AgentKit

AgentKit is an AI agent framework designed to help developers build reliable agent-based applications, from simple model calls to more advanced multi-agent workflows. It is built by Inngest and focuses on orchestration, which means it helps developers structure how agents think, call tools, pass work between steps, and complete tasks in a predictable way. Instead of creating disconnected prompts or one-off chatbot logic, AgentKit gives teams a more organized foundation for building agents that can use tools, manage workflows, and run inside real software products.

The platform is especially useful for engineering teams that want to build support agents, coding agents, research assistants, workflow automation agents, or internal AI systems. AgentKit supports composable primitives, allowing developers to create agents, connect them to tools, and coordinate multiple agents when a task requires more than one responsibility. It also supports different model providers, including OpenAI, Anthropic, Gemini, and OpenAI-compatible models, which gives teams more flexibility when choosing the AI model behind their workflow.

One of AgentKit’s main strengths is that it is designed for testing and production readiness, not just prototyping. Developers can build locally, inspect agent behavior, debug workflows, and improve reliability before deployment. This is important because AI agents can be unpredictable if they are not properly structured and monitored. AgentKit helps reduce that risk by giving developers a clearer way to build, test, and deploy agentic applications at scale.

Overall, AgentKit is a strong option for technical teams that want to create custom AI agents with tool usage, orchestration, and multi-agent logic. It is not a no-code chatbot builder, but it is valuable for developers who need control, flexibility, and a more structured way to build AI agent systems.