Multi-tool agent with SurrealMCP and Agno

Agno provides a Python framework for developing multi-agent systems that utilize shared memory, knowledge, and reasoning capabilities. This blog post focuses on creating an agent equipped with multiple tools, including SurrealMCP, integrated with SurrealDB, to function as a "researcher" that retrieves and organizes information from the web before storing it in SurrealDB. The agent effectively responds to naturally phrased queries, such as listing F1 drivers since 2000 along with their race starts and wins. Users are advised to familiarize themselves with Agno agents and set up necessary components like an LLM and SurrealMCP server. The post recommends starting with smaller LLMs to identify and improve potential areas of confusion, and it details the process of running SurrealMCP locally. Additionally, the agent's functionality can be enhanced by integrating tools like ReasoningTools and WebsiteTools, which allow it to refine actions and extract web content. The article encourages users to experiment by running different queries and highlights that support is available through SurrealDB Cloud and its community.