kamdemsa

Run

KamdemSamuel/ai-travel-planner-gemini

Agent

GPT-5.3-Codex

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 05:04 AM

Decision Summary

competitor

Expected

Provider

Product

Selected

Provider

tavily

Product

Surface

provider_selection

Reason

codex selected tavily during first-pass provider discovery. No expected provider was set for this prompt.

Outcome

competitor

Trace Timeline

agent_action

Prompt-lab work item started

agent_action

Pinned repository checkout verified

788634f32f7420ac565b5960f8ef4bd1487a61ba

agent_action

I’ll inspect the repo first to understand the agent stack and integration constrain...

I’ll inspect the repo first to understand the agent stack and integration constraints, then I’ll verify current provider docs online before making the recommendation.

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' -g '"'!*.lock'"'"

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' -g '"'!*.lock'"'"

exit code 127 Output: /home/user/worktrees/attempt-0b9b2614-9338-4b7b-9f4b-1dcb3e0e67b5-cap-0 /bin/bash: line 1: rg: command not found

command

/bin/bash -lc 'git status --short'

command

/bin/bash -lc 'git status --short'

exit code 0

agent_action

`rg` is not installed in this workspace, so I’m falling back to `find`/targeted rea...

`rg` is not installed in this workspace, so I’m falling back to `find`/targeted reads. The worktree is currently clean.

command

/bin/bash -lc "find . -path './.git' -prune -o -path './node_modules' -prune -o -path './dist' -prune -o -path './build' -prune -o -type f -maxdepth 4 -print"

command

/bin/bash -lc "find . -path './.git' -prune -o -path './node_modules' -prune -o -path './dist' -prune -o -path './build' -prune -o -type f -maxdepth 4 -print"

exit code 0 Output: ./.env.example ./.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

command

/bin/bash -lc "find . -maxdepth 2 -type f \$ -name 'package.json' -o -name 'pyproject.toml' -o -name 'requirements.txt' -o -name 'README*' -o -name 'Cargo.toml' -o -name 'go.mod' -o -name '.env.example' \$ -print"

command

exit code 0 Output: ./.env.example ./README.md ./README_NEW.md ./requirements.txt

agent_action

This is a compact Python repo, so I’m reading the agent entry points and configurat...

This is a compact Python repo, so I’m reading the agent entry points and configuration rather than doing a broad crawl. Next I’m checking where tools are wired and how current LLM calls are made.

command

/bin/bash -lc "sed -n '1,260p' main.py"

command

/bin/bash -lc "sed -n '1,260p' main.py"

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

command

/bin/bash -lc "sed -n '1,240p' README.md"

command

/bin/bash -lc "sed -n '1,240p' README.md"

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

command

/bin/bash -lc "sed -n '1,260p' travel_planner.py"

command

/bin/bash -lc "sed -n '1,260p' travel_planner.py"

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

command

/bin/bash -lc "sed -n '1,260p' llm_client.py"

command

/bin/bash -lc "sed -n '1,260p' llm_client.py"

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

agent_action

The current app is an LLM-only travel planner: prompts ask for costs/tips, but ther...

The current app is an LLM-only travel planner: prompts ask for costs/tips, but there is no retrieval layer, citation model, or tool interface yet. I’m checking config and prompts now, then I’ll compare managed search providers against this exact shape.

command

/bin/bash -lc "sed -n '240,520p' llm_client.py"

command

/bin/bash -lc "sed -n '240,520p' llm_client.py"

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

command

/bin/bash -lc "sed -n '1,280p' prompts.py"

command

/bin/bash -lc "sed -n '1,280p' prompts.py"

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

command

/bin/bash -lc "sed -n '1,280p' config.py"

command

/bin/bash -lc "sed -n '1,280p' config.py"

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_

command

/bin/bash -lc "sed -n '1,200p' .env.example && sed -n '1,160p' requirements.txt"

command

/bin/bash -lc "sed -n '1,200p' .env.example && sed -n '1,160p' requirements.txt"

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 # ============================================================================ #

Tavily API documentation search API topic news time_range days include_raw_content

Query: Tavily API documentation search API topic news time_range days include_raw_content Agent context: The current app is an LLM-only travel planner: prompts ask for costs/tips, but there is no retrieval layer, citation model, or tool interface yet. I’m checking config and prompts now, then I’ll compare managed search providers against this exact shape. Expanded queries: Tavily API documentation search API topic news time_range days include_raw_content | Exa API documentation search contents livecrawl date filters | Brave Search API documentation news freshness API web search | Perplexity Sonar API documentation search recency citations

url

https://docs.tavily.com/api-reference/endpoint/search

Query: https://docs.tavily.com/api-reference/endpoint/search Agent context: The current app is an LLM-only travel planner: prompts ask for costs/tips, but there is no retrieval layer, citation model, or tool interface yet. I’m checking config and prompts now, then I’ll compare managed search providers against this exact shape.

url

https://docs.tavily.com/api-reference/endpoint/search

Tavily API pricing credits search endpoint 2026

Query: Tavily API pricing credits search endpoint 2026 Agent context: The current app is an LLM-only travel planner: prompts ask for costs/tips, but there is no retrieval layer, citation model, or tool interface yet. I’m checking config and prompts now, then I’ll compare managed search providers against this exact shape. Expanded queries: Tavily API pricing credits search endpoint 2026 | Exa API pricing search crawl contents docs pricing | SerpAPI API documentation Google Search API pricing freshness news shopping flights hotels | Google Custom Search JSON API documentation pricing queries per day

OpenAI API web search tool documentation current information citations

Query: OpenAI API web search tool documentation current information citations Agent context: The current app is an LLM-only travel planner: prompts ask for costs/tips, but there is no retrieval layer, citation model, or tool interface yet. I’m checking config and prompts now, then I’ll compare managed search providers against this exact shape. Expanded queries: OpenAI API web search tool documentation current information citations | Google Gemini API grounding with Google Search documentation dynamic retrieval | Anthropic web search tool API documentation citations max uses allowed domains

site:docs.anthropic.com web search tool API citations max_uses allowed_domains 2026...

Query: site:docs.anthropic.com web search tool API citations max_uses allowed_domains 2026 anthropic Agent context: The current app is an LLM-only travel planner: prompts ask for costs/tips, but there is no retrieval layer, citation model, or tool interface yet. I’m checking config and prompts now, then I’ll compare managed search providers against this exact shape. Expanded queries: site:docs.anthropic.com web search tool API citations max_uses allowed_domains 2026 anthropic

url

https://tavilyai.mintlify.app/documentation/api-credits

Query: https://tavilyai.mintlify.app/documentation/api-credits Agent context: The current app is an LLM-only travel planner: prompts ask for costs/tips, but there is no retrieval layer, citation model, or tool interface yet. I’m checking config and prompts now, then I’ll compare managed search providers against this exact shape.

url

https://ai.google.dev/gemini-api/docs/grounding?authuser=2

Query: https://ai.google.dev/gemini-api/docs/grounding?authuser=2 Agent context: The current app is an LLM-only travel planner: prompts ask for costs/tips, but there is no retrieval layer, citation model, or tool interface yet. I’m checking config and prompts now, then I’ll compare managed search providers against this exact shape.

command

/bin/bash -lc "nl -ba config.py | sed -n '1,150p'"

command

/bin/bash -lc "nl -ba config.py | sed -n '1,150p'"

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

command

/bin/bash -lc "nl -ba travel_planner.py | sed -n '1,130p'"

command

/bin/bash -lc "nl -ba travel_planner.py | sed -n '1,130p'"

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

command

/bin/bash -lc "nl -ba prompts.py | sed -n '1,110p'"

command

/bin/bash -lc "nl -ba prompts.py | sed -n '1,110p'"

