Speakeasy

Open-source agentic development environment for parallel AI agents

Emdash is an open-source agentic development environment for orchestrating many coding agents in parallel. It runs each agent in an isolated Git worktree, presents tasks in a dashboard, auto-detects installed CLIs, works with 25+ agents including Claude Code, Codex, Cursor, Amp and Gemini, and supports MCP server connections for tool access.

AI website wireframe and design generator

Generate complete website sitemaps and wireframes with AI in minutes. Relume outputs Figma and Webflow-ready component libraries, with a built-in library of 1000+ components and style guides. Ideal for designers and agencies who need to rapidly prototype client projects without starting from scratch every time.

Run a team of AI coding agents in parallel on your Mac

Web-based dashboard for monitoring and managing multiple AI coding agent sessions. Provides real-time visibility into what each agent is doing, resource consumption, and task progress across your team. Centralizes agent management so team leads can assign work, review outputs, and track productivity across multiple concurrent AI-assisted development workflows from a single interface.

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.