Agno Documentation
by Agno
Build agents, teams, and workflows in plain Python — then ship them on a FastAPI runtime.
Overview
Agno's own tagline is "Build, run, and manage agent platforms," and the docs split along exactly those lines: an SDK for building agents, teams, and workflows; AgentOS, a stateless, secure FastAPI backend for running them in production; and a Control Plane UI for monitoring and managing them. The framework's design position is that you write plain Python — no graphs, chains, or convoluted patterns — while the library supplies the pieces that actually get you to production: memory, knowledge (RAG), guardrails, state management, human-in-the-loop, session handling, and observability. Multi-agent work is modeled as teams that plan, communicate, and delegate, which suits research pipelines, data processing, and support triage. Its modular design lets you swap the LLM, database, or vector store without rewriting agent code, and it advertises 100+ integrations. The core framework is Apache-2.0 licensed and free (the GitHub repo is at 41.5k stars); Agno's hosted control plane adds a Pro tier at $150/mo for managing live production systems, with an Enterprise tier above it. A full documentation index for LLM consumption is published at docs.agno.com/llms.txt.
At a Glance
- Topic
- Agentic
- Level
- Intermediate
- Format
- Documentation
- Cost
- Freemium
- Duration
- Self-paced; first agent in ~15 min
- Provider
- Agno
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Build a single agent with tools, memory, and structured output in plain Python
- ✓Compose multi-agent teams that delegate and coordinate on multi-step tasks
- ✓Attach knowledge bases and vector stores to give agents RAG grounding
- ✓Deploy agents behind AgentOS, a stateless FastAPI runtime, rather than a notebook
- ✓Add guardrails, session state, and human-in-the-loop checkpoints
- ✓Swap model providers, databases, and vector stores without rewriting agent logic
Highlights
- •No graph or chain DSL — agent logic stays readable Python
- •Ships a production runtime (AgentOS) rather than leaving deployment as an exercise
- •Apache-2.0 core with 100+ integrations
- •Publishes an llms.txt doc index, so coding agents can consume the docs directly
Who It’s For
Best For
- ✓Python engineers building multi-agent systems for production
- ✓Teams comparing agent frameworks (LangGraph, CrewAI, Pydantic AI, Agno)
- ✓Backend developers who want agents behind a FastAPI service
- ✓Builders of research pipelines, data processing, or support-triage agents
Prerequisites
- •Intermediate Python
- •Familiarity with LLM APIs and tool/function calling
- •Basic FastAPI and REST knowledge for the deployment sections
FAQ
What is Agno Documentation?
Official documentation for Agno, an Apache-2.0 Python framework and runtime for building multi-agent systems. For engineers who want production agents without graph DSLs or chain abstractions.
Is Agno Documentation free?
Agno Documentation offers free content, with paid options for certificates or premium features.
What level is Agno Documentation for?
Agno Documentation is aimed at a intermediate audience. Recommended background: Intermediate Python, Familiarity with LLM APIs and tool/function calling, Basic FastAPI and REST knowledge for the deployment sections.
How long does Agno Documentation take?
Expect roughly Self-paced; first agent in ~15 min. Most learners work through it at their own pace.
What will I learn from Agno Documentation?
You'll learn: Build a single agent with tools, memory, and structured output in plain Python; Compose multi-agent teams that delegate and coordinate on multi-step tasks; Attach knowledge bases and vector stores to give agents RAG grounding; Deploy agents behind AgentOS, a stateless FastAPI runtime, rather than a notebook; Add guardrails, session state, and human-in-the-loop checkpoints; Swap model providers, databases, and vector stores without rewriting agent logic.