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AI Consultant Agent with adk
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Madhu
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# AI Consultant Agent with Google ADK
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A powerful business consultant powered by Google's Agent Development Kit that provides comprehensive market analysis, strategic planning, and actionable business recommendations.
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## Features
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- **Market Analysis**: Leverages Google search and AI insights to analyze market conditions and opportunities
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- **Strategic Recommendations**: Generates actionable business strategies with timelines and implementation plans
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- **Risk Assessment**: Identifies potential risks and provides mitigation strategies
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- **Interactive UI**: Clean Google ADK web interface for easy consultation
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- **Evaluation System**: Built-in evaluation and debugging capabilities with session tracking
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## How It Works
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1. **Input Phase**: User provides business questions or consultation requests through the ADK web interface
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2. **Analysis Phase**: The agent uses market analysis tools to process the query and generate insights
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3. **Strategy Phase**: Strategic recommendations are generated based on the analysis
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4. **Synthesis Phase**: The agent combines findings into a comprehensive consultation report
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5. **Output Phase**: Actionable recommendations with timelines and implementation steps are presented
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## Requirements
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- Python 3.8+
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- Google API key (for Gemini model)
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- Required Python packages (see `requirements.txt`)
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## Installation
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1. Clone this repository:
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```bash
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git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
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cd advanced_ai_agents/single_agent_apps
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```
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2. Install the required packages:
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```bash
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pip install -r requirements.txt
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```
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## Usage
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1. Set your Google API key:
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```bash
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export GOOGLE_API_KEY=your-api-key
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```
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2. Start the Google ADK web interface:
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```bash
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adk web
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```
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3. Open your browser and navigate to `http://localhost:8000`
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4. Select "AI Business Consultant" from the available agents
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5. Enter your business questions or consultation requests
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6. Review the comprehensive analysis and strategic recommendations
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7. Use the Eval tab to save and evaluate consultation sessions
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## Example Consultation Topics
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- "I want to launch a SaaS startup for small businesses"
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- "Should I expand my retail business to e-commerce?"
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- "What are the market opportunities in healthcare technology?"
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- "How should I position my new fintech product?"
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- "What are the risks of entering the renewable energy market?"
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## Technical Details
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The application uses specialized analysis tools:
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1. **Market Analysis Tool**: Processes business queries and generates market insights, competitive analysis, and opportunity identification.
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2. **Strategic Recommendations Tool**: Creates actionable business strategies with priority levels, timelines, and implementation roadmaps.
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The agent is built on Google ADK's LlmAgent framework using the Gemini 2.0 Flash model, providing fast and accurate business consultation capabilities.
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## Evaluation and Testing
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The agent includes built-in evaluation features:
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- **Session Management**: Track consultation history and progress
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- **Test Case Creation**: Save successful consultations as evaluation cases
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- **Performance Metrics**: Monitor tool usage and response quality
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- **Custom Evaluation**: Configure metrics for specific business requirements
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from .ai_consultant_agent import root_agent, session_service, runner, APP_NAME
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from . import agent
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__all__ = ['root_agent', 'session_service', 'runner', 'APP_NAME', 'agent']
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from .ai_consultant_agent import root_agent
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# Export for ADK CLI discovery
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__all__ = ['root_agent']
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import logging
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from typing import Dict, Any, List, Union
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from dataclasses import dataclass
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import base64
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# Google ADK imports
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from google.adk.agents import LlmAgent
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from google.adk.tools import google_search_tool
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from google.adk.sessions import InMemorySessionService
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from google.adk.runners import Runner
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# Define constants for the agent configuration
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MODEL_ID = "gemini-2.5-flash"
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APP_NAME = "ai_consultant_agent"
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USER_ID = "consultant-user"
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SESSION_ID = "consultant-session"
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def sanitize_bytes_for_json(obj: Any) -> Any:
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"""
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Recursively convert bytes objects to strings to ensure JSON serializability.
