Frequently Asked Questions

An AI agent is an autonomous software system powered by large language models (LLMs) that can perform tasks, make decisions, and take actions with minimal human intervention. Unlike simple chatbots, AI agents can reason, plan, use tools, and complete complex multi-step tasks.

We evaluate AI agents across four key dimensions: Overall performance (combined score), Speed (response time and task completion), Accuracy (correctness of outputs and decisions), and Cost efficiency (value for price). Each score is on a 0-100 scale based on standardized testing protocols.

No, AI agents have different pricing models. We categorize them as: Free (completely free), Freemium (free tier with paid upgrades), Paid (subscription or usage-based pricing), and Open Source (free to use and modify). You can filter agents by pricing model on our Explore page.

Consider your specific use case (coding, research, automation, etc.), budget constraints, technical requirements, and integration needs. Use our comparison feature to evaluate agents side-by-side, and check benchmark scores for performance insights.

Multi-agent systems are frameworks that allow multiple AI agents to collaborate on complex tasks. They enable specialized agents to work together, each handling different aspects of a problem. Examples include CrewAI, MetaGPT, and LangChain Agents.

We aim to update benchmarks quarterly or whenever significant updates are released by AI agent providers. The "Last tested" date on each agent page indicates when the most recent evaluation was performed.

Yes! We welcome submissions of new AI agents. Please contact us at hello@smart-agents.io with details about the agent, including its name, description, website, GitHub repository (if applicable), and pricing model.

LLMs (Large Language Models) like GPT-4 or Claude are the underlying AI models that power agents. AI agents build on top of LLMs by adding capabilities like memory, tool use, planning, and autonomous action-taking. Think of LLMs as the brain and agents as the complete system that can act in the world.

Safety varies by agent and use case. Open-source agents offer transparency but require careful setup. Commercial agents typically have more safety guardrails. Always review permissions, limit access to sensitive data, and start with controlled environments when testing new agents.

Most AI agents offer APIs, SDKs, or no-code interfaces for integration. Check the specific agent documentation on their website. For custom solutions, consider our Blueprints section which provides implementation guides and best practices.