Agno
High-performance framework for building multi-modal AI agents with built-in memory and tool use.
Agno is an open-source Python framework and agent platform designed for building high-performance multi-modal AI agents. Originally known as PhiData before rebranding, Agno has grown into a full-stack agent infrastructure solution that covers everything from local development to production deployment.
At its core, Agno consists of two main products: the Agent Framework and AgentOS. The Agent Framework is the open-source Python library for constructing individual agents or multi-agent teams with built-in memory, knowledge retrieval, and tool-use capabilities. AgentOS is the production runtime layer — a hosted control plane that lets teams deploy, monitor, and trace agents in their own cloud environment.
The framework is model-agnostic, supporting any LLM provider including OpenAI, Anthropic, Google, and open-source models. It is also database-agnostic for memory and storage backends. This flexibility means developers are not locked into a particular vendor stack. Agents can be connected to a wide variety of external interfaces including Telegram, Slack, WhatsApp, and custom UIs.
One of Agno's distinguishing characteristics is its emphasis on speed and performance. Users and community testimonials consistently cite how quickly agents can be set up — often within minutes — and how the runtime performs compared to alternatives like LangGraph or CrewAI. The framework is engineered with production concerns built in from the start, rather than treated as afterthoughts.
For teams, AgentOS adds a built-in control plane accessible from the browser, offering real-time chat, distributed tracing, and monitoring without shipping data to third-party systems. Security features include JWT authentication, role-based access control (RBAC), and request-level isolation — making it a practical choice for enterprise or regulated environments where data egress is a concern.
In the broader agent framework ecosystem, Agno competes with LangChain/LangGraph, CrewAI, and AutoGen. Where LangGraph offers fine-grained state machine control and LangChain provides a large ecosystem of integrations, Agno prioritizes simplicity and speed of development. Developers moving from LangGraph to Agno often cite cleaner abstractions and faster iteration cycles. Compared to CrewAI, Agno provides more direct infrastructure control and better suited for teams who want to self-host.
Agno is particularly well-suited for Python developers and teams that need to go from prototype to production without switching frameworks mid-project. The combination of the open-source framework and the optional AgentOS runtime means teams can start small and scale without architectural rewrites.
Key Features
- Model-agnostic agent framework supporting any LLM provider (OpenAI, Anthropic, Google, and open-source models)
- Built-in memory, knowledge retrieval, and guardrails for self-learning agents
- Multi-agent orchestration for building teams of coordinating agents
- AgentOS production runtime with deploy-anywhere architecture for running agents as a service
- Browser-based control plane for real-time chat, distributed tracing, and monitoring
- Security-first design with JWT authentication, RBAC, and request-level isolation
- Integrations with external interfaces including Telegram, Slack, WhatsApp, and custom UIs
- Private, self-hosted deployment — no data egress or third-party data retention
Pros & Cons
Pros
- Fast to get started — working agents can be running in minutes according to community feedback
- Full-stack solution covering both agent development and production deployment in a single platform
- Strong security and privacy posture with self-hosted AgentOS and no mandatory data sharing
- Model and database agnostic, avoiding vendor lock-in
- Active community and frequent updates, with a public changelog and Discord
Cons
- Relatively newer brand (rebranded from PhiData) meaning ecosystem maturity and third-party content is still growing
- AgentOS production runtime requires self-managed cloud infrastructure, adding operational overhead for smaller teams
- Python-only framework limits accessibility for teams working in other languages
- Less community documentation and tutorials compared to more established frameworks like LangChain
Pricing
Agno offers a pricing page on its website. The Agent Framework is open-source. AgentOS, the production runtime and control plane, has separate pricing tiers. Visit the official website for current pricing details.
Who Is This For?
Agno is best suited for Python developers and engineering teams that need to build production-ready multi-agent systems with serious security and privacy requirements. It is a strong fit for organizations that want full control over their infrastructure — including self-hosted deployment — and need to move quickly from prototype to a stable, monitored production service without changing frameworks.
Similar to Agno