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https://github.com/deepseek-ai/deepseek-harness
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40 lines
3.7 KiB
Markdown
40 lines
3.7 KiB
Markdown
# acp-agent example
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The DeepSeek Harness agent demo exposed as an **Agent Client Protocol (ACP)** server over JSON-RPC stdio — drive it from Zed or any other ACP client.
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```sh
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pnpm run demo:acp # needs DEEPSEEK_API_KEY (repo-root .env or env)
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```
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This example is just a leaf `cordis.yml`: it loads the [`@deepseek-ai/dsh-acp-agent`](../../packages/ui/acp-agent) app (which bundles the [`@deepseek-ai/dsh-agent-core`](../../packages/core/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), the swappable DeepSeek, bash, and filesystem backends, and the model-facing `read`/`write`/`edit`/`subagent`/`subagent_fork`/`todo_write` tool entries. 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.
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## stdout is the protocol
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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).
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## Zed configuration
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Add to your Zed `settings.json` under `agent_servers`:
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```json
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{
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"agent_servers": {
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"DeepSeek Harness": {
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"command": "pnpm",
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"args": ["--dir", "/path/to/deepseek-harness", "run", "demo:acp"],
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"env": { "DEEPSEEK_API_KEY": "sk-…" }
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}
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}
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}
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```
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The editor sets each session's `cwd` to the project it opens; both the agent's bash tools and the `read`/`write`/`edit` filesystem tools resolve relative paths against that per-session workspace (see the per-session `cwd` note in `packages/ui/acp` and [the per-session cwd RFC](../../docs/rfc/implemented/architecture/2026-07-02-fs-per-session-cwd.md)), so the server can be launched anywhere and each session still acts on its own project directory.
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## Snapshot tests (record-once / replay-deterministic)
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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 [the ACP snapshot tests RFC](../../docs/rfc/implemented/testing/2026-06-19-acp-snapshot-tests.md) for the full design.
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## MVP limitations
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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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