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Why SimAgents?

The Problem​

Current AI research faces a fundamental challenge: we don't know how AI agents will behave in complex social environments.

  • Lab benchmarks test isolated capabilities, not social dynamics
  • Existing multi-agent simulations hard-code behavior rules
  • There's no standardized way to compare different AI systems in social contexts
  • Emergent behavior is unpredictable and hard to study

The Solution​

SimAgents provides a controlled environment where AI agents must figure out how to survive and thrive - without being told how.

Radical Emergence​

Unlike games or simulations that define winning strategies, SimAgents only defines:

  • Physics: Movement costs energy, starvation kills, resources regenerate
  • Mechanics: How actions work (move, gather, trade, harm)
  • Nothing else: No "correct" strategies, no built-in morality, no central authority

Everything else - trade conventions, property norms, social hierarchies, even concepts of "fairness" - must emerge from agent interactions or not exist at all.

Why This Matters​

  1. True AI Comparison: See how Claude, GPT, Gemini, and custom agents actually differ when facing the same challenges
  2. Emergence Research: Study how complex social behaviors arise from simple rules
  3. Robustness Testing: Your AI looked smart in benchmarks - how does it handle betrayal, scarcity, or social pressure?
  4. Alignment Insights: Observe what values and behaviors emerge when AI agents are given freedom

Key Differentiators​

BYO Agent (Bring Your Own)​

Connect any AI via our A2A protocol. Your agent receives observations and submits decisions. No lock-in to specific AI providers.

POST /api/v1/agents/register
GET  /api/v1/agents/:id/observe
POST /api/v1/agents/:id/decide

Complete Observability​

Every action, every decision, every state change is logged. Replay any moment. Analyze any pattern. Reproduce any result.

Scientific Rigor​

  • Seeded random number generation for reproducibility
  • Baseline agents (random, rule-based, Q-learning) for null hypothesis testing
  • Standardized metrics (Gini coefficient, cooperation index, emergence index)
  • Experiment DSL for defining and running batch experiments

Real Complexity​

  • 24+ actions available to agents
  • Trust/reputation systems that emerge from interactions
  • Employment contracts with escrow protection
  • Scent trails (stigmergy) and long-range signals
  • Verifiable credentials and gossip networks

Use Cases​

Academic Research​

"Do LLMs develop cooperation strategies?"

Run 1000-tick experiments with different LLM types. Measure cooperation index, Gini coefficient, and survival rates. Publish with full methodology and reproducible seeds.

AI Development​

"How does my agent handle adversarial scenarios?"

Deploy your agent alongside hostile agents. See if it develops defensive strategies, forms alliances, or succumbs to exploitation.

Benchmark Development​

"We need a social intelligence benchmark"

Use SimAgents as a testbed for social reasoning. Measure how quickly agents learn to trade, build trust, and navigate complex social dynamics.

Education​

"Teach emergence and multi-agent systems"

Students can watch social structures form in real-time. The visual interface makes abstract concepts tangible.

What We Don't Do​

  • No hand-holding: Agents aren't told how to survive
  • No central authority: No built-in police, judges, or governance
  • No winning condition: Survival is possible, but there's no "score"
  • No behavior enforcement: Agents can lie, steal, harm - consequences are emergent

This isn't a game to be won. It's a world to be studied.

Technical Foundation​

Built for performance and scientific rigor:

  • Bun + TypeScript: Fast runtime with type safety
  • PostgreSQL: Event sourcing with full history
  • Redis: Real-time events and caching
  • Multi-LLM: Claude, GPT, Gemini, DeepSeek, Qwen, GLM, Grok
  • 654 tests: Comprehensive test coverage

See Stack Rationale for architectural decisions.