Run

P6

788634f32f7420ac565b5960f8ef4bd1487a61ba

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: /home/user/worktrees/attempt-7a7f4a61-41f8-48e3-a552-70bbfbb4dca6-cap-1/README.md /home/user/worktrees/attempt-7a7f4a61-41f8-48e3-a552-70bbfbb4dca6-cap-1/README_NEW.md /home/user/worktrees/attempt-7a7f4a61-41f8-48e3-a552-70bbfbb4dca6-cap-1/requirements.txt Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 1 # 🌍 AI Travel Planner — LLM API Demo 2 3 **Generate smart, detailed travel itineraries using modern Large Language Model APIs.** 4 5 --- 6 7 ## 📋 What is This? 8 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, an Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Provider mentions: openai

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 1 # Core LLM APIs 2 google-generativeai>=0.3.0 # Google Gemini API 3 openai>=1.3.0 # OpenAI API 4 anthropic>=0.7.0 # Anthropic Claude API 5 httpx>=0.24.0 # Async HTTP client for OpenRouter 6 7 # Web Interface 8 gradio>=4.0.0 # Modern web UI framework 9 10 # Configuration & Utilities 11 python-dotenv>=1.0.0 # Environment variable management 12 pydantic>=2.0.0 # Data validation with Python types 13 14 # Async Support 1 Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Provider mentions: openai

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: total 148 drwxr-xr-x 2 user user 4096 Jun 6 04:53 . drwxr-xr-x 4 user user 4096 Jun 6 04:53 .. -rw-r--r-- 1 user user 3253 Jun 6 04:53 .env.example -rw-r--r-- 1 user user 170 Jun 6 04:53 .git -rw-r--r-- 1 user user 4628 Jun 6 04:53 .gitignore -rw-r--r-- 1 user user 1088 Jun 6 04:53 LICENSE -rw-r--r-- 1 user user 3136 Jun 6 04:53 README.md -rw-r--r-- 1 user user 10298 Jun 6 04:53 README_NEW.md -rw-r--r-- 1 user user 2 Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 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) 1 Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Provider mentions: openai

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 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 Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Provider mentions: openai

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 1 # ============================================================================ 2 # AI TRAVEL PLANNER - ENVIRONMENT CONFIGURATION 3 # ============================================================================ 4 # Copy this file to .env and fill in your API keys 5 # IMPORTANT: Never commit .env with real API keys! 6 7 # ============================================================================ 8 # GOOGLE GEMINI A Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Provider mentions: openai

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 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( Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 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 1 Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 1 """ 2 Main Application - Gradio Web Interface for AI Travel Planner 3 Provides interactive UI for travel itinerary generation 4 """ 5 6 import asyncio 7 import logging 8 from typing import Optional 9 10 import gradio as gr 11 12 from travel_planner import TravelPlanner 13 from config import LLMProvider, config 14 15 # Setup logging 16 logging.basicConfig(level=logging.INFO) 17 logger = logging.getLogger(__name__) 1 Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 1 """ 2 Utility Functions - Formatting, validation, and helper functions 3 """ 4 5 import re 6 from typing import Optional, Dict, Any 7 from datetime import datetime 8 9 10 class ItineraryFormatter: 11 """Format itinerary content for better presentation""" 12 13 @staticmethod 14 def add_emojis(text: str) -> str: 15 """Add emojis to enhance readability""" 16 text = re.sub(r'Day (\d+):', r'✨ Day \1:', text) 17 text = r Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 1 # 🌍 AI Travel Planner - Professional LLM Integration Showcase 2 3 [](https://www.python.org/downloads/) 4 [](LICENSE) 5 [](https://github.com/KamdemSamuel/ai-tra Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: 788634f docs: concise, clean, and realistic README for LLM API-focused travel planner d351a3a ✨ Add: Unified LLM client with multi-provider support 9434119 ✨ Add: Optimized prompt templates for travel planning 02a25cc ✨ Add: Core travel planner logic with streaming support 752f605 ✨ Add: Professional Gradio web interface 8deb864 ✨ Add: Python dependencies for LLM-based travel planner 1926091 ✨ Add: Example environmen Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: Found 1 file main.py Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: I'll research the repository structure and stack first, then evaluate web-search providers. Tool result summary: <cell id="927607bf">print("Hello master")</cell id="927607bf"> Hello master <cell id="7e4205f9">print("Test......Test ok bro")</cell id="7e4205f9"> Test......Test ok bro <cell id="a04ed64c"><cell_type>markdown</cell_type># ✈️ AI-Powered Travel Itinerary Generator ### *A Gemini-Powered Capstone Project* **By Kamdem Samuel Yedidya** 🔗 [LinkedIn](https://www.linkedin.com/in/samuel-yedidya-tchuenche-kamdem-9b651034a?utm Agent context: I'll research the repository structure and stack first, then evaluate web-search providers.

