News & Intel
LiveDaily AI-curated intelligence on Palantir Foundry, Ontology, AIP, Apollo, contracts, and community feedback. Updated automatically via GitHub Actions every day at 7 AM UTC.
semblance-foundry added to PyPI
Unofficial local HTTP simulation of selected Palantir Foundry API v2 ontology operations
semblance-foundry 0.1.1
Unofficial local HTTP simulation of selected Palantir Foundry API v2 ontology operations
semblance-foundry 0.1.2
Unofficial local HTTP simulation of selected Palantir Foundry API v2 ontology operations
semblance-foundry 0.1.3
Unofficial local HTTP simulation of selected Palantir Foundry API v2 ontology operations
Generic Internal Workshop Error
I’m trying to package a Workshop application as an output in DevOps/Marketplace, but the packaging step consistently fails with a generic internal Workshop error. The output is: REDACTED Source: Workshop The error shown is: Error Name: Default:Internal Error Code: INTERNAL Error ID: 250c49fc-3042-4e99-a78b-5cae387119ec The UI only says: “Something went wrong, please contact Palantir support.” I’ve retried the packaging operation and get the same blocker, but I’m not seeing any additional validation message or indication of which Workshop resource/configuration is causing it. The package also includes a custom widget set/repository and ontology resources, but the failure is specifically being reported against the Workshop application output. Has anyone encountered this before, or is there somewhere in Workshop/DevOps where I can get more detailed packaging logs or identify the resource that is causing the failure? I’m using a personal/tra
mintmory-core added to PyPI
MintMory core: Palantir-inspired typed memory ontology with SQLite storage
Automates and Ontology links on project-scoped mode: How to import?
Hello! We are running into issues when automates run an action that includes some sort of object traversal within the function. If we do something like function getFlightAirplane(flight: Flight): string{ const plane = flight.plane.get() return plane.id We get failures in automatic execution because the link seems to be outside of the scope of the automate/project. If we do something like (pseudo code): function getFlightAirplane(flight: Flight): string{ const plane = Objects.search().plane().filter(p => p.id.exactMatch(flight.planeId)).all().at(0) return plane.id it seems to work, but I end up just keeping the automate user scoped so it executes as me. How can I import the links manually into the automate? Is this lack of awareness that a link is needed a product limitation? 1 post - 1 participant Read full topic
Does AIP Chatbot Studio Action tool support array inputs for action parameters?
I’m using AIP Chatbot Studio and want to know whether the Action tool supports array-typed action parameters. The documentation I found explains that chatbots can execute ontology edits via actions, but it doesn’t clearly say whether array inputs are supported or how reliably they are populated by the model in prompted vs native tool calling modes. Has anyone confirmed support for array parameters in Action tools, or found recommended patterns/workarounds? 1 post - 1 participant Read full topic
Building a Prompt Management Workflow in Workshop – Sharing Implementation & Seeking Feedback
Hi Everyone, I recently designed a Prompt Governance & Versioning Module using Foundry Workshop and Ontology to handle LLM prompt lifecycles. Rather than just sharing the final result, I’d love to share the technical approach used to handle multi-tab state isolation, and get input from the community on potential optimizations. How it was implemented: Data Model: Used Prompt Project and Prompt Version object types linked via an Ontology relationship. Version Comparison Filter: Solved multi-page variable conflict by isolating dynamic dropdown variables and using On a link object set filtering rather than plain string equality. Governance: Bound radio selection UI directly to Ontology Actions to handle Draft -> Active -> Archive status transitions. Questions for the Community: How are others managing prompt versioning and status propagation across multi-tenant spaces in Workshop? Is using Ontology Actions the preferre
Unsupported Object Security feature in DevOps
Hey there, I’m currently getting an error OntologyMetadata:CurrentlyUnsupportedMarketplaceFeature in DevOps when I attempt to package some object types that use a UserID as part of the policy in their Object Security. The error is clear: Cannot have a comparison condition using a UserId for Object Security Policies. but this feature is paramount to several workflows we are building. Is there a time frame on when this feature will be implemented? Thanks in advance! 1 post - 1 participant Read full topic
My dev tier stack has crashed - how can I restore/recover?
