41 Repos
Centralized error management and status reporting.
Distinguishing note: Focuses on the recovery and reporting aspect of error handling.
Explore 41 awesome GitHub repositories matching software engineering & architecture · Error Recovery. Refine with filters or upvote what's useful.
This project provides a modular framework for building and orchestrating autonomous AI agents. It functions as an agentic workflow engine that manages the full lifecycle of task execution, including model reasoning, tool invocation, and the integration of results. By utilizing a centralized orchestration platform, the system enables the creation of multi-agent teams that collaborate on complex objectives through structured communication and shared task graphs. The framework distinguishes itself through its focus on persistent, stateful operations and multi-agent coordination. It employs file-
Classifies execution failures and applies automated retry strategies and model fallbacks to maintain system robustness.
Agent-skills is a collection of structured instructions and behavioral personas designed to standardize how AI coding agents perform engineering tasks. It functions as a workflow orchestrator that maps natural language intent to repeatable technical sequences and verification checklists. The project distinguishes itself through the use of specialized markdown-defined roles, such as security auditors or test engineers, to apply targeted domain expertise. It employs an evidence-based verification model that requires runtime data or passing tests as mandatory exit criteria to ensure AI-generated
Provides structured triage workflows to distinguish between code bugs and environment mismatches during failure analysis.
Echo is a high-performance, lightweight web framework for Go designed for building scalable RESTful APIs and web services. It provides a centralized environment for mapping network requests to handler functions, utilizing a fast radix-tree routing engine to ensure efficient request dispatching. The framework is built around a modular, middleware-centric pipeline that allows developers to execute reusable logic for cross-cutting concerns like authentication, logging, and security across the entire application. What distinguishes Echo is its focus on developer productivity through structured da
Manages errors centrally by returning consistent status codes and messages to ensure reliable feedback when requests fail.
Tree-sitter is a parsing system and incremental parsing framework designed to generate high-performance syntax trees from source code. It functions as a language parsing engine that compiles formal grammar definitions into portable code, which can then be integrated into text editors and development tools to facilitate structural analysis, code navigation, and syntax highlighting. The project distinguishes itself through its ability to maintain valid, usable syntax tree structures even when source code contains syntax errors or incomplete fragments. It utilizes a generalized parsing algorithm
Maintains valid syntax tree structures even when encountering incomplete or malformed source code during parsing.
Semantic-release is an automated release management tool that determines version increments, generates changelogs, and publishes software packages by analyzing commit history against standardized conventions. It functions as a plugin-based orchestrator that integrates directly into continuous integration pipelines to manage the entire release lifecycle, from verifying environment conditions to distributing artifacts. The project distinguishes itself through its commit-message-driven approach, which enforces consistent versioning standards and automates the creation of release notes based on t
Reports failures using standardized error types to ensure the automation process correctly identifies issues and triggers recovery.
This project provides a framework for managing multi-agent systems, designed to automate complex software development, infrastructure, and business workflows. It functions as a multi-agent workflow orchestrator that routes tasks to domain-specific workers while maintaining state persistence and infrastructure automation. By leveraging large language models, the system decomposes high-level objectives into actionable plans, ensuring that complex operations are executed with consistency and reliability. The framework distinguishes itself through its hierarchical agent registry and policy-driven
Implements fallback strategies and health monitoring to ensure resilient, graceful failure recovery in multi-agent systems.
The Reactive Extensions for JavaScript
Errors skip all subsequent operators in the chain and immediately notify the observer, with no recovery unless a retry operator is inserted.
PUA is an agentic workflow orchestrator and behavioral governance tool designed to enhance the reliability and autonomy of AI coding assistants. It functions as a prompting framework and extension that implements strict engineering standards and verification requirements to prevent hallucinations and premature task completion. The project distinguishes itself through high-agency enforcement mechanisms, including escalating prompt pressure and failure-driven recovery loops that automatically pivot problem-solving strategies after repeated errors. It utilizes a diagnosis-first workflow that man
Implements systematic loops and escalating prompt pressure to help AI agents pivot strategies after repeated failures.
