16 repositorios
Mechanisms for fetching outputs from completed asynchronous processes using unique identifiers.
Distinct from Query Result Fetching: Distinct from database query fetching: focuses on retrieving results from long-running workflow executions rather than database records.
Explore 16 awesome GitHub repositories matching data & databases · Workflow Result Retrieval. Refine with filters or upvote what's useful.
Prefect is a workflow orchestration platform designed to define, schedule, and monitor complex data pipelines as Python code. It functions as a container-native engine that wraps individual tasks in isolated environments, ensuring consistent dependencies and resource allocation across diverse infrastructure. By utilizing a state-machine-based orchestration model, the system tracks execution progress through discrete transitions and persistent event logs to maintain reliable and observable task processing. The platform distinguishes itself through a decoupled worker-API architecture, which sep
Provides mechanisms for fetching outputs from completed asynchronous workflow executions using unique identifiers.
Temporal is a distributed workflow orchestration engine designed to manage fault-tolerant, stateful, and long-running background processes. It functions as a platform for coordinating complex cross-service operations, ensuring consistency and reliability in distributed environments by decoupling workflow orchestration from task execution. The platform distinguishes itself through a deterministic, event-sourced execution model that reconstructs workflow state by re-executing code from an immutable event log. This approach isolates non-deterministic side effects into managed activities, allowin
Enables synchronous or asynchronous retrieval of outputs from completed workflow processes.
Unstructured is an enterprise-grade data orchestration engine designed to transform raw, unstructured files into structured, machine-readable formats. It functions as a comprehensive platform for document ingestion, partitioning, and enrichment, specifically engineered to prepare complex data for retrieval-augmented generation and agentic AI workflows. The platform distinguishes itself through its sophisticated document processing strategies, which combine rule-based extraction with vision-language models to handle diverse file layouts, tables, and images. It provides a modular architecture t
Fetches and filters lists of existing data processing workflows to support operational visibility.
Automatisch is an open-source, self-hosted automation platform designed to orchestrate multi-stage workflows across various third-party web services. It functions as a no-code integration engine that allows users to connect disparate applications, enabling the automated movement of data and the execution of tasks without manual intervention. By running the platform on private infrastructure, users maintain full control over their data and ensure compliance with internal security policies. The platform distinguishes itself through a focus on secure, local credential management and flexible int
Captures and returns workflow step outputs to enable data utilization in subsequent multi-stage tasks.
Subfinder is a security reconnaissance framework designed for subdomain enumeration and attack surface management. It functions as a discovery engine that identifies and maps internet-exposed infrastructure, cloud-hosted assets, and network ranges to maintain a comprehensive inventory of an organization's digital footprint. The project distinguishes itself through a modular, template-driven scanning engine that executes security checks against discovered assets. It leverages cloud-native asset discovery to query provider APIs and infrastructure metadata, while supporting distributed agent orc
Fetches collected asset discovery results using flexible filtering criteria.
Metaflow is a Python machine learning framework and MLOps workflow orchestrator designed to manage the lifecycle of data pipelines from local prototyping to production. It serves as a distributed compute manager and an experiment tracking system, enabling the creation of reproducible pipelines that transition between development and high-availability production environments. The framework distinguishes itself through an integrated checkpointing system that automatically persists intermediate data artifacts to remote storage, allowing failed runs to be resumed from the last successful step. It
Enables access to data and outputs from previous pipeline executions for analysis or reuse.
The inspector is a diagnostic and validation tool for the Model Context Protocol. It provides an interactive interface and a transport proxy to discover, inspect, and execute the tools, prompts, and resources provided by an MCP server. The project serves as a debugger and compliance tester to verify that server implementations adhere to the protocol specification and JSON-RPC standards. It allows for real-time monitoring of message exchanges and logs between clients and servers across various transport layers, such as standard input/output and Server-Sent Events. The tool covers a broad rang
Fetches the final outputs of completed long-running asynchronous processes using unique task identifiers.
