In our project, the observability system allows us to monitor full agentic workflows, link experiments to execution providers, and inspect both high-level sequences and detailed LLM calls.
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Client’s Challenge
Generative AI projects often involve thousands of generations across different models, prompts, and environments, making it difficult to track results, audit outputs, and debug issues. Without a centralized observability system, generations are scattered, metadata is incomplete, and teams struggle with repeated work and slow delivery. Observability is typically deprioritized in short-term projects, and retrofitting it into large codebases can be technically and organizationally challenging.
Our Solution
We developed a lightweight observability library requiring only a single line of code to integrate, even in complex, nested codebases. It supports models across modalities (text-to-text, text-to-image, etc.) and works with both local and API-based providers. The backend leverages a relational database with SQLAlchemy, built-in storage solutions, and integrates seamlessly with Transformers, Diffusers, and LiteLLM to ensure detailed tracking of generations and metadata.
Client’s Benefits
In our project, the observability system allows us to monitor full agentic workflows, link experiments to execution providers, and inspect both high-level sequences and detailed LLM calls. This has made debugging easier, accelerated development, and improved collaboration by offering a centralized view of generation history across team members and environments.
