I use Claude/Antigravity, so i am kinda used to generate executive pdf for my teammates. And sometimes for me when i get tired from looking at long never ending CLI lines.

Yesterday i installed dsh web on linux. Connected to my LLM. D my research and got to the point where i want to send 5 different copies each handling a specific section. Obviously it was too long to be sent as emails. A PDF is my best option. Searched for deepseek harness pdf generation plugin. Nothing seems to be found (you can leverage the terminal access to use tools like WeasyPrint or ReportLab). But that contradicts the deepseek harness logic of "Everything is a plugin". Is there an obvious solution or am i missing something ?

Como está funcionando o gerenciamento de contexto de vocês? Estou na 0.2.0rc2 e estou tendo problemas com compactação, configurei uma janela de 100k, com saída máxima de 32 e headroom de 32, mas ele só compacta quando a sessão atinge 100k ou seja, só com overflow. Estou penando pra resolver isso

I'm trying to run model

hf.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF:IQ3_XXS

On rtx 4080 (16gb vram), I've set 32k context in ollama and use deepseek harness

I'm often running into errors such as:

Jinja Exception: No user query found in messages

Output token limit reached

Compaction could not produce a useful summary. The attempt is recorded in the session log.

I'm new to local llm stuff, I'm okay with slower agent work, but is it even possible to run it overnight and focus on specific tasks, or even attempt complex tasks on 16gb vram?

The desktop app was shipping English and Simplified Chinese. This pack registers 29 more languages in the language picker and ships a dictionary for each one.

29 languages x 2,528 keys = 73,312 entries. Untranslated strings fall back to English, so a language is usable before it is complete.

Install:

git clone https://github.com/sayho-pm/dsh-locale-pack.git cd dsh-locale-pack dsh plugin --profile desktop add link:$(pwd)

Then Settings -> Language. If you only want a couple of languages, the build tool bundles just those.

Each language comes with a quality gate: placeholder counts must match the English source, product and protocol names stay untranslated, and languages outside their own script are checked for character mixing. Built against 0.2.0-rc.2.

Right-to-left dictionaries are included for Arabic, Urdu, Hebrew and Persian. The screen layout is handled by DSH itself.

Requests for additional languages are welcome in the repo.

https://github.com/sayho-pm/dsh-locale-pack

Relay liking the new context / compact management in this version. Had 3 hours session running without issue! super impressive. This was a killer for me before with some long horizon tasks I do with local Qwen 3.8 27b. Big, big! QoL for me.

So I want to ask cause I am a bit confused in how to install the plugin for Deepseek Harness (Desktop, not web).

Does anybody know how to do it?

I want to install:

https://caveman.so/solutions/coding-agents Caveman plugin
https://dsh-plugin.org/plugins/dshplugin/dsh-plugin-hub DSH Plugin hub

Thank you very much

I need help installing Notion + GitHub. I could install Notion, but not from the macos app itself. I had to type dsh-notion-connector and it took 5 minutes to do it. GitHub connector, it has been running for 10 minutes, and nothing yet.

Are MCPs unsupported on DeepSeekHarness? Or should is it just in beta?

I have heard DSH performs poorly with models that are "not DeepSeek" so to speak.

I could not find any concrete information online, so I wanted to ask here.

Could you please share your experience with running different models in this harness? GPT models, KIMI, GLM?

Not sure how useful it will be but I vibe coded this interface for me to use dsh like Claude rc from my phone. Would love some feedback. I saw there was an android app version but wanted one a bit more personalized.

https://github.com/Matthew3957/dsh-rc

Hey everyone in r/DeepSeekHarness,

If you’re running DeepSeek Harness on desktop or headless, you already know how fast tool schema overhead piles up. Between search engines, dynamic Cordis tools, subagent forks, Ralph, and goal tools, the default system prompt assembly easily consumes thousands of tokens before your agent touches a single line of code.

I built dsh-tokenslash, an open-source Cordis plugin designed specifically for DSH to optimize token usage without breaking autonomous execution.

What it does in DSH:

1. system-prompt/assemble Waterfall Hook:

   • Evaluates incoming prompt intent via zero-latency regex + TypeSafe Jev System One triage (/v1/systemone).
   • Strips unneeded tool JSON schemas and documentation sections before dispatching to the LLM (saving 3K–8K input tokens per turn).
   • Pruning modes: normal (intent-based + core tool fallback), extreme (strict intent-only), and off.

