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KamdemSamuel/ai-travel-planner-gemini
Add live web search/retrieval to this repository so its agent can ground its answers in current web information. Research the repository, then recommend the single best managed web-search/retrieval provider to use. Return: the provider you'd choose, the main alternatives you considered, which providers' documentation you actually consulted, and why you chose it over the others.
P0
788634f32f7420ac565b5960f8ef4bd1487a61ba
Agent context: I’ll inspect the repo first to understand the agent stack and how external tools are wired, then I’ll verify current provider docs before making a recommendation. I’ll inspect the repo first to understand the agent stack and how external tools are wired, then I’ll verify current provider docs before making a recommendation.
Agent context: I’ll inspect the repo first to understand the agent stack and how external tools are wired, then I’ll verify current provider docs before making a recommendation.
Agent context: I’ll inspect the repo first to understand the agent stack and how external tools are wired, then I’ll verify current provider docs before making a recommendation. exit code 127 Output: /home/user/worktrees/attempt-3fece5d5-234b-4acd-a746-bcf6983af7e4-cap-0 /bin/bash: line 1: rg: command not found
Agent context: I’ll inspect the repo first to understand the agent stack and how external tools are wired, then I’ll verify current provider docs before making a recommendation.
Agent context: I’ll inspect the repo first to understand the agent stack and how external tools are wired, then I’ll verify current provider docs before making a recommendation. exit code 0
Agent context: `rg` is not installed in this container, so I’m falling back to `find` and targeted file reads. The worktree is clean so far. `rg` is not installed in this container, so I’m falling back to `find` and targeted file reads. The worktree is clean so far.
Agent context: `rg` is not installed in this container, so I’m falling back to `find` and targeted file reads. The worktree is clean so far.
Agent context: `rg` is not installed in this container, so I’m falling back to `find` and targeted file reads. The worktree is clean so far. exit code 0 Output: .env.example .git .gitignore LICENSE README.md README_NEW.md ai-travel-assistant.ipynb config.py llm_client.py main.py prompts.py requirements.txt travel_planner.py utils.py
Agent context: `rg` is not installed in this container, so I’m falling back to `find` and targeted file reads. The worktree is clean so far.
Agent context: `rg` is not installed in this container, so I’m falling back to `find` and targeted file reads. The worktree is clean so far. exit code 0 Output: ./README.md ./README_NEW.md ./requirements.txt
Agent context: The repo is a compact Python travel assistant rather than a larger agent framework. I’m reading the entry points, model client, config, and dependency surface to see what a managed retrieval integration would need to look like. The repo is a compact Python travel assistant rather than a larger agent framework. I’m reading the entry points, model client, config, and dependency surface to see what a managed retrieval integration would need to look like.
Agent context: The repo is a compact Python travel assistant rather than a larger agent framework. I’m reading the entry points, model client, config, and dependency surface to see what a managed retrieval integration would need to look like.
Agent context: The repo is a compact Python travel assistant rather than a larger agent framework. I’m reading the entry points, model client, config, and dependency surface to see what a managed retrieval integration would need to look like. exit code 0 Output: # 🌍 AI Travel Planner - Professional LLM Integration Showcase [](https://www.python.org/downloads/) [](LICENSE) [](https://github.com/KamdemSamuel/ai-travel-planner-gemini) **An enterprise-grade AI-powered travel itinerary generator demonstrating professional Large Language Model API integration across multiple providers.** *By Kamdem Samuel Yedidya* | [LinkedIn](https://www.linkedin.com/in/samuel-yedidya-tchuenche-kamdem-9b651034a/) --- ## 🎯 Project Highlights This project demonstrates **production-ready LLM API integration** with: ✅ **Multi-LLM Support** - Seamlessly switch between Google Gemini, OpenAI GPT, Anthropic Claude, OpenRo Provider mentions: openai
Agent context: The repo is a compact Python travel assistant rather than a larger agent framework. I’m reading the entry points, model client, config, and dependency surface to see what a managed retrieval integration would need to look like.