Model Context Protocol is a standardized framework for connecting large language models to external data sources and executable tools. It enables the creation of a universal interface where servers expose tools, resources, and prompts that can be discovered and utilized by various AI clients. The protocol utilizes a JSON-RPC message system that is transport-agnostic, supporting both standard input/output for local processes and HTTP with server-sent events for remote connections. It emphasizes security and control by delegating model sampling to the client to keep API keys secure from servers
Fetches the final output of a completed background task once it reaches a terminal status.
Elsa Core is a workflow engine framework designed for defining, executing, and managing long-running business processes. It functions as a distributed workflow orchestrator and event-driven trigger system, capable of operating as a multi-tenant platform with secure data isolation. The project distinguishes itself through a flexible approach to workflow definitions, supporting a visual drag-and-drop designer, programmatic C# definitions, and portable JSON specifications. It provides a highly extensible architecture allowing for the development of custom activities and the use of a dynamic expr
Filters and retrieves workflow executions based on status, correlation IDs, or timestamps.
Refly is an open-source platform for building, running, and sharing deterministic agent skills. It provides a visual workflow compiler that converts natural language descriptions into executable, versioned agent workflows, and includes a runtime that deploys these compiled skills as APIs, webhooks, Slack bots, or native tools for AI coding platforms like Claude Code and Cursor. The platform distinguishes itself through a central skill registry with versioning and audit logging, enabling teams to manage agent capabilities as governed corporate assets. It supports human-in-the-loop automation,
Fetches output messages and files produced by completed or running workflow executions.
Hatchet is an open-source durable workflow engine and task orchestration platform. It provides a framework for building and executing fault-tolerant, multi-step pipelines as directed acyclic graphs (DAGs), with automatic retries, scheduling, and real-time observability. The system is built around durable task checkpointing, which persists execution state after each step so work can resume from the last checkpoint after a worker crash or restart, and it supports event-driven task resumption that pauses a task until a matching external event arrives. The platform distinguishes itself through it
Returns typed task results synchronously or asynchronously, replacing Celery's result retrieval pattern.
Helicone is an AI gateway and observability platform designed to intercept, manage, and monitor interactions with large language models. By acting as a reverse-proxy, it provides a centralized layer for routing requests across multiple AI providers, allowing developers to maintain consistent application logic while gaining deep visibility into model performance, usage, and costs. The platform distinguishes itself through a robust suite of traffic management and prompt engineering tools. It enables policy-driven control, including automatic failover between providers, rate limiting, and edge-b
Attaches custom JSON properties to AI requests to categorize and filter logs by specific workflow names, environments, or versioning identifiers.
LXD is a unified platform for managing both system containers and virtual machines through a single REST API and command-line interface. It provides a programmatic HTTP interface for controlling the full lifecycle of instances, enabling automation and integration with external tools. The system runs unprivileged containers with per-instance UID/GID mappings, seccomp filters, and AppArmor profiles for kernel-level isolation, while supporting multiple storage backends including directory, Btrfs, LVM, ZFS, Ceph, LINSTOR, and TrueNAS through a unified driver interface. The platform distinguishes
Updates the host with the instance's current readiness status using a simple HTTP PATCH request.
ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself
Fetches historical execution data and output artifacts from completed pipeline runs for analysis, auditing, or downstream consumption.
Dramatiq is a distributed task queue and workload manager used to offload function execution to background workers. It functions as an asynchronous task orchestrator that enables the distribution of computational tasks across a cluster using a pluggable transport layer supporting RabbitMQ and Redis. The framework provides specialized tools for complex task orchestration, including the ability to link background jobs into sequences, pipelines, and barriers. It further manages distributed concurrency through the use of shared mutexes, rate limiters, and exponential backoff retries to prevent re
Provides mechanisms to persist and fetch the return values of completed background tasks using unique identifiers.
Judge0 is an online code execution engine and multi-language compiler API designed to compile and run source code within isolated sandboxes. It functions as an asynchronous job processor that handles code submissions via a queue and provides a secure environment to run arbitrary programs while preventing unauthorized system access. The system distinguishes itself through a multi-stage compilation pipeline and a flexible execution model that supports both single-file submissions and multi-file program execution via archives. It employs an isolate-based sandboxing mechanism to enforce strict ha
Provides mechanisms to retrieve standard output, error logs, and compilation messages for completed execution jobs.