How to Build an AI Agent with Function Calling in Node.js Using Google Gemini
July 24, 2026 / #Node.js How to Build an AI Agent with Function Calling in Node.js Using Google Gemini Zia Ullah Last year, a client asked me to add a conversational interface to their internal reporting tool. Staff would type a question, and the system would pull a live answer from the database. But a week in, a tester typed: "What is the weather in Berlin, and how much would 500 EUR convert to in USD right now?" The model called the weather function, returned that answer, and ignored the second half of the question entirely.
- ▪July 24, 2026 / #Node.js How to Build an AI Agent with Function Calling in Node.js Using Google Gemini Zia Ullah Last year, a client asked me to add a conversational interface to their internal reporting tool.
- ▪Staff would type a question, and the system would pull a live answer from the database.
- ▪But a week in, a tester typed: "What is the weather in Berlin, and how much would 500 EUR convert to in USD right now?" The model called the weather function, returned that answer, and ignored the second half of the question entirely.
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| Original publisher | freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More |
| Canonical URL | https://www.freecodecamp.org/news/how-to-build-ai-agent-function-calling-nodejs-gemini/ |
| Publication time | Fri, 24 Jul 2026 18:30:52 +0000 |
| Retrieval time | 2026-07-26T10:02:19.283Z |
| Last seen | 2026-07-26T10:02:19.283Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | oKYEVL8eQEY6 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
July 24, 2026 / #Node.js How to Build an AI Agent with Function Calling in Node.js Using Google Gemini Zia Ullah Last year, a client asked me to add a conversational interface to their internal reporting tool. Staff would type a question, and the system would pull a live answer from the database. I had the first version running in a day. Single questions worked. But a week in, a tester typed: "What is the weather in Berlin, and how much would 500 EUR convert to in USD right now?" The model called the weather function, returned that answer, and ignored the second half of the question entirely. That is the gap between a chatbot and an agent. A chatbot works from training data. That's its limit. An agent doesn't have that limit.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More .
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