Timepoint Pro
Synthetic time travel through social simulation. The first practical SNAG engine: Social Network Augmented Generation. Like RAG retrieves documents to ground generation, SNAG synthesizes and maintains a structured social graph—complete with causal provenance, knowledge flow, emotional states, and temporal consistency—to ground LLM generation in complex group dynamics. This transforms LLMs from fragile, drifting storytellers into reliable multi-agent reasoners. Naive single-prompt simulations collapse beyond ~10 entities or ~20 interactions due to inconsistency and token explosion. SNAG’s structured propagation, variable-depth fidelity, and composable mechanisms let you scale to dozens of entities across hundreds of timepoints—while keeping costs low and causality auditable.Quick Start
Get up and running in 5 minutes
Core Concepts
Understand SNAG and temporal modes
API Reference
Explore the CLI and Python API
Examples
See real simulation scenarios
Why SNAG Matters
| RAG | SNAG (Timepoint Pro) | |
|---|---|---|
| Grounds LLMs in | Retrieved documents | Synthesized social graphs |
| Maintains | Document relevance | Causal provenance + temporal consistency |
| Scales to | Millions of documents | Dozens of entities, hundreds of timepoints |
| Output | Grounded answers | Auditable causal simulations + training data |
Cost-effective at scale: 1.00 per simulation run. All 21 templates verified February 2026.
Key Features
Five Temporal Modes
FORWARD, PORTAL, BRANCHING, CYCLICAL, DIRECTORIAL—each with unique causal semantics
Heterogeneous Fidelity
95% cost reduction: entities scale from TENSOR_ONLY (~200 tokens) to TRAINED (~50k tokens)
Knowledge Provenance
Track who learned what, from whom, when—with exposure events and causal audit trails
Dialog Synthesis
Per-character generation with voice discipline, archetype profiles, and naturalness scoring
19 Mechanisms
Composable building blocks for fidelity, temporal reasoning, knowledge tracking, and more
Training Data Export
TDF, JSONL, SQLite, Fountain formats—ready for fine-tuning and ML pipelines
Architecture Overview
Temporal Modes
FORWARD — Strict causality
FORWARD — Strict causality
Standard forward timeline with knowledge provenance. Entities only know what they’ve witnessed or been told.
PORTAL — Backward reasoning
PORTAL — Backward reasoning
Start from a target outcome (mission failure, election won) and work backward to discover critical paths and pivot points.
BRANCHING — Counterfactuals
BRANCHING — Counterfactuals
Explore “what-if” scenarios with divergent timelines. Run multiple survival strategies, pitch outcomes, or decision paths.
CYCLICAL — Prophecy loops
CYCLICAL — Prophecy loops
Future constrains past. Perfect for mythic sagas, generational stories, and bootstrap paradoxes.
DIRECTORIAL — Dramatic tension
DIRECTORIAL — Dramatic tension
Five-act structure with camera system. Events driven by narrative arc rather than pure causality.
Flagship Examples
| Template | Mode | Key Feature | Entities | Timepoints | Cost |
|---|---|---|---|---|---|
| mars_mission_portal | PORTAL | Backward reasoning from 2031 failure | 4 | 6 | ~$0.18 |
| castaway_colony_branching | BRANCHING | Counterfactual survival strategies | 8 | 16 | ~$0.35 |
| vc_pitch_branching | BRANCHING | Investor reactions across pitches | 5 | 16 | ~$0.30 |
Use Cases
Strategic Foresight
PORTAL maps critical paths backward from any outcome
Decision Testing
Run scenarios multiple ways, measure causal convergence
Training Data
Full causal ancestry, provenance, counterfactuals baked in
Social Forecasting
Variable-depth fidelity: low-res for long horizons, high-res at pivots
Quick Installation
Run your first simulation
Python 3.10+ required. OpenRouter API key needed for LLM access.
Timepoint Suite Integration
Timepoint Pro is part of the open-source Timepoint Suite for temporal AI:- Flash — Reality Writer: renders grounded historical moments
- Pro — SNAG simulation engine (this project)
- Clockchain — Temporal Causal Graph: Rendered Past + Rendered Future
- SNAG Bench — Quality certifier: measures Causal Resolution
- Proteus — Settlement layer: prediction markets for Rendered Futures
- TDF — Data format: JSON-LD interchange across all services
Learn more about the Timepoint Suite
Explore how Pro integrates with Flash, Clockchain, and other services
Next Steps
Quickstart Guide
Run your first simulation in 5 minutes
Understanding SNAG
Learn the core architecture and philosophy
Temporal Modes Deep Dive
Master FORWARD, PORTAL, BRANCHING, and more
19 Mechanisms
Explore the composable building blocks
Open Source — Apache 2.0 License · GitHub · @seanmcdonaldxyz