Serverless AI agent platform with composable pipes, managed memory, and one-click deployment for building production AI agents.
A serverless platform for building AI agents — create and deploy agents with built-in memory and tool access in minutes.
Langbase is a serverless AI agent development platform that abstracts away infrastructure complexity, letting developers build, deploy, and scale AI agents using composable building blocks called Pipes. Each Pipe is a serverless function that wraps an LLM call with configurable prompts, tools, memory, and guardrails — and Pipes can be chained together to create sophisticated agent behaviors.
The platform's core innovation is treating AI agent components as composable, serverless primitives. A Pipe can be a simple prompt-response function, a RAG pipeline with attached memory, a tool-calling agent, or a multi-step workflow. Developers configure Pipes through a web UI or API, test them in an integrated playground, and deploy with one click — no containers, no infrastructure, no cold starts to manage.
Langbase Memory is the platform's managed RAG solution. Users upload documents or connect data sources, and Langbase handles chunking, embedding, indexing, and retrieval automatically. Memory can be attached to any Pipe, instantly giving agents access to custom knowledge. The system supports multiple embedding models and retrieval strategies.
The pricing model is purely usage-based — you pay for LLM tokens consumed through your Pipes, with no separate platform fees for the free tier. This makes it extremely accessible for experimentation and small-scale deployments, with costs scaling linearly with actual usage.
Langbase provides SDKs for JavaScript/TypeScript and Python, a REST API for any language, and streaming support for real-time applications. The platform includes built-in analytics showing token usage, latency, and error rates across all Pipes.
For developers who want the power of frameworks like LangChain without managing infrastructure, Langbase offers a compelling middle ground. It's more flexible than no-code builders but simpler than running your own agent infrastructure. The composable Pipe model naturally supports the modular agent architectures that are becoming best practice — each capability is isolated, testable, and independently scalable.
The platform has gained particular traction for building internal tools, customer support agents, and content generation pipelines where rapid iteration and zero-ops deployment are priorities.
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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.
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.
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.
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.
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.
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.
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