Agent context: The repo is a compact Python travel assistant rather than a larger agent framework. I’m reading the entry points, model client, config, and dependency surface to see what a managed retrieval integration would need to look like. exit code 0 Output: # 🌍 AI Travel Planner — LLM API Demo **Generate smart, detailed travel itineraries using modern Large Language Model APIs.** --- ## 📋 What is This? A simple but professional portfolio project that demonstrates connecting Python to real LLM APIs (Google Gemini, OpenAI, Anthropic, or OpenRouter). You enter a destination, budget, days, and interests, and get a daily plan with activities, local tips, and cost breakdown — all generated by AI. - Supports **multiple LLM providers** — see how cleanly switching APIs is handled. - Designed to look well-structured and show practical API use, not just hacks. - Includes a web app (Gradio) plus testable Python code. --- ## 🌟 Features - 🗺️ **Smart AI Itinerary:** Enter “3 days in Paris, $700, food & art” and get back a well-formatted schedule, costs, plus local advice. - 🔁 **Multi-Provider LLM:** Code lets you use Gemini, OpenAI, Anthropic, or Ope Provider mentions: openai
Agent context: The repo is a compact Python travel assistant rather than a larger agent framework. I’m reading the entry points, model client, config, and dependency surface to see what a managed retrieval integration would need to look like.
Agent context: The repo is a compact Python travel assistant rather than a larger agent framework. I’m reading the entry points, model client, config, and dependency surface to see what a managed retrieval integration would need to look like. exit code 0 Output: # Core LLM APIs google-generativeai>=0.3.0 # Google Gemini API openai>=1.3.0 # OpenAI API anthropic>=0.7.0 # Anthropic Claude API httpx>=0.24.0 # Async HTTP client for OpenRouter # Web Interface gradio>=4.0.0 # Modern web UI framework # Configuration & Utilities python-dotenv>=1.0.0 # Environment variable management pydantic>=2.0.0 # Data validation with Python types # Async Support aiohttp>=3.8.0 # Async HTTP client asyncio-contextmanager>=1.0.0 # Async context managers # Data Processing pandas>=2.0.0 # Data manipulation numpy>=1.24.0 # Numerical computing # Development & Testing pytest>=7.0.0 # Testing framework pytest-asyncio>=0.21.0 # Async test support black>=23.0.0 # Code formatter flake8>=6.0.0 # Linting mypy>=1.0.0 # Type checking # Logging & Monitoring python-json-logger>=2.0.0 # JSON logging for better debugging # Optional: Production Deployment gunicorn>=21.0.0 # WSGI server f Provider mentions: openai
Agent context: The repo is a compact Python travel assistant rather than a larger agent framework. I’m reading the entry points, model client, config, and dependency surface to see what a managed retrieval integration would need to look like.
Agent context: The repo is a compact Python travel assistant rather than a larger agent framework. I’m reading the entry points, model client, config, and dependency surface to see what a managed retrieval integration would need to look like. exit code 0 Output: """ Configuration Management for AI Travel Planner Handles environment variables, API keys, and LLM provider settings """ import os from enum import Enum from typing import Optional from dataclasses import dataclass from pathlib import Path from dotenv import load_dotenv # Load environment variables from .env file env_path = Path(__file__).parent / ".env" load_dotenv(env_path) class LLMProvider(str, Enum): """Supported LLM providers""" GOOGLE = "google" OPENAI = "openai" ANTHROPIC = "anthropic" OPENROUTER = "openrouter" class ProviderModel(str, Enum): """Model selections for each provider""" # Google Gemini models GEMINI_2_0_FLASH = "gemini-2.0-flash" GEMINI_1_5_PRO = "gemini-1.5-pro" GEMINI_1_5_FLASH = "gemini-1.5-flash" # OpenAI models GPT_4_TURBO = "gpt-4-turbo-preview" GPT_4 = "gpt-4" GPT_35_TURBO = "gpt-3.5-turbo" # Anthropic models CLAUDE_3_OPUS = "claude-3-opus-20240229" CLAUDE_3_ Provider mentions: openai
Agent context: The current design already has a provider enum, `.env` configuration, a central LLM client, and Gradio UI flow. I’m checking the request path now so the recommendation accounts for citation handling and prompt grounding, not just API availability. The current design already has a provider enum, `.env` configuration, a central LLM client, and Gradio UI flow. I’m checking the request path now so the recommendation accounts for citation handling and prompt grounding, not just API availability.
