Powabase is an Application Platform and backend-as-a-service for building and deploying AI applications. Each project receives an isolated backend that includes PostgreSQL with pgvector, authentication, file storage, real-time services, an application programming interface (API) gateway, and an AI runtime.

The platform combines retrieval-augmented generation (RAG), AI Agent Builder capabilities, Multi-Agent Orchestration, and Agentic Workflow Orchestration. Developers can use those modules independently or combine them through a REST API to build applications that retrieve from proprietary content, call tools, reason over tasks, and trigger downstream actions.

Key Capabilities

Per-project backend infrastructure: Powabase provisions a dedicated data plane for each project, including a PostgreSQL database, pgvector, authentication service, object storage, API gateway, and AI service worker. The platform exposes database access through PostgREST and direct database connections.
RAG and document processing: The Context Engineering suite ingests PDFs, office files, images, spreadsheets, and other sources; extracts content with optical character recognition (OCR) where needed; and indexes it for vector, full-text, hybrid, or document-structure retrieval. It supports several indexing strategies, including structured document-to-JSON extraction.
Agent design and tool use: Developers can configure agents with instructions, knowledge bases, session memory, and tools. Agents use a Reason + Act (ReAct) loop and can call built-in database, storage, HTTP, and code-execution tools, as well as custom endpoints and Model Context Protocol (MCP) servers.
Multi-agent coordination: A coordinator agent can delegate work to specialized agents, each with its own role, tools, and knowledge base, then combine their outputs. Human-approval flows can pause sensitive tool calls before execution.
Workflow automation: Powabase workflows use directed acyclic graphs (DAGs) of triggers, conditions, agent runs, code, HTTP calls, and response blocks. Workflows can run from an API request, webhook, or schedule.
Deployment options: Powabase Cloud provides managed hosting with backups, updates, and scaling. The platform is also available as an Apache 2.0-licensed open-source stack for self-hosting with Docker Compose or Kubernetes.

Audience & Use Cases

Audience: Application developers, platform engineers, AI-product teams, and technical teams building production AI features.
Use cases: Building document-grounded assistants; creating tool-using or multi-agent systems; automating AI-assisted business workflows; providing a backend for AI-generated applications; and deploying AI applications under managed-cloud or self-hosted operating models.

Technical Specifications

Database: Per-project PostgreSQL with pgvector.
Interfaces: REST API, PostgREST, direct database access, webhooks, Server-Sent Events (SSE) streaming, custom HTTP tools, and MCP-server connections.
Retrieval: Vector search, BM25 full-text search, hybrid retrieval, tree-based retrieval, and optional reranking.
LLM model access: Supports bring-your-own model-provider keys and platform-managed inference options.

Powabase

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Categories & Use Cases