dsh-agentdebugx

A separately packaged DeepSeek Harness plugin kept under the AgentDebugX repository. It connects Harness sessions to AgentDebugX without coupling Harness-specific code into the agentdebug Python package.

The plugin:

• starts no AgentDebugX process when Harness loads the plugin;
• captures and diagnoses Harness turns only when an AgentDebugX tool or command is explicitly used;
• stores trajectories in AgentDebugX SQLite storage;
• exposes /agentdebug status|capabilities|diagnose|open;
• exposes model-facing saved-session discovery, session diagnosis, saved-trace analysis, and capability-discovery tools;
• reads Harness's own persisted sessions, including the concatenated-Zstandard session.jsonl.zstd container;
• opens the AgentDebugX viewer only when explicitly requested or configured;
• records replayable agentdebug/start and agentdebug/result session events;
• keeps all Harness-specific code isolated under integrations/dsh-agentdebugx.

Status and compatibility

This first integration targets DeepSeek Harness 0.1.1-rc.2 and AgentDebugX 0.3.x. Harness is in developer preview and may introduce breaking changes.

The bridge exposes AgentDebugX's deterministic heuristic pipeline and the DeepDebug profile. The external protocol deliberately leaves room for GUI RCA, discussion, and rerun operations without requiring those host-specific concerns to enter AgentDebugX core.

Install from npm

Prerequisites:

• Node.js 22.19+ or 24+;
• a Node-installed DeepSeek Harness profile;
• Python 3.9+;
• AgentDebugX with the optional dashboard dependencies.

Install the Python runtime and the DSH bundle independently:

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python -m pip install "agentdebugx[ui]>=0.3.1,<0.4"
dsh plugin --profile web add dsh-agentdebugx
dsh --profile web --dump-config
dsh web

The npm package intentionally does not install Python or execute install scripts. This keeps installation auditable and lets users choose the Python environment that owns AgentDebugX.

Install from a local checkout

Prerequisites:

• Node.js 22.19+ or 24+;
• a Node-installed DeepSeek Harness profile;
• Python 3.9+;
• AgentDebugX with the optional dashboard dependencies.

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powershell
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cd C:\path\to\AgentDebugX
python -m pip install -e ".[ui]"
dsh plugin --profile web add C:\path\to\AgentDebugX\integrations\dsh-agentdebugx
dsh --profile web --dump-config
dsh web

When running Harness from its source checkout, prefix the DSH commands with pnpm:

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powershell
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pnpm dsh plugin --profile web add C:\path\to\AgentDebugX\integrations\dsh-agentdebugx
pnpm dsh --profile web --dump-config
pnpm dsh web

The plugin does not use deepseek-harness-sdk's bundled Python runtime. The Node Harness host starts the packaged bridge script with the configured local Python interpreter, which also works on Windows.

Updating with AgentDebugX

For local development, both sides are linked rather than copied:

• pip install -e ".[ui]" points Python at the current AgentDebugX src/;
• dsh plugin ... add <directory> links the DSH profile to this plugin folder.

After pulling or editing AgentDebugX, restart dsh web; Python imports the updated AgentDebugX source when the bridge process starts. Re-run the editable install only when pyproject.toml, dependencies, or package metadata changed. After editing the JavaScript bridge/plugin, restart DSH as well. No repack or reinstall is needed for this linked development setup.

Published npm/tarball installations are copies instead of links. For those, bump the plugin version, run pnpm pack or publish to npm, update the DSH profile dependency, and restart DSH.

Maintaining the system prompt

SYSTEM_PROMPT.md is the shipped source of truth for the model-facing AgentDebugX instructions. index.js resolves it relative to import.meta.url and strictly renders its capture, open, and sessions-root policy placeholders from plugin configuration when registering the prompt. Semantic edits must preserve the persisted-session contract: list candidates first, present them to the user, and confirm the selected saved session before diagnosis.

Use

AgentDebugX is loaded as a Cordis plugin, not a Harness skill. After at least one Harness turn has completed:

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/agentdebug status
/agentdebug capabilities
/agentdebug diagnose
/agentdebug open

The model-facing tools are:

• agentdebug_list_sessions: list or search persisted DSH sessions beneath the configured sessions root without starting Python or the dashboard;
• agentdebug_diagnose: diagnose this DSH session through its latest completed turn boundary;
• agentdebug_analyze_trace: normalize and diagnose an existing trajectory file or OSWorld trajectory directory inside a configured trace root;
• agentdebug_capabilities: return the installed integration contract, formats, diagnosis mode, and current limitations.

An ambiguous reference to a past or external DSH conversation must start with agentdebug_list_sessions, followed by presenting the candidates and asking the user to choose. agentdebug_diagnose is reserved for requests that clearly identify the current, latest, or just-now conversation. Once a saved candidate is confirmed, its path can be passed directly to agentdebug_analyze_trace.

Both diagnosis tools take a mode:

• heuristic (default) runs AgentDebugX's deterministic Detect-Attribute-Recover pipeline and makes no model calls;
• deep runs the DeepDebug profile, seeded with the heuristic findings.

Deep mode needs no extra API key: AgentDebugX's LLMClient protocol is satisfied by an adapter that calls back into the Harness host over the same pipe, so diagnosis runs on the model the session already uses. Set llmProvider and llmModel together to pin a different model, which is also how you get a second opinion from a model that did not produce the trace.

If a deep run fails, the bridge returns the deterministic report with a deepError explaining why, rather than discarding the result. Automatic per-turn capture never calls a model.

