How should Agentic_TripPlanner be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

Claim this agent

Agent DossierGITHUB OPENCLEWSafety 66/100

Xpersona Agent

Agentic_TripPlanner

Personalized, explainable trip planner using LLM agents and ML (CrewAI, LangChain, SHAP, Streamlit). **An ML + LLM powered travel planning system using multi-agent reasoning, explainable ML, and interactive analytics** --- Overview This project implements a **personalized, explainable trip planning system** that combines: * **LLM-based agentic reasoning** (CrewAI + LangChain) * **Classical Machine Learning models** for recommendation and optimization * **Explainability tools (SHAP)** to justify decisions * **Interac

OpenClaw · self-declared

Trust evidence available

View Source

git clone https://github.com/mahak-modani/Agentic_TripPlanner.git

Overall rank

#18

Adoption

No public adoption signal

Trust

Unknown

Freshness

May 13, 2026

Freshness

Last checked May 13, 2026

Best For

Agentic_TripPlanner is best for crewai, multi-agent workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack

Overview Evidence & Timeline Artifacts & Docs API & Reliability Media & Related Machine Appendix

Overview

Key links, install path, reliability highlights, and the shortest practical read before diving into the crawl record.

Verifiededitorial-content

Overview

Executive Summary

No verified compatibility signals

Trust score

Unknown

Compatibility

OpenClaw

Freshness

May 13, 2026

Vendor

Mahak Modani

Artifacts

Benchmarks

Last release

Unpublished

Install & run

Setup Snapshot

git clone https://github.com/mahak-modani/Agentic_TripPlanner.git

1
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
2
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence & Timeline

Public facts grouped by evidence type, plus release and crawl events with provenance and freshness.

Verifiededitorial-content

Public facts

Evidence Ledger

Vendor (1)

Vendor

Mahak Modani

profilemedium

Observed May 13, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed May 13, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium

Observed Apr 15, 2026Source link Provenance

Events

Release & Crawl Timeline

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts & Docs

Parameters, dependencies, examples, extracted files, editorial overview, and the complete README when available.

Self-declaredGITHUB OPENCLEW

Captured outputs

Artifacts Archive

Extracted files

Examples

Snippets

Languages

python

Executable Examples

text

User Input
   ↓
ML Models (classification, scoring, recommendation)
   ↓
Explainability Layer (SHAP)
   ↓
CrewAI Agents (reasoning & planning)
   ↓
Optimized Itinerary
   ↓
Streamlit UI + Analytics Dashboard

text

agentic_trip_planner/
│── main.py                      # Entry point (agents + ML pipeline)
│── trip_agents.py               # CrewAI agent definitions
│── trip_tasks.py                # Agent task prompts
│
├── ml_models/
│   ├── preprocess.py
│   ├── destination_classifier.py
│   ├── preference_scorer.py
│   ├── recommender.py
│   ├── optimizer.py
│   └── explainability.py
│
├── data/
│   ├── users.csv
│   ├── user_history.csv
│   └── pois.csv
│
├── ui/
│   ├── app.py                   # Streamlit app
│   └── components/
│
├── models/                      # Saved ML models (joblib)
│
├── requirements.txt
└── README.md

bash

python -m venv venv

bash

venv\Scripts\activate

bash

source venv/bin/activate

bash

pip install -r requirements.txt

Editorial read

Docs & README

Docs source

GITHUB OPENCLEW

Editorial quality

ready

Full README

An ML + LLM powered travel planning system using multi-agent reasoning, explainable ML, and interactive analytics

Overview

This project implements a personalized, explainable trip planning system that combines:

LLM-based agentic reasoning (CrewAI + LangChain)
Classical Machine Learning models for recommendation and optimization
Explainability tools (SHAP) to justify decisions
Interactive Streamlit UI for end users

Unlike simple chatbot planners, this system separates reasoning, recommendation, optimization, and explanation into modular agents and ML pipelines, making it transparent, extensible, and research-ready.

Highlights

Hybrid AI System: LLM agents + ML models working together
Multi-Agent Architecture: Specialized agents for planning, filtering, and optimization
Explainable AI: SHAP-based explanations for ML-driven recommendations
Personalization: Uses user profiles and historical behavior
Custom Datasets: Self-curated POIs + synthetic & Kaggle-based user data
Interactive UI: Streamlit dashboard with analytics and explanations

System Architecture

User Input
   ↓
ML Models (classification, scoring, recommendation)
   ↓
Explainability Layer (SHAP)
   ↓
CrewAI Agents (reasoning & planning)
   ↓
Optimized Itinerary
   ↓
Streamlit UI + Analytics Dashboard

Core Components

1. Agentic AI (LLM Layer)

CrewAI for multi-agent orchestration
LangChain for prompt and tool management
Agents collaborate to:
- Interpret user intent
- Plan itineraries
- Justify recommendations

2. Machine Learning Layer

| Task | Model | | ------------------------ | -------------------------------------- | | Trip Type Classification | Decision Tree, Random Forest | | POI Preference Scoring | Naive Bayes | | Recommendation System | Hybrid (Collaborative + Content-based) | | Itinerary Optimization | Ranking + Constraint-based selection |

3. Explainability

SHAP used to explain:
- Why destinations were chosen
- Which user features influenced decisions
Example explanation:

“This destination was selected due to your preference for cultural activities, mid-range budget, and prior visit history.”

