Langbase

Serverless platform for building, shipping, and scaling AI agents and apps

4.8 (4)

Geprüft von Daniel Nikulshyn·Aktualisiert Mai 2026

Serverless RAG Collaboration AI Agents API LLM Orchestration Developer Tools

Übersicht

Langbase is a developer-focused platform for designing and deploying AI agents and AI-powered features without managing infrastructure. It provides a serverless runtime, APIs, and tooling that let teams move from prototype to production with minimal setup. The platform centers on composable building blocks—pipes, memory, and agent workflows—so developers can connect multiple LLMs, manage prompts, and add retrieval or long-term context. Built-in collaboration features make it easier for teams to iterate on prompts, share components, and version their AI logic. With deployment, observability, and scaling handled out of the box, Langbase targets engineers who want to embed AI into products quickly while keeping cost, latency, and model choice under their control.

Hauptfunktionen

Serverless AI agent runtime
Composable pipes for chaining models and tools
Long-term memory and RAG support
Multi-model and multi-provider routing
Prompt versioning and team collaboration
APIs and SDKs for production integration

Anwendungsfälle

Deploy production AI agents without infrastructure

Engineers can ship AI agents to production using Langbase's serverless runtime, skipping server setup, scaling, and observability configuration.

Build multi-model AI workflows with pipes

Developers chain multiple LLMs, tools, and providers using composable pipes to route requests and orchestrate complex AI logic in one workflow.

Add long-term memory and RAG to apps

Teams embed context-aware features into products by leveraging built-in memory and retrieval, enabling agents to recall prior interactions and reference knowledge bases.

Collaborate on prompts with version control

Cross-functional teams iterate on prompts, share reusable components, and version AI logic together, streamlining prototyping-to-production handoffs.

Pro & Contra

Pro

Serverless deployment removes infra overhead
Supports multiple LLM providers in one workflow
Built-in memory and retrieval for context-aware agents
Team collaboration on prompts and pipes

Contra

Requires developer skills to get full value
Newer ecosystem with evolving documentation
Vendor lock-in risk for platform-specific abstractions

Bewertungen

4.8

Durchschnitt aus 4 Bewertungen.

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Sofia Lindqvist

Use it every day

Honestly didn't expect to like it this much. Prompt versioning and team collaboration is exactly what I needed, and team collaboration on prompts and pipes. I do wish newer ecosystem with evolving documentation, but I reach for it almost every day now and it just clicks.

Ahmed Saleh

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on serverless AI agent runtime, and serverless deployment removes infra overhead caught me off guard. still, I'd recommend giving it a real trial.

Jamal Carter

Solid for our team

We rolled this out across the team last quarter and serverless deployment removes infra overhead. APIs and SDKs for production integration fits neatly into how we already work, and multi-model and multi-provider routing removed a step we used to do by hand. Vendor lock-in risk for platform-specific abstractions, which is the main caveat, but it has held up under daily use.

Marcus Bell

Compared a few options

Evaluated this against two competitors. Where it wins: aPIs and SDKs for production integration and supports multiple LLM providers in one workflow. On balance the feature set — especially long-term memory and RAG support — justifies the 5 stars for our use case.