OpenCode + Ollama in 2026: Run a Local AI Coding Agent on Your PC

AI coding agents are moving from autocomplete toward a workflow where the model can inspect a repository, edit files and run commands. OpenCode is one of the open-source projects building around that idea, and Ollama makes it possible to connect that workflow to local models.

This guide focuses on the practical path: what OpenCode is, how it differs from a model runtime, how Ollama connects to it, what a local coding-agent loop looks like, and where the setup can go wrong.

What Is OpenCode?

OpenCode is an open-source AI coding agent available through terminal and other interfaces. Its core job is not to be the model; it is to provide the agent workflow around the model.

The agent can work with a repository, inspect files, edit code and run commands. Ollama's integration documentation describes OpenCode as a coding agent that runs in the terminal, reads projects, edits files and executes commands.

OpenCode vs Ollama

This is similar to the relationship between a model server and a higher-level AI application: the agent layer is where tools, files and execution become part of the loop.

Why OpenCode Is Interesting in 2026

OpenCode has a rapidly changing release stream. The official GitHub releases page currently shows v1.18.32 as the latest listed release, while v1.18.31 was published on September 14, 2026. The project supports desktop and terminal workflows, and the ecosystem includes MCP-related functionality.

More importantly for local AI users, Ollama documents a direct OpenCode launch path.

Start OpenCode With Ollama

Ollama's official integration documentation provides a simple launch command:

ollama launch opencode

This lets Ollama configure OpenCode for a local or cloud-backed workflow.

For manual setups, Ollama also documents an OpenAI-compatible provider configuration pointing at the local Ollama endpoint:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "ollama": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Ollama",
      "options": {
        "baseURL": "http://localhost:11434/v1"
      }
    }
  }
}

How a Local Coding-Agent Loop Works

The important difference between chat and agentic coding is the loop:

1. You describe a task.
2. The model inspects the relevant project files.
3. The agent decides what tool or command to use.
4. It changes files or runs a command.
5. It observes the result.
6. It continues until the task is complete or requires your intervention.

That makes agent quality depend on much more than raw model intelligence. Tool access, repository context, command permissions and the model's ability to follow the workflow all matter.

What Model Should You Use Locally?

There is no universal best local model for coding agents. The practical choice depends on your RAM or VRAM, context requirements, repository size and the specific model's tool-use behavior.

Our local-model-by-RAM guide is a useful starting point, but agent workloads can require more memory than ordinary chat because the model may need a large repository context plus tool outputs.

OpenCode With a Small Local Model

Smaller models can be attractive because they run comfortably on consumer hardware, but coding agents are not just code-generation benchmarks. The model must also understand tool results, follow instructions, maintain context and recover from failures.

That means a model that writes a good standalone function may still struggle when asked to inspect a large project and perform a multi-step change.

Repository Rules Matter More Than People Expect

Before giving an agent a large codebase, create simple rules for how it should work:

• Read project instructions before editing.
• Do not modify generated files unless explicitly requested.
• Run tests after changes.
• Never delete data or production configuration without confirmation.
• Summarize changed files before finishing.

Good rules reduce the blast radius of a weak or confused model.

OpenCode + MCP

OpenCode also sits in the larger tool-use ecosystem where MCP can provide standardized access to external tools and context. This can turn a local coding agent into a broader development assistant, but it also increases the number of systems the model can influence.

For that reason, our MCP guide and agent-security guide are worth reading before adding powerful integrations.

Windows, macOS and Linux

The official OpenCode release ecosystem includes desktop assets for multiple platforms, and the Ollama integration works with a local Ollama endpoint. The exact installation path depends on your operating system and whether you use terminal or desktop workflows.

Common OpenCode + Ollama Problems

The model is available in Ollama but not in OpenCode

Check the provider configuration and model name. An installed Ollama model does not automatically mean every external application has discovered it using the same identifier.

The agent keeps running long commands

Command execution is a tool capability. Use timeouts and project instructions, and avoid granting unrestricted access to sensitive directories.

The model forgets earlier instructions

Large repository context can compete with your system instructions. Keep agent rules short, explicit and easy to preserve.

The local model is too slow

Check model size, quantization, context length, GPU offload and concurrent workloads. Coding agents can generate many tool turns, so latency compounds quickly.

OpenCode vs a Traditional Coding Chatbot

How to Use OpenCode Safely

Think of an AI coding agent as a junior developer with shell access, not as a text box.

• Use Git before major changes.
• Work in a disposable branch or workspace.
• Keep secrets out of the repository.
• Limit filesystem permissions where possible.
• Review destructive commands.
• Run tests and inspect diffs.

OpenCode + Ollama vs Qwen Code + Ollama

Both are approaches to local AI coding, but they emphasize different agent experiences and integrations. We recently published a separate Qwen Code + Ollama + LM Studio guide. Reading both gives you a better feel for the broader local coding-agent ecosystem than treating one tool as the only option.

FAQ

Can OpenCode use Ollama?

Yes. Ollama has an official OpenCode integration guide and provides the ollama launch opencode command.

Can OpenCode run with a local model?

Yes, when you configure a compatible local provider such as Ollama.

Is OpenCode only a terminal tool?

The project supports terminal-oriented workflows and desktop/other interfaces depending on the current release.

Does a coding agent need a huge model?

No single model size is mandatory, but larger or more capable models can handle complex tool-use tasks more reliably. Hardware and context limits still matter.

Official Sources

OpenCode Releases · Ollama OpenCode Integration

Final Take

OpenCode becomes much more interesting when paired with Ollama because the stack cleanly separates the model runtime from the agent workflow. For developers who want a local coding assistant with repository access and command execution, that combination is worth understanding before jumping straight to another cloud-only coding tool.