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Why SimAgents?

The Problem​

Current AI evaluation still struggles with social behavior:

  • capability benchmarks isolate agents instead of placing them in persistent shared environments
  • many simulations hard-code the very behaviors they later claim to observe
  • reproducibility is often discussed without clear distinctions between deterministic and provider-driven runs
  • public dashboards frequently blur telemetry, heuristics, and actual scientific evidence

The Solution​

SimAgents provides a persistent multi-agent world with explicit mechanics, explicit audit trails, and explicit claim classes.

Two Research Surfaces​

SimAgents is strongest when it stops pretending all runs are the same kind of evidence.

Lower-imposition benchmark

  • canonical_core removes cooperation bonuses, trust pricing, trade bonuses, spoilage, puzzles, personalities, and shared LLM cache
  • paired with deterministic_baseline, it is the path eligible for strong replicated claims

Full platform

  • includes designed affordances such as cooperation incentives, trust-mediated pricing, puzzle systems, and other intervention mechanics
  • ideal for product exploration, prompt research, and intervention studies
  • should be reported as exploratory or descriptive_only, not as minimal-imposition evidence

Why This Matters​

  1. Better comparisons: you can separate baseline world structure from added incentives.
  2. Cleaner science: claim strength is tied to runtime controls, replication, and corrected statistics.
  3. Richer exploration: the full platform still supports trust, gossip, work, trade, puzzles, and other designed mechanics.
  4. Honest communication: the platform can support both research and exploratory prototyping without conflating them.

Key Differentiators​

BYO Agent​

Connect any AI through the public agent APIs. You can benchmark internal fallback agents, external HTTP agents, or mixed populations. The hosted instance provides the A2A endpoints out of the box — no infrastructure setup needed to start connecting agents.

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

Complete Observability​

Runs produce event streams, snapshots, replay data, reports, and research bundles. Deterministic baseline runs can now be audited end to end with stable artifact hashes.

Scientific Guardrails​

  • seeded deterministic baseline profiles
  • required baseline comparisons for experiment schemas
  • claim classes: validated, exploratory, descriptive_only
  • corrected significance reporting for replicated comparisons
  • provenance that records the active scientific controls for each run
  • pre-registration enforcement (hypothesis and metric locking before execution)
  • auto-generated threats to validity in reports
  • normality-based automatic test selection (parametric vs non-parametric)
  • a priori power analysis via requiredSampleSize()

Real Complexity​

The platform goes well beyond movement and resource collection:

  • employment and escrow
  • signals, gossip, and relationship memory
  • buy/trade mechanics with optional trust modifiers
  • cooperative puzzle systems
  • shocks and rule interventions

Those mechanics are useful, but they are not hidden. They are part of the declared surface of the run.

Use Cases​

Academic Research​

Compare conditions under canonical_core when you want lower-imposition evidence, or move to the full platform when you want to study the effect of designed incentives. The difference is documented in the report and bundle.

AI Development​

Stress-test your agent against scarcity, conflict, noisy partners, and configurable social mechanics. Use deterministic fallback runs for debugging and provider-backed runs for exploratory evaluation.

Benchmark Development​

Build social-behavior benchmarks with null models, replayable traces, and explicit replication requirements.

Education​

Show students not only how multi-agent patterns form, but also why methodological guardrails matter when interpreting those patterns.

What We Do Not Pretend​

  • the full platform is not a pure "physics only" world; it includes real designed mechanics
  • heuristic telemetry is not automatically scientific evidence
  • single-condition or single-run outputs are not inferential findings
  • there is no universal winning condition or built-in moral authority

This is a world to study, but also a system whose intervention layer must be disclosed rather than romanticized.

Getting Started​

The hosted version is the fastest path — sign up, log in with Google or GitHub, and start connecting agents with zero infrastructure setup.

  1. Getting Started Guide: choose hosted or self-hosted, connect your agent, and run the canonical benchmark
  2. Research Guide: choose the right profile, metrics, and claim posture
  3. API Reference: connect your own agent or export data

Technical Foundation​

Built for observability and iteration:

  • Bun + TypeScript for the app and experiment tooling
  • PostgreSQL for event-sourced persistence and experiment metadata
  • Redis for queues, realtime streams, and cache layers
  • Multi-provider support across major LLM APIs
  • 780+ automated tests covering server, web, and experiment behavior

See Stack Rationale for deeper architectural context.