mark3labs/mcp-go is a production-oriented Go SDK implementing both server and client sides of the Model Context Protocol (MCP). The codebase is a library, not a standalone application: it exposes server.NewMCPServer, client.NewClient, and transport constructors for stdio, SSE, Streamable HTTP, and in-process communication. It supports tools, resources, prompts, tasks, sampling, elicitation, roots, completions, subscriptions, multi-round-trip requests, OAuth, OpenTelemetry tracing, schema validation, and cursor-based pagination. The SDK maintains dual-era protocol support, negotiating between legacy handshake-based versions (2024-11-05 through 2025-11-25) and the modern stateless 2026-07-28 core, with automatic per-request era detection and bridging for server-initiated requests. Architecturally, the repository is organized as modular Go packages: mcp defines protocol types and schema helpers, server implements JSON-RPC dispatch, sessions, middleware, hooks, tasks, and HTTP/SSE/stdio transports, while client provides a high-level client plus a transport abstractions layer. Notable engineering strengths include defensively coded transports, resumable SSE streams with event replay, an OAuth layer hardened against RFC 9728 resource-binding attacks, cached JSON Schema validation, and an extensive test suite including conformance tests, race-condition tests, and e2e HTTP tests. The repository also ships a separate otel submodule for OpenTelemetry integration and a docs site under www/. This is purely an application/infrastructure repository; there are no data pipelines, notebooks, or research artifacts. The primary data flow is JSON-RPC messages exchanged between MCP servers and clients, with SSE used for notifications, streaming, and server-to-client requests. The code is strongly standards-driven and tightly tracked to the evolving MCP specification. While the dual-era protocol support adds significant complexity and maintenance burden, the implementation is well-tested, security-conscious, and contains several genuinely reusable components. A few stale example files and commented-out tests exist, but they do not materially affect the quality of the core SDK.
Invisible Dots is a self-hosted, open-source platform for deploying and managing persistent AI agents ('Dots'). Each Dot operates within its own dedicated QEMU virtual machine, providing a secure, isolated environment with a full Linux desktop, browser, and file system. The platform is designed for undetectability and long-running autonomy, serving as an open-source alternative to proprietary agent services. The system is a polyglot monorepo with a strict separation between the host control plane (TypeScript) and the guest agent (Go) and engine (Python). The host manages VM lifecycle, scheduling, a web UI, CLI, and messaging channels (Telegram/WhatsApp). The guest runs a Go daemon for low-level system access and a Python engine (forked from nanobot) that orchestrates LLM calls, tool execution, and permissions. Key features include a granular permission system with approval workflows, a cron-like task/automation system, cost capping per task, and a comprehensive benchmark suite for evaluating agent capabilities. The codebase demonstrates exceptional engineering craftsmanship with clean abstractions (Database, Channels, Computer), robust testing strategies (contract tests, fakes, e2e), and reusable utility libraries (ISO writer, SSE parser, atomic file operations). While primarily an application, the project includes a significant research-oriented benchmark suite (tests/bench) and the engine itself is a research fork (nanobot), giving it a hybrid character. The core value, however, is as a production-grade application platform.
global-stock-data is a comprehensive financial data pipeline designed for AI coding assistants to access US and Hong Kong market data. It implements a 13-layer architecture that fetches data from 11 diverse sources including exchanges (CBOE), regulatory bodies (SEC, FINRA), financial data providers (Eastmoney, Yahoo Finance), and macroeconomic sources (Treasury, CFTC). The system normalizes data from various formats (JSON, CSV, text) into consistent schemas for real-time quotes, historical prices, technical indicators, fundamentals, options data, SEC filings, short volume, and macroeconomic indicators. Unlike a traditional application, it functions as a reusable data toolkit where each layer can be independently accessed to solve specific financial data acquisition problems.
Agent Reach is a Python CLI tool that serves as a capability layer for AI agents, giving them one-click access to ~15 internet platforms (Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu, LinkedIn, etc.) without requiring manual setup. It does not wrap these platforms itself; instead, it selects, installs, health-checks, and routes to the most reliable upstream CLI tools (e.g., twitter-cli, yt-dlp, gh CLI, OpenCLI, mcporter) for each platform. The architecture is built around a multi-backend channel pattern: each platform has an ordered list of backends (primary + fallbacks), and the `doctor` command probes each candidate in order to determine the active backend. This design handles upstream tool instability gracefully by allowing fallback routing without code changes. The codebase demonstrates strong engineering practices: a clean Channel ABC with `check()` and `can_handle()` contracts, a robust probe system that distinguishes missing/broken/timeout/error by actual execution, security-hardened file operations (atomic private writes, symlink rejection, bounded reads), and SSRF protection in URL normalization. The CLI provides commands for installation (safe-by-default, read-only mode), diagnostics, configuration, and skill registration for agent environments (Claude Code, OpenClaw, Cursor, OpenCode). An optional MCP server exposes read-only status. Agent Reach is purely an application — there are no notebooks, data pipelines, or ML components. Its value lies in reducing the friction of giving agents internet access, with a focus on zero-config defaults for public platforms and cookie-based auth for login-required ones. The project is actively maintained, with documented responses to upstream API changes (e.g., Bilibili blocking yt-dlp, Twitter API pay-per-use).
Flowcraft is a workflow orchestration framework for building, executing, and monitoring durable workflows. It provides a type-safe, composable API for defining workflows as directed graphs of nodes and edges, supporting both programmatic (fluent) and declarative (JSON) definitions. The framework enables progressive scaling from in-memory execution to distributed systems via adapters for message queues and cloud services, with built-in support for patterns like retries, timeouts, human-in-the-loop, batch processing, and subflows. Key architectural pieces include a dependency-free core runtime, a sophisticated context tracking system for efficient state persistence, a plugin-based adapter architecture for distributed execution, and LLM tooling for workflow introspection and control via natural language.
