@samchon/openapi

flowchart
  subgraph "OpenAPI Specification"
    v20("Swagger v2.0") --upgrades--> emended[["OpenAPI v3.1 (emended)"]]
    v30("OpenAPI v3.0") --upgrades--> emended
    v31("OpenAPI v3.1") --emends--> emended
  end
  subgraph "OpenAPI Generator"
    emended --normalizes--> migration[["Migration Schema"]]
    migration --"Artificial Intelligence"--> lfc{{"LLM Function Calling"}}
    lfc --"OpenAI"--> chatgpt("ChatGPT")
    lfc --"Google"--> gemini("Gemini")
    lfc --"Anthropic"--> claude("Claude")
    lfc --"<i>Google</i>" --> legacy_gemini("<i> (legacy) Gemini</i>")
    legacy_gemini --"3.0" --> custom(["Custom JSON Schema"])
    chatgpt --"3.1"--> custom
    gemini --"3.1"--> standard(["Standard JSON Schema"])
    claude --"3.1"--> standard
  end

Transform OpenAPI documents into type-safe LLM function calling applications.

@samchon/openapi converts any version of OpenAPI/Swagger documents into LLM function calling schemas for OpenAI GPT, Claude, and Gemini. It supports every OpenAPI version (Swagger 2.0, OpenAPI 3.0, and OpenAPI 3.1) with full TypeScript type definitions. The library also works with MCP (Model Context Protocol) servers, enabling seamless AI agent development.

Key Features:

• Universal OpenAPI Support: Works with Swagger 2.0, OpenAPI 3.0, and OpenAPI 3.1
• LLM Function Calling: Auto-generates function schemas for OpenAI, Claude, and Gemini
• Type-Safe Validation: Built-in validation with detailed error feedback for LLM responses
• MCP Integration: Compose function calling schemas from MCP servers
• Emended Specification: Standardized OpenAPI v3.1 format that removes ambiguities

Live Demo:

https://github.com/user-attachments/assets/e1faf30b-c703-4451-b68b-2e7a8170bce5

Watch how @samchon/openapi powers an AI shopping chatbot with @agentica

• View Backend Repository
• Explore OpenAPI Document

Quick Start

npm install @samchon/openapi

Transform your OpenAPI document into an LLM function calling application in just a few lines:

import { HttpLlm, OpenApi } from "@samchon/openapi";

// Load and convert your OpenAPI document
const document: OpenApi.IDocument = OpenApi.convert(swagger);

// Generate LLM function calling schemas
const application: IHttpLlmApplication<"chatgpt"> = HttpLlm.application({
  model: "chatgpt", // "chatgpt" | "claude" | "gemini"
  document,
});

// Find a function by path and method
const func: IHttpLlmFunction<"chatgpt"> | undefined = application.functions.find(
  (f) => f.path === "/bbs/articles" && f.method === "post"
);

// Execute the function with LLM-composed arguments
const result: unknown = await HttpLlm.execute({
  connection: { host: "http://localhost:3000" },
  application,
  function: func,
  arguments: llmGeneratedArgs, // from OpenAI/Claude/Gemini
});

That's it! Your HTTP backend is now callable by AI.

OpenAPI Definitions

@samchon/openapi provides complete TypeScript definitions for all OpenAPI versions and introduces an "emended" OpenAPI v3.1 specification that serves as a universal intermediate format.

flowchart
  v20(Swagger v2.0) --upgrades--> emended[["<b><u>OpenAPI v3.1 (emended)</u></b>"]]
  v30(OpenAPI v3.0) --upgrades--> emended
  v31(OpenAPI v3.1) --emends--> emended
  emended --downgrades--> v20d(Swagger v2.0)
  emended --downgrades--> v30d(Swagger v3.0)

Supported Specifications:

• Swagger v2.0
• OpenAPI v3.0
• OpenAPI v3.1
• OpenAPI v3.1 (emended) - Standardized format

What is "Emended" OpenAPI?

The emended specification removes ambiguities and duplications from OpenAPI v3.1, creating a cleaner, more consistent format. All conversions flow through this intermediate format.