This project is a collection of guidelines and best practices for the Go programming language, providing a comprehensive style guide and set of programming standards. It establishes a framework for writing maintainable and performant source code through standardized naming, structuring, and organizational conventions. The guide focuses on specific patterns for concurrency, error handling, and performance optimization. It details methods for managing goroutine lifecycles to prevent race conditions, designing structured error wrapping and routing to maintain observability, and implementing memo
Emphasizes returning errors over triggering panics to ensure predictable application recovery.
hls.js is a JavaScript player library that enables HTTP Live Streaming playback in web browsers using the Media Source Extensions API. It functions as an adaptive bitrate streaming engine and a media streamer that manages the buffering and playback of video segments. The library features specialized capabilities for low latency streaming and digital rights management, allowing it to decrypt protected content via Encrypted Media Extensions. It includes a metadata processor for handling ID3 tags and date-range elements, as well as a pluggable loader interface to replace default network request
Implements retry and emergency-switch mechanisms to restore playback after network or parsing failures.
This project is an educational framework designed to teach the fundamentals of building core distributed systems and web services from scratch in Go. It provides a collection of modular implementations that demonstrate how to construct essential infrastructure components, including web servers, remote procedure call systems, distributed caches, and database abstraction layers. The framework distinguishes itself by focusing on the internal mechanics of these systems rather than providing a high-level abstraction for production use. It covers the implementation of complex architectural patterns
Catches unexpected runtime errors during request processing to prevent service crashes and return standard error responses.
ioredis is a performance-focused Redis client for Node.js designed to execute commands and manage data connections. It provides a specialized interface for interacting with standalone servers, sharded clusters, and high-availability setups. The library distinguishes itself with native support for Redis Cluster, featuring automatic slot discovery and network address mapping, and Redis Sentinel for master node discovery and automatic failover. It also includes a dedicated Lua scripting interface that utilizes server-side caching to ensure atomic operations. The project covers a broad set of ca
Resets connections and retries commands automatically when specific server errors occur.
GenericAgent is an LLM agent framework and autonomous system controller designed to manage local systems, web browsers, and hardware interfaces through action and observation loops. It functions as a tool orchestrator that routes model calls to local executors, enabling the automation of complex tasks on a host machine. The project is distinguished by its self-evolving AI agent capabilities, which convert successful execution paths into reusable procedural scripts and skill trees to reduce future reasoning overhead. It employs a context optimization engine that utilizes layered memory hierarc
Recovers from tool errors through a sequence of local correction, strategy shifting, and human escalation.
Agent-S is a multimodal AI agent and LLM desktop automation framework designed to control operating systems through graphical user interface interactions. It functions as a computer use interface, utilizing vision-language grounding to translate natural language goals into precise screen coordinates and system actions. The project differentiates itself by combining structured accessibility tree inspection with vision-based element localization. It manages cross-application workflows by mapping conceptual descriptions to physical pixels and simulating low-level keyboard and mouse events to mov
Detects failures in GUI actions and self-corrects by adapting navigation or grounding methods.
This project provides educational materials and courseware focused on the theoretical and practical foundations of distributed systems design. It serves as a comprehensive curriculum covering the disciplines of consensus, data consistency, reliability engineering, and scalability. The instructional content focuses on achieving cluster agreement through consensus algorithms and managing system-wide state via coordination frameworks. It includes a dedicated guide to data theory, exploring replication strategies, consistency models, and data convergence. The courseware covers a broad capability
Provides guidance on designing automated recovery sequences to coordinate responses across multiple system components.
Buffalo ist ein umfassendes MVC-Webframework und eine Full-Stack-Toolchain für den Aufbau von Webanwendungen in Go. Es bietet eine strukturierte Entwicklungsumgebung, die Modelle, Views und Controller trennt und ein Webframework mit einem dedizierten Object-Relational-Mapper-Wrapper für Datenbankschema- und Datensatzverwaltung integriert. Das Projekt zeichnet sich durch eine Reihe von Entwicklungs-Utilities zum Bootstrapping von Projektstrukturen, zur Verwaltung von Assets und zum Erstellen produktionsreifer Container aus. Es verfügt über ein ausgeklügeltes Routing-System, das konventionsbasiertes RESTful-Ressourcen-Generieren, domänenbasiertes Virtual Hosting und die Erstellung von Route-Helpern für konsistente interne Verlinkungen unterstützt. Das Framework deckt ein breites Spektrum an Web-Kernfunktionen ab, einschließlich Middleware-basierter Request-Pipelines, dynamischem Template-Rendering und Request-Scoped-State-Management. Es enthält zudem integrierte Unterstützung für Hintergrundaufgabenverarbeitung, Session-Management, E-Mail-Versand und Graceful Server Shutdown.
