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41 Repos

Awesome GitHub RepositoriesError Recovery

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.

Awesome Error Recovery GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • shareai-lab/learn-claude-codeAvatar von shareAI-lab

    shareAI-lab/learn-claude-code

    67,975Auf GitHub ansehen↗

    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.

    Pythonagentagent-developmentai-agent
    Auf GitHub ansehen↗67,975
  • addyosmani/agent-skillsAvatar von addyosmani

    addyosmani/agent-skills

    60,849Auf GitHub ansehen↗

    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.

    Shellagent-skillsantigravityantigravity-ide
    Auf GitHub ansehen↗60,849
  • labstack/echoAvatar von labstack

    labstack/echo

    32,451Auf GitHub ansehen↗

    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.

    Goechogohttp2
    Auf GitHub ansehen↗32,451
  • tree-sitter/tree-sitterAvatar von tree-sitter

    tree-sitter/tree-sitter

    23,848Auf GitHub ansehen↗

    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.

    Rustcincrementalparser
    Auf GitHub ansehen↗23,848
  • semantic-release/semantic-releaseAvatar von semantic-release

    semantic-release/semantic-release

    23,332Auf GitHub ansehen↗

    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.

    JavaScriptautomationchangelogpackage
    Auf GitHub ansehen↗23,332
  • voltagent/awesome-claude-code-subagentsAvatar von VoltAgent

    VoltAgent/awesome-claude-code-subagents

    21,906Auf GitHub ansehen↗

    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.

    Shellai-agent-frameworkai-agent-toolsai-agents
    Auf GitHub ansehen↗21,906
  • reactive-extensions/rxjsAvatar von Reactive-Extensions

    Reactive-Extensions/RxJS

    19,353Auf GitHub ansehen↗

    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.

    JavaScript
    Auf GitHub ansehen↗19,353
  • tanweai/puaAvatar von tanweai

    tanweai/pua

    18,283Auf GitHub ansehen↗

    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.

    TypeScriptagencyagentpip
    Auf GitHub ansehen↗18,283
  • uber-go/guideAvatar von uber-go

    uber-go/guide

    17,573Auf GitHub ansehen↗

    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.

    Makefilebest-practicesgogolang
    Auf GitHub ansehen↗17,573
  • video-dev/hls.jsAvatar von video-dev

    video-dev/hls.js

    16,767Auf GitHub ansehen↗

    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.

    TypeScript
    Auf GitHub ansehen↗16,767
  • geektutu/7days-golangAvatar von geektutu

    geektutu/7days-golang

    16,812Auf GitHub ansehen↗

    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.

    Gogolanglearningscratch
    Auf GitHub ansehen↗16,812
  • luin/ioredisAvatar von luin

    luin/ioredis

    15,295Auf GitHub ansehen↗

    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.

    TypeScript
    Auf GitHub ansehen↗15,295
  • lsdefine/genericagentAvatar von lsdefine

    lsdefine/GenericAgent

    13,017Auf GitHub ansehen↗

    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.

    Pythonai-agentautomationautonomous-agent
    Auf GitHub ansehen↗13,017
  • simular-ai/agent-sAvatar von simular-ai

    simular-ai/Agent-S

    11,855Auf GitHub ansehen↗

    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.

    Pythonagent-computer-interfaceai-agentscomputer-automation
    Auf GitHub ansehen↗11,855
  • aphyr/distsys-classAvatar von aphyr

    aphyr/distsys-class

    9,717Auf GitHub ansehen↗

    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.

    Auf GitHub ansehen↗9,717
  • gobuffalo/buffaloAvatar von gobuffalo

    gobuffalo/buffalo

    8,392Auf GitHub ansehen↗

    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.

    Go
    Auf GitHub ansehen↗8,392
  • jd/tenacityAvatar von jd

    jd/tenacity

    8,375Auf GitHub ansehen↗

    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.

