Bloom Open-Source Agentic Framework

Bloom open-source agentic framework for automated behavioral evaluations of frontier AI models delivers scalable, measurable behavioral tests for frontier AI. It automates seed driven generation, judgment, and meta-judgment across simulated rollouts. This agentic system uses seed.yaml and behaviors.json to define target behaviors.

Because automated behavioral evaluations scale better than manual tests, they reduce oversight gaps. Moreover, Bloom integrates LiteLLM, Anthropic, and OpenAI backends through one unified interface. Therefore teams can run large sweeps with Weights and Biases and Inspect exports.

Anthropic used Bloom to build alignment relevant suites with hundreds of behavior consistent rollouts. As a result, evaluators gain quantitative metrics like elicitation rate and reproducible transcripts. In short, Bloom accelerates frontier AI safety research while providing open-source transparency and extensibility.

It ships as a Python pipeline under the MIT license on GitHub for immediate adoption. Therefore researchers and engineers can iterate faster, test edge behaviors, and quantify risks. Join the community to extend and validate evaluations.

Bloom is an open source agentic framework that automates behavioral evaluations for frontier AI. It runs a Python pipeline that turns a single behavior definition into many rollouts. Because it uses agents to ideate and judge scenarios, Bloom produces behavior consistent transcripts at scale.

At the core sits a seed configuration. The seed files seed.yaml and behaviors.json define target behaviors, role prompts, and run parameters. For example, seed.yaml encodes the initial scenario and constraints. Therefore Bloom expands that seed into diverse simulated rollouts automatically.

This automation matters for frontier AI safety and performance. It scales testing far beyond manual labeling, and it finds edge behaviors faster. Moreover, Bloom integrates with model backends and tooling, including Anthropic models at Anthropic and Weights and Biases at Weights and Biases. As a result teams can run large sweeps and export Inspect compatible transcripts.

Bloom ships under an MIT license and encourages community extensions. Researchers can fork the pipeline, add judge models, and measure elicitation rates. In short, Bloom accelerates reproducible, quantitative behavioral evaluations for cutting edge models.

Below is a concise comparison that highlights Bloom’s strengths for behavior-driven evaluation.

Moreover, it focuses on automation, reproducibility, and scalable sweeps.

Bloom evaluation stages: Understanding, Ideation, Rollout, Judgment and meta-judgment

Bloom runs a four stage evaluation pipeline to convert seeds into scored rollouts. In practice, the framework uses agentic subroutines at each stage. Claude Opus 4.1 serves as the primary automated judge across stages for consistent scoring. For reference, see the Claude page.

Understanding

• Purpose: Parse the seed, extract intent, constraints, and success criteria.
• Example: Identify whether a seed targets misinformation, jailbreak attempts, or social engineering.
• Technical note: Agents produce structured interpretation objects that guide later steps.

Ideation

• Purpose: Generate diverse scenario variants that probe edge cases and failure modes.
• Example: Create user prompts that escalate ambiguity or request sensitive steps.
• Technical note: Ideation agents apply controlled perturbations to produce behavior consistent rollouts.

Rollout

• Purpose: Execute interactions between scenario actors and target models.
• Example: Run 100 distinct dialogues to measure how often a model elicits the target behavior.
• Technical note: Bloom records Inspect compatible transcripts and time stamped events.

Judgment and meta-judgment

• Purpose: Score transcripts, adjudicate borderline cases, and estimate judge reliability.
• Example: Claude Opus 4.1 rates each transcript, while meta-judgment aggregates confidence and disagreement.
• Technical note: Outputs include elicitation rate, reproducible statistics, and Spearman correlations used in validation.

Together, these stages let teams scale reproducible evaluations and quantify risk across frontier models.

Bloom Open-Source Agentic Framework

Bloom open-source agentic framework for automated behavioral evaluations of frontier AI models offers a scalable, reproducible path to safer systems. It automates seed-driven generation, ideation, rollout, and judgment with quantitative outputs like elicitation rate. As a result, researchers gain measurable metrics and Inspect-compatible transcripts for rigorous analysis.

Because Bloom turns one behavior definition into full evaluation suites, teams test edge cases faster. Moreover, the MIT-licensed Python pipeline integrates with LiteLLM, Anthropic, OpenAI, and Weights and Biases. Claude Opus 4.1 serves as a reliable judge, yielding high correlation with human labels. Therefore Bloom accelerates alignment research while keeping evaluations transparent and extensible.

Because Bloom is open-source, teams can fork, extend judge models, and reproduce results. Community contributions accelerate coverage across behaviors and model organisms. Moreover, Bloom complements tools like Petri for benchmark-driven comparisons.

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