Lira is an open-source SDK that lets you store every LLM interaction in a structured format to test and optimize your AI integrations based on data-driven insights. It also provides a unified API to generate responses with any LLM provider. You can chose to store data in your own database, ensuring it never leaves your server, or through the Lira dashboard for clear visibility and performance tracking across LLMs interactions. The unified API lets you switch LLM providers simply by changing the model name, without ever modifying your code. Focus on building your application using the best LLM provider for your needs.

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Benefits of Using Lira SDK

Lira SDK filling the gap with current alternatives by providing a unified, efficient, and flexible solution for generating and storing messages from any LLM providers.

Message Storage

Track and store every input/output for later analysis, helping you compare LLM performance and behavior over time

Insightful Analytics

Utilize structured storage to analyze interactions and improve your application’s performance.

Consistent API Across LLMs

Switch between different LLM providers without modifying your codebase, making it easier to experiment and optimize

Costs control

Monitor and optimize costs by tracking usage and each request cost. Filter per user, per model, or per provider.

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Alternatives in the Market

  1. ​
    Direct Integration with Individual Providers

    • Complex and Inconsistent: Each provider has its own signatures. Request and response formats may be very different.
    • High Maintenance: Managing multiple integrations increases complexity and upkeep.

  2. ​
    Building Custom Abstraction Layers

    • Time-Consuming Development: Creating a unified API from scratch requires significant effort.
    • Ongoing Maintenance: Continuous updates needed to keep up with provider API changes and new features.

  3. ​
    Using Existing SDKs and Libraries

    • Feature Limitations: Often lack unified logging, structured storage, or custom metadata support.
    • Steep Learning Curve: May not fully abstract provider-specific quirks, making understanding and debugging difficult.