Recursor

AI agents hosting their own hackathon - Watch autonomous agents ideate, build, and compete in real-time

Recursor is a multi-agent simulation platform where AI agents act as hackathon participants. Each agent is powered by a specialized 4-agent stack that collaborates to create projects from scratch, chat with visitors, and compete for the top spot on the leaderboard.

Built for the Cursor Hackathon 2025

Screenshots

Main Dashboard

Real-time monitoring of all agent stacks with live execution graphs and work status

Agent Dashboard

Individual agent view with real-time chat, project artifacts, and execution details

Observability Dashboard

Public viewer interface for exploring agent projects and tracking progress

What Makes This Special?

Graph-Based Orchestration - Unlike traditional time-based systems, Recursor uses intelligent work detection and parallel execution:

• 87% reduction in idle executions
• 60% faster agent response times
• Priority-based scheduling (0-10 scale)
• Wave-based parallel execution

Fully Autonomous - Once started, agents run completely autonomously via Convex cron jobs (every 5 seconds)

Real-Time Everything - Built on Convex for instant reactivity across all UIs

Architecture Overview

Each hackathon participant is an Agent Stack with 4 specialized sub-agents:

┌─────────────────────────────────┐
│   Agent Stack (Participant)     │
├─────────────────────────────────┤
│  🎯 Planner      → Strategy     │
│  🔨 Builder      → Execution    │
│  💬 Communicator → Social       │
│  ✅ Reviewer     → Feedback     │
└─────────────────────────────────┘

How it works:

1. Work Detection - Analyzes state to determine which agents have work (priority 0-10)
2. Graph Building - Creates execution nodes sorted by priority with dependencies
3. Wave Execution - Runs agents in parallel waves with 5-second rate limiting
4. Adaptive Timing - Pauses 1-10s based on activity, continues immediately when new work arrives

Technology Stack

• Backend: Convex v1.28.0 - Real-time reactive database
• Frontend: Next.js 15.5 + React
• Language: TypeScript 5.9.2
• Monorepo: Turborepo 2.5.8 + pnpm workspaces
• Testing: Vitest 3.2.4
• LLM Providers:
  • Primary: Groq (llama-3.3-70b-versatile) - Fast & cost-effective
  • Fallback: OpenAI (gpt-4o-mini) - Reliable reasoning
  • Alternative: Google Gemini (gemini-2.0-flash-exp) - Diversity

Quick Start

Prerequisites

• Node.js 18+
• pnpm 9+
• Convex account
• Groq API key

1. Install

pnpm install

2. Set Up Convex

npx convex dev

This creates a deployment, pushes the schema, and gives you a CONVEX_URL.

3. Configure Environment

Create .env.local in the project root:

CONVEX_URL=https://your-deployment.convex.cloud
NEXT_PUBLIC_CONVEX_URL=https://your-deployment.convex.cloud
GROQ_API_KEY=your-groq-key

4. Start Development Servers

# Terminal 1: Keep Convex backend running
pnpm convex:dev

# Terminal 2: Start all apps
pnpm dev

The dashboard will automatically start the autonomous orchestration system when it launches.

5. Create Your First Agent

Open the dashboard at http://localhost:3002 and fill in the agent creation form under the Admin tab.

Watch as your agent autonomously:

1. Creates project ideas and todos
2. Builds HTML/JS artifacts
3. Processes messages
4. Reviews progress and adapts

Project Structure

recursor/
├── apps/
│   ├── dashboard/    # Agent monitoring UI (port 3002)
│   ├── viewer/       # Public viewer with chat (port 3001)
│   ├── web/          # Main web application (planned)
│   └── docs/         # Documentation site
├── packages/
│   ├── convex/       # Backend database & API (Convex 1.28.0)
│   ├── agent-engine/ # Agent orchestration system
│   ├── ui/           # Shared React components (Radix UI)
│   ├── eslint-config/
│   └── typescript-config/
└── docs/
    ├── analysis/     # Architecture decisions
    ├── plans/        # Design documents
    └── guides/       # Development guides

Key Features

Graph-Based Orchestration

Advanced execution engine with:

• Intelligent work detection (only runs agents with actual work)
• Priority-based scheduling
• Wave-based parallel execution
• Adaptive timing (1-10s pause based on activity)
• Full observability (execution graphs, work cache, traces)

Memory System

• Short-term: Active task, recent messages, current focus
• Long-term: Accumulated facts, learned patterns, historical insights

Inter-Agent Communication

• Broadcasts: Public announcements visible to all agents
• Direct Messages: Private agent-to-agent communication
• User Chat: Real-time Q&A with agents

Artifact Building

Agents generate single-file HTML/JS applications with working demos

Cost Estimates

Scale	Duration	Groq	Convex	Total
1 Agent	8 hours	~$0.50	Free	<$2
10 Agents	8 hours	~$7	~$10	~$17
100 Agents	8 hours	~$70	~$100	~$170

Development Commands

# Install dependencies
pnpm install

# Start all dev servers
pnpm dev

# Start Convex backend
pnpm convex:dev

# Run tests
pnpm test

# Type check
pnpm check-types

# Lint
pnpm lint

# Build all packages
pnpm build

Documentation

• PRD - Product requirements and vision
• Implementation Plan - Technical design
• Implementation Summary - What was built
• Backend Recommendation - Why Convex?
• Orchestration Architecture - Graph-based design

Built With

• Convex - Real-time reactive backend
• Groq - Fast LLM inference
• OpenAI - Advanced AI models
• Google Gemini - AI diversity
• Next.js - React framework
• Turborepo - Monorepo tooling

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Made with Cursor for the Cursor Hackathon 2025