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Description

Agno is a streamlined framework designed for creating agents equipped with memory, knowledge, tools, and reasoning capabilities. It allows developers to construct a variety of agents, including reasoning agents, multimodal agents, teams of agents, and comprehensive agent workflows. Additionally, Agno features an attractive user interface that facilitates communication with agents and includes tools for performance monitoring and evaluation. Being model-agnostic, it ensures a consistent interface across more than 23 model providers, eliminating the risk of vendor lock-in. Agents can be instantiated in roughly 2μs on average, which is about 10,000 times quicker than LangGraph, while consuming an average of only 3.75KiB of memory—50 times less than LangGraph. The framework prioritizes reasoning, enabling agents to engage in "thinking" and "analysis" through reasoning models, ReasoningTools, or a tailored CoT+Tool-use method. Furthermore, Agno supports native multimodality, allowing agents to handle various inputs and outputs such as text, images, audio, and video. The framework's sophisticated multi-agent architecture encompasses three operational modes: route, collaborate, and coordinate, enhancing the flexibility and effectiveness of agent interactions. By integrating these features, Agno provides a robust platform for developing intelligent agents that can adapt to diverse tasks and scenarios.

Description

Eve serves as an open-source framework for developing AI agents that you fully control, structured around a file system and equipped with a runtime suitable for production use. Defining an agent is as straightforward as creating an instructions.md file, while additional components like model configurations, skills, tools, sandboxes, channels, connections, subagents, schedules, and evaluations can be incorporated as the agent evolves. Tools can easily be established by placing TypeScript files into the designated tools directory, and skills are formulated as Markdown playbooks that activate only when needed. These agents have the capability to integrate with platforms such as GitHub, Stripe, and Linear via secure authenticated connections, and they can interact through various channels like Slack, Discord, Teams, web chats, APIs, cron jobs, command-line interfaces, and tailored applications—all originating from the same codebase. Moreover, specialized subagents are capable of managing delegated tasks, utilizing their own unique prompts, tools, and sandboxes, enhancing the overall functionality and adaptability of the agent. This versatility allows developers to customize their AI systems to fit a wide range of applications and use cases.