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Bloom
Summary
Anthropic's evaluation framework that auto-generates targeted test suites to measure specific AI behaviors at scale.
Bloom tackles a real gap in AI safety work: most evaluations are either broad (and miss edge cases) or manual (and don't scale). This tool generates reproducible test scenarios for narrow, high-stakes behaviors—like refusal consistency or prompt injection resilience—then scores models across thousands of permutations. The catch is structural: it's only as good as the seed prompts and judging logic you feed it, so teams still need to validate results manually for ambiguous cases. Free to use, but assumes you have both the domain expertise to set it up and the infrastructure to run it.
Bottom line: *Use this if you're a safety researcher or red-teaming group who needs systematic, repeatable measurements of specific model risks.*
Pricing Plans
Free- Free Tier
- No limits; fully open-source
Open Source
Freely available open-source framework
- Full agentic evaluation pipeline
- Four-stage system (Understanding, Ideation, Rollout, Judgment)
- Weights & Biases integration
- Inspect transcript export
- Interactive transcript viewer
View full pricing on anthropic.com →
Pricing may have changed since last verified. Check the official site for current plans.
Community Performance Report Card
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Pros
Sign in to edit- Reproducible and targeted evaluations that quantify frequency and severity across automatically generated scenarios
- Evaluations correlate strongly with hand-labelled judgments and reliably separate baseline models from intentionally misaligned ones
- Researchers can extensively configure Bloom's behavior, through choosing models for each stage, adjusting interactions' length and modality
- Using Bloom evaluations took only a few days to conceptualize, refine and generate
- Integrates with Weights & Biases for experiments at scale and exports Inspect-compatible transcripts
Cons
Sign in to edit- Bloom is only as robust as the seeds and judging logic that power it; teams should treat seeds as living governance artifacts, and for ambiguous or highly contextual behaviors, periodic manual review is still necessary
- Bloom's evaluation suite is unlikely to match the precise distribution of scenarios found in existing benchmarks, and since model behavior can be sensitive to context and prompt variations, direct comparisons are unreliable
About
- Platforms
- Python; integrates with Anthropic and OpenAI models via LiteLLM; supports Weights & Biases
- Languages
- Python
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-04-20T21:16:31.339Z
Best For
Who it's for
- Regression testing, release gating, and tracking mitigations over time
- AI safety and alignment research teams
- Studying narrow but critical risks that may be missed by broader evaluations
- Evaluating frontier AI models for specific behavioral traits
- Automating evaluation suite generation without manual engineering
What it does well
- Measuring behaviors like delusional sycophancy, long-horizon sabotage, self-preservation and self-preferential bias
- Regression testing, release gating, and tracking mitigations over time
- Investigating jailbreak susceptibility, self-preferential bias, and long-horizon sabotage risks
- Quantifying frequency and severity of target behaviors across generated scenarios
- Baseline model comparison and intentional misalignment detection
Integrations
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Sign Up to ContributeFrequently Asked Questions
- Is Bloom free?
- Yes — Bloom is fully free to use. There is no paid tier.
- Is Bloom open source?
- No — Bloom is a closed-source tool. Source code is not publicly available.
- Does Bloom have an API?
- Yes. Bloom exposes a developer API. See the official documentation at https://anthropic.com for details.
- Can I self-host Bloom?
- Yes. Bloom supports self-hosting on your own infrastructure.
- When was Bloom released?
- Bloom was first released in 2025.
- What platforms does Bloom support?
- Bloom is available on: Python; integrates with Anthropic and OpenAI models via LiteLLM; supports Weights & Biases.
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Anthropic's evaluation framework that auto-generates targeted test suites to measure specific AI behaviors at scale.
Bloom tackles a real gap in AI safety work: most evaluations are either broad (and miss edge cases) or manual (and don't scale). This tool generates reproducible test scenarios for narrow, high-stakes behaviors—like refusal consistency or prompt injection resilience—then scores models across thousands of permutations. The catch is structural: it's only as good as the seed prompts and judging logic you feed it, so teams still need to validate results manually for ambiguous cases. Free to use, but assumes you have both the domain expertise to set it up and the infrastructure to run it.
*Use this if you're a safety researcher or red-teaming group who needs systematic, repeatable measurements of specific model risks.*