Predictive intelligence through agent-based population simulation.
Website · Announcement · CLI Reference · Architecture
Extropy creates synthetic populations grounded in real-world distributions, connects them in social networks, and simulates how they respond to events — each agent reasoning individually via LLM.
Simulate anything: Policy changes. Pricing decisions. Product launches. Crisis response. Any scenario where humans form opinions, make decisions, and influence each other.
Development Notice
[!CAUTION] "Extropy is still in active development and behavior may change between versions." For reliable execution and triage, we strongly recommend running Extropy through an agentic harness (for example: Codex or Claude Code) rather than manual ad-hoc CLI usage. Simulation can be expensive at scale (especially high-fidelity, multi-timestep runs). Start with small runs first; we recommend beginning at around
500agents before scaling up.
Install
pip install extropy-run
export OPENAI_API_KEY=sk-...
# or: ANTHROPIC_API_KEY=... / AZURE_API_KEY=... + AZURE_ENDPOINT=...
Requires Python 3.11+. uv recommended.
Quick Start
# Create study folder and build population spec
extropy spec "Austin TX commuters" -o congestion-tax -y
cd congestion-tax
# Create scenario with events and outcomes
extropy scenario "Response to $15/day congestion tax" -o congestion-tax -y
extropy persona -s congestion-tax -y
# Sample agents and generate network
extropy sample -s congestion-tax -n 500 --seed 42
extropy network -s congestion-tax --seed 42
# Run simulation
extropy simulate -s congestion-tax --seed 42
# View results
extropy results
extropy results segment income
How It Works
- Population — LLM discovers attributes, researches real-world distributions, samples agents
- Network — Builds structural + similarity edges; edge types affect information flow
- Two-pass reasoning — Agent role-plays reaction, then classifier extracts outcomes
- Propagation — Opinions spread through network; agents update after hearing from peers
Features
| Feature | Description |
|---|---|
| Population | |
| Any geography | US, Japan, India, Brazil — define attributes with your distributions |
| Real grounding | LLM researches actual demographics, cites sources |
| Household mode | Correlated partners, NPC dependents, assortative mating |
| Agent focus | Primary adult, couples, or full families as reasoning agents |
| Network | |
| Structural edges | Partner, household, coworker, neighbor, congregation, school parent |
| Similarity edges | Acquaintances and online contacts from attribute similarity |
| Small-world | Calibrated clustering coefficient and path lengths |
| Simulation | |
| Two-pass reasoning | Role-play first, classify second — reduces schema anchoring in outcome extraction |
| Conversations | Agents talk to each other; both update state independently |
| Memory | Persistent memory traces with fidelity-based prompt trimming |
| Conviction | Affects sharing probability and flip resistance |
| THINK vs SAY | Internal monologue separate from public statement |
| Timeline events | New information injected at specified timesteps |
| Outcomes | |
| Categorical | Known decision space (buy/wait/skip) |
| Boolean | Binary decisions (will share, will switch) |
| Float | Intensity measures (sentiment, likelihood) |
| Open-ended | Free text — discover categories post-hoc |
Development
git clone https://github.com/exaforge/extropy.git && cd extropy
pip install -e ".[dev]"
pytest
License
1 maintainer
Metadata
1 maintainer
Release files for extropy-run 0.4.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| extropy_run-0.4.0.tar.gz | 2.1 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| extropy_run-0.4.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.5 MB
Release files / extropy_run-0.4.0.tar.gz
| Download URL | extropy_run-0.4.0.tar.gz |
|---|---|
| Size | 2.1 MB |
| Tags | Source |
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| Tags | Python 3 |
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| Uploaded via |
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