PromptStream Python SDK

A lightweight Python client for PromptStream. Execute prompts, run A/B experiments and track engagement from any Python environment.

1. Introduction

PromptStream’s Python SDK lets you run prompts and capture user events from your Python applications. Key concepts:

Prompt – a versioned template you create in the dashboard
Prompt Run – a single execution of a prompt with inputs
Experiment – groups runs for A/B testing
Variant – the specific prompt version assigned to a run
Events – clicks, conversions, feedback, ratings, or engagement metrics

2. Installation

Requirements

Python \u2265 3.x
promptstream and its dependencies

Install via pip

pip install promptstream

Importing

from promptstream_sdk import PromptStreamClient

3. Authentication & Client Initialization

Generate an API key from your PromptStream dashboard. Pass it directly or via the PROMPTSTREAM_API_KEY environment variable.

client = PromptStreamClient(api_key="YOUR_API_KEY")

4. Core API

4.1 `run_prompt`

run_prompt(
    prompt_id: str,
    inputs: dict,
    external_user_id: Optional[str] = None
) -> RunResponse

Parameters:

Name	Type	Description
`prompt_id`	`str`	UUID of your PromptStream prompt
`inputs`	`dict`	Mapping of input names to values
`external_user_id`	`str?`	Optional stable end-user ID

Returns RunResponse with:

run_id
experiment_id
variant_assigned
output

Errors

Authentication failures, invalid prompt IDs, and network issues raise exceptions.

4.2 Event-Tracking Methods

All event methods accept prompt_run_id, experiment_id, and optional external_user_id.

4.2.1 `track_click`

track_click(
    prompt_run_id: str,
    experiment_id: str,
    label: str,
    external_user_id: Optional[str] = None,
    source: Optional[str] = None,
    metadata: Optional[dict] = None
)

4.2.2 `track_conversion`

track_conversion(
    prompt_run_id: str,
    experiment_id: str,
    label: str = "conversion",
    external_user_id: Optional[str] = None,
    source: Optional[str] = None,
    metadata: Optional[dict] = None
)

4.2.3 `track_feedback`

track_feedback(
    prompt_run_id: str,
    experiment_id: str,
    thumbs_up: bool,
    external_user_id: Optional[str] = None,
    source: Optional[str] = None,
    metadata: Optional[dict] = None
)

4.2.4 `track_rating`

track_rating(
    prompt_run_id: str,
    experiment_id: str,
    rating: float,
    external_user_id: Optional[str] = None
)

4.2.5 `track_engagement`

track_engagement(
    prompt_run_id: str,
    experiment_id: str,
    engagement_score: float,
    external_user_id: Optional[str] = None
)

5. Response Types & Data Models

RunResponse fields:

Field	Type	Description
`run_id`	`str`	Unique identifier for this run
`experiment_id`	`str`	Experiment the run belongs to
`variant_assigned`	`str`	Version used
`output`	`str`	Prompt output text

Errors may include AuthenticationError, PromptNotFoundError, and RateLimitError.

6. Usage Examples

Basic Prompt Run

resp = client.run_prompt("PROMPT_ID", {"text": "Hi"}, external_user_id="user_123")
print(resp.output)

Batch Runs & Bulk Tracking

Loop over your data to run prompts and track outcomes in bulk.

7. Best Practices

Use idempotent identifiers for runs when possible
Standardize event labels and metadata
Handle retries for transient failures
Avoid storing PII or send hashes when required for GDPR compliance

SDKs

​PromptStream Python SDK

​1. Introduction

​2. Installation

​Requirements

​Install via pip

​Importing

​3. Authentication & Client Initialization

​4. Core API

​4.1 run_prompt

​Errors

​4.2 Event-Tracking Methods

​4.2.1 track_click

​4.2.2 track_conversion

​4.2.3 track_feedback

​4.2.4 track_rating

​4.2.5 track_engagement

​5. Response Types & Data Models

​6. Usage Examples

​Basic Prompt Run

​Batch Runs & Bulk Tracking

​7. Best Practices