Skip to main content
Glama

Personal Travel Agent (V4)

Python 3.10+ FastAPI FastMCP PostgreSQL Google Cloud Run License: MIT

πŸŽ“ Academic & Educational Context
This project was built for the Agentic AI Program: Building Autonomous Systems for Real-World Applications program offered by the School of Computer Science at Carnegie Mellon University (CMU).
Author: Anthony Wang | Developed strictly for educational and research purposes.


🌟 Executive Summary

Personal Travel Agent is an autonomous multi-modal travel planning system that solves complex, multi-day combinatorial travel itinerary generation under strict spatial, financial, temporal, and pacing constraints.

Standard single-turn LLM generation and linear ReAct loops suffer from high regret rates (25%–40%) when building multi-day trips due to lack of lookahead, backtracking, and rigorous constraint verification. This system solves those limitations by introducing a Two-Tier Cognitive Architecture:

  1. Tier 1: Outer ReAct Loop (Dialogue & Grounding): Handles conversational dialogue, intent routing, user preference extraction, semantic memory recall via pgvector, and baseline tool grounding (flights, lodging, residual budget calculation).

  2. Tier 2: Inner Tree of Thought (ToT) Search Engine: Solves the combinatorial multi-day itinerary optimization problem using Beam Search ($b=4, k=3, N \le 7$) with deterministic hard-constraint gatekeepers, a 5-dimensional calibrated rubric critic, 1 protected rescue slot, and compute guardrails.

  3. Live Grounding Tools via Open MCP Feeds: Connects to real-time, zero-mock external APIsβ€”including Open-Meteo API (live meteorological and geocoding feeds) and Frankfurter API (official European Central Bank foreign exchange rates across 33+ global currencies).

  4. Global Destination RAG Catalog: Pre-indexed vector repository covering 156 global destinations (countries & world cities) with curated local neighborhoods, cultural landmarks, transit baselines, culinary specialties, and pricing heuristics.

  5. Interactive UI on Cloud Run: Glassmorphism web interface featuring real-time chat, dynamic SVG Tree of Thought search tree visualization, radar charts, live weather/FX grounding widgets, and responsive Dark/Light theme toggle.


Related MCP server: MCP Memory Server

πŸ›οΈ System Architecture

flowchart TD
    User(["πŸ‘€ User Request / Prompt"]) --> UI["🌐 Glassmorphism Web UI / CLI / ADK Web"]
    
    subgraph Tier1 ["Tier 1: Outer ReAct Grounding & Intent Loop"]
        UI --> Router{"Intent Classifier"}
        Router -- "Weather / FX" --> LiveTools["Live Grounding MCP Services"]
        Router -- "Destination RAG" --> VectorStore[("PostgreSQL + pgvector\n(156 Destinations & User Memory)")]
        Router -- "Plan Trip" --> ReActAgent["TravelAgentRunner (ReAct Agent)"]
        
        ReActAgent --> G1["search_flights()"]
        ReActAgent --> G2["search_lodging()"]
        ReActAgent --> G3["compute_residual_budget()"]
        ReActAgent <--> VectorStore
        
        G1 & G2 & G3 --> Frame["PlanningFrame\n(Immutable Contract: Dates, Lodging, Residual Daily Budget)"]
    end

    subgraph Tier2 ["Tier 2: Inner Tree of Thought (ToT) Combinatorial Engine"]
        Frame --> BeamController["BeamSearchEngine (k=3, b=4, N<=7)"]
        
        BeamController --> Gen["DayPlanGenerator\n(Proposes 4 anchor-diverse candidates per node)"]
        Gen --> Stage1{"Stage 1: Hard Constraint Gatekeeper\n- Budget ceiling\n- Daily transit <= 120m\n- Operating hours"}
        
        Stage1 -- Fail --> Pruned["Mark PRUNED\n(Pruning floor < 0.45)"]
        Stage1 -- Pass --> Stage2["Stage 2: 5D Calibrated Rubric Critic\n(Headroom, Geo, Prefs, Variety, Feasibility)"]
        
        Stage2 --> RescueLogic{"Rescue Slot Activation\n(Confidence < 0.60 or Ξ”score <= 0.10)"}
        RescueLogic -- Reserve 1 slot --> BeamNodes["Active Beam Set (k=3 nodes / depth)"]
        RescueLogic -- Top-ranked --> BeamNodes
        
        BeamNodes <--> FastMCP["FastMCP tot-state Server\n(State persistence in PostgreSQL)"]
        BeamNodes --> Termination{"d == N or Budget Exhausted?"}
        Termination -- No --> Gen
        Termination -- Yes --> BestPlan["Select Highest Scoring Complete Path"]
    end

    subgraph LiveMCP ["Live Open MCP Grounding Feeds"]
        LiveTools --> OpenMeteo["🌀️ Open-Meteo API\n(Real-time Weather & Geocoding)"]
        LiveTools --> Frankfurter["πŸ’± Frankfurter API\n(Live ECB Exchange Rates for 33+ Currencies)"]
    end

    BestPlan --> Formatter["Response Formatter & Graph Generator"]
    Formatter --> UI

πŸ“ Tree of Thought (ToT) Mathematical Formulation

1. Search Parameters

  • Branching Factor ($b$): $4$ anchor-diverse candidate thoughts generated per active beam node.

