All Open Source Apps

The open source AI engineering platform for debugging, evaluating, monitoring, and optimizing production LLMs and agents at scale.

Build, run, and manage agent platforms with a full production stack — SDK, runtime, and control plane included.

The most powerful node-based AI workflow engine for creating images, video, 3D models, and audio with full control over every generation step.

Open source AI gateway and Python SDK that gives you one OpenAI-compatible interface to call 100+ LLM providers, with built-in routing, cost tracking, guardrails, and virtual keys.

Build enterprise-grade AI agents with RAG, workflows & multi-modal support

Open-source vector database and search engine built in Rust for production-grade AI applications — from semantic search to RAG pipelines and recommendation systems.

Open-source, embedded vector database built on the Lance columnar format for fast multimodal search across billions of vectors, backed by Y Combinator (W23).

Build, test, and deploy AI agents and RAG workflows visually with native API and MCP server export.

Git, but better — a modern version control client with stacked branches, parallel workflows, unlimited undo, and first-class support for AI-powered development.

Run and fine-tune LLMs, diffusion, audio and embedding models on your own hardware, from a native desktop app, a browser UI, or a Python library.

An open source AI-native proxy that secures, observes, and governs agent-to-LLM, agent-to-tool, and agent-to-agent communication through MCP, A2A, and unified LLM routing.

The batteries-included Python agent harness — planning, sub-agents, filesystem, shell, memory, and skills bundled in, built on LangGraph.

Run LLM-generated Python code safely inside your agent—no containers, no CPython, no compromise—with sub-microsecond startup.

The open-source API layer that turns local LLMs into production private AI applications with full Claude API compatibility

An incremental data indexing engine that keeps AI agent context perpetually fresh by reprocessing only what changed.

Give your LLM agents on-the-job training—ART lets you apply GRPO reinforcement learning to any multi-step agentic workflow with minimal code changes.

Compress everything your AI agent reads — tool outputs, logs, RAG chunks, and files — before it reaches the LLM, achieving 60–95% fewer tokens with the same answers.

An open-source context database that gives AI agents a unified filesystem for memory, resources, and skills with hierarchical tiered retrieval.

Local-first AI memory with verbatim storage, pluggable backends, and 96.6% retrieval recall on LongMemEval — no API key required.

Mesh LLM pools GPUs and memory across every machine you own into one OpenAI-compatible API, so agents tap distributed compute instead of a single GPU box or a metered cloud bill.

Drop your files and search them instantly — no vector DB, no indexing pipeline, just raw data queried by a self-evolving intelligence layer.

A Microsoft-built training framework that optimizes AI agents with reinforcement learning, automatic prompt optimization, or supervised fine-tuning — with near-zero code changes to your existing agent, in any framework.

Transform messy, unstructured documents into persistent, navigable memory that AI agents can actually use.

A self-hosted, OpenAI-compatible LLM gateway that puts one endpoint in front of 40+ providers, with virtual API keys, per-user budgets enforced before spend, and full usage tracking.