Comparison Preset
Agno is the more suitable choice due to its enterprise-friendly Apache-2.0 license, which avoids the legal and compliance risks associated with AutoGen's CC-BY-4.0 license. Agno's architecture is explicitly designed for production environments, offering critical features like RBAC, isolated sessions, audit logs, and self-hosted data ownership. While Agno has a known critical vulnerability that requires mitigation, AutoGen's stagnant development, with no commits in 83 days, presents a greater long-term support and maintenance risk. Agno's active maintenance and clear state management model provide a more defensible foundation for a long-term enterprise platform.
Overview
The bottom line — what this framework is, who it's for, and when to walk away.
Bottom Line Up Front
Agno provides an SDK and runtime (AgentOS) for building, running, and managing agent platforms. It enables the creation of multi-agent systems and step-based workflows, offering production features like isolated sessions, RBAC, and data ownership. Teams can deploy intelligent software in their own cloud environments.
AutoGen is a Python framework for building AI agents and applications, supporting everything from no-code prototyping to scalable, distributed multi-agent systems. It features a modular design with components for core agent orchestration, conversational interactions, and integrations with external services. The framework facilitates deterministic and dynamic agentic workflows for business processes and research.
Best For
Building and productionizing multi-agent platforms, in-product copilots, and AI-driven data workflows.
Building scalable multi-agent AI systems, complex agentic workflows, and multi-agent research.
Avoid If
no data
Your task requires only a simple single-agent LLM call without complex interaction.
Strengths
- +Provides an SDK for building agents, multi-agent teams, and step-based agentic workflows.
- +AgentOS runtime offers multi-user, isolated sessions with tracing, scheduling, RBAC, and audit logs.
- +Allows running agents as a service with a unified control plane for management.
- +Runs in your cloud using your database, ensuring ownership of session, memory, and trace data.
- +Natively typesafe and multi-modal, suitable for data labeling, extraction, and classification.
- +Can productionize agents built with any framework, model, or cloud.
- +Supports rapid no-code prototyping of agents via AutoGen Studio.
- +Provides a programming framework for conversational single and multi-agent applications (AgentChat).
- +Offers an event-driven core for building scalable multi-agent AI systems and workflows.
- +Facilitates research on multi-agent collaboration and distributed agents.
- +Includes extensions for integrating with external services like OpenAI Assistant API, Docker code execution, and gRPC for distribution.
- +Supports deterministic and dynamic agentic workflows for business processes.
Weaknesses
- −Requires Python 3.10+.
- −Learning curve associated with its modular architecture (Core, AgentChat, Studio, Extensions) for complex system design.
- −Lacks first-party emphasis on local LLM integration in the provided examples, prioritizing OpenAI models.
- −Potentially complex for simple single-agent tasks, as it is designed for multi-agent systems and scalable solutions.
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
AgentOS runtime manages state through multi-user, isolated sessions, allowing users to own their session, memory, and trace data.
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Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
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