For a long time, we’ve known Ollama as the ultimate model-runner. It was the go-to tool for downloading weights and spinning up a local chat interface in seconds. But as of early 2026, Ollama is outgrowing that label. With the introduction of the ollama launch command, it has evolved into a full-scale launchpad for AI agents.

The Shift: From Running to Acting

The difference is simple: a model-runner just gives you a place to talk to an AI. An agent-host gives the AI the tools to actually work on your system. By integrating powerful applications directly into the ecosystem, Ollama is moving from "answering questions" to "executing tasks."

The New Agentic Lineup

Ollama now supports launching four major agentic tools that transform your local LLM into a functional colleague:

  • Claude Code: Developed by Anthropic, this is a professional-grade CLI agent that can research your entire repository, write code, run terminal commands, and execute test suites directly from your command line using ollama launch claude.
  • OpenCode: Created by AnomalyCo, this tool provides an interactive Terminal User Interface (TUI) that offers a fully open-source, provider-agnostic "Plan and Build" workflow for developers who want to architect features locally using ollama launch opencode.
  • OpenClaw: Originally created by Peter Steinberger (PSPDFKit), this is a background daemon that acts as an autonomous personal assistant; it bridges your local models to apps like Slack or WhatsApp to manage your calendar and emails via ollama launch openclaw.
  • Codex: This is a high-speed CLI developer assistant from OpenAI that Ollama now allows you to run 100% locally, specializing in quick code modifications and script execution within your working directory using ollama launch codex.

The "Memory" Secret: The Context Window

If you are going to use these agents, you need to understand the Context Window. A common misconception is that the model "remembers" your past messages. In reality, AI models are stateless—they have no memory of their own.

Here is how the "illusion" of memory is formed:

  • Ollama (The Runner): When you launch a model, Ollama reserves a specific amount of RAM for the "Context Window." Think of this as a digital workspace or a "bucket."
  • The Application (The Librarian): Tools like Claude Code or OpenClaw act as the record-keepers. Every time you send a new message, the application grabs your entire chat history and the files it's reading, bundles them together, and hands the whole package back to the model.
  • The Process: The model re-reads everything from scratch every single time you press Enter.

This is why a 64k context window (roughly 50,000 words) is so important for agents. Because these tools are constantly feeding the model your source code and history, the "bucket" fills up fast. If the window is too small, the agent will literally lose its train of thought as older information is pushed out to make room for the new.

Why This Matters for Local AI

The "model-runner" era was about privacy and accessibility. This new "agentic" era is about utility. We are moving from "What can this model tell me?" to "What can this agent do for me?"

Regardless of your operating system, these tools provide a massive boost to local productivity. Just remember that agents are "chatty"—give them enough context space in your settings, and they’ll become the most productive teammates you’ve ever had.