An open source autonomous agent from Nous Research: finished tasks harden into reusable skills that keep improving, preferences live in local files that survive sessions, and it plugs into more than twenty chat platforms.

An AI assistant platform from Tencent Cloud, MIT licensed, where conversations, workspaces and keys stay on your own machine and nine chat platforms route into the same set of experts.

Search, scrape and interact go through one interface, and what comes back is clean markdown, templated JSON or a screenshot. Proxies, anti-bot handling and rendering are the service's problem — which is exactly the tedious part.

Four layers split by depth — a no-code studio, a conversational layer, an event-driven core and an extension pack. The framework is in maintenance mode, so the honest question is whether to start on it or only keep running it.

Nodes are steps, edges are transitions, state travels between them — and the same graph may mix deterministic code with model judgement. Resume points, approval gates and inspectable state are primitives, not patches.

Agents, flows and tasks form the skeleton; a control plane on top traces every model call and memory read and costs it. The framework itself is MIT, and the discovery and optimisation stages depend on platform-scale data.

Four screens split the work: describe an agent in a sentence, drag blocks for a complex flow, watch runs and spending, and reuse what others published. A platform directory under a source-available licence sits beside a classic MIT one.

An assistant that lives in the chat apps you already use and keeps every trace of its memory on your own disk. A non-profit foundation holds the project, there is no paid tier, and one gateway process routes 29 channels into a coding agent.

One half takes a task and returns a result, the other rents your own Playwright code a remote browser with a stealth profile. The open library is MIT licensed, and the billing is per hour and per token rather than a subscription.

A platform where knowledge base Q&A, visual workflows, agent orchestration and tool calling share one workspace. The licence allows internal commercial use but not resale as a service, and the retrieval side is where the effort went.

The project that made visual LLM flows mainstream has stopped: the repository was archived in August 2026 and the core team has left. The code stays under an Apache licence, which turns the question from what it can do into whether your team can carry it.

An open-source Python framework where you drag components into a flow, swap models to compare, then ship the result as an API or an MCP server. Local install is one command on port 7860, and nothing about the canvas stops you editing the code underneath.

Visual blocks plus prompts define how an app thinks, retrieves, decides and asks a human. Cloud, private VPC and self-hosted run the same code, and the Sandbox tier starts free with 200 message credits and 5 apps.

One drag-and-drop canvas, JavaScript or Python at any node, more than 500 integrations, MCP and RAG support, and a choice between your own server and the official cloud. Billing counts full executions rather than steps, and every tier allows unlimited users.

More than 300 verified programs for local AI, each packaged with its own installation script: one press brings down the models, sorts the dependencies, starts the service and opens the interface in a window, all inside its own sandbox.