AI agents that can interact with the file system are genuinely useful. Whether it's reading a config file, searching through source code, or writing multiple files in one shot, Toolpack SDK's file system tools make it all possible out of the box.

In this post, we'll explore all 18 tools in the filesystem category — from core read/write operations to advanced search, glob matching, and atomic batch writes.

Toolpack SDK ships with a lot of built-in tools across categories like file system, web, databases, Git, and code analysis. But every project has unique needs - internal APIs, proprietary systems, domain-specific operations. Custom tools let you extend your AI agent's capabilities without limits.

This guide shows you exactly how to build custom tools, based on real production implementations from the Toolpack CLI project.

By default, the Toolpack SDK provides a unified AI agent runtime with full access to an extensive toolset. But giving an AI full runtime access is not always the correct architectural choice. Sometimes you need a chat-only assistant, a read-only DevOps reviewer, or a highly specialized agent confined to a strict workflow.

This is exactly what Modes solve. A "Mode" shapes the AI's persona, its capabilities, and its operational boundaries. In this guide, we will explore what modes do, how the built-in modes work, and how to create powerful custom ones from scratch.

Toolpack SDK ships with built-in support for OpenAI, Anthropic, Gemini, and Ollama. But sometimes you need to integrate a different provider -- xAI's Grok, Groq, DeepSeek, Azure OpenAI, or your company's own model. Custom providers make that possible without giving up any SDK features like tool calling, streaming, or embeddings.

This guide walks through exactly how to build one, based on the real xAI adapter used in the Toolpack CLI project.

Configuration is the backbone of any flexible SDK. Toolpack SDK implements a sophisticated hierarchical configuration system that balances simplicity for quick starts with power for complex production deployments. Whether you're integrating AI into your app or managing multiple projects, understanding how configuration works will unlock the full potential of the SDK.

Let's dive deep into how toolpack.config.json works, the three-tier hierarchy, and best practices for managing configurations across different environments.

AI agents need to access real-time information from the web. Whether it's searching for current events, fetching documentation, or scraping data, Toolpack SDK's web tools make it seamless.

In this post, we'll explore the web search fallback system (Tavily → Brave → DuckDuckGo Lite) and the web fetch tools that power web-aware AI applications.

Tools are the foundation of what makes AI agents useful. While LLMs are great at reasoning and generating text, they need tools to interact with the real world—reading files, making API calls, querying databases, and executing code.

Toolpack SDK ships with a deep catalog of built-in tools (with more landing every release) and makes it trivial to add your own. Let's explore how the tool system works and why it matters.

The AI tooling ecosystem is growing fast—but it's also fragmenting. Developers face vendor lock-in, inconsistent tool execution, and a lack of standardization when building AI-powered systems. Toolpack SDK is our answer to these problems.