Agno
A Python framework for building multi-agent systems with memory, knowledge, and tools — formerly Phidata, now with the AgentOS runtime.
Agno (formerly Phidata) is a Python framework for building multi-agent systems with memory, knowledge, and tools. You compose Agents, Teams, and Workflows over 20+ model providers, and can run them in production with the AgentOS runtime. The core library is open source under Apache-2.0.
Agno is a Python framework for building multi-agent systems with memory, knowledge, and tools. It was formerly known as Phidata and was renamed to Agno in early 2025, with its GitHub organization moving to agno-agi. You compose three core abstractions — Agents, Teams, and Workflows — over 20+ model providers, and the framework is model-agnostic so you can swap providers without rewriting agent logic.
It is aimed at developers who want to go beyond a single prototype agent to a managed platform: the project pairs the open-source SDK with AgentOS, a FastAPI-based runtime for serving agents in production with tracing, scheduling, and RBAC. That makes it a fit for an AI-assisted workflow where you scaffold agents locally and then run them as a real service.
Highlights
- Agents, Teams, Workflows — build a single tool-using agent, coordinate a team of specialists, or wire deterministic step-based workflows from the same SDK.
- Memory and knowledge — attach session memory and knowledge stores (backed by common vector databases) so agents retain context and ground answers in your data.
- Tools and integrations — give agents tools through a large library of prebuilt toolkits, or register your own Python functions.
- AgentOS runtime — serve agents as a production service with tracing, scheduling, RBAC, and a control plane that runs in your own cloud.
In an AI-assisted workflow
Use Agno to define a tool-equipped agent in a few lines, then iterate with your coding assistant before promoting it to AgentOS. See Agent Frameworks in 2026 for where it sits among the alternatives.
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
tools=[DuckDuckGoTools()],
markdown=True,
)
agent.print_response("Summarize the latest news on agent frameworks")TIP
Start with a single Agent, then graduate to a Team or Workflow only when one agent's responsibilities clearly split. Verify import paths and class names against the current docs, since Agno's API changed across the 2.0 release.
Good to know
The core Agno library is open source under Apache-2.0 and free to self-host — you supply your own model provider and database. A free control-plane tier plus paid Pro and Enterprise plans (AgentOS) add hosted management, live connections, retention, and team seats; confirm the current tiers and limits on the official pricing page before relying on them. For patterns on coordinating multiple agents, see Multi-Agent Orchestration.
Frequently asked questions
- What is Agno?
- Agno is an open-source Python framework for building multi-agent systems with memory, knowledge, and tools. It was formerly known as Phidata and was renamed to Agno in early 2025 (its GitHub org moved to agno-agi). You compose Agents, Teams, and Workflows, connect 20+ model providers, and can run them in production with the AgentOS runtime.
- Is Agno free?
- The core Agno framework is open source under Apache-2.0 and free to self-host; you bring your own model provider and database. There is also a free control-plane tier plus paid Pro and Enterprise plans (AgentOS) that add hosted management, live connections, and team features. Confirm current pricing on the official site.
- How does Agno compare to CrewAI?
- Both are Python multi-agent frameworks. CrewAI centers on a role/goal 'crew' metaphor, while Agno gives you Agents, Teams, and Workflows plus a built-in AgentOS runtime for serving and managing agents, and emphasizes fast, low-memory agent instantiation. Agno leans toward the full build-run-manage lifecycle; pick based on how much production tooling you want bundled in.
Related
- Which Agent Framework in 2026? LangGraph vs CrewAI vs AutoGen vs OpenAI Agents SDK vs Claude Agent SDKA decision guide to the major AI agent frameworks — control vs. abstraction, multi-agent models, state and durability, and which fits your project.
- LangGraph vs CrewAI: Agent Frameworks Compared (2026)LangGraph vs CrewAI — explicit state-machine control vs role-based crew abstractions. Which agent framework fits your reliability bar and team.
- Multi-Agent OrchestrationFour patterns for coordinating multiple agents — fan-out, pipeline, orchestrator-worker, and verify/critic — and when each earns its overhead.
- Agent Memory Architecture: Short-Term, Long-Term, and When to Use EachHow AI agents remember — working memory vs. persistent long-term memory, what to store, how to retrieve it, and how to keep context small.
- Agentic RAG: When Retrieval Needs an Agent in the LoopWhat agentic RAG is — retrieval as a tool an agent uses iteratively, with query planning, self-correction, and multi-source routing — and when the upgrade pays.