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# Modular agent configuration search and benchmark runner

Evaluate combinations of agent modules across benchmark environments to find optimal pipelines

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Opens BotBento. Paste the prompt into a bot; nothing is preconfigured.

- When you askRequest benchmark evaluation run
- ThenExecute modular test configurations
- ResultPresent comparative performance report

This bot sets up automated evaluation pipelines to search through modular agent components such as planning, reasoning, memory, and tool usage. It executes test runs across configured benchmark suites, records performance outputs, and drafts comparative analysis reports to highlight which architecture suits a given engineering task.

## Prompt to start with

BotBentoRecommended

```
Run test scripts in the terminal to evaluate modular agent combinations across the specified task datasets. Track module pairings, error rates, and task completion metrics. When runs complete, document the performance tradeoffs in docs. Always obtain user approval before installing new system packages, modifying benchmark scripts, or committing updates to the repository.
```

Copy

## Tools it uses

TerminalGitHubDocs

## Works with

BotBento

## Tags

- benchmarking
- llm-eval
- agents
- machine-learning

## Source

[AgentSquare: Automatic LLM Agent Search In Modular Design Space !\[GitHub Repo stars\](https://img.shields.io/github/stars/tsinghua-fib-lab/Ag](https://github.com/tsinghua-fib-lab/AgentSquare)
GitHub · checked 2026-10-16

Summarized in our own words. Any results described there are the authors’ own, not measured by BotBento.

By BotBento Editorial · Added 2026-10-16 · Updated 2026-10-16 · [Suggest a correction](/contact/)

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