Programming with LLMs requires careful orchestration of prompts with workflow logic and agentic patterns. Unfortunately, previous frameworks rely on deeply buried prompts that work well for a specific model and pattern but are difficult to adapt to new settings. We designed PDL, a new programming language for LLMs. PDL keeps prompts at the forefront using YAML, with a small set of simple but powerful logic blocks to assemble workflows or agents. Prompt contexts are accumulated implicitly, simplifying model chaining. PDL comes with visual tools for observability and experimentation and the implementation automatically parallelizes model calls. PDL supports a variety of model providers, including but not limited to IBM's watsonx-ai with Granite models and the new granite-io library.