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apromisedland/trustworthy-agent-simulation

AI agentsPython
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Auditable LLM multi-agent town simulation with AgentScope, Mesa, replay, policy experiments, and Streamlit

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About the project

Auditable LLM multi-agent town simulation built on AgentScope and Mesa: agents message, work, trade and react to policies, with all decisions recorded for replay.

Useful for
  • Run the town simulation with 18 residents and inspect economic metrics in the Streamlit dashboard
  • Run a batch policy experiment with subsidies and compare paired effects across seeds
  • Replay a specific simulation tick without calling any model

README summarized by DeepSeek V4.1 Flash. Details may be inaccurate.

1.7 Early signal Magnitude out of 10 — how fast interest is growing, not a quality score.

Star growth looks organic. Data as of October 5, 2026, 12:02 UTC.

The star-growth assessment does not verify whether the project is safe to run.

Stars per day

01020September 27, 2026October 5, 2026

Bars show daily stars; the line is a moving average of the available history. Short histories do not yet establish a reliable usual pace. Red marks spike days.

Why it’s trending

  • Star-counter measurements on 2026-10-05 (UTC), 10:59–11:54: 73 → 73 stars (+0). This is the change over that interval.
  • Estimated end-of-day forecast: about +4 stars, using observed gains and the previous day.
  • The repository is 6 days old and already has 73 stars. With less than two weeks of history, there's no usual pace to compare the spike against yet.
  • Top new repositories this week: #169.

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Three GitHub discoveries in each edition

What they do, why they are gaining interest, and what to check before using them.

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Numbers

Total stars
73
Today
+0 stars measured 2026-10-05, 10:59–11:54 UTC, ≈ 4 by evening
Forks
0
Issues and pull requests
0
Watchers
0
Language
Python
License
Apache-2.0
Created
September 29, 2026
Last push
September 29, 2026

Star trust

Growth looks organic: forks and discussion are in line with active projects, and stars arrive unevenly, the way people give them.

These are heuristics, not a verdict: we judge by the repository’s behavior, not by a list of stargazers.

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