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Share your thoughts about these announcements in our Developer Community Forum ↗.
AIP Analyst now supports AIP skills, analysis lookup, time series analysis, Ontology interfaces, and Foundry exports
Date published: 2026-08-18
AIP Analyst now supports new ways to capture reusable knowledge, analyze time series data, work with Ontology interfaces, and export results to Foundry resources.
Capture reusable knowledge with AIP skills and past analyses
Skills provide a first-class way to capture and share organizational knowledge. Users can configure which skills AIP Analyst has access to in the settings menu. AIP Analyst can load existing skills when they are relevant, create new skills from an analysis via the new export options, and modify existing skills. Skills are also supported as inputs to the AIP Analyst Workshop widget.
Configure what skills AIP Analyst has access to in the settings menu.
Additionally, the new Analysis lookup tool turns past analyses into a source of reusable knowledge. AIP Analyst can load a recent or favorite analysis by its RID directly into context, letting it understand how a related question was previously answered. The loaded analysis acts as a template: it shows what resources and tools were used, but carries no live results, so any relevant tool is rerun in the current analysis and always reflects current data.
Export conversations into Foundry resources
AIP Analyst now supports exporting conversations into a Notepad document, a Quiver analysis or dashboard, a Contour analysis, or an AIP skill. These tools are available in a redesigned Export button, which also includes the Print to PDF option. They are also available in-chat, by using a prompt such as “Export this conversation to a Notepad document”. These tools also support modifying existing resources, so you can ask for example “Update this notepad {notepadRid} with the results from this conversation”. To add these resource types to the conversation, select Add context, or drag and drop the resource into the chat window.
Open the export menu to export to PDF, Notepad, AIP Skills, Quiver, or Contour.
Analyze time series data
You can now import time series into your AIP Analyst chat in one of three ways:
- Add an object type with a time series property
- Add a time series property through the Add context button
- Add a time series sync using the Add context button
A full suite of time series transformations are available to manipulate the data, including filters, aggregates, mathematical operations, and event-based operations. You can export these time series charts into corresponding Quiver analyses and dashboards or included as Notepad images.
The time series transform tool uses built-in transform operations to analyze time series data at scale.
Work with Ontology interfaces
AIP Analyst now supports Ontology interfaces as a first class resource. You can import interfaces, search for them like object types, and use interface-scoped object sets in all of the regular object-set-supporting tools. Add them using the Add context button, or continue with your analyses as usual and they will be discovered by the object type search tool.
Alongside this change, AIP Analyst can now support multi-scope object sets. Unions, intersections, and differences can be created within AIP Analyst and consumed by most downstream tools, with warnings thrown if they are not. Both of these changes are compatible with Workshop.
The Add context button supports adding many new options, such as interfaces.
We want to hear from you
We welcome your feedback on AIP Analyst, and your posts help the team prioritize what to build next. Share your thoughts with Palantir Support channels or our Developer Community ↗ using the aip-analyst ↗ tag.
Introducing AIP Evolve: Coordinate AI FDE agents to improve AI systems in Foundry
Date published: 2026-08-18
AIP Evolve is now available in beta for enrollments with AIP enabled and access to AI FDE. AIP Evolve coordinates fleets of AI FDE agents to improve AI systems in AIP. Define a target, optimization goal, validation strategy, and operational constraints, then review the resulting proposal and agent activity before merging changes.
Early adopters of AIP Evolve have used it to autonomously cut AI costs, improve eval performance, and migrate workloads to open source models. Learn more about AIP Evolve.
Key features
AIP Evolve supports iterative improvement workflows with the following features:
- Guided setup for model migration, cost reduction, latency reduction, evaluation score improvement, and custom goals.
- Flexible validation using selected test cases or existing evaluation suites.
- Configurable scoring criteria and acceptable output divergence.
- Agent constraints that control permitted change types and the maximum number of iterations.
- A proposal view containing proposed changes, validation results, output comparisons, supporting evidence, and confidence assessments.
- An interactive agent graph for monitoring progress and inspecting agent goals, insights, and artifacts.
- Integration with Global Branching for reviewing proposed changes before merging.
- The ability to resume an evolution in AI FDE with additional instructions.
Requirements
To use AIP Evolve:
- Enable AIP and ensure that you can access AI FDE.
- Install the AIP Evolve Marketplace product in an Ontology.
- Ensure that you have access to the target resource and any data or evaluation suites used for validation.
Getting started
The AIP Evolve setup screen, showing the "Review" stage with a fully specified evolution.
Open AIP Evolve and select New to create an evolution. Select the Foundry resource you want to evolve, then configure the following:
- Goal: Choose a predefined optimization goal or describe a custom objective.
