Discover / LLM Ops & Observability

PromptLayer

by MagnivOrgPython

Platform and SDK for tracking, managing and versioning prompts used in LLM applications.

Toolexperimental

Maturity: experimental because active but has never tagged a release. Derived from release and commit history, not a rating.

Stars
782
Forks
94
Downloads / mo
421k
Last commit
2026-07-31
License
Apache-2.0
Open issues
25

Market and trust evidence

Edition not yet matched

No exact skills.sh identity match is available for this repository. Repository adoption and freshness remain visible above; install momentum is not inferred.

Trust analysis is a screening signal, not a security warranty. Read the ranking and trust methodology.

In practice

Written by AI from this repository’s README · high confidence

Prompts live scattered in code with no version history and no record of what each request returned.

Use it when

Use it when you want prompt templates managed outside code plus request logging, tracing and scorecards.

Not the right pick when

It is a client for a hosted service, so it does nothing without a PromptLayer account and API key.

Capabilities

  • fetch prompt templates with input variables
  • sync and async clients for every method
  • provider proxies that wrap the OpenAI and Anthropic SDKs
  • OpenTelemetry tracing export with enable_tracing
  • request tracking, groups and manual logging
  • in memory prompt template caching

Requirements

  • PromptLayer API key, passed as api_key or PROMPTLAYER_API_KEY
  • Python 3.9+

Cost: Needs a paid API or account

Install

Derived from the published package name in the repository, not from a model.

Video walkthroughs

Third-party YouTube uploads matched to this tool by title, channel and repository name on 2026-08-03. Not made, reviewed or endorsed by SkillPilot. View counts and publish months are as of the match date and the month is approximate. Nothing loads from YouTube until you press play.

What the repository ships

Has testsHas examplesCI configured

Detected from the actual files in the repository root.

Tags

README

<div align="center">

🍰 PromptLayer

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

<a href="https://www.python.org/"><img alt="Python" src="https://img.shields.io/badge/-Python 3.9+-blue?style=for-the-badge&logo=python&logoColor=white"></a>

<a href="https://docs.promptlayer.com"><img alt="Docs" src="https://custom-icon-badges.herokuapp.com/badge/docs-PL-green.svg?logo=cake&style=for-the-badge"></a>

<a href="https://www.loom.com/share/196c42e43acd4a369d75e9a7374a0850"><img alt="Demo with Loom" src="https://img.shields.io/badge/Demo-loom-552586.svg?logo=loom&style=for-the-badge&labelColor=gray"></a>


<div align="left">

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

Installation


pip install promptlayer

Optional extras (learn more):


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

Quick Start

To follow along, you need a PromptLayer API key. Once logged in, go to Settings to generate a key.

Create a client and fetch a prompt template from PromptLayer:


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:


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:


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 the OpenAI SDK 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.

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.

OpenAI SDK Auto-Instrumentation

Install the OpenAI-only tracing extra:


pip install "promptlayer[otel-genai-instrumentation]" openai

Then enable tracing before making direct OpenAI SDK calls:


from openai import OpenAI
from promptlayer import PromptLayer

promptlayer_client = PromptLayer(api_key="pl_xxxxx", enable_tracing=True)
openai_client = OpenAI()

response = openai_client.chat.completions.create(
    model="gpt-4.1-mini",
    messages=[{"role": "user", "content": "Say hello."}],
)

This preserves the existing PromptLayer-managed tracing provider and additionally

enables only the official OpenAI SDK instrumentor. It does not instrument the

OpenAI Agents SDK or any other model provider.

Applications that only use the direct OpenAI SDK can enable the same

instrumentation without creating a PromptLayer client:


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.

Applications with advanced OpenTelemetry configuration can continue to use

configure_tracing() directly and pass an application-owned tracer_provider.

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:


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:


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

Recalculate and fetch the calculation:


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:


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 that instrument agent runs and export their traces to PromptLayer. |

| promptlayer.integrations.claude_agents | Configuration utilities for the claude-agent-sdk SDK 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(...).

Related tools