Lamatic.ai

This is where no-code AI workflows take shape. You build agents by placing nodes on a canvas rather than writing code end to end. AI nodes handle text, JSON, images, classification, and retrieval; data nodes cover search, memory, and vector stores; logic nodes add branches, loops, and conditions. The RAG workflows here are the common starting point: drop in a retriever, a vector store, and a text node, and you have a working answer pipeline. A flow editor with sticky notes keeps large graphs readable, and you can test each step as you go.

Lamatic gives you one place to call text, image, and embedding models from major providers alongside open models. You swap the model per node, so a cheap model can draft while a stronger one reviews. Teams that want a specific provider can add a custom model instead of waiting for it to land in the catalog.

Connectors cover tools such as Google Drive, Google Sheets, Gmail, Airtable, AWS S3, GitHub, Firecrawl, and SQL databases. They act as the ingestion layer, pulling files and rows into a flow for vectorization or indexing. This is the part that usually takes a month of setup when you build it yourself, and here it is a click.

Once a flow works, Lamatic hosts it on serverless infrastructure and scales it as traffic grows. Edge deployment and request caching aim to keep latency low, and the company states you can cut latency in half and serve millions of users without managing servers.

Real-time tracing, logs, and performance dashboards show what each agent actually did on a run. You can spot a failing step before customers report it, and log retention and scheduled jobs support ongoing tuning. Reports and failover handling cover the operational side.

The platform ships JavaScript, Next.js, and Python SDKs plus a GraphQL API, so you can execute a flow from your own code in a line or two. Webhooks let external systems trigger flows, and embeddable widgets let you drop an agent into a web page without building a front end.