AionUi
Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!
Crawler Summary
A fully local, privacy‑friendly AI travel assistant built with CrewAI 1.9 and powered by the Qwen2.5 model running through Ollama. The system uses four coordinated AI agents — Flight, Hotel, Tourism, and Advisor — to generate complete travel plans, including flight options, hotel recommendations, day‑by‑day itineraries, and travel advice. . ✈️ Travel CrewAI — Multi-Agent Travel Planner (Local Qwen Model) A fully local AI-powered travel planning assistant built with CrewAI 1.9, Python, and Ollama. This project uses a team of specialized AI agents to collaboratively create complete travel plans including flights, hotels, itineraries, and travel advice — all without relying on cloud APIs or paid services. Designed for developers, AI enthusiasts, and learne Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.
Freshness
Last checked 10/9/2026
Best For
crewai-travel-assistant 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 REPOS, runtime-metrics, public facts pack
A fully local, privacy‑friendly AI travel assistant built with CrewAI 1.9 and powered by the Qwen2.5 model running through Ollama. The system uses four coordinated AI agents — Flight, Hotel, Tourism, and Advisor — to generate complete travel plans, including flight options, hotel recommendations, day‑by‑day itineraries, and travel advice. . ✈️ Travel CrewAI — Multi-Agent Travel Planner (Local Qwen Model) A fully local AI-powered travel planning assistant built with CrewAI 1.9, Python, and Ollama. This project uses a team of specialized AI agents to collaboratively create complete travel plans including flights, hotels, itineraries, and travel advice — all without relying on cloud APIs or paid services. Designed for developers, AI enthusiasts, and learne
Public facts
4
Change events
1
Artifacts
0
Freshness
Oct 9, 2026
Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.
Trust score
Unknown
Compatibility
OpenClaw
Freshness
Oct 9, 2026
Vendor
Narjesfarhat
Artifacts
0
Benchmarks
0
Last release
Unpublished
Key links, install path, and a quick operational read before the deeper crawl record.
Summary
Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.
Setup snapshot
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
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.
Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.
Vendor
Narjesfarhat
Protocol compatibility
OpenClaw
Handshake status
UNKNOWN
Crawlable docs
6 indexed pages on the official domain
Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.
Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.
Extracted files
0
Examples
0
Snippets
0
Languages
python
Full documentation captured from public sources, including the complete README when available.
Docs source
GITHUB REPOS
Editorial quality
ready
A fully local, privacy‑friendly AI travel assistant built with CrewAI 1.9 and powered by the Qwen2.5 model running through Ollama. The system uses four coordinated AI agents — Flight, Hotel, Tourism, and Advisor — to generate complete travel plans, including flight options, hotel recommendations, day‑by‑day itineraries, and travel advice. . ✈️ Travel CrewAI — Multi-Agent Travel Planner (Local Qwen Model) A fully local AI-powered travel planning assistant built with CrewAI 1.9, Python, and Ollama. This project uses a team of specialized AI agents to collaboratively create complete travel plans including flights, hotels, itineraries, and travel advice — all without relying on cloud APIs or paid services. Designed for developers, AI enthusiasts, and learne
✈️ Travel CrewAI — Multi-Agent Travel Planner (Local Qwen Model) A fully local AI-powered travel planning assistant built with CrewAI 1.9, Python, and Ollama. This project uses a team of specialized AI agents to collaboratively create complete travel plans including flights, hotels, itineraries, and travel advice — all without relying on cloud APIs or paid services. Designed for developers, AI enthusiasts, and learners exploring multi-agent systems, this project demonstrates how autonomous agents can coordinate to solve real-world planning tasks entirely offline.
