ShengranHu/ADAS

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1631 stars · 237 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

ADAS (Automated Design of Agentic Systems) is the official research code for the ICLR 2025 paper introducing Meta Agent Search, where a meta agent iteratively programs novel agent designs in Python code. It provides self-contained experiment folders for benchmark domains like ARC, DROP, MGSM, and MMLU, with an OpenAI API key as the main dependency.

Use cases

• automatically discover new agent designs with LLMs
• run Meta Agent Search experiments on benchmark domains
• adapt agent search to a custom evaluation domain
• reproduce ICLR 2025 ADAS paper results
• research automated agentic system design
• evolve and archive LLM agent programs

When to choose

• you are doing research on automated agent design or agent architecture search
• you want to reproduce or extend the Meta Agent Search algorithm
• you need a code-based framework where agents invent new agents programmatically

When to avoid

• you need a production-ready agent framework for deploying chatbots or pipelines
• you want a no-code or low-code agent builder
• you lack an OpenAI API key or budget for many LLM calls
• you need a stable, well-documented library rather than research code

Facets

library · maturity active

agent-framework llm-inference machine-learning prompt-engineering developer-tools artificial-intelligence large-language-models python cli cross-platform adas meta-agent-search automated-agent-design iclr-2025 research-code llm-agents code-generation ai-agents research automation

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