Hatchet

Open-source background jobs and AI workflows for TypeScript

Trigger.dev is an open-source platform for building and deploying background jobs, AI agents, and long-running workflows in TypeScript. It eliminates serverless timeouts with durable task execution, automatic retries, queue-based concurrency control, and elastic scaling. Used by 30,000+ developers at companies like MagicSchool and Icon.com, it processes hundreds of millions of agent runs monthly. Backed by a $16M Series A led by Dalton Caldwell's Standard Capital fund.

Durable workflow orchestration for serverless and edge

Inngest is a workflow orchestration platform that replaces queues, state management, and scheduling with durable step functions. Write functions in TypeScript, Python, or Go that survive failures, sleep for days between steps, and retry only failed steps. SDKs integrate natively with Next.js, Vercel, and serverless platforms. Free tier includes 50,000 runs/month. Used by Resend, Mintlify, and Ocoya for background jobs, AI orchestration, and event-driven workflows.

Durable execution for fault-tolerant workflows

Temporal is an open-source durable execution platform that ensures application code runs to completion regardless of failures or outages. It captures workflow state at every step, enabling seamless recovery without custom retry logic. With SDKs for Go, Java, Python, TypeScript, and .NET, Temporal powers mission-critical orchestration at Netflix, Nvidia, and other enterprises. Valued at $5B, it replaces fragile cron jobs, state machines, and saga patterns with resilient workflow-as-code.

Durable execution engine for workflows and AI agents

Restate is a durable execution engine that provides reliable workflow orchestration for AI agents and backend services. It runs as a single binary with no external dependencies, delivering sub-50ms latency and 94K+ actions per second. Supports TypeScript, Python, Go, Java, and Kotlin SDKs with built-in retries, sagas, and virtual object state. MIT licensed with 3,700+ GitHub stars.

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.