Agno is an open-source agent framework and high-performance runtime for building, running, and managing multi-agent systems at scale. With 38,000+ GitHub stars and 402 contributors, it has become one of the most widely adopted frameworks for production agentic software. Agno is designed for the shift from deterministic request-response architectures to reasoning systems that plan, call tools, remember context, and make decisions. ## Three-Layer Architecture Agno is structured into three distinct layers. The SDK Layer provides the programming primitives: agents, teams, workflows, memory, knowledge, tools, guardrails, and approval flows. The Engine Layer handles model calls, tool orchestration, structured outputs, and runtime enforcement. The AgentOS Layer delivers streaming APIs, isolation, authentication, approval enforcement, tracing, and a control plane for production operations. This layered design means developers can start simple with the SDK and gradually adopt more sophisticated features as their agent systems mature. ## Streaming and Long-Running Execution Agno treats streaming and long-running execution as first-class behaviors rather than afterthoughts. Agents stream reasoning, tool calls, and results in real-time. They can pause mid-execution, wait for human approval, and resume later. This is essential for production agent systems where tasks may take minutes or hours and require human oversight at critical decision points. ## Per-User and Per-Session Isolation Multi-tenant deployments are supported natively with per-user and per-session isolation. Each user's agent sessions, memory, and knowledge are stored separately, enabling shared infrastructure without data leakage between tenants. This addresses a common challenge when scaling agent systems for SaaS applications. ## Governance and Auditability Agno includes built-in guardrails, evaluations, traces, and audit logging. Runtime approval enforcement allows organizations to define governance policies that are enforced during agent execution rather than relying on post-hoc review. Every decision point, tool call, and model response is traceable, providing the auditability required for regulated industries. ## Infrastructure Ownership Unlike hosted agent platforms, Agno runs entirely in the user's infrastructure. Sessions, memory, knowledge, and traces are stored in the user's own database. There is no vendor-hosted component that retains user data. This self-hosted model provides full control over data residency, compliance, and operational costs. ## Model Provider Flexibility Agno supports 40+ model providers including Gemini, Claude, GPT, Llama, Mistral, DeepSeek, Groq, Ollama, and vLLM. MCP integration provides additional tool access through the Model Context Protocol ecosystem. The framework is model-agnostic, allowing teams to switch providers or use multiple models within the same agent system. ## Production Deployment A production-ready API can be deployed from minimal code. The runtime is stateless and horizontally scalable, supporting background execution across 50+ APIs. The latest release (v2.5.3, February 19, 2026) continues active development with 168 releases and a growing cookbook of practical examples.