment

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh  # macOS/Linux

# Upgrade Python if needed
python --version  # Should be 3.11+

# Uninstall v3
pip uninstall droidrun

Step 2: Install v4

# For CLI + Python integration
uv pip install 'droidrun[google,anthropic,openai,deepseek,ollama,openrouter]'

# Or for CLI only
uv tool install 'droidrun[google,anthropic,openai,deepseek,ollama,openrouter]'

Step 3: Create Configuration

# Run any command to auto-generate config.yaml
droidrun ping

Edit config.yaml to add your LLM profiles:

llm_profiles:
  - name: "my-gemini"
    provider: "GoogleGenAI"
    model: "models/gemini-2.5-flash"
    
  - name: "my-gpt4"
    provider: "OpenAI"
    model: "gpt-4o"

default_profile: "my-gemini"
vision: false
reasoning: false
max_steps: 15

Step 4: Set Environment Variables

export GOOGLE_API_KEY=your-api-key
export OPENAI_API_KEY=your-api-key
export ANTHROPIC_API_KEY=your-api-key

Step 5: Update Your Code

Before (v3):

import asyncio
from droidrun import AdbTools, DroidAgent
from llama_index.llms.google_genai import GoogleGenAI

async def main():
    tools = AdbTools()
    llm = GoogleGenAI(
        api_key="YOUR_GEMINI_API_KEY",
        model="gemini-2.5-flash",
    )
    
    agent = DroidAgent(
        goal="Open Settings and check battery level",
        llm=llm,
        tools=tools,
        vision=True,
        reasoning=False,
    )
    
    result = await agent.run()
    print(f"Success: {result['success']}")
    if result.get('output'):
        print(f"Output: {result['output']}")

if __name__ == "__main__":
    asyncio.run(main())

After (v4):

import asyncio
from droidrun import DroidAgent
from droidrun.config_manager.config_manager import DroidrunConfig

async def main():
    config = DroidrunConfig()
    
    agent = DroidAgent(
        goal="Open Settings and check battery level",
        config=config,
    )
    
    result = await agent.run()
    print(f"Success: {result.success}")
    print(f"Reason: {result.reason}")
    print(f"Steps: {result.steps}")

if __name__ == "__main__":
    asyncio.run(main())

Step 6: Test Your Migration

# Test device connection
droidrun ping

# Test simple command
droidrun "Open Settings"

# Test your Python script
python your_script.py

New Features in v4

Droidrun v4 introduces several powerful new features that weren’t available in v3. Here’s a quick overview:

1. Structured Output Extraction

Extract typed data from device interactions using Pydantic models:

from pydantic import BaseModel, Field
from droidrun import DroidAgent
from droidrun.config_manager import DroidrunConfig

class ContactInfo(BaseModel):
    name: str = Field(description="Full name")
    phone: str = Field(description="Phone number")
    email: str = Field(description="Email address")

config = DroidrunConfig()

agent = DroidAgent(
    goal="Find John Smith's contact information",
    config=config,
    output_model=ContactInfo,  # NEW in v4
)

result = await agent.run()
contact = result.structured_output  # Typed Pydantic object
print(f"Name: {contact.name}, Phone: {contact.phone}")

The agent automatically extracts structured data into your Pydantic model after completing the task. → See Structured Output Documentation for detailed examples and best practices.

2. Credential Management

Securely manage passwords, API keys, and tokens without hardcoding them:

from droidrun import DroidAgent
from droidrun.config_manager import DroidrunConfig

# In-memory credentials (recommended for SDK)
credentials = {
    "EMAIL_USER": "user@example.com",
    "EMAIL_PASS": "secret_password",
    "API_KEY": "sk-1234567890"
}

config = DroidrunConfig()

agent = DroidAgent(
    goal="Login to Gmail with my credentials",
    config=config,
    credentials=credentials,  # NEW in v4
)

result = await agent.run()

Credentials are never logged or exposed - agents use type_secret() to securely input sensitive data. You can also store credentials in a credentials.yaml file. → See Credential Management Documentation for YAML configuration and advanced usage.

