Agno Review, Pricing & Alternatives (2026)

Agno is an open source Python SDK and self-hostable runtime for building agent platforms. The project was formerly known as Phidata before adopting the Agno name in January 2025. Its main primitives are agents, teams, and workflows. Agents combine models, instructions, and tools, teams coordinate multiple agents, and workflows define sequential, parallel, conditional, looping, or nested steps. The SDK supports more than 30 model providers and more than 100 integrations, along with multimodal input and output, structured Pydantic based I/O, memory, knowledge, sessions, learning, guardrails, and state. Developers choose their model vendors, databases, and deployment environment instead of buying bundled inference. PitchBook lists Agno as founded in 2023, private, with nine employees and $5.4 million in funding.

A typical Agno project starts with an agent configured with a model, instructions, and tools. Teams coordinate agents when a task needs several roles, while workflows organize application logic into code-defined pipelines. AgentOS supplies the runtime layer and is built with FastAPI. It exposes REST, streaming, WebSocket, MCP, A2A, AG-UI, Slack, Telegram, and WhatsApp interfaces. The runtime also provides sessions, memory and knowledge storage, background execution, checkpointing, cancellation, approvals, schedules, tracing, evaluations, and JWT authorization. Studio adds a visual editor for composing, testing, versioning, and publishing agents, teams, and workflows. The Control Plane manages AgentOS instances. Agno keeps that management layer separate from the runtime, allowing the runtime to remain in customer infrastructure.

Agno separates its open source runtime from its hosted management service. The SDK and AgentOS have no Agno charges for events, tokens, compute, storage, retention, or egress. Developers connect supported model providers and expose agents through REST, MCP, or chat interfaces. The project changed its name from Phidata to Agno in January 2025, with namespaces moving from phi to agno and the command line interface changing from phi to ag. Documentation lists AgentOS capabilities such as checkpointing, human approval, scheduling, JWT authorization, and integrations with Claude Code, Codex, and Cursor. This structure gives developers a Python implementation layer for building agents and a separate operational layer for deployed systems, while leaving model and infrastructure costs with customers.

Agno's Free plan costs $0 per month and includes unlimited usage and retention, local AgentOS connections, the local Control Plane, Studio, sessions, traces, memory, knowledge, learnings, metrics, evaluations, approvals, scheduling, templates, and community support. Pro costs $150 per month and adds one live AgentOS connection, three team seats, basic role-based access control, and email support. Extra seats cost $30 per month each, and extra live connections cost $95 per month each. SAML SSO is a $300 per month add-on on Pro, while Enterprise pricing is custom. Enterprise adds end-user management, audit logs, identity and roles, custom resource-scoped RBAC, support commitments, security reviews, compliance support, and optional self-hosted Control Plane services.

Agno leaves deployment choices and several security responsibilities with the customer. Users select model providers, databases, infrastructure, authentication settings, and application controls. AgentOS can validate JWTs, enforce endpoint scopes, use service-account tokens, assign roles, and enable per-user data isolation. Isolation is off by default, so custom handlers need explicit scope helpers. Agno's Trust Center reports encryption in transit and at rest, access controls, MFA for critical services, scanning, backups, and disaster-recovery practices. LangGraph focuses on graph-based orchestration, while CrewAI emphasizes teams of agents, and AutoGen centers on multi-agent conversation patterns. Pick Agno if you want a Python-first open-source runtime with optional hosted management and control over deployment. This approach suits teams willing to configure infrastructure and application security themselves.