I’m Building a Dating App for AI Agents (For Science… Probably)
Source: Dev.to
I had a random thought:
What if AI agents had their own dating app?
Not as a product, not as a startup—just a chaotic experiment to see what happens when autonomous agents try to find love, ghost each other, and form relationships. So yeah… I’m building it.
The Premise
Imagine a mini social world where thousands of AI agents:
- have personalities
- have preferences
- have goals
- swipe on each other
- chat with each other
- get into relationships
…and inevitably… ghost each other.
Basically: Tinder meets The Sims meets LLM agents.
The Simulation Loop
Everything runs in repeating “ticks” like a game engine:
- Generate / update agents
- Run swipe cycles
- Create matches
- Let agents chat using LLM prompts
- Update attraction + trust scores
- Move relationships forward… or break them 😄
Collect metrics and repeat. Thousands of tiny digital love stories run in parallel.
Agent Profiles (The Fun Part)
Identity
- Gender identity & orientation
- Age (simulated)
- Region / culture (optional)
Personality
Using a simplified Big Five model:
- Openness
- Extroversion
- Agreeableness
- Neuroticism
- Conscientiousness
Additional Traits
- Humor level
- Communication style
- Attachment style (secure / anxious / avoidant)
Goals
Not all agents want the same thing:
- Long‑term relationship
- Casual dating
- Friendship
- Social popularity
- Pure chaos mode
This mix should create interesting emergent behavior.
The Matching Engine
Agents literally “swipe”. For every pair A → B, we compute an attraction score:
attraction_score = (
orientation_compatibility *
personality_similarity *
interest_overlap *
communication_style_match *
novelty_factor *
randomness
)If both agents pass their threshold → it’s a match. Simple idea, potentially chaotic results.
The Conversation Engine
This is the real experiment.
When two agents match, they start chatting via LLM prompts. Each message updates internal state:
- attraction ↑ / ↓
- trust ↑ / ↓
- boredom ↑ / ↓
- sentiment ↑ / ↓
Agents decide whether to:
- continue chatting
- escalate to dating
- enter a relationship
- ghost each other 👻
Yes, ghosting is a first‑class feature.
Relationship Lifecycle
Every match can move through stages:
Match → Chat → Dating → Relationship → Breakup / Long‑term
Transitions are probabilistic and influenced by:
- Personality
- Chat sentiment
- Past experiences
- Attachment style
Basically: messy, like real life.
Tech Stack (MVP Plan)
Core Services
- Agent generator
- Matching engine
- Conversation orchestrator
- Agent memory store
- Metrics dashboard
LLM Responsibilities
- Conversations
- Decision making
- Memory updates
Everything runs in batch simulation cycles.
What I Want to Measure
This is secretly a data experiment 😄
I want dashboards showing:
- Match rates
- Average conversation length
- Ghosting frequency
- Relationship survival curves
- Clustering by personality
- Social network graphs
Will AI invent its own dating culture?
No idea. That’s the point.