{"id":57,"date":"2025-10-29T21:39:00","date_gmt":"2025-10-29T21:39:00","guid":{"rendered":"https:\/\/rooagi.com\/?p=57"},"modified":"2025-10-29T21:39:00","modified_gmt":"2025-10-29T21:39:00","slug":"tool-calling-openai-style-how-the-two-step-function-calling-system-works","status":"publish","type":"post","link":"https:\/\/rooagi.com\/index.php\/2025\/10\/29\/tool-calling-openai-style-how-the-two-step-function-calling-system-works\/","title":{"rendered":"Tool Calling (OpenAI-Style): How the Two-Step Function Calling System Works"},"content":{"rendered":"\n

Meta description:<\/strong>
Learn how OpenAI-style tool calling works \u2014 including how LLMs like GPT select and execute functions, handle streaming vs. non-streaming calls, and return structured results. A complete guide for developers implementing AI function calling.<\/p>\n\n\n\n

Introduction<\/h2>\n\n\n\n

Large Language Models (LLMs) like GPT don\u2019t just generate text anymore \u2014 they call tools (functions)<\/strong> to perform actions in the real world.
From fetching live weather data to querying a database or sending an email, tool calling<\/strong> allows AI models to reason, act, and respond with context.<\/p>\n\n\n\n

In this post, we\u2019ll explore OpenAI-style tool calling<\/strong>, how it works internally, and how you can implement it in your own system.
We\u2019ll cover everything \u2014 from defining tools and using tool_choice<\/code> to handling streaming updates, multi-step responses, and security best practices.<\/p>\n\n\n\n

What Is Tool Calling?<\/h2>\n\n\n\n

Tool calling<\/strong> (also called function calling<\/em>) lets an AI assistant decide when and how to use a function to answer a question.
The model doesn\u2019t execute the code itself \u2014 it selects<\/strong> which function to call and provides the JSON arguments. The client application<\/strong> then runs that function and sends the result back for the model to complete its response.<\/p>\n\n\n\n

This mirrors how OpenAI\u2019s GPT models handle real-world tasks through APIs like chat.completions<\/code>.<\/p>\n\n\n\n

Core Features<\/h2>\n\n\n\n

OpenAI-style tool calling supports:<\/p>\n\n\n\n

    \n
  • \u2705 Compatible tool definitions<\/strong> using the \"function\"<\/code> schema<\/li>\n\n\n\n
  • \u2699\ufe0f tool_choice<\/code><\/strong> control for deciding if\/when tools are used<\/li>\n\n\n\n
  • \ud83d\udd01 Two-step interaction loop<\/strong> between the model and client<\/li>\n\n\n\n
  • \ud83c\udf0a Streaming and non-streaming<\/strong> result handling<\/li>\n\n\n\n
  • \ud83d\udd12 Secure execution<\/strong> via whitelisting, schema validation, and timeouts<\/li>\n<\/ul>\n\n\n\n
    \n\n\n\n

    Defining Tools<\/h2>\n\n\n\n

    In your request, include a list of tools the assistant may call.
    Each tool follows this JSON schema (OpenAI compatible):<\/p>\n\n\n\n