    Pythonfailurehacktoberfestpython
    Auf GitHub ansehen↗8,375
  • crazyguitar/pysheeetAvatar von crazyguitar

    crazyguitar/pysheeet

    8,150Auf GitHub ansehen↗

    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.

    Python
    Auf GitHub ansehen↗8,150
  • k8sgpt-ai/k8sgptAvatar von k8sgpt-ai

    k8sgpt-ai/k8sgpt

    7,922Auf GitHub ansehen↗

    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.

    Go
    Auf GitHub ansehen↗7,922
  • boundaryml/bamlAvatar von BoundaryML

    BoundaryML/baml

    7,636Auf GitHub ansehen↗

    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.

    Rustbamlboundarymlguardrails
    Auf GitHub ansehen↗7,636
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  1. Home
  2. Software Engineering & Architecture
  3. Error Recovery

Unter-Tags erkunden

  • AgentClear cached errors on an agent and resume normal operation after an update action has thrown an exception. **Distinct from Error Recovery:** Distinct from Error Recovery: focuses on agent-specific error state clearing and resumption, not general error management.
  • AgenticAutonomous recovery loops that pivot AI problem-solving strategies after repeated failures. **Distinct from Error Recovery:** Focuses on agent behavioral pivots and strategy shifts rather than general system status reporting.
  • All Error Catchers1 Sub-TagCatching every recoverable error from an effect and running a fallback effect in its place. **Distinct from Error Recovery:** Distinct from Error Recovery: specifically catches all recoverable errors to run a fallback, not general recovery or reporting.
  • Automated Torrent RemediationAutomated sequences of actions to recover torrents in an error state. **Distinct from Error Recovery:** Specific to torrent state recovery (pause/recheck/resume) rather than general software error reporting.
  • Distributed Recovery Flows1 Sub-TagAutomated recovery sequences for coordinating responses across multiple system components. **Distinct from Error Recovery:** Focuses on distributed coordination of recovery, distinct from centralized error management.
  • Error Shortcut TerminationsErrors that skip all subsequent operators in a chain and immediately notify the observer, with no recovery unless a retry operator is inserted. **Distinct from Error Recovery:** Distinct from Error Recovery: focuses on immediate error propagation that bypasses downstream operators, not on recovery or fallback logic.
  • LLM Response RepairUsing secondary AI prompts to autonomously correct malformed or invalid structured outputs. **Distinct from Error Recovery:** Specifically addresses using an LLM to repair its own output, whereas general error recovery is broader.
  • LLM-Driven Self-HealingUses language models to analyze runtime failures and automatically generate code fixes for recovery. **Distinct from Error Recovery:** Specifically uses LLMs to generate fixes, whereas general error recovery focuses on management and reporting.
  • Request-Level RecoveryRecovery logic and custom response definitions for maintaining consistency across request failures. **Distinct from Error Recovery:** Focuses on HTTP request-level failure recovery rather than general system error reporting or data import recovery.
  • Runtime Error CategorizationClassifying runtime failures into specific categories to facilitate targeted debugging and recovery strategies. **Distinct from Error Recovery:** Focuses on the classification of errors into types rather than the general act of recovery or reporting.
  • Specific Error CatchersCatching only specific types of recoverable errors from an effect and running a fallback effect for those cases. **Distinct from Error Recovery:** Distinct from Error Recovery: catches only specific error types rather than all errors, with a targeted fallback.
  • Trace-Aware RecoveryRecovery logic that utilizes the captured call stack to determine how to handle an error. **Distinct from Error Recovery:** Distinct from general error recovery by specifically using stack trace information to drive the recovery decision.
  • Transition Interception and Recovery MechanismsMechanisms to intercept active navigation transitions for aborts or redirection flows and execute centralized error recovery. **Distinct from Error Recovery:** Distinct from generic error recovery: focuses specifically on intercepting active routing transitions and handling navigation errors.
  • Triage Workflows2 Sub-TagsStructured processes for reproducing, localizing, and fixing system failures. **Distinct from Error Recovery:** Focuses on the iterative triage process rather than just the recovery mechanisms or status reporting.