3. App Instruction Cards

Teach agents how to use specific apps with custom instruction guides:

# App cards are enabled by default
droidrun run "Send an email to john@example.com" --reasoning

When your agent opens an app (like Gmail, Chrome, WhatsApp), it automatically loads app-specific instructions that help it:

Navigate the UI efficiently
Find buttons and features quickly
Use app-specific shortcuts and search syntax
Complete tasks with higher success rates

Create your own app cards:

# 1. Create app cards directory
mkdir -p config/app_cards

# 2. Create a markdown file for your app
touch config/app_cards/chrome.md

# 3. Register in app_cards.json
echo '{"com.android.chrome": "chrome.md"}' > config/app_cards/app_cards.json

→ See App Instruction Cards Documentation for examples, best practices, and troubleshooting.

4. Custom Tools

Extend agent capabilities with custom Python functions for external APIs, data processing, or domain-specific logic:

from droidrun import DroidAgent
from droidrun.config_manager import DroidrunConfig

def calculate_tax(amount: float, rate: float, **kwargs) -> str:
    """Calculate tax for a given amount."""
    tax = amount * rate
    total = amount + tax
    return f"Tax: ${tax:.2f}, Total: ${total:.2f}"

custom_tools = {
    "calculate_tax": {
        "arguments": ["amount", "rate"],
        "description": "Calculate tax for a given amount and rate",
        "function": calculate_tax
    }
}

config = DroidrunConfig()

agent = DroidAgent(
    goal="Calculate tax for $100 at 8% rate",
    config=config,
    custom_tools=custom_tools,  # NEW in v4
)

result = await agent.run()

Custom tools can access device state, shared memory, and credentials. → See Custom Tools Documentation for accessing device state, API integration patterns, and advanced examples.

5. Advanced Configuration System

v4 introduces granular configuration for all aspects of agent behavior:

from droidrun.config_manager import (
    DroidrunConfig,
    AgentConfig,
    CodeActConfig,
    ManagerConfig,
    ExecutorConfig,
    ScripterConfig,
    AppCardConfig,
    DeviceConfig,
    LoggingConfig,
    TracingConfig
)

config = DroidrunConfig(
    agent=AgentConfig(
        max_steps=30,
        reasoning=True,
        after_sleep_action=1.5,
        codeact=CodeActConfig(vision=True, safe_execution=True),
        manager=ManagerConfig(vision=True),
        executor=ExecutorConfig(vision=False),
        scripter=ScripterConfig(enabled=True, max_steps=10),
        app_cards=AppCardConfig(enabled=True, mode="local")
    ),
    device=DeviceConfig(serial="emulator-5554", use_tcp=False),
    logging=LoggingConfig(debug=True, save_trajectory="action"),
    tracing=TracingConfig(enabled=True)
)

agent = DroidAgent(goal="Complex task", config=config)

→ See Configuration Reference Documentation for all available configuration classes and parameters.

6. Per-Agent LLM Configuration

Use different LLMs for different agent roles (planning, execution, code generation):

from droidrun import DroidAgent
from droidrun.config_manager import DroidrunConfig
from llama_index.llms.openai import OpenAI
from llama_index.llms.gemini import Gemini

config = DroidrunConfig()

agent = DroidAgent(
    goal="Complex multi-step task",
    config=config,
    llms={
        "manager": OpenAI(model="gpt-4o"),                    # Planning
        "executor": Gemini(model="models/gemini-2.5-flash"),  # Action selection
        "codeact": Gemini(model="models/gemini-2.5-pro"),     # Code generation
        "text_manipulator": Gemini(model="models/gemini-2.5-flash"),  # Text input
        "app_opener": OpenAI(model="gpt-4o-mini"),            # App launching
        "scripter": Gemini(model="models/gemini-2.5-flash"),  # Off-device scripts
        "structured_output": Gemini(model="models/gemini-2.5-flash"),  # Output extraction
     }
)

This allows you to optimize cost and performance by using appropriate models for each task. → See SDK Configuration Documentation for all LLM keys and configuration options.

