Documentation Index
Fetch the complete documentation index at: https://docs.powabase.ai/llms.txt
Use this file to discover all available pages before exploring further.
The Problem with Assembling Your Own Stack
Building a production AI application typically requires stitching together 5–7 separate tools: a vector database for RAG, an agent framework for tool calling, a workflow engine for automation, an LLM gateway for model routing, an auth system for users, a file storage service for documents, and a database for application state. Each tool has its own API, deployment model, and failure modes. Agentic Platform replaces this entire assembly with a single, unified REST API — one endpoint, one auth model, one database, one deployment.
Comparison Overview
vs Supabase
Supabase is an excellent general-purpose backend-as-a-service (Postgres, auth, storage, real-time) that Agentic Platform actually builds on — each project’s infrastructure uses Supabase components. The difference is purpose: Supabase provides the database and infrastructure primitives common to most SaaS apps, while Agentic Platform provides a good number of prebuilt agentic abstractions on top to accelerate development of AI-native applications.
| Capability | Agentic Platform | Supabase |
|---|
| Database | Postgres + pgvector per project (included) | Postgres + pgvector (included) |
| Auth & Storage | GoTrue + Storage API per project (included) | GoTrue + Storage API (included) |
| Document ingestion | Upload API → automatic extraction (PDF, DOCX, images w/ OCR) | No — build your own extraction pipeline |
| RAG pipeline | 4 indexing strategies, 4 retrieval methods, reranking, chunking | pgvector similarity search only — chunking, embedding, and retrieval pipeline are DIY |
| Agent framework | ReAct loop, 6 builtin tools, custom tools, MCP, hooks, approval flow | No agent framework — integrate LangChain or similar |
| Multi-agent orchestration | Supervisor, Sequential, Parallel strategies | None |
| Workflow automation | DAG-based workflows with webhooks, schedules, AI Copilot builder | Edge Functions (serverless compute, no workflow engine) |
| Streaming | SSE for agents, orchestrations, and workflows with event lifecycle | Real-time subscriptions (row-level changes, not AI events) |
vs LangChain / LangGraph
LangChain is the most popular AI framework, and LangGraph extends it with graph-based agent orchestration and durable state. They provide powerful abstractions for building AI applications — but they are frameworks, not infrastructure. You write code using their libraries, then deploy and operate everything yourself.
| Capability | Agentic Platform | LangChain / LangGraph |
|---|
| Deployment model | Managed API — no infrastructure to operate | Framework — you deploy, scale, and monitor your own services |
| Database | Postgres + pgvector included per project | None — bring your own (Pinecone, Weaviate, pgvector, etc.) |
| Auth | GoTrue included per project | None — build your own auth layer |
| Document ingestion | Upload API with automatic extraction | Document loaders (community-maintained, varying quality) |
| RAG | 4 indexing strategies, 4 retrieval methods, managed pipeline | Components for assembly — you build and maintain the pipeline |
| Agent framework | Managed ReAct loop with streaming SSE, tools, hooks, approval | LangGraph agents with durable state, checkpointing, time-travel debugging |
| Multi-agent | 3 orchestration strategies via API | Graph-based agent coordination (flexible but complex) |
| Observability | Run history, events, and usage stored per session | LangSmith (paid, starts at $39/user/month) |
| Workflow automation | Visual + API workflow builder with triggers and scheduling | LangGraph workflows (code-defined, no visual builder in OSS) |
vs Agno
Agno (formerly Phidata) is a lightweight Python agent framework with built-in agentic RAG and multi-agent teams. It emphasizes simplicity and speed — agents are pure Python objects, not graphs or chains. AgentOS provides a monitoring and management control plane.
| Capability | Agentic Platform | Agno |
|---|
| Deployment model | Managed API with per-project isolation | Framework — you deploy agents in your own infrastructure |
| Database & Auth | Postgres + pgvector + GoTrue included | None — bring your own database and auth |
| RAG pipeline | 4 indexing strategies, 4 retrieval methods, managed document ingestion | Agentic RAG with hybrid search and reranking — but you manage the vector DB |
| Agent framework | Managed ReAct with hooks, approval flow, session persistence | Lightweight agents with tool calling and memory |
| Multi-agent | 3 strategies (Supervisor, Sequential, Parallel) via API | Teams with role-based collaboration |
| Workflow automation | DAG workflows with webhooks, schedules, AI Copilot | Agno Workflows (code-defined sequential/parallel) |
| MCP support | Runtime tool discovery via MCP servers | MCP server connections supported |
| Management | Built-in dashboard, settings, per-project config | AgentOS control plane (monitoring, playground) |
vs Vectara
Vectara is a fully managed RAG-as-a-Service platform with excellent document processing, hybrid search, and built-in hallucination detection. It’s the strongest pure-RAG competitor — but it’s RAG-only.