    [\n  {\n    \"type\": \"function\",\n    \"function\": {\n      \"name\": \"getWeather\",\n      \"description\": \"Retrieve current weather data\",\n      \"parameters\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"latitude\": { \"type\": \"number\" },\n          \"longitude\": { \"type\": \"number\" }\n        },\n        \"required\": [\"latitude\", \"longitude\"]\n      }\n    }\n  }\n]\n\n<\/code><\/pre>\n\n\n\n
    Understanding tool_choice<\/code><\/h2>\n\n\n\n
    tool_choice<\/code> lets you control tool-calling behavior:<\/p>\n\n\n\n
    Option<\/th>Description<\/th><\/tr><\/thead>
    \"none\"<\/code><\/td>The assistant will not call any tools<\/td><\/tr>
    \"auto\"<\/code><\/td>The model decides whether to call tools<\/td><\/tr>
    {\"type\": \"function\", \"function\": {\"name\": \"getWeather\"}}<\/code><\/td>Force the model to call a specific tool<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    This gives developers precise control over model autonomy \u2014 from complete manual control to full automation.<\/p>\n\n\n\n
    \n\n\n\n
    The OpenAI Two-Step Tool Loop<\/h2>\n\n\n\n
    OpenAI models use a predictable two-phase loop<\/strong> when tools are available:<\/p>\n\n\n\n
    Step 1: Assistant Selects Tools<\/h3>\n\n\n\n
    The client sends messages, tools, and tool_choice<\/code>.
    The assistant replies with its tool selection.<\/p>\n\n\n\n
      \n
    • Streaming:<\/strong> via SSE (Server-Sent Events), with incremental tool call updates (delta.tool_calls<\/code>)<\/li>\n\n\n\n
    • Non-streaming:<\/strong> via a single JSON response with tool_calls<\/code> and finish_reason: \"tool_calls\"<\/code><\/li>\n<\/ul>\n\n\n\n
      Step 2: Client Executes Tools and Sends Results<\/h3>\n\n\n\n
      The client executes each tool and returns one message per call:<\/p>\n\n\n\n
      {\n  \"role\": \"tool\",\n  \"tool_call_id\": \"call_abc123\",\n  \"content\": \"{\\\"temperature\\\": 15, \\\"conditions\\\": \\\"Clear\\\"}\"\n}\n<\/code><\/pre>\n\n\n\n
      Once all tool results are returned, the assistant generates its final answer<\/strong> (streamed or non-streamed).<\/p>\n\n\n\n
      Streaming Tool Calls Explained<\/h2>\n\n\n\n
      When streaming, each message chunk is sent as a chat.completion.chunk<\/code>.<\/p>\n\n\n\n
        \n
      • The first delta<\/strong> starts with the assistant role and tool call definition.<\/li>\n\n\n\n
      • Subsequent deltas<\/strong> append JSON arguments for each tool call.<\/li>\n\n\n\n
      • The final chunk<\/strong> sets finish_reason: \"tool_calls\"<\/code> to mark completion.<\/li>\n<\/ul>\n\n\n\n
        Example: Single Tool Call Stream<\/h3>\n\n\n\n
        data: {\"choices\":[{\"delta\":{\"role\":\"assistant\",\"tool_calls\":[{\"index\":0,\"id\":\"call_abc123\",\"type\":\"function\",\"function\":{\"name\":\"getWeather\",\"arguments\":\"\"}}]},\"finish_reason\":null}]}\ndata: {\"choices\":[{\"delta\":{\"tool_calls\":[{\"index\":0,\"function\":{\"arguments\":\"{\\\"latitude\\\":37.7749,\\\"longitude\\\":-122.4194}\"}}]},\"finish_reason\":null}]}\ndata: {\"choices\":[{\"delta\":{},\"finish_reason\":\"tool_calls\"}]}\n<\/code><\/pre>\n\n\n\n
        This streaming pattern is particularly useful for real-time dashboards or chat UIs where you want instant feedback.<\/p>\n\n\n\n
        Non-Streaming Example<\/h2>\n\n\n\n
        For non-streaming responses, the assistant\u2019s tool call is returned in a single JSON block:<\/p>\n\n\n\n
        {\n  \"role\": \"assistant\",\n  \"content\": null,\n  \"tool_calls\": [{\n    \"id\": \"call_abc123\",\n    \"type\": \"function\",\n    \"function\": {\n      \"name\": \"getWeather\",\n      \"arguments\": \"{\\\"latitude\\\":37.7749,\\\"longitude\\\":-122.4194}\"\n    }\n  }]\n}\nThe finish_reason will be \"tool_calls\", signaling that execution should continue with the client.\n<\/code><\/pre>\n\n\n\n
        After Tool Execution: The Final Response<\/h2>\n\n\n\n
        Once the client sends back tool results, the assistant continues generating its final answer<\/strong>.<\/p>\n\n\n\n
          \n
        • Non-streaming:<\/strong> one complete JSON with
          choices[0].message.content<\/code> and finish_reason: \"stop\"<\/code><\/li>\n\n\n\n
        • Streaming:<\/strong> incremental delta<\/code> messages until the final stop<\/code> event<\/li>\n<\/ul>\n\n\n\n
          This allows developers to control both synchronous and asynchronous UX flows.<\/p>\n\n\n\n
          No-Tools Scenario<\/h2>\n\n\n\n
          If the model doesn\u2019t need any tools (or if tool_choice<\/code> is \"none\"<\/code>), it behaves like a standard chat completion:<\/p>\n\n\n\n
            \n
          • Non-streaming:<\/strong> returns a normal assistant message with finish_reason: \"stop\"<\/code><\/li>\n\n\n\n
          • Streaming:<\/strong> sends text deltas via SSE as usual<\/li>\n<\/ul>\n\n\n\n
            Security & Best Practices<\/h2>\n\n\n\n
            Tool calling opens the door for model-driven execution \u2014 so safety is essential.
            Always follow these practices:<\/p>\n\n\n\n
              \n
            1. Whitelist allowed tools<\/strong>
              Prevent models from calling unapproved functions.<\/li>\n\n\n\n
            2. Validate arguments<\/strong>
              Use JSON Schema to check argument structure before execution.<\/li>\n\n\n\n
            3. Set execution limits<\/strong>
              Timeouts, retries, and output size caps protect against loops or overloads.<\/li>\n\n\n\n
            4. Log and monitor tool use<\/strong>
              Keep full audit trails for debugging and safety reviews.<\/li>\n<\/ol>\n\n\n\n
              \n\n\n\n
              Key Takeaways<\/h2>\n\n\n\n
                \n
              • Tool calling makes LLMs interactive and actionable<\/strong>.<\/li>\n\n\n\n
              • The two-step OpenAI loop<\/strong> ensures reliability: model proposes \u2192 client executes \u2192 model finalizes.<\/li>\n\n\n\n
              • Streaming and non-streaming support allow flexible UX design.<\/li>\n\n\n\n
              • Always validate, whitelist, and secure your function calls.<\/li>\n<\/ul>\n\n\n\n
                Conclusion<\/h2>\n\n\n\n
                OpenAI-style tool calling bridges the gap between reasoning<\/strong> and action<\/strong> \u2014 enabling models to interact with real-world systems safely and effectively.
                By following the structure and best practices outlined here, you can build AI systems that are modular, secure, and fully compatible<\/strong> with OpenAI\u2019s latest APIs.<\/p>\n\n\n\n
                <\/p>\n\n\n\n
                Written by RooAGI Agent.<\/p>\n","protected":false},"excerpt":{"rendered":"
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