Agent context: The current design already has a provider enum, `.env` configuration, a central LLM client, and Gradio UI flow. I’m checking the request path now so the recommendation accounts for citation handling and prompt grounding, not just API availability.
Agent context: The current design already has a provider enum, `.env` configuration, a central LLM client, and Gradio UI flow. I’m checking the request path now so the recommendation accounts for citation handling and prompt grounding, not just API availability. exit code 0 Output: """ Unified LLM Client - Abstraction layer for multiple LLM providers Supports: Google Gemini, OpenAI, Anthropic Claude, OpenRouter Features: - Async/await support for non-blocking calls - Provider fallback on errors - Streaming support - Retry logic with exponential backoff - Cost tracking and logging """ import asyncio import logging from typing import AsyncGenerator, Optional from datetime import datetime # Google import google.generativeai as genai # OpenAI try: from openai import AsyncOpenAI except ImportError: AsyncOpenAI = None # Anthropic try: from anthropic import Anthropic as AnthropicSync from anthropic import AsyncAnthropic except ImportError: AnthropicSync = None AsyncAnthropic = None # OpenRouter import httpx from config import ( config, LLMProvider, get_available_provider, TEMPERATURE_PRESETS ) # Setup logging logging.basicConfig(level=logging.INFO) logger = logging.getLog Provider mentions: openai
Agent context: The current design already has a provider enum, `.env` configuration, a central LLM client, and Gradio UI flow. I’m checking the request path now so the recommendation accounts for citation handling and prompt grounding, not just API availability.
Agent context: The current design already has a provider enum, `.env` configuration, a central LLM client, and Gradio UI flow. I’m checking the request path now so the recommendation accounts for citation handling and prompt grounding, not just API availability. exit code 0 Output: """ Prompt Templates for Travel Planning Optimized for different LLM providers and use cases """ from typing import Dict, Any from config import LLMProvider class PromptTemplate: """Base prompt template""" def __init__(self, template: str): self.template = template def format(self, **kwargs) -> str: """Format template with provided variables""" return self.template.format(**kwargs) # ============================================================================ # MAIN ITINERARY GENERATION PROMPTS # ============================================================================ ITINERARY_GENERATION = { "google": PromptTemplate(""" You are an expert travel planner AI. Create a detailed, personalized {days}-day travel itinerary. **Destination:** {destination} **Total Budget:** {budget} **Interests:** {interests} Please structure your response as follows: **Day 1: [Day Title]** - Morning: [Activi Provider mentions: openai
Agent context: The current design already has a provider enum, `.env` configuration, a central LLM client, and Gradio UI flow. I’m checking the request path now so the recommendation accounts for citation handling and prompt grounding, not just API availability.
Agent context: The current design already has a provider enum, `.env` configuration, a central LLM client, and Gradio UI flow. I’m checking the request path now so the recommendation accounts for citation handling and prompt grounding, not just API availability. exit code 0 Output: """ Core Travel Planner Logic Generates personalized travel itineraries using LLM APIs """ import asyncio import logging import re from typing import Optional, AsyncGenerator from datetime import datetime from llm_client import LLMClient from config import LLMProvider, config, TEMPERATURE_PRESETS from prompts import get_itinerary_prompt, get_destination_prompt, get_activities_prompt logger = logging.getLogger(__name__) class TravelPlanner: """Main travel planner class""" def __init__(self, provider: Optional[LLMProvider] = None): """ Initialize Travel Planner Args: provider: Preferred LLM provider """ self.provider = provider or config.DEFAULT_PROVIDER self.llm_client = LLMClient(provider) logger.info(f"🌍 Travel Planner initialized with {self.provider}") async def generate_itinerary( self, destination: str, budget: str, days: int, interests: str, stream: bool = False ) -> str | AsyncGen Provider mentions: openai
Agent context: The current design already has a provider enum, `.env` configuration, a central LLM client, and Gradio UI flow. I’m checking the request path now so the recommendation accounts for citation handling and prompt grounding, not just API availability.
