Agno: A Python Framework and Runtime for Building Production Agent Platforms
Build, run, and manage agent platforms. Build agents, run them as a service, manage your platform using a web UI.
At a glance
- What is it?
- Agno is an Apache-2.0 Python framework that combines an SDK for defining agents with a runtime called AgentOS, providing a REST API, JWT-based access control, storage, and a web UI for managing agent deployments. It targets teams who want to own their agent stack rather than relying on a managed cloud service.
- Who is it for?
- Agno suits a team that needs a full agent platform stack, including a REST API, multi-user isolation, session storage, and observability, without vendor lock-in on the runtime. It is not the right choice for a developer who just wants a lightweight library for chaining LLM calls: AgentOS adds real infrastructure (Postgres, Docker, a control plane) that is overhead for simple prototypes.
- Can I use it commercially?
- Yes. Apache-2.0 is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 4 days ago.
- What is it written in?
- Mainly Python, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 25, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
What Agno Builds and Who It Is For
Agno addresses the gap between a single-agent demo and a production agent service that multiple users interact with. It provides an SDK for writing agents in Python and a runtime called AgentOS that exposes those agents as a REST API, handles user sessions and memory, manages traces, and enforces access control. The README describes the goal as owning your agent stack, meaning the operator retains control of data, memory, and security posture rather than delegating those to a SaaS platform.
The intended users are engineering teams building agent-powered products: internal tools, customer-facing assistants, or automated pipelines where the operator needs audit logs, role-based access, or multi-tenant isolation. Single-developer prototypes are a supported starting point (the README links to a first-agent tutorial in 20 lines), but the platform features are the main offering.
The README notes that Agno sends one telemetry event per agent run to help the team prioritize model provider support. This can be disabled by setting the environment variable AGNO_TELEMETRY=false.
SDK, AgentOS Runtime, and the Starter Template Approach
Agno separates the agent definition layer (the SDK) from the infrastructure layer (AgentOS). The SDK handles how agents are described, which tools they have access to, and how they interact with models. AgentOS handles deployment: REST endpoints, database persistence, observability, and the web UI.
The recommended starting path uses a coding agent. The README provides a prompt to hand to a coding agent such as Claude Code or Cursor:
Help me set up my agent platform.
Clone https://github.com/agno-agi/agentos-railway into a folder called
agent-platform, cd in, read the README, and follow the get started guide.The coding agent clones a starter template, sets up the platform locally with Docker, and provides a REST API, Postgres database, MCP server, and control plane. The starter templates are structured around deployment targets: agentos-railway for Railway, agentos-docker for plain Docker, agentos-aws, agentos-gcp, agentos-azure, agentos-fly, agentos-render, agentos-modal, and agentos-helm for Kubernetes.
Developers who prefer to configure the platform by hand can follow the docs at docs.agno.com rather than the coding-agent path.
Production API, Storage, and AgentOS Runtime Features
AgentOS provides over 50 REST API endpoints with SSE and websockets, making it usable as a backend for chat interfaces, Slack bots, or any client that can make HTTP requests. Session, memory, knowledge, and trace data are stored in a developer-owned Postgres database, not in a third-party service.
The runtime includes several features that require explicit configuration: human approval pauses agent runs for user confirmation and can block specific tools from running without admin sign-off; observability uses OpenTelemetry tracing with run history and audit logs; security provides JWT-based RBAC with multi-user and multi-tenant isolation built in. Context Providers allow agents to pull live data from Slack, Drive, wikis, MCP servers, and custom sources at runtime.
Scheduling is handled inside AgentOS using cron-based scheduling and background jobs without requiring an external queue service. The agent interface list in the README includes Slack, Telegram, WhatsApp, Discord, AG-UI, and A2A protocols.
Tool Integrations and Telemetry Opt-Out
The README lists over 100 integrations covering GitHub, Slack, Postgres, and more through pre-built toolkits. These are accessed through the SDK when defining what tools an agent can use.
For developers using coding agents to work with Agno, the README recommends adding the Agno documentation as an MCP server by pointing a coding agent to docs.agno.com/mcp. An alternative is to index https://docs.agno.com/llms-full.txt in tools like Cursor, VSCode, or Windsurf.
Telemetry is enabled by default. The README specifies that only a per-run event is sent and that prompts, messages, and outputs are never included. To disable it entirely:
export AGNO_TELEMETRY=falseThis must be set in the environment before starting the agent runtime, not at the application level inside Python code.
Where Agno Is Not the Right Fit
Agno is infrastructure, not just a library. A full local setup requires Docker, a Postgres database, and the starter template structure. For a developer who wants to test a single agent concept quickly, this overhead is real.
The framework does not document a purely in-process, single-file option equivalent to writing a few Python functions with a model API call. The 20-line first-agent tutorial at docs.agno.com may cover that, but the README focuses on the platform setup path.
LangGraph is an alternative agent framework from LangChain that approaches agent orchestration as a stateful graph with typed edges and nodes. The design model is fundamentally different: LangGraph is graph-based state management, while Agno is a service-oriented runtime with REST endpoints and storage as first-class concerns. The choice between them depends on whether the team needs the platform infrastructure Agno provides or the graph-based control flow LangGraph offers.
Maintenance, Licence, and Repository Layout
The last push to the repository was on 2026-09-25. Recent releases include v3.0.11 on 2026-09-23, v3.0.10 on 2026-09-16, and v3.0.9 on 2026-09-08. The release cadence indicates active development on the v3 series.
Agno is licensed under Apache-2.0. This is a permissive licence that allows commercial use, modification, and redistribution without requiring derivatives to be open-source. There is no copyleft clause.
The repository layout separates the SDK (in libs/ and cookbook/) from the runtime starter templates (in separate repositories under the agno-agi organization). The CLAUDE.md and AGENTS.md files in the root suggest the project is designed to be worked on with AI coding agents. Contributions follow the CONTRIBUTING.md guide.
Editorial conclusion
Agno suits a team that needs a full agent platform stack, including a REST API, multi-user isolation, session storage, and observability, without vendor lock-in on the runtime. It is not the right choice for a developer who just wants a lightweight library for chaining LLM calls: AgentOS adds real infrastructure (Postgres, Docker, a control plane) that is overhead for simple prototypes. Before adopting it, verify that the target deployment environment supports Docker and that the JWT-based RBAC model matches the organization's authentication requirements.
Frequently asked questions
Is Agno AI free?
The Agno framework and the AgentOS runtime are open-source under the Apache-2.0 licence, which permits commercial use at no cost. The infrastructure you run it on (cloud servers, Postgres, Docker) carries its own costs from your chosen provider.
What is the Agno framework?
Agno is a Python framework for building agent platforms, consisting of an SDK for defining agents and an AgentOS runtime that exposes agents as a REST API with Postgres storage, JWT-based access control, observability, and a web UI. It is designed for production deployments where the operator wants to self-host the entire agent stack.
How do I disable Agno telemetry?
Set the environment variable AGNO_TELEMETRY=false before starting the runtime. The README states that only per-run events are sent by default and that prompts, messages, and outputs are never included in the telemetry data.
What deployment platforms does Agno support?
Agno provides starter templates for Railway, Docker, AWS, GCP, Azure, Fly.io, Render, Modal, and Kubernetes via Helm. Each template is identical in structure except for the deployment scripts, so switching platforms means pointing to a different starter repository.
Official sources
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