Practical overview
When it fits
Use MachineSoM when you need research code to investigate how agent traits and collaboration mechanisms affect multi-agent experiments, rather than a hosted agent service.
Who it is for
- Researchers studying multi-agent collaboration with large language models.
- Developers who want to examine or adapt an experimental framework for agent debate, self-reflection or interaction strategies.
Documented uses
- Simulate agent societies using different collaborative strategies.
- Compare experiments that vary the number of agents or collaboration rounds.
- Evaluate experiment results with table-generation and significance-test routines.
- Generate figures for experiment analyses.
Requirements & limits
The repository is public and specifies an MIT license. Running experiments requires configuring an API key for an inference service named in the README.
- The documented environment uses Conda and Python 3.9, with dependencies installed from requirements.txt.
- The README names Replicate, Dashscope, OpenAI and Anyscale as inference-service options.
- Experiment data is available in eval_data; raw datasets for MMLU, Math and chess move validity are described as separate downloads.
- Evaluation routines expect experiment and dataset identifiers, and paper-result evaluation follows a documented results-directory arrangement.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare the MachineSoM Python environment
Create the documented Conda environment and install the repository dependencies.
Before you start
- The MachineSoM repository
- Conda
- Python 3.9 environment specification
- The repository's requirements.txt
Steps
Create a Conda environment named masom with Python 3.9.
Install dependencies with pip install -r requirements.txt.
Expected outcome
A Conda environment named masom with the repository dependencies installed.
Guide limitations
- This covers environment setup only, not running or validating experiments.
Run and evaluate a documented collaboration experiment
Configure an inference-service API key, launch a documented experiment script, and use the evaluation interface for results.
Before you start
- The prepared Python environment
- An API key for a supported inference service, added in src/api.py
- A dataset available in the repository's eval_data folder or separately downloaded as described in the README
Steps
Add the relevant API key to the configuration in src/api.py.
Run a documented script from src, such as run_main.sh, run_agent.sh, run_turn.sh or run_strategy.sh.
Use evaluate.py with a documented experiment type and dataset to produce a table, run a significance test or draw a figure.
Expected outcome
The code is configured to launch one of the described experiment variants and invoke the corresponding evaluation routine.
Guide limitations
- The README does not give one universal command for launching each script.
- Evaluation depends on available experiment results and matching experiment and dataset names.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- zjunlp/MachineSoM · Official homepage title · retained in its source language