Agent context: This is a research-and-recommend task — I'll deliver a provider recommendation, not code. The repo is a Python async multi-provider LLM travel planner (Gemini/OpenAI/Anthropic/OpenRouter) with no current web search. Let me consult the actual documentation of the leading managed search providers. This is a research-and-recommend task — I'll deliver a provider recommendation, not code. The repo is a Python async multi-provider LLM travel planner (Gemini/OpenAI/Anthropic/OpenRouter) with no current web search. Let me consult the actual documentation of the leading managed search providers. Provider mentions: openai

Agent context: Now I'll consult the actual documentation of the leading candidates in parallel — focusing on recency, freshness, and real-time coverage. Now I'll consult the actual documentation of the leading candidates in parallel — focusing on recency, freshness, and real-time coverage.

Query/prompt: Summarize the Tavily Search API: what it does, parameters related to recency/freshness/real-time (e.g., time range, days, topic=news, include raw content), pricing model, Python SDK availability, and how results are returned for LLM grounding. Note anything about news/real-time coverage. Agent context: Now I'll consult the actual documentation of the leading candidates in parallel — focusing on recency, freshness, and real-time coverage. Fetch prompt: Summarize the Tavily Search API: what it does, parameters related to recency/freshness/real-time (e.g., time range, days, topic=news, include raw content), pricing model, Python SDK availability, and how results are returned for LLM grounding. Note anything about news/real-time coverage. Tool result summary: # Tavily Search API Summary ## Core Function Tavily Search is a REST API designed to provide search capabilities optimized for LLM agents, enabling semantic search with result ranking and content extraction. ## Recency & Real-Time Parameters The API offers several mechanisms for controlling content freshness: - **Topic filtering**: The `topic` parameter accepts `news` for real-time updates, particularly for "politics Agent context: Now I'll consult the actual documentation of the leading candidates in parallel — focusing on recency, freshness, and real-time coverage.

Query/prompt: Summarize the Exa search API: neural vs keyword search, livecrawl / freshness options, ability to get current/real-time content, date filtering (startPublishedDate), contents/highlights, Python SDK, and pricing. Focus on how fresh/real-time the results can be. Agent context: Now I'll consult the actual documentation of the leading candidates in parallel — focusing on recency, freshness, and real-time coverage. Fetch prompt: Summarize the Exa search API: neural vs keyword search, livecrawl / freshness options, ability to get current/real-time content, date filtering (startPublishedDate), contents/highlights, Python SDK, and pricing. Focus on how fresh/real-time the results can be. Tool result summary: REDIRECT DETECTED: The URL redirects to a different host. Original URL: https://docs.exa.ai/reference/search Redirect URL: https://exa.ai/docs/reference/search Status: 307 Temporary Redirect To complete your request, I need to fetch content from the redirected URL. Please use WebFetch again with these parameters: - url: "https://exa.ai/docs/reference/search" - prompt: "Summarize the Exa search API: neural vs keyword Agent context: Now I'll consult the actual documentation of the leading candidates in parallel — focusing on recency, freshness, and real-time coverage.

Query/prompt: Summarize the Perplexity Sonar API: which models provide online/real-time web search grounding, how recency/freshness is handled (search_recency_filter, search domain filters), citations, pricing, and OpenAI-compatibility. Focus on real-time/current information coverage. Agent context: Now I'll consult the actual documentation of the leading candidates in parallel — focusing on recency, freshness, and real-time coverage. Fetch prompt: Summarize the Perplexity Sonar API: which models provide online/real-time web search grounding, how recency/freshness is handled (search_recency_filter, search domain filters), citations, pricing, and OpenAI-compatibility. Focus on real-time/current information coverage. Tool result summary: # Perplexity Sonar API Summary ## Web Search Models The API offers four models with online grounding capabilities: - **sonar** (base) - **sonar-pro** (higher quality) - **sonar-deep-research** (extended analysis) - **sonar-reasoning-pro** (reasoning-focused) All support real-time web search when enabled. ## Recency & Freshness Handling The platform provides granular temporal filtering: - `search_recency_filter`: Quic Agent context: Now I'll consult the actual documentation of the leading candidates in parallel — focusing on recency, freshness, and real-time coverage. Provider mentions: openai

