palantir-hello-world

name: palantir-hello-world description: 'Create a minimal working Palantir Foundry example querying Ontology objects.

Use when starting a new Foundry integration, testing your setup,

or learning basic Foundry API and Ontology patterns.

Trigger with phrases like "palantir hello world", "palantir example",

"palantir quick start", "foundry first query".

' allowed-tools: Read, Write, Edit, Bash(pip:), Bash(npm:) version: 2.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

• saas
• palantir
• foundry
• ontology
• getting-started compatibility: Designed for Claude Code, also compatible with Codex and OpenClaw

Palantir Hello World

Overview

Build a minimal working example that connects to Palantir Foundry, queries Ontology objects via the REST API, reads a dataset, and applies an action. Uses real foundry-platform-sdk Python API calls.

Prerequisites

• Completed palantir-install-auth setup
• Valid bearer token or OAuth2 credentials
• At least one Ontology with object types configured in your Foundry enrollment

Instructions

Step 1: List Available Ontologies

import os
import foundry

client = foundry.FoundryClient(
    auth=foundry.UserTokenAuth(
        hostname=os.environ["FOUNDRY_HOSTNAME"],
        token=os.environ["FOUNDRY_TOKEN"],
    ),
    hostname=os.environ["FOUNDRY_HOSTNAME"],
)

# List all ontologies you have access to
for ont in client.ontologies.Ontology.list():
    print(f"Ontology: {ont.api_name}  RID: {ont.rid}")

Step 2: Query Ontology Objects

# List objects of type "Employee" from the default ontology
# The object type api_name comes from your Ontology configuration
ONTOLOGY = "your-ontology-api-name"
OBJECT_TYPE = "Employee"

objects = client.ontologies.OntologyObject.list(
    ontology=ONTOLOGY,
    object_type=OBJECT_TYPE,
    page_size=5,
)

for obj in objects.data:
    props = obj.properties
    print(f"  {props.get('fullName', 'N/A')} — {props.get('department', 'N/A')}")

Step 3: Get a Single Object by Primary Key

employee = client.ontologies.OntologyObject.get(
    ontology=ONTOLOGY,
    object_type=OBJECT_TYPE,
    primary_key="EMP-001",
)
print(f"Found: {employee.properties}")

Step 4: Read a Dataset

# Read rows from a Foundry dataset (tabular)
DATASET_RID = "ri.foundry.main.dataset.xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"

# Get dataset metadata
dataset = client.datasets.Dataset.get(dataset_rid=DATASET_RID)
print(f"Dataset: {dataset.name}, Path: {dataset.path}")

# Read rows from the dataset (CSV format)
content = client.datasets.Dataset.read(
    dataset_rid=DATASET_RID,
    branch_id="master",
    format="arrow",  # or "csv"
)
print(f"Read {len(content)} bytes of data")

Step 5: Apply an Ontology Action

# Actions modify objects — e.g., updating an employee's department
result = client.ontologies.Action.apply(
    ontology=ONTOLOGY,
    action_type="updateDepartment",
    parameters={
        "employeeId": "EMP-001",
        "newDepartment": "Engineering",
    },
)
print(f"Action result: {result.validation}")

Step 6: Run and Verify

set -euo pipefail
python hello_foundry.py
# Expected output:
# Ontology: my-company  RID: ri.ontology.main.ontology.xxx
# Employee: Jane Doe — Engineering
# Action result: VALID

Output

• Authenticated connection to Palantir Foundry
• Listed ontologies and object types
• Retrieved objects with property values
• Read dataset content
• Applied an action to modify an object

Error Handling

Examples

Using the REST API Directly (curl)

# List objects via REST
curl -s -H "Authorization: Bearer $FOUNDRY_TOKEN" \
  "https://$FOUNDRY_HOSTNAME/api/v2/ontologies/my-ontology/objects/Employee?pageSize=5" \
  | python -m json.tool

TypeScript OSDK Equivalent

import { createClient } from "@osdk/client";
import { Employee } from "@my-app/sdk";  // generated from OSDK

const employees = await client(Employee)
  .where({ department: "Engineering" })
  .fetchPage({ pageSize: 10 });

employees.data.forEach(emp => console.log(emp.fullName));

Resources

• Foundry API Introduction
• Get Object API
• Python SDK PyPI
• Code Examples

Next Steps

• Set up iterative development: palantir-local-dev-loop
• Build data pipelines with transforms: palantir-core-workflow-a
• Query and link objects: palantir-core-workflow-b