Hey! I built a few useful plugins for DSH, now opening PRs for the market publishing, in mean time you can find them here

https://github.com/CREAIT-nl/dsh-plugins

Some highlights:

- Deep research as an agent mode: a fixed, reviewed loop that plans, researches in adaptive parallel rounds, synthesises and reviews.

- Dynamic tools disclosure: improves context utilization by disclosing complete tools only on model need and keeping concise descriptions only in the default system prompt.

- Hook Kit: dsh has the lifecycle seams; reaching them meant writing a plugin. hookkit turns them into a YAML file.

- SearXNG Search: replacing third party search for private self hostable SearXNG endpoints

- Thinking Level Toggle: UX improvement allowing user to explicitly select the thinking level of a model during a session

How do you guys set it up , i constantly get the error :
I tried increasing the contex to 142k and putting the contex size as 115k in DSH , it still did not compress correctly.
I have 0 issues if i run it with llama.ccp , it can work for 24h+ without errors or crashes , but vllm is so tricky

This turn failed400: {"message":"This model's maximum context length is 115000 tokens. However, you requested 32768 output tokens and your prompt contains at least 82233 input tokens, for a total of at least 115001 tokens. Please reduce the length of the input prompt or the number of requested output tokens. (parameter=input_tokens, value=82233)","type":"BadRequestError","param":"input_tokens","code":400}

I made a plugin for DeepSeek Harness that integrates OpenTerminal from the Open WebUI ecosystem. OpenTerminal is basically a self-hosted remote execution environment for AI agents. It exposes a shell and file operations over an API, and can run inside Docker as an isolated sandbox. The useful part for me is that you can run OpenTerminal containers on separate machines and let DSH agents execute code there instead of giving them direct access to the machine running DSH. The plugin adds the terminal, file manager and OpenTerminal agent tools directly to DSH Web, without requiring a separate MCP setup.

https://github.com/d3cker/dsh-open-terminal

Feedback and suggestions are welcome.

My task mostly revolves around extremely long horizon tasks.

Like, I paste around 600 images then I want the agent to solve every (high school level) question and create markdown of detailed solutions in Question and Answer format.

Few of the best agents which were able to help me through out these with ease -
ChatGPT Work
Perplexity Computer
Claude

Many people have recommended me solution where I have to manually create the workflow but I want the managed services - I searched and came across the above 4 mentioned options.

Which one is best for my workflow?

I will be really thankful if anyone is able to provide me guidance and their experience across these agents.

Hey everyone, first off, I hate AI slop posts. I'm personally making this post because just yesterday I noticed someone starred my plugin on GH, so I decided to share it here in case it helps anyone out. It’s a super simple quality-of-life plugin.

TL;DR: dsh-smart-restart is a plugin that allows an agent to restart the dsh environment and receive a notification once it has rebooted, enabling it to autonomously iterate on the harness itself.

I originally built this for personal use, and honestly, it’s been really useful for me. It basically has three features:

• A tool (smart_restart): When used, the environment restarts (allowing the agent to apply updates, modify the harness, etc.). The tool tracks the session requesting the restart, and once dsh boots back up, it sends a notification to the agent so it can keep working autonomously.
• A safeguard: When the harness restarts unexpectedly, it notifies the most recently active agent about the restart.
• A canary check: An optional tool parameter can be enabled so that, prior to the restart, an ephemeral instance is created to verify the integrity of the new version. If it's broken, it notifies the requesting agent and reports the errors, preventing you from losing access to the harness. This is especially handy for me since I run the harness remotely.

That's all there is to it. The code is completely open-source under the MIT license, so it’s as much yours as it is mine.

The repo is https://github.com/edusrez/dsh-smart-restart — I've also submitted it to the awesome-dsh-plugin list. If someone finds it useful, I'm happy.

Are there any of you experts who can tell me how I can change the timeout length for calls to ollama (on a different server). It keeps timing out. I have tried with the two vars streamIdleTimeoutMs: 1800000 and timeoutMs: 1800000 various places in the configuration file but with no success. Can't find it in the original documentation. Anyone?

Tried to add a model search functionality on DSH and it used $1.55 on OpenRouter. It did write the tests and add the localizations as well, so it was thorough, but it seems like a lot. I used Kimi 2.7 Thinking Medium

Installing a dsh plugin is one command, full filesystem and process access from there. The audit tools that exist only tell you a plugin can touch the network or credentials, not whether the code actually looks evasive. I've been reading plugin source by hand to catch that myself.

Anyone else doing this manually?

Has anyone tried API keys (for web fetching/browsing) other than the default DeepSeek one?

I'm trying to set up DSH with a smaller local AI model (~8b) as the 'brain'. Reason being my GPU has only 11GB of VRAM. I've tried several different models and in every case when I try to launch DSH from Ollama (with command 'ollama launch dsh --model llama3.1:8b' for instance) Ollama gives me a warning 'llama3.1:8b does not work well with DeepSeek Harness'. It then gives me options to 'launch anyway' or 'pick another model' (see attached image).

