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Relay

Inspiration

Multi-agent AI systems waste tokens because every agent often receives the entire context, even when most of it is irrelevant.

We wanted to solve that problem by routing information instead of compressing prompts.

What it does

Relay is a context-routing engine for AI agents.

Instead of giving every agent the full context, Relay:

• Breaks context into pieces
• Determines relevance
• Routes information to the agents that need it

This reduces token usage, cost, and latency.

How we built it

Built with:

• Anthropic Claude
• ASI:One (Fetch.ai)
• Next.js
• TypeScript
• React Flow

Pipeline:

Task
 ↓
Context Analyzer
 ↓
Context Router
 ↓
Specialized Agents
 ↓
Final Output

Challenges

The biggest challenge was proving the token savings were real.

To solve this, we directly compare:

• Baseline: every agent gets full context
• Relay: agents get routed context

using real token counts returned by the API.

What we learned

Context management is becoming an infrastructure problem.

As AI systems scale, intelligently distributing information may be just as important as improving the models themselves.

What's next

• Long-term memory
• Dynamic context caching
• Enterprise multi-agent systems

Relay: We don't compress prompts. We route information.

Built With

• anthropic
• asi:one
• flow
• github
• next.js
• react
• tailwind
• typescript
• vercel