@agentica/core

The simplest Agentic AI library, specialized in LLM Function Calling.

Don't compose complicate agent graph or workflow, but just deliver Swagger/OpenAPI documents or TypeScript class types linearly to the @agentica/core. Then @agentica/core will do everything with the function calling.

Look at the below demonstration, and feel how @agentica/core is easy and powerful.

import { Agentica } from "@agentica/core";
import typia from "typia";

const agent = new Agentica({
  controllers: [
    await fetch(
      "https://shopping-be.wrtn.ai/editor/swagger.json",
    ).then(r => r.json()),
    typia.llm.application<ShoppingCounselor>(),
    typia.llm.application<ShoppingPolicy>(),
    typia.llm.application<ShoppingSearchRag>(),
  ],
});
await agent.conversate("I wanna buy MacBook Pro");
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https://github.com/user-attachments/assets/01604b53-aca4-41cb-91aa-3faf63549ea6

Demonstration video of Shopping AI Chatbot

How to Use

Setup

npm install @agentica/core @samchon/openapi typia
npx typia setup
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Install not only @agentica/core, but also @samchon/openapi and typia.

@samchon/openapi is an OpenAPI specification library which can convert Swagger/OpenAPI document to LLM function calling schema. And typia is a transformer (compiler) library which can compose LLM function calling schema from a TypeScript class type.

By the way, as typia is a transformer library analyzing TypeScript source code in the compilation level, it needs additional setup command npx typia setup. Also, if you're not using non-standard TypeScript compiler (not tsc) or developing the agent in the frontend environment, you have to setup @ryoppippi/unplugin-typia too.

Chat with Backend Server

import { IHttpLlmApplication } from "@samchon/openapi";
import { Agentica, createHttpLlmApplication } from "@agentica/core";
import OpenAI from "openai";
import { IValidation } from "typia";

const main = async (): Promise<void> => {
  // LOAD SWAGGER DOCUMENT, AND CONVERT TO LLM APPLICATION SCHEMA
  const application: IValidation<IHttpLlmApplication<"chatgpt">> =
    createHttpLlmApplication({
      model: "chatgpt",
      document: await fetch("https://shopping-be.wrtn.ai/editor/swagger.json").then(
        (r) => r.json()
      ),
    });
  if (application.success === false) {
    console.error(application.errors);
    throw new Error("Type error on the target swagger document");
  }

  // CREATE AN AGENT WITH THE APPLICATION
  const agent: Agentica<"chatgpt"> = new Agentica({
    model: "chatgpt",
    vendor: {
      api: new OpenAI({
        apiKey: "YOUR_OPENAI_API_KEY",
      }),
      model: "gpt-4o-mini",
    },
    controllers: [
      {
        protocol: "http",
        name: "shopping",
        application: application.data,
        connection: {
          host: "https://shopping-be.wrtn.ai",
        },
      },
    ],
    config: {
      locale: "en-US",
    },
  });

  // ADD EVENT LISTENERS
  agent.on("select", async (select) => {
    console.log("selected function", select.operation.function.name);
  });
  agent.on("execute", async (execute) => {
    consoe.log("execute function", {
      function: execute.operation.function.name,
      arguments: execute.arguments,
      value: execute.value,
    });
  });

  // CONVERSATE TO AI CHATBOT
  await agent.conversate("What you can do?");
};
main().catch(console.error);
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Just load your swagger document, and put it into the @agentica/core.

Then you can start conversation with your backend server, and the API functions of the backend server would be automatically called. AI chatbot will analyze your conversation texts, and executes proper API functions by the LLM (Large Language Model) function calling feature.

From now on, every backend developer is also an AI developer.

Chat with TypeScript Class

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

class BbsArticleService {
  /**
   * Create a new article.
   *
   * Writes a new article and archives it into the DB.
   *
   * @param props Properties of create function
   * @returns Newly created article
   */
  public async create(props: {
    /**
     * Information of the article to create
     */
    input: IBbsArticle.ICreate;
  }): Promise<IBbsArticle>;

  /**
   * Update an article.
   *
   * Updates an article with new content.
   *
   * @param props Properties of update function
   * @param input New content to update
   */
  public async update(props: {
    /**
     * Target article's {@link IBbsArticle.id}.
     */
    id: string & tags.Format<"uuid">;

    /**
     * New content to update.
     */
    input: IBbsArticle.IUpdate;
  }): Promise<void>;
}

const main = async (): Promise<void> => {
  const api: OpenAI = new OpenAI({
    apiKey: "YOUR_OPENAI_API_KEY",
  });
  const agent: Agentica<"chatgpt"> = new Agentica({
    model: "chatgpt",
    vendor: {
      api: new OpenAI({
        apiKey: "YOUR_OPENAI_API_KEY",
      }),
      model: "gpt-4o-mini",
    },
    controllers: [
      {
        protocol: "class",
        name: "vectorStore",
        application: typia.llm.application<
          BbsArticleService,
          "chatgpt"
        >(),
        execute: new BbsArticleService(),
      },
    ],
  });
  await agent.conversate("I wanna write an article.");
};
main().catch(console.error);
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You also can chat with a TypeScript class.