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Args:
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obj: Any object that might contain bytes
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Returns:
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Object with all bytes converted to strings
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"""
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if isinstance(obj, bytes):
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try:
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# Try to decode as UTF-8 text first
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return obj.decode('utf-8')
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except UnicodeDecodeError:
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# If not valid UTF-8, encode as base64 string
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return base64.b64encode(obj).decode('ascii')
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elif isinstance(obj, dict):
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return {key: sanitize_bytes_for_json(value) for key, value in obj.items()}
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elif isinstance(obj, list):
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return [sanitize_bytes_for_json(item) for item in obj]
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elif isinstance(obj, tuple):
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return tuple(sanitize_bytes_for_json(item) for item in obj)
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else:
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return obj
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def safe_tool_wrapper(tool_func):
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"""
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Wrapper to ensure tool functions never return bytes objects.
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Args:
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tool_func: The original tool function
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Returns:
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Wrapped function that sanitizes output
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"""
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def wrapped_tool(*args, **kwargs):
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try:
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result = tool_func(*args, **kwargs)
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return sanitize_bytes_for_json(result)
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except Exception as e:
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logger.error(f"Error in tool {tool_func.__name__}: {e}")
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return {
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"error": f"Tool execution failed: {str(e)}",
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"tool": tool_func.__name__,
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"status": "error"
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}
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# Preserve function metadata
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wrapped_tool.__name__ = tool_func.__name__
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wrapped_tool.__doc__ = tool_func.__doc__
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return wrapped_tool
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@dataclass
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class MarketInsight:
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"""Structure for market research insights"""
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category: str
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finding: str
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confidence: float
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source: str
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def analyze_market_data(research_query: str, industry: str = "") -> Dict[str, Any]:
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"""
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Analyze market data and generate insights
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Args:
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research_query: The business query to analyze
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industry: Optional industry context
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Returns:
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Market analysis insights and recommendations
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"""
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# Simulate market analysis - in real implementation this would process actual search results
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insights = []
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if "startup" in research_query.lower() or "launch" in research_query.lower():
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insights.extend([
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MarketInsight("Market Opportunity", "Growing market with moderate competition", 0.8, "Market Research"),
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MarketInsight("Risk Assessment", "Standard startup risks apply - funding, competition", 0.7, "Analysis"),
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MarketInsight("Recommendation", "Conduct MVP testing before full launch", 0.9, "Strategic Planning")
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])
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if "saas" in research_query.lower() or "software" in research_query.lower():
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insights.extend([
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MarketInsight("Technology Trend", "Cloud-based solutions gaining adoption", 0.9, "Tech Analysis"),
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MarketInsight("Customer Behavior", "Businesses prefer subscription models", 0.8, "Market Study")
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])
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if industry:
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insights.append(
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MarketInsight("Industry Specific", f"{industry} sector shows growth potential", 0.7, "Industry Report")
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)
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return {
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"query": research_query,
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"industry": industry,
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"insights": [
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{
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"category": insight.category,
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"finding": insight.finding,
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"confidence": insight.confidence,
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"source": insight.source
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}
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for insight in insights
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],
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"summary": f"Analysis completed for: {research_query}",
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"total_insights": len(insights)
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}
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def generate_strategic_recommendations(analysis_data: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""
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Generate strategic business recommendations based on analysis
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Args:
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analysis_data: Market analysis results
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Returns:
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List of strategic recommendations
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"""
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recommendations = []
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# Generate recommendations based on insights
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insights = analysis_data.get("insights", [])
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if any("startup" in insight["finding"].lower() for insight in insights):
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recommendations.append({
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"category": "Market Entry Strategy",
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"priority": "High",
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"recommendation": "Implement phased market entry with MVP testing",
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"rationale": "Reduces risk and validates market fit before major investment",
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"timeline": "3-6 months",
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"action_items": [
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"Develop minimum viable product",
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"Identify target customer segment",
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"Conduct market validation tests"
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]
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})
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if any("saas" in insight["finding"].lower() for insight in insights):
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recommendations.append({
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"category": "Technology Strategy",
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"priority": "Medium",
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"recommendation": "Focus on cloud-native architecture and subscription model",
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"rationale": "Aligns with market trends and customer preferences",
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"timeline": "2-4 months",
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"action_items": [
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"Design scalable cloud infrastructure",
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"Implement subscription billing system",
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"Plan for multi-tenant architecture"
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]
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})
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# Always include risk management
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recommendations.append({
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"category": "Risk Management",
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"priority": "High",
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"recommendation": "Establish comprehensive risk monitoring framework",
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"rationale": "Proactive risk management is essential for business success",
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"timeline": "1-2 months",
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"action_items": [
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"Identify key business risks",
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"Develop mitigation strategies",
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"Implement monitoring systems"
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]
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})
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return recommendations
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# Define the consultant tools with safety wrappers
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consultant_tools = [
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google_search_tool,
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safe_tool_wrapper(analyze_market_data),
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safe_tool_wrapper(generate_strategic_recommendations)
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]
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INSTRUCTIONS = """You are a senior AI business consultant specializing in market analysis and strategic planning.
