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
mnemon
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LLM-supervised persistent memory for AI agents — graph-based recall, cross-session knowledge, single binary. Works with DeepSeek Harness, Claude Code, OpenClaw, and any agent runtime.
phi
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a coding agent, rpc plugin, sub-agents, hashline edits, and mcp
sivtr
by ariestar
A unified memory workspace for agents and people, making terminal output and AI session context searchable and reusable across local workspaces.
caliper
by edonadei
Run your real agent with and without your skills, MCPs, and rules. See which ones actually help, and what they cost in tokens. Supports Claude Code, Codex, Pi, and Hermes.
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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