agno-agi/agno

What it solves

Building and running AI agents is often messy: you have to wire models, tools, memory, and deployment yourself, and agents never get better from real usage. Agno solves this by giving you one platform to build agents with code or no-code tools, run them as reliable services with APIs and MCP support, and then systematically improve them using data from actual conversations and traces — so they get smarter over time.

How it works

You start from a pre-built template (e.g., for Railway, Docker, AWS) that sets up the whole platform locally or in your cloud. That includes an API server, an MCP server, PostgreSQL storage for conversations and memory, and an OS Manager UI for reviewing and managing everything. You build agents using the Agno SDK (or even Claude Code or Codex), connect them to tools, give them long-term memory and knowledge, and organize them into teams or workflows. The platform handles durable execution, scheduling, human approval, and security. For improvement, you use the /eval-and-improve skill in coding agents like Claude Code or Codex: they get access to source code, conversations, traces, and evaluations, then test changes to instructions, tools, knowledge, and code, and propose pull requests for you to review or adopt.

Who it’s for

This is for developers and teams who want to build production-grade AI agents rather than prototypes. It’s useful if you’re shipping agents to your product, your team, or your customers and need reliable deployment, observability, and a way to continuously refine agent behavior based on real usage. The no-code UI and OS Manager also make it accessible to less technical folks who still want to manage and improve agents.

Highlights

• Build agents with any model and connect them to your own tools, databases, and MCP servers.
• Run as durable services — you get an API and MCP server out of the box, with background execution and persistent state.
• Improve with use — built-in skills let coding agents investigate failures, simulate scenarios, and test changes, returning pull requests for review.
• Deploy anywhere — templates for Docker, Railway, AWS, GCP, Azure, Fly.io, Render, Modal, and Helm.
• Manage everything in one UI — the AgentOS UI shows conversations, traces, knowledge, and activity, with human approval and scheduled runs.