Tag
6 catalogue entries use this topic; each Project's license policy is labeled separately.
Build and deploy AI agents and RAG pipelines visually
Dify is where a prompt experiment becomes something a team can operate. Workflows are assembled on a canvas, datasets are indexed and versioned, and every deployment gets an API endpoint with logging and cost tracking attached.
Drag-and-drop builder for agents and RAG pipelines
Langflow turns pipeline construction into a graph you can see. Components are Python underneath, so a flow that outgrows the canvas can be exported and run as code rather than rebuilt.
Let AI agents drive a real browser
Browser Use gives an agent a real browser rather than an HTTP client. It handles the translation between a model's intent and DOM actions, keeps a structured representation of the page in context, and recovers from the usual navigation failures.
Autonomous agents that complete real engineering tasks
OpenHands runs agents that plan and execute multi-step engineering work: reading a repository, editing files, running tests, and iterating until a task is done. Each agent works inside a sandboxed runtime rather than on the host machine.
A control layer for customer-facing AI agents
Parlant addresses the problem that stops most support agents from shipping: a model that is helpful ninety-five percent of the time is not safe to put in front of customers. It manages behaviour through explicit, context-matched guidelines rather than one long prompt.
Hosted headless browser infrastructure for agents
Browserbase runs the browsers so you do not have to. Sessions, proxies, captcha handling, and stealth configuration are managed, which removes the least interesting and most fragile part of building anything that scrapes or automates the web.