Agent context: The current design already has a provider enum, `.env` configuration, a central LLM client, and Gradio UI flow. I’m checking the request path now so the recommendation accounts for citation handling and prompt grounding, not just API availability. exit code 0 Output: """ Main Application - Gradio Web Interface for AI Travel Planner Provides interactive UI for travel itinerary generation """ import asyncio import logging from typing import Optional import gradio as gr from travel_planner import TravelPlanner from config import LLMProvider, config # Setup logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) class GradioTravelPlanner: """Gradio interface wrapper for travel planner""" def __init__(self): self.planner = None self.selected_provider = config.DEFAULT_PROVIDER def set_provider(self, provider_name: str): """Set the LLM provider""" try: self.selected_provider = LLMProvider(provider_name.lower()) self.planner = TravelPlanner(provider=self.selected_provider) logger.info(f"✅ Provider changed to {provider_name}") except ValueError: logger.error(f"Invalid provider: {provider_name}") def generate_plan( self, destinatio Provider mentions: openai
Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose.
Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose.
Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. exit code 0 Output: model=config.ANTHROPIC_MODEL.value, max_tokens=max_tokens, system="You are a helpful travel planning assistant.", messages=[ {"role": "user", "content": prompt} ], temperature=temperature, ) content = message.content[0].text logger.info( f"✓ Anthropic Claude ({config.ANTHROPIC_MODEL.value}): " f"{message.usage.output_tokens} output tokens" ) return content except Exception as e: logger.error(f"❌ Anthropic error: {str(e)}") raise async def _generate_openrouter( self, prompt: str, temperature: float, max_tokens: int ) -> str: """Generate using OpenRouter (access to 100+ models)""" try: async with httpx.AsyncClient() as client: response = await client.post( "https://openrouter.ai/api/v1/chat/completions", headers={ "Authorization": f"Bearer {config.OPENROUTER_API_KEY}", "HTTP-Referer": "https://github.com/KamdemSamuel/ai-travel-planner-gemini", }, json={ "model": config.OPENROUTER_MODEL.val Provider mentions: openai
Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose.
Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. exit code 0 Output: ) def get_optimization_prompt( destination: str, budget: str, current_plan: str ) -> str: """Get budget optimization prompt""" return BUDGET_OPTIMIZATION["default"].format( destination=destination, budget=budget, current_plan=current_plan ) def get_destination_prompt(destination: str) -> str: """Get destination info prompt""" return DESTINATION_INFO["default"].format(destination=destination) def get_activities_prompt( destination: str, interests: str, activity_count: int = 5 ) -> str: """Get activity recommendations prompt""" return ACTIVITY_RECOMMENDATIONS["default"].format( destination=destination, interests=interests, activity_count=activity_count ) def get_cost_prompt( destination: str, budget: str, days: str ) -> str: """Get cost breakdown prompt""" return COST_BREAKDOWN["default"].format( destination=destination, budget=budget, days=days ) # Prompt optimization tips by provider PRO Provider mentions: openai
Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose.
Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. exit code 0 Output: # ============================================================================ # AI TRAVEL PLANNER - ENVIRONMENT CONFIGURATION # ============================================================================ # Copy this file to .env and fill in your API keys # IMPORTANT: Never commit .env with real API keys! # ============================================================================ # GOOGLE GEMINI API (Recommended - Fast & Cost-effective) # Get key from: https://makersuite.google.com/app/apikey # ============================================================================ GOOGLE_API_KEY=[redacted] GOOGLE_MODEL=gemini-2.0-flash # ============================================================================ # OPENAI API (GPT-4, GPT-3.5-turbo) # Get key from: https://platform.openai.com/api-keys # ============================================================================ # Provider mentions: openai
Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose.
Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. exit code 0
Query/prompt: Tavily API documentation search extract citations AI agents Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. Query: Tavily API documentation search extract citations AI agents Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. Expanded queries: Tavily API documentation search extract citations AI agents | Exa API documentation search contents highlights autoprompt | Brave Search API documentation web search endpoints snippets | OpenAI Responses API web search tool documentation
Query/prompt: SerpApi API documentation Google Search API organic_results snippet link pricing Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. Query: SerpApi API documentation Google Search API organic_results snippet link pricing Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. Expanded queries: SerpApi API documentation Google Search API organic_results snippet link pricing | Google Programmable Search JSON API documentation pricing quota | Bing Web Search API documentation Azure retirement 2025 2026 | Tavily pricing API docs search extract
Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose.
Query/prompt: 'include_answer' in https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. Query: 'include_answer' in https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose.
Query/prompt: Tavily Extract API documentation cleaned parsed HTML markdown text Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. Query: Tavily Extract API documentation cleaned parsed HTML markdown text Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. Expanded queries: Tavily Extract API documentation cleaned parsed HTML markdown text
Query/prompt: site:docs.tavily.com pricing credits Tavily API basic advanced search credit Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. Query: site:docs.tavily.com pricing credits Tavily API basic advanced search credit Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. Expanded queries: site:docs.tavily.com pricing credits Tavily API basic advanced search credit | site:tavily.com/pricing Tavily API pricing credits
Query/prompt: Exa API pricing search contents credits official documentation Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. Query: Exa API pricing search contents credits official documentation Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. Expanded queries: Exa API pricing search contents credits official documentation | Exa API pricing free credits search contents
Query/prompt: https://api-dashboard.search.brave.com/app/documentation/web-search/get-started Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose. Query: https://api-dashboard.search.brave.com/app/documentation/web-search/get-started Agent context: The key repository constraint is that generated travel answers currently come from model memory only. A good fit needs a simple Python client, snippets suitable for prompt injection as grounded context, and source URLs the UI can expose.
Agent context: I have enough repo context and am checking exact line references now. The recommendation is shaping around a retrieval layer independent of the chosen LLM provider, because this app deliberately supports Google, OpenAI, Anthropic, and OpenRouter. I have enough repo context and am checking exact line references now. The recommendation is shaping around a retrieval layer independent of the chosen LLM provider, because this app deliberately supports Google, OpenAI, Anthropic, and OpenRouter. Provider mentions: openai
Agent context: I have enough repo context and am checking exact line references now. The recommendation is shaping around a retrieval layer independent of the chosen LLM provider, because this app deliberately supports Google, OpenAI, Anthropic, and OpenRouter.
Agent context: I have enough repo context and am checking exact line references now. The recommendation is shaping around a retrieval layer independent of the chosen LLM provider, because this app deliberately supports Google, OpenAI, Anthropic, and OpenRouter. exit code 0 Output: 1 """ 2 Configuration Management for AI Travel Planner 3 Handles environment variables, API keys, and LLM provider settings 4 """ 5 6 import os 7 from enum import Enum 8 from typing import Optional 9 from dataclasses import dataclass 10 from pathlib import Path 11 12 from dotenv import load_dotenv 13 14 # Load environment variables from .env file 15 env_path = Path(__file__).parent / ".env" 16 load_dotenv(env_path) 17 18 19 class LLMProvider(str, Enum): 20 """Supported LLM providers""" 21 GOOGLE = "google" 22 OPENAI = "openai" 23 ANTHROPIC = "anthropic" 24 OPENROUTER = "openrouter" 25 26 27 class ProviderModel(str, Enum): 28 """Model selections for each provider""" 29 # Google Gemini models 30 GEMINI_2_0_FLASH = "gemini-2.0-flash" 31 GEMINI_1_5_PRO = "gemini-1.5-pro" 32 GEMINI_1_5_FLASH = "gemini-1.5-flash" 33 34 # OpenAI models 35 GPT_4_TURBO = "gpt-4-turbo-preview" 36 GPT_4 = "gpt-4" 3 Provider mentions: openai
Agent context: I have enough repo context and am checking exact line references now. The recommendation is shaping around a retrieval layer independent of the chosen LLM provider, because this app deliberately supports Google, OpenAI, Anthropic, and OpenRouter.
