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Share your thoughts about these announcements in our Developer Community Forum ↗.

Access the most relevant Workshop modules directly from the new Workflow Builder landing page

Date published: 2025-06-26

An improved experience is now available when creating new or empty graphs in Workflow Builder. In addition to using the keyboard shortcuts Cmd + i (macOS) or Ctrl + i (Windows) from a Workshop module, function repository, or, newly, an ontology object to jump into Workflow Builder, there is now another convenient way to select the most relevant Workshop modules directly from the application. From Workflow Builder's landing page, you can now see a list of the most recent or the most viewed Workshop modules.

You can now access the most relevant Workshop modules directly from the new Workflow Builder landing page.

You can now access the most relevant Workshop modules directly from the new Workflow Builder landing page.

What’s New?

When you open a new or empty graph, you will now see:

The most recent Workshop applications: Quickly access the most recently opened Workshop applications in your workspace.
The most viewed Workshop applications: Instantly discover the most popular and impactful workflows being used by your team. This option shows the most viewed Workshop applications in the last 30 days by all users.

Select from the most viewed Workshop modules.

Select from the most viewed Workshop modules.

When you select a Workshop application, Workflow Builder will automatically populate the Workflow Builder graph with the backing resources, similar to how it populates the graph when a user uses the Cmd + i keyboard combination directly from a Workshop module.

This update makes it easier than ever to get started; explore, learn, and build with the most important workflows at your fingertips.

Learn more about Workflow Builder features in the documentation.

We want to hear from you

Share your feedback with us through Palantir Support or our Developer Community ↗ using the workflow-builder tag ↗.

Version range dependencies for functions

Date published: 2025-06-26

Previously, Foundry applications could only depend on functions at pinned versions. With the introduction of version range dependencies for functions, applications like Workshop, Actions, and Automate can now depend on functions at version ranges. This enables automatic upgrades at runtime, saving you time in your development cycle and providing a downtime-free upgrade experience for deployed Python functions.

Configure a version range dependency for a function-backed widget in Workshop using the new Auto upgrade toggle.

Configure a version range dependency for a function-backed widget in Workshop using the new Auto upgrade toggle.

Review the version range dependencies documentation to better understand feature behavior and considerations.

Versioning your functions

The introduction of version range dependencies makes proper release versioning more critical than ever. To help you in this process, new backward compatibility checks are now available when tagging your functions.

Backward compatibility checks run automatically when you tag a new version of your functions in Code Repositories.

Backward compatibility checks run automatically when you tag a new version of your functions in Code Repositories.

Learn how to properly version your functions to ensure that you provide function consumers with a stable and reliable experience.

Your feedback matters

We want to hear about your experience with functions and welcome your feedback. Share your thoughts with Palantir Support channels or on our Developer Community ↗ using the functions tag ↗.

Python 3.12 support in Code Repositories and VS Code Workspaces now available

Date published: 2025-06-26

Python 3.12 support in Code Repositories and VS Code Workspaces is now available across all enrollments, adding Python startup time improvements, optimized comprehensions, reduced memory footprint, and other new features. This version is available across all Python environments, including during preview, checks, and builds for Python transform operations.

Highlights from Python 3.12 include:

Faster startup: Reduced interpreter initialization time (10-15% improvement in all Python process startup times)
Memory optimization: Lower memory usage overall
Improved multithreading: Enhanced performance with subinterpreters
Inlined comprehensions: Significantly faster Python comprehensions

To benefit from the latest performance improvements and essential security fixes, upgrade your repositories and set your Python environment to recommended version or explicitly select Python 3.12.

Build an advanced to-do application with OSDK, now available in VS Code workspaces

Date published: 2025-06-24

The advanced to-do application, available through VS Code workspaces, demonstrates how to use advanced features of the Ontology SDK (OSDK) and Platform SDK by building a to-do application as a practical example. You will learn about many OSDK features, including the following:

Ontology interfaces: Use ontology interfaces to interact seamlessly with backend services.
Media content handling with media sets: Handle media content efficiently within your to-do application.
Runtime-derived properties: Leverage runtime-derived properties for dynamic data handling and real-time updates.
Platform SDKs: Integrate with the platform SDK for enhanced application functionality.