  • Beam Width ($k$): $3$ active branches retained per day depth $d \in [1, N]$ ($N \le 7$).

  • Pruning Floor: $\text{Composite Score} < 0.45 \implies \text{PRUNED}$.

  • Acceptance Threshold: $\text{Composite Score} \ge 0.75$.

  • Rescue Slot: $1$ protected slot reserved in the beam for high-potential candidates facing evaluation uncertainty ($\text{Confidence} < 0.60$ or $\Delta \text{score} \le 0.10$).

  • Compute Guardrails: Strict limits of 40 LLM calls and 45.0 seconds wall-clock time per search session.

2. 5-Dimensional Calibrated Rubric

$$\text{Composite Score} = 0.20 \cdot S_{\text{headroom}} + 0.20 \cdot S_{\text{geo}} + 0.25 \cdot S_{\text{pref}} + 0.20 \cdot S_{\text{quality}} + 0.15 \cdot S_{\text{forward}}$$

Dimension

Weight

Description

Constraint Headroom ($S_{\text{headroom}}$)

0.20

Heuristic safety margin evaluating remaining budget and buffer against daily transit ceilings ($\le 120$ min).

Geographic Coherence ($S_{\text{geo}}$)

0.20

Spatial clustering metric that penalizes zig-zagging across non-adjacent city wards/districts.

Preference Alignment ($S_{\text{pref}}$)

0.25

Semantic cosine similarity between user interests (e.g. culinary, modern art, historic temples) and activity themes.

Experience Quality ($S_{\text{quality}}$)

0.20

Evaluates daily pacing ($\le 2$ major activities for relaxed pace), meal timing, and neighborhood variety.

Forward Feasibility ($S_{\text{forward}}$)

0.15

Lookahead heuristic projecting whether remaining budget can sustain future days ($0.10$ critic projection $+ 0.05$ budget margin).


πŸ“Š Benchmark & Ablation Study

We evaluated the Two-Tier Tree of Thought architecture against a traditional Linear ReAct baseline across 100 multi-day travel requests with strict budget and transit constraints (including the Priya Tokyo worked example):

Metric

Linear ReAct Baseline

Tree of Thought (V4)

Net Improvement

Hard Constraint Satisfaction Rate

68.0%

100.0%

+32.0%

Search Regret / Beam Collapse Rate

32.0%

0.0%

-100.0%

Mean Composite Quality Score

0.742

0.945

+27.4%

Budget Compliance Accuracy

71.0%

100.0%

+29.0%

Average LLM Calls per Plan

3.6 calls

28–38 calls

Within 40-call budget


πŸš€ Quickstart & Local Setup

1. Prerequisites

  • Python 3.10+

  • (Optional) Docker & Docker Compose for local PostgreSQL + pgvector

  • (Optional) Google Cloud SDK (gcloud) if deploying to GCP

2. Clone Repository & Setup Virtual Environment

git clone https://github.com/anthonywang-sg/Personal-Travel-Agent.git
cd Personal-Travel-Agent

# Create and activate virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies in editable mode
pip install -e ".[dev]"

3. Environment Configuration

Copy the template configuration file:

cp .env.example .env

Edit .env if using Gemini Enterprise on Google Cloud, or supply your GEMINI_API_KEY:

# .env
APP_NAME="Personal Travel Agent V4"
ENVIRONMENT="development"

# Gemini Enterprise Agent Platform (or leave blank for standard API Key)
GOOGLE_GENAI_USE_ENTERPRISE=true
GOOGLE_CLOUD_PROJECT=your-gcp-project-id
GOOGLE_CLOUD_LOCATION=global

# Database (Optional local Docker default)
DATABASE_URL="postgresql+psycopg://postgres:postgres@localhost:5432/travel_agent"

4. Run the Web Application

uvicorn travel_agent.web.app:app --host 0.0.0.0 --port 8080 --reload

Navigate to http://localhost:8080 to access the interactive web interface.


πŸ’» CLI Tools & Evaluation Harness

The system provides a rich command-line suite powered by Typer and Rich:

1. Plan a Multi-Day Trip

# Plan a 3-Day Tokyo culinary trip
travel-agent plan --destination Tokyo --days 3 --budget 2200

# Plan a 4-Day Cairo historic trip
travel-agent plan --destination Cairo --days 4 --budget 1800

# Plan a personalized trip for Priya (User Persona benchmark)
travel-agent plan --user-id priya_01 --destination Tokyo --days 4 --budget 2500 --lodging Shinjuku

2. Run the Offline Regret & Ablation Evaluation

travel-agent evaluate --trials 5

3. Run Pre-Flight Open Source Secret & Hygiene Scanner

travel-agent scan-secrets

πŸ§ͺ Automated Test Suite

The test suite covers unit models, heuristic grounding tools, FastMCP client/server lifecycles, Tree of Thought search engine, ReAct agent integration, and repository security:

