Overview
Langbase is a serverless AI developer platform designed to simplify building, deploying, and scaling AI agents with semantic memory capabilities. It eliminates complexity from traditional AI frameworks, making sophisticated AI development accessible to developers without deep ML expertise.
The platform processes billions of AI messages and tokens daily through a globally distributed, highly scalable serverless architecture.
Core Concepts
Pipes (AI Agents)
Pipes are serverless, composable AI agents that can be connected together in multi-agent architectures. Each pipe is independently deployed and can communicate with other pipes, enabling complex workflows and orchestrations. The composable nature mirrors Docker containers, React components, or Lego blocks—allowing developers to build intricate systems from simple, interchangeable parts.
Memory (Semantic Memory with RAG)
Memory leverages Retrieval-Augmented Generation (RAG) technology to create semantic understanding of your data through a sophisticated multi-step process:
- Parsing - Extracts document structure and semantic meaning
- Chunking - Splits content into meaningful chunks while preserving context
- Embedding - Converts chunks into numerical vectors capturing semantic meaning
- Indexing - Stores embeddings in vector database for fast retrieval
- Reranking - Prioritizes most contextually relevant results for query
- Generation - Passes relevant context to LLM for grounded answers
Key Features
Multi-Model Access
- Access to 600+ AI models through a single unified API
- Seamless model switching without managing multiple integrations
- Support for latest frontier models and open-source options
Development Options
- AI Studio - Visual interface for building and deploying agents
- Langbase SDK - TypeScript/Node.js developer experience
- HTTP API - Works with any language (Python, Go, PHP, etc.)
- BaseAI.dev - Open-source, local-first web AI framework
Collaboration & Workflow
- Team collaboration similar to GitHub
- Built-in testing, versioning, and optimization tools
- Shared workspaces for multi-developer teams
Advanced Capabilities
- Model Context Protocol (MCP) integration for standardized tool/data source connections
- Cost prediction for spending optimization
- Secure key storage for API credentials
- Response evaluation for coherence and source grounding
Target Users
- Developers building AI agents and chatbots
- Teams creating context-aware customer support systems
- Organizations needing documentation assistants
- Multi-agent system builders
- Anyone building memory-based, context-aware AI applications
Practical Use Cases
Knowledge Base Assistants - Build AI that understands entire company knowledge bases and provides accurate, context-grounded responses to user queries.
Document Analysis - Create agents that can intelligently select top relevant paragraphs from terabytes of data and generate context-aware answers.
Multi-Agent Systems - Compose multiple specialized agents working together on complex tasks.
Custom Tool Integration - Connect AI models to documentation, APIs, databases, and custom tools through MCP.
Developer Experience
Langbase emphasizes accessibility and ease of use. The platform removes infrastructure management burden—developers focus on building AI logic while the platform handles deployment and scaling automatically. Collaborative features enable teams to work together on AI projects with version control and testing built-in.
Related Concepts
- Retrieval-Augmented Generation (RAG) - Core memory technology
- LLM - Large Language Models accessible through platform
- Model Context Protocol - Tool integration standard
- Multi-Agent Systems - Advanced composition pattern
- Semantic Memory - Vector embeddings and vector databases