AgentKit

The framework provides the scaffolding for the reasoning loop that gives agents their planning capability: breaking complex objectives into subtasks, selecting and calling appropriate tools for each step, evaluating results, and adjusting plans when initial approaches fail.

This structured approach to agent construction produces more reliable, debuggable agents than prompt-engineering approaches that attempt to accomplish everything in a single context window.

AgentKit's tool abstraction layer makes it straightforward to extend agents with new capabilities by defining tool schemas that the LLM can understand and call correctly, without requiring changes to the core reasoning loop.

Pre-built tool integrations cover common agent capabilities including web search, code execution, file system access, HTTP API calls, database queries, and browser automation, allowing developers to assemble capable agents quickly by selecting from available tools rather than building each capability from scratch.

The framework supports multiple LLM backends including OpenAI, Anthropic Claude, and open-source models, making it adaptable to different cost, performance, and data privacy requirements.

AgentKit includes observability and debugging tools that surface the agent's reasoning process the chain of thought, tool calls, tool results, and plan adjustments made during task execution making it possible to identify and fix failure modes that are invisible in pure output evaluation.

Human-in-the-loop hooks allow agents to pause and request confirmation before taking consequential actions, adding a safety mechanism appropriate for agents with real-world effects like sending emails, modifying databases, or making API calls to external services.