Key Improvements:

• Operations: Merges parameters from path and operation levels, resolves all references
• JSON Schema: Eliminates mixed types, unifies nullable handling, standardizes array/tuple representations
• Schema Composition: Consolidates anyOf, oneOf, allOf patterns into simpler structures

Converting Between Versions

import { OpenApi } from "@samchon/openapi";

// Convert any version to emended format
const emended: OpenApi.IDocument = OpenApi.convert(swagger); // Swagger 2.0/3.0/3.1

// Downgrade to older versions if needed
const v30: OpenApiV3.IDocument = OpenApi.downgrade(emended, "3.0");
const v20: SwaggerV2.IDocument = OpenApi.downgrade(emended, "2.0");

Validating OpenAPI Documents

Use typia for runtime validation with detailed type checking - far more accurate than other validators:

import { OpenApi, OpenApiV3, OpenApiV3_1, SwaggerV2 } from "@samchon/openapi";
import typia from "typia";

const document: any = await fetch("swagger.json").then(r => r.json());

// Validate with detailed error messages
const result: typia.IValidation<SwaggerV2.IDocument | OpenApiV3.IDocument | OpenApiV3_1.IDocument> =
  typia.validate<SwaggerV2.IDocument | OpenApiV3.IDocument | OpenApiV3_1.IDocument>(document);

if (result.success) {
  const emended: OpenApi.IDocument = OpenApi.convert(result.data);
} else {
  console.error(result.errors); // Detailed validation errors
}

Try it in the playground: Type assertion | Detailed validation

LLM Function Calling

flowchart
  subgraph "OpenAPI Specification"
    v20("Swagger v2.0") --upgrades--> emended[["OpenAPI v3.1 (emended)"]]
    v30("OpenAPI v3.0") --upgrades--> emended
    v31("OpenAPI v3.1") --emends--> emended
  end
  subgraph "OpenAPI Generator"
    emended --normalizes--> migration[["Migration Schema"]]
    migration --"Artificial Intelligence"--> lfc{{"LLM Function Calling"}}
    lfc --"OpenAI"--> chatgpt("ChatGPT")
    lfc --"Google"--> gemini("Gemini")
    lfc --"Anthropic"--> claude("Claude")
    lfc --"<i>Google</i>" --> legacy_gemini("<i> (legacy) Gemini</i>")
    legacy_gemini --"3.0" --> custom(["Custom JSON Schema"])
    chatgpt --"3.1"--> custom
    gemini --"3.1"--> standard(["Standard JSON Schema"])
    claude --"3.1"--> standard
  end

Turn your HTTP backend into an AI-callable service. @samchon/openapi converts your OpenAPI document into function schemas that OpenAI, Claude, and Gemini can understand and call.

Supported AI Models

IChatGptSchema - For OpenAI GPT

• Fully compatible with OpenAI's strict mode
  • strict mode is not recommended
  • validation feedback strategy is much powerful
• Uses JSDoc tags in description to bypass OpenAI's schema limitations

IClaudeSchema - For Anthropic Claude ⭐ Recommended

• Follows JSON Schema standard most closely
• No artificial restrictions - cleanest type definitions
• Ideal default choice when you're unsure which model to use
  • working on every models unless OpenAI's strict mode or legacy Gemini

IGeminiSchema - For Google Gemini

• Supports nearly all JSON Schema specifications (as of Nov 2025)
• Previous versions had severe restrictions, but these are now removed

[!NOTE]

You can also compose ILlmApplication from a TypeScript class using typia.

https://typia.io/docs/llm/application

import { ILlmApplication } from "@samchon/openapi";
import typia from "typia";

const app: ILlmApplication<"chatgpt"> =
  typia.llm.application<YourClassType, "chatgpt">();

Complete Example

Here's a full example showing how OpenAI GPT selects a function, fills arguments, and you execute it:

Resources:

• Full Example Code
• User Prompt Example
• LLM-Generated Arguments
• Function Calling Schema

import { HttpLlm, OpenApi, IHttpLlmApplication, IHttpLlmFunction } from "@samchon/openapi";
import OpenAI from "openai";

