Activepieces

Workflow automation with AI nodes

n8n is a source-available workflow automation platform for connecting apps, APIs, data, and AI models through visual workflows and code. It supports self-hosted deployments and n8n Cloud, with integrations across communication, databases, CRM, project management, and model providers. Teams can combine deterministic automation with AI-powered steps and agent workflows while retaining control over deployment and data.

No-code automation platform connecting 9,000+ apps

The most popular no-code automation platform connecting 9,000+ apps and app connections to automate workflows without writing code. Features multi-step Zaps with conditional logic, AI Copilot for natural language workflow creation, Tables, Forms, and MCP integration for AI orchestration. Task-based pricing with a free tier at 100 tasks/month. Used by businesses from solo operators to enterprise teams for eliminating repetitive work across their software stack.

Visual automation platform for complex workflows

Visual workflow automation platform formerly known as Integromat, built around a drag-and-drop canvas for complex multi-step workflows. Features routers for conditional branching, iterators for array processing, aggregators, webhooks, and HTTP modules for custom API calls. Best suited to power users and technical teams that need granular data transformation and workflow logic rather than only simple trigger-action automations.

Heterogeneous CPU-GPU inference and SFT for large MoE models

Open-source framework for running and fine-tuning large Mixture-of-Experts models with heterogeneous CPU-GPU execution, optimized kernels, limited VRAM and SGLang or LLaMA-Factory integrations.

Official Kubernetes and Helm reference stack built on the vLLM inference engine

Official vLLM reference implementation for scaling the existing inference engine on Kubernetes with Helm, request routing, KV-cache offload, autoscaling and Prometheus/Grafana observability.

Distributed inference orchestration above vLLM, SGLang and TensorRT-LLM

Open-source, datacenter-scale orchestration layer that coordinates vLLM, SGLang and TensorRT-LLM across nodes with disaggregated serving, KV-aware routing, multi-tier cache management and automatic scaling.

Open-source GPU control plane for scalable AI model serving

Open-source GPU cluster manager that configures vLLM, SGLang, TensorRT-LLM or custom engines, serves models through compatible APIs, and provisions SSH-accessible GPU instances across on-premises, Kubernetes and cloud environments.

Disaggregated KV cache storage and transfer for LLM serving

Open-source infrastructure for disaggregated LLM serving that pools KV caches across prefill and decode workers, with high-performance transfer, distributed storage and integrations for vLLM and SGLang.

Reusable KV cache infrastructure for scalable LLM inference

Open-source KV cache management layer that persists, offloads and reuses model key-value caches across requests and serving engines to reduce repeated prefill work and improve inference throughput.