| Capability | Agentic Platform | Vectara |
|---|
| RAG pipeline | 4 indexing strategies (ChunkEmbed, PageIndex, GraphIndex, Doc2JSON), 4 retrieval methods, reranking | ML-based chunking, hybrid search (neural + lexical), Boomerang reranker, hallucination detection |
| Document ingestion | Upload API, OCR, multi-format extraction | 100+ format ingestion, zero-config |
| Agent framework | Full ReAct loop with tools, hooks, MCP, streaming | Limited — vectara-agentic library (thin wrapper on LlamaIndex) |
| Multi-agent orchestration | 3 strategies via API | None |
| Workflow automation | DAG workflows with triggers | None |
| Database for app data | Postgres + PostgREST for custom tables | None — document corpus only |
| Auth for end users | GoTrue with RLS | API authentication only (not end-user auth) |
| Self-hosting | Yes (Docker or Kubernetes) | No — managed cloud only |
| Multi-language support | API-driven (any language) | 100+ languages for search out of the box |
vs Dify
Dify is a popular open-source LLM application builder with a visual workflow canvas, built-in RAG, and multiple app types (chatbot, agent, workflow). It’s a strong platform for prototyping and building AI apps, especially with its visual interface.
| Capability | Agentic Platform | Dify |
|---|
| RAG depth | 4 indexing strategies including tree-based (PageIndex) and structured extraction (Doc2JSON) | Standard chunking + embedding with configurable strategies |
| Agent framework | ReAct with hooks, approval flow, MCP, 6 builtin tools | ReAct agents with tool calling and conversation variables |
| Multi-agent | 3 orchestration strategies (Supervisor, Sequential, Parallel) | Workflow chaining of LLM/agent nodes (no native multi-agent collaboration) |
| Workflow engine | API-first DAGs with webhooks, cron/interval schedules | Visual canvas with branching, loops, error handling |
| Database for app data | Postgres + PostgREST (your own tables with RLS) | Internal Postgres only (for Dify state, not user data) |
| Auth for end users | GoTrue with email/OAuth/magic links | Admin auth only (no end-user auth, enterprise SSO is paid) |
| API-first design | Every feature accessible via REST API | Visual-first design — API is secondary |
| Per-project isolation | Fully isolated infrastructure per project | Shared infrastructure, workspace-level isolation |
vs n8n
n8n is a powerful general-purpose workflow automation platform with 400+ integration nodes. Its AI capabilities (AI Agent node, LLM nodes, memory nodes) are add-ons to a workflow engine, not purpose-built AI primitives.
| Capability | Agentic Platform | n8n |
|---|
| Primary focus | AI application backend (RAG, agents, orchestration) | General-purpose workflow automation with AI add-ons |
| RAG pipeline | Managed end-to-end: ingest, index (4 strategies), retrieve (4 methods), rerank | None built-in — connect to external vector DBs and embedding APIs via nodes |
| Agent depth | ReAct with tools, hooks, approval, session memory, streaming | AI Agent node with tool calling and memory (no approval flow, limited streaming) |
| Integrations | 6 builtin tools, custom HTTP tools, MCP servers | 400+ pre-built integration nodes (Slack, Salesforce, databases, etc.) |
| AI streaming | SSE with full event lifecycle (tool calls, approval, steps) | Not designed for streaming AI responses |
| Workflow triggers | API, webhook, cron/interval schedules | Manual, webhook, cron, and app-specific triggers |
vs CrewAI
CrewAI is a Python framework focused on multi-agent orchestration with role-based teams. It excels at modeling agent collaboration patterns — hierarchical delegation, sequential pipelines, and consensual decision-making.
| Capability | Agentic Platform | CrewAI |
|---|
| Deployment model | Managed API | Framework — deploy in your infrastructure |
| Multi-agent | 3 strategies via API (Supervisor, Sequential, Parallel) | 3 process types (sequential, hierarchical, consensual) — code-defined |
| RAG | 4 indexing strategies, 4 retrieval methods, managed pipeline | Basic RAG with ChromaDB (no hybrid search, no reranking) |
| Database & Auth | Postgres + GoTrue included | None included |
| Workflow automation | DAG workflows with visual builder and AI Copilot | CrewAI Flows (code-defined, no visual builder) |
| Human-in-the-loop | Approval hooks with SSE events and approve endpoint | None built-in |
| Language support | REST API — any language | Python only |
| Differentiator | What It Means |
|---|
| Unified API | RAG, agents, orchestration, workflows, database, auth, and storage — all from one REST API with one auth model |
| Deep RAG (not just vector search) | 4 indexing strategies (including LLM-powered tree indexing and structured JSON extraction), 4 retrieval methods, cross-encoder reranking |
| Per-project isolation | Each project gets its own Postgres, API gateway, auth service, storage, and AI worker — no shared state between projects |
| API-first, language-agnostic | Every feature works via REST. Build in Python, TypeScript, Go, Rust, or any language that speaks HTTP |
| Human-in-the-loop as a primitive | Approval hooks pause agent execution via SSE and wait for a decision via API — built into the agent runtime, not bolted on |
| Three orchestration strategies | Supervisor (autonomous delegation), Sequential (pipeline), Parallel (fan-out + merge) — choose the right pattern for your use case |
| AI Copilot for workflows | Describe what you want in natural language and the copilot generates the workflow graph |
| Managed infrastructure | No vector DB to provision, no agent server to deploy, no auth system to build — it’s all included and running |
Next Steps
Platform Overview
Understand the three core modules and how they work together.
Quickstart
Build an end-to-end RAG agent in 5 minutes.
Architecture
Per-project isolation, database schemas, and request routing.
