Everything your AI needs, in one place.

firecrawl

Convert any website into clean, structured data with Firecrawl — API-first web scraping service. Use when someone asks to "turn a website into markdown", "scrape website for LLM", "Firecrawl", "extrac

haystack

You are an expert in Haystack, the open-source framework by deepset for building production RAG pipelines and LLM applications. You help developers create composable pipelines with document stores, re

lancedb

Embedded vector database with LanceDB — serverless, zero-config vector search for AI applications. Use when someone asks to "vector search without a server", "embedded vector database", "LanceDB", "lo

langchain

Build LLM-powered applications with LangChain. Use when a user asks to create AI chains, build RAG pipelines, implement agents with tools, set up document loaders, create vector stores, build conversa

llamaindex

Assists with building RAG pipelines, knowledge assistants, and data-augmented LLM applications using LlamaIndex. Use when ingesting documents, configuring retrieval strategies, building query engines,

markdown-new

Convert any public URL into clean, LLM-ready Markdown using the markdown.new service. Use for content extraction, RAG ingestion, article summarization, research, archiving, and token-efficient web rea

mastra

You are an expert in Mastra, the TypeScript framework for building AI agents, RAG pipelines, and workflows. You help developers create production AI applications with type-safe agent definitions, tool

mem0

You are an expert in Mem0, the memory infrastructure for AI applications. You help developers add persistent, personalized memory to LLM-powered apps and agents — storing user preferences, conversatio

onyx-ai

Deploy and configure Onyx, a self-hosted AI chat platform with RAG and 25+ data connectors. Use when: setting up private ChatGPT alternative, connecting AI to company documents, building enterprise AI

opendataloader-pdf

Parse PDFs into AI-ready structured data — extract text, tables, images, and metadata with high accuracy. Use when: processing PDF documents for RAG, extracting data from invoices/contracts, building

pgvector

Store and search vector embeddings in PostgreSQL with pgvector — no separate vector database needed. Use when someone asks to "vector search in Postgres", "store embeddings", "pgvector", "similarity s

qdrant

You are an expert in Qdrant, the high-performance vector search engine written in Rust. You help developers build semantic search, RAG retrieval, recommendation systems, and anomaly detection with bil

ragas

Expert guidance for Ragas, the framework for evaluating Retrieval-Augmented Generation pipelines. Helps developers measure and improve the quality of their RAG systems across retrieval accuracy, answe

supermemory

Add persistent memory to AI agents using Supermemory API -- the #1 ranked AI memory engine. Use when: building AI assistants that remember users, adding long-term memory to chatbots, creating personal

trieve

Expert guidance for Trieve, the all-in-one search infrastructure that combines full-text, semantic, and hybrid search with built-in RAG capabilities. Helps developers implement production search with