{"name":"Observers: A Lightweight Library for AI Observability in Python","description":"Observers is a Python library designed for AI observability, enabling developers to track and store interactions with generative AI APIs. It provides a flexible framework with various observers for popular LLM providers and multiple storage backends. This tool helps in monitoring, debugging, and analyzing AI model behavior effectively.","github":"https://github.com/cfahlgren1/observers","url":"https://osrepos.com/repo/cfahlgren1-observers","source":"osrepos.com","sourceDescription":"This repository profile is provided by osrepos.com, an open source repository discovery platform.","repositoryProfile":"https://osrepos.com/repo/cfahlgren1-observers","generatedFor":"open source discovery and AI-assisted research","markdown":"https://osrepos.com/repo/cfahlgren1-observers.md","json":"https://osrepos.com/repo/cfahlgren1-observers.json","topics":["Python","AI Observability","LLM","Generative AI","Hugging Face","OpenAI","Data Tracking","Machine Learning"],"keywords":["Python","AI Observability","LLM","Generative AI","Hugging Face","OpenAI","Data Tracking","Machine Learning"],"stars":null,"summary":"Observers is a Python library designed for AI observability, enabling developers to track and store interactions with generative AI APIs. It provides a flexible framework with various observers for popular LLM providers and multiple storage backends. This tool helps in monitoring, debugging, and analyzing AI model behavior effectively.","content":"## Introduction\n\nObservers is a lightweight Python library dedicated to AI observability. It simplifies the process of tracking interactions with generative AI APIs, such as OpenAI, Hugging Face transformers, and other LLM providers. The library is structured around two core concepts: **observers**, which wrap AI APIs to track interactions, and **stores**, which synchronize these observations to different storage backends.\n\n## Installation\n\nGetting started with Observers is straightforward. You can install the base SDK using pip:\n\nbash\npip install observers\n\n\nFor integrating with other LLM providers via AISuite or Litellm, install with the respective extra:\n\nbash\npip install observers[aisuite] # or observers[litellm]\n\n\nIf you plan to use OpenTelemetry for tracing, install the OpenTelemetry extra:\n\nbash\npip install observers[opentelemetry]\n\n\n## Examples\n\nObservers allows you to easily wrap your existing LLM clients to start logging interactions. Here's a basic example demonstrating how to wrap an OpenAI client and log requests to a Hugging Face dataset using the default `DatasetsStore`:\n\npython\nfrom openai import OpenAI\nfrom observers import wrap_openai\n\nopenai_client = OpenAI()\n\nclient = wrap_openai(openai_client)\n\nresponse = client.chat.completions.create(\n    model=\"gpt-4o\",\n    messages=[{\"role\": \"user\", \"content\": \"Tell me a joke.\"}],\n)\nprint(response)\n\n\n**Supported Observers** include OpenAI and compatible APIs, Hugging Face transformers, Hugging Face Inference Client, AISuite, and Litellm. This broad support ensures compatibility with a wide range of generative AI models and services.\n\n**Supported Stores** offer flexible options for data persistence and analysis. These include Hugging Face Datasets, DuckDB, Argilla, and OpenTelemetry. Each store provides different capabilities for viewing, querying, and annotating your AI interaction data.\n\n## Why Use Observers\n\nObservers provides a crucial layer for understanding and improving your AI applications. By automatically tracking interactions, it helps in:\n\n*   **Debugging and Performance Monitoring**: Gain insights into how your LLMs are performing, identify errors, and monitor latency or token usage.\n*   **Data Collection for Fine-tuning**: Easily collect interaction data that can be used to fine-tune models or improve prompts.\n*   **Compliance and Auditing**: Maintain a clear record of AI model inputs and outputs for compliance or auditing purposes.\n*   **Flexibility**: Its modular design allows you to switch between different LLM providers and storage solutions without significant code changes.\n*   **Rich Querying Capabilities**: Stores like Hugging Face Datasets and DuckDB allow for powerful SQL-based querying of your observation data, making analysis straightforward.\n\n## Links\n\n*   **GitHub Repository**: [https://github.com/cfahlgren1/observers](https://github.com/cfahlgren1/observers){:target=\"_blank\"}\n*   **Hugging Face Datasets Viewer**: [https://huggingface.co/docs/hub/en/datasets-viewer](https://huggingface.co/docs/hub/en/datasets-viewer){:target=\"_blank\"}\n*   **Argilla Quickstart**: [https://docs.argilla.io/latest/getting_started/quickstart/](https://docs.argilla.io/latest/getting_started/quickstart/){:target=\"_blank\"}","metrics":{"detailViews":3,"githubClicks":1},"dates":{"published":null,"modified":"2026-06-28T00:31:41.000Z"}}