I’d been testing the Foundry and Ontology MCPs all afternoon for a demo, part way through final test run nothing loaded, now I get this when I try to log back in Can anyone help? 3 posts - 1 participant Read full topic
Action edit limit appears to be 25,001 objects, not the documented 10,000
Action edit limit appears to be 25,001 objects, not the documented 10,000 Written with the help of AI-FDE. The docs under Action types → Scale and property limits ( link ) state: Objects edited per action submission: 10,000 I tested this directly with a single, simple action and observed a real ceiling of 25,001 objects by passing an object set to a function-backed action type. The action input Parameter Type Description games ObjectSet<Games> The set of games whose scores should be incremented by 1 The function TypeScript v2, backing the action as an ontology-edit function: import { Client, ObjectSet } from "@osdk/client"; import { Games } from "@games/sdk"; import { createEditBatch, Edits, Long } from "@osdk/functions"; type OntologyEdit = Edits.Object<Games>; export default async function incrementAllGames( client: Client, games: ObjectSet<Games> ): Promise<OntologyEdit[]> { co
Support for AI FDE to launch subagents with another model
At the moment, AI FDE doesn’t let you switch models when launching subagents. Concrete example: when building an OSDK React app, I’d like to do the research and design with Opus and produce a well-structured Epic with sprint notebooks — but I can’t then launch a subagent to implement Sprint 1 with Sonnet. Being able to choose the model per subagent would help a lot. It would keep token consumption (and cost) down for the implementation-heavy work, and it would remove the manual session switching that currently breaks the flow. 1 post - 1 participant Read full topic
Super repo doesnt work?
I started using superrepo and get the same error no matter what. Ontology SDK fails and its just internal error. No helpful logs for debugging. anybody else facing this issue? Attached the screenshot below but it isnt much help anyway. 5 posts - 3 participants Read full topic
How should I expose multiple scientific-computing APIs for integration with Databricks and an ontology platform?
I’m building a portfolio of around 30 scientific-computing applications covering physics, quantum calculations, protein/biomolecular modeling, signal processing, and related mathematical tools. The applications currently run on platforms such as Replit and Base44. I am considering exposing their computational capabilities through stable, documented APIs and integrating those services with Palantir Foundry. The architecture I’m considering; Scientific APIs → Integration/API Layer → Foundry → Ontology → Actions/Functions → Customer-facing applications The solvers would remain independently deployed computational services. Foundry would provide the data, ontology, orchestration, permissions, and operational interface around them. For example, a solver might expose: POST /v1/quantum/solve Input: { "parameters": {...}, "model": "...", "version": "1.2" } Output: { "result": {...}, "metadata": {...}, "model_version": "1.2" } I would then want a Foundry workflo
🚀 Munich Builder Mapping AI Transformation Across the Top 40 Global Reinsurers — Fellow Builders & Advisors Welcome!
Hey Palantir Community! Munich-based builder here — first post, so bear with me! The Project I’m building a code repository and data pipeline to track AI transformation across the top 40 reinsurance companies worldwide — pulling signals straight out of financial and annual reports and turning them into a living dashboard of who’s talking about AI, who’s actually shipping it, and how that shifts year over year. The Approach Under the hood it’s an ontology-management problem as much as a data problem: structuring messy annual-report data into something queryable, then layering NLP extraction and benchmarking on top. Happy to turn this into a mini workshop if others are wrestling with similar report-parsing challenges. Looking For Fellow builders working on similar pipeline-builder style projects and code repositories , folks with ideas on report parsing, NLP extraction, or benchmarking approaches, and advisors who’ve wrangled messy unstructu
Question about the layer between Ontology and action
Question about the layer between Ontology and action Palantir gives AI a governed representation of operational reality through the Ontology, then provides logic, models, agents, tools, actions, and Evals around that reality. What I’m trying to understand is whether Palantir has a general reasoning architecture that governs how the AI interprets that operational reality before deciding when and how to act , rather than leaving that reasoning largely to application-specific prompts, logic, tools, and evaluators. Put differently: is there a reusable layer that determines whether the system has identified the right problem, governing constraint, decision risk, and justified first move before intervention? I’m asking because I’ve developed a framework specifically for that layer and I’m trying to determine whether I’m identifying a genuine gap in the current architecture or simply missing something Palantir already has. Curious how people here would think about that.
Fractals documentation details request
In one of our internal demo meeting, Team walked through us the “Fractals” foundry capability , related to agents developments ( could sense it is more on agentic workflow development, agent configs, related ontology config, analysis on runs). Tried to see in documentation, but failed. Could anyone help me to route to the documentation. Would like to explore more on it. Thanks Attached the screen for reference: 1 post - 1 participant Read full topic