Parabable is a simulation intelligence platform for testing complex agent interactions at scale. Design scenarios, run simulations, and understand emergent behavior before deploying to production.
Everything you need to design, run, and analyse multi-agent simulations with production-grade infrastructure.
Define agent populations, environmental rules, and interaction constraints using a structured scenario editor. Template from six proven archetypes.
Run multi-agent simulations with configurable tick rates, population sizes, and action spaces. Real-time progress tracking and intervention controls.
Emergent pattern detection, coalition analysis, and behavioral trajectory mapping. Compare runs across parameter sweeps.
Apache AGE graph database storing every agent, action, and relationship. Full temporal lineage for every simulation state.
pgvector-powered embedding store for agent memory retrieval. Agents recall relevant past interactions using semantic similarity search.
18 tools exposed via Model Context Protocol. Integrate simulations into any LLM workflow, CI pipeline, or external agent framework.
Parabable is built on a layered architecture where each ring has clear ownership and can evolve independently. From the OASIS simulation engine at the core to the operational shell at the edge.
Start from proven behavioral templates or design custom scenarios. Each archetype encodes characteristic interaction patterns.
Agents optimise for collective benefit
Zero-sum resource contests
Creative problem solving under constraints
Rule formation and compliance dynamics
Exchange, pricing, and equilibrium
Stress response and cascading failures
From scenario design to behavioral analysis in minutes, not months.
Define your agent population, action space, and environmental rules. Choose an archetype template or build from scratch.
Run the scenario with configurable parameters. Watch agent interactions unfold in real-time. Pause, intervene, or adjust mid-run.
Explore emergent patterns, coalition structures, and behavioral trajectories. Compare runs across parameter variations.
Any domain where multiple autonomous agents interact under complex constraints.
Test how populations of AI agents behave when given competing objectives. Detect misalignment patterns before deployment.
Simulate trading agent interactions, order book dynamics, and flash crash scenarios. Validate algorithmic strategies against adversarial actors.
Model swarm behavior for drone fleets, autonomous vehicles, or robotic systems. Test coordination protocols at scale.
Design emergent NPC behaviors and faction dynamics. Test game economy balance and social systems before launch.
Simulate how populations of rational agents respond to policy changes. Predict unintended consequences and gaming behaviors.
Model multi-party supply networks under disruption scenarios. Identify single points of failure and cascading risk paths.
18 tools exposed via MCP. Connect any LLM or agent framework.
// Connect via SSE transport
POST /mcp
Authorization: Bearer <token>
{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "create_scenario",
"arguments": {
"name": "Market Stability Test",
"archetype": "market",
"agents": 50,
"ticks": 1000
}
}
}
Parabable is in private alpha. We are partnering with teams building multi-agent systems who want to understand emergent behavior before it reaches production.