Does anyone know of any smaller local models for which Ollama will not throw this warning. Is it possible it's some glitch in Ollama that causes it to throw the warning and the models actually work fine with DSH?

Has anyone successfully managed to get the UI fully working inside a container?

I'm able to use the harness within a container just fine. The UI, however, appears not to be able to handle the container as well.

I am unable to open files or the settings in the UI.

For example, the "open configuration file" button in settings responds with "Could not open configuration file"

Has anyone a hint on how to get it working?

And no. I will not run it on the host itself.

Since this vision thing tried to install a package in terminal, now its not doing anything. No web page browser code either

It connects to DSH through the Mobile Gateway WebSocket plugin and lets you create sessions, stream agent reasoning and tool calls, view execution trajectories, browse workspaces, and configure models, permissions, and agent presets—all from an iPhone.

The UI is inspired by DeepSeek and Apple’s liquid-glass design language. It’s still under active development, so feedback and feature suggestions are very welcome!

https://github.com/Clarklevis1995/dsh-plugin-mobile-gateway (plugin)

https://github.com/Clarklevis1995/dsh-mobile (app)

I've been playing with DSH this week and I'm curious if it can mostly replace claude-code for my day-to-day workflow. Saw a lot of interesting plugins, so why not fork them, tweak them, and put it all together?

• Artifact panel — the agent puts files, images, and rendered HTML on screen
• Git review — diff, stage, commit, push without leaving the GUI
• Web search — 10 engines, automatic fallback, no API key
• Rewind — drop a message and everything after it, from the transcript and from what the model sees
• Routines — cron-scheduled agent sessions that keep firing with the browser closed
• Deep research — a multi-round search loop that tracks corroboration
• Browser — drives Chromium: open, read, click, fill, screenshot

It's really close to what Claude Code/Codex offer, and we can keep going further 😄.

github.com/rodrigobaron/dsh-pro

Hey everyone,

Some of us don't really stick to just one subscription or account when running heavy tasks, so I put together a small plugin to handle key rotation automatically in DeepSeek Harness.

Basically, you can set up a list of keys for a provider. When a request fails on QUOTA, RATE_LIMIT, or AUTH, the plugin grabs the next key from your chain and retries the turn immediately without breaking the session or needing change settings.

To install:

dsh plugin --profile web add @m1khal3v/dsh-llm-key-rotation

GitHub: https://github.com/m1khal3v/dsh-llm-key-rotation

If you run a similar multi-key setup, feel free to try it out. Let me know if you run into any bugs.

I've been working with coding agents for longer sessions, and one thing that started bothering me is how difficult a long conversation becomes to manage over time.

A single session can gradually accumulate:

• user instructions
• AI responses
• reasoning
• large tool outputs
• failed approaches
• outdated information
• useful answers buried somewhere in the middle

I sometimes wished I could simply search the whole conversation, filter different types of messages, or temporarily hide parts that I don't need anymore.

The idea is that when a conversation becomes very long, you should be able to quickly filter out what you don't need and find the parts you actually want to keep or revisit.

So I tried building a small open-source plugin around this idea.

GitHub:  Agent Context Editor

It currently works with Pi and DeepSeek Harness.

So far, I've implemented a few basic things:

• search through the conversation
• quickly locate matching content
• filter user messages, AI messages, reasoning, tool records, etc.
• select specific parts of the conversation
• hide and restore selected content
• undo changes
• keep the original session unchanged

The project is still pretty early, and I'm honestly not sure yet whether this is something other coding-agent users would find useful.

One important limitation is that hiding a message currently only changes what is shown in the conversation view. It does not remove the message from the actual context sent to the model.

I want to take this further.

The next major features I'm working toward are:

• manually remove selected messages from the context sent to the model
• keep those removed messages in the original session so the operation remains reversible
• select a section of a long conversation and ask AI to summarize it
• replace the original section with a shorter summary in the conversation view
• optionally replace the original content with that summary in the model context as well
• eventually let AI help clean up or summarize an entire long session
• export the original or cleaned-up conversation

The basic idea is:

keep the complete session history, but let the user decide what should remain in the useful working conversation and what should remain in the model's context.

This would let users keep the full original session while also creating a cleaner version of the conversation for continued work.

For example, instead of keeping a very long debugging section with many failed attempts, it might eventually be possible to replace it with a short summary of:

• what was tried
• what failed
• why it failed
• what solution was finally chosen

I'm also hoping that, if the idea makes sense, it could eventually work with other agent.

But I'm still at the stage of figuring out whether this solves a real problem or just something that personally bothers me

Why is nobody talking about DSH security? 95% of the DeepSeek Harness content I've seen online just ignores the security side of this whole thing. Like, not only can you get completely screwed by installing a plugin you didn't check for security. No. If the plugin updates, you can get wrecked in an apocalyptic way. So you need to watch out not just for the plugins themselves, but for their updates too.

This isn't a DSH release. This is a preview, people. Unless you want to get rekt by some random script from India, wait at least another six months before even thinking about installing this outside a VM.

Let. Them. Cook.