Pathway

Declarative orchestration for data, AI, and infra

Kestra is an open-source orchestration platform that uses declarative YAML to define event-driven and scheduled workflows for data pipelines, infrastructure automation, and AI workloads. With over 1,200 plugins, it connects to databases, cloud services, APIs, and SaaS tools without custom glue code. Kestra reached version 1.0 LTS with agentic AI capabilities, SDKs for Python, TypeScript, Java, and Go, and SOC 2 compliance. Clients include Leroy Merlin, Huawei, Tencent, and Decathlon.

Visual framework for building multi-agent AI apps

LangFlow is an open-source visual framework for building multi-agent AI apps with drag-and-drop. Built on LangChain, it lets developers compose chains, agents, and RAG pipelines by connecting modular components visually. Features real-time interaction, Python customization, one-click deployment, and export to LangChain code. Supports all major LLM providers, vector stores, and tools. With 146K+ GitHub stars, it bridges visual prototyping and production deployment.

TypeScript AI agent framework

TypeScript-native framework for building AI agents and workflows with great developer experience. Provides primitives for agents with tool calling, RAG pipelines, workflow orchestration with branching/parallel steps, and integration connectors. First-class TypeScript support with type-safe tool definitions. Local dev server with playground UI for testing. Growing as a LangChain alternative for TypeScript developers building AI apps.

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.