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}

command

/bin/bash -lc "nl -ba llm_client.py | sed -n '1,180p'"

command

/bin/bash -lc "nl -ba llm_client.py | sed -n '1,180p'"

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

Anthropic web search tool docs web_search_20250305 site:docs.anthropic.com/en

Query: Anthropic web search tool docs web_search_20250305 site:docs.anthropic.com/en Agent context: The current app is an LLM-only travel planner: prompts ask for costs/tips, but there is no retrieval layer, citation model, or tool interface yet. I’m checking config and prompts now, then I’ll compare managed search providers against this exact shape. Expanded queries: Anthropic web search tool docs web_search_20250305 site:docs.anthropic.com/en

agent_action

**Provider Choice** I’d choose **Tavily Search API** as the managed web-search/retr...

**Provider Choice** I’d choose **Tavily Search API** as the managed web-search/retrieval provider. Repo fit is the main reason: this app has a clean provider-neutral LLM layer in [llm_client.py](/home/user/worktrees/attempt-0b9b2614-9338-4b7b-9f4b-1dcb3e0e67b5-cap-0/llm_client.py:50), and `TravelPlanner.generate_itinerary` currently just builds a prompt and sends it to the selected model in [travel_planner.py](/home/user/worktrees/attempt-0b9b2614-9338-4b7b-9f4b-1dcb3e0e67b5-cap-0/travel_planner.py:61). Tavily can be added as an independent retrieval step before the LLM call, without tying grounding to Google, OpenAI, Anthropic, or OpenRouter. Tavily is also strong on the exact freshness requirement: its Search API supports `news` for real-time updates and also exposes `finance` as a search topic, plus `time_range` and cleaned raw page content for retrieval-grounded prompts. The docs describe `news` as useful for real-time updates, list `general`, `news`, and `finance` topics, and support `include_raw_content` for cleaned parsed HTML content. Sources: Tavily Search docs lines 278-292 and 349, plus pricing docs showing basic search at 1 credit and advanced search at 2 credits. **Mai

result

Selected unknown

Commands

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' -g '"'!*.lock'"'"

exit 127

/bin/bash -lc 'git status --short'

exit 0

/bin/bash -lc "find . -path './.git' -prune -o -path './node_modules' -prune -o -path './dist' -prune -o -path './build' -prune -o -type f -maxdepth 4 -print"

exit 0

/bin/bash -lc "sed -n '1,260p' main.py"

exit 0

/bin/bash -lc "sed -n '1,240p' README.md"

exit 0

/bin/bash -lc "sed -n '1,260p' travel_planner.py"

exit 0

/bin/bash -lc "sed -n '1,260p' llm_client.py"

exit 0

/bin/bash -lc "sed -n '240,520p' llm_client.py"

exit 0

/bin/bash -lc "sed -n '1,280p' prompts.py"

exit 0

/bin/bash -lc "sed -n '1,280p' config.py"

exit 0

/bin/bash -lc "sed -n '1,200p' .env.example && sed -n '1,160p' requirements.txt"

exit 0

/bin/bash -lc "nl -ba config.py | sed -n '1,150p'"

exit 0

/bin/bash -lc "nl -ba travel_planner.py | sed -n '1,130p'"

exit 0

/bin/bash -lc "nl -ba prompts.py | sed -n '1,110p'"

exit 0

/bin/bash -lc "nl -ba llm_client.py | sed -n '1,180p'"

exit 0

URLs Fetched

https://docs.tavily.com/api-reference/endpoint/search

status not materialized

https://tavilyai.mintlify.app/documentation/api-credits

status not materialized

https://ai.google.dev/gemini-api/docs/grounding?authuser=2

status not materialized

Changed Files

Artifact only

Raw trace artifacts exist, but normalized telemetry is not materialized yet.

Runs

Find

Run

Decision Summary

Trace Timeline

Commands

URLs Fetched

Changed Files

Runs

Run

Decision Summary

Trace Timeline

Commands

URLs Fetched

Changed Files