> For the complete documentation index, see [llms.txt](https://agentic.gitbook.io/agentic/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://agentic.gitbook.io/agentic/home.md).

# Home

## Introduction to Agentic

Welcome to the documentation for `@obayd/agentic`!

`@obayd/agentic` is a powerful, lightweight framework designed to streamline the development of LLM-powered agents in Node.js environments. It focuses on simplifying the complexities around function calling (tool usage), conversation management, and asynchronous streaming, allowing you to focus on building intelligent agent behavior.

### What Problem Does It Solve?

Building sophisticated LLM agents often involves several challenges:

1. **Function Calling Logic:** Defining functions (tools) the LLM can call, parsing the LLM's requests, executing the functions, and feeding the results back requires significant boilerplate.
2. **Conversation Flow:** Managing the back-and-forth between the user, the LLM, and tool executions can become complex, especially with multiple potential tool calls per turn.
3. **Streaming Responses:** Handling streamed responses from LLMs effectively to provide a responsive user experience while also parsing for potential function calls requires careful state management.
4. **Prompt Engineering:** Crafting effective system prompts that instruct the LLM on how and when to use available tools is crucial but can be tedious.
5. **LLM Agnosticism:** Integrating with different LLM providers often means adapting to slightly different API request/response formats.

`@obayd/agentic` addresses these challenges by providing a structured and intuitive API.

### Key Features

* **✨ Fluent Tool Definition:** Define tools with parameters, descriptions, required flags, enums, and even raw text input using a clean, chainable API (`Tool.make().description().param()...`).
* **📦 Toolpacks:** Group related tools into `Toolpack`s. Enable or disable entire sets of tools dynamically within a conversation using the built-in `enable_toolpack` tool.
* **🌊 Streaming First:** Designed around async generators for handling LLM responses and tool events. Process information as it arrives for a more interactive feel.
* **🔌 Flexible LLM Integration:** Connect to virtually any LLM that supports function calling and streaming responses by providing a simple `llmCallback` async generator function. A utility (`fetchResponseToStream`) is included for common SSE formats.
* **🗣️ Automated Conversation Management:** The `Conversation` class manages message history, builds system prompts incorporating tool definitions, parses LLM responses for tool calls, executes tools, and formats results back for the LLM.
* **⚙️ Dynamic System Prompts:** Define system prompt content using a mix of static strings, `Tool` instances, `Toolpack` instances, or even asynchronous functions that return content dynamically based on the conversation state.
* **🔒 Type-Safe:** Comes with comprehensive TypeScript declaration files (`.d.ts`) for robust type checking and excellent developer experience (autocompletion!) in both TypeScript and JavaScript projects.
* **☀️ Zero-Dependency:** No external dependencies, making it easy to integrate with your existing project.

Ready to build your agent? Head over to the [Getting Started](/agentic/getting-started.md) guide!