prompt-layer-library

KO | EN

gitlite — search

#python #react #ai #go #machine-learning #rust #linux #docker #awesome #cli #awesome-list #security #mcp #open-source

prompt-layer-library

(★ 782)

🍰 PromptLayer - Maintain a log of your prompts and OpenAI API requests. Track, debug, and replay old completions.

prompt-layer-library 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 🍰 PromptLayer

**Version, test, and monitor every prompt and agent with robust evals, tracing, and regression sets.**

---

This library provides convenient access to the PromptLayer API from applications written in python.

## AI coding agents

- Python SDK guide: https://docs.promptlayer.com/sdks/python
- Curated docs index (`llms.txt`): https://docs.promptlayer.com/llms.txt
- Full docs corpus (`llms-full.txt`): https://docs.promptlayer.com/llms-full.txt

Install PromptLayer skill files and the Docs MCP server into your coding agents:

```bash
promptlayer setup
```

This writes the PromptLayer docs skill and the SDK evals skill (`sdk-eval-builder`) for Cursor and Claude Code, and adds the Docs MCP server (`https://docs.promptlayer.com/mcp`) to their project configs. Useful variants:

```bash
promptlayer setup skills
promptlayer setup mcp
promptlayer setup --agent cursor --agent claude
promptlayer setup --force
```

## Installation

```bash
pip install promptlayer
```

Optional extras [(learn more)](#integration-modules):

```bash
pip install "promptlayer[openai-agents]"
pip install "promptlayer[claude-agents]"
```

## Quick Start

To follow along, you need a [PromptLayer](https://www.promptlayer.com/) API key. Once logged in, go to Settings to generate a key.

Create a client and fetch a prompt template from PromptLayer:

```python
from promptlayer import PromptLayer

pl = PromptLayer(api_key="pl_xxxxx")

prompt = pl.templates.get(
    "support-reply",
    {
        "input_variables": {
            "customer_name": "Ada",
            "question": "How do I reset my password?",
        }
    },
)

print(prompt["prompt_template"])
```

Async client:

```python
import asyncio

from promptlayer import AsyncPromptLayer

async def main():
    pl = AsyncPromptLayer(api_key="pl_xxxxx")

prompt = await pl.templates.get(
        "support-reply",
        {
            "input_variables": {
                "customer_name": "Ada",
                "question": "How do I reset my password?",
            }
        },
    )

print(prompt["prompt_template"])

asyncio.run(main())
```

Every method has an async version.

You can also use the client as a proxy around supported provider SDKs:

```python
from promptlayer import PromptLayer

pl = PromptLayer(api_key="pl_xxxxx")
openai = pl.openai

response = openai.chat.completions.create(
    model="gpt-4.1-mini",
    messages=[{"role": "user", "content": "Say hello in one short sentence."}],
    pl_tags=["proxy-example"],
)
```

## Configuration

### Client Options

`PromptLayer(...)` and `AsyncPromptLayer(...)` accept these parameters:

- `api_key: str | None = None`: Your PromptLayer API key. If omitted, the SDK looks for `PROMPTLAYER_API_KEY`.
- `enable_tracing: bool = False`: Enables OpenTelemetry tracing export to PromptLayer and auto-instruments installed OpenAI, Anthropic, Google GenAI, and AWS Bedrock SDKs when the tracing extra is installed.
- `base_url: str | None = None`: Overrides the PromptLayer API base URL. If omitted, the SDK uses `PROMPTLAYER_BASE_URL` or the default API URL.
- `throw_on_error: bool = True`: Controls whether SDK methods raise PromptLayer exceptions or return `None` for many API errors.
- `cache_ttl_seconds: int = 0`: Enables in-memory prompt-template caching when greater than `0`.
- `tracer_provider: TracerProvider | None = None`: Uses an application-owned OpenTelemetry SDK tracer provider instead
  of the default PromptLayer-managed provider.
- `tracing_providers: Iterable[str] | None = None`: Selects provider SDKs to auto-instrument. Defaults to all supported
  providers; pass an empty iterable to export spans without provider SDK auto-instrumentation.

