# ContextNet

- **Event:** [Agentic Memory & Context Engineering Hackathon](https://cerebralvalley.ai/e/mongoDB-hackathon)
- **When:** Sat, Oct 11 at 9:00 AM – 10:00 PM (PDT)
- **Where:** Shack15, San Francisco, CA
- **Team:** [Bhavain Shah](https://cerebralvalley.ai/u/bhavain), [Vineet Khadloya](https://cerebralvalley.ai/u/vineetkhadloya), [Abhishek Pokala](https://cerebralvalley.ai/u/AbhishekPokala), [Aniket Shendre](https://cerebralvalley.ai/u/Aniketshendre)
- **GitHub:** https://github.com/AbhishekPokala/ContextNet
- **Demo video:** https://www.loom.com/share/a6e49c875d3849fa8ed66a7c1c927fa6?sid=6537500f-4c8c-4099-b33d-34695f5a311d
- **Gallery:** https://cerebralvalley.ai/e/mongoDB-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/mongoDB-hackathon/hackathon/gallery/67

ContextNet: Real-Time Multi-Agent Collaboration Framework
Traditional multi-agent AI systems are painfully slow and sequential - agents can't see what others are doing, causing duplicated work and long wait times. ContextNet solves this with MongoDB-powered real-time context sharing that enables AI agents to work in parallel. Our system demonstrates this with five agents collaborating to plan events: while ThemeAgent sets parameters, PartnerAgent, VenueAgent, JudgeAgent, and FoodAgent work simultaneously using shared context. Built on MongoDB Change Streams for instant updates and featuring a live React dashboard, ContextNet transforms how AI agents collaborate - eliminating bottlenecks, reducing costs, and providing unprecedented visibility into multi-agent decision-making for complex workflows.

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Markdown version of https://cerebralvalley.ai/e/mongoDB-hackathon/hackathon/gallery/67. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt