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DeepSeek, Kimi Coding, OpenCode Go, Zhipu, MiniMax, OpenRouter 및 OpenAI Codex용 작성기 도크 잔액/사용량 표시 — 제공자 기반, 2초 폴링, 5분 캐시.

LoongPort DeepSeek Harness (dsh) Cordis 번들 및 설정 CLI

DeepSeek Harness 플러그인: OpenAI 호환 /v1/models 목록에서 모델과 역량을 가져오고(게이트웨이 역량 확장), 설정 페이지에서 모델별 역량(추론, 비전)을 편집합니다.

첨부된 이미지를 구성 가능한 모델(15개 이상의 OpenAI 호환 및 Anthropic 공급업체)을 통해 텍스트로 변환하고, DeepSeek이 계속 답변하는 동안 처리하는 비전 제공업체 경로.

첨부 및 로컬 이미지를 설정 가능한 OpenAI Responses, Chat Completions, 또는 Anthropic Messages 엔드포인트를 통해 분석하면서, 이미지 지원 라우트는 네이티브로 유지하는 텍스트 전용 DeepSeek 라우트용 비전 브리지입니다.

텍스트 전용 DeepSeek용 비전 툴킷입니다. 모델 호출형 `vision` 도구, deepseek/opencode-go용 래퍼 어댑터(v4 flash/pro), Antigravity IDE 쿼터(default, flash/pro) / 모든 OpenAI 호환 VLM / Gemini / 로컬 Ollama 채널, 압축 재복원 기능이 있는 증거 메모리, 콘텐츠 해시 캐시, 그리고 이중 언어 클라이언트 패널을 제공합니다.

sub2api 게이트웨이를 DeepSeek Harness에 연결: 하나의 베이스 URL 뒤에 OpenAI 호환 멀티 프로바이더 라우트(OpenAI / Claude / Grok / Gemini), 키별 모델 검색, 사용량 조회, 전역 비전/이미지 도구.

DSH에서 ChatGPT/Codex 구독을 사용하세요. 이미지 생성 및 웹 검색 지원.

DeepSeek Harness용 OpenAI Codex OAuth 프로바이더 및 네이티브 설정 UI

DeepSeek Harness 에이전트를 인증된 Codex App Server 세션에 연결

DeepSeek Harness(DSH)용 개인 WeChat ClawBot 플러그인: OpenClaw가 WeChat 채널(QR 로그인/메시지 송수신)을 담당하고, DSH가 두뇌 역할을 담당하며, 둘은 OpenAI 호환 프로토콜로 연결

OpenAI Codex의 메모리, 스킬, 설정을 DeepSeek Harness로 브리지

컴포저에 첨부된 이미지를 텍스트 전용 DeepSeek 모델에 전달하기 전에 OpenAI 호환 비전 모델이 텍스트로 변환

只填 Key 的服务商预设：50 家渠道为 DSH 一键写入官方 llm-pi-ai 路由与凭据（插件），并可写入腾讯 WorkBuddy、OpenCode（Key 走其 auth.json）与 cc-switch 的配置（CLI）；核心为纯 JS，可被更多应用的外壳复用。

Keeps a project's documentation in step with its decisions on DeepSeek Harness: the same append-only decision log and derived design snapshot, kept fresh in-process instead of by a hook process.

Drive DSH from anything that speaks HTTP: 47 routes (REST + SSE) for workspaces, sessions (create/prompt, live SSE generation, history, stats, todos, cancel, ask-user Q&A), models and the global default model, providers, agent presets, settings namespaces, skills, and workspace files including resumable chunked upload — plus an OpenAI-compatible /chat/completions, a built-in OpenAPI 3.0 spec and an interactive request page. It only injects the host webServer and tools, so it rides on the DSH webserver (default prefix /api/v1) or its own port with optional API-key auth, and it ships a skill that teaches the DSH agent to call the API.

OpenAI-compatible vLLM client (default :8000).

OpenAI-compatible client for llama.cpp / Unsloth Desktop (default :8080).

Fronts stdio MCP servers with three router tools (list_capabilities, describe_tool, call_tool) so schemas load on demand instead of riding every request; shapes every tool result through a deterministic output ladder that archives the original first (retrieve_spill reads it back); prices tool groups in tokens and can hide built-in groups per agent. Bridges to a standard-library Python router, so Python 3.9+ is required.

Compaction fallback for compaction-basic: when its LLM summary throws, comes back empty or cannot fit the window, the span is compiled deterministically with a seq pointer on every line (vendored dsh-compaction-instant compiler). Adds recall and search tools, /recall and /context commands, an idle trigger at 45% of the window and an on-disk span archive. The engine mounts as one row in the agent preset (docs/dsh-integration.md).

Pool AI provider accounts per platform and route them through a local loopback proxy that bridges the OpenAI, OpenAI-Responses, Anthropic and Gemini protocols, then write the proxy into Codex, Claude Code, Gemini CLI, Grok, OpenCode, OpenClaw and Hermes config files with snapshot-and-rollback safe writes; registers no agent tools.

Registers the imagegen skill on the skill registry, so generating and editing raster images needs no hand-copied files under $DSH_HOME/skills. The agent drives a bundled image_gen.py CLI against any OpenAI-compatible GPT Image endpoint for text-to-image, reference-image editing, and transparent-background cutouts.

Per-reply read-aloud with a multi-provider settings page — MiniMax or any OpenAI-compatible /audio/speech endpoint, with voice, emotion, speed and model selection plus an auto-read toggle.

Live token cost for every chat in the DSH Web UI: the chat, its subagents and the whole session tree, priced from a bundled models.dev snapshot of seven providers and 139 priced models, with cache read and write rates wherever the provider publishes them and DeepSeek from its own peak and off-peak table. Every finished answer carries its own price in the transcript, and a chat that is not currently open is priced from the log instead of disappearing. Spend is costed per interval from an append-only JSONL ledger in the project folder, so logged work keeps the tariff it was billed at. On top of it: budget-aware work plans packed against a money limit, routing scenarios that show which models are worth connecting, a generated plan document in English, Chinese or Russian, and a warning when spend belongs to no plan unit.