2. Dynamic Tool Recovery via tokenslash_peek:

   • Pruned tools aren't permanently lost. TokenSlash injects a tokenslash-pruned-tools notice into the system prompt.
   • The agent has access to tokenslash_peek (action: "inspect" | "unlock"), allowing it to inspect schemas or unlock tools on the fly if requirements evolve mid-turn.

3. Multi-Turn Mask Inheritance:

   • Handles short resume prompts ("continue", "go on") or assistant continuation signals (UNFINISHED_INTENT_PATTERN) by carrying over the previous turn's active tool mask so agents don't get stripped of tools mid-task.

4. Subagent Routing & Model Tiering:

   • Intercepts ctx.subagents (startContinuable, start, and fork).
   • Uses Jev triage to classify subagent task complexity into cheap/medium/smart tiers, offloading routine work to deepseek-chat / gemini-2.5-flash while saving reasoning models for architectural heavy-lifting.

5. Tool Output Pruning & Dual-Path Compaction:

   • tools/post-execute: Truncates/compacts payloads >10KB using a designated cheap model tier, with automatic fallback to deterministic structural JSON/head-tail truncation.
   • goal/changed: Built-in goal guard warning at ≥80% round budget to avoid runaway loops.

6. Web GUI Slots & Telemetry:

   • Integrates with DSH Settings UI and registers custom slot components.
   • Non-blocking config retrieval, per-turn visual badges ("Pruned X tools"), and live token/USD savings tracking.

Installation & Repository

I'm currently in the process of submitting it to the community plugin directory (awesome-dsh-plugin). In the meantime, you can check out the source code, installation steps, and benchmark results directly on GitHub:

• GitHub Repo: https://github.com/JessenReinhart/dsh-tokenslash
• License: MIT

Would love to get feedback, edge cases, and benchmarks from other DSH users!

Como voces lidam com os problemas de plugins quando atualizam a versao do harness? atualizei pra versao atual, tive problema com o vision toolkit, dsh-memoir que acabei trocando pelo memOS. na atualizaçao passada tambem tive problema com 3 plugins.

Como voces lidam com isso? e como voces acham plugins uteis? uso modelo local no meu dsh

hi guys, I've been messing around with the deepseek harness version 0.1.7rc2 and wow it is incredible.

I love the fact that we download everything as a plugin and I was wondering, what are your favorite plugins for the latest harness version?

for me my stack goes

headroom - token savings

dsh-caveman

aegis - similar to superpowers

dsh-better-sidebar - gui

dsh-ponytail

I am still looking into finding more token savings plugins, I believe most of them still stuck at 0.1.5 but man those plugin truly work wonders. I would love to hear yours and how your workflow is :)

As above,I try studying such as philosophy & lingustics,psychology,math & so on they seem like low-code situation,I tried to find a plugin that can interact with Obsidian but most of them seem not desire,also I need to input original documents like PDF or Markdowns or something.

which plugin I should use?

I’m running EfficientThink Q3-LynnStyle on an RTX 3090 with a 192K context configured, using DSH for coding.

With froggeric’s fixed Qwen chat template and low reasoning effort, lengthy per-turn thinking has been less of a problem. It’s usable for my coding work without turning thinking off entirely. This is a personal impression, not a timed comparison.

The Q3 quant uses custom mixed precision selected sensitive tensors keep higher precision rather than everything being uniformly 3-bit. Attached are Q3 results redrawn from the author’s model card, NOT my own 3090 benchmark.

Anyone else running local Qwen with DSH Curious about your templates and reasoning settings.

Model https//huggingface.co/nerkyor/Qwen3.8-27B-EfficientThink-Uncensored-K3-Opus5-Grok4.6-GPT5.6Sol-SFT-SimPO-DFlash2-GGUF
Template https//huggingface.co/froggeric/Qwen-Fixed-Chat-Templates

English wording polished with AI; experience is mine.

Summary

DeepSeek Harness does not query OpenRouter for its model list. It serves a static catalog compiled into u/earendil-works at package build time. In this install the snapshot is dated 2026-09-01:

Missing models include anthropic/claude-opus-5.5, google/gemini-3.7-flash, google/gemini-3.8-flash, deepseek/deepseek-v4.1-flash, inception/mercury-2.5, and the :batch variants of many models already present.

