kedro-org/kedro 1.2.0 on GitHub

Major features and improvements

Added @experimental decorator to mark unstable or early-stage public APIs.
Added support for running multiple pipelines in a single Kedro session run via the --pipelines CLI option and pipeline_names argument in KedroSession.run() method.
Updated the spaceflights-pyspark starter to use the new SparkDatasetV2 integration, enabling local, Databricks-native, and remote Spark execution workflows.

Added experimental llm_context_node and LLMContextNode for assembling LLMs, prompts, and tools into a runtime LLMContext within Kedro pipelines.
Added experimental preview_fn argument to Node class to add support for user-injectable node preview functions.
Added new experimental support-agent-langgraph starter, which supports the above experimental features. This starter contains pipelines that leverage LangGraph for agentic workflows and Langfuse or Opik for prompt management and tracing.

Set raise_errors=True in find_pipelines() calls in the project template's pipeline_registry.py to ensure pipeline discovery errors are raised during project runs.
Fixed packaged runs logging the current working directory name; they now log the installed package name (or project path) instead.

Added beginner-friendly notes on uvx installation.
Updated Databricks deployment docs to cover Spark Connect and Unity Catalog – first workflows, and local-to-remote development.

Many thanks to the following Kedroids for contributing PRs to this release: