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📚Complete Guide

Langbase Tutorial: Get Started in 5 Minutes [2026]

Master Langbase with our step-by-step tutorial, detailed feature walkthrough, and expert tips.

Get Started with Langbase →Full Review ↗

🔍 Langbase Features Deep Dive

Explore the key features that make Langbase powerful for agent workflows.

Composable Pipes

What it does:

Serverless AI functions that can be configured, chained, and deployed independently — each wrapping LLM calls with prompts, tools, memory, and guardrails.

Use case:

Building a content pipeline with separate Pipes for research, writing, editing, and fact-checking that compose into a workflow.

Managed Memory (RAG)

What it does:

Upload documents and data sources with automatic chunking, embedding, and retrieval — attach to any Pipe for instant knowledge access.

Use case:

Creating a product documentation agent by uploading docs and attaching the memory to a customer support Pipe.

One-Click Deployment

What it does:

Deploy any Pipe as a serverless API endpoint instantly with no infrastructure configuration, containers, or cold start management.

Use case:

Shipping an AI feature to production within minutes of prototyping it in the playground.

Integrated Playground

What it does:

Test and iterate on Pipes directly in the browser with real-time streaming, variable injection, and conversation simulation.

Use case:

Tuning prompts and retrieval parameters for a RAG agent before deploying to production.

Multi-Model Support

What it does:

Switch between OpenAI, Anthropic, Google, and other LLM providers without code changes — just reconfigure the Pipe.

Use case:

A/B testing different models for a customer support agent to find the best quality/cost tradeoff.

Usage-Based Pricing

What it does:

Pay only for LLM tokens consumed through Pipes, with no platform fees on the free tier and linear cost scaling.

Use case:

Starting with free experimentation and scaling to production without pricing tier jumps or commitments.

❓ Frequently Asked Questions

How is Langbase different from LangChain?

LangChain is a framework you run on your own infrastructure. Langbase is a serverless platform — you configure Pipes through UI/API and Langbase handles deployment, scaling, and infrastructure.

What's a Pipe?

A Pipe is a serverless AI function that wraps an LLM call with configurable prompts, tools, memory access, and guardrails. Pipes are the building blocks for agent behaviors in Langbase.

Can I use my own LLM API keys?

Yes, you can bring your own API keys for OpenAI, Anthropic, Google, and other providers. Langbase routes calls through your keys.

Is it suitable for production?

Yes, Langbase is designed for production use with serverless scaling, monitoring, and enterprise security features.

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Ready to Get Started?

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Start Using Langbase Today

Follow our tutorial and master this powerful agent tool in minutes.

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Tutorial updated March 2026