Building Manus AI-like Systems with Google's Agent Development Kit: Implementation Framework

April 10, 2025
Nicholas Harmes - Grok3 and Claude 3.7 Sonnet
Enterprise Technology

Google's Agent Development Kit (ADK), released on April 9, 2025, at Google Cloud Next 2025, provides enterprise developers with a comprehensive framework for building multi-agent AI systems. Our technical analysis indicates that ADK possesses the necessary architecture components to replicate functionality similar to autonomous systems like Manus AI, which has gained attention for its ability to independently handle complex tasks from travel planning to stock analysis. This implementation guide explores the technical requirements, architectural mapping, and step-by-step development process to leverage ADK for enterprise-grade autonomous agent systems.

Understanding Manus AI and Google ADK: Architectural Alignment

To assess ADK's capability to replicate Manus AI-like functionality, we first need to understand the core architectural components of both systems. Manus AI, launched on March 6, 2025, is a general AI agent that can autonomously execute complex tasks across domains. It employs a multi-agent architecture, integrating various language models (including Claude 3.5 Sonnet and Qwen) with specialized tools for different domains.

Google's ADK offers several key features that align well with Manus AI's architecture:

  • Multi-Agent Framework: ADK supports hierarchical composition of specialized agents, allowing for complex coordination and delegation similar to Manus AI's multi-agent system.
  • Tool Integration Ecosystem: ADK includes pre-built tools, custom function support, and integration with third-party libraries, enabling the rich tool ecosystem needed for domain-specific tasks.
  • Flexible Orchestration: ADK's workflow agents and LLM-driven dynamic routing support create the adaptive behavior necessary for autonomous execution.
  • Deployment Readiness: Production-ready deployment options on Vertex AI Agent Engine, Cloud Run, or Docker provide the enterprise-grade infrastructure needed for reliable operation.
  • Model Flexibility: While optimized for Gemini, ADK supports other LLMs, allowing integration with various models similar to Manus AI's approach.

Implementation Strategy: Building Blocks Approach

The most efficient approach to building a Manus AI-like system using ADK is to leverage the sample agents provided in the ADK ecosystem and adapt them for specific use cases. Based on our analysis, the following sample agents provide relevant starting points:

Manus AI Domain ADK Sample to Adapt Key Components Implementation Difficulty
Travel Planning Travel Concierge Agent Multi-agent system with inspiration, planning, and booking agents; integrated tools for maps and search Medium - requires API integrations for real booking
Financial Analysis FOMC Research Agent Analysis, summarization, and report generation; adaptable for stock analysis High - requires financial API integration and custom analysis tools
Task Coordination Orchestrator Sample Core routing logic for delegating to specialized agents Medium - requires customization for specific domain routing
Tool Integration Custom Tool Samples Python function tools, API connectors, search tools Medium to High - depends on specific tool APIs

Step-by-Step Implementation Framework

Based on our analysis, here is a structured implementation framework for enterprise teams looking to build a Manus AI-like system using Google's ADK:

  1. System Architecture Design (2-3 weeks):
    • Define task domains and required agent specializations (e.g., travel, finance, general research)
    • Determine orchestration patterns for agent coordination
    • Map tool requirements to available ADK components and identify integration gaps
    • Document API requirements and service dependencies
  2. Agent Component Development (4-6 weeks):
    • Implement domain-specific agents starting with ADK samples:
      • Adapt Travel Concierge for travel planning functionality
      • Customize FOMC Research for financial analysis
      • Develop additional specialized agents for other domains
    • Create custom tool integrations with third-party APIs
    • Implement state management for persistent context across interactions
  3. Orchestration Implementation (3-4 weeks):
    • Develop central orchestrator agent for routing user requests
    • Implement hierarchical agent composition patterns
    • Create communication protocols between specialized agents
    • Build fallback mechanisms for handling unexpected requests
  4. Tool Integration (2-3 weeks per domain):
    • For travel domain: integrate booking APIs, map services, and travel advisories
    • For financial domain: connect to market data providers, analysis libraries, and visualization tools
    • Implement Python function tools for domain-specific calculations and operations
  5. Testing and Refinement (3-4 weeks):
    • Develop comprehensive test suites using ADK's built-in evaluation tools
    • Conduct user acceptance testing with domain experts
    • Optimize agent prompts and tool integrations based on performance metrics
    • Fine-tune orchestration logic for improved task routing
  6. Deployment and Scaling (2 weeks):
    • Configure deployment on Vertex AI Agent Engine or Cloud Run
    • Implement monitoring and observability infrastructure
    • Establish CI/CD pipelines for ongoing updates
    • Document deployment architecture and scaling considerations

Technical Implementation Details

Travel Planning Agent Implementation

For travel planning functionality, start with the Travel Concierge sample and modify it with these key technical enhancements:


# Example ADK agent implementation for travel planning
from google.adk import Agent, Tool, Workflow
from google.adk.tools import GoogleSearchTool, MapsTool

# 1. Define custom tools for real booking integrations
class FlightBookingTool(Tool):
    def execute(self, flight_details):
        # Integration with real flight booking API
        # Replace mock implementation from the sample
        ...