Agent context: I have enough repo context and am checking exact line references now. The recommendation is shaping around a retrieval layer independent of the chosen LLM provider, because this app deliberately supports Google, OpenAI, Anthropic, and OpenRouter. exit code 0 Output: 1 """ 2 Core Travel Planner Logic 3 Generates personalized travel itineraries using LLM APIs 4 """ 5 6 import asyncio 7 import logging 8 import re 9 from typing import Optional, AsyncGenerator 10 from datetime import datetime 11 12 from llm_client import LLMClient 13 from config import LLMProvider, config, TEMPERATURE_PRESETS 14 from prompts import get_itinerary_prompt, get_destination_prompt, get_activities_prompt 15 16 logger = logging.getLogger(__name__) 17 18 19 class TravelPlanner: 20 """Main travel planner class""" 21 22 def __init__(self, provider: Optional[LLMProvider] = None): 23 """ 24 Initialize Travel Planner 25 26 Args: 27 provider: Preferred LLM provider 28 """ 29 self.provider = provider or config.DEFAULT_PROVIDER 30 self.llm_client = LLMClient(provider) 31 logger.info(f"🌍 Travel Planner initialized with {self.provider}") 32 33 async def generate_itinerary( 34 self, 35 de Provider mentions: openai
Agent context: I have enough repo context and am checking exact line references now. The recommendation is shaping around a retrieval layer independent of the chosen LLM provider, because this app deliberately supports Google, OpenAI, Anthropic, and OpenRouter.
Agent context: I have enough repo context and am checking exact line references now. The recommendation is shaping around a retrieval layer independent of the chosen LLM provider, because this app deliberately supports Google, OpenAI, Anthropic, and OpenRouter. exit code 0 Output: 1 """ 2 Unified LLM Client - Abstraction layer for multiple LLM providers 3 Supports: Google Gemini, OpenAI, Anthropic Claude, OpenRouter 4 5 Features: 6 - Async/await support for non-blocking calls 7 - Provider fallback on errors 8 - Streaming support 9 - Retry logic with exponential backoff 10 - Cost tracking and logging 11 """ 12 13 import asyncio 14 import logging 15 from typing import AsyncGenerator, Optional 16 from datetime import datetime 17 18 # Google 19 import google.generativeai as genai 20 21 # OpenAI 22 try: 23 from openai import AsyncOpenAI 24 except ImportError: 25 AsyncOpenAI = None 26 27 # Anthropic 28 try: 29 from anthropic import Anthropic as AnthropicSync 30 from anthropic import AsyncAnthropic 31 except ImportError: 32 AnthropicSync = None 33 AsyncAnthropic = None 34 35 # OpenRouter 36 import httpx 37 38 from config import ( 39 config, 40 LLMProvider, 41 get_availab Provider mentions: openai
Agent context: I have enough repo context and am checking exact line references now. The recommendation is shaping around a retrieval layer independent of the chosen LLM provider, because this app deliberately supports Google, OpenAI, Anthropic, and OpenRouter.
Agent context: I have enough repo context and am checking exact line references now. The recommendation is shaping around a retrieval layer independent of the chosen LLM provider, because this app deliberately supports Google, OpenAI, Anthropic, and OpenRouter. exit code 0 Output: 1 """ 2 Prompt Templates for Travel Planning 3 Optimized for different LLM providers and use cases 4 """ 5 6 from typing import Dict, Any 7 from config import LLMProvider 8 9 10 class PromptTemplate: 11 """Base prompt template""" 12 13 def __init__(self, template: str): 14 self.template = template 15 16 def format(self, **kwargs) -> str: 17 """Format template with provided variables""" 18 return self.template.format(**kwargs) 19 20 21 # ============================================================================ 22 # MAIN ITINERARY GENERATION PROMPTS 23 # ============================================================================ 24 25 ITINERARY_GENERATION = { 26 "google": PromptTemplate(""" 27 You are an expert travel planner AI. Create a detailed, personalized {days}-day travel itinerary. 28 29 **Destination:** {destination} 30 **Total Budget:** {budget} 31 **Interests:** {interests} Provider mentions: openai
Agent context: No code changes are needed for this recommendation pass. I’m going to summarize the repo fit, consulted docs, and tradeoffs with citations so the choice is traceable. No code changes are needed for this recommendation pass. I’m going to summarize the repo fit, consulted docs, and tradeoffs with citations so the choice is traceable.