Just deliver the TypeScript type to the @agentica/core, and start conversation. Then @agentica/core will call the proper class functions by analyzing your conversation texts with LLM function calling feature.

From now on, every TypeScript classes you've developed can be the AI chatbot.

Multi Agent Orchestration

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

class OpenAIVectorStoreAgent {
  /**
   * Retrieve Vector DB with RAG.
   *
   * @param props Properties of Vector DB retrievelance
   */
  public query(props: {
    /**
     * Keywords to look up.
     *
     * Put all the keywords you want to look up. However, keywords
     * should only be included in the core, and all ambiguous things
     * should be excluded to achieve accurate results.
     */
    keywords: string;
  }): Promise<IVectorStoreQueryResult>;
}

const main = async (): Promise<void> => {
  const api: OpenAI = new OpenAI({
    apiKey: "YOUR_OPENAI_API_KEY",
  });
  const agent: Agentica<"chatgpt"> = new Agentica({
    model: "chatgpt",
    context: {
      api: new OpenAI({
        apiKey: "YOUR_OPENAI_API_KEY",
      }),
      model: "gpt-4o-mini",
    },
    controllers: [
      {
        protocol: "class",
        name: "vectorStore",
        application: typia.llm.application<
          OpenAIVectorStoreAgent,
          "chatgpt"
        >(),
        execute: new OpenAIVectorStoreAgent({
          api,
          id: "YOUR_OPENAI_VECTOR_STORE_ID",
        }),
      },
    ],
  });
  await agent.conversate("I wanna research economic articles");
};
main().catch(console.error);
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In the @agentica/core, you can implement multi-agent orchestration super easily.

Just develop a TypeScript class which contains agent feature like Vector Store, and just deliver the TypeScript class type to the @agentica/core like above. The @agentica/core will centralize and realize the multi-agent orchestration by LLM function calling strategy to the TypeScript class.

If you want drastically improves function selection speed

Use the @agentica/pg-vector-selector

Just initialize and set the config
when use this adapter, you should run the connector-hive

import { Agentica } from "@agentica/core";
import { AgenticaPgVectorSelector } from "@agentica/pg-vector-selector";

import typia from "typia";


// Initialize with connector-hive server
const selectorExecute = AgenticaPgVectorSelector.boot<"chatgpt">(
  'https://your-connector-hive-server.com'
);


const agent = new Agentica({
  model: "chatgpt",
  vendor: {
    model: "gpt-4o-mini",
    api: new OpenAI({
      apiKey: process.env.CHATGPT_API_KEY,
    }),
  },
  controllers: [
    await fetch(
      "https://shopping-be.wrtn.ai/editor/swagger.json",
    ).then(r => r.json()),
    typia.llm.application<ShoppingCounselor>(),
    typia.llm.application<ShoppingPolicy>(),
    typia.llm.application<ShoppingSearchRag>(),
  ],
  config: {
    executor: {
      select: selectorExecute,
    }
  }
});
await agent.conversate("I wanna buy MacBook Pro");
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Principles

Agent Strategy

sequenceDiagram
actor User
actor Agent
participant Selector
participant Caller
participant Describer
activate User
User-->>Agent: Conversate:
user says
activate Agent
Agent->>Selector: Deliver conversation text
activate Selector
deactivate User
Note over Selector: Select or remove candidate functions
alt No candidate
  Selector->>Agent: Talk like plain ChatGPT
  deactivate Selector
  Agent->>User: Conversate:
agent says
  activate User
  deactivate User
end
deactivate Agent
loop Candidate functions exist
  activate Agent
  Agent->>Caller: Deliver conversation text
  activate Caller
  alt Contexts are enough
    Note over Caller: Call fulfilled functions
    Caller->>Describer: Function call histories
    deactivate Caller
    activate Describer
    Describer->>Agent: Describe function calls
    deactivate Describer
    Agent->>User: Conversate:
agent describes
    activate User
    deactivate User
  else Contexts are not enough
    break
      Caller->>Agent: Request more information
    end
    Agent->>User: Conversate:
agent requests
    activate User
    deactivate User
  end
  deactivate Agent
end
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When user says, @agentica/core delivers the conversation text to the selector agent, and let the selector agent to find (or cancel) candidate functions from the context. If the selector agent could not find any candidate function to call and there is not any candidate function previously selected either, the selector agent will work just like a plain ChatGPT.

