Tianyi Cui
e68496fd79
The snapshot tier was built single-session: dsh-llm-replay served calls from one global positional cursor, and the harness harvested one session log. A subagent runs as a second agent with its own session, so a parent→child scenario could neither replay deterministically nor harvest the child's log. This resolves the TODO(subagent-snapshots) deferral from the subagent RFC. - Stamp the calling session id onto the model request: GenerateOptions.sessionId (typed Branded<'SessionId'> to avoid the dsh-llm↔dsh-session cycle), set by the agent loop from agent.session.id. Adapters ignore it; an llm/stream listener routes by it. - Key replay per session: dsh-llm-replay loads the parent log plus one per child (childFiles / $DSH_SNAPSHOT_CHILD_FILES), derives a script per recorded session, and binds each live (freshly-random) session to a recorded script by first-call order — parent first (earliest createdAt, first to stream). Keys by WHO calls, so it survives a future concurrent/backgrounded subagent; a global cursor would not. An unrecorded extra session fails loud. - Harvest every log: the harness collects all .jsonl across cwd buckets, ordered primary-first (top-level, then children by createdAt), and RunResult exposes the plural sessionLogs. The spec writes each back on record (session.jsonl + session.<n>.jsonl) and diffs each against its fixture on replay. - Wire the subagent seam + spawn + fork + tool into the acp-agent example (both cordis configs) and add two nested scenarios recorded against the real API: subagent-spawn (parent + 1 child) and subagent-multi (parent + 2 children, 3 sessions). Both replay keyless in the default gate. A new RFC documents the design (docs/rfc/implemented/testing/). Single-session replay is unchanged (a call with no sessionId is one anonymous primary session). TODO follow-up: a dedicated branded-ids package could own the SessionId brand and dissolve the cross-package cycle note; out of scope for this testing PR.
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 a leaf has no logger entry to get wrong by default — keeping stdout pure for JSON-RPC.
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 includes no logger entry, so this leaf has none to get wrong by default; do not add one (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.