AssetOpsBench is a unified open framework for building, orchestrating, and evaluating domain-specific AI agents in industrial asset operations and maintenance (Industry 4.0). It provides a suite of MCP (Model Context Protocol) servers that expose domain tools for IoT telemetry, work order lifecycle, failure mode analysis, time-series forecasting/anomaly detection, vibration diagnostics, and utility functions. The framework includes multiple agent runners (plan-execute, Claude, OpenAI, Deep, Stirrup, OpenCode, direct LLM) that consume these tools, and a comprehensive evaluation pipeline with LLM-as-judge and deterministic structured-answer scorers. The system is designed to run reproducible benchmark scenarios with ground truth, producing leaderboard reports for agent comparison. It is accepted at KDD 2026 and powers multiple competitions. Architecturally, the system is a collection of Python packages with 14 CLI entry points defined in pyproject.toml. Each MCP server is a standalone FastMCP application using stdio transport, backed by CouchDB for persistence. Agent runners share a common trajectory model and observability layer (OpenTelemetry with file-based export). The evaluation pipeline is pluggable with a scorer registry. The codebase also includes a significant data/research component: a time-series feature extraction library (130+ extractors), a model catalog with foundation models (TinyTimeMixer, sktime wrappers), synthetic data generation for vibration signals, and showcase notebooks demonstrating end-to-end model lifecycle. The hybrid nature makes it both a software benchmark framework and a research platform for industrial AI.

This repository is a production-grade CLI application (`mcp-cli`) for interacting with the Model Context Protocol (MCP). It serves as an orchestration layer over MCP servers and Large Language Models (LLMs), providing a unified interface across chat, interactive shell, and scriptable command modes. The architecture is a monolithic Python application built on the `chuk-*` ecosystem, featuring a sophisticated unified command system, a streaming display manager, a WebSocket-based dashboard, multi-agent orchestration, and a planning subsystem for autonomous tool execution. It manages the full lifecycle of MCP server connections, tool discovery/execution, LLM provider abstraction, and session persistence. The codebase demonstrates high craftsmanship through its extensive use of Pydantic models for type safety, a well-isolated command pattern, and security-conscious utilities for SSRF protection and secret redaction. Key reusable components include the unified command framework (`UnifiedCommand`, `CommandRegistry`), the streaming display manager with content-type detection, and the `McpToolBackend` adapter that bridges planning systems to tool execution with guard integration. The project is actively maintained with a large test suite, though its heavy coupling to the `chuk-*` library ecosystem and cutting-edge Python version (3.14) represent notable technical dependencies.

IBM fp-go is a comprehensive functional programming library for Go, heavily inspired by fp-ts (TypeScript). It provides a wide range of algebraic abstractions including Option, Either, Result, IO, Reader, State, Writer, and their monad transformer stacks (IOEither, ReaderIOEither, ReaderIOResult, StateReaderIOEither, etc.). The library employs Go generics to simulate higher-kinded types and includes optics (lenses, prisms, isomorphisms, traversals), lazy iterators, retry policies, resource management via Bracket, dependency injection with Reader, and code generation tools. It also offers idiomatic Go wrappers that return (A, error) tuples while preserving functional composition. The codebase is split into two parallel module paths (root and v2/), with v2 representing a newer generation requiring Go 1.24+ and adding result types, effect packages, and context-aware variants. While highly engineered and feature-rich, the dual tree structure, some incomplete components, and non-standard pragmas introduce maintenance complexity.