Agno is a comprehensive runtime for developing, deploying, and managing scalable agentic software, including single agents, coordinated teams, and structured workflows. It provides a framework for building, a stateless FastAPI runtime for serving, and a control plane for production monitoring. Agno operates within the user's infrastructure, ensuring data ownership and auditability.

The OpenAI Agents SDK is a lightweight, Python-first framework for building production-ready agentic AI applications with minimal abstractions. It provides core primitives like agents, handoffs, and guardrails, along with built-in tracing and session management. The SDK prioritizes ease of learning while offering customization, making it suitable for rapid development and debugging of AI-driven workflows.

• +Provides a complete runtime for agentic software, supporting agents, teams, and workflows.
• +Offers 100+ integrations for building agents with memory, knowledge, and guardrails.
• +Serves systems as scalable, stateless, session-scoped FastAPI backends for production.
• +Includes AgentOS UI for testing, monitoring, and managing systems in production.
• +Ensures per-user and per-session isolation with native tracing and full auditability.
• +Runs in user's infrastructure, providing full data ownership and control.

• +Offers a lightweight, easy-to-use package with few abstractions for rapid development.
• +Provides a built-in agent loop that handles tool invocation and LLM interaction until task completion.
• +Enables Python-first orchestration and chaining of agents using native language features.
• +Features 'Agents as tools' (Handoffs) for powerful coordination and delegation across multiple agents.
• +Includes Guardrails for parallel input validation, safety checks, and fail-fast execution.
• +Automatically generates schemas and provides Pydantic-powered validation for Python function tools.
• +Offers a persistent memory layer via Sessions for maintaining working context across agent turns.
• +Supports built-in mechanisms for involving human users across agent runs (Human in the loop).
• +Comes with built-in tracing for visualizing, debugging, and monitoring workflows, supporting OpenAI evaluation and fine-tuning.
• +Allows building real-time voice agents with features like interruption detection and context management using `gpt-realtime-1.5`.

• −The strong specialization in 'agentic software' may introduce complexity or be over-engineered for simpler, non-agentic applications.
• −Requires users to manage their own infrastructure, which adds operational overhead compared to a fully managed service.
• −Building and governing 'distributed, governed multi-agent systems' can entail a significant learning curve and implementation complexity.

• −Optimal functionality, especially for tracing, evaluation, and real-time features, is tied to the OpenAI ecosystem.
• −Limited to Python applications, making it unsuitable for polyglot environments or teams focused on other languages.
• −The 'very few abstractions' design may limit flexibility for highly complex or specialized agent architectures beyond its core primitives.

State is managed as stateless, session-scoped operations, with sessions, memory, knowledge, and traces persisted in the user's database.

The SDK provides 'Sessions', a persistent memory layer designed to maintain working context across turns within an agent loop.