All Open Source Apps

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

AI-Native Distributed SQL Database for Agentic Workloads

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

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

Run Llama, Gemma, DeepSeek, and other open LLMs on your own machine with one command and an OpenAI-compatible API.

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.

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

A Postgres-backed orchestration engine for background tasks, AI agents, and durable workflows that replaces Redis queues and multi-datastore durable execution platforms with a single self-hostable service.

Turn coding agents into real teammates — assign issues, track progress, and compound reusable skills across a vendor-neutral, self-hosted platform.

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

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.

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.

Run AI workloads at scale with a Pythonic serverless runtime that handles GPU inference, background jobs, and sandboxes with zero infrastructure overhead.

An open-source CLI that packages machine learning models into standard, production-ready Docker containers — no Dockerfile wrangling, no CUDA version hell.

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.

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

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.

A single graph+vector+temporal database for AI workloads — Neo4j-compatible, sub-millisecond hybrid search, and built-in memory decay.