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Tianyi Cui e2bde2902c refactor(examples): extract the app spine into dsh-agent-core + app packages

Implements docs/rfc/.../2026-06-20-extract-example-app-packages.md. Each
example was thick — a hand-rolled start.ts, an infra preamble, nested
base.yml/base-core.yml/acp-tail.yml includes, and a coupled front-door
cluster enforced only by prose. This moves the composition into packages so
each example is a thin leaf cordis.yml: pick the swappable backends, load one
app package.

New packages:
  - @deepseek-ai/dsh-agent-core (packages/core/agent-core): one bundle plugin
    that loads the providerless/executor-less/UI-less spine (timer + llm +
    sessions + system-prompt + tools + agents + invariants + tool-bash +
    agent-loop) via ctx.plugin(...) inside apply(), and forwards agent-loop's
    `agents` list as its own Config (export const Config = AgentLoop.Config,
    default []).
  - @deepseek-ai/dsh-stdio-agent (packages/ui/stdio-agent): terminal chat APP —
    agent-core + console logger + readline UI + a pre-created `main` agent, with
    a bin. The demo:echo/coding front door.
  - @deepseek-ai/dsh-acp-agent (packages/ui/acp-agent): ACP server APP —
    agent-core + JSONL persistence + the acp bridge, NO stdout logger, with a
    bin. The stdout-purity footgun is structurally unreachable from the leaf.

Amendment to the RFC: hmr stays a LEAF cordis.yml entry, not baked into
dsh-stdio-agent. hmr is a Loader-only dev plugin (throws without
--expose-internals; the in-process test tier can't even import its decorator
form), so a package statically importing it could never carry the per-file
coverage gate. Unlike the console logger, a stray hmr is not a stdout-purity
footgun, so leaving it at the leaf costs no safety. With hmr out, all three new
packages carry in-process unit specs at 100%.

Boot glue (Loader tail, .env load, snapshot-mode selection, stdin-dispose
lifecycle) moves into each app's bin; start.ts and base.yml/base-core.yml/
acp-tail.yml are deleted. Each app package gets a keyless real-load-path test
that boots through its bin + the cordis Loader (guarding the unwrapExports
export-shape bug class, postmortem 0001). ACP snapshot replay stays green
against the existing committed goldens (pure boot restructuring). RFC moved
proposed->implemented with the amendment recorded; package/example/architecture
docs and the module graph updated.

2026-06-21 12:06:38 +08:00

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acp-agent example

The DeepSeek Harness coding agent exposed as an Agent Client Protocol (ACP) server over JSON-RPC stdio — drive it from Zed or any other ACP client.

pnpm run demo:acp          # needs DEEPSEEK_API_KEY (repo-root .env or env)

This example is just a leaf cordis.yml: it loads the @deepseek-ai/dsh-acp-agent app (which bundles the @deepseek-ai/dsh-agent-core spine, JSONL session persistence, and the @deepseek-ai/dsh-acp bridge — with no pre-created agents, since ACP session/new creates them on demand) plus the two swappable backends (llm-deepseek, bash-local). The app package bakes in the no-stdout-logger cluster, so the stdout-purity guarantee is a property of the artifact, not a leaf convention.

stdout is the protocol

This example loads no stdout logger — stdout carries the JSON-RPC frames, and any other write corrupts them. @deepseek-ai/dsh-acp-agent contains no logger entry, so the footgun is structurally unreachable from this leaf. Use a stderr exporter if you need logs.

Zed configuration

Add to your Zed settings.json under agent_servers:

{
  "agent_servers": {
    "DeepSeek Harness": {
      "command": "pnpm",
      "args": ["--dir", "/path/to/deepseek-harness", "run", "demo:acp"],
      "env": { "DEEPSEEK_API_KEY": "sk-…" }
    }
  }
}

The editor sets each session's cwd to the project it opens; the agent's bash tools run there (see the per-session cwd note in packages/ui/acp), so launch the server from the harness repo with pnpm --dir … and let ACP carry the workspace path per session.

Snapshot tests (record-once / replay-deterministic)

This example is the home of the harness's snapshot tests — they boot this server as a real subprocess, drive it with a deterministic input script, and diff its normalized output against committed golden files. The model is made deterministic by @deepseek-ai/dsh-llm-replay, a function/namespace plugin that installs an llm/stream waterfall listener and short-circuits it, serving model streams reconstructed from a recorded session JSONL fixture (<scenario>/session.jsonl) — so replay needs no API key. The fixture IS the persisted session log: its assistant/chunk events carry every StreamChunk, so grouping them by (turn, step) reconstructs each stream() call (one model call per loop step). Recording is therefore "run the real agent once and harvest the .jsonl". The two failure modes not expressible as logged chunks — a pure throw before any chunk, and cancel/hang — use an optional <scenario>/replay.override.json sidecar (a ReplayEntry[] that replaces the derived script). A scenario that needs the agent to operate on existing files ships an optional <scenario>/workspace/ directory — the harness copies its contents into the temp cwd before the run (see workspace-edit). See docs/rfc/implemented/2026-06-19-acp-snapshot-tests.md for the full design.

MVP limitations

The bridge supports N concurrent sessions per connection, each in its own workspace cwd (RFC 011). Remaining limits: prompts support ACP's baseline text and resource_link blocks only, additionalDirectories and mcpServers are rejected, and the tool-permission gate is deferred (TODO(rfc010-permission-gate) — tools run with the executor's full authority). See packages/ui/acp/README.md for the full contract.

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