Query/prompt: Summarize the Brave Search API: freshness parameter, independent index, real-time/news coverage, pricing tiers, response format, and whether there's an LLM-focused grounding endpoint. Focus on recency and freshness capabilities. Agent context: Now I'll consult the actual documentation of the leading candidates in parallel — focusing on recency, freshness, and real-time coverage. Fetch prompt: Summarize the Brave Search API: freshness parameter, independent index, real-time/news coverage, pricing tiers, response format, and whether there's an LLM-focused grounding endpoint. Focus on recency and freshness capabilities. Tool result summary: # Brave Search API: Freshness & LLM Capabilities ## Freshness Parameters Brave Web Search offers robust date-based filtering through the `freshness` parameter with options including: - **Last 24 Hours** (`pd`) - **Last 7 Days** (`pw`) - **Last 31 Days** (`pm`) - **Last Year** (`py`) - **Custom date ranges** (e.g., `2022-04-01to2022-07-30`) ## Independent Index & Coverage The service provides "access to our comprehens Agent context: Now I'll consult the actual documentation of the leading candidates in parallel — focusing on recency, freshness, and real-time coverage.

Agent context: Let me fetch the Exa redirect and check pricing/freshness details for the top candidates. Let me fetch the Exa redirect and check pricing/freshness details for the top candidates.

Query/prompt: Summarize the Exa search API: neural vs keyword search, livecrawl / freshness options, ability to get current/real-time content, date filtering (startPublishedDate), contents/highlights, Python SDK, and pricing. Focus on how fresh/real-time the results can be. Agent context: Let me fetch the Exa redirect and check pricing/freshness details for the top candidates. Fetch prompt: Summarize the Exa search API: neural vs keyword search, livecrawl / freshness options, ability to get current/real-time content, date filtering (startPublishedDate), contents/highlights, Python SDK, and pricing. Focus on how fresh/real-time the results can be. Tool result summary: # Exa Search API Summary ## Search Types & Freshness Exa offers multiple search modes optimized for different needs: - **Instant**: Lowest latency for real-time applications - **Fast**: Lower-latency search models - **Auto** (default): Intelligently selects the best mode - **Deep variants**: In-depth research with synthesis capabilities ## Content Freshness Control The API provides granular freshness options: **maxAg Agent context: Let me fetch the Exa redirect and check pricing/freshness details for the top candidates.

Query/prompt: What are Tavily's pricing tiers and free plan limits (free credits per month, paid plan costs)? Summarize the pricing. Agent context: Let me fetch the Exa redirect and check pricing/freshness details for the top candidates. Fetch prompt: What are Tavily's pricing tiers and free plan limits (free credits per month, paid plan costs)? Summarize the pricing. Tool result summary: # Tavily Pricing Summary **Free Plan:** "You get 1,000 free API Credits every month. **No credit card required.**" **Monthly Subscription Tiers:** | Plan | Monthly Credits | Cost | |------|-----------------|------| | Project | 4,000 | $30 | | Bootstrap | 15,000 | $100 | | Startup | 38,000 | $220 | | Growth | 100,000 | $500 | **Additional Options:** - Pay-as-you-go at $0.008 per credit - Enterprise plans with custom p Agent context: Let me fetch the Exa redirect and check pricing/freshness details for the top candidates.

Query/prompt: Bing Web Search API retirement 2025 deprecation Azure Agent context: Let me fetch the Exa redirect and check pricing/freshness details for the top candidates. Query: Bing Web Search API retirement 2025 deprecation Azure 10 search result links captured Tool result summary: Based on the search results, here's what happened with the Bing Web Search API retirement: ## Retirement Timeline Bing Search APIs will be retired on August 11, 2025. The announcement came in May 2025, but the situation was more complicated: - Creation of new Bing Search resources in Azure was disabled in February 2025, the formal retirement notice landed on May 13, 2025, and the public endpoints stopped responding e Agent context: Let me fetch the Exa redirect and check pricing/freshness details for the top candidates.

