palantir-hello-world

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".

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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

Error	Cause	Solution
`ObjectTypeNotFound`	Wrong `api_name`	Check Ontology Manager for exact object type names
`ObjectNotFound`	Invalid primary key	Verify the key exists; keys are case-sensitive
`ActionValidationFailed`	Missing required params	Check action definition for required parameters
`DatasetNotFound`	Wrong RID or no access	Verify RID in Foundry UI; check project permissions
`401 Unauthorized`	Token expired	Regenerate in Developer Console

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

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