76fceae5b3
Establishes the standard way to give a snapshot scenario a non-empty starting
workspace: an optional `<scenario>/workspace/` directory whose contents the
harness copies into the temp cwd before the run (for both record and replay),
so the agent's bash tools see the seeded files. The cwd is normalized in the
goldens, so seeded paths stay stable.
The new `workspace-edit` scenario demonstrates the full read→write→verify cycle
on a seeded file: it ships `workspace/greeting.txt` ("hello"), prompts the agent
to append a WORLD line and cat it back. The recorded log captures the real bash
edits (`echo WORLD >> greeting.txt`, then `cat` showing `hello\nWORLD`), and it
replays deterministically with no key.
Also hardens runScenario teardown (Codex review): workspace seeding and spawn
now run inside the try whose finally removes both temp dirs, so a seeding/spawn
failure can't leak them. Documents the convention in the RFC + example README.
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 boots @deepseek-ai/dsh-acp over the shared provider/tool core (../base.yml), with agent-loop configured with no pre-created agents (ACP session/new creates them on demand) and JSONL session persistence (so session/load works).
stdout is the protocol
This example loads no stdout logger — stdout carries the JSON-RPC frames, and any other write corrupts them. Do not add @cordisjs/plugin-logger-console or a stdio UI here. 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": ["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/acp), so the server does not need to be launched in the workspace.
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 src/llm-replay.ts, 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: text-only prompts, additionalDirectories rejected (a session operates in its single cwd), and the tool-permission gate is deferred (TODO(rfc010-permission-gate) — tools run with the executor's full authority). See packages/acp/README.md for the full contract.