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Your expertise includes:
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- Business strategy development and recommendations
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- Risk assessment and mitigation planning
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- Implementation planning with timelines
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- Market analysis using your knowledge and available tools
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- Real-time market research using Google search capabilities
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When consulting with clients:
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1. Use Google search to gather current market data, competitor information, and industry trends
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2. Use the market analysis tool to process business queries and generate insights
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3. Use the strategic recommendations tool to create actionable business advice
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4. Provide clear, specific recommendations with implementation timelines
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5. Focus on practical solutions that drive measurable business outcomes
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**Core Responsibilities:**
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- Conduct real-time market research using Google search for current data
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- Analyze competitive landscapes and market opportunities using search results and your knowledge
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- Provide strategic guidance with clear action items based on up-to-date information
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- Assess risks and suggest mitigation strategies using current market conditions
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- Create implementation roadmaps with realistic timelines
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- Generate comprehensive business insights combining search data with analysis tools
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**Critical Rules:**
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- Always search for current market data, trends, and competitor information when relevant
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- Base recommendations on sound business principles, current market insights, and real-time data
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- Provide specific, actionable advice rather than generic guidance
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- Include timelines and success metrics in recommendations
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- Prioritize recommendations by business impact and feasibility
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- Use Google search to validate assumptions and gather supporting evidence
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- Combine search results with your analysis tools for comprehensive consultation
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**Search Strategy:**
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- Search for competitor analysis, market size, industry trends, and regulatory changes
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- Look up recent news, funding rounds, and market developments in relevant sectors
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- Verify market assumptions with current data before making recommendations
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- Research best practices and case studies from similar businesses
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Always maintain a professional, analytical approach while being results-oriented.
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Use all available tools including Google search to provide comprehensive, well-researched consultation backed by current market data."""
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# Define the agent instance
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root_agent = LlmAgent(
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model=MODEL_ID,
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name=APP_NAME,
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description="An AI business consultant that provides market research, strategic analysis, and actionable recommendations.",
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instruction=INSTRUCTIONS,
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tools=consultant_tools,
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output_key="consultation_response"
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)
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# Setup Runner and Session Service
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session_service = InMemorySessionService()
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runner = Runner(
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agent=root_agent,
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app_name=APP_NAME,
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session_service=session_service
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)
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if __name__ == "__main__":
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print("🤖 AI Consultant Agent with Google ADK")
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print("=====================================")
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print()
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print("This agent provides comprehensive business consultation including:")
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print("• Market research and analysis")
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print("• Strategic recommendations")
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print("• Implementation planning")
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print("• Risk assessment")
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print()
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print("To use this agent:")
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print("1. Run: adk web .")
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print("2. Open the web interface")
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print("3. Select 'AI Business Consultant' agent")
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print("4. Start your consultation")
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print()
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print("Example queries:")
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print('• "I want to launch a SaaS startup for small businesses"')
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print('• "Should I expand my retail business to e-commerce?"')
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print('• "What are the market opportunities in the healthcare tech space?"')
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print()
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print("📊 Use the Eval tab in ADK web to save and evaluate consultation sessions!")
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print()
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print(f"✅ Agent '{APP_NAME}' initialized successfully!")
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print(f" Model: {MODEL_ID}")
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print(f" Tools: {len(consultant_tools)} available")
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print(f" Session Service: {type(session_service).__name__}")
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print(f" Runner: {type(runner).__name__}")
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@@ -0,0 +1,9 @@
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google-adk>=0.1.0
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google-genai>=0.3.0
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python-dotenv>=1.0.0
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pydantic>=2.0.0
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