{"version":1,"type":"story","url":"https://digestai.news/story/author-builds-local-travel-planner-with-hermes-agent-and-ollama","json":"https://digestai.news/story/author-builds-local-travel-planner-with-hermes-agent-and-ollama.json","markdown":"https://digestai.news/story/author-builds-local-travel-planner-with-hermes-agent-and-ollama.md","slug":"author-builds-local-travel-planner-with-hermes-agent-and-ollama","headline":"Author builds local travel planner with Hermes Agent and Ollama","summary":"A software engineer created a multi-agent travel research system using open-source tools: **Hermes Agent** (version 0.21.1) and **Ollama** with the **gemma4:31b** model. The system, called *Wanderlist*, runs entirely on local hardware, coordinating four agents—destination research, accommodation suggestions, flight links, and memory—to plan trips without cloud dependencies or subscription costs. The project highlights how open-source agent frameworks and local models can replace cloud-based AI for specific tasks like web searches and tool integration, though with trade-offs in speed and capability compared to frontier models.\n\nThe coordinator agent delegates tasks to specialists, each with defined roles: one searches for current entry requirements and attractions, another finds budget-friendly neighborhoods, and a third generates real Google Flights search links. The system avoids inventing prices or outdated rules by explicitly instructing agents to fetch live data. Memory persistence lets the system retain user preferences across sessions. The author notes the project’s practicality for research-heavy tasks, emphasizing privacy and cost savings over raw performance.","keyPoints":["Hermes Agent 0.21.1 and Ollama with gemma4:31b model power a local travel-planning system","Four agents research destinations, stays, flights, and store preferences in a SQLite database","Flight links are generated via a custom Python tool, avoiding API keys or subscription costs"],"whyItMatters":"Demonstrates how open-source agent frameworks and local models can replace cloud AI for niche tasks like travel planning, appealing to privacy-conscious users or those avoiding recurring costs. Useful for developers exploring agentic workflows without vendor lock-in.","category":{"slug":"agents","name":"Agents & Tools","url":"https://digestai.news/category/agents"},"entities":{"companies":["Nous Research","Ollama"],"models":["gemma4:31b"],"people":["Shittu Olumide"]},"firstPublishedAt":"2026-11-05T13:00:00Z","updatedAt":"2026-11-05T13:00:00Z","sourceCount":1,"hasPrimarySource":false,"sources":[{"outlet":"KDnuggets","title":"What I Did with Hermes Agent, Ollama & Local Models","url":"https://kdnuggets.com/what-i-did-with-hermes-agent-ollama-local-models","publishedAt":"2026-11-05T13:00:00Z","type":"press","primary":false,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"Author builds local travel planner with Hermes Agent and Ollama\", 5 November 2026, https://digestai.news/story/author-builds-local-travel-planner-with-hermes-agent-and-ollama","publisher":"Digest AI","title":"Author builds local travel planner with Hermes Agent and Ollama","datePublished":"2026-11-05T13:00:00Z","url":"https://digestai.news/story/author-builds-local-travel-planner-with-hermes-agent-and-ollama"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}