# 2. Create specialized agents
inspiration_agent = Agent.from_config({
    "name": "TravelInspirationAgent",
    "description": "Suggests travel destinations based on user preferences",
    "model": "gemini-1.5-pro",  # Can be replaced with other supported LLMs
    "tools": [GoogleSearchTool()]
})

planning_agent = Agent.from_config({
    "name": "TravelPlanningAgent",
    "description": "Creates detailed travel itineraries",
    "model": "gemini-1.5-pro",
    "tools": [MapsTool(), FlightBookingTool(), HotelBookingTool()]
})

# 3. Create orchestration workflow
travel_workflow = Workflow.sequential([
    inspiration_agent,
    planning_agent,
    booking_agent
])

Financial Analysis Implementation

For financial analysis capabilities, adapt the FOMC Research agent with these modifications:


# Example ADK implementation for stock analysis
from google.adk import Agent, Tool, Workflow
from google.adk.tools import DataVisualizationTool

# 1. Create custom financial data tool
class StockDataTool(Tool):
    def execute(self, ticker, timeframe):
        # Integrate with financial data API (e.g., Alpha Vantage)
        ...

# 2. Create analysis agent
stock_analysis_agent = Agent.from_config({
    "name": "StockAnalysisAgent",
    "description": "Analyzes stock performance and generates insights",
    "model": "gemini-1.5-pro",
    "tools": [StockDataTool(), DataVisualizationTool()]
})

# 3. Create reporting agent
financial_report_agent = Agent.from_config({
    "name": "FinancialReportAgent",
    "description": "Generates comprehensive financial reports with visualizations",
    "model": "gemini-1.5-pro",
    "tools": [DataVisualizationTool()]
})

Architectural Considerations for Enterprise Implementation

Model Selection Strategy

While ADK is optimized for Google's Gemini models, enterprise implementations may require flexibility in model selection based on specific domain performance. Consider these approaches:

  • Hybrid Model Approach: Use Gemini for general coordination but integrate domain-specific models (like Claude for reasoning tasks) where appropriate.
  • Performance Benchmarking: Test different models on domain-specific tasks before finalizing architecture.
  • Custom Adapter Patterns: Implement adapter classes to standardize interactions with different model providers.

Security and Compliance Framework

Enterprise implementations must address security and compliance requirements with these technical measures:

  • Data Governance: Implement strict controls on what data agents can access and store.
  • Agent Guardrails: Configure explicit constraints on agent actions, particularly for financial transactions or sensitive operations.
  • Audit Trails: Implement comprehensive logging of all agent decisions and actions for compliance purposes.
  • Access Control: Develop role-based access control for different agent functionalities.

Comparative Analysis: Manus AI vs. ADK Implementation

Feature Manus AI ADK Implementation Enterprise Considerations
Multi-Agent Architecture Proprietary multi-agent system with specialized components Hierarchical agent composition with workflow orchestration ADK provides more transparency and control over agent interactions
Model Integration Claude 3.5 Sonnet and fine-tuned Qwen Primary support for Gemini with adaptability for other models Enterprise implementations may need custom adapters for preferred models
Tool Integration Proprietary tool integrations Rich ecosystem with custom function support ADK's open architecture allows easier integration with enterprise systems
Deployment Options Limited information on deployment infrastructure Google Cloud, Vertex AI, Docker, on-premises options ADK offers more flexibility for enterprise deployment scenarios
Development Time N/A (commercial product) 14-22 weeks for full implementation Incremental deployment possible with ADK's modular approach

Conclusion: Strategic Advantages of ADK Implementation

Google's Agent Development Kit provides enterprise organizations with a viable framework for building Manus AI-like autonomous agent systems. The key strategic advantages of this approach include:

  • Transparency and Control: ADK's code-first approach offers greater visibility into agent behavior compared to commercial black-box systems.
  • Enterprise Integration: Native connectivity with Google Cloud services and flexible deployment options simplify enterprise adoption.
  • Customization Potential: Ability to tailor agents for specific enterprise use cases and domains beyond Manus AI's capabilities.
  • Cost Efficiency: Open-source foundation reduces licensing costs, though implementation requires significant development resources.
  • Future-Proofing: Google's continued investment in ADK ensures ongoing improvements and support for emerging AI capabilities.

While replicating Manus AI's full functionality requires substantial development resources, ADK's sample-based approach significantly accelerates implementation. The framework outlined in this article provides enterprise architects and development teams with a structured methodology to assess, plan, and execute such implementations with predictable timelines and resource requirements.

Resources for Implementation

Report completed on April 10, 2025