AgentOps is an open-source framework designed to streamline the development, deployment, and management of autonomous agents powered by Large Language Models (LLMs). It addresses the complexities of building production-ready agents by providing a structured approach encompassing agent creation, observability, evaluation, and continuous improvement. The core philosophy revolves around treating agents as software, applying DevOps principles to their lifecycle, and fostering collaboration between developers, data scientists, and operations teams.

At its heart, AgentOps offers a modular architecture. It defines key components like `Agents`, `Tools`, `Memories`, and `Workflows`. `Agents` represent the core logic, utilizing LLMs to process inputs and generate outputs. `Tools` are functionalities agents can leverage – APIs, databases, web search, etc. – extending their capabilities beyond the LLM's inherent knowledge. `Memories` provide agents with persistent storage for context and learning, crucial for long-running tasks and personalization. `Workflows` define the sequence of steps an agent takes to achieve a goal, enabling complex, multi-stage operations. This modularity promotes reusability and simplifies maintenance.

A significant feature is the AgentOps CLI, a command-line interface for managing the entire agent lifecycle. This includes creating new agents from templates, deploying them to various environments (local, cloud), monitoring their performance, and triggering updates. The CLI integrates with popular orchestration tools like Docker and Kubernetes, facilitating scalable deployments. Furthermore, AgentOps provides a robust evaluation framework. It allows users to define metrics and run automated tests to assess agent performance, identify weaknesses, and track improvements over time. This is critical for ensuring agents behave reliably and achieve desired outcomes.

Observability is a cornerstone of AgentOps. The framework incorporates logging, tracing, and metrics collection, providing deep insights into agent behavior. It supports integration with popular observability platforms like Prometheus and Grafana, enabling real-time monitoring and alerting. This allows teams to quickly diagnose issues, understand agent decision-making processes, and optimize performance. The framework also emphasizes the importance of responsible AI, offering features for tracking agent provenance and ensuring transparency.

Beyond the core framework, AgentOps provides a growing collection of pre-built agents, tools, and workflows, accelerating development. These examples cover common use cases like customer support, data analysis, and content creation. The project actively encourages community contributions, fostering a collaborative ecosystem for sharing best practices and expanding the framework's capabilities. Essentially, AgentOps aims to be the "DevOps for LLM Agents," providing the infrastructure and tooling needed to move beyond experimentation and build truly impactful, production-grade autonomous systems.