🍰 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:
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:
promptlayer setup skills
promptlayer setup mcp
promptlayer setup --agent cursor --agent claude
promptlayer setup --force
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 forPROMPTLAYER_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 usesPROMPTLAYER_BASE_URLor the default API URL.throw_on_error: bool = True: Controls whether SDK methods raise PromptLayer exceptions or returnNonefor many API errors.cache_ttl_seconds: int = 0: Enables in-memory prompt-template caching when greater than0.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:
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:
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:
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:
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:
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:
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_secondswhen creatingPromptLayerorAsyncPromptLayer. - 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_filtersormodel_parameter_overridesbypass 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(...)orclient.templates.invalidate(...).
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