Browsing: calling

  # Introduction  Agentic AI systems depend on a model’s ability to reliably call tools, selecting the right function, formatting arguments correctly, and integrating results into multi-step…

In this tutorial, we build a Groq-powered agentic research workflow that runs directly using Groq’s free OpenAI-compatible inference endpoint. We configure LangChain’s ChatOpenAI interface to work…

class QwenChat: def __init__(self, model, processor, system=None, tools=None): self.model, self.processor = model, processor self.tokenizer = processor.tokenizer self.history: list[dict] = [] if system: self.history.append({“role”: “system”, “content”: system})…

Imagine asking your AI model, “What’s the weather in Tokyo right now?” and instead of hallucinating an answer, it calls your actual Python function, fetches live…

In this tutorial, we build an advanced, production-ready agentic system using SmolAgents and demonstrate how modern, lightweight AI agents can reason, execute code, dynamically manage tools,…

A hacker has compromised a backend system for Doublespeed, an a16z-funded startup that uses a phone farm to flood social media with AI-generated TikTok accounts, and…

Agentic tool calling is what makes AI agents useful in production. It’s how they query databases, trigger workflows, retrieve real-time data, and act on a user’s…

print(“\n” + “=” * 70) print(“🤖 SECTION 8: Multi-Tool Agentic Loop”) print(“=” * 70) print(“Build a complete agent that can use multiple tools across turns.\n”) class…

Nearly all graphics cards are expensive right now, but the ongoing RAM shortage is at least partly to blame for the current state of things. But,…

In this tutorial, we implement IWE: an open-source, Rust-powered personal knowledge management system that treats markdown notes as a navigable knowledge graph. Since IWE is a…