# Run all 21 automated tests
pytest tests/ -v
============================== test session starts ==============================
tests/test_beam_search_engine.py::test_beam_search_4_day_itinerary PASSED  [  4%]
tests/test_beam_search_engine.py::test_beam_search_guardrails_and_best_effort PASSED [  9%]
tests/test_beam_search_engine.py::test_beam_search_rescue_slot_activation PASSED [ 14%]
tests/test_cli_eval.py::test_cli_plan_command PASSED                     [ 19%]
tests/test_cli_eval.py::test_ablation_harness_metrics PASSED             [ 23%]
tests/test_domain_models.py::test_day_plan_serialization PASSED          [ 28%]
tests/test_domain_models.py::test_planning_frame_immutability PASSED     [ 33%]
tests/test_generator_critic.py::test_thought_generator_diversity PASSED  [ 38%]
tests/test_generator_critic.py::test_thought_critic_evaluation_rubric PASSED [ 42%]
tests/test_global_rag_and_mcp.py::test_global_destinations_catalog_loading_and_rag_search PASSED [ 47%]
tests/test_global_rag_and_mcp.py::test_external_mcp_services_and_client PASSED [ 52%]
tests/test_global_rag_and_mcp.py::test_end_to_end_multi_destination_planning PASSED [ 57%]
tests/test_grounding_heuristics.py::test_grounding_tools PASSED          [ 61%]
tests/test_grounding_heuristics.py::test_hard_constraint_evaluation PASSED [ 66%]
tests/test_grounding_heuristics.py::test_heuristic_calculation PASSED    [ 71%]
tests/test_mcp_tot_state.py::test_mcp_client_tree_lifecycle PASSED       [ 76%]
tests/test_priya_worked_example.py::test_priya_worked_example_full_verification PASSED [ 80%]
tests/test_priya_worked_example.py::test_priya_ablation_superiority PASSED [ 85%]
tests/test_react_agent_integration.py::test_travel_agent_end_to_end_planning_flow PASSED [ 90%]
tests/test_storage_repositories.py::test_tot_branch_repository_crud PASSED [ 95%]
tests/test_storage_repositories.py::test_semantic_memory_chunk_filter_and_search PASSED [100%]
============================== 21 passed in 16.06s ==============================

☁️ Google Cloud Deployment

The repository includes automated provisioning scripts for Google Cloud:

  • Compute / Frontend: Google Cloud Run (Containerized Web UI)

  • Agent Orchestration: Gemini Enterprise Agent Platform (reasoningEngines)

  • Reasoning Model: gemini-3.7-flash (Location: global)

  • Persistence & Vector Search: Cloud SQL PostgreSQL 16 + pgvector

  • Artifact Storage: Google Cloud Storage (gs://personal-travel-agent-artifacts-*)

# 1. Provision Cloud Infrastructure
export GOOGLE_CLOUD_PROJECT=your-gcp-project-id
./deploy/provision_gcp.sh

# 2. Deploy Web UI to Cloud Run
./deploy/cloudrun_ui.sh

# 3. Deploy to Agent Engine
./deploy/agent_engine_deploy.sh

πŸ”’ Security & Open-Source Hygiene

  • Zero Hardcoded Secrets: Scanned via custom repository sanitization skill (.agents/skills/sanitizing-repo-for-open-source/).

  • No Leaked PII: All benchmarks and user personas are 100% synthetic.

  • Environment Isolation: Sensitive configuration loaded strictly via .env or cloud secret managers.


πŸ“„ License & Academic Attribution

This project is licensed under the MIT License - see the LICENSE file for details.

Developed by Anthony Wang as part of the Agentic AI Program: Building Autonomous Systems for Real-World Applications by the School of Computer Science at Carnegie Mellon University.

A
license - permissive license
Not graded
quality - not tested
B
maintenance

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Servers

  • -
    license
    Not graded
    quality
    Not graded
    maintenance
    Enables AI assistants to store and retrieve long-term memories using PostgreSQL with vector similarity search. Supports semantic memory operations, tagging, and real-time updates for persistent learning across conversations.
  • F
    license
    Not graded
    quality
    D
    maintenance
    Enables AI agents to persist and retrieve structured thinking graphs using SQLite-backed memory with support for CRUD operations, graph search, and path finding.
    1
  • A
    license
    Not graded
    quality
    F
    maintenance
    Persistent semantic memory for AI agents using PostgreSQL and vector embeddings, enabling cross-session continuity and semantic search.
    AGPL 3.0

View all related MCP servers

Related MCP Connectors

  • Persistent memory for AI agents β€” verbatim conversations, searchable by meaning.

  • Persistent memory for AI agents. Search, store, and recall across sessions.

  • Analytical memory for AI agents: a real Postgres queried in plain English over MCP. One command.

View all MCP Connectors

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/anthonywang-sg/Personal-Travel-Agent'

If you have feedback or need assistance with the MCP directory API, please join our Discord server