From DeepReinforce, Ornith hands task writing, scaffolding and solution writing to the model itself, comes in three sizes from 9B to 397B, and ships under MIT with a 256K context and local deployment.

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.

An open source agent runtime from DeepSeek with a desktop build that bundles Node.js, pnpm and a Python environment, four preset modes, a plugin page and a trace view.

An open extension that plugs into the editor you already use and talks to whichever model you point it at: cloud, local, or a private deployment inside the company network, with the configuration written in a file you own.

Not an application but a paper and its reference implementation, trained purely on synthetic degradations and loosely licensed enough that a whole shelf of GUI products wraps it. Three ways in, with different costs.

A desktop application that enlarges images on your own graphics card and touches no network. The catch is stated early: it needs a Vulkan-capable GPU, and a cloud tier handles the jobs your hardware cannot.

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.