And @agentica/core enters to a loop statement until the candidate functions to be empty. In the loop statement, caller agent tries to LLM function calling by analyzing the user's conversation text. If context is enough to compose arguments of candidate functions, the caller agent actually calls the target functions, and let decriber agent to explain the function calling results. Otherwise the context is not enough to compose arguments, caller agent requests more information to user.

Such LLM (Large Language Model) function calling strategy separating selector, caller, and describer is the key logic of @agentica/core.

Validation Feedback

import { FunctionCall } from "pseudo";
import { ILlmFunction, IValidation } from "typia";

export const correctFunctionCall = (p: {
  call: FunctionCall;
  functions: Array<ILlmFunction<"chatgpt">>;
  retry: (reason: string, errors?: IValidation.IError[]) => Promise<unknown>;
}): Promise<unknown> => {
  // FIND FUNCTION
  const func: ILlmFunction<"chatgpt"> | undefined =
    p.functions.find((f) => f.name === p.call.name);
  if (func === undefined) {
    // never happened in my experience
    return p.retry(
      "Unable to find the matched function name. Try it again.",
    );
  }

  // VALIDATE
  const result: IValidation<unknown> = func.validate(p.call.arguments);
  if (result.success === false) {
    // 1st trial: 50% (gpt-4o-mini in shopping mall chatbot)
    // 2nd trial with validation feedback: 99%
    // 3nd trial with validation feedback again: never have failed
    return p.retry(
      "Type errors are detected. Correct it through validation errors",
      {
        errors: result.errors,
      },
    );
  }
  return result.data;
}
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Is LLM function calling perfect?

The answer is not, and LLM (Large Language Model) vendors like OpenAI take a lot of type level mistakes when composing the arguments of the target function to call. Even though an LLM function calling schema has defined an Array<string> type, LLM often fills it just by a string typed value.

Therefore, when developing an LLM function calling agent, the validation feedback process is essentially required. If LLM takes a type level mistake on arguments composition, the agent must feedback the most detailed validation errors, and let the LLM to retry the function calling referencing the validation errors.

About the validation feedback, @agentica/core is utilizing typia.validate<T>() and typia.llm.application<Class, Model>() functions. They construct validation logic by analyzing TypeScript source codes and types in the compilation level, so that detailed and accurate than any other validators like below.

Such validation feedback strategy and combination with typia runtime validator, @agentica/core has achieved the most ideal LLM function calling. In my experience, when using OpenAI's gpt-4o-mini model, it tends to construct invalid function calling arguments at the first trial about 50% of the time. By the way, if correct it through validation feedback with typia, success rate soars to 99%. And I've never had a failure when trying validation feedback twice.

C.V. means class-validator

OpenAPI Specification

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 --"Anthropic"--> claude("Claude")
    lfc --"Google"--> gemini("Gemini")
    lfc --"Meta"--> llama("Llama")
  end
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@agentica/core obtains LLM function calling schemas from both Swagger/OpenAPI documents and TypeScript class types. The TypeScript class type can be converted to LLM function calling schema by typia.llm.application<Class, Model>() function. Then how about OpenAPI document? How Swagger document can be LLM function calling schema.

The secret is on the above diagram.

In the OpenAPI specification, there are three versions with different definitions. And even in the same version, there are too much ambiguous and duplicated expressions. To resolve these problems, @samchon/openapi is transforming every OpenAPI documents to v3.1 emended specification. The @samchon/openapi's emended v3.1 specification has removed every ambiguous and duplicated expressions for clarity.

With the v3.1 emended OpenAPI document, @samchon/openapi converts it to a migration schema that is near to the function structure. And as the last step, the migration schema will be transformed to a specific LLM vendor's function calling schema. LLM function calling schemas are composed like this way.

Why do not directly convert, but intermediate?

If directly convert from each version of OpenAPI specification to specific LLM's function calling schema, I have to make much more converters increased by cartesian product. In current models, number of converters would be 12 = 3 x 4.

However, if define intermediate schema, number of converters are shrunk to plus operation. In current models, I just need to develop only (7 = 3 + 4) converters, and this is the reason why I've defined intermediate specification. This way is economic.