Comparison Preset
Agno is the more suitable choice for an enterprise environment due to its maturity and focus on production-grade operations. It is a more established project (1475 vs 530 days old) and its architecture supports enterprise needs like self-hosted data, OpenTelemetry tracing, RBAC, and human approval workflows. While both frameworks report critical vulnerabilities that must be vetted, Agno’s state management, which uses your own database, provides superior control and auditability for long-term systems. Its strong bus factor score of 8/10 and Apache-2.0 license align well with enterprise risk management requirements.
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.
SmolAgents is a Python library for rapidly building LLM agents with minimal code, emphasizing simplicity. It supports both code-writing agents with sandboxed execution and traditional tool-calling, integrating flexibly with various models and tools. Its design prioritizes ease of use and broad compatibility across modalities and sources.
Best For
Orchestrating, deploying, and managing a fleet of AI agents for product, ML, and operations workflows.
Rapidly building and deploying LLM agents with code execution and flexible tool/model integration.
Avoid If
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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.
- +Extremely easy to build and run agents with minimal code, designed for simplicity.
- +Supports Code Agents capable of writing actions in code, with secure sandboxed execution options (Modal, Blaxel, E2B, Docker).
- +Flexible integration with various LLM providers and models, including local Transformers and Ollama.
- +Agnostic to tool sources, allowing integration from MCP servers, LangChain, or Hugging Face Spaces.
- +Handles diverse input modalities beyond text, including vision, video, and audio inputs.
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
Agno stores all session, memory, and trace data in the user's own database within their cloud environment.
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Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
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