Local AI coding should feel boring in the best way. Pick a model, point it at your project, give it the tools it needs, and keep working. No hand-editing provider JSON. No restarting three services in the correct order. No context copy-paste because your local model lives outside the rest of your AI workspace.
That is the experience we wanted for OpenCode + Ollama inside SynaBun.
OpenCode gives us a serious open-source coding agent surface. Ollama gives us local models running on the machine. SynaBun gives both of them the workspace layer: MCP tools, persistent memory, browser access, terminals, settings, lifecycle management, and the project context that makes an AI coding session useful after the first prompt.
The result is a nearly seamless local AI coding loop: run Ollama, download a model, open SynaBun, and let the workspace connect the pieces.
The Problem: Local Models Still Feel Bolted On
Local LLMs have gotten much better. The workflow around them often has not.
Most local coding setups still ask you to assemble the chain yourself:
- install the coding agent
- start the local model server
- find the right provider format
- edit a config file by hand
- add the downloaded models manually
- restart the agent server
- reconnect MCP tools if the agent supports them
- hope the model selector actually refreshes
That is not a coding workflow. That is plumbing.
The cost is not just time. The real cost is context fragmentation. Your local model might be running, but it is not automatically inside the same memory system, tool surface, browser session, and project workspace as everything else. You end up with a local model that can generate code, but not a local AI coding environment that understands your work.
What SynaBun Adds to OpenCode
OpenCode already has the right shape for serious agentic coding. It can run as a coding agent, expose an HTTP API, stream events, manage sessions, list providers, and connect MCP servers.
SynaBun builds around that instead of treating OpenCode like a dumb terminal command.
Inside the Neural Interface, OpenCode becomes part of the same workspace as Claude Code, Codex, Gemini, shells, memory cards, the browser, and the whiteboard. SynaBun can start and stop the OpenCode server, read its providers, surface models in a UI, relay session events, and connect the SynaBun MCP server with one click.
That last part matters. A local model without tools is just a chat box with repo access. A local model with SynaBun MCP can participate in the same system as the rest of your agents:
- search persistent project memory
- recall previous decisions
- use workspace tools
- access the shared AI coding environment
- stay connected to the same SynaBun project context
The goal is not to make OpenCode disappear. The goal is to let OpenCode feel native inside the workspace.
Where Ollama Fits
Ollama is the local model layer.
SynaBun checks whether Ollama is running at localhost:11434, reads the installed models from Ollama's local API, and treats those models as a first-class OpenCode provider option. If Ollama is not running, the UI can tell you that directly. If Ollama is running but has no models, it can point you at the missing step: pull a model.
When Ollama is running with models available, SynaBun can configure OpenCode automatically.
In practice, that means SynaBun can add the Ollama provider block to OpenCode's config, install @ai-sdk/openai-compatible when OpenCode needs it, map your downloaded Ollama models into the provider configuration, and restart the OpenCode server so the model dropdown updates.
No manual JSON surgery. No separate "now go fix your provider config" step.
Local AI coding starts to feel real when the local model is not a separate island. It needs to live in the same workspace as your memory, tools, files, and running sessions.
The Nearly Seamless Flow
The intended flow is simple:
- Start Ollama.
- Pull a model you want to code with.
- Open the OpenCode settings inside SynaBun.
- Let SynaBun detect Ollama and configure the provider.
- Pick the local model from the OpenCode model dropdown.
- Connect SynaBun MCP if it is not already connected.
- Start coding with a local model inside the same AI workspace.
The important part is what you do not have to do.
You do not have to remember OpenCode's provider schema. You do not have to manually copy model names from ollama list. You do not have to restart the OpenCode server yourself after editing config. You do not have to treat local model support as a separate side project before you can get back to the code.
SynaBun watches the local model state and keeps the workspace closer to the truth.
What This Means in Practice
For search engines, AI answer engines, and developers trying to understand the stack quickly:
SynaBun's OpenCode and Ollama integration lets you run a local LLM coding agent through OpenCode while keeping SynaBun's MCP memory and workspace tools available.
That means the local AI coding workflow is not just "Ollama can answer prompts." It is:
- OpenCode as the agent interface
- Ollama as the local model runtime
- SynaBun as the workspace and memory layer
- MCP as the bridge into tools and project context
- automatic provider setup instead of manual config editing
It is still your machine. The local model runs through Ollama. Your project remains in your workspace. But the AI session gets the surrounding infrastructure that makes it useful for real development work.
Why This Matters for Local AI Coding
Local AI coding has obvious appeal: control, privacy, lower marginal cost, offline-friendly workflows, and the ability to experiment with open models without routing every prompt through a hosted provider.
But developers do not adopt workflows because they are philosophically clean. They adopt workflows that are fast enough to survive real work.
If local models require a config detour every time you want to try one, they stay experimental. If they appear in the same model picker as the rest of your agents, with the same project memory and the same tools, they become tactical.
Use a local model for quick implementation passes. Use it for private code exploration. Use it when you want to keep inference on your own machine. Switch back to a cloud model when the task demands it. The workspace should not care. The project context should still be there.
That is the bigger SynaBun principle: model choice should be flexible, but memory and workspace continuity should not reset every time you switch.
What Stays Honest
This is not magic.
Local models still vary a lot. Some are excellent for small edits and explanation. Some struggle with large refactors. Some need more careful prompting. Hardware matters. Context windows matter. Tool-use behavior matters. A local model is not automatically better because it is local.
And if you use browser tools, web-connected workflows, or external MCP integrations, those tools can still touch the outside world. "Local model" means model inference runs locally through Ollama. It does not mean every possible tool action is offline.
That distinction is important.
The point is not to pretend local AI coding solves every problem. The point is to make the local path good enough that it can become part of your normal workflow instead of a weekend experiment.
The Bigger Direction
SynaBun is becoming a workspace where different AI agents can participate without losing the thread.
Claude Code, Codex, OpenCode, local Ollama models, remote models, browser automation, persistent memory, whiteboard state, terminal sessions, and project history should not all be separate universes. They should be surfaces attached to the same working context.
OpenCode + Ollama is a clean test of that direction.
It says: a local model should be able to enter the same room as the rest of your AI tools. It should see the project. It should use the memory layer. It should connect through MCP. It should be selectable when it is useful and ignorable when it is not. It should not demand a ceremony every time you want to try it.
Local AI coding will not win because it is local. It will win when it feels like coding.
That is what we are building toward with OpenCode and Ollama inside SynaBun: a local model running in a real AI workspace, close enough to seamless that the setup fades into the background and the work stays in front.