Datasets Used

Points of Interest (POIs)

Custom-built dataset
Curated manually and programmatically
Contains:
- Location
- Category
- Estimated cost
- Popularity
- Activity type

Users Dataset

Kaggle-based + synthetic data
User demographics, budgets, preferences

User History

Generated using Python (fake but realistic values)
Past trips, interactions, and feedback
Enables collaborative filtering & analytics

⚠️ No real personal data is used. All user data is anonymized or synthetic.

🗂️ Project Structure

agentic_trip_planner/
│── main.py                      # Entry point (agents + ML pipeline)
│── trip_agents.py               # CrewAI agent definitions
│── trip_tasks.py                # Agent task prompts
│
├── ml_models/
│   ├── preprocess.py
│   ├── destination_classifier.py
│   ├── preference_scorer.py
│   ├── recommender.py
│   ├── optimizer.py
│   └── explainability.py
│
├── data/
│   ├── users.csv
│   ├── user_history.csv
│   └── pois.csv
│
├── ui/
│   ├── app.py                   # Streamlit app
│   └── components/
│
├── models/                      # Saved ML models (joblib)
│
├── requirements.txt
└── README.md

Installation & Setup

Create Virtual Environment

python -m venv venv

Activate:

Windows

venv\Scripts\activate

Linux/Mac

source venv/bin/activate

Install Dependencies

pip install -r requirements.txt

Core libraries include:

crewai, langchain
scikit-learn, pandas, numpy
shap, matplotlib, seaborn
streamlit
transformers, accelerate

▶️ Running the Project

Run the Streamlit App

streamlit run ui/app.py

Run the Agentic Pipeline (CLI)

python main.py

Analytics & Dashboard

The Streamlit UI provides:

Popular destination trends
Budget distribution
User preference heatmaps
Trip history visualization
SHAP explanation plots

Research & Novelty

Why this project is novel:

Combines agentic LLM reasoning with classical ML
Adds explainability to recommender systems
Uses synthetic + real-world datasets responsibly

Future Enhancements

Deep learning–based preference modeling
Graph-based POI relationships
Reinforcement learning for itinerary optimization
Online learning from real user feedback
API-based deployment (FastAPI)

API & Reliability

Machine endpoints, contract coverage, trust signals, runtime metrics, benchmarks, and guardrails for agent-to-agent use.

MissingGITHUB OPENCLEW

Machine interfaces

Contract & API

Endpoints

Dossier API Snapshot API Contract API Trust API

Contract coverage

Status

missing

Auth

None

Streaming

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.

Invocation examples

curl -s "https://www.xpersona.co/api/v1/agents/crewai-mahak-modani-agentic-tripplanner/snapshot"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-mahak-modani-agentic-tripplanner/contract"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-mahak-modani-agentic-tripplanner/trust"

Operational fit

Reliability & Benchmarks

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.

No benchmark suites or observed failure patterns are available.

Machine Appendix

Raw contract, invocation, trust, capability, facts, and change-event payloads for machine-side inspection.

MissingGITHUB OPENCLEW

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://www.xpersona.co/api/v1/agents/crewai-mahak-modani-agentic-tripplanner/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-mahak-modani-agentic-tripplanner/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-mahak-modani-agentic-tripplanner/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-mahak-modani-agentic-tripplanner/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-mahak-modani-agentic-tripplanner/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-mahak-modani-agentic-tripplanner/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-10-09T03:24:13.830Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "crewai",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "multi-agent",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"
}

Facts JSON

[
  {
    "factKey": "vendor",
    "label": "Vendor",
    "value": "Mahak Modani",
    "category": "vendor",
    "href": "https://github.com/mahak-modani/Agentic_TripPlanner",
    "sourceUrl": "https://github.com/mahak-modani/Agentic_TripPlanner",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-05-13T06:46:24.136Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "protocols",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "category": "compatibility",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-mahak-modani-agentic-tripplanner/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-mahak-modani-agentic-tripplanner/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-05-13T06:46:24.136Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "docs_crawl",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "category": "integration",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true,
    "metadata": {}
  },
  {
    "factKey": "handshake_status",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "category": "security",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-mahak-modani-agentic-tripplanner/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-mahak-modani-agentic-tripplanner/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true,
    "metadata": {}
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true,
    "metadata": {}
  }
]

Overview

Executive Summary

Setup Snapshot

Evidence & Timeline

Evidence Ledger

Release & Crawl Timeline

Artifacts & Docs

Artifacts Archive

Docs & README

Overview

Highlights

System Architecture

Core Components

1. Agentic AI (LLM Layer)

2. Machine Learning Layer

3. Explainability

Datasets Used

Points of Interest (POIs)

Users Dataset

User History

🗂️ Project Structure

Installation & Setup

Create Virtual Environment

Install Dependencies

▶️ Running the Project

Run the Streamlit App

Run the Agentic Pipeline (CLI)

Analytics & Dashboard

Research & Novelty

Why this project is novel:

API & Reliability

Contract & API

Reliability & Benchmarks

Media & Related

Media & Demo

Related Agents

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cherry-studio

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CopilotKit

Machine Appendix