数据标注团队和机器学习工程师终于能摆脱重复劳动——Adala 用自主智能体自动学习标注技能，大幅缩短项目周期，建议做数据预处理或模型训练的团队直接试试。
Adala 是一个开源框架，利用自主智能体自动完成数据标注，取代传统的手工标注方式。用户只需提供少量标注示例作为环境，智能体通过观察、反思和迭代学习技能，自动生成高质量标注。每个技能运行在语言模型运行时中，支持不同模型切换，实现学生-教师模式以降低推理成本。框架支持严格或灵活的输出格式，并可直接集成到 Python 笔记本中处理大规模数据。
Adala just killed manual data labeling with autono…
Adala just killed manual data labeling with autonomous agents.
Most data labeling still happens by hand.
Teams burn weeks tagging examples to train one model. This is an open-source framework for autonomous data labeling agents.
They learn skills on their own using a ground truth dataset. Here is the loop in plain terms: 1. You define the environment with labeled examples 2. The agent acts, observes, and reflects on mistakes 3. It updates the skill until outputs hold up Each skill runs inside a runtime.
That is just the language model behind the scenes. The same skill can swap between different models.
This unlocks student teacher setups for cheaper inference at scale. You can also configure how results come out.
Strict schemas or flexible answers are both allowed. It plugs straight into Python notebooks for working with massive dataframes.