- Validation strategy: Define the test data, scoring approach, and acceptable output divergence.
- Agent constraints: Select the types of changes agents may propose and set an iteration policy.
Review the generated prompt, then select Evolve. AIP Evolve opens AI FDE in a new tab and starts the evolution. You can also select Write custom prompt to provide your own instructions.
An example AIP Evolve proposal, where the system presents a model swap for cost reduction and the evidence that supports why the change is safe to make.
Open Evolutions to monitor active and completed evolutions. Use Proposal to review results and proposed changes, or Agent graph to inspect the agents involved in the evolution. When a proposal is ready, open it in Global Branching for final review and merging.
The AIP Evolve agent graph, showing each of the subagents that were spawned along the way to optimize the target AI component.
Your feedback matters
As we continue developing AIP Evolve, we welcome feedback about your experience. Share your thoughts through Palantir Support channels.
Process two streams with custom logic using CoProcess user-defined functions in Pipeline Builder
Date published: 2026-08-13
CoProcess user-defined functions (UDFs) are now supported in Pipeline Builder. A keyed CoProcess UDF allows users to define custom stateful logic for processing events from two different streams and merging them into a single output stream.
A keyed CoProcess UDF takes a separate stream on each of its left and right inputs.
Rows can be processed and emitted from either stream, which allows sophisticated real-time pipelines that go beyond standard join patterns. As with any stateful UDF, keyed CoProcess UDFs can be imported into a pipeline and used like any other transform.
Configure a keyed CoProcess UDF by mapping the columns of each input to the UDF.
What's new
With keyed CoProcess UDFs, you can:
- Maintain custom state across both input streams. State is shared between the two inputs rather than scoped to a single stream.
- Emit output rows from either stream independently. Neither stream has to wait on the other to produce output.
- Implement complex matching logic that standard joins cannot express.
Example use cases
- Flight tracking: Join a stream of live aircraft positions with flight plan schedule updates to detect route deviations in real time.
- Supply chain: Join shipment tracking events with inventory updates to suggest restocking before a warehouse runs out.
- Wildfire alerting: Join satellite thermal detections with ground-level weather data to escalate alerts when hotspots coincide with high-risk conditions.
Share your feedback
As we continue to add features to Pipeline Builder, we want to hear about your experiences and welcome your feedback. Share your thoughts with Palantir Support channels or our Developer Community using the pipeline-builder tag ↗.
Observe and optimize your agents, objects, and workflows with object timeline
Date published: 2026-08-11
The new object timeline feature provides observability into actions performed on an object. See edits performed by agents or by manual action along with full attribution for each one. Monitor token usage, runtime, and agentic coverage to find where to optimize. Trace any edit back to the execution that made it.
Object timeline showing edits to an object, with attribution, token usage, runtime, and change costs.
View a demonstration of object timeline on the Palantir Developers YouTube channel. ↗.
Summary metrics
The object's summary metrics give you a starting point for optimization:
- Total token usage: The number of tokens consumed across all executions.
- Total runtime: The length of time from the first to the last execution.
- Waiting time: The amount of time spent waiting for human input, shown in the same cell as total runtime.
- Agentic coverage: How much of the object's history was driven by agents versus humans.
Explore the timeline
Each row represents an agent, human, or resource. Bars show each execution, and diamond markers show each edit made to the object in the ontology through an action. Together, the timeline shows how each has worked on the same object over time, including where activity overlapped or one picked up from another.
Select any bar or diamond to open a side panel with the metrics for that execution and the edits it made to the object.
Optimize your workflow
- Token usage per step lets you quickly identify which steps consume the most, so you know where to focus, whether to refine your prompt or switch models. Select any diamond on the timeline to view detailed metrics.
- Easily spot that most of the token usage in this use case comes from the agent that creates a new campaign. At a glance, you can see where to prioritize optimization, whether that is refining the prompt, switching models, or reducing token usage another way.
- Time breakdown helps you spot where you are losing time in a run, so you can identify manual steps to automate and reduce overall workflow duration.
The notional example above shows that 98% of the time is spent waiting on human input for the final review. Knowing this metric can help you understand where you can intervene to speed up the runtime.
Access object timeline from Autopilot or Object Explorer
You can access object timeline from Autopilot, as well as from Object Explorer if edit history is turned on.
Access the object timeline from Autopilot's graph view or from the More menu in Object Explorer.
Share your feedback
As we continue to add features to Autopilot, we want to hear about your experiences and welcome your feedback. Share your thoughts with Palantir Support channels or our Developer Community ↗ using the aip-autopilot tag ↗.
