I've been tracking Hermes Agent since Nous Research shipped the first public build last year. It's one of the few open-source agent frameworks with a real learning loop — it builds reusable skills from past task executions instead of starting from scratch every time. That's a genuinely useful idea, not just a demo trick. The latest release (v0.11.0 and the subsequent patches) tightens up a few things that were getting in the way of production use. Let me walk through what changed, why it matters, and how you can connect it to NeuroAPI's MCP server so your Hermes agent can scrape, crawl, and extract data from the live web. What's New in the Latest Hermes Agent Release Hermes Agent runs locally (or on a VPS) and orchestrates tasks across LLMs, memory stores, and external tool servers via MCP. The recent update focused on three areas: tool management, self-improvement mechanics, and deployment ergonomics. Smarter Tool Schema Handling Previous versions sent the full JSON schema of every registered MCP tool on every LLM API call. If you had 40 tools registered across a few MCP servers, that's a lot of tokens burned on context the model didn't need for the current turn. The new release filters tool schemas based on relevance to the active task, which cuts latency and cost on every request. Skill Persistence and Evolution The learning loop got a meaningful upgrade. Hermes now diffs your manual corrections against its initial output and stores the delta as a reusable skill. Over a few weeks of active use, your agent accumulates a personalized skill library. The update also added skill versioning, so you can roll back if a learned behavior starts degrading. Faster Setup and Deployment The config surface got simpler. You can now define MCP servers, model providers, and memory backends in a single YAML file without chaining together environment variables. The project also ships a Docker image and a one-command VPS install script, which makes the 'just get it running' phase much less painful. Why MCP Matters Here MCP (Model Context Protocol) is the connector layer that lets Hermes talk to external tool servers. Think of it as USB-C for AI agents — a standard plug that any compatible tool server can implement once, and any MCP-aware agent can consume. No custom SDKs, no glue code per integration. This is where NeuroAPI fits in. NeuroAPI's MCP server exposes the full scraping and extraction stack — single-page scrape, batch scrape, crawl, map, search, structured extraction, and more — as MCP-native tools. You point Hermes at the server, and your agent can pull live web data on demand. Wiring NeuroAPI into Hermes Agent Here's the minimal config to add NeuroAPI as an MCP server in Hermes. You'll need a NeuroAPI API key (grab one from the NeuroAPI dashboard). # hermes-config.yaml mcpServers: neuroapi: transport: streamable-http url: https://mcp.neuroapi.me/mcp headers: Authorization: Bearer YOUR_NEUROAPI_KEY tools: include: - scrape - batch_scrape - crawl - map - search - extract - summary - screenshot Once Hermes starts, it discovers the available tools from NeuroAPI's MCP server automatically. You can then ask your agent things like: 'Scrape the pricing page at example.com and summarize it.' 'Crawl docs.example.com and list all API endpoints.' 'Search for Python async web scraping and extract the top 3 results into structured JSON.' The agent decides which tool to call based on the task. You don't need to write orchestration logic — that's the whole point of the agent framework. A Practical Example: Research Agent Let's say you want Hermes to monitor competitor pricing. Here's a prompt you can feed it after wiring up NeuroAPI: 'Go to https://competitor-pricing-page.com. Scrape the pricing table. Extract the plan names, monthly costs, and feature summaries into a JSON object. Then do the same for two other competitors I'll specify. Save all three as a comparison sheet.' Under the hood, Hermes will call NeuroAPI's /v1/scrape endpoint for each URL, then /v1/extract with a JSON schema to pull structured data. It stores the results in its memory backend and composes the comparison. If you correct an extraction (say, it missed a plan tier), Hermes learns from that correction and applies it next time. That feedback loop is what separates Hermes from a simple script. It gets better the more you use it. How to Think About This Hermes Agent isn't going to replace your pipeline overnight. But it's a genuinely useful layer for tasks that are too complex for a single API call and too routine to do manually every time. The MCP integration means you can plug in real web data without building custom scraping infrastructure from scratch. If you're already running Hermes, adding NeuroAPI's MCP server takes about five minutes. If you're evaluating agent frameworks, Hermes is worth a look — especially now that tool management doesn't burn half your token budget on irrelevant schemas. Start with the NeuroAPI docs for MCP setup, and check the Hermes MCP config reference for the full set of filtering and policy options. Two reads, one config file, and you're up.