Agno: Agent Framework and High-Performance Runtime for Multi-Agent Systems

Categories: Coding & Developer Tools, Automation / Agents, Productivity | Pricing: Freemium | Official Website ↗

Agno is a framework-agnostic platform for building, deploying, and managing production-ready AI agents and multi-agent systems.

Agno provides an open-source Python framework for developing AI agents and AgentOS, a high-performance runtime for deploying and managing these agents as production services. It allows developers to build agents using various frameworks like LangGraph, DSPy, Claude Agent SDK, or their own code, and then handles the infrastructure for APIs, sessions, streaming, scaling, authentication, and observability. The platform aims to reduce the burden of maintaining complex agent infrastructure, offering features like per-session state, JWT authentication, SSE streaming, tracing, rate limiting, and an approval queue. Agno ensures that the production layer remains flexible, allowing users to switch underlying agent frameworks without re-platforming. It also includes a secure UI control plane for monitoring, managing, and evaluating agent performance, memory, and knowledge, with all data remaining private within the user's cloud environment.

Key Features

Framework-agnostic agent platform
High-performance runtime (AgentOS)
APIs, sessions, streaming, scaling, auth, observability
FastAPI scaffolding
Per-session state management
JWT auth, RBAC
SSE Streaming
Tracing (OpenTelemetry)

Pros

Framework-agnostic, allowing use of various agent frameworks
Provides a robust production-ready infrastructure layer
Offers high performance and scalability for multi-agent systems
Includes a control plane for monitoring and managing agents
Ensures data privacy by keeping all data within the user's cloud

Cons

Pricing for production use starts at $150/month
Requires technical expertise for deployment and management
Specific integrations beyond general API patterns are not explicitly listed
No explicit mention of a free trial for paid plans
Focuses heavily on Python, potentially limiting other language users

Use Cases

Deploying AI agents as production services
Building and managing multi-agent systems
Creating AI-powered workflows and applications
Developing custom AI solutions with robust infrastructure
Monitoring and evaluating AI agent performance in real-time

Best For

AI developers
Engineering teams building AI products
Startups deploying AI agents
Organizations requiring scalable multi-agent systems

Integrations: LangGraph, DSPy, Claude Agent SDK, FastAPI, Redis, OpenTelemetry

Platforms: Web

Watch demo on YouTube ↗

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