The advanced to-do application, with annotations where advanced OSDK features are used.

The advanced to-do application, with annotations where advanced OSDK features are used.

Get started

To access and install the advanced to-do application example, open the Code Workspaces application and choose to create a + New Workspace. Then, select VS Code > Applications and find the application in the curated examples list.

You can also search for the application in the platform Examples (Build with AIP), or by searching the Ontology SDK reference examples in Developer Console.

Create an application in a VS Code workspace to choose the advanced to-do application example.

Create an application in a VS Code workspace to choose the advanced to-do application example.

The advanced to-do application application will then be installed through Marketplace.

What's included in this example?

When you first install the example, it will include the Advanced to-do application ontology, an ontology that can be installed only once in each space to ensure API names remain unique and constant. After installation, be sure that all objects were synced to the ontology before you run the application; you can verify this by opening the Advanced to-do object types in Object Explorer. After syncing, the data from the ontology should automatically appear when you open the example.

The installed example will open the code repository and run the application in a VS Code workspace using the Palantir extension for Visual Studio Code. Review the included Markdown documents to learn more about the project and detailed explanations for data services. You can also explore our public documentation to learn more about the architecture and configuration of the application and the custom React hooks used.

What's next?

We are currently working on more examples to demonstrate other features available with the OSDK, including write operations with ontology interfaces and other advanced filtering patterns. Share your feedback with Palantir Support or our Developer Community using the ontology-sdk tag ↗ if you have ideas for more examples and tutorials that you would like to see.

Introducing metric objectives for AIP Evals

Date published: 2025-06-19

Metric objectives are now available in AIP Evals, giving you more control over how your evaluation results are measured and interpreted. Previously, users could see raw metric values for each test case. However, it was difficult to determine at a glance whether a model’s output truly met expectations, especially when dealing with multiple metrics or large suites.

With metric objectives, you can now define what success looks like for each metric in your evaluation suite. For Boolean metrics, you can choose whether true or false indicates a passing result. For numeric metrics, you can select whether higher or lower values are better, and optionally set a threshold for passing. This makes it easier to enforce standards and quickly identify regressions or improvements in your models.

Pass/fail configurations configured for Boolean and numerical metrics.

Pass/fail configurations configured for Boolean and numerical metrics.

When you run an evaluation suite, AIP Evals will automatically determine and display the pass/fail status for each metric and test case based on your configured objectives. The run results dialog now clearly visualizes which metrics and test cases passed or failed according to your criteria, making it easier to track quality and communicate results.

Pass/fail status for metrics and test cases in the run results dialog.

Pass/fail status for metrics and test cases in the run results dialog.

Learn more about metric objectives in AIP Evals.

For additional assistance with metric objectives for AIP Evals, contact Palantir Support or visit our Community Forum ↗.

Introducing multi-type retrieval context support for AIP Agents

Date published: 2025-06-19

AIP Agents now support multiple types within the retrieval context. This means that you can provide multiple object sets, functions, and documents simultaneously as retrieval context. Each context is resolved to a string prompt in the order the types were added to the agent, on each new user message. Multiple contexts of the same type are supported, enabling semantic search on one document collection and full-text extraction on another within the same retrieval context prompt.

You can now provide your agent additional retrieval context using multiple types.

Citation configuration for agents is now centralized in order to support multiple context types. Previously, citation configuration was nested within individual retrieval context configurations such as document, ontology, or function RAG. As all existing citation configurations have been migrated to the new centralized citation settings, no further user action is required to benefit from multi-type retrieval context.

Configure custom citation behavior per object type in the AIP Agent Studio sidebar.

