dsh-open-dream-rsi

ODR — DeepSeek Harness adapter

DeepSeek Harness (dsh) is an all-plugin agent harness. ODR speaks MCP, and DSH ships a first-party MCP client bridge (@deepseek-ai/dsh-mcp-client) — so this adapter is pure configuration: a Cordis overlay that inserts the ODR MCP server as an external stdio (or Streamable HTTP) server. DSH starts the child with its plugin lifecycle, discovers the six odr_* tools, and exposes them as mcp__open_dream_rsi__.

No code ships here — only two overlay files and this guide.

Files

file	transport	use when
odr.cordis.yml	stdio	DSH and the ODR checkout on one machine
odr-http.cordis.yml	streamable-http	ODR served remotely (tunnel / other host)
cordis.patch.yml + package.json	bundle (npm)	installable dsh plugin add package

The npm bundle ships the ODR Python server inside the package (open_dream_rsi/, stdlib-only) plus a tiny bridge (bridge.js) that publishes where it lives; the layer inserts the in-box @deepseek-ai/dsh-mcp-client row pointing the system python3 at the vendored entry point (run_mcp.py). No pip, no PYTHONPATH, no build step and no build allowance even for git installs. Bundle defaults resolve tasks.json / .dream_rsi beside the directory dsh launches from; override the mcp-open-dream-rsi row from your profile's cordis.patch.yml (same id — a config override replaces the whole config).

Install as a bundle (dsh plugin add)

The bundle ships the Python server inside the package — no pip, no PYTHONPATH. Only a system python3 (3.10+) on PATH is required. From a directory holding your tasks.json:

# from the npm registry, a tarball, a checkout, or straight from GitHub — pick one:
dsh plugin --profile  add dsh-open-dream-rsi
dsh plugin --profile  add /path/to/dsh-open-dream-rsi-0.1.3.tgz
dsh plugin --profile  add /path/to/open-dream-rsi/plugins/deepseek_harness
dsh plugin --profile  add github:patrykorwat/open-dream-rsi#path:plugins/deepseek_harness

dsh --profile  --dump-config    # shows the "# == dsh-open-dream-rsi" layer

Git installs fetch sources; this bundle needs no build step and no build allowance. Pin a commit so a later push cannot silently change what runs:

dsh plugin --profile  add "github:patrykorwat/open-dream-rsi#&path:plugins/deepseek_harness"

(The pin must point at a commit that already contains this directory — pnpm fetches sources, not releases.)

Install (stdio, --patch overlay)

1. Make the server importable (not on PyPI — install from the repo): python3 -m pip install git+https://github.com/patrykorwat/open-dream-rsi, or export PYTHONPATH= in the overlay's env: block.

2. Edit both /ABSOLUTE/PATH/... placeholders in odr.cordis.yml to your project's tasks.json and memory dir (DSH does not expand ~; use absolute paths).

3. Apply it once:

   dsh --patch /ABS/PATH/TO/odr.cordis.yml web
   

   To persist across runs, merge the overlay's - insert: entry into $DSH_HOME/profiles/web/cordis.patch.yml (do not overwrite that file — it may already hold unrelated patches). Verify the layer mounted:

   dsh --patch /ABS/PATH/TO/odr.cordis.yml --dump-config | grep -A2 open_dream_rsi
   

Install (remote)

Serve the endpoint first, then apply odr-http.cordis.yml the same way. The URL is a credential — see the note in the overlay.

Environment scrubbing

DSH scrubs the spawned child's environment (names matching KEY|PASSWORD|SECRET|TOKEN and everything DSH_*). The stdio overlay therefore re-passes OPENAI_BASE_URL / OPENAI_API_KEY / ODR_LLM_* explicitly from the harness process's environment. Without them the dreamer falls back to http://127.0.0.1:8000/v1 (vLLM/Ollama default) and auto-detects the served model. No key is needed for a local endpoint.

Timeouts

toolCallTimeoutMs: 1800000 (30 min) replaces the 60 s default because odr_run_once / odr_dream run bounded improvement cycles that can take tens of minutes. odr_status, odr_recipes, odr_lessons and odr_add_task return in milliseconds.

Verify

In a DSH session:

1. mcp__open_dream_rsi__odr_status — returns the loop's learned-state summary (policies, recipes, events). Expected on a fresh memory dir: zeros plus a hint, not an error.
2. mcp__open_dream_rsi__odr_add_task with a small task (prompt + tests), then mcp__open_dream_rsi__odr_run_once — the cycle needs a resolvable LLM endpoint (env, goose config, or localhost vLLM); with none reachable it reports the resolution failure instead of hanging.

Model-facing behavior notes (recipes as warm start, add-task-then-dream workflow) live in the main README's "Plug in your favourite agent" section.