Master Agno (formerly Phidata) with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Environment setup and package installation Configure authentication and core services Create and test your first agent with tools
💡 Quick Start: Follow these 1 steps in order to get up and running with Agno (formerly Phidata) quickly.
Explore the key features that make Agno (formerly Phidata) powerful for ai memory & search workflows.
Revolutionary runtime system that transforms individual agents into scalable production infrastructure. AgentOS handles automatic scaling, load balancing, fault tolerance, and service orchestration, allowing agents to operate as enterprise-grade microservices. The runtime abstracts infrastructure complexity while providing comprehensive monitoring, tracing, and management capabilities through a unified API.
Benchmark-proven performance with 529× faster agent instantiation than LangGraph, 57× faster than PydanticAI, and 70× faster than CrewAI. Memory efficiency improvements of 24× compared to LangGraph enable higher-density deployments. These performance characteristics make Agno the only framework suitable for real-time applications and high-throughput production environments.
Native support for processing text, images, audio, and video inputs within a single agent framework. Agents can analyze visual documents, transcribe audio, generate multimedia content, and understand complex data formats automatically. The framework handles format conversions, optimizations, and media processing pipeline management transparently.
Built-in security with JWT authentication, role-based access control (RBAC), and request-level isolation. Data sovereignty guarantee ensures all information remains within customer infrastructure with zero egress. Comprehensive audit logging, encryption at rest and in transit, and custom SSO integration support enterprise compliance requirements.
Persistent memory architecture that enables agents to learn from interactions, retain context across sessions, and access vast knowledge repositories. The system supports custom datasets, real-time data integration, and intelligent information retrieval. Memory management includes automated cleanup, privacy controls, and performance optimization for large-scale deployments.
Sophisticated coordination system enabling teams of specialized agents to collaborate on complex workflows. Includes automatic task routing, conflict resolution, result aggregation, and error recovery. Teams can operate hierarchically with supervisory agents, or horizontally with peer collaboration patterns. The orchestration layer handles communication protocols and state synchronization automatically.
Agno is the evolved brand name for what was previously known as Phidata. The core technology, team, and capabilities remain the same, but the platform has been enhanced with improved performance, expanded features, and a more robust production runtime. All existing Phidata projects seamlessly transition to the Agno ecosystem.
Agno's performance comes from optimized runtime architecture, efficient memory management, and streamlined agent instantiation processes. The AgentOS runtime eliminates overhead common in other frameworks, while optimized Python implementations and reduced abstraction layers contribute to the 529× speed improvement over LangGraph.
Yes, Agno supports all major language model providers including OpenAI, Anthropic Claude, Google AI, Cohere, and open-source models like Llama and Mistral. The framework is designed to be model-agnostic, allowing you to switch providers or use multiple models within the same agent system without code changes.
Agno implements a privacy-by-design architecture where all agent data, conversations, metrics, and traces remain within your cloud infrastructure. No information is transmitted to Agno servers, and the self-hosted control plane ensures complete data control. This eliminates compliance concerns and egress costs while maintaining full operational visibility.
Agno requires Python programming knowledge for agent development and deployment. While the framework provides intuitive APIs that minimize boilerplate code, effective use requires understanding of Python classes, async programming, and basic deployment concepts. No specialized AI or machine learning expertise is required.
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Tutorial updated March 2026