// 1. Convert OpenAPI to LLM function calling application
const document: OpenApi.IDocument = OpenApi.convert(swagger);
const application: IHttpLlmApplication<"chatgpt"> =
  HttpLlm.application({
    model: "chatgpt",
    document,
  });

// 2. Find the function by path and method
const func: IHttpLlmFunction<"chatgpt"> | undefined = application.functions.find(
  (f) => f.path === "/shoppings/sellers/sale" && f.method === "post"
);
if (!func) throw new Error("Function not found");

// 3. Let OpenAI GPT call the function
const client: OpenAI = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const completion: OpenAI.ChatCompletion = await client.chat.completions.create({
  model: "gpt-4o",
  messages: [
    { role: "system", content: "You are a helpful shopping assistant." },
    { role: "user", content: "I want to sell Microsoft Surface Pro 9..." }
  ],
  tools: [{
    type: "function",
    function: {
      name: func.name,
      description: func.description,
      parameters: func.parameters,
    }
  }],
});

// 4. Execute the function call on your actual server
const toolCall: OpenAI.ChatCompletionMessageToolCall =
  completion.choices[0].message.tool_calls![0];
const result: unknown = await HttpLlm.execute({
  connection: { host: "http://localhost:37001" },
  application,
  function: func,
  input: JSON.parse(toolCall.function.arguments),
});

Validation Feedback - Fixing LLM Mistakes

The Problem: LLMs make type errors. A lot.

Even when your schema says Array<string>, GPT might return just "string". In real-world testing with OpenAI GPT-4o-mini on a shopping service:

• 1st attempt: 70% success rate ❌
• 2nd attempt (with validation feedback): 98% success rate ✅
• 3rd attempt: Never failed ✅

The Solution: Validate LLM output and send errors back for correction.

import { HttpLlm, OpenApi, IHttpLlmApplication, IHttpLlmFunction, IValidation } from "@samchon/openapi";

// Setup application
const document: OpenApi.IDocument = OpenApi.convert(swagger);
const application: IHttpLlmApplication<"chatgpt"> = HttpLlm.application({
  model: "chatgpt",
  document,
});
const func: IHttpLlmFunction<"chatgpt"> = application.functions[0];

// Validate LLM-generated arguments
const result: IValidation<unknown> = func.validate(llmArguments);

if (result.success === false) {
  // Send detailed error feedback to LLM
  return await retryWithFeedback({
    message: "Type errors detected. Please correct the arguments.",
    errors: result.errors, // Detailed error information
  });
} else {
  // Execute the validated function
  const output: unknown = await HttpLlm.execute({
    connection: { host: "http://localhost:3000" },
    application,
    function: func,
    input: result.data,
  });
  return output;
}

The validation uses typia.validate<T>(), which provides the most accurate validation and extremely detailed error messages compared to other validators:

C.V. means class-validator

Model Context Protocol

flowchart
  subgraph "JSON Schema Specification"
    schemav4("JSON Schema v4 ~ v7") --upgrades--> emended[["OpenAPI v3.1 (emended)"]]
    schema2910("JSON Schema 2019-03") --upgrades--> emended
    schema2020("JSON Schema 2020-12") --emends--> emended
  end
  subgraph "OpenAPI Generator"
    emended --normalizes--> migration[["Migration Schema"]]
    migration --"Artificial Intelligence"--> lfc{{"LLM Function Calling"}}
    lfc --"OpenAI"--> chatgpt("ChatGPT")
    lfc --"Google"--> gemini("Gemini")
    lfc --"Anthropic"--> claude("Claude")
    lfc --"<i>Google</i>" --> legacy_gemini("<i> (legacy) Gemini</i>")
    legacy_gemini --"3.0" --> custom(["Custom JSON Schema"])
    chatgpt --"3.1"--> custom
    gemini --"3.1"--> standard(["Standard JSON Schema"])
    claude --"3.1"--> standard
  end

@samchon/openapi provides better MCP function calling than using the mcp_servers property directly.

While MCP (Model Context Protocol) can execute server functions directly through the mcp_servers property, @samchon/openapi offers significant advantages through model specification support, validation feedback, and selector agent filtering for context optimization.

