@samchon/openapi

flowchart TB
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 "LLM Function Calling"
  emended --normalizes--> migration[["Migration Schema"]]
  migration --"AI-Ready"--> schema{{"LLM Function Schema"}}
end
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Transform OpenAPI documents into LLM function calling applications.

@samchon/openapi converts OpenAPI/Swagger documents into LLM function calling schemas. With full TypeScript type safety, automatic validation, and support for every OpenAPI version, it's the simplest way to make your HTTP backend AI-callable.

Key Features

• 🌐 Multi-Provider Support: Works with OpenAI, Claude, Qwen, Llama, and other LLM providers
• 📝 Complete OpenAPI Coverage: Swagger 2.0, OpenAPI 3.0, and OpenAPI 3.1 fully supported
• 🔒 Type-Safe Validation: Built-in validation with detailed error feedback for LLM responses
• 🔄 MCP Integration: Compose function calling schemas from Model Context Protocol servers
• 📊 Emended Specification: Standardized OpenAPI v3.1 format that removes ambiguities
• ✅ Production Ready: Battle-tested with 98%+ success rates in real-world LLM applications

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
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Transform your OpenAPI document into an LLM function calling application:

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

// 1. Load and convert your OpenAPI document
const document: OpenApi.IDocument = OpenApi.convert(swagger);

// 2. Generate LLM function calling schemas
const application: IHttpLlmApplication = HttpLlm.application({
  document,
});

// 3. Find a function to call
const func: IHttpLlmFunction | undefined = application.functions.find(
  (f) => f.path === "/bbs/articles" && f.method === "post"
);

// 4. Use with any LLM provider (OpenAI, Claude, Qwen, etc.)
const completion = await llm.chat.completions.create({
  model: "gpt-4o", // or claude-3-5-sonnet, qwen-plus, etc.
  messages: [...],
  tools: [{
    type: "function",
    function: {
      name: func.name,
      description: func.description,
      parameters: func.parameters,
    }
  }],
});

// 5. Execute with validation
const result = await HttpLlm.execute({
  connection: { host: "http://localhost:3000" },
  application,
  function: func,
  input: llmGeneratedArgs,
});
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That's it! Your HTTP backend is now AI-callable across all major LLM providers.

OpenAPI Definitions

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

flowchart TB
v20(Swagger v2.0) --upgrades--> emended[["OpenAPI v3.1 (emended)"]]
v30(OpenAPI v3.0) --upgrades--> emended
v31(OpenAPI v3.1) --emends--> emended
emended --downgrades--> v20d(Swagger v2.0)
emended --downgrades--> v30d(OpenAPI v3.0)
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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");
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Validating OpenAPI Documents

Use typia for runtime validation with detailed type checking:

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.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
}
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Try it: Type assertion | Detailed validation

LLM Function Calling

Turn your HTTP backend into an AI-callable service. @samchon/openapi converts your OpenAPI document into function calling schemas that work with OpenAI GPT, Claude, Qwen, Llama, and other LLM providers.

Type Definitions:

Complete Example

Here's a full example showing LLM function calling with OpenAI (works identically with Claude, Qwen, etc.):

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 = HttpLlm.application({
  document,
});

// 2. Find the function by path and method
const func: IHttpLlmFunction | 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 = completion.choices[0].message.tool_calls![0];
const result = await HttpLlm.execute({
  connection: { host: "http://localhost:37001" },
  application,
  function: func,
  input: JSON.parse(toolCall.function.arguments),
});
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Works with Any LLM Provider:

// OpenAI
const openai = new OpenAI({ apiKey: "..." });

// Anthropic Claude
const anthropic = new Anthropic({ apiKey: "..." });

// Alibaba Qwen via DashScope
const qwen = new OpenAI({
  apiKey: "...",
  baseURL: "https://dashscope.aliyuncs.com/compatible-mode/v1",
});

// All use the same func.parameters schema
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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, IHttpLlmFunction, IValidation } from "@samchon/openapi";

const func: IHttpLlmFunction = 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 = await HttpLlm.execute({
    connection: { host: "http://localhost:3000" },
    application,
    function: func,
    input: result.data,
  });
  return output;
}
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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

Human-AI Collaboration (Separating Parameters)

Sometimes you need both human input and AI-generated parameters. Use the separate option to split parameters between LLM and human:

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

const application = HttpLlm.application({
  document,
  options: {
    separate: (schema) =>
      LlmTypeChecker.isString(schema) &&
      !!schema.contentMediaType?.startsWith("image"),
  },
});

const func = application.functions.find(
  (f) => f.path === "/shoppings/sellers/sale" && f.method === "post"
)!;

// func.separated.llm - for AI to fill (text, numbers, etc.)
// func.separated.human - for human to provide (file uploads, images)

const result = await HttpLlm.execute({
  connection: { host: "http://localhost:37001" },
  application,
  function: func,
  input: HttpLlm.mergeParameters({
    function: func,
    llm: llmGeneratedArgs,
    human: {
      content: {
        files: [...], // Human provides files
        thumbnails: [...], // Human provides images
      },
    },
  }),
});
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Model Context Protocol

flowchart TB
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 "AI Ecosystem"
  emended --normalizes--> migration[["Migration Schema"]]
  migration --AI-Ready--> schema{{"LLM Function Schema"}}
  schema --supports--> all("All LLM Providers")
end
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@samchon/openapi provides better MCP function calling than using the mcp_servers property directly.

While MCP can execute server functions directly through the mcp_servers property, @samchon/openapi offers significant advantages through 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 works across all LLM providers and includes validation feedback.

MCP supports all JSON schema specifications:

• 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 = McpLlm.application({
  tools: [...], // MCP tools
});

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

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

if (result.success) {
  // Execute validated function
  console.log("Valid arguments:", result.data);
} else {
  // Send errors back to LLM for correction
  console.error("Validation errors:", result.errors);
}
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import { ILlmApplication } from "@samchon/openapi";
import typia from "typia";

const app: ILlmApplication = typia.llm.application<YourClassType>();
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Utilization Cases

Agentica

https://github.com/wrtnlabs/agentica

Agentic AI framework that converts OpenAPI documents into LLM function calling schemas. 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({
  vendor: {
    api: new OpenAI({ apiKey: "********" }),
    model: "gpt-4o-mini",
  },
  controllers: [
    // Functions from TypeScript class
    typia.llm.controller(
      "filesystem",
      MobileFileSystem(),
    ),
    // Functions from Swagger/OpenAPI
    // Uses @samchon/openapi under the hood
    assertHttpController({
      name: "shopping",
      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");
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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({
  vendor: {
    api: new OpenAI({ apiKey: "********" }),
    model: "gpt-4o-mini",
  },
  controllers: [
    // Compiler functions that receive/produce OpenApi.IDocument
    typia.llm.controller(
      "api",
      new OpenApiWriteApplication(),
    ),
  ],
});
await agent.conversate("Design API specification and generate backend app.");

class OpenApiWriteApplication {
  // LLM calls this function with OpenApi.IDocument structure
  public async write(document: OpenApi.IDocument): Promise<void> {
    // Compiler validates schema structure before code generation
    ...
  }
}
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License

MIT License

Copyright (c) 2024 Jeongho Nam

For detailed API documentation, visit: https://samchon.github.io/openapi/api/