Comparison Preset
Choose Agno for an enterprise deployment due to its significantly lower risk profile regarding licensing and long-term support. Agno's Apache-2.0 license is standard for commercial use, whereas AutoGen's CC-BY-4.0 license introduces legal and compliance complexities that are unacceptable for most enterprise products. Furthermore, Agno's very active development (last commit 0 days ago) and high maintainer count indicate strong, ongoing support, while AutoGen's repository has been inactive for over 100 days. While the critical vulnerability in Agno must be addressed immediately, the risk of adopting a seemingly unmaintained framework with a problematic license is far greater.
Overview
The bottom line ā what this framework is, who it's for, and when to walk away.
Bottom Line Up Front
Agno provides an SDK, a stateless FastAPI runtime (AgentOS), and a control plane for building and deploying production-ready AI agent platforms. It supports agents with memory, knowledge, and integrations, offering cloud-agnostic deployment.
AutoGen is a Python framework for developing AI agents and applications, ranging from no-code prototyping to scalable multi-agent systems. It offers a modular architecture, enabling conversational agents, complex workflows, and integration with external services. The framework supports research and distributed applications.
Best For
Deploying, monitoring, and managing production-grade AI agent platforms with structured workflows.
Building conversational multi-agent AI applications, research, and scalable business workflows.
Avoid If
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Strengths
- +Provides a rich SDK for building agents, teams, and complex workflows.
- +Offers extensive integrations (100+) for agent capabilities.
- +Features a production-ready runtime (AgentOS) based on a stateless FastAPI backend.
- +Includes an integrated control plane with a UI for monitoring and management.
- +Supports deployment across multiple cloud providers and containerization options, including AWS, GCP, Azure, Kubernetes, and Docker.
- +Supports no-code agent prototyping via AutoGen Studio.
- +Provides a programming framework for building conversational single and multi-agent applications (AgentChat).
- +Offers an event-driven core for building scalable multi-agent AI systems.
- +Facilitates deterministic and dynamic agentic workflows for business processes.
- +Enables research on multi-agent collaboration.
- +Supports distributed agents for multi-language applications through extensions like GrpcWorkerAgentRuntime.
- +Extensible with built-in and community components for external services, including OpenAI API and Docker for code execution.
Weaknesses
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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
The SDK enables agents to manage internal state via its 'memory' feature, while the AgentOS runtime itself operates statelessly.
The framework manages state through conversational message exchanges and event-driven interactions between agents.
Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
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