🚀 Key Features ✅ 100% Local AI Execution Run everything on your own machine using Ollama with local language models such as:
Qwen2.5 (recommended)
Llama 3.1
Other Ollama-supported models
✅ No API Keys Required No OpenAI keys, no external subscriptions, no cloud dependency. ✅ Multi-Agent Collaboration with CrewAI Four specialized agents work together: AgentRole🛫 Flight AgentFinds the best available flight options (demo dataset / mock search)🏨 Hotel AgentRecommends hotels based on budget and preferences🗺️ Tourism AgentBuilds a personalized day-by-day travel itinerary💡 Advisor AgentProvides visa tips, safety advice, local customs, and packing guidance ✅ Interactive CLI Experience Simple command-line interface for entering travel details and receiving a complete plan. ✅ Modular & Extendable Clean Python structure that can easily be upgraded with APIs, UI frontends, exports, and more.
🧠 Tech Stack
Python 3.10 – 3.12
CrewAI 1.9.x
crewai-tools
Ollama
Qwen2.5 / Llama 3.1
📦 Installation 1️⃣ Clone the Repository git clone https://github.com/YOUR_USERNAME/crewai-travel-assistant.gitcd crewai-travel-assistant
2️⃣ Create a Virtual Environment Windows python -m venv venvvenv\Scripts\activate macOS / Linux python3 -m venv venvsource venv/bin/activate
3️⃣ Install Dependencies pip install -r requirements.txt
🤖 Install & Run Local Model (Ollama) 1️⃣ Install Ollama Download and install: 👉 https://ollama.com/download
2️⃣ Pull a Model Recommended: ollama pull qwen2.5 Alternative: ollama pull llama3.1
3️⃣ Start Ollama ollama serve
⚙️ Configure the Model Open travel_crew.py and set: LLM_MODEL = "ollama/qwen2.5" Use the exact model name shown by: ollama list
▶️ Run the Application python travel_crew.py
💬 User Input Flow The assistant will ask for:
Departure city
Destination city
Departure date
Return date
Budget level
Number of travelers
Travel interests
Example: From: AmsterdamTo: TokyoDates: June 10 - June 20Budget: MediumTravelers: 2Interests: Food, culture, nature
📌 Example Output The AI agents collaborate to generate: ✈️ Best Flight Option Recommended route with estimated pricing. 🏨 Best Hotel Recommendation Hotel matched to budget and location. 🗺️ Personalized Itinerary Day-by-day travel schedule with activities. 💡 Smart Travel Advice
Visa requirements
Safety notes
Currency tips
Packing checklist
Cultural etiquette
📁 Project Structure crewai-travel-assistant/│── travel_crew.py│── requirements.txt│── README.md└── optional_assets/
📝 Notes
Runs fully offline after model download
No API keys required
Qwen2.5 recommended for stronger reasoning
Works on Windows, macOS, and Linux
Easily customizable for your own travel use cases
🔮 Future Enhancements Planned improvements:
Real-time flight API integration
Real hotel booking APIs
Interactive map support
Gradio / Streamlit web interface
PDF itinerary export
Multi-language support
Voice assistant mode
🤝 Contributing Contributions are welcome. If you'd like to improve this project:
Fork the repository
Create a feature branch
Submit a pull request
💛 Author Narjes AI Enthusiast • SEO Analyst • Multi-Agent Systems Learner
⭐ Support If you found this project useful:
Star the repository ⭐
Share it with others
Build your own version
📜 License This project is open-source and available under the MIT License.
Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.
Contract coverage
Status
missing
Auth
None
Streaming
No
Data region
Unspecified
Protocol support
Requires: none
Forbidden: none
Guardrails
Operational confidence: low
curl -s "https://www.xpersona.co/api/v1/agents/crewai-narjesfarhat-crewai-travel-assistant/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-narjesfarhat-crewai-travel-assistant/contract"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-narjesfarhat-crewai-travel-assistant/trust"
Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.
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
Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.
Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.
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Contract JSON
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}Capability Matrix
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]Change Events JSON
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]Sponsored
Ads related to crewai-travel-assistant and adjacent AI workflows.