Keywords AI
Compare Agno and Llama Stack side by side. Both are tools in the Agent Frameworks category.
| Category | Agent Frameworks | Agent Frameworks |
| Website | agno.com | github.com |
Key criteria to evaluate when comparing Agent Frameworks solutions:
Agno (formerly Phidata) is a framework for building multi-modal AI agents.
Llama Stack is Meta's standardized API and SDK for building AI applications on top of Llama models. It provides a unified interface for inference, safety, memory, and agentic workflows — with swappable providers for local, cloud, and on-device deployment. As the official framework for the Llama ecosystem, it is becoming the default for teams building on open-source Llama models.
Developer frameworks and SDKs for building autonomous AI agents with tool use, planning, multi-step reasoning, and orchestration capabilities.
Browse all Agent Frameworks tools →An agent framework provides the building blocks for creating AI agents that can autonomously plan, use tools, and complete multi-step tasks. Instead of building tool use, memory, and orchestration from scratch, you get pre-built components that handle the common patterns.
For simple single-tool agents, raw API calls work fine. Frameworks become valuable when you need multi-step planning, tool orchestration, error recovery, memory, or multi-agent coordination. They save significant development time for complex agent architectures.
LangChain and LlamaIndex are the most mature with the largest ecosystems. CrewAI is best for multi-agent workflows. Vercel AI SDK is ideal for TypeScript/Next.js applications. Evaluate based on your language preference, use case complexity, and integration needs.