Finally, citations can now be enabled or disabled globally for all retrieval contexts. Citation settings allow configuration of overrides for ontology citations, with each object type mapped to its own citation override. For example, a citation from object type A can be configured to open an external link, while a citation from object type B can be configured to open a media item.

Citations allow users to see the information source used by the AIP Agent.

Enable citations to allow users to see the information source used by the AIP Agent.

For more information on multi-type retrieval context support, review the Retrieval context documentation.

We want to hear about your experiences and welcome your feedback. Share your thoughts with Palantir Support channels or our Developer Community ↗ and use the aip-agent-studio ↗ tag.

Introducing `use_sidecar`: Run models in dedicated containers within Python transforms

Date published: 2025-06-12

Starting from palantir_models version 0.1673.0, the ModelInput class exposes a use_sidecar parameter in Python transforms. When use_sidecar is set to True, the model is run in a separate container provisioned on top of the machines running the Spark transform itself, thereby ensuring easy, portable and reliable production usage of models across the platform. This feature prevents dependency conflicts that can occur when importing models built in a different repositories or code workspaces into Python transforms for inference. Furthermore, this guarantees that your models operate with the exact dependencies with which they were built, protecting users from unexpected behavior or runtime failures.

Note that use_sidecar is not supported in lightweight transforms, and previewing transforms with a sidecar ModelInput is also not supported.

Key features and benefits

Dependency isolation: use_sidecarensures your model runs in a controlled environment with its original dependencies, preventing conflicts with your transform's libraries.
Guaranteed reproducibility: Execute your models with the assurance that they are using the identical library versions with which they were trained and validated for consistency and reliability.
Simplified cross-repository and multiple model usage: When employing models built in a different repository or a Code Workspace, use_sidecar automatically manages the loading of the correct model adapter code. This removes the need to manually update dependencies in your transform's repository and run checks if the adapter code or dependencies changed with a new model version, and allows you to import multiple models into the same repository without worrying about clashes.
Customizable resources: Specify CPU, memory, and GPU resources for the sidecar container via the sidecar_resources parameter.

How to use

To load the model in a container, simply set use_sidecar=True. No other code changes are necessary.

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from transforms.api import Input, Output, transform, TransformInput, TransformOutput
from palantir_models.transforms import ModelInput, ModelAdapter

@transform(
    out=Output('path/to/output'),
    model_input=ModelInput(
        "path/to/my/model",
        use_sidecar=True,
        sidecar_resources={
            "cpus": 2.0,
            "memory_gb": 4.0,
            "gpus": 1
        }
    ),
    data_in=Input("path/to/input")
)

def my_transform(out: TransformOutput, model_input: ModelAdapter, data_in: TransformInput) -> None:
    inference_outputs = model_input.transform(data_in)
    out.write_pandas(inference_outputs.output_df)

To learn more, review the ModelInput class reference documentation.

Configure custom enrollment roles in Control Panel

Date published: 2025-06-10

You can now create custom roles to grant granular enrollment-level workflows from the Enrollment permissions page in Control Panel.

Configure your enrollment custom roles through the Enrollment permissions configuration page in Control Panel.

Configure your enrollment custom roles through the Enrollment permissions configuration page in Control Panel.

Custom roles are useful in situations when users or groups require permissions for particular workflows that do not match existing default roles. For example, by creating a custom IT group, you can allow permissions for that group to add or modify domains without granting permissions to change ingress or egress settings.

Select the individual workflows to grant to members of a new custom role.

Select the individual workflows to grant to members of a new custom role.

To get started creating custom roles, navigate to Enrollment Permissions settings in Control Panel, or learn more in our public documentation.

For additional assistance with custom roles, contact Palantir Support or visit our Community Forum ↗.

Announcing Machinery, for building, automating, and optimizing processes [GA]

Date published: 2025-06-05

Machinery is an application for modeling real-world events, such as healthcare procedures, insurance assessments, and government operations, as processes that can be explored in real-time through custom AI-powered applications tailored to your needs. As of the first week of June, Machinery is now generally available across all enrollments.

