93a6dc6716
Record the `todo-plan` snapshot scenario: a real prompt drives the model to call todo_write, and the golden captures the resulting `plan` sessionUpdate (three entries, priority synthesized as medium, status 1:1) plus the persisted todo/write event. Registered in SCENARIOS; replays deterministically keyless. Add a with-key coding-agent e2e that verifies the WORLD — a real model call to todo_write lands a todo/write event whose snapshot is a valid, one-in-progress list — not the agent's self-report. Wire tool-todo into the e2e harness.
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.