Project Info

Relay AI: Context Routing for Multi-Agent AI

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: 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.

Relay

Context-routing engine for multi-agent AI systems. Instead of sending every agent the full prompt, Relay routes only the context each agent actually needs.

Goal

Demonstrate 70–95% token reduction while maintaining output quality.

Stack

Next.js 15 + TypeScript + Tailwind
Anthropic API + ASI:One API (OpenAI-compatible fallback)
React Flow (agent graph) + Recharts (metrics)

Setup

npm install
cp .env.example .env.local
# Add your API keys to .env.local
npm run dev

Open http://localhost:3000.

Environment Variables

Variable	Description
`ANTHROPIC_API_KEY`	Anthropic API key (primary provider)
`ASI_ONE_API_KEY`	ASI:One API key (fallback provider)
`LLM_PROVIDER`	`anthropic` or `asi_one` (default: anthropic)

Pipeline

User Task → Task Analyzer → Context Graph → Context Router
  → Architect / Performance / Verification / Critic Agents
  → Merger Agent → Final Output

Modes

Baseline: Every agent receives full context
Relay: Each agent receives targeted context slices

Demo

Default prompt: "Design an AI accelerator for robotics under a 50W power budget."

Click Run Baseline + Relay to compare token usage, cost, and output quality side-by-side.

Pitch

Traditional multi-agent systems send every agent all context. Relay routes only relevant information — lower cost, lower latency, fewer tokens, same output quality.

Analysis

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Metric

Vedant
2

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only

AnthropicIn code
CSSIn code
Next.jsIn code
ReactIn code
Tailwind CSSIn code
TypeScriptIn code
VercelClaimed

6 of 7 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

95 KB

Source files

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.