Tianyi Cui
d5e894edf4
Keep DeepSeekHarness.run() reusable, but make ownership of its lazy runtime process explicit. Document the context-manager/close contract and update every construction example to use a context manager so repeated runs remain valid without encouraging leaked subprocesses. Contain notification predicate failures at the subscription boundary. Remove only the subscriber whose callback raised, deliver that exception through its queue, and continue dispatching to healthy subscribers so arbitrary callback code cannot terminate the shared reader thread or strand later requests. Enforce one in-flight prompt per server session with an atomic activePrompt guard. Route overlap through the existing -32603 handler-error response and clear the guard in finally, preserving parallel prompts across sessions and sequential reuse without changing JSON-RPC request or notification shapes. Use StringDecoder for line framing so a UTF-8 code point split across Buffer chunks is not corrupted. Add a queued-write flush barrier, and make memoized shutdown await it before disposal and exit while retaining exactly-once cleanup when shutdown calls race or flushing fails. Cover callback isolation, same-session exclusion, cross-session concurrency, split multibyte input, delayed writes, racing shutdown, and flush failure with deterministic tests.
DeepSeek Harness Python SDK
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Python subprocess SDK for driving DeepSeek Harness over JSON-RPC stdio. The
runtime inherits normal DeepSeek Harness environment variables such as
DEEPSEEK_BASE_URL and DEEPSEEK_API_KEY, so callers can use real model
endpoints directly or point those variables at a local proxy during
benchmark runs.
Installing deepseek-harness installs the exact same-version deepseek-harness-runtime-bin platform wheel. The normal entry point therefore needs no executable argument:
from deepseek_harness import DeepSeekHarness
with DeepSeekHarness() as harness:
result = harness.run("Say hi.")
DeepSeekHarness keeps its lazily started runtime subprocess for reuse across calls. Use it as a context manager, as above, or call close() explicitly when finished.
By default, the SDK launches the bundled single-file dsh-jsonrpc-agent executable from the deepseek-harness-runtime-bin package and injects that package's default configuration (the stdio JSON-RPC server, agent core, preloaded DeepSeek adapter, JSONL session persistence, local bash) via DSH_CORDIS_CONFIG. To run a plugin composition of your own, keep the @deepseek-ai/dsh-jsonrpc entry in the config and pass the Cordis config path.
from deepseek_harness import DeepSeekHarness
with DeepSeekHarness(
model="deepseek-v4-flash",
cordis="examples/dsbench-coding-agent/cordis.yml",
) as harness:
result = harness.run("Make the requested code change.")
TurnResult.final_response is the text content from the last
assistant/message event in the turn. Use TurnResult.events for the complete
event stream, including intermediate assistant messages and tool activity.
The same behavior can be selected for the runtime subprocess with DSH_CORDIS_CONFIG. The injection lives in HarnessClient.start(), so the low-level client's default launch gets it too: when the launch resolves to the bundled runtime and neither cordis nor a non-empty DSH_CORDIS_CONFIG is set (the runtime treats an empty value as absent, and so does the injection check), the bundled default configuration is used; an explicit runtime_bin or launch_args_override disables the injection entirely. See the sdk-runtime README for the runtime carriers (production exe vs dev-only node closure) and how to obtain them.
cwd and runtime_cwd are resolved to absolute paths before subprocess launch, environment injection, and the wire handshake. The public API exposes only applied options: deployment persona and persistence belong in cordis.yml, while session_root remains the high-level convenience that sets DSH_SESSION_ROOT.