MagnivOrg/prompt-layer-library

What it solves

PromptLayer is a management layer for LLM applications that solves the problem of prompt versioning, testing, and monitoring. It allows developers to manage prompt templates centrally rather than hard-coding them in their application code, and provides tools to evaluate and trace the performance of prompts and agents.

How it works

The library acts as a Python SDK that connects to the PromptLayer API. It can be used in three primary ways:

1. Prompt Management: It fetches prompt templates from a central repository, allowing for updates to prompts without redeploying code.
2. Proxying: It can wrap around provider SDKs (like OpenAI, Anthropic, Google GenAI, and AWS Bedrock) to automatically log requests and responses.
3. Tracing: Using OpenTelemetry, it auto-instruments GenAI SDKs to export spans and traces to PromptLayer for monitoring and debugging.

Additionally, it provides a specific API for "Table Scorecards" to configure, migrate, and calculate quality scores for LLM outputs.

Who it’s for

Developers building LLM-powered applications and agents who need a way to track prompt iterations, monitor production logs, and run evaluations to prevent regressions.

Highlights

• Multi-Provider Support: Auto-instrumentation for OpenAI, Anthropic, Google GenAI, and AWS Bedrock.
• Centralized Prompt Templates: Decouple prompt engineering from application logic via API-based retrieval.
• OpenTelemetry Integration: Uses industry-standard tracing to monitor function calls and LLM interactions.
• In-Memory Caching: Optional caching of prompt templates to reduce API latency and provide fallbacks during outages.
• Agent Integration: Specialized tracing utilities for openai-agents and claude-agent-sdk.