Agent context: ## Recommendation: **Tavily Search API** For this repo — a provider-agnostic, async LLM travel planner (Gemini/OpenAI/Anthropic/OpenRouter) with a clean search-then-prompt integration point at `travel_planner.py:62-79` and `prompts.py` — **Tavily** is the best fit. **Why Tavily wins here:** - **Built for LLM grounding, provider-neutral.** It returns ranked, deduplicated, LLM-ready content (plus an optional synthesized `answer`) that you inject into the existing prompt context. Critically, it does *not* couple you to one LLM — preserving the repo's whole multi-provider design. - **Strong recency controls** for the "current prices/news/availability" requirement: `topic=news` for real-time events, `time_range` (day/week/month) and `start_date`/`end_date`, plus `include_raw_content` to pull fresh page text. - **Frictionless adoption:** official Python SDK, async-friendly, 1,000 free credits/month (no card), then $0.008/credit. Drops into their async `httpx`/SDK pattern with minimal change. **Main alternatives considered:** - **Exa** — Excellent freshness (`maxAgeHours=0` forces a live fetch; `startPublishedDate` filtering) and neural search. But it's tuned for semantic *discovery/research* more than fast real-time news/price lookups, and fresh-fetch calls cost more. Strong second choice for richer retrieval. - **Perplexity Sonar** — Best-in-class recency + citations (`search_recency_filter` down to the hour). But it's an *LLM-answer* API, not a search tool — adopting it would collide with the repo's existing multi-provider LLM layer (it'd be a competing model, not a grounding source). Better as an alternative provider than as the search backend. - **Brave Search API** — Independent index, `freshness` param (`pd`/`pw`/`pm`), a new LLM Context endpoint, and low cost. A solid, cheaper alternative, but returns more raw-SERP-shaped results needing extra shaping for grounding. - **Serper / SerpAPI** — Freshest possible (real Google SERPs) but raw, unshaped results — more glue code, no LLM-native answer. - **Bing Web Search API** — **Eliminated.** Retired Aug 11, 2025 (endpoints now return HTTP 410); migration path is locked into Azure AI Agents. Not viable. **Docs I actually consulted:** Tavily (search endpoint + pricing/credits pages), Exa (search reference), Perplexity (Sonar chat-completions reference), Brave (web-search get-started), and the Microsoft Lifecycle notice confirming Bing's retirement. **Bottom line:** Tavily gives the best balance of real-time freshness (news topic + time filters), LLM-ready output, provider independence, and trivial adoption — matching this codebase's architecture without forcing a rewrite of its multi-provider core. Sources: - [Tavily Search API Reference](https://docs.tavily.com/documentation/api-reference/endpoint/search) - [Exa Search Reference](https://exa.ai/docs/reference/search) - [Perplexity Sonar Chat Completions](https://docs.perplexity.ai/api-reference/chat-completions-post) - [Brave Search API Docs](https://api-dashboard.search.brave.com/app/documentation/web-search/get-started) - [Bing Search APIs Retiring August 11, 2025 — Microsoft Lifecycle](https://learn.microsoft.com/en-us/lifecycle/announcements/bing-search-api-retirement) ## Recommendation: **Tavily Search API** For this repo — a provider-agnostic, async LLM travel planner (Gemini/OpenAI/Anthropic/OpenRouter) with a clean search-then-prompt integration point at `travel_planner.py:62-79` and `prompts.py` — **Tavily** is the best fit. **Why Tavily wins here:** - **Built for LLM grounding, provider-neutral.** It returns ranked, deduplicated, LLM-ready content (plus an optional synthesized `answer`) that you inject into the existing prompt context. Critically, it does *not* couple you to one LLM — preserving the repo's whole multi-provider design. - **Strong recency controls** for the "current prices/news/availability" requirement: `topic=news` for real-time events, `time_range` (day/week/month) and `start_date`/`end_date`, plus `include_raw_content` to pull fresh page text. - **Frictionless adoption:** official Python SDK, async-friendly, 1,000 free credits/month (no card), then $0.008/credit. Drops into their async `httpx`/SDK pattern with minimal change. **Main alternatives considered:** - **Exa** — Excellent freshness (`maxAgeHours=0` forces a live fetch; `startPublishedDate` filtering) and neural search. But it's tuned for semantic *discovery/resea Provider mentions: openai

## Recommendation: **Tavily Search API** For this repo — a provider-agnostic, async LLM travel planner (Gemini/OpenAI/Anthropic/OpenRouter) with a clean search-then-prompt integration point at `travel_planner.py:62-79` and `prompts.py` — **Tavily** is the best fit. **Why Tavily wins here:** - **Built for LLM grounding, provider-neutral.** It returns ranked, deduplicated, LLM-ready content (plus an optional synthesized `answer`) that you inject into the existing prompt context. Critically, it does *not* couple you to one LLM — preserving the repo's whole multi-provider design. - **Strong recency controls** for the "current prices/news/availability" requirement: `topic=news` for real-time events, `time_range` (day/week/month) and `start_date`/`end_date`, plus `include_raw_content` to pull fresh page text. - **Frictionless adoption:** official Python SDK, async-friendly, 1,000 free credits/month (no card), then $0.008/credit. Drops into their async `httpx`/SDK pattern with minimal change. **Main alternatives considered:** - **Exa** — Excellent freshness (`maxAgeHours=0` forces a live fetch; `startPublishedDate` filtering) and neural search. But it's tuned for semantic *discovery/research* more than fast real-time news/price lookups, and fresh-fetch calls cost more. Strong second choice for richer retrieval. - **Perplexity Sonar** — Best-in-class recency + citations (`search_recenc Provider mentions: openai