Packages

Flomo MCP server enabling AI agents to create and retrieve memos in Flomo — the popular Chinese minimalist note-taking and knowledge capture app — sending notes to Flomo via its API, tagging entries, and integrating Flomo's frictionless capture workflow into agent-driven knowledge management and journaling pipelines.

MCP server for Moodle — the world's most popular open-source Learning Management System. Enables AI agents to interact with Moodle courses, enroll students, query grades, manage course content, and interact with the LMS through Moodle's Web Services API. Supports educational automation and AI-assisted learning workflows.

Penpot MCP server enabling AI agents to interact with Penpot — the open-source design and prototyping tool — creating and modifying design elements, managing design components, accessing design tokens, and integrating Penpot's design capabilities into agent-driven UI design and development workflows.

SharpTools MCP server enabling AI agents to interact with SharpTools smart home automation platform — controlling SmartThings and Hubitat devices, reading device states, executing SharpTools rules and routines, accessing dashboard data, and integrating SharpTools' home automation hub into agent-driven smart home control and home automation workflows.

Vite-powered static site generator optimized for documentation. VitePress converts Markdown files to fast static sites with Vue components support for interactive content. Built by the Vue.js team, used for Vue.js documentation itself. Features include full-text search (Algolia or built-in), dark mode, i18n, versioned docs, and a clean default theme. Much simpler than Docusaurus for pure Markdown documentation.

Async SQLite library for Python — thin wrapper around sqlite3 that runs database operations in a thread pool to avoid blocking the event loop. aiosqlite features: async context manager interface (async with aiosqlite.connect()), async cursor (async for row in cursor), execute/executemany/executescript, fetchone/fetchall/fetchmany, row_factory for Row namedtuple access, connection.commit()/rollback(), WAL mode for concurrent reads, and full compatibility with sqlite3 module API. Enables SQLite use in asyncio/FastAPI applications without blocking.

In-memory Redis implementation for Python testing — drop-in replacement for redis-py that runs entirely in-memory without a Redis server. fakeredis features: fakeredis.FakeRedis() matching redis-py API, fakeredis.FakeServer for shared state between clients, async support (fakeredis.aioredis.FakeRedis), all Redis data structures (string, hash, list, set, sorted set, stream), TTL support, Lua scripting, pub/sub, transaction support, pytest plugin (pytest-fakeredis), and version-specific behavior modes. Used for unit testing Redis-dependent code without external Redis server.

Fix text encoding issues in Python — automatically repairs mojibake (garbled text from wrong encoding), normalizes Unicode, and fixes common text encoding problems. ftfy features: fix_text() for general text repair, fix_encoding() for encoding-specific fixes, fix_and_explain() for diagnostic output, explain_unicode() for character analysis, remove_control_chars() for control character removal, uncurl_quotes() for smart/curly quote normalization, fix_surrogates() for surrogate pair repair, fix_latin_ligatures() for ligature expansion, and normalization (NFC/NFKC). Handles Windows-1252-as-UTF-8, Latin-1 mojibake, and other real-world encoding disasters.

Go mock generation and testing framework. gomock generates mock implementations of Go interfaces using the mockgen tool — run mockgen on an interface file to get a type-safe mock that records calls and allows expectations. The most widely used Go mocking library. Forked to uber-go/mock (maintained) after the original google/gomock became unmaintained.

Python bindings for LZ4 compression algorithm — provides extremely fast compression and decompression with reasonable ratios. lz4 features: lz4.frame.compress()/decompress() for standard LZ4 frame format, lz4.block.compress()/decompress() for raw block compression, lz4.frame.open() for streaming compression, LZ4FrameCompressor/LZ4FrameDecompressor for incremental processing, compression_level parameter (0=fast, 9-16=high compression), block_size options, content_checksum for integrity, block_linked for better ratio, and store_size for size-prepended blocks. LZ4 is 2-5x faster than zlib/gzip at similar ratios.

