* **Structured Outputs:** Uses grammar-constrained decoding (logit biasing/masking) to enforce strict JSON schema compliance during inference. Best for deterministic data transformation.
* **Function Calling:** Utilizes instruction tuning to enable model reasoning over tool definitions. Best for agentic workflows and external state mutation.
| Feature | Structured Outputs | Function Calling |
| :--- | :--- | :--- |
| **Mechanism** | Constrained decoding (Grammar/Regex) | Instruction-tuned intent detection |
| **Reliability** | 100% Schema Compliance | Probabilistic (requires retry logic) |
| **Primary Use Case** | ETL, Query Gen, Reasoning traces | API Triggers, RAG, Task Routing |
| **Latency/Cost** | Low overhead; optimized decoding | Higher overhead due to tool-definition tokens |
* **ETL & Extraction:** Use Structured Outputs to ensure downstream parsers never fail on malformed JSON.
* **Agentic Loops:** Use Function Calling for multi-turn interactions where the model must decide *which* tool to invoke based on context.
* **Hybrid Pattern (Controller/Formatter):** Deploy a "Function Calling" agent as the **Controller** to select tools, then pipe results through a "Structured Output" layer as the **Formatter** to ensure clean data ingestion into databases or UIs.
* **Function Calling:** Utilizes instruction tuning to enable model reasoning over tool definitions. Best for agentic workflows and external state mutation.
| Feature | Structured Outputs | Function Calling |
| :--- | :--- | :--- |
| **Mechanism** | Constrained decoding (Grammar/Regex) | Instruction-tuned intent detection |
| **Reliability** | 100% Schema Compliance | Probabilistic (requires retry logic) |
| **Primary Use Case** | ETL, Query Gen, Reasoning traces | API Triggers, RAG, Task Routing |
| **Latency/Cost** | Low overhead; optimized decoding | Higher overhead due to tool-definition tokens |
* **ETL & Extraction:** Use Structured Outputs to ensure downstream parsers never fail on malformed JSON.
* **Agentic Loops:** Use Function Calling for multi-turn interactions where the model must decide *which* tool to invoke based on context.
* **Hybrid Pattern (Controller/Formatter):** Deploy a "Function Calling" agent as the **Controller** to select tools, then pipe results through a "Structured Output" layer as the **Formatter** to ensure clean data ingestion into databases or UIs.
This tutorial demonstrates how to build a local, privacy-first tool-calling agent using the Google Gemma 4 model family and Ollama. It explains the transition from static language models to dynamic autonomous agents through function calling, allowing models to interact with external APIs and real-world data. The guide provides a practical Python implementation using a zero-dependency approach to create tools for weather retrieval, news fetching, time checking, and currency conversion.
- Overview of the Gemma 4 model family and its native agentic capabilities.
- The architectural shift from closed-loop conversationalists to tool-enabled agents.
- Setting up a local inference environment using Ollama and the gemma4:e2b model.
- Implementing Python functions and mapping them to JSON schemas for model instruction.
- Orchestrating the agentic workflow loop to execute tools and synthesize live context.
- Overview of the Gemma 4 model family and its native agentic capabilities.
- The architectural shift from closed-loop conversationalists to tool-enabled agents.
- Setting up a local inference environment using Ollama and the gemma4:e2b model.
- Implementing Python functions and mapping them to JSON schemas for model instruction.
- Orchestrating the agentic workflow loop to execute tools and synthesize live context.
The llama.cpp server has introduced support for the Anthropic Messages API, a highly requested feature that allows users to run Claude-compatible clients with locally hosted models. This implementation enables powerful tools like Claude Code to interface directly with local GGUF models by internally converting Anthropic's message format to OpenAI's standard. Key features of this update include full support for chat completions with streaming, advanced tool use through function calling, token counting capabilities, vision support for multimodal models, and extended thinking for reasoning models. This development bridges the gap between proprietary AI ecosystems and local, privacy-focused inference pipelines, providing a seamless experience for developers working with agentic workloads and coding assistants.
ANTHROPIC_AUTH_TOKEN, ANTHROPIC_MODEL=
ANTHROPIC_AUTH_TOKEN, ANTHROPIC_MODEL=
This article explains how to implement function calling with Google’s Gemma 3 27B model. It covers the concept of function calling, the step‑by‑step workflow, and provides a practical example using a Python `convert` function to turn $200,000 into EUR. The post walks through prompting Gemma, parsing its `tool_code` output, executing the function with `eval`, and returning a friendly final response. It also demonstrates how to set up the Google‑GenAI SDK, create a chat session, and extract tool calls. The discussion highlights Gemma’s multilingual, multimodal, and agentic capabilities, making it suitable for real‑world AI assistants that need to interact with external APIs and tools.
This article details a coding implementation of ClawTeam, an open-source Agent Swarm Intelligence framework. It demonstrates how to orchestrate multi-agent systems using OpenAI function calling, focusing on a leader agent that decomposes tasks, specialized worker agents for execution, a shared task board with dependency resolution, and an inter-agent messaging system. The implementation is designed to run seamlessly in Colab, requiring only an OpenAI API key, and showcases key components like task management, agent communication, and team registry. The tutorial provides a practical example of building and running a multi-agent swarm.
This guide explains how to use tool calling with local LLMs, including examples with mathematical, story, Python code, and terminal functions, using llama.cpp, llama-server, and OpenAI endpoints.
This article compares Model Context Protocol (MCP), Function Calling, and OpenAPI Tools for integrating tools and resources with language models, outlining their strengths, limits, security considerations, and ideal use cases.
This document details the features, best practices, and migration guidance for GPT-5, OpenAI's most intelligent model. It covers new API features like minimal reasoning effort, verbosity control, custom tools, and allowed tools, along with prompting guidance and migration strategies from older models and APIs.
LLM 0.26 introduces tool support, allowing LLMs to access and utilize Python functions as tools. The article details how to install, configure, and use these tools with various LLMs like OpenAI, Anthropic, Gemini, and Ollama models, including examples with plugins and ad-hoc functions. It also discusses the implications for building 'agents' and future development plans.
This tutorial demonstrates how to integrate Google’s Gemini 2.0 with an in-process Model Context Protocol (MCP) server using FastMCP, creating tools for weather information and integrating them into Gemini's function calling workflow.
