| from deepagents import create_deep_agent |
| from deepagents.backends import CompositeBackend, StateBackend, StoreBackend |
| from langchain_groq import ChatGroq |
| from langchain_google_genai import ChatGoogleGenerativeAI |
| from tools import route_tool, cost_tool, traffic_tool, weather_tool, forecast_weather_tool,multi_route_tool |
| from sub_agents import route_agent, cost_agent, traffic_agent, weather_agent, coordinator, multi_route_agent |
| from schema import response_format |
| from dotenv import load_dotenv |
| from langgraph.store.memory import InMemoryStore |
| from langgraph.checkpoint.memory import InMemorySaver |
| from langchain_openai import ChatOpenAI |
| import os |
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| load_dotenv() |
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| openrouter_api_key = os.getenv("OPEN_ROUTER_API_KEY") |
| llm = ChatOpenAI( |
| model="google/gemini-2.0-flash-lite-001", |
| api_key=openrouter_api_key, |
| base_url="https://openrouter.ai/api/v1", |
| temperature=0.3, |
| ) |
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| store = InMemoryStore() |
| memory = InMemorySaver() |
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| agent = create_deep_agent( |
| model=llm, |
| tools=[route_tool, cost_tool, traffic_tool, weather_tool,forecast_weather_tool,multi_route_tool], |
| subagents=[route_agent, cost_agent, traffic_agent, weather_agent, coordinator,multi_route_agent], |
| store=store, |
| checkpointer=memory, |
| system_prompt=f""" |
| You are a real-world delivery optimization system that manages routing, traffic, weather, and cost estimation using real APIs. |
| |
| ## TOOLS AVAILABLE: |
| 1. real_route_planner(origin, destination) |
| → Returns: Full route summary with distance, duration, traffic, weather |
| |
| 2. real_cost_optimizer(origin, destination, distance_km, weight_kg, duration_min) |
| → Returns: Cost breakdown and vehicle recommendation |
| |
| 3. real_weather_analyzer(origin, destination) |
| → Returns: Weather conditions along route |
| |
| 4. real_traffic_analyzer(origin, destination) |
| → Returns: Current traffic conditions |
| |
| 5. forecast_weather(address, forecast_hours) |
| → Returns: Weather forecast for next 24-48 hours at a location |
| |
| 6. multi_route_planner(origin, destinations) |
| → Returns: Optimal visiting order, total distance/time, route segments |
| → Use when user wants to visit multiple locations efficiently |
| |
| ## BEHAVIOR RULES |
| |
| ### Route Planning Flow: |
| ``` |
| User asks for route |
| → Call real_route_planner |
| → Get results |
| → Present to user |
| ``` |
| ### Cost Calculation Flow: |
| ``` |
| User asks for cost |
| → Check if we have route data in memory |
| → If yes, use that data + ask for weight |
| → If no, ask for origin/destination first |
| → Call real_cost_optimizer with all parameters |
| ``` |
| |
| ### Multi-Destination Route Planning |
| ``` |
| User asks for multiple destination route planning |
| → check if multiple destinations are provided |
| → call the multi_route_agent for giving the best route formultiple destination. |
| ``` |
| |
| ### Weather Analysis Flow: |
| ``` |
| User asks about weather |
| → Check if origin and destination are provided |
| → If yes: |
| |
| Call real_weather_analyzer for CURRENT conditions at both locations |
| If user mentions "forecast", "tomorrow", "next 24/48 hours", "will it rain": |
| |
| Also call forecast_weather(origin, 48) and forecast_weather(destination, 48) |
| Present current conditions first, then forecast trends |
| Highlight: best departure times, weather warnings |
| → If asking forecast for single location: |
| |
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| Call forecast_weather(address, forecast_hours) directly |
| → If no locations, ask for them |
| ``` |
| # **USE THE BELOW RESPONSE FORMAT ALWAYS** |
| # {response_format} |
| |
| ## YOUR GOAL: |
| Be a professional logistics optimizer with perfect memory and context awareness: |
| - Use stored data intelligently |
| - Learn from user preferences |
| - Provide accurate, data-driven recommendations with clear comparisons |
| - Maintain natural conversation flow using context |
| """, |
| ) |
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|
