#nodellm

Back when we introduced MCP support, we ended on a teaser: Phase 3 would tackle Sampling—letting...

Integration is the primary scaling bottleneck for production agents. Historically, giving an agent...

"NodeLLM 1.15 introduces automated schema self-correction, enabling agents to recover from validation failures without manual intervention. This release also brings fine-grained middleware lifecycle control and hardened type safety."

The release of NodeLLM 1.16 marks a significant milestone in our journey to provide production-grade...

The AI ecosystem has a tendency to make simple concepts overly complicated. Lately, the term "AI...

One of the biggest frustrations I've had while building NodeLLM is how hard it is to write a reliable...

Introducing @node-llm/monitor—a production-grade observability layer for LLM applications. Track costs, latency, token usage, and debug AI interactions with a built-in real-time dashboard.

Prompt injection is the new SQL injection. This post explores the security architecture required for 2026 AI Agents and how NodeLLM provides the necessary guardrails.

AI Agents are more than chatbots—they're systems that can reason, plan, and act. This post breaks down what an agent actually is, how the execution loop works under the hood, and gives you a copy-paste-ready agent using NodeLLM.

LLMs are great at thinking, but terrible at doing. Learn how Tool Calling bridges the gap between text generation and real-world action.