Agno is an agent platform designed to build, deploy, and manage AI agents in production environments. It supports agents built using any framework or no-code UI, providing production-grade features like tracing, scheduling, and RBAC. Agno allows teams to automate diverse tasks from data labeling and document extraction to product copilots while maintaining data ownership.

Pydantic AI is a Python agent framework for building production-grade Generative AI applications and workflows, emphasizing type safety, observability, and durable execution. It is model-agnostic, integrating with various LLM providers and offering extensive capabilities like web search, tool approval, and graph support. The framework aims to bring the ergonomic development experience of FastAPI to GenAI.

• +Productionizes agents built with any framework, offering flexibility in agent creation.
• +Provides robust production features for agents, including tracing, scheduling, role-based access control (RBAC), and audit trails.
• +Supports management of the entire agent development lifecycle using coding agents.
• +Offers native typesafety and multi-modal capabilities for various input/output modalities, including structured output.
• +Ensures data ownership by storing all session, memory, and trace data in the user's own database and cloud.
• +Enables auto-improvement of agents using production usage data via provided code mechanisms.

• +Built by the Pydantic Team, leveraging widely adopted Pydantic Validation.
• +Model-agnostic, supporting numerous LLM providers and custom model implementations.
• +Seamless observability through tight integration with Pydantic Logfire, compatible with other OpenTelemetry platforms.
• +Fully type-safe design enhances auto-completion and type checking, shifting errors to write-time.
• +Powerful evaluation capabilities allow systematic testing and performance monitoring of agentic systems.
• +Extensible by design, supporting composable capabilities, a Harness library, third-party packages, and YAML/JSON agent definitions.
• +Integrates with Model Context Protocol (MCP), Agent2Agent (A2A), and UI event stream standards for broad interoperability.
• +Provides human-in-the-loop tool approval for flagged tool calls based on context.
• +Supports durable execution to preserve agent progress across failures and manage long-running workflows.
• +Offers streamed structured outputs with immediate validation for real-time data access.
• +Includes graph support to define complex applications with type hints, preventing spaghetti code.
• +Utilizes dependency injection via `RunContext` for type-safe customization and simplified testing.
• +Guarantees structured outputs conform to Pydantic models, including schema generation and validation with self-correction.

Agno stores all session, memory, and trace data in the user's own database within their cloud environment.

Agents preserve their progress across failures and restarts via durable execution, while `RunContext` manages runtime dependencies and contextual data passed into instructions and tool functions.