Inkeep (YC W23) – Agent Builder to create agents in code or visually
Hi HN! I'm Nick from Inkeep. We built an agent builder with true 2-way sync between code and a drag-and-drop visual editor, so devs and non-devs can collaborate on the same agents. Here’s a demo video: https://go.inkeep.com/video. As a developer, the flow is: 1) Build AI Chat Assistants or AI Workflows with the TypeScript SDK 2) Run `inkeep push` from your CLI to publish 3)Edit agents in the visual builder (or hand off to non-technical teams) 4) Run `inkeep pull to edit in code again. We built this because we wanted the accessibility of no-code workflow builders (n8n, Zapier),…
In plain words
Inkeep is an agent builder that enables developers and non-technical users to collaborate on AI chat assistants and workflows through bidirectional sync between code and a visual drag-and-drop editor. Developers write agents using TypeScript SDK, then push them to the visual builder for editing or handoff to team members, then pull changes back to code. It combines the accessibility of no-code platforms with the flexibility of code-based frameworks, while supporting interactive chat interfaces beyond workflows alone.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN! I'm Nick from Inkeep. We built an agent builder with true 2-way sync between code and a drag-and-drop visual editor, so devs and non-devs can collaborate on the same agents. Here’s a demo video: https://go.inkeep.com/video. As a developer, the flow is: 1) Build AI Chat Assistants or AI Workflows with the TypeScript SDK 2) Run `inkeep push` from your CLI to publish 3)Edit agents in the visual builder (or hand off to non-technical teams) 4) Run `inkeep pull to edit in code again. We built this because we wanted the accessibility of no-code workflow builders (n8n, Zapier), but the flexibility and devex of code-based agent frameworks (LangGraph, Mastra). We also wanted first-class support for chat assistants with interactive UIs, not just workflows. OpenAI got close, but you can only do a one-time export from visual builder to code and there’s vendor lock-in. How I've used it: I bootstrapped a few agents for our marketing and sales teams, then was able to hand off so they can maintain and create their own agents. This has enabled us to adopt agents across technical and non-technical roles in our company on a single platform. To try it, here’s the quickstart: https://go.inkeep.com/quickstart. We leaned on open protocols to make it easy to use agents anywhere: An MCP endpoint, so agents can be used from Cursor/Claude/ChatGPT A Chat UI library with interactive elements you can customize in React An API endpoint compatible with the Vercel AI SDK `useChat` hook Support for Agent2Agent (A2A) so they work with other agent ecosystems We made some practical templates like a customer_support, deep_research, and docs_assistant. Deployment is easy with Vercel/Docker with a fair-code license and there's a traces UI and OTEL logs for observability. Under the hood, we went all-in on a multi-agent architecture. Agents are made up of LLMs, MCPs, and agent-to-agent relationships. We’ve found this approach to be easier to maintain and more flexible than traditional “if/else” approaches for complex workflows. The interoperability works because the SDK and visual builder share a common underlying representation, and the Inkeep CLI bridges it with a mix of LLMs and TypeScript syntactic sugar. Details in our docs: https://docs.inkeep.com. We’re open to ideas and contributions! And would love to hear about your experience building agents - what works, hasn’t worked, what’s promising?
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