### Environment Variables

The SDK relies on the following environment variables:

| Variable | Required | Description |
| --- | --- | --- |
| `PROMPTLAYER_API_KEY` | Yes, unless passed as `api_key=` | API key used to authenticate requests to PromptLayer. |
| `PROMPTLAYER_BASE_URL` | No | Overrides the PromptLayer API base URL. Defaults to `https://api.promptlayer.com`. |
| `PROMPTLAYER_OTLP_TRACES_ENDPOINT` | No | Overrides the OTLP trace endpoint (`/v1/traces`) used when SDK tracing is enabled. |
| `PROMPTLAYER_TRACEPARENT` | No | Optional trace context passed through the Claude Agents integration. |

## Client Resources

The main resources surfaced by `PromptLayer` and `AsyncPromptLayer` are:

| Resource | Description |
| --- | --- |
| `client.templates` | Prompt template retrieval, listing, publishing, and cache invalidation. |
| `client.run()` and `client.run_workflow()` | Helpers for running prompts and workflows. |
| `client.log_request()` | Manual request logging. |
| `client.track` | Request annotation utilities for metadata, prompt linkage, scores, and groups. |
| `client.group` | Group creation for organizing related requests. |
| `client.traceable()` | Decorator for tracing your own functions and sending those spans to PromptLayer when tracing is enabled. |
| `client.skills` | Skill collection pull, create, publish, and update operations. |
| `client.tables.sheets.scorecards` | Table scorecard configuration, migration, recalculation, and row-level result retrieval. |
| `client.openai` and `client.anthropic` | Provider proxies that wrap those SDKs and log requests to PromptLayer. |

Note: When tracing is enabled, spans are exported to PromptLayer using OpenTelemetry.

### GenAI SDK Auto-Instrumentation

Install the tracing extra and the provider SDKs used by your application:

```bash
pip install "promptlayer[otel-genai-instrumentation]" openai anthropic google-genai boto3
```

The extra includes the official OpenTelemetry instrumentors for:

| Provider | Instrumented APIs |
| --- | --- |
| OpenAI and Azure OpenAI | Chat Completions, structured-output parsing, Embeddings, and Responses; sync, async, and streaming |
| Anthropic and Anthropic Vertex | Messages create, parse, and stream; sync and async |
| Google GenAI | Generate Content, streaming Generate Content, Embeddings, and supported Interactions releases; Gemini Developer API and Vertex AI modes |
| AWS Bedrock | Botocore Bedrock Runtime Converse and InvokeModel APIs, including streaming |

`PromptLayer(enable_tracing=True)` auto-instruments every supported provider SDK
that is installed:

```python
from anthropic import Anthropic
from promptlayer import PromptLayer

promptlayer_client = PromptLayer(api_key="pl_xxxxx", enable_tracing=True)
anthropic_client = Anthropic()

response = anthropic_client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=256,
    messages=[{"role": "user", "content": "Say hello."}],
)
```

Advanced OpenTelemetry configurations can select instrumentors explicitly.
The `google` instrumentor supports both Gemini and `google-genai` clients
created with `vertexai=True`:

```python
from promptlayer import configure_tracing

tracer_provider = configure_tracing(
    providers=("openai", "anthropic", "google", "bedrock"),
)
```

The `openai.azure` provider alias selects the underlying OpenAI SDK
instrumentor. The `amazon.bedrock` and `aws.bedrock` aliases select the
Botocore instrumentor. Because Botocore instrumentation operates at the AWS
SDK layer, selecting Bedrock also traces other Botocore service calls made by
the process.

PromptLayer defaults OpenTelemetry message-content capture to `SPAN_ONLY` when
it enables provider instrumentation. To exclude prompts and responses from
spans, opt out before configuring tracing:

```bash
export OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=NO_CONTENT
```

PromptLayer preserves explicit official OpenTelemetry values: `NO_CONTENT`,
`SPAN_ONLY`, `EVENT_ONLY`, and `SPAN_AND_EVENT`. Set the value before tracing
is configured because provider instrumentors read it during initialization.
Message content can contain sensitive data and is sent to the configured trace
destination when capture is enabled.

Applications that only use the direct OpenAI SDK can continue to use the
OpenAI-specific convenience API:

```python
from openai import OpenAI
from promptlayer import instrument_openai

tracer_provider = instrument_openai()
openai_client = OpenAI()
```

`instrument_openai()` reads the PromptLayer API key and endpoint from the
environment, is safe to call repeatedly with the same tracer provider, and
returns the configured provider so short-lived processes can flush it.

All provider instrumentors in this extra require Python 3.10 or newer. The core
PromptLayer package continues to support Python 3.9 without auto-instrumentation.

## Table Scorecards

New scorecard APIs are preferred for new table scoring workflows. Legacy `/score` endpoints remain supported for existing integrations. If both a legacy score configuration and a scorecard exist on the same sheet, `/score` continues to return legacy score behavior; use the `/scorecard` endpoints through `client.tables.sheets.scorecards` to access scorecard state and results.