For example, the GitHub MCP server has 30 functions. Loading all of them via mcp_servers creates huge context that often causes AI agents to crash with hallucinations. Function calling with proper filtering avoids this problem.

https://github.com/user-attachments/assets/72390cb4-d9b1-4d31-a6dd-d866da5a433b

GitHub MCP server via mcp_servers often crashes.

However, function calling to GitHub MCP with @agentica works properly.

• Function calling demo: https://www.youtube.com/watch?v=rLlHkc24cJs

Creating MCP applications:

Use McpLlm.application() to create function calling schemas from MCP tools. The returned IMcpLlmApplication includes the IMcpLlmFunction.validate() function for validation feedback.

MCP supports all JSON schema specifications without restrictions:

• JSON Schema v4, v5, v6, v7
• JSON Schema 2019-03
• JSON Schema 2020-12

import {
  IMcpLlmApplication,
  IMcpLlmFunction,
  IValidation,
  McpLlm,
} from "@samchon/openapi";

const application: IMcpLlmApplication<"chatgpt"> = McpLlm.application({
  model: "chatgpt",
  tools: [...],
});

const func: IMcpLlmFunction<"chatgpt"> = application.functions.find(
  (f) => f.name === "create",
)!;

const result: IValidation<unknown> = func.validate({
  title: "Hello World",
  body: "Nice to meet you AI developers",
  thumbnail: "https://wrtnlabs.io/agentica/thumbnail.jpg",
});
console.log(result);

Utilization Cases

Agentica

https://github.com/wrtnlabs/agentica

Agentic AI framework that converts OpenAPI documents into LLM function calling schemas for ChatGPT, Claude, and Gemini. Uses @samchon/openapi to transform backend REST APIs into callable functions with automatic parameter validation and type-safe remote execution.

import { Agentica, assertHttpController } from "@agentica/core";
import OpenAI from "openai";
import typia from "typia";

import { MobileFileSystem } from "./services/MobileFileSystem";

const agent = new Agentica({
  model: "chatgpt",
  vendor: {
    api: new OpenAI({ apiKey: "********" }),
    model: "gpt-4.1-mini",
  },
  controllers: [
    // functions from TypeScript class
    typia.llm.controller<MobileFileSystem, "chatgpt">(
      "filesystem",
      MobileFileSystem(),
    ),
    // functions from Swagger/OpenAPI
    // Uses @samchon/openapi under the hood:
    // 1. OpenApi.convert() to emended format
    // 2. HttpLlm.application() to create IHttpLlmApplication<"chatgpt">
    // 3. IChatGptSchema composed for each API operation
    assertHttpController({
      name: "shopping",
      model: "chatgpt",
      document: await fetch(
        "https://shopping-be.wrtn.ai/editor/swagger.json",
      ).then(r => r.json()),
      connection: {
        host: "https://shopping-be.wrtn.ai",
        headers: { Authorization: "Bearer ********" },
      },
    }),
  ],
});
await agent.conversate("I wanna buy MacBook Pro");

AutoBE

https://autobe.dev

AI backend code generator achieving 100% compilation success by using function calling to construct compiler AST instead of generating code text. For API specification design, uses @samchon/openapi types - AI calls compiler functions to build OpenAPI document structures that define REST endpoints and request/response schemas.

import { MicroAgentica } from "@agentica/core";
import { OpenApi } from "@samchon/openapi";

const agent = new MicroAgentica({
  model: "chatgpt",
  vendor: {
    api: new OpenAI({ apiKey: "********" }),
    model: "gpt-4.1-mini",
  },
  controllers: [
    // Compiler functions that receive/produce OpenApi.IDocument
    typia.llm.controller<OpenApiWriteApplication>(
      "api",
      new OpenApiWriteApplication(),
    ),
  ],
});
await agent.conversate("Design API specification, and generate backend app.");

class OpenApiWriteApplication {
  // LLM calls this function with OpenApi.IDocument structure
  // The type guarantees all operations have valid IJsonSchema definitions
  public async write(document: OpenApi.IDocument): Promise<void>  {
    // document.paths contains OpenApi.IOperation[]
    // Each operation.parameters, requestBody, responses use OpenApi.IJsonSchema
    // Compiler validates schema structure before code generation
    ...
  }
}