EverMemOS Competition Starter Kit

EverMemOS Competition Starter Kit

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Welcome to the EverMemOS Competition! This starter kit will help you get up and running quickly so you can focus on building amazing AI memory applications.

Join our Discord to brainstorm ideas, find teammates, and get help from the community!

Table of Contents

• Quick Start (5 Minutes)
• Competition Tracks
• How to Submit
• Evaluation Criteria
• API Cheatsheet
• Sample Data
• Example Projects
• Resources
• Tips for Success

Quick Start (5 Minutes)

1. Clone and Start Services

# Clone the repository
git clone https://github.com/EverMind-AI/EverMemOS.git
cd EverMemOS

# Start all services with Docker
docker compose up -d

# Verify services are running
curl http://localhost:1995/health

2. Store Your First Memory

curl -X POST "http://localhost:1995/api/v1/memories" \
  -H "Content-Type: application/json" \
  -d '{
    "message_id": "msg_001",
    "create_time": "2025-01-20T10:00:00+00:00",
    "sender": "user_001",
    "content": "I love building AI applications with memory!"
  }'

3. Search Memories

curl -X GET "http://localhost:1995/api/v1/memories/search" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What does the user love?",
    "user_id": "user_001",
    "retrieve_method": "hybrid"
  }'

You're ready to build!

Competition Tracks

Track 1: Agent + Memory

Build intelligent agents with long-term, evolving memories.

Track 2: Platform Plugins

Develop plugins or SDKs to integrate EverMemOS everywhere developers work.

Track 3: OS Infrastructure

Optimize the EverMemOS platform: core functionality, performance tuning, and system improvements.

How to Submit

Note: A submission portal will be available soon. Stay tuned for updates!

Required

Optional (Preferred)

Submission Checklist

• GitHub repository is public and accessible
• README includes project overview and setup instructions
• README explains how EverMemOS powers your solution
• Video demo is 3-5 minutes and covers functionality + concept
• (Optional) Live demo URL included in README

Evaluation Criteria

Projects will be evaluated based on the following priorities:

Innovation

We value creative and original approaches. Does your project solve a problem in a new way? Is the use of memory capabilities imaginative? Does it stand out from existing solutions?

Technical Depth

We look for well-engineered projects with clean implementation, effective use of EverMemOS features, and appropriate technical sophistication for the problem being solved.

Consumer Value

We prioritize projects that solve real problems for real users. Is it useful? Is it intuitive? Could it scale or be commercialized?

API Cheatsheet

Core Endpoints

Key Parameters

Response Structure

{
  "status": "ok",
  "message": "Success description",
  "result": {
    // Response data here
  }
}

Sample Data

Pre-loaded sample conversations are available:

Load sample data:

uv run python src/bootstrap.py src/run_memorize.py \
  --input data/group_chat_en.json \
  --scene group_chat \
  --api-url http://localhost:1995/api/v1/memories

Example Projects

Resources

Documentation

• API Documentation - Complete API reference
• Architecture - System design overview
• Group Chat Guide - Multi-user conversations

Examples

• Batch Operations - Processing multiple messages
• Retrieval Strategies - Optimizing search

External

• Paper - Research paper
• Discord - Community support

Tips for Success

1. Start Simple - Get the basic flow working before adding complexity
2. Use Hybrid Retrieval - Best balance of speed and accuracy for most use cases
3. Group Your Memories - Use group_id for multi-user or multi-agent scenarios
4. Check Pending Messages - Memories may not extract immediately (boundary detection)
5. Ask for Help - Join our Discord for real-time support

Good luck and happy building!