Generative Agents Behavior Simulator
A notebook-based, locally runnable simulation of human-like behavior in a D&D town setting.
- Source repo
- mkturkcan/generative-agents
- Stars
- ★ 993
- Last updated
- 1y ago
- License
- MIT
- Primary language
- Jupyter Notebook
- FA score
- 16/100 · Major gaps
At a glance
- Works with
- Universal · cross-platform
- You'll need
- Typical use
- A researcher with local GPU hardware who wants to run an implementation related to the model type described in the Generative Agents paper.
- Main limitation
- The current version works best with 16GB VRAM; although sub-8GB VRAM environments are supported, no performance or feature trade-offs are documented.
- Source review
- 16/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
This is a working implementation of the kind of model described in Generative Agents: Interactive Simulacra of Human Behavior. It is distributed as notebooks that can run locally or on Google Colab, with JupyterLab recommended for local use. The stable model is in notebook/Release, work-in-progress models with newer features are in notebook/WIP, and a WIP library is under game_simulation. The current model simulates Phandalin, the town from an introductory D&D 5e adventure, using a setting described as more free-form than the paper's simple scenario. It uses lower-parameter, locally runnable models to address the cost of high-quality instruction models and many long-context queries, while noting that future instruction-tuned models are expected to perform better.
A user selects a notebook from notebook/Release or notebook/WIP and runs it locally or in Google Colab. The model runs a simulation of the town of Phandalin, aiming to model the kind of human-like behavior described by the paper. A WIP library is also available under game_simulation. The supplied material does not identify notebook filenames, inputs, prompts, model calls, cell order, or output artifacts, so those end-to-end details cannot be verified.
- A researcher with local GPU hardware who wants to run an implementation related to the model type described in the Generative Agents paper.
- An experimenter who wants to try the notebooks in Google Colab instead of deploying a service.
- A prototype developer studying simulated behavior in the more open-ended Phandalin setting from an introductory D&D 5e adventure.
- A user who wants to start with a stable version and compare notebook/Release with notebook/WIP when newer features matter.
- An experimental team constrained by the cost of high-quality instruction models and long-context queries that wants to explore a lower-parameter local-model approach.
How do you install or deploy this agent?
No copyable installation commands, dependency list, model acquisition method, environment versions, or credential requirements are provided. The documented execution boundary is that notebooks run locally or in Google Colab, JupyterLab is recommended for local use, and the notebooks should work as-is in Google Colab. A reproducible setup still needs the specific notebook filename, dependency installation steps, model-file source, and first-run workflow.
How do you use this agent?
Choose the model described as most stable in notebook/Release, or choose a newer WIP version in notebook/WIP; open and run the applicable notebook in local JupyterLab or Google Colab. A WIP library is also available under game_simulation. The material does not document a first notebook, command, parameters, or cell execution order, so no more specific verified invocation can be supplied.
What are this agent's strengths and limitations?
- Documents two notebook execution paths: local runs and Google Colab.
- Separates a stable release, newer WIP notebooks, and a WIP library across notebook/Release, notebook/WIP, and game_simulation.
- Uses the Phandalin D&D setting, described as more free-form than the paper's simple scenario.
- Explicitly uses lower-parameter locally runnable models to address the cost of high-quality instruction models and long-context queries.
- The current version works best with 16GB VRAM; although sub-8GB VRAM environments are supported, no performance or feature trade-offs are documented.
- No installation commands, dependency versions, model-download method, credentials, or notebook execution sequence are provided.
- The material acknowledges room for improvement in the current lower-parameter local models and expects future instruction-tuned models to perform better.
- There is no verifiable documentation of an API, CLI, evaluation results, output format, or production deployment interface.
How does this agent compare with similar options?
The material contrasts this project with the paper's approach, which requires a high-quality instruction model such as GPT-3 and many long-context queries: this implementation instead uses lower-parameter, locally runnable models to reduce running cost.
Key facts side by side with the most closely related agents.
| Agent | Source review | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|
| Generative Agents Behavior Simulator This agent | 16 · Major gaps | ★ 993 | 1y ago | Jupyter Notebook | — |
| Concordia: A Library for Generative Social Simulation | 39 · Major gaps | ★ 1.7k | 9d ago | Python | — |
| Principia | 66 · Some gaps | ★ 840 | 8d ago | Rich Text Format | — |
| EDSL (Expected Parrot Domain-Specific Language) | 63 · Some gaps | ★ 497 | 5d ago | Python | OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows no documentation on permission management, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. Therefore all trust criteria score 0.
Self-consistency: README describes a working implementation but no tests or verification are provided, only partial support. Dependency availability: No dependencies or versions listed, cannot assess. Failure messages: No error handling or failure messages provided.
Audience and scenarios: Aimed at researchers and hobbyists, scenario is D&D town simulation. Capability boundaries: Mentions use of low-parameter models but no explicit boundaries. Trigger precision: No trigger mechanisms defined. Environment fit: Mentions local and Colab but no specific configurations.
Information architecture: Has README and directory structure but no detailed documentation. Install notes: Provides basic setup but not detailed steps. Naming stability: Not mentioned. Examples and FAQ: None. Known limitations: Has limitations section. License: MIT license but copyright info incomplete. Versioning and changelog: None. Maintenance responsibility: Not clear.
Output usability: Described as working implementation but no output examples. Marginal value: Provides low-cost local running solution but no comparison with existing solutions. Cost-benefit: Mentions low-parameter models but no specific cost data.
Claim traceability: Cites paper but no implementation details. Cross-source corroboration: None. Fact-inference separation: Not distinguished.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- The repository provides no security or permission management documentation; assess risks before use.
- Dependencies are not listed, so supply chain security cannot be confirmed.
- No tests or verification are provided, raising reliability concerns.