Packages

Azure's unified communications PaaS that gives AI agents programmatic access to SMS, transactional email, voice/video calling, push notifications, and chat via REST API and multi-language SDKs.

Ruby-DSL configuration management tool that uses cookbooks and recipes to define desired system state, with a chef-client agent converging nodes against a Chef Server or in local mode.

MCP server that bridges AI assistants (Cursor, Claude Desktop) with DaVinci Resolve's scripting API. Enables natural language control of Resolve for project management, timeline operations, media pool management, marker placement, and page/workspace navigation.

Open-source data platform wrapping any SQL database (Postgres, MySQL, SQLite) with an instant REST/GraphQL API, admin UI, file management, auth, and role-based access control.

Ghost Content API and Admin API — headless CMS REST APIs for creating, managing, and publishing blog posts, newsletters, and membership content, with support for members, subscriptions, and email newsletters.

Kit (formerly ConvertKit) — creator-focused email marketing platform with a REST API for managing subscribers, tags, sequences, and broadcasts for independent creators and online businesses.

Email testing and production delivery API that provides a safe sandbox environment for testing email workflows and a production SMTP/API path for transactional sending.

Open-source platform for building native product integrations with 250+ pre-built API connectors. Nango handles OAuth flows, token refresh, credential storage, and provides a unified interface to call any connected API. Designed for SaaS products that need to integrate with user-authorized third-party APIs (Salesforce, HubSpot, Slack, GitHub, etc.) without managing OAuth complexity per provider. Unlike Fivetran/Airbyte (ETL focus), Nango enables real-time API interactions on behalf of users.

Open-source notification infrastructure API that orchestrates multi-channel notifications (email, SMS, push, in-app, chat) through a single unified API with workflow templates.

OpenFGA is an open-source fine-grained authorization engine (Google Zanzibar-inspired, by Okta) that evaluates relationship-based access control (ReBAC) via a tuple model — agents write (user, relation, object) tuples and call the Check API to determine if a user has a specific permission on a specific resource.

A Java thread dump and heap analysis tool that runs as a GUI application, VisualVM plugin, or headless MCP server, enabling AI agents to parse thread dumps, detect deadlocks, identify long-running threads, and analyze virtual thread pinning.

Tianshu is an enterprise AI data preprocessing platform that converts unstructured documents (PDF, Word, Excel, PPT), images, audio, and video into AI-ready Markdown/JSON formats using MinerU and PaddleOCR-VL engines. It exposes document parsing capabilities via an MCP server for integration with AI assistants.

An MCP server that connects AI assistants to TradingView market data, enabling real-time screening for top gainers/losers, technical indicator analysis (RSI, MACD, Bollinger Bands), and pattern recognition across crypto and stock exchanges.

Distributed SQL query engine for querying data across multiple sources (S3/HDFS Parquet/ORC, Hive, Iceberg, Delta Lake, PostgreSQL, MySQL, Kafka, Redis, MongoDB, and more) using standard ANSI SQL. Trino (formerly PrestoSQL) runs federated queries spanning multiple data sources in a single SQL statement — JOIN a Hive table with PostgreSQL with a Kafka topic. Excels at interactive analytics on large datasets. Widely used as the query engine behind data platforms at Meta, LinkedIn, Netflix, and others.

An MCP server that enables AI agents to publish content, search posts, retrieve recommendations, manage comments, and access user profiles on Xiaohongshu (小红书/RedNote) via headless browser automation.

Zen is a self-hosted, single-binary notes app that stores markdown notes in SQLite with tag-based organization, BM25 full-text search, and an optional MCP server for AI agent integration with note operations.

Python bindings for llama.cpp — runs quantized LLMs locally on CPU/GPU without PyTorch or CUDA required. llama-cpp-python features: Llama class (load GGUF model, generate text), OpenAI-compatible API server (llama_cpp.server), llama(prompt) call interface, n_gpu_layers for partial/full GPU offload, chat completion format (llm.create_chat_completion), streaming responses, function calling support, embedding generation (llm.create_embedding), context window control, and mmap for large model loading. Runs Llama, Mistral, Phi, Gemma, and other GGUF-format models. Primary tool for CPU-only LLM inference in agent pipelines.

Python refactoring library — provides programmatic code refactoring operations on Python projects. rope features: rename (variables, functions, classes, modules), extract function/variable, move (class/function to different module), inline variable, introduce parameter, change function signature, auto-import suggestions, code completion via analysis, find-occurrences for symbol lookup, organize imports, undo/redo for refactoring operations, and Project abstraction for multi-file refactoring. Used by IDE plugins (Vim, Emacs, Sublime) for Python refactoring support.

Built-in WebSocket framework for Ruby on Rails — integrates WebSockets with Rails app for real-time features using channels. Action Cable features: Channel classes (ApplicationCable::Channel), subscriptions via JavaScript consumer, broadcasting with ActionCable.server.broadcast, stream_from / stream_for for pub/sub, authentication via connection.rb (current_user), Redis or async adapter for pub/sub backend, cable.yml configuration, Action Cable JavaScript consumer (consumer.subscriptions.create), and full Rails session/cookie access in connection. Replaced Need for Pusher in many Rails apps. ChatChannel, NotificationChannel, AgentStatusChannel patterns enable real-time agent updates without external WebSocket service.

Python-native stateful stream processing framework built on a Rust runtime. Bytewax brings the developer experience of Python to stream processing — write dataflows as Python functions without JVM, Java/Scala knowledge, or complex cluster configuration. Processes data from Kafka, Kinesis, files, or custom sources with stateful windowing, joining, and aggregation. Runs locally for development and scales to distributed execution. Positioned as the 'Python Flink' for teams that want streaming without the JVM ecosystem.