Phidata

Agno (formerly Phidata) is one of the most complete open-source agent frameworks we've reviewed. It doesn't just give you a Python SDK—it gives you a production runtime (AgentOS) with governance features that most teams would otherwise have to build themselves: JWT-based RBAC, per-session isolation, human-in-the-loop approval flows, and audit logs. That combination is rare; most open-source agent frameworks stop at the SDK layer and leave you to figure out deployment, security, and observability. The private-by-default deployment model is a major plus. AgentOS runs in your own cloud—AWS, GCP, Railway, or airgapped—so your agents' conversations, memory, knowledge, and traces never leave your environment. That's a big deal for companies with strict data residency or compliance requirements. It also means no per-token or per-event fees; you pay for your own infrastructure, which can be more predictable at scale. The unified API across 30+ model providers is another strength. You can switch models without rewriting your agent logic, and it works with Chat, Responses, and Interactions APIs. The ability to run agents built with Claude Agent SDK, LangGraph, or DSPy in the same runtime is a practical migration path for teams already invested in other frameworks. We particularly like the async support. Agno gives you a single agent that works seamlessly in sync and async modes—no event-loop headaches. That's a common pain point in Python agent frameworks, and Agno handles it well. The free tier is genuinely useful for learning and local development—you can build agents and run them against a local AgentOS Control Plane, with session monitoring, memory and knowledge management, and system evaluations. But to go into production with a live connection, you need Pro at $150/mo, which includes one live connection and four total seats. Additional seats are $30/mo and extra live connections are $95/mo. That's not cheap for small teams or hobbyists, but it's reasonable for companies running real agent systems. One limitation is that you manage your own infrastructure—deploying AgentOS on your cloud is on you. The docs provide coding-agent prompts to help with deployment on Railway, Docker, AWS, Fly, GCP, Kubernetes, Azure, Render, and Modal, which lowers the barrier, but it's still more work than a fully hosted solution. We also note that recent updates (v2.5.9, v2.8.6, v2.7.3) show active development, adding features like follow-up suggestions, text-to-speech, Valkey support, and Redmine tools. The roadmap seems strong. Where Agno might not fit: if you need heavy custom graph-based workflows or complex state machines, LangGraph's explicit graph model might be a better match. And if you're looking for a completely free way to run agents in production, Agno's Free tier won't cut it—you'll need Pro. For teams that want a self-managed runtime without the AgentOS dependency, you can use the SDK alone but you'll lose the governance and control plane benefits.