Comparison Preset
Agno is the only viable choice here due to its enterprise-friendly Apache-2.0 license, which avoids the significant legal and compliance risks of AutoGen's CC-BY-4.0 license. It is explicitly designed for production environments, offering self-hosting for data sovereignty, RBAC, and human approval workflows that align with enterprise governance. However, you must immediately plan to mitigate its 2 known vulnerabilities, one of which is CRITICAL, a serious risk factor not present in AutoGen. Despite this, its active maintenance (25 commits/week) and architecture designed for control and observability make it the more defensible choice for long-term maintainability. AutoGen's license and recent inactivity make it unsuitable for enterprise use.
Overview
The bottom line — what this framework is, who it's for, and when to walk away.
Bottom Line Up Front
Agno is an agent platform designed to build, deploy, and manage AI agents in production environments. It supports agents built using any framework or no-code UI, providing production-grade features like tracing, scheduling, and RBAC. Agno allows teams to automate diverse tasks from data labeling and document extraction to product copilots while maintaining data ownership.
AutoGen is a Python framework for building AI agents and applications, ranging from no-code prototyping to scalable, event-driven multi-agent systems. It supports conversational applications, complex workflows, and features like secure code execution and distributed agents.
Best For
Orchestrating, deploying, and managing a fleet of AI agents for product, ML, and operations workflows.
Building scalable multi-agent AI systems, including business workflows and collaborative AI research.
Avoid If
no data
Requires an older Python version than 3.10.
Strengths
- +Productionizes agents built with any framework, offering flexibility in agent creation.
- +Provides robust production features for agents, including tracing, scheduling, role-based access control (RBAC), and audit trails.
- +Supports management of the entire agent development lifecycle using coding agents.
- +Offers native typesafety and multi-modal capabilities for various input/output modalities, including structured output.
- +Ensures data ownership by storing all session, memory, and trace data in the user's own database and cloud.
- +Enables auto-improvement of agents using production usage data via provided code mechanisms.
- +Provides a web-based UI (AutoGen Studio) for no-code agent prototyping.
- +Features an event-driven core for scalable multi-agent AI systems and workflows.
- +Facilitates conversational single and multi-agent application development with AgentChat.
- +Enables secure code execution within Docker containers using built-in extensions.
- +Supports distributed agents and offers an extensible architecture for external services and community contributions.
Weaknesses
- −Requires Python 3.10 or a newer version for AgentChat.
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
Agno stores all session, memory, and trace data in the user's own database within their cloud environment.
AutoGen is an event-driven framework designed for conversational multi-agent systems, where state is managed through agent interactions and event flows.
Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
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