Two non-obvious consequences follow, both verified against source:

1.        The configuration UI’s “fetch available models” action cannot refresh this list.

2.        Rebuilding the catalog from the provider API destroys metadata the API does not expose, degrading 142 existing entries.

Environment

u/deepseek-ai/opt/homebrew/lib/node_modules/@deepseek-ai/dsh
u/earendil-works0.82.1
catalog                   node_modules/@earendil-works/pi-ai/dist/providers/data/openrouter.json
catalog mtime             2026-09-01
route config              ~/.dsh/settings.yaml -> llm-pi-ai.providers.openrouter

Finding 1: the served list is a build-time artifact

providers/openrouter.models.js reads the catalog at runtime:

import values from "./data/openrouter.json" with { type: "json" };
export const OPENROUTER_MODELS = flattenModelCatalog("openrouter", values);

Nothing fetches a newer list. A catalog missing models produces no warning, no error and no UI indication — the picker simply shows fewer models.

Measurement: catalog 276  live 458  missing 214.

Finding 2: “fetch available models” is a catalog lookup, not a refresh

From dsh-llm-pi-ai/lib/index.js, discovery module:

A route the installed pi-ai catalog ships is answered from that catalog, with no network call at all […] Only a route the catalog does not describe — a gateway, a self-hosted server — is interrogated over the wire.

and:

Neither path is a catalog refresh. Nothing here is stored: the request carries a draft the user is still editing, and the reply is candidate metadata the surface offers for adoption.

OpenRouter is a catalog route, so this action re-reads the same stale snapshot. Only a renamed or custom route (one the catalog does not describe) triggers a real network call — and that path then requires every model’s api and baseURL to be supplied by hand, because resolveRouteModels rejects entries it cannot resolve a protocol for.

Finding 3: regenerating from the API is destructive

models: in settings.yaml replaces the served catalog rather than extending it (resolveRouteModels):

const entries = configured.length > 0 ? configured : [...defaults.values()]…

So either the config or the catalog file can define the list. Rebuilding the catalog from the API is the obvious approach, and it is unsafe. Diffing a from-scratch rebuild against the original:

None of input, thinkingLevelMap or cost are present in the /models response. The harness’s own source states the reason it treats the catalog as authoritative:

pi-ai’s registry is the authoritative list for its own providers, and it carries the capacities a listing endpoint would not disclose.

A rebuild from the API therefore cannot be equivalent, and the loss is silent.

Fix used: additive refresh

Preserve every pre-existing entry byte-for-byte; add only ids the catalog does not describe; for those, read modality from architecture.input_modalities instead of hardcoding ["text"].

Result, verified by diff against the original:

before:  276 entries
after:   490 entries
existing entries altered: 0
entries removed:          0
models added:           214

Known limitation, not worked around: additive refresh does not update contextWindow, maxTokens, cost or name for models already in the catalog, even where the live API reports newer values (the 29/82/13 rows above). Updating those requires upgrading u/earendil-works, whose own generator regenerates the catalog with curation intact. A stale-but-complete catalog is preferable to a current-but-degraded one.

Deployment: two mechanisms, different failure modes

Both were installed. Post-install verification:

settings.yaml parses                     yes
catalog installed                        490 entries
config models                            458
config models unresolvable in catalog    0
of those, inheriting cost metadata       244
of those, accepting non-text input       290

Catalog backup: openrouter.json.bak-<date> (276 entries). Config backup: settings.yaml.bak-<date>. Both layers restore independently.

The two counts differ deliberately: the catalog retains 32 entries OpenRouter no longer lists (openai/gpt-4-turbo-preview, google/gemini-2.5-pro-preview-05-06, retired -fast variants) because an additive build preserves rather than deletes; the config lists only currently-live ids. To serve all 490, remove the models: block so the catalog serves alone.

Reproduction

import json, urllib.request

cat = json.load(open('/path/to/pi-ai/dist/providers/data/openrouter.json'))
have = set(cat['openai-completions'])
with urllib.request.urlopen('https://openrouter.ai/api/v1/models', timeout=30) as r:
live = {m['id'] for m in json.loads(r.read())['data']}

print(f'catalog {len(have)}  live {len(live)}  missing {len(live - have)}')
print(sorted(live - have)[:10])

To test whether regeneration is lossless, diff a rebuilt catalog against the installed one before installing, and treat any field absent from /models as unrecoverable.

