If you’ve been following what I write about agents and MCP, you know I’m a little obsessed with the shift from “AI that talks” to “AI that acts.” Chatbots answer. Agents do.
OpenCode is one of the clearest examples of that shift I’ve come across lately, and it’s open source — which, if you know me, is basically a guaranteed way to get my attention.
So let’s break it down: what it actually is, why I think it matters, and where it fits next to tools like Claude Code and Cursor.
What Is OpenCode, Really?
OpenCode is an open-source AI coding agent that lives in your terminal, your desktop, or your IDE. Instead of just answering “how do I write this function,” it works inside your project — reading files, editing code, running commands, and pulling in outside systems through MCP.
That last part is the piece I keep coming back to. It’s not a smarter autocomplete. It’s a coding partner that can actually touch your repo and your infrastructure.
Why This Matters to Me
Modern codebases are messy and huge. Nobody’s real problem today is “finish this one line of code.” The real problem is:
- Understanding a repo with thousands of files
- Making a change that touches five different modules
- Running the terminal commands to test it
- Doing all of that without breaking something in production
Autocomplete never solved any of that. Agents are at least trying to.
Plan Mode: My Favorite Feature
Here’s the thing about autonomous coding tools that used to make me nervous — you ask for a change, and it just… changes stuff. OpenCode’s Plan Mode fixes that.
Before touching a single file, it proposes how it plans to solve the problem. You read the plan, you approve it (or push back), and only then does it move into Build Mode and start editing, running tests, and refactoring.
💡 Why this matters: In an enterprise setting, this is the difference between “AI agent” and “AI agent I’m actually allowed to point at production code.”
It’s Not Locked to One Model
This is a big one for me. OpenCode doesn’t force you into a single vendor. You can wire it up to Anthropic, OpenAI, Gemini, Groq, OpenRouter, or even a local Ollama model.
That flexibility matters more than people give it credit for. Cost, privacy, latency, compliance — every org weighs these differently, and OpenCode lets you actually make that call instead of having it made for you.
MCP Is Where It Gets Interesting
You’ve seen me write about MCP a lot this year, and OpenCode leans into it hard. Instead of an agent that only knows what’s in your local files, MCP lets it reach out to things like:
- SQL Server / PostgreSQL
- GitHub
- Slack, Jira
- Internal APIs and enterprise services
Picture this: someone on your team asks “why are service jobs down this month?” A properly wired-up OpenCode setup could hit a SQL Server MCP connector, pull the right metadata, write and run the query, and hand back an explanation — not just a SQL snippet you still have to run yourself.
That’s the direction I think agentic tooling is heading in general, and it’s nice to see an open-source project leaning into it early.
Specialized Agents
Newer versions of OpenCode let you configure agents for specific jobs — Build, Plan, Explore, Scout, Summary are the built-ins, but you can roll your own with different prompts, models, tool permissions, and temperature settings.
In practice, this means you could have one agent tuned for SQL performance review and a completely different one for documentation, each with its own guardrails.
Getting It Running
Install through npm, Docker, Chocolatey, Scoop, or the one-line install script — whichever fits your setup. Once it’s in:
- Point it at your preferred AI provider
cdinto your project- Launch OpenCode
- Run the init command (this generates an AGENTS.md that captures your project’s structure and conventions)
- Start working with it like a teammate, not a search bar
That AGENTS.md file is worth calling out — it’s what makes the next session smarter than the last one, instead of starting from zero every time.
Where It Falls Short (For Now)
I’m not going to pretend it’s magic:
- Setup takes real time, especially wiring up MCP servers correctly
- Output quality still depends heavily on which model you point it at
- You still need a human reviewing anything that touches production
- Permission policies need real thought in an enterprise environment — this isn’t “install and forget”
My Take
OpenCode isn’t trying to be a better autocomplete. It’s trying to be an engineering partner — one that plans before it acts, works with whatever model you trust, and can reach into your actual systems through MCP instead of guessing from static context.
If you’re already deep into the agent/MCP world like I am, it’s worth an afternoon to set up and poke at. If you’re just dipping a toe in, this is a pretty good on-ramp to see what “agentic coding” actually looks like day to day — not just in a keynote demo.