Configure a scorecard:

```python
await client.tables.sheets.scorecards.configure(
    table_id,
    sheet_id,
    {
        "name": "Quality Scorecard",
        "evaluated_column_ids": [],
        "aggregation": {
            "method": "weighted_mean",
            "required_step_failure_behavior": "fail",
            "pass_threshold": 0.8,
            "warn_threshold": 0.6,
        },
        "steps": [],
    },
)
```

Migrate a legacy score safely. `delete_legacy_score` defaults to `False`, so migration does not remove legacy score configuration unless you explicitly request it:

```python
await client.tables.sheets.scorecards.migrate_legacy_score(
    table_id,
    sheet_id,
    {"delete_legacy_score": False},
)
```

Recalculate and fetch the calculation:

```python
run = await client.tables.sheets.scorecards.recalculate(table_id, sheet_id)

result = await client.tables.sheets.scorecards.get_calculation(
    table_id,
    sheet_id,
    run["calculation_id"],
)
```

Fetch row breakdowns:

```python
rows = await client.tables.sheets.scorecards.list_rows(
    table_id,
    sheet_id,
    {
        "calculation_id": run["calculation_id"],
        "verdict": "fail",
    },
)

row = await client.tables.sheets.scorecards.get_row(
    table_id,
    sheet_id,
    0,
    {"calculation_id": run["calculation_id"]},
)
```

Migration caveat: custom legacy scoring cannot be automatically converted into scorecard criteria. Review migrated criteria before relying on scorecard results in production.

## Integration Modules

Optional modules that are imported directly rather than accessed through the client:

| Module | Description |
| --- | --- |
| `promptlayer.integrations.openai_agents` | Tracing utilities for the [openai-agents SDK](https://github.com/openai/openai-agents-python) that instrument agent runs and export their traces to PromptLayer. |
| `promptlayer.integrations.claude_agents` | Configuration utilities for the [claude-agent-sdk SDK](https://github.com/anthropics/claude-agent-sdk-python) that load the PromptLayer plugin and required environment settings so Claude agent runs send traces to PromptLayer. |

## Error Handling

The SDK raises `PromptLayerError` as the base exception for SDK failures, with more specific subclasses for common API and validation cases.

| Error type | Description |
| --- | --- |
| `PromptLayerValidationError` | Invalid input passed to the SDK before or during a request. |
| `PromptLayerAPIConnectionError` | The SDK could not connect to PromptLayer. |
| `PromptLayerAPITimeoutError` | A PromptLayer request or workflow run timed out. |
| `PromptLayerAuthenticationError` | Authentication failed, usually because the API key is missing or invalid. |
| `PromptLayerPermissionDeniedError` | The API key does not have permission for the requested operation. |
| `PromptLayerNotFoundError` | The requested resource, such as a prompt or workflow, was not found. |
| `PromptLayerBadRequestError` | The request was malformed or used invalid parameters. |
| `PromptLayerConflictError` | The request conflicts with the current state of a resource. |
| `PromptLayerUnprocessableEntityError` | The request was well-formed but semantically invalid. |
| `PromptLayerRateLimitError` | PromptLayer rejected the request because of rate limiting. |
| `PromptLayerInternalServerError` | PromptLayer returned a 5xx server error. |
| `PromptLayerAPIStatusError` | Other non-success API responses that do not map to a more specific error type. |

By default, the clients raise these exceptions. If you initialize `PromptLayer` or `AsyncPromptLayer` with `throw_on_error=False`, many resource methods return `None` instead of raising on PromptLayer API errors.

## Caching

When enabled, the SDK caches fetched prompt templates in memory for faster repeat reads, locally re-renders them with new variables, and falls back to stale cache on temporary API failures.
- Caching is disabled by default and is enabled by setting `cache_ttl_seconds` when creating `PromptLayer` or `AsyncPromptLayer`.
- The cache applies to prompt templates fetched through `client.templates.get(...)`.
- Cached entries are stored in memory and keyed by prompt name, version, label, provider, and model.
- Requests that include `metadata_filters` or `model_parameter_overrides` bypass the cache.
- Templates that require server-side rendering behavior, such as placeholder messages or tool-variable expansion, are not cached for local rendering.
- If a cached template is stale and PromptLayer returns a transient error, the SDK can serve the stale cached version as a fallback.
- You can clear cached entries with `client.invalidate(...)` or `client.templates.invalidate(...)`.

prompt-layer-library

prompt-layer-library 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

prompt-layer-library

prompt-layer-library 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README