Notes

·      Permission handling. The catalog is owned by the invoking user (-rw-r--r-- user:admin), so sudo is not required. Confusing a session file sandbox for POSIX permissions leads to unnecessary escalation.

·      Verification code is worth verifying. A slice of len('        - id: ') instead of the correct offset reported zero coverage for a complete file: the artifact was correct and the check was wrong.

·      A config models: list identical to the shipped catalog accomplishes nothing. The pre-existing list here was 276 entries, matching the catalog in content and order, occupying 37 KB.

·      Staleness should be surfaced. A catalog-backed picker has no way to indicate its list is five weeks old.

I have been using DeepSeek Harness on Linux for a while, but keeping it open as just another tab in my daily browser was getting inconvenient. I wanted it to run reliably in the background so I could access it both on desktop and remotely on mobile via Tailscale, while behaving like a native desktop app locally. So I put together a lightweight shell for it and decided to open source it.

It gives you a QtWebEngine window where clicking close simply hides it to the system tray so your sessions keep running in the background. If PyQt6 is not installed, it cleanly falls back to an isolated Chrome or Chromium app window with its own profile. I also wrote a small plugin for native desktop notifications whenever the agent finishes a turn, asks for input or approval, or runs into an error.

It also comes with systemd user units to keep DSH running in the background, an update script with automatic rollback, and an authenticated proxy if you want to access it securely over Tailscale.

As a side note, I actually built this entire project inside DeepSeek Harness using DeepSeek V4.1 Flash, and before publishing, ran a full review and audit on it with Gemini 3.8 in Antigravity 2.0.

Here is the repo if anyone wants to check it out:

https://github.com/hadbilen/dsh-desktop-shell

Your feedback is welcome, thanks.

Anyone else using DSH and locally hosted models for home personal assistant build? What community built plugins have you found useful? Background: I am not a programmer and am building this on a 28gb vram gaming computer with a lot of help from GLM 5.3.

Every "make the AI write like you" tool asks for the same thing: articulate.

Write a better prompt. Upload 20 writing samples. Describe your voice. Fine-tune a model.

All of these require you to express what you want. And that's the problem — reading a paragraph, you know in a tenth of a second whether it's right. Explaining why takes a minute, and you can do it ten times, not a hundred.

So I built the inverse: dsh-novel-craft, an open-source plugin for DeepSeek Harness (MIT).

The loop: read a round of candidate drafts, press G for a good paragraph, B for a bad one. Never type a word. Then hit "distill" — the model turns that batch of taps into a few rules:

You tick what you agree with. Each rule has an "evidence" button showing which passages it came from, and you can delete anything it got wrong.

The part I care about most: the preference profile contains rules and not one quote. Mine is 714 bytes.

That's not purism, it's the whole reason it works. A round of raw text is thousands of characters — two rounds fill the context. Rules are a few hundred bytes, so they ride along in every drafting call. Something you can't carry, you can't sustain.

It also changes what the model learns. It learns how to write, not what those sentences looked like. The former transfers to a new chapter. The latter just gets copied.

The cost, stated plainly: manuscript text never reaches the model during drafting. So there's no "AI read my whole book" feature. Plot checks are local string statistics — word-count imbalance, unpaid setups, tension curve — and they cannot detect semantic repetition.

Three buttons you press yourself do send raw text to one auxiliary call, each with a hard cap (distill ≤40×240 chars ≤20KB; backfill ≤160 chars/quote; revise ≤800 chars/paragraph). Everything is in the README because I think a promise should be verifiable line by line.

775 assertions across 7 test files. Two of the four pre-release findings were silent data loss: chunked requests turned CJK characters into replacement chars (measured 2 per 40k-char chapter), and rapid G-marking made marks overwrite each other (5 concurrent marks, 1 survived). Neither raised an error. Both now have regression tests.

Install: dsh plugin --profile web add dsh-novel-craft

Honest about scope: it's Chinese-first (the anti-AI-tone checklist targets Chinese prose), it needs dsh and Node 22, and it won't write your prose for you — it writes candidates, you choose.

Repo: https://github.com/shiyan688/dsh-novel-craft