Additional open-weight models now available in AIP through AWS Bedrock
Date published: 2026-08-04
Select models from DeepSeek, Z.ai, and Moonshot AI are now available in AIP through AWS Bedrock on non-georestricted, US, EU, UK, and JP commercial enrollments.
Model overviews
DeepSeek V3.2 ↗ is DeepSeek's mixture-of-experts reasoning model optimized for reasoning, coding, and instruction following. It supports client-side tool use and a 164,000-token context window with up to 8,000 output tokens.
GLM 5 ↗ is Z.ai's multimodal reasoning model, designed for coding, multilingual understanding, and agentic workflows. It supports text and image inputs, client-side tool use, and a 256,000-token context window with up to 16,000 output tokens.
Kimi 2.5 ↗ is Moonshot AI's frontier-class reasoning model, optimized for complex systems engineering and long-horizon agentic tasks. It supports advanced reasoning, mathematics, coding, and client-side tool use, with a 200,000-token context window and up to 128,000 output tokens.
Getting started
To use these models:
- Confirm that your enrollment administrator has enabled the relevant model family.
- Review token costs and pricing.
- See the complete list of models available in AIP.
Your feedback matters
We want to hear about your experiences using language models in the Palantir platform and welcome your feedback. Share your thoughts with Palantir Support channels or on our Developer Community ↗ using the language-model-service tag ↗.
Introducing SuperRepo: Build full-stack Ontology applications in a pro-code monorepo
Date published: 2026-08-04
As of the week of August 3, SuperRepo is now available in beta, bringing a pro-code way to build complex full-stack applications anchored around the Ontology. A SuperRepo is a single monorepo that holds your Ontology definitions, your functions, and your React application together, so you can develop, build, and deploy them as one versioned artifact.
With SuperRepo, you ship a feature that spans the Ontology, a function, and a frontend from one repository, in a single edit-and-preview cycle.
Iterate across the whole application locally
The Foundry CLI runs your SuperRepo on your own machine so you can change the Ontology, your functions, and your application together:
- Ontology-as-code: Declare object types, links, interfaces, and actions in TypeScript. Your code definitions are the source of truth, and they materialize as real entities on your enrollment when your product is deployed.
- Embedded Ontology preview: Local servers reproduce the Ontology's behavior on your machine. Function-backed actions route through the embedded Ontology, and functions run in language-specific preview runtimes. Seed data files populate a fresh local database each time the server starts.
- Locally generated Ontology SDK: SDK bindings are generated from your Ontology-as-code definitions and regenerated automatically whenever those definitions change, then consumed directly by your functions and your Ontology SDK application. You can extend the Ontology and use the new types in a single loop, with no SDK republish in between.
Track Ontology SDK rebuild progress in the ontology section of the preview output.
Deploy programmatically to any enrollment
A SuperRepo compiles natively into a Marketplace product: a self-contained, reproducible, and cryptographically signed bundle you can install on one or more enrollments. Run foundry deploy configure once to map your deployment inputs into an env.yml file, commit it, and both deployment paths read the same configuration:
- From the Palantir platform: If your source is hosted in the platform, tag a version of your repository and Foundry CI builds and deploys the product for you.
- From your own CI system: If your source is hosted elsewhere, build and deploy from your terminal or from any CI provider, such as GitHub Actions or CircleCI, with the Foundry CLI.
The interactive foundry deploy configure flow writes your deployment configuration to env.yml.
Use the Ontology you already have
A SuperRepo does not require a separate Ontology. Entities that already exist on your enrollment, including those built in the Ontology Manager, can be imported into your SuperRepo instead of being redefined in code, and types created in code are visible in the UI. Applications are never siloed by how their Ontology types were created.
Getting started
Create a SuperRepo in whichever environment you prefer:
- In the platform, select New > Code repository, then Applications > SuperRepo, and select Open in VS Code to launch it in VS Code workspaces.
- Locally, install the Foundry CLI and run
foundry create.
Then follow the end-to-end tutorial to import an object type, define a link type, add a TypeScript function, expose it as a function-backed action, and consume all of it from a React application without leaving your editor. When you are ready to install on an enrollment, review Prepare for your first deployment.
Learn more about SuperRepo in our documentation.
What is coming next
The following are in active development and not available yet:
- Python functions
- Agent engine and Agent SDK
- External sources
- Automate
- Data pipelines
Review Coming in the future and the FAQ for details.
We want to hear from you
We welcome your feedback on SuperRepo, and your posts help the team prioritize what to build next. Share your thoughts with Palantir Support channels or our Developer Community ↗ using the superrepo ↗ tag.