8 repository-uri
Structured systems for chaining tasks and managing state transitions.
Distinguishing note: Focuses on the structural definition of workflows, distinct from execution control.
Explore 8 awesome GitHub repositories matching artificial intelligence & ml · Workflow Definitions. Refine with filters or upvote what's useful.
CrewAI is a multi-agent orchestration framework designed for building autonomous systems that execute complex, multi-step workflows. It provides a development platform where specialized agents are defined with specific roles, goals, and tool sets to perform tasks collaboratively. By leveraging a declarative workflow engine, the system manages task dependencies, state transitions, and execution logic, allowing for the creation of structured, stateful sequences of operations. The framework distinguishes itself through its hierarchical management capabilities, which utilize manager agents to coo
Chains methods with decorators to manage execution order, state transitions, and task dependencies within a structured system.
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
Constructs directed acyclic graphs by chaining partitioning, enrichment, chunking, and embedding nodes to transform unstructured data for downstream applications.
Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, che
Creates predefined flowcharts with clear topologies to ensure predictable and deterministic execution paths.
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,
Compiles natural-language descriptions into deterministic, versioned agent workflows using a visual canvas.
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
Lists, updates, and removes workflow definitions through a dedicated workflows client.
Provides an endpoint to fetch a complete workflow definition and metadata by ID.
Acest proiect este un motor de flux de lucru pentru machine learning containerizat și orchestrator conceput pentru a automatiza ciclul de viață end-to-end al modelelor de machine learning pe clustere Kubernetes. Funcționează ca un compilator de pipeline MLOps care transformă un limbaj specific domeniului în specificații structurate pentru implementare portabilă și scalabilă. Platforma oferă un mediu multi-tenant cu namespace-uri izolate și autentificare prin furnizor de identitate. Se distinge printr-o combinație de izolare a sarcinilor bazată pe containere, gestionarea artefactelor puternic tipizate pentru transferul de date și caching-ul rezultatelor adresabile prin conținut pentru a evita calculele redundante. Sistemul acoperă orchestrarea cuprinzătoare a fluxului de lucru, inclusiv execuția sarcinilor în paralel, programarea recurentă a rulărilor și logica de ramificare condiționată. De asemenea, suportă urmărirea experimentelor, colectarea metricilor fluxului de lucru și gestionarea componentelor de pipeline reutilizabile, cu posibilitatea de a configura cerințe specifice de resurse hardware pentru CPU, memorie și GPU. Software-ul este distribuit printr-un SDK Python și poate fi implementat în medii standalone, locale sau multi-tenant.
Transforms a domain-specific language into structured pipeline specifications to execute complex workloads across a distributed cluster.
gh-aw is a GitHub automation platform and orchestration framework that uses an agentic workflow engine to automate repository management and code reviews. It translates natural language markdown and configuration files into secure, automated task sequences driven by large language models. The system integrates a Model Context Protocol gateway to route calls between AI agents and external tools. It distinguishes itself through a comprehensive security guardrail system that provides sandboxed execution for protocol servers, network egress controls via domain allowlists, and human-in-the-loop ap
Translates natural language markdown and configuration blocks into executable files for system execution.