OPC Skills is a curated marketplace and toolchain of AI agent skills designed for solopreneurs, indie hackers, and one-person companies. The repository contains 9 skills (reddit, twitter, producthunt, domain-hunter, logo-creator, banner-creator, nanobanana, seo-geo, requesthunt, archive) that each provide structured prompts and Python scripts to automate tasks like market research, SEO/GEO optimization, domain hunting, logo/banner creation, and social media analysis. Skills are distributed via the Agent Skills standard (`npx skills add`) and also available as a Claude Code plugin. The project includes a full website at opc.dev powered by a Cloudflare Worker (website/worker.js) with per-skill pages, a blog, SEO/GEO optimization, and install statistics tracking. CI/CD pipelines deploy the website, test installs across platforms, and fetch install stats daily. The codebase follows a consistent architecture: each skill has a SKILL.md with YAML frontmatter, optional Python scripts following a credential → API wrapper → per-endpoint script pattern with TOON (Token-Oriented Object Notation) output, and dependencies declared between skills. While primarily an application, it has data-research characteristics due to its heavy use of external API scraping for market signals (Reddit, Twitter, ProductHunt) and SEO analytics. The repository is actively maintained with frequent releases, a changelog, and an agent-assisted development workflow (Ralph). Notable strengths include the reusable skill template pattern, cross-session memory via the archive skill, and the standalone SEO audit script. Key risks include reliance on multiple third-party API keys, tight coupling between some skills, and fragility in the install stats scraper.
This repository is the official Model Context Protocol (MCP) reference implementation, containing seven standalone servers (everything, filesystem, memory, sequentialthinking, fetch, git, time) written in TypeScript or Python. These servers demonstrate the full MCP protocol—tools, prompts, resources, transports (stdio, SSE, Streamable HTTP), subscriptions, sampling, elicitation, tasks, and structured content. They are designed as educational examples for developers building their own MCP servers, not as production-ready solutions. The codebase is well-structured with consistent patterns (server factory, transport abstraction, security-focused path validation, atomic file operations, knowledge graph management) and includes extensive test suites. While actively maintained, some Python servers show drift from the latest MCP SDK (v2 API renames in progress), and the Everything server's demo code is tightly coupled to experimental protocol features. The repository also serves as a central hub for the MCP ecosystem via a curated community resource directory (ADDITIONAL.md).
Playwright MCP is the npm distribution package for Microsoft's Model Context Protocol server that exposes Playwright browser automation to LLM clients as MCP tools. Despite the large star count, this repository contains almost no product source: the actual MCP tool implementation (browser_click, browser_navigate, browser_snapshot, network routing, storage, vision, PDF, devtools, and testing capabilities) lives in the Playwright monorepo at packages/playwright-core/src/tools/mcp. cli.js and index.js are thin shims that re-export tools.decorateMCPCommand and tools.createConnection from playwright-core's internal bundles, and src/README.md openly states where the source is. This repo functions as the release-shipping wrapper: it pins a Playwright alpha build, rolls it forward via roll.js, regenerates README tool/config documentation from the bundled tool schemas via update-readme.js, publishes the @playwright/mcp npm package (GitHub Actions OIDC, Azure ESRP, and the MCP Registry with server.json/package.json version validation), and builds multi-arch Docker images. Classified as a pure application, not data_research.
NyaProxy is a lightweight API gateway/proxy service built with FastAPI and Uvicorn that manages authentication, credential rotation, load balancing, rate limiting, and observability for HTTP APIs. It acts as an intermediary between clients and upstream services, injecting credentials, enforcing quotas, and providing features like retry logic, key quarantine, and request/response transformation. The system includes a web dashboard for monitoring traffic, metrics, and queue status, and exposes Prometheus metrics for external monitoring. The architecture follows a modular design with separate concerns: core proxy logic (queue-first request handling), traffic management (load balancing and rate limiting), configuration management (using Nacho for layered config and schema validation), services (metrics, state persistence), and a dashboard interface. Requests are processed via a priority queue with worker pools, enabling prioritization of retries and high-priority requests. State persistence saves rate limit windows and key cooldowns to disk using SHA256-hashed keys to avoid storing secrets, allowing survival across restarts. Key entry points include the CLI (`python -m nya` or `nyaproxy` script) and the main application factory in `nya/server/app.py`. The system relies on dependencies like FastAPI, Uvicorn, Pydantic, Nacho, and the Prometheus client, with explicit version pins in `pyproject.toml` and regular updates shown in the changelog. The codebase demonstrates strong engineering practices including comprehensive test coverage (unit and end-to-end), secure secret handling, and clean separation of concerns.
This repository is a curated list (``awesome list'' style) of high-quality datasets and tools for LLM fine-tuning, post-training, and alignment. It is not a software application, data pipeline, or research codebase; it contains no executable code, notebooks, or data files. The primary artifact is a README.md that categorizes and links to over 70 external datasets (instruction, preference, multilingual, agent, etc.) and tools for data scraping, filtering, generation, and exploration. The repo serves as a reference guide for practitioners seeking datasets, with a focus on accuracy, diversity, and complexity. It is maintained by Maxime Labonne and has a large number of stars and forks, indicating community value. The repository has no code to analyze, test, or reproduce; its value lies in the curation and annotation of external resources.