High-performance fake data generation library for Python — generates realistic test data for 50+ languages/locales with rich provider system. mimesis features: Person (name, email, phone), Address, Finance, Internet, Text, Datetime, File, Cryptographic, Code (ISBN, EAN, PIN), Transport (VIN, aircraft), Medical, Food, Science, Hardware, BinaryFile providers, Generic for all providers, Schema for structured data generation, Field/Fieldset for database seeding, locale support (en, de, ja, zh, etc.), and 2-5x faster than Faker for bulk generation.

Drop-in datetime replacement with timezone support — extends Python's datetime with intuitive API, timezone handling, and human-friendly output. pendulum features: pendulum.now() for current time with automatic DST handling, pendulum.parse() for ISO 8601 and RFC 2822 parsing, DateTime.add()/subtract() for date arithmetic, DateTime.diff() returning Duration objects, Duration with in_seconds()/in_hours() methods, Period between two datetimes, is_past()/is_future()/is_today() predicates, for_humans() for natural language diffs, timezone() for IANA timezone lookup, datetime() factory, in_timezone() for conversion, and timezone-aware datetime replacing pytz.

ASCII table formatting for Python — generates formatted table output for terminal display and text reports. prettytable features: PrettyTable() for table creation, add_row()/add_rows() for data, field_names for column headers, get_string() for ASCII output, get_html_string() for HTML tables, get_json_string() for JSON, get_csv_string() for CSV, sortby/reversesort for ordering, border/header/hrules for styling, align for column alignment (l/r/c), max_width for column truncation, start/end for pagination, and from_csv()/from_json()/from_db_cursor() for data import.

Advanced interactive terminal UI library for Python — provides readline replacement with autocompletion, syntax highlighting, history, multi-line editing, and key bindings. prompt_toolkit features: prompt() for single-line input with completion, PromptSession for stateful sessions, completer=WordCompleter/NestedCompleter/FuzzyCompleter, history=FileHistory, lexer= for syntax highlighting (Pygments), vi_mode=True, key_bindings for custom shortcuts, HTML style formatting, multiline=True for multi-line editing, bottom_toolbar, rprompt, and async support (prompt_async()). Powers IPython, ptpython, and many CLIs.

Non-interactive Python debugging via automatic variable tracing — traces function execution and prints variable changes without a debugger. snoop features: @snoop decorator for automatic trace logging, snoop() context manager for block tracing, pp() for pretty-print with expression display, watch= parameter for tracking non-local variables, depth= for tracing called functions, out= for file/stream output, prefix= for log identification, columns= for timestamp/thread display, enabled= flag for conditional tracing, and pysnooper compatibility. Prints every line executed with variable values — debugging without breakpoints.

Lightning-fast ASGI server for Python — production-grade async web server using uvloop and httptools. uvicorn features: HTTP/1.1 and HTTP/2, WebSocket support, worker processes (--workers N), hot reload (--reload for dev), SSL/TLS (--ssl-keyfile/--ssl-certfile), Unix socket support, graceful shutdown, access logging, --proxy-headers for reverse proxy, --root-path for sub-path deployment, --limit-concurrency for backpressure, programmatic startup via uvicorn.run() and Config, Gunicorn integration via UvicornWorker, and lifespan protocol support.

Python bindings for Zstandard (zstd) compression — provides fast, high-ratio compression developed by Facebook. zstandard features: ZstdCompressor/ZstdDecompressor for streaming, compress()/decompress() for one-shot, level parameter (1-22, default 3), trained dictionary support for repetitive data, streaming compression via compressor.stream_writer(), multi-threaded compression, frame parameters (magic, checksum, dict_id), content size in frame, chunked streaming decompression, CFFI and C extension backends, and significant improvement over LZ4 (better ratio) and gzip (much faster).

MCP server combining chart generation and MinIO object storage — enabling AI agents to generate charts and visualizations from data, store them in MinIO (S3-compatible object storage), and retrieve URLs for embedding in documents and reports. Enables agents to create visual data outputs that persist beyond the conversation context.

Extends pandas with a GeoDataFrame type and geospatial operations including spatial joins, overlays, dissolves, and CRS transformations for vector geospatial data.

Self-hosted containerized version of Google AlloyDB that runs on any hardware or cloud, bringing AlloyDB's columnar analytics engine and vector search to on-premises and multi-cloud environments.