Defines custom error responses and recovery logic for specific service groups to ensure consistent failure handling.
Tenacity is a Python retry library and fault tolerance framework designed to automatically re-execute failing functions based on custom conditions, wait intervals, and stop criteria. It provides a mechanism to apply retry logic to both synchronous functions and asynchronous coroutines. The library implements exponential backoff to increase delays between retries, helping to manage transient network failures and prevent the overloading of services. Its capabilities cover the definition of retry conditions based on exception types or return values, as well as the enforcement of duration limits
Tracks failure rates and attempt counts for critical operations to monitor system reliability.
pysheeet ist eine technische Referenzbibliothek, die eine kuratierte Sammlung von Code-Snippets und Implementierungsmustern für fortgeschrittene Python-Entwicklung, Systemintegration und High-Performance-Computing bereitstellt. Sie dient als umfassender Leitfaden für die Implementierung von Low-Level-Netzwerkprogrammierung, nativen C-Erweiterungen sowie asynchroner und nebenläufiger Programmierung. Das Projekt bietet spezialisierte Frameworks für die Entwicklung und Bereitstellung von Large Language Models, einschließlich Werkzeugen für verteilte GPU-Inferenz und High-Performance-Serving. Es enthält zudem detaillierte Muster für die Orchestrierung von High-Performance-Computing-Clustern, die GPU-Ressourcenzuweisung und Multi-Node-Workload-Management abdecken. Die Bibliothek deckt ein breites Spektrum an Funktionen ab, einschließlich sicherer Netzwerkkommunikation und Kryptografie, Object-Relational-Mapping und Datenbankverwaltung sowie die Implementierung komplexer Datenstrukturen und Algorithmen. Sie bietet zudem Utilities für Speicherverwaltung, native Interoperabilität via Foreign-Function-Interfaces und systemnahe OS-Integration.
Implements runtime error recovery patterns using try-except-finally structures to ensure resource cleanup.
k8sgpt ist eine Suite von Kubernetes-fokussierten Tools, die für KI-gestütztes Debugging, Cluster-Diagnose und Selbstheilung entwickelt wurden. Es fungiert als automatisierter Analysator und Debugger, der große Sprachmodelle verwendet, um Cluster-Fehler zu erklären, Sanierungsschritte vorzuschlagen und Ressourcenfehler zu identifizieren. Das Projekt zeichnet sich durch ein erweiterbares Analyse-Framework aus, das benutzerdefinierte Diagnose-Plugins und einen Model Context Protocol-Server unterstützt, der Cluster-Diagnosen als Tools für KI-Assistenten bereitstellt. Es enthält einen Selbstheilungs-Agenten, der in der Lage ist, automatisch Korrekturen für erkannte Anomalien zu generieren und anzuwenden, sowie eine Middleware zur Datenanonymisierung, um sensible Informationen vor der Übertragung an externe KI-Anbieter zu maskieren. Das Toolset deckt ein breites Spektrum an operativen Funktionen ab, einschließlich kontinuierlicher Gesundheitsüberwachung über einen Operator, Compliance-Audits gegen Richtlinien-Engines und Multi-Cluster-Orchestrierung zur Identifizierung weit verbreiteter Fehlermuster. Es bietet zudem Beobachtbarkeitsfunktionen wie den Export von Diagnoseergebnissen, die Integration von Beobachtbarkeitsmetriken und die Fehlerbehebung bei Pod-Ausfällen.
Triages system problems using analyzers to identify errors and provide automated resolutions.
BAML is a prompt engineering framework and LLM client generator that defines AI prompts as type-safe functions. It serves as a structured data extraction tool and workflow orchestrator, transforming unstructured model responses into strongly typed objects using a custom schema language and alignment algorithms. The project distinguishes itself by using a compiler to generate language-specific boilerplate code for API communication and output parsing. It features a dedicated environment for designing complex prompt templates with conditional logic and reusable snippets, and employs genetic alg
BAML uses a secondary LLM prompt to automatically repair malformed responses by passing parsing errors back to a function.