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Hermes AI Agent handles multi-step workflows well. The planning layer holds up. Memory across sessions works. What kept breaking down was the tool layer. Once a workflow touched three or four external systems, I was spending more time on auth configs, mismatched response formats, and per-tool retry logic than on the workflows themselves. I fixed this by routing all external tool calls through a unified MCP gateway. The agent logic stayed the same. The integration complexity moved into one place I could actually manage. This post walks through how that works, how to set it up, and where it is genuinely useful. How Hermes runs tasks Hermes is an open-source, self-hosted agent runtime from Nous Research, released in February 2026 under the MIT license. It runs persistently on your own infrastructure and executes goals as structured, stateful workflows. Four layers handle execution. The planning layer breaks a goal into sequenced steps and adjusts them as intermediate results come in The execution layer runs each step and fires tool calls when external data or action is needed The memory layer stores task state and session history in SQLite with FTS5, so context carries over across restarts The skills layer captures completed workflows as reusable documents retrieved on future tasks After a task finishes, Hermes writes a skill file with the procedure and known failure points, then stores it for retrieval next time a similar task runs. Tool execution is embedded in the runtime loop. External capabilities come through MCP-based interfaces, which is where the gateway plugs in. What breaks when integrations live inside the agent In a standard MCP setup, each client connects one-to-one with a specific MCP server. That works fine with two or three tools. With ten, it becomes a maintenance problem that grows with every tool you add. A task spanning a web search, a product API, and a SERP scraper means three separate auth setups, three response formats to parse, and three diffe

2026-06-15 原文 →

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