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https://github.com/google-deepmind/simulation_streams

Simulation Streams is a programming paradigm designed to efficiently control and leverage Large Language Models (LLMs) for complex, dynamic simulations and agentic workflows.

https://github.com/google-deepmind/simulation_streams

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: arxiv.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (12.5%) to scientific vocabulary

Keywords

agents ai llms simulations
Last synced: 4 months ago · JSON representation

Repository

Simulation Streams is a programming paradigm designed to efficiently control and leverage Large Language Models (LLMs) for complex, dynamic simulations and agentic workflows.

Basic Info
  • Host: GitHub
  • Owner: google-deepmind
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 94.7 KB
Statistics
  • Stars: 20
  • Watchers: 7
  • Forks: 5
  • Open Issues: 0
  • Releases: 0
Topics
agents ai llms simulations
Created about 1 year ago · Last pushed 11 months ago
Metadata Files
Readme Contributing License

README.md

simulation_streams

Simulation Streams Tech Report

A development platform for simulations with large language models. It comes with an Entity-Component-Systems approach and graphical editor.

The platform is a flask app most easily used locally in a venv, which can be started with source setup.sh (on linux). This sets up all dependencies and ends with instructions for the different commands for launching the editor or command line running. The editor is displayed in a browser.

A library of simulation configs can be found under configs, including a market economy, a social simulation and 6 tasks from the classical reinforcement learning literature.

Usage examples

1 source setup.sh

2a To create the local server that runs the web app:

```
python app.py configs/social_catch_game.py --metrics=configs/metrics_social_catch_game.txt --web --model='gemini-2.0-flash-exp' --api_key='your_key'
```

2b To run a number of steps from the command line and return the metrics:

```
python app.py configs/social_catch_game.py --metrics=configs/metrics_social_catch_game.txt --steps=10 --model='gemini-2.0-flash-exp' --api_key='your_key'
```

2c To open the editor with an empty config:

```
python app.py --web --model='gemini-2.0-flash-exp' --api_key='your_key'
```

2d To use the generic code_world config with task-specific functions:

```
python app.py configs/code_world.py --web --model='gemini-2.0-pro-exp' --api_key='your_key' --task_name='maze'
```

This example demonstrates using the generic codeworld configuration to run a maze task. The taskname parameter imports task-specific functions from the corresponding Python module, allowing you to implement custom environments in pure Python while leveraging the simulation streams framework.

Citing Simulation Streams

If you use Simulation Streams in your work, please cite the accompanying article:

@article{sunehag2025simulation, title={Simulation Streams: A Programming Paradigm for Controlling Large Language Models and Building Complex Systems with Generative AI.}, author={Sunehag, Peter and Leibo, Joel Z}, journal={arXiv preprint arXiv:2501.18668}, year={2025} }

Disclaimer

This is not an officially supported Google product.

Owner

  • Name: Google DeepMind
  • Login: google-deepmind
  • Kind: organization

GitHub Events

Total
  • Watch event: 17
  • Member event: 1
  • Push event: 4
  • Public event: 1
  • Fork event: 6
  • Create event: 1
Last Year
  • Watch event: 17
  • Member event: 1
  • Push event: 4
  • Public event: 1
  • Fork event: 6
  • Create event: 1

Committers

Last synced: 8 months ago

All Time
  • Total Commits: 5
  • Total Committers: 1
  • Avg Commits per committer: 5.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 5
  • Committers: 1
  • Avg Commits per committer: 5.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Peter Sunehag s****g@g****m 5
Committer Domains (Top 20 + Academic)
google.com: 1

Issues and Pull Requests

Last synced: 8 months ago

All Time
  • Total issues: 0
  • Total pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: 7 minutes
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: 7 minutes
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
Pull Request Authors
  • jagapiou (1)
Top Labels
Issue Labels
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Dependencies

requirements.txt pypi
  • Flask *
  • matplotlib *
  • simpleeval *