tool calling
PulseAugur coverage of tool calling — every cluster mentioning tool calling across labs, papers, and developer communities, ranked by signal.
5 day(s) with sentiment data
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New Node.js library shields LLM API calls from intermittent JSON errors
A new Node.js library called `@coder12-z/llm-shield` has been developed to address intermittent JSON parsing errors when interacting with large language models like OpenAI's GPT-4o mini, Anthropic, and Gemini. These err…
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AI agents: understanding workflows, tool-calling, and hidden logic gaps
This article delves into the nuances of AI agents, distinguishing between simple workflows and more complex agents capable of tool-calling. It highlights how AI agents can get stuck in loops and explores the subtle logi…
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AI Product Technologies: Tool Calling, MCP, Agents, Workflows, and RAG Explained
The article explains how various AI technologies, including tool calling, MCP (Model-Centric Programming), agents, workflows, and retrieval-augmented generation (RAG), are interconnected and often used together in AI pr…
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Python tutorial details Text-to-SQL agent with separate tool definition and implementation
This tutorial demonstrates how to build a Text-to-SQL agent in Python by separating tool definitions from their implementations. The approach involves placing model details, system prompts, and database schemas in a das…
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Developers can estimate LLM API costs by understanding token pricing and cost-saving levers
Estimating the cost of using Large Language Model (LLM) APIs requires understanding that pricing is based on tokens, with output tokens being significantly more expensive than input tokens. Developers can calculate basi…
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New benchmark ToolRobustBench diagnoses LLM tool-calling failures
Researchers have introduced ToolRobustBench, a new benchmark designed to evaluate and diagnose failures in tool-calling agents, which are LLM systems that use external tools to complete tasks. The benchmark systematical…
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LLM API Integration: Beyond Demos to Robust Production Systems
This article discusses the challenges and best practices for integrating Large Language Models (LLMs) with real-world APIs, moving beyond basic demonstrations to robust production systems. It emphasizes treating LLM API…
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LLM agents slash token use with new 'Code Mode' approach
A new approach called "Code Mode" aims to significantly reduce the number of tokens required by large language models when interacting with multiple tools. Instead of serializing all tool definitions and intermediate re…
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Liquid AI releases 2.6B model capable of 128K context on phones
Liquid AI has released LFM2.5-2.6B, a compact language model designed for local AI applications. Despite its small size of 2.69 billion parameters, the model boasts a 128K context window and supports tool calling, makin…
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Google's A2A protocol finds niche in agent-to-agent communication
Google's Agent2Agent (A2A) protocol, introduced in April 2025, aims to standardize communication between independent AI agents from various vendors and frameworks. Initially met with skepticism due to market saturation …
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New RL methods enhance LLM training stability and efficiency · 7 sources tracked
Researchers have developed several new methods to improve the stability and efficiency of reinforcement learning (RL) in large language models (LLMs). STARE addresses policy entropy collapse by reweighting token-level a…
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AI terms like Agents and RAG are reshaping careers, demanding new knowledge
This article discusses the growing prevalence of AI terms like AI Agents, Automation, Tool Calling, RAG, and Multi-Agent Systems. It suggests that understanding these concepts is crucial for career preservation in the f…
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Microsoft details its Agent Framework for building AI applications
Microsoft has released the third part of its "Agent Framework – Building Blocks for AI" series on the .NET blog. This installment delves into the creation of AI agents, focusing on essential components for their develop…