Agno (formerly Phidata) Review 2026

Build, run, and manage production-ready AI agents with a Python framework for agent systems, memory, tools, and AgentOS deployment.

Agno (formerly Phidata) is best for Python engineering teams building production AI agents; it starts free as an open-source framework, with Pro listed at $150/month including 4 seats and 1 live connection, plus $30/month per extra seat and $95/month per extra connection, while Enterprise is custom priced. Agno is an open-source Python framework and agent platform for building agents, teams, workflows, memory-backed applications, knowledge/RAG systems, and production AgentOS deployments. It is a better fit for developers who want framework-level control than for nontechnical users looking for a no-code automation builder. The core development path is code-first: teams define agents, connect model providers, add tools, configure memory and knowledge, test locally, and then move selected systems into AgentOS for serving, monitoring, tracing, and operational management. Official documentation describes AgentOS as the runtime and control plane layers for an Agno-built agent platform, with the SDK used to build, the runtime used to run, and the control plane used to manage. Five concrete facts help frame the product: the pricing record lists a free open-source tier; Pro is listed at $150/month with 4 total seats and 1 live connection; additional Pro seats are listed at $30/month each and additional connections at $95/month each; AgentOS documentation describes a production API with 50+ ready-to-use endpoints and SSE-compatible streaming; Agno documentation lists broad model support, including 40+ model providers in examples, 100+ tools, 2000+ examples, and vector database support that includes PgVector, Pinecone, Qdrant, Weaviate, Chroma, and other backends. The strongest use cases are internal engineering automation, private agent infrastructure, multi-agent task orchestration, RAG applications, research workflows, and production services where the team wants to own runtime behavior and data storage. AgentOS documentation also describes sessions, memory, knowledge, and traces being stored in the customer database, browser-to-runtime control plane connections, JWT-based RBAC, request isolation, traces, approvals, human-in-the-loop flows, schedules, and user management. Those are meaningful operational capabilities, but teams should still verify exact plan limits, deployment architecture, compliance obligations, retention settings, and benchmark methodology against the current Agno documentation before adopting it for regulated or high-volume production workloads. Agno competes most directly with developer-first agent frameworks such as LangChain, LangGraph, CrewAI, and LlamaIndex rather than fully hosted no-code agent products. Its value is highest when a team has Python expertise, wants production agent infrastructure without building everything from scratch, and is willing to validate integrations and security controls in its own environment.