Agno provides a production-grade runtime for building, deploying, and managing agentic software, from single agents to coordinated teams and workflows. It features a scalable, stateless FastAPI backend, robust state management in your database, and a control plane for monitoring and governance. It emphasizes user ownership of data and infrastructure.

Pydantic AI is a Python framework for building robust, type-safe generative AI agents, leveraging Pydantic validation and comprehensive observability. It offers features like model-agnosticism, durable execution, and rich tool integration to streamline production-grade AI applications. Its design aims to bring the "FastAPI feeling" to GenAI development.

• +Runtime specifically designed for agentic software, teams, and workflows.
• +Includes memory, knowledge, guardrails, and over 100 integrations for agent development.
• +Provides a stateless, horizontally scalable FastAPI backend for production deployment.
• +Offers a control plane (AgentOS UI) for testing, monitoring, and managing systems in production.
• +Supports per-user and per-session isolation with runtime approval enforcement.
• +Features native tracing and full auditability.
• +Stores sessions, memory, knowledge, and traces in the user's database, ensuring data ownership.
• +Runs in the user's infrastructure, not Agno's.

• +Built by the Pydantic Team, leveraging Pydantic Validation as a core foundation.
• +Model-agnostic, supporting a wide range of LLMs and providers with custom model implementation.
• +Seamless observability, integrating tightly with Pydantic Logfire for real-time debugging, tracing, evals, and cost tracking.
• +Fully type-safe, utilizing Python type hints for static analysis and reduced runtime errors.
• +Powerful evals enable systematic testing and performance monitoring of agentic systems over time.
• +Extensible by design, allowing agents to be built from composable capabilities and defined in YAML/JSON.
• +Integrates the Model Context Protocol (MCP), Agent2Agent (A2A), and UI event stream standards.
• +Supports human-in-the-loop tool approval for critical or sensitive tool calls.
• +Durable execution allows agents to preserve progress across API failures, application errors, or restarts.
• +Provides streamed outputs with immediate Pydantic validation for real-time data access.
• +Includes graph support for defining complex application flows using type hints.
• +Offers a dependency injection system for type-safe agent customization and testing.
• +Automatically validates structured outputs and tool arguments with Pydantic, enabling LLM self-correction.

State is managed externally in the user's database for sessions, memory, knowledge, and traces, with a stateless, session-scoped FastAPI backend.

State is managed through durable agents that can preserve progress across failures and restarts, and RunContext for passing dependencies during an agent run.