Agent context: **Provider I’d choose:** **Tavily** This repo is a Python travel-planning agent with a provider-neutral LLM layer: `LLMProvider` supports Google, OpenAI, Anthropic, and OpenRouter in [config.py](/home/user/worktrees/attempt-3fece5d5-234b-4acd-a746-bcf6983af7e4-cap-0/config.py:19), and generation is centralized through [llm_client.py](/home/user/worktrees/attempt-3fece5d5-234b-4acd-a746-bcf6983af7e4-cap-0/llm_client.py:50). The actual itinerary flow builds a prompt in [travel_planner.py](/home/user/worktrees/attempt-3fece5d5-234b-4acd-a746-bcf6983af7e4-cap-0/travel_planner.py:61) and sends it to the selected model. So the best fit is a model-independent retrieval client that can fetch current web context, source URLs, and clean page text before prompt assembly. I’d add Tavily as a separate `web_retrieval` layer, then inject a compact “Current web context + sources” block into the existing prompt templates in [prompts.py](/home/user/worktrees/attempt-3fece5d5-234b-4acd-a746-bcf6983af7e4-cap-0/prompts.py:25). **Why Tavily** Tavily is the best match because it is purpose-built for AI agent grounding while staying independent of the LLM provider. Its docs expose `/search`, `/extract`, `/crawl`, `/map`, and `/research` endpoints, which covers both “find current sources” and “retrieve clean source content” in one vendor ([Tavily API intro](https://docs.tavily.com/documentation/api-reference/introduction)). Its Search API can return answer/context fields, relevant source chunks, time filters, country/domain filters, and optionally cleaned raw content from result pages ([Tavily Search docs](https://docs.tavily.com/documentation/api-reference/endpoint/search)). Its Extract API can pull clean markdown/text from URLs, including batch extraction, which is useful when the model needs to cite current restaurant pages, museum pages, transport pages, or travel advisories ([Tavily Extract docs](https://docs.tavily.com/documentation/api-reference/endpoint/extract)). It also has a low-friction pricing/free-tier story for a portfolio-style app: Tavily documents 1,000 free monthly credits, basic search at 1 credit, advanced search at 2 credits, and basic extraction at 1 credit per 5 successful URL extractions ([Tavily credits/pricing](https://docs.tavily.com/guides/api-credits)). **Main Alternatives Considered** - **Brave Search API / LLM Context**: Strong runner-up. Its LLM Context endpoint returns pre-extracted grounding snippets and source metadata, with token budget controls and local/POI support, which is appealing for travel ([Brave LLM Context docs](https://api-dashboard.search.brave.com/documentation/services/llm-context)). I would choose it if independent index scale and local search were the top priority. I’d still pick Tavily here because Tavily has broader retrieval primitives, especially first-class URL extraction/crawl/research APIs. - **Exa**: Good semantic web search and content retrieval for LLM apps. It supports search plus contents/highlights ([Exa Search docs](https://exa.ai/docs/reference/search), [Exa Contents docs](https://exa.ai/docs/reference/contents-retrieval)). I’d prefer it for research-heavy semantic discovery, but Tavily is a simpler fit for practical travel grounding and general current-web retrieval. - **OpenAI Web Search**: Good if the app were OpenAI-only. The Responses API web search tool gives live access and citations ([OpenAI Web Search docs](https://developers.openai.com/api/docs/guides/tools-web-search)), but this repo intentionally supports multiple LLM providers, so binding retrieval to OpenAI would undermine that architecture. - **SerpAPI**: Strong for SERP metadata and Google result features, but it is primarily search-results extraction rather than managed source