Getting Started with AgentKit
https://chatgpt.com/share/68fc28b4-127c-8004-8c4a-8c690f5d7d00 AgentKit is a framework/toolkit for building, orchestrating, testing and deploying AI-agents (from single-model agents to multi-agent systems). agentkit.inngest.com +2 agentkit.inngest.com +2 Key features: Agents + Networks + Routers + Tools + State. agentkit.inngest.com +1 Visual workflow builder (“Agent Builder”) + Chat embedding (“ChatKit”) + external connectors/tools. OpenAI +1 Built for production-scale orchestration (not only simple prompts). blott.com +1 Because you’re already building automated workflows (you use platforms like Make.com + Avatar generation + publishing pipelines), AgentKit can nicely plug into your stack (e.g., agents that drive content workflows, route tasks, integrate tools). 2. Prerequisites & Setup Before diving in, ensure you have the basics in place: Access to an LLM provider (e.g., OpenAI API key) or a supported model. Skywork +1 Language/Stack knowledge: JS/TypeScript (Node.js) is supported. agentkit.inngest.com +1 Basic familiarity with tools, workflows, tool-calls (you already have this via your automation experience). Strategic thinking about the “agent task” you want: what job it will do, which tools it needs, what scope. (Optional but beneficial) Familiarity with workflow automation concepts (you have Make.com, Google Sheets, asset generation pipelines) — you can treat AgentKit as a “workflow engine” for AI-agents. 3. Quick-Start Example Here’s how you can spin up a minimal AgentKit agent locally — you’ll get a “hello agent” and then you can build on it. For example, using the Inngest version: Create a JS/TS project: mkdir my-agentkit-project && cd my-agentkit-project npm init -y npm install @inngest/agent-kit inngest agentkit.inngest.com 2. Create an index.ts (or .js) file: import { createAgent, anthropic } from '@inngest/agent-kit'; const dbAgent = createAgent({