@obayd/agentic

Agentic (@obayd/agentic) is a powerful, lightweight framework for building LLM agents in Node.js. It simplifies managing conversations, defining tools (function calling), handling streaming responses, and integrating with various Large Language Models.

Full Documentation (GitBook)

Key Features

• Fluent Tool Definition: Easily define tools (functions) the LLM can call using a clean, chainable API (Tool.make()...).
• Toolpacks: Group related tools and manage their availability.
• Streaming First: Built with streaming in mind. Process LLM responses and tool events chunk by chunk using async generators.
• Flexible LLM Integration: Works with any LLM API that provides streaming responses via a simple async generator function. Includes a helper fetchResponseToStream for common SSE formats.
• Conversation Management: Automatically handles message history, system prompts, and the flow between user messages, assistant responses, and tool calls/results.
• Dynamic System Prompts: Define system prompt content using strings, tools, toolpacks, or even functions for dynamic generation.
• Type-Safe: Includes TypeScript declaration files (.d.ts) for excellent autocompletion and type safety, even when used in JavaScript projects.
• Zero-Dependency: No external dependencies, making it easy to integrate with your existing project.

Installation

npm install @obayd/agentic
# or
npm install obaydmerz/agentic # Will install directly from github ( Not recommended )

Quick Start

import { Conversation, Tool, fetchResponseToStream } from "@obayd/agentic";

// 1. Define your LLM callback (Example using fetch and a helper)
//    Replace with your actual LLM API call logic
async function* llmCallback(messages, options) {
  // You can use OpenRouter or OpenAI here
  const response = await fetch("YOUR_LLM_API_ENDPOINT", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      Authorization: `Bearer YOUR_API_KEY`,
    },
    body: JSON.stringify({
      model: "YOUR_MODEL_NAME",
      messages: messages, // Pass the formatted messages without modification
      stream: true, // Ensure streaming is enabled

      // Add any other required parameters for your API
    }),
  });

  // Use the provided helper to process Server-Sent Events (SSE)
  yield* fetchResponseToStream(response);
}

// 2. Define Tools
const getCurrentWeather = Tool.make("get_current_weather")
  .description("Gets the current weather for a specified location.")
  .param("location", "The city and state, e.g., San Francisco, CA", {
    required: true,
    type: "string",
  })
  .param("unit", "Temperature unit", {
    enum: ["celsius", "fahrenheit"],
    required: false,
    type: "string",
  })
  .action(async (params) => {
    // Simulate API call
    await new Promise((resolve) => setTimeout(resolve, 50)); // Simulate delay
    const location = params.location.toLowerCase();
    const unit = params.unit || "celsius";
    let temperature;

    if (location.includes("tokyo")) temperature = 15;
    else if (location.includes("san francisco")) temperature = 12;
    else temperature = 20; // Default

    if (unit === "fahrenheit") {
      temperature = (temperature * 9) / 5 + 32;
    }

    return JSON.stringify({
      // Return data for the LLM
      temperature: temperature,
      unit: unit,
      condition: "Sunny", // Keep it simple for the example
      location: params.location,
    });
  });

// 3. Create a Conversation instance
const conversation = new Conversation(llmCallback);

// 4. Define conversation content (system prompt, tools)
conversation.content([
  "You are a helpful assistant.",
  getCurrentWeather, // Add the tool
]);

// 5. Send a message and process the response stream
async function runConversation() {
  const userInput = "What's the weather like in Tokyo?";
  console.log(`\nUSER: ${userInput}`);
  console.log("\nASSISTANT:");

  let fullAssistantResponse = "";

  try {
    const stream = conversation.send(userInput);

    for await (const event of stream) {
      switch (event.type) {
        case "assistant":
          process.stdout.write(event.content); // Stream text to console
          fullAssistantResponse += event.content;
          break;
        case "tool.generating":
          // Optionally show that the LLM is generating raw tool input
          // console.log(`\n[Tool Generating Raw Chunk (${event.name})]: ${event.rawChunk}`);
          break;
        case "tool.calling":
          process.stdout.write(
            `\n[Calling Tool: ${event.name} with params: ${JSON.stringify(
              event.params
            )}]`
          );
          if (event.raw) {
            process.stdout.write(` [Raw: ${event.raw}]`);
          }
          process.stdout.write("\n");
          break;
        case "tool": // Tool result is back
          console.log(`\n[Tool Result (${event.name})]:`);
          console.log(event.result); // Log the raw result object
          // The conversation loop automatically sends this result back to the LLM
          console.log("\nASSISTANT (after tool):"); // Indicate LLM will respond next
          break;
        case "error":
          console.error(`\n[Conversation Error]: ${event.content}`);
          break;
      }
    }
    console.log("\n\n--- Conversation End ---");
    // console.log("Full Assistant Response:", fullAssistantResponse);
    // console.log("Final Message History:", conversation.messages);
  } catch (error) {
    console.error("\n[Error Running Conversation]:", error);
  }
}

runConversation();

Documentation

For detailed usage, guides, and API reference, please visit the Full Documentation GitBook.

Contributing

Contributions are welcome! Please feel free to open an issue or submit a pull request.

• Fork the repository.
• Create your feature branch (git checkout -b feature/AmazingFeature).
• Commit your changes (git commit -m 'Add some AmazingFeature').
• Push to the branch (git push origin feature/AmazingFeature).
• Open a Pull Request.

License

Distributed under the MIT License. See LICENSE file for more information. Author Abderrahmene Merzoug ([email protected])