Comparison Preset
Semantic Kernel is the more prudent choice for an enterprise environment due to its focus on stability and integration with existing systems. Its support for C#, Python, and Java, along with a stated commitment to non-breaking changes post-v1.0, reduces long-term maintenance risk. The framework's adoption is validated by its 205 dependent repositories—a stark contrast to Agno's zero—indicating a more robust and trusted ecosystem. It also has a slightly higher bus factor score of 9/10, suggesting a more resilient project. While both frameworks list critical vulnerabilities requiring due diligence, Semantic Kernel's design for integrating with existing code makes it a lower-risk choice.
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.
Semantic Kernel is a lightweight, open-source development kit for building AI agents and integrating AI models into C#, Python, or Java applications. It functions as efficient middleware, enabling rapid delivery of enterprise-grade solutions by orchestrating existing APIs and supporting future model advancements.
Best For
Building, deploying, and managing scalable, production-ready agentic software, teams, and workflows.
Building AI agents, integrating AI models with existing code/APIs, automating business processes.
Avoid If
no data
no data
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.
- +Lightweight, open-source development kit for AI agents and model integration.
- +Efficient middleware enabling rapid delivery of enterprise-grade AI solutions.
- +Flexible, modular, and observable architecture for responsible AI at scale.
- +Includes security-enhancing capabilities like telemetry support, hooks, and filters.
- +Offers Version 1.0+ support across C#, Python, and Java with commitment to non-breaking changes.
- +Allows easy expansion of existing chat-based APIs to support modalities like voice and video.
- +Designed to be future-proof, allowing new AI models to be swapped without extensive code rewrites.
- +Combines prompts with existing APIs to perform actions and automate business processes.
- +Utilizes OpenAPI specifications for easily sharing extensions with other developers.
Weaknesses
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.
no data
Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
Your expertise shapes what we build next.
We build for engineers who make real architectural decisions. If something is missing, inaccurate, or could be more useful — we want to hear it.
FrameworkPicker — The technical decision engine for the agentic AI era.