LangbaseServerless platform for building, shipping, and scaling AI agents and apps
Overview
Key features
- 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
Pricing
- Model
- Freemium
- Category
- Large Language Models (LLMs)
- Rating
- 4.8 / 5 (4)
Use cases
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.
Pros & Cons
Pros
- 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
Cons
- Requires developer skills to get full value
- Newer ecosystem with evolving documentation
- Vendor lock-in risk for platform-specific abstractions
Reviews
Average from 4 ratings.
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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.
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.
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.
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.
Q&A
Do I need to manage any infrastructure for long‑term memory or RAG capabilities?
No, Langbase’s serverless offering includes built‑in long‑term memory, vector store file storage, and a retrieval engine for RAG, handling all infrastructure concerns behind the scenes.
Asked by Marcus Bell · Jul 13, 2025
What collaboration features are available for teams working on prompts and agents?
The platform includes prompt versioning, shared components, and built‑in collaboration tools so teams can iterate together, track changes, and reuse pipes or memory configurations across projects.
Asked by Ximena Torres · Jul 12, 2025
Can I use multiple LLM providers in a single Langbase workflow?
Yes, Langbase supports multi‑model and multi‑provider routing, allowing you to chain different LLMs and tools within composable pipes in the same agent workflow.
Asked by Thandiwe Dlamini · Jun 30, 2025
How does Langbase handle deployment and scaling for AI agents?
Langbase provides a serverless runtime that automatically manages deployment, observability, and scaling, so developers can move from prototype to production without provisioning or maintaining infrastructure.
Asked by Timur Nazarov · Jun 12, 2025
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