Orchestration AI apps

Open-source visual builder to create, deploy, and orchestrate AI agents.

An open-source workspace for building AI agents on a Figma-like drag-and-drop canvas, conversationally, or in code. It connects LLMs to 1,000+ integrations plus knowledge bases and structured tables, then deploys a workflow as an API, scheduled job, webhook handler, or standalone chat app. Apache-2.0, YC-backed, and runnable in Sim's cloud or self-hosted via npm or Docker.

TypeScript framework for building AI agents and workflows.

An open-source TypeScript stack for AI applications: agents, a graph-based workflow engine (.then/.branch/.parallel), memory, tools, and built-in observability behind one API. Run it self-hosted under Apache 2.0, or deploy to Mastra Cloud with a free Starter tier and paid Teams/Enterprise plans.

Type-safe Python agent framework, the Pydantic way.

An open-source Python framework for building production-grade agents with validated, structured outputs instead of raw-string parsing. Model-agnostic across OpenAI, Anthropic, Gemini and many more, with composable tools, durable execution, MCP support, and built-in observability via Pydantic Logfire. MIT-licensed from the team behind Pydantic.

Visually build AI agents and LLM workflows — drag-and-drop, self-hosted.

A low-code visual builder for AI agents, chatflows, and multi-agent systems on a drag-and-drop canvas. It self-hosts locally through npm or Docker, or deploys to major clouds, and is provider-agnostic across many LLMs via its node ecosystem. The core is Apache-2.0; a managed Flowise Cloud tier exists, making it freemium.

The data framework for LLM apps — RAG, agents, and document workflows.

An open-source framework (Python + TypeScript) for connecting LLMs to your data — ingestion, indexing, retrieval, and agentic document workflows. Pairs with the managed LlamaCloud (LlamaParse) for production parsing and extraction. The most-used RAG framework after LangChain.

Visual platform for agentic workflows, RAG pipelines, and LLM apps.

An LLMOps platform that bundles a drag-and-drop workflow builder, RAG pipelines, agent tooling, model management, and observability into one surface — prototype to production without much glue code. Connects hundreds of proprietary and open models across providers. Self-host the source-available edition for free, or use Dify Cloud's paid tiers.

Multi-agent framework with explicit roles and tasks.

Python framework for orchestrating crews of specialised agents — researcher, writer, reviewer — coordinated through shared context. Opinionated about roles, sequencing, and delegation; good fit for content-and-research pipelines.

TypeScript SDK for streaming, tool-calling, and structured outputs.

Vercel's batteries-included TypeScript framework for LLM-powered apps. Streaming primitives, structured outputs, tool calling, and React hooks for chat UIs — works with every major provider out of the box.

Graph-based agent orchestration. Stateful loops with checkpoints.

LangChain's agent layer. Model agents as nodes in a state graph with persistent checkpoints, human-in-the-loop steps, and durable execution. Strong when you need a long-running, debuggable agent rather than a one-shot chain.

Durable workflow engine for AI background jobs.

Event-driven, durable execution engine — pause/resume, retries, fan-out, scheduling — designed for long-running AI jobs that can't sit in a request/response cycle. TypeScript-first; framework-agnostic.

The default open-source framework for composing LLM apps.

Python + TypeScript framework for chaining prompts, tools, retrievers, and memory into LLM applications. Ubiquitous in the ecosystem; pairs with LangGraph for agent orchestration and LangSmith for tracing.

Fair-code workflow automation with first-class AI nodes.

Visual workflow builder bridging APIs, databases, and AI providers. Self-hostable; commercial cloud available. The default Zapier-style surface for the agentic-workflow crowd.