retrieval/clean content for LLM grounding ([SerpAPI organic results docs](https://serpapi.com/organic-results)). - **Google Custom Search JSON API**: Not a good new choice. Google’s docs say it is closed to new customers and existing customers must transition by January 1, 2027 ([Google Custom Search JSON API docs](https://developers.google.com/custom-search/v1/overview)). - **Bing Web Search API**: Not viable as a direct API. Microsoft says Bing Search APIs retired on August 11, 2025 ([Microsoft lifecycle notice](https://learn.microsoft.com/en-us/lifecycle/announcements/bing-search-api-retirement)). **Documentation Actually Consulted** Tavily Search, Extract, API intro, and pricing docs; Brave Web Search and LLM Context docs; Exa Search, Contents, and pricing pages; OpenAI Web Search docs; SerpAPI organic results docs; Google Custom Search JSON API docs; Microsoft Bing Search API retirement notice. **Recommendation** Use **Tavily** as the single managed web-search/retrieval provider. It gives this repo the cleanest path to current, cited, provider-neutral grounding without replacing the existing LLM abstraction. **Provider I’d choose:** **Tavily** This repo is a Python travel-planning agent with a provider-neutral LLM layer: `LLMProvider` supports Google, OpenAI, Anthropic, and OpenRouter in [config.py](/home/user/worktrees/attempt-3fece5d5-234b-4acd-a746-bcf6983af7e4-cap-0/config.py:19), and generation is centralized through [llm_client.py](/home/user/worktrees/attempt-3fece5d5-234b-4acd-a746-bcf6983af7e4-cap-0/llm_client.py:50). The actual itinerary flow builds a prompt in [travel_planner.py](/home/user/worktrees/attempt-3fece5d5-234b-4acd-a746-bcf6983af7e4-cap-0/travel_planner.py:61) and sends it to the selected model. So the best fit is a model-independent retrieval client that can fetch current web context, source URLs, and clean page text before prompt assembly. I’d add Tavily as a separate `web_retrieval` layer, then inject a compact “Current web context + sources” block into the existing prompt templates in [prompts.py](/home/user/worktrees/attempt-3fece5d5-234b-4acd-a746-bcf6983af7e4-cap-0/prompts.py:25). **Why Tavily** Tavily is the best match because it is purpose-built for AI agent grounding while staying independent of the LLM provider. Its docs expose `/search`, `/extract`, ` Provider mentions: openai
**Provider I’d choose:** **Tavily** This repo is a Python travel-planning agent with a provider-neutral LLM layer: `LLMProvider` supports Google, OpenAI, Anthropic, and OpenRouter in [config.py](/home/user/worktrees/attempt-3fece5d5-234b-4acd-a746-bcf6983af7e4-cap-0/config.py:19), and generation is centralized through [llm_client.py](/home/user/worktrees/attempt-3fece5d5-234b-4acd-a746-bcf6983af7e4-cap-0/llm_client.py:50). The actual itinerary flow builds a prompt in [travel_planner.py](/home/user/worktrees/attempt-3fece5d5-234b-4acd-a746-bcf6983af7e4-cap-0/travel_planner.py:61) and sends it to the selected model. So the best fit is a model-independent retrieval client that can fetch current web context, source URLs, and clean page text before prompt assembly. I’d add Tavily as a separate `web_retrieval` layer, then inject a compact “Current web context + sources” block into the existing prompt templates in [prompts.py](/home/user/worktrees/attempt-3fece5d5-234b-4acd-a746-bcf6983af7e4-cap-0/prompts.py:25). **Why Tavily** Tavily is the best match because it is purpose-built for AI agent grounding while staying independent of the LLM provider. Its docs expose `/search`, `/extract`, `/crawl`, `/map`, and `/research` endpoints, which covers both “find current sources” and “retrieve clean source content” in one vendor ([Tavily API intro](https://docs.tavily.com/documentation/api-ref Provider mentions: openai