Use Machinery to mine or implement a process, identify unwanted behaviors, and make measured progress towards achieving desired outcomes. Additionally, facilitate human intervention to reduce inefficiencies and improve your process performance over time.

Implement a process from scratch review and optimize with Machinery.

Implement a process from scratch, review, and optimize with Machinery.

Common workflows for Machinery include:

Resolving process inefficiencies with the help of AIP and automation
Managing an AIP use case end-to-end by orchestrating several AI agents
Building operational applications to supervise AIP workflows with real-time human intervention
Mining an ongoing process from external event logs to gain visibility into an existing process
Defining and monitoring performance metrics and expectations to identify process bottlenecks

Optimize your process with automation and AIP

Implementing a process in the Palantir platform involves many individual resources, such as object types, actions, and automations. Machinery now provides a comprehensive view for all these components and lets you define an ordered flow of automations and manual actions. Its unique state-centric perspective allows you to make incremental progress towards desired outcomes while handling and resolving the edge cases of your organization. Value types and submission criteria can then provide an additional layer of conformance guarantees.

Automation nodes can be built into a Machinery graph.

Automation nodes can be built into a Machinery graph.

Machinery boasts a custom layout algorithm with the auto-layout feature allowing for visually appealing graphs without need of manual manipulation. Users can also disable this feature and freely move elements, allowing for customized adjustments tailored to individual preferences.

Multiple object type modeling in a single Machinery graph

Create subprocesses and parallel processes to benefit from greater flexibility and control over workflows. Subprocesses allow you to create nested processes within your main Machinery process, providing a structured way to manage complex tasks and enabling seamless integration into the larger workflow. This modular approach also supports parallel processes, allowing multiple processes to run concurrently, thereby enhancing efficiency and reducing bottlenecks.

You can now build multiple linked processes in Machinery allowing you to model processes acting across your whole organization.

You can now build multiple linked processes in Machinery, allowing you to model processes acting across your whole organization.

The focus view feature further elevates user experience by allowing users to zoom in on specific subprocesses, providing a detailed view that simplifies navigation and management of intricate workflows. With these capabilities, users can manage complexity across the organization for a higher level of process automation, ultimately leading to improved productivity and streamlined operations.

Mining mode

After configuring the log ontology, users can enter mining mode, which presents a distinct graph highlighting both existing states and potential edits in sepia color. Users can exclude certain states from mining, effectively reducing noise and focusing on relevant data. Use the transition frequency slider to filter out less important nodes, ensuring that only the most critical transitions are highlighted while benefiting from the clean auto-layout graph.

Mining an entire process in Machinery mining mode.

Mining an entire process in Machinery mining mode.

Machinery mining mode, with a transition filter to filter out nodes that have less than 71.5 objects passing through.

Machinery mining mode, with a transition filter to filter out nodes that have less than 71.5% objects passing through.

Bootstrap your development by generating a Workshop module

To operationalize your process in the Palantir platform, you can quickly bootstrap a Workshop module ("Machinery Express application") with a single click to initiate your application development. The express application serves as a dynamic playground where you can conduct analysis and intervene with your agentic workflows in real-time. Then, jump back into Machinery to refine, update, or optimize your processes with actions, automations, and AIP logic functions.

Whether you choose to use Machinery Express as a ready-to-use analysis tool or as a foundation to build your own applications, it facilitates a fluid interaction between process exploration and refinement. Additionally, the Machinery Express application enables you to immediately share this process with your operational users, providing them with the necessary context to effectively engage with the system in real time.

Generate a Machinery Express application to help get you started on application development in Workshop.

Generate a Machinery Express application to help get you started on application development in Workshop.

If you prefer to build a new application from scratch or add Machinery to an existing Workshop module, you may add a Machinery Process Overview widget in Workshop.

For more information on Machinery, review the documentation.