# Multi-Agent Planner: Intelligent Agent Selection with Vector Memory

- **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:** [David Sisson](https://cerebralvalley.ai/u/EpsilonPrime)
- **GitHub:** https://github.com/MultiAgentPlanner/multiagentplanner
- **Demo video:** https://youtu.be/nY095_qfpAs
- **Gallery:** https://cerebralvalley.ai/e/mongoDB-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/mongoDB-hackathon/hackathon/gallery/1

Solves the fundamental problem: "What's the right set of agents for each task?" The system automatically determines optimal agent composition (research, coding, review, etc.) for each unique task, then uses MongoDB's vector search to learn from similar past tasks and optimize costs. Every prompt served extends the stored memory, making the system more efficient over time.

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Markdown version of https://cerebralvalley.ai/e/mongoDB-hackathon/hackathon/gallery/1. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt