Self-evolving Router for DSH

English | 简体中文

Run deterministic programs before DeepSeek Harness calls a model. Unsupported, ambiguous or failed requests fall back to the base agent. The agent can use permitted work trajectories to edit, test and persistently publish these programs.

Research alpha · Current entry point: v2 · Automated execution is limited to read-only public files. Continuous-context capability development has been demonstrated; broad reliability and net lifecycle cost savings have not. This is an independent project, not an official DeepSeek plugin.

Find all guides in the documentation index. Each guide's language is listed there.

How it works

flowchart LR
    Q[Request] --> R[Deterministic parsing and selection]
    R -->|Supported| E[Isolated read-only execution]
    E --> O[Result with zero model calls]
    R -->|Unknown or ambiguous| A[Base DSH agent]
    E -->|Failure| A
    H[Permitted work trajectories] --> D[Agent edits and tests]
    D --> P[Host replays checks and publishes]
    P --> R

Routing makes no model calls; development uses your configured model. Passing agent-written tests is development evidence, not independent correctness certification. Current v2 learning requires an explicit development or training run. Ordinary requests do not start background training.

Quick start: no API required

Install Node.js 22+ (including npm) and start Docker, then run:

git clone https://github.com/sra-research/self-evolving-router-dsh.git
cd self-evolving-router-dsh
npm run demo

The demo checks Docker, downloads the pinned image if missing, creates an isolated example, initializes the capability and runs the request. Expect 2 lines · 0 model calls. Repeat runs use fresh .sandbox/demo-* directories and never overwrite existing tasks.

This deterministic example needs no npm ci, DSH profile or API credentials. If Colima is not your default context, use ROUTER_DOCKER_CONTEXT=colima npm run demo. The repository directory must be shared with Docker. See getting started (中文) for full-agent installation and manual commands.

From routing to learning

Purpose	Guide
Installation, full DSH and API configuration	Getting started · 中文
Environment checks and common errors	npm run doctor · Troubleshooting · 中文
Capability packages, development, publication and rollback	v2 design · 中文
Continuous tasks and agent-chosen development timing	Training experiment · English
Evidence and limitations	Results · 中文
Previous cli.js evolve workflow	Archived v1 guide · 中文

The initial v2 package only lists files and counts newline characters. File, byte and word counts learned in experiments are not built in. v1 and v2 use different state, history and maintenance interfaces; use the guide for your backend.

Current experiment

After 24 training tasks in one context, the guided autonomous agent chose to develop in one batch:

Condition	Training model calls	Training tokens	Correct frozen automation
Fixed batches	96	2,944,523	12/12
Guided, agent-chosen timing	87	1,116,190	12/12

This is one development comparison across six read-only counting operations, authored requests and modified file environments. It is not an official InterCode score, a sealed test or evidence of broad coding competence. Reuse in this run did not amortize learning costs. Earlier failures and repaired reruns remain in the results summary (中文).

Development and contributions

npm run check
npm test

Default tests skip opt-in Docker/profile integration checks; skips are not passes. Full commands are in Contributing. Tests use offline model substitutes; live scripts and real benchmarks call APIs and must be started explicitly.

Installation reports, boundary failures, minimal reproductions and documentation improvements are welcome. See execution boundaries, security, changelog and third-party notices. MIT license.