Скачать с ютуб Agno Framework - Building AI Agents and Teams for Deep Research в хорошем качестве

Agno Framework - Building AI Agents and Teams for Deep Research

Agno: https://docs.agno.com/introduction Based on the sources, Agno can be effectively used for what could be considered "deep research" through several mechanisms, particularly within the context of the Compere (I1) multi-persona system and the broader capabilities demonstrated in the provided code examples. The Compere (I1) implementation itself envisions a system where different personas contribute their specialities to address user requests. One of these personas, Kick La Metta | Knowledge Graph Analyst, is specifically designed for navigating and translating complex information into accessible insights. In the implemented system, Kick La Metta is an `agno.Agent` instance that Compere can invoke as a tool to request analysis or insight extraction. This demonstrates Agno's capability to facilitate specialised research functions by creating agents with specific roles and instructions focused on analysis and information retrieval. Furthermore, Agno's `@tool` decorator allows Compere, or any other agent, to call upon these specialised personas to perform specific tasks. This enables a collaborative research process where different aspects of a research query can be delegated to the most suitable persona. Compere's instructions guide it to analyse user tasks, identify the relevant personas (tools), initiate collaboration, and synthesise the results, which aligns with a research workflow involving task decomposition and information synthesis. Beyond the specific Compere (I1) implementation, the provided code snippets in `prompt_teams.txt` showcase various ways Agno supports research-oriented tasks: Tool Usage for External Information Access: Several examples demonstrate agents equipped with tools like `DuckDuckGoTools` and `HackerNewsTools`. These tools allow Agno agents to actively search and retrieve information from the web and specific platforms, which is fundamental to research. Integration with Knowledge Bases: Agno provides extensive support for integrating various types of knowledge bases, including those derived from ArXiv, websites, PDFs, CSV files, and even YouTube videos. Agents can be configured to search these knowledge bases, enabling retrieval-augmented generation (RAG) for informed responses based on a body of knowledge. This is crucial for in-depth research within specific domains or on curated datasets. Multi-Agent Teams for Distributed Research: The examples of `Team` usage in Agno illustrate how multiple agents with different specialisations can work together on a common goal. For instance, the "Stock Team" uses a "Stock Searcher" and a "Web Searcher" to gather information from different sources. The "Discussion Team" has agents researching on Reddit and HackerNews to form a comprehensive analysis. These examples show Agno's capacity to build sophisticated research workflows by orchestrating the efforts of multiple AI agents. Agentic Context and Team History: Agno allows for the management of context and history within teams, enabling agents to build upon previous findings and maintain coherence across a research process. This supports more iterative and in-depth research. In summary, Agno provides a powerful framework for deep research by enabling the creation of specialised research agents, equipping them with tools for information access, integrating various knowledge sources, and facilitating collaborative research through multi-agent systems with memory and context management. The implementation of Compere (I1) with its Knowledge Graph Analyst persona serves as a practical example of these capabilities in action.