AgentKit vs LangChain

AgentKit is a developer toolkit by Inngest designed to simplify the creation of AI agents that interact with APIs and event-driven workflows.

It provides lightweight abstractions for integrating large language models into structured processes, enabling agents to handle real-world tasks such as scheduling, notifications, or workflow automation.

AgentKit emphasizes event-driven design, allowing developers to build agents that respond to triggers from apps, systems, or users.

Its framework supports modularity, making it easy to extend functionality or connect with other services.

LangChain is one of the most widely adopted frameworks for building applications powered by large language models (LLMs). It simplifies the development of AI agents and workflows by providing modular components for connecting LLMs with external data sources, APIs, and memory.

LangChain enables developers to design chatbots, research assistants, and automation systems that move beyond static prompts into dynamic, context-aware applications.

Its architecture supports chains (sequences of prompts), agents (LLM-driven decision-makers), and tools (integrations such as APIs, databases, or search engines).

LangChain also offers integrations with vector databases, enabling retrieval-augmented generation (RAG) for more accurate, knowledge-grounded responses.