safety-research/bloom

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1392 stars · 171 forks observed · 2026-08-28

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

Bloom is a Python tool that automatically generates behavioral evaluation suites for LLMs, probing target models for behaviors like sycophancy, self-preservation, and political bias. It creates diverse test scenarios from a seed configuration, runs conversations with the target model, and scores the results.

Use cases

• evaluate llm for sycophancy
• test if a model exhibits self-preservation behavior
• probe political bias in a chatbot
• generate custom behavioral benchmarks for a language model
• run automated safety evaluations on an llm api
• measure behavior stability under emotional pressure variations

When to choose

• you need to probe a specific LLM behavior beyond fixed benchmarks
• you want reproducible, seed-configurable evaluation suites
• you're doing AI safety research on model behaviors

When to avoid

• you need a fixed, widely-cited benchmark with stable results
• you want actively developed features - the repo is frozen and moved to Meridian Labs
• you need non-LLM software testing

Facets

library · maturity maintenance

llm-inference testing benchmarking agent-framework artificial-intelligence large-language-models machine-learning developer-tools python cli cross-platform llm-evaluation ai-safety behavioral-testing red-teaming sycophancy benchmark-generation

Member repositories

For agents

markdown · JSON · MCP: product_card(name="safety-research/bloom")