Yesterday I installed the deepseek harness after hearing a lot of noise about it from different channels. It’s minimal but with the idea that everything is a plugin and agents can create plugins dynamically so it basically has infinite flexibility.

Today I decided to get it to build a human/agent collaboration pipeline. For each workspace (project), an AGENTS.kanban is created that has a kanban board formatted as json to match the vscode extension portable-kanban. The board is fully viewable and functional in the harness web ui (as well as vscode) with an identical set of features to portable-kanban. Then I can use the command: /kanban next to instruct the agent to pickup the next todo task from the Doing or To Do boards and implement it.

To implement all this I used Ornith-1.5-35B running on my AI PC (with a modest 32+8 memory), and when there were complex issues or the harness was throwing errors, I enlisted the help of GLM-5.3-Flash through the lite qwen token plan ($6!). I ran into two issues that caused previous dsh sessions to fail to parse both were debugged and fixed by GLM.

Now I’m at end of day, I have a fully functional kanban collab plugin which I used to develop the plugin itself. The agent also started contributing new tasks into the backlog (ones marked with “Confirm” label).

I’m not sure where I’ll go next with it, but it’s already usable! which is astonishing.

My experience with deepseek harness so far:

The Good

  • Coming from opencode, dsh is superior in its clarity and runtime loop
  • It’s noticeably lighter to run than opencode on my system despite my running it through the web interface (dsh web) rather than TUI
  • The everything is a plugin philosophy is a great approach imo
  • Using workspaces as a first class citizen, workspaces map perfectly to projects and all are active and accessible always. So I can launch tasks on 2+ projects concurrently from the same interface

The Bad

  • Opinionated towards DeepSeek API. I still don’t know how to turn off their default web search tool from settings. It requires a DeepSeek API key and fails when models call it if a key is not provided. Even when I setup my own MCP for web search, models default to attempting to use the built-in tool and fail every time (then tries the mcp tool).
  • Not clear which parts of the harness can be changed on the fly and update immediately, and which parts require a full harness restart to apply.
  • Has the same issue as opencode: uses a hardcoded list of models per provider rather than fetching the model list from v1/models endpoint; leading some providers to show a stale list of models (such as qwen’s token plan).
  • Functionality breaking bugs but that’s expected

If you’ve tried it, what’s been your experience with deepseek harness?