7. Custom Variables

Pass non-sensitive contextual data to agents:

from droidrun import DroidAgent
from droidrun.config_manager import DroidrunConfig

config = DroidrunConfig()

agent = DroidAgent(
    goal="Send report to the project lead",
    config=config,
    variables={
        "project_id": "12345",
        "lead_email": "alice@example.com",
        "api_endpoint": "https://api.example.com"
    }
)

Variables are accessible throughout execution in shared_state.custom_variables and can be used in custom tools or referenced by agents. → See Custom Variables Documentation for usage patterns.

8. Tracing and Trajectory Recording

Monitor agent execution with real-time Phoenix tracing and local trajectory recording:

from droidrun import DroidAgent
from droidrun.config_manager import DroidrunConfig, TracingConfig, LoggingConfig

config = DroidrunConfig(
    tracing=TracingConfig(enabled=True),  # Phoenix tracing
    logging=LoggingConfig(save_trajectory="step")  # Local recording
)

agent = DroidAgent(goal="Open Settings", config=config)
result = await agent.run()

Or via CLI:

# Enable Phoenix tracing
droidrun run "Open Settings" --tracing

# Enable trajectory recording (screenshots + UI state)
droidrun run "Open Settings" --save-trajectory step

Phoenix Tracing provides real-time monitoring of:

LLM calls with prompts and token usage
Agent workflow execution
Tool invocations and results

Trajectory Recording saves locally:

Screenshots per step/action
UI state and element hierarchies
Agent reasoning and decisions

Trajectories are saved to trajectories/ directory for offline debugging. → See Tracing Documentation for Phoenix setup, configuration options, and trajectory analysis.

Last updated: November 2025

Introduction

Features

Guides

Concepts

SDK Reference

​Overview of Major Changes

​Installation Changes

​v3 Installation

​v4 Installation

​Migration Steps

​Configuration System Changes

​v3: No Config Files

​v4: Configuration-Driven Approach

​Migration Steps

​API Changes

​Agent Initialization

​Key Differences

​Result Object Changes

​v3: Dictionary Result

​v4: ResultEvent Object

​Migration Steps

​CLI Changes

​New CLI Flags

​Breaking Changes Summary

​1. Python Version

​2. Installation Tool

​3. Import Changes

​4. Agent Constructor

​5. LLM Management

​6. Tools Loading

​7. Result Access

​Step-by-Step Migration Guide

​Step 1: Update Environment

​Step 2: Install v4

​Step 3: Create Configuration

​Step 4: Set Environment Variables

​Step 5: Update Your Code

​Step 6: Test Your Migration

​New Features in v4

​1. Structured Output Extraction

​2. Credential Management

​3. App Instruction Cards

​4. Custom Tools

​5. Advanced Configuration System

​6. Per-Agent LLM Configuration

​7. Custom Variables

​8. Tracing and Trajectory Recording

Overview of Major Changes

Installation Changes

v3 Installation

v4 Installation

Migration Steps

Configuration System Changes

v3: No Config Files

v4: Configuration-Driven Approach

Migration Steps

API Changes

Agent Initialization

Key Differences

Result Object Changes

v3: Dictionary Result

v4: ResultEvent Object

Migration Steps

CLI Changes

New CLI Flags

Breaking Changes Summary

1. Python Version

2. Installation Tool

3. Import Changes

4. Agent Constructor

5. LLM Management

6. Tools Loading

7. Result Access

Step-by-Step Migration Guide

Step 1: Update Environment

Step 2: Install v4

Step 3: Create Configuration

Step 4: Set Environment Variables

Step 5: Update Your Code

Step 6: Test Your Migration

New Features in v4

1. Structured Output Extraction

2. Credential Management

3. App Instruction Cards

4. Custom Tools

5. Advanced Configuration System

6. Per-Agent LLM Configuration

7. Custom Variables

8. Tracing and Trajectory Recording