MIT-licensed, DeepSeek-native coding agent in a single Go binary — terminal, desktop, browser or editor via ACP. Standalone; the README describes no DeepSeek Harness (dsh) integration.
DSH integration
Ecosystem-related
Author-claimed
Safety audit
Unaudited
Last verified
2026-08-21
License
MIT
01What can it help you accomplish?
Run long autonomous coding tasks from the terminal with an agent you can leave running
Code changes produced by a single local engine, guarded by plan mode, permissions, a workspace sandbox and per-turn checkpoints so runs stay readable and undoable
Developers who want a DeepSeek-native coding agent for long unattended terminal sessions
Drive the same local Reasonix engine from your editor
Native chat, editor context, tool-call approvals, model selection and workspace sessions in VS Code / VSCodium / Eclipse Theia via the `reasonix acp` backend
Developers who prefer coding-agent sessions inside their editor instead of a separate terminal
02How to install into DeepSeek Harness
Prerequisites
- npm on any supported platform, or Homebrew on macOS, for the prebuilt native binary path
- Go 1.25+ only if building the CLI from source (desktop builds additionally need Node 24+, pnpm 10 and the Wails CLI)
Installation steps
- 01
Install the CLI: `npm i -g reasonix` (any OS; pulls the prebuilt native binary) or `brew install esengine/reasonix/reasonix` (macOS)
$ npm i -g reasonix
- 02
Run `reasonix setup` to configure a provider and model
- 03
Start an interactive session with `reasonix`, or run a one-off task with `reasonix run "implement the TODOs in main.go"`
Verify the integration
Not specified by the author
03DSH integration and capability boundaries
Standalone DeepSeek-native coding agent — the README describes no DeepSeek Harness (dsh) integration; ecosystem-level relation via the built-in DeepSeek model preset
Autonomous coding runs across four entry points
a task or prompt from the terminal CLI/TUI, desktop app, browser, or an editor over ACP→code changes from one local engine, with plan mode, permissions, a workspace sandbox and per-turn checkpoints
the agent writes and modifies project files in your workspace during a runmodel calls go to the configured provider endpoint over the networkConfig-driven multi-model engine
a `reasonix.toml` declaring providers, the agent, enabled tools and plugins→no hardcoded models — DeepSeek as a preset, any OpenAI-compatible endpoint as a config entry, optional executor + planner pair in separate cache-stable sessions
MCP servers and Extension Protocol plugins
MCP servers or Extension Protocol v1 sidecars declared in the config→extra tools, prompts and resources from MCP; sidecars can intercept runtime events, contribute Providers and structured UI, and ship versioned plugin packages
extension sidecars can intercept runtime eventsCache-aware context maintenance
a long-running session against prefix-cache-friendly endpoints→a small stable environment summary injected at startup; stale tool output snipped/pruned before summary compaction
04Who is it for? When not to use it?
Good for
- Developers who want a DeepSeek-native coding agent for long unattended terminal sessions
- Developers who prefer coding-agent sessions inside their editor instead of a separate terminal
Not for
- Reasonix does not work out of the box without a model provider: you must run `reasonix setup` to configure a provider and model before starting a session, and all generation traffic goes to that configured endpoint.
05Compatibility, maintenance and safety notes
- Reasonix does not work out of the box without a model provider: you must run `reasonix setup` to configure a provider and model before starting a session, and all generation traffic goes to that configured endpoint.
- Prebuilt binaries cover darwin/linux/windows × amd64/arm64, but building from source requires Go 1.25+ (pinned toolchain), and the desktop build additionally needs Node 24+, pnpm 10 and the Wails CLI.
- The README describes no DeepSeek Harness (dsh) integration: Reasonix is a standalone agent, so there is no documented in-harness install path — its DeepSeek relation is that DeepSeek ships as a built-in model preset.
MIT · actively maintained (latest release desktop-v1.31.0, 2026-08-20)
06Frequently asked questions
How do I install DeepSeek Reasonix?
Run `npm i -g reasonix` (any OS; pulls the prebuilt native binary) or `brew install esengine/reasonix/reasonix` on macOS, then run `reasonix setup` to configure a provider and model. Prebuilt archives for darwin/linux/windows × amd64/arm64 with SHA256SUMS are on every GitHub release.
Does it integrate with DeepSeek Harness (dsh)?
The README describes no dsh integration. Reasonix is a standalone agent; its DeepSeek ecosystem relation is that DeepSeek ships as a built-in model preset, and any OpenAI-compatible endpoint can be added as a config entry in reasonix.toml.
Can I use it inside VS Code?
Yes — install the CLI first, then add the `SivanLiu.reasonix-agent` extension from the Visual Studio Marketplace or Open VSX Registry. It starts your local `reasonix acp` backend and adds native chat, editor context, tool-call approvals, model selection and workspace sessions.
How are models and tools configured?
Everything — providers, the agent, enabled tools and plugins — is declared in `reasonix.toml` with no hardcoded models. You can optionally run two models together (executor + planner) in separate cache-stable sessions, and MCP servers can contribute tools, prompts and resources.
What keeps long autonomous runs safe?
Plan mode, permissions, a workspace sandbox and per-turn checkpoints keep a long autonomous run something you can still read and undo. The whole engine is a self-contained static binary — nothing to install on the target machine beyond the binary itself.
07Related DSH workflows
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phi
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a coding Agent, rpc plugin, sub-agents, hashline edits, and mcp
sivtr
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A unified memory workspace for agents and people, making terminal output and AI session context searchable and reusable across local workspaces.
deepseek-harness-docker
by runzhliu
Community Docker and Kubernetes packaging for DeepSeek Harness (@deepseek-ai/dsh), with a hardened image, Compose stack, Helm chart, Web UI, and headless CLI.
08Data and sources
DeepSeek ships as a preset; any OpenAI-compatible endpoint is a config entry, not new code.
One local engine, four ways in — terminal, desktop app, browser, or your editor over ACP. Plan mode, permissions, a work…
This page is generated from the project’s public documentation, repository metadata and a structured parse of DSH Plugins; last verified on 2026-08-21. Found an error? Submit a correction.
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