2 articles tagged with "Node.js"

Choosing a Node.js LLM library in 2026 is mostly a question of which trade-offs your team can carry for the next two years. The realistic shortlist is the provider SDKs (openai, @anthropic-ai/sdk, @google/genai), the Vercel AI SDK, LangChain.js with LangGraph.js, Mastra, the OpenAI Agents SDK, and lighter multi-provider libraries such as IntelliNode.

Streaming chat inside a React or Next.js product points to the Vercel AI SDK. Stateful agent workflows with tracing point to LangGraph.js or Mastra. A vendor you will never change points to its own SDK. One small backend API across OpenAI, Claude, Gemini and local models, with a Python twin, is where IntelliNode fits. Below are the numbers, the code, and where each option falls short.

To build AI agents in Node.js you need three parts: tools the model can call, a loop that runs those tools and feeds the results back, and a model that is good at deciding when to call them. Memory stores, graphs and dashboards are optional, and most first agents skip them.

This guide builds one support agent end to end with the intellinode npm package. It answers order questions with a plain JavaScript tool, gets the same tool from an MCP server, waits for a person before it issues a refund, and falls back to another provider when one is down. You develop on a local Ollama model with no API key, then move to OpenAI, Claude or Gemini by changing one line.

Every snippet was run on Node.js 20 against Ollama with qwen2.5:0.5b. Where the tiny model got things wrong, we say so: those failures are worth seeing before you ship.