Build AI Agents with Langflow

Langflow sits at the intersection of LLM orchestration, agent design, RAG, and production deployment, offering a practical on‑ramp for teams moving from experimentation to real AI systems. This compact course provides the entry points engineers need to integrate their data, workflows, and checkpoints to create and deploy applications quickly.

First, learn how to build AI agents using Langflow, focusing on creating dynamic and context-aware systems. Explore Langflow capabilities for adding memory, leveraging APIs, and incorporating retrieval-augmented generation for better outputs. Learn about deploying applications using Docker and Kubernetes, ensuring your agents are production-ready and reliable. Discover how to implement guardrails to secure your applications and route requests based on user inputs and sentiments. Engage with tools and techniques to connect your agents with external systems, like Linear via MCP servers, expanding their functionality. Benefit from practical, hands-on guidance that takes you from prototype creation to a full-scale AI solution. By the end of the course, you'll be equipped to design, build, and deploy robust AI agents tailored to various needs and environments.

Learning objectives
Install and configure Langflow to build and run LLM-powered workflows locally.
Create and iterate on Langflow flows to prototype LLM applications quickly.
Add memory, variables, and tool use to enable more capable, context-aware agents.
Implement RAG (retrieval-augmented generation) to ground responses in external knowledge sources.
Deploy Langflow-based agents with Docker to standardize and ship applications reliably.
Improve safety, reliability, and determinism with guardrails, routing, and structured outputs
Connect Langflow agents to an MCP server and manage state for scalable, multi-step agent behavior.