Comparison Preset
Agno is the more appropriate choice for an enterprise context, primarily due to its architecture and license. Its design emphasizes user-owned infrastructure and data, with features like native tracing and full auditability that are critical for governance and compliance. The Apache-2.0 license is standard and well-understood, avoiding the legal and compliance risks of AutoGen's CC-BY-4.0 license. However, the existing CRITICAL vulnerability is a major risk that must be immediately addressed and mitigated before any production deployment. Despite this, Agno's high commit frequency (25x/week) suggests it is actively maintained, making it a more viable long-term investment than the less active AutoGen.
Overview
The bottom line — what this framework is, who it's for, and when to walk away.
Bottom Line Up Front
Agno provides a production-grade runtime for building, deploying, and managing agentic software, from single agents to coordinated teams and workflows. It features a scalable, stateless FastAPI backend, robust state management in your database, and a control plane for monitoring and governance. It emphasizes user ownership of data and infrastructure.
AutoGen is a Python framework for building AI agents and applications, offering layered components for prototyping, conversational agents, and scalable multi-agent systems. It supports deterministic and dynamic agentic workflows, with extensibility for external services and custom components. Developers can choose between a no-code UI, a Python agent chat framework, or an event-driven core for complex systems.
Best For
Building, deploying, and managing scalable, production-ready agentic software, teams, and workflows.
Building scalable, conversational, and multi-agent AI systems with deterministic or dynamic workflows.
Avoid If
no data
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Strengths
- +Runtime specifically designed for agentic software, teams, and workflows.
- +Includes memory, knowledge, guardrails, and over 100 integrations for agent development.
- +Provides a stateless, horizontally scalable FastAPI backend for production deployment.
- +Offers a control plane (AgentOS UI) for testing, monitoring, and managing systems in production.
- +Supports per-user and per-session isolation with runtime approval enforcement.
- +Features native tracing and full auditability.
- +Stores sessions, memory, knowledge, and traces in the user's database, ensuring data ownership.
- +Runs in the user's infrastructure, not Agno's.
- +Provides AutoGen Studio, a web-based UI for prototyping agents without writing code.
- +Offers AgentChat, a programming framework for building conversational single and multi-agent applications.
- +Built on Core, an event-driven framework for building scalable multi-agent AI systems.
- +Supports deterministic and dynamic agentic workflows for business processes.
- +Enables research on multi-agent collaboration and distributed agents for multi-language applications.
- +Features an extension mechanism to interface with external services and libraries.
- +Includes built-in extensions for using Model-Context Protocol (MCP) servers and OpenAI Assistant API.
- +Supports running model-generated code in a Docker container via a built-in extension.
- +Facilitates distributed agents via GrpcWorkerAgentRuntime.
Weaknesses
- −Requires Python 3.10 or newer, which may limit compatibility with older environments.
Project Health
Is this project alive, well-maintained, and safe to bet on long-term?
Bus Factor Score
Maintainers
Open Issues
Fit
Does it support the workflows, patterns, and capabilities your team actually needs?
State Management
State is managed externally in the user's database for sessions, memory, knowledge, and traces, with a stateless, session-scoped FastAPI backend.
AutoGen supports conversational and multi-agent applications, requiring state management to maintain context throughout interactions.
Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
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