Theme

⚡️ Bridge the gap: LLMs meet structured graphs! Prototype next-gen Model Context Protocol (MCP) integrations for GraphRAG databases using Scalingo and Neo4j. Focus on context persistence, dynamic retrieval, and multi-agent collaboration. Can your team build the future of explainable, context-smart AI? 🧠🔗🚀
Golden Pass Value: Your acceptance grants you a free pass to GenerationAI, apidays, AND GreenIO Paris—that’s 4 conferences normally valued at 999€, for the price of zero: To access the apidays conference component, use this promo link for a Regular Ticket.

Detailed Schedule

Day 1: Tuesday, December 9, 2025

Time	Activity	Notes
8:30 AM	Breakfast	@ Partner Village
9:00 AM	Welcome and Opening Remarks	@ Amphi
9:15 AM	General Keynotes
10:35 AM	Coffee Break / Networking / Expo	@ Partners Village
11:00 AM - 11:25 AM	AI Tinkerers Paris Hackathon Launch	@ Dattier 3
11:30 PM - 11:55 PM	Neo4j Tech Talk: Introduction to GraphRAG Workshop
12:00 PM - 12:25 PM	Team Formation	Hackathon portal/ Teams
12:30 PM - 12:55 PM	Tinkering
1:00 PM	Lunch Break	@ Partner Village
2:00 PM - 3:55 PM	Tinkering	@ Dattier 3
4:00 PM	Coffee Break / Networking / Expo	@ Partners Village
4:30 PM - 5:30 PM	Tinkering	Final push before evening
5:30 PM	End of Hackathon Day 1
6:00 PM	Optional Builder Station (separate registration required)	@ Holiburton school

Day 2: Wednesday, December 10, 2025

Time	Activity	Notes
8:45 AM	Breakfast @ Partner Village
9:15 AM - 10:30 AM	Tinkering	@ Dattier 3
10:50 AM	Coffee Break / Networking / Expo	@ Partners Village
11:20 AM - 12:45 PM	Tinkering	@ Dattier 3
12:50 PM - 2:00 PM	Lunch Break	@ Partner Village
2:00 - 2:30 PM	Last chance to polish demos	@ Dattier 3
2:30 PM - 3:15 PM	Final submission	Hackathon Portal/ Compete/ Submit Entry tab
3:15 PM - 3:50 PM	Judging	Hackathon Portal/ Voting Tool
4:00 PM	Coffee Break / Networking / Expo	@ Partner Village
4:30 PM - 4:50 PM	Finalist Presentations & Jury Deliberation	@ Partner Village
4:50 PM	Winners Announced & Closing Ceremony	Hackathon Close

Day 3 (optional): Thursday, December 11, 2025

See the official program of the conferences

Judging Criteria

Criterion	Focus & High Score Example	Scoring (1 to 5)
Working Prototype	Focus: Is the solution functional, deployable (Scalingo friendly), and does it demonstrate the core concept? High Score (5): Flawless execution; a live demo running the GraphRAG pipeline end-to-end.	1: Project does not run or is entirely conceptual. 2: Major bugs prevent core functionality demonstration. 4: Runs, but has minor bugs or dependency issues. 5: Flawless execution and stable demonstration.
Theme Alignment & MCP Integration	Focus: How deeply does the project implement the “Model Context Protocol Integrations for GraphRAG”? High Score (5): Achieves seamless, bidirectional MCP ↔ Graph integration, demonstrating advanced features like context persistence across nodes or multi-agent collaboration using structured memory.	1: Barely relates to LLMs or graphs. 2: Superficial use of RAG or a simple graph structure. 4: Clear, meaningful incorporation of GraphRAG concepts. 5: Perfectly embodies and elevates MCP integration with Neo4j/Graph data.
Contextual Reasoning & Retrieval	Focus: Does the project leverage the graph structure for smarter context retrieval and reasoning, moving beyond simple vector search? High Score (5): Implements dynamic, context-aware reasoning (e.g., pathfinding, relationship inference) that directly informs the LLM’s output, with clear explainability trails.	1: Retrieval is basic keyword/vector search only. 2: Graph is used only for static data storage. 4: Demonstrates dynamic retrieval based on a simple graph query. 5: Advanced, context-smart reasoning unlocks novel LLM capabilities.
Developer Experience (UX/DX)	Focus: How usable is the resulting API/interface for developers or data teams? (As per judging notes: “Clear UX for developers and data teams”). High Score (5): Provides an elegant, well-documented API wrapper (Scalingo deployable) or a visual query builder that makes complex graph context easy to consume.	1: No clear interface or documentation. 2: Usable only by the original hacker. 4: Clear API endpoints or a simple interface exists. 5: Elegant, well-documented, and immediately useful for other developers/data teams.

Prizes

Handbook View Handbook

🛠️ Participant Handbook: GraphRAG & MCP Integration

This handbook provides key resources, sponsor tools, and starter ideas to help you build cutting-edge Model Context Protocol (MCP) integrations with GraphRAG databases.

🔗 Core Technologies & Setup

Neo4j for GraphRAG

Neo4j is your structured memory layer. Use it to store context, relationships, and reasoning trails.

Neo4j Sandbox/AuraDB: Get a free instance running quickly. [Link to Neo4j Free Tier/Sandbox to be provided]
Cypher Query Language: Master the basics for efficient data retrieval. [Link to Cypher Documentation to be provided]
Graph Data Science (GDS): Explore advanced algorithms for context ranking and relationship inference. [Link to GDS Library to be provided]

Scalingo for Deployment

Scalingo is your PaaS for deploying your API backend, RAG service, or multi-agent orchestration layer.

Scalingo Documentation: Learn how to deploy your Python/Node.js/etc. backend. [Link to Scalingo Docs to be provided]
Database Integration: Instructions on connecting your app to a managed database (if not using Neo4j Aura). [Link to Scalingo DB Docs to be provided]
Free Trial/Credits: Information on any special hackathon credits available to be provided.

💡 Starter Project Ideas

Context-Aware Agent Orchestrator: Build a multi-agent system where agents use graph-persisted context (via MCP messages) to coordinate tasks, improving explainability.
Dynamic Retrieval Pipeline: Create an RAG system where the retrieval step queries Neo4j based on the current LLM state (stored in a graph node) to fetch highly specific, relationship-aware context chunks.
Interpretable Context Trail: Implement a system that visualizes the path of context retrieval from the graph, through the LLM prompt, and back into a new graph node, demonstrating explainability.
Graph-Enhanced Memory: Use Neo4j to store long-term memory for an LLM, structured as knowledge graph triplets, and use MCP to inject relevant memories into the context window dynamically.

🤝 Mentorship & Support

Sponsors: Check the mentors page.
Communication Channel: Message Center for public, teams, judges, sponsors or volunteers dedicated channels and DMs

GenerationAI Hackathon - MCP Integration for Graph DBs

Theme

Detailed Schedule

Day 1: Tuesday, December 9, 2025

Day 2: Wednesday, December 10, 2025

Day 3 (optional): Thursday, December 11, 2025

Judging Criteria

Prizes

Handbook View Handbook

🛠️ Participant Handbook: GraphRAG & MCP Integration

🔗 Core Technologies & Setup

Neo4j for GraphRAG

Scalingo for Deployment

💡 Starter Project Ideas

🤝 Mentorship & Support

Quick Actions

Key Deadlines

🛠️ Participant Handbook: GraphRAG & MCP Integration

🔗 Core Technologies & Setup

Neo4j for GraphRAG

Scalingo for Deployment

💡 Starter Project Ideas

🤝 Mentorship & Support

AI Generated Options