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LLMFreemiumOpen Source
PROMPTLAYER
Log, version, and analyze every prompt your LLM ever sees
Apache-2.0
ABOUT
When an LLM application produces a bad response, developers have no record of what prompt was used, what the model returned, or which version of the prompt template generated the output. Debugging becomes guesswork. PromptLayer captures every prompt and response sent to any LLM provider — including OpenAI, Anthropic, Cohere, and local models — with metadata like model version, temperature, and latency. You can search historical prompts, compare responses across model versions, and track prompt performance over time, making LLM debugging as systematic as traditional software debugging.
INSTALL
pip install promptlayerINTEGRATION GUIDE
1. Log every LLM API call with prompt templates, model parameters, and response content for full traceability
2. Search and replay historical prompts to debug why an LLM produced a specific bad response
3. Version-control prompt templates and track which version produced the best outcomes via A/B metrics
4. Compare responses from different models or parameters on the same prompt to optimize cost and quality
5. Set up automated regression detection that alerts when prompt changes degrade response quality
TAGS
llmobservabilityprompt-managementtrackingevaluationpythonversion-controllogging