LLM judge, OSWorld GUI root-cause analysis, standalone LLM attribution, rerun, batch processing, and Error Hub sharing stay on the agentdebug CLI against the same store; agentdebug_capabilities reports them so the model recommends the real command instead of assuming the product lacks the feature. That tool reads the installed package's own registries (version, ingest formats, and every detect/attribute/recover component with its default and LLM requirement), so the answer cannot drift from the AgentDebugX build in use.

On-demand runtime and visualization

By default the plugin is dormant: loading DSH registers its tools and commands but starts neither the Python bridge nor the AgentDebugX dashboard. The first status or capabilities request starts only the bridge. A diagnosis or /agentdebug open also starts the local dashboard, waits for /healthz, and reuses it for the rest of the DSH process:

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http://127.0.0.1:7777/trace/<trace_id>/event/<event_id>

The plugin-owned dashboard stops when DSH exits. If dashboardUrl already has a healthy AgentDebugX server, the plugin reuses it and does not stop that external process.

autoCapture is disabled by default. When explicitly enabled, every completed turn is captured and therefore may start the bridge. autoOpen accepts turn, session, and off (default); enabling it together with autoCapture starts the dashboard and opens the matching trace page. Explicit diagnosis keeps the dashboard available but does not pop a browser when autoOpen is off; /agentdebug open always opens it.

Heuristic detection reasons over events, so a benchmark trace scored as a failure can still return zero findings. When the source trace carries an outcome, the tool result repeats it under recordedOutcome, so "no findings" is never mistaken for "the task succeeded". Use the CLI (agentdebug diagnose --mode gui-rca|judge|deep) for the model-backed root-cause modes.

You can still start the dashboard separately; the plugin will detect and reuse it:

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agentdebug serve --store-sqlite .agentdebug\agentdebug.sqlite

Then open http://127.0.0.1:7777.

Configuration

The default bundle row is:

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- insert:
    - id: agentdebugx
      name: dsh-agentdebugx
      config:
        python: python
        store: .agentdebug/agentdebug.sqlite
        dashboardUrl: http://127.0.0.1:7777
        traceRoots:
          - .
        timeoutMs: 120000
        autoCapture: false
        autoOpen: off

Environment shortcuts:

• AGENTDEBUGX_PYTHON
• AGENTDEBUGX_STORE
• AGENTDEBUGX_DASHBOARD_URL
• AGENTDEBUGX_TRACE_ROOTS (semicolon-separated on Windows, colon-separated elsewhere)
• AGENTDEBUGX_AUTO_CAPTURE (true enables per-turn capture)
• AGENTDEBUGX_AUTO_OPEN (turn, session, or off)

deepTimeoutMs (default 900000) bounds a deep run, which issues several model calls and therefore takes much longer than timeoutMs allows for the heuristic path.

Harness patch layers replace a row's complete config; when overriding this row, repeat every setting you need.

Data and security

The plugin runs in the trusted Harness host process and launches a local Python process. Session snapshots may contain prompts, model responses, tool arguments, command output, paths, and system prompt material. Storage remains local by default. The dashboard binds to 127.0.0.1; do not expose it remotely without a separate authentication and TLS boundary.

agentdebug_analyze_trace can read only paths under traceRoots. Keep this allowlist narrow; add an OSWorld results directory explicitly when the model needs to analyze traces outside the DSH working directory.

$DSH_HOME/sessions is appended to the readable roots automatically so the model can debug Harness's own past sessions. Point dshSessionsRoot at a different directory to override it, or set it to an empty string to keep Harness's session history out of reach.

agentdebug_list_sessions searches only that configured sessions root. It does not accept a caller-supplied root, follow symlinked files or directories, or make a model call. Persisted prompts and filesystem paths are sensitive local data; the tool returns only bounded identification metadata and limits results to at most 25 candidates.

Debugging saved traces

agentdebug_analyze_trace accepts two sources beyond the live session:

• a past Harness session, stored as $DSH_HOME/sessions/<workspace>/session-<uuid>/session.jsonl.zstd (on Windows $DSH_HOME defaults to a dsh-* folder under %TEMP%). Pass either the session directory or the log file;
• trace and trajectory files in the open workspace, including OSWorld trajectory directories.

When the exact saved session is unknown, call agentdebug_list_sessions with optional remembered text. Candidates show the session id, analyzable absolute path, cwd/workspace, bounded first user prompt, and log modification time:

1. search with any remembered id, path, workspace, cwd, or prompt text;
2. present the returned candidates and ask the user to choose one;
3. pass the chosen candidate's path to agentdebug_analyze_trace.

Matching is deterministic and local: query text is normalized case-insensitively into whitespace-separated tokens, then ranked by token coverage, full-query presence, matched fields, recency, and finally lexical path. With no query, newest sessions come first. Missing, unreadable, partially written, or corrupt logs are skipped and reported through bounded aggregate counts and warnings rather than failing the entire listing.

Persisted session logs are a concatenated-Zstandard container that Node decodes frame by frame, and they are mapped through the same code path as the live session feed, so turn, step, and tool-call linkage is preserved rather than flattened by generic format detection.

assistant/chunk deltas are not duplicated into AgentDebugX. The assembled assistant message is retained, while the number of skipped chunks is recorded as trajectory metadata.

Distribution and discovery

DeepSeek Harness currently does not accept external pull requests. Community plugins are distributed independently:

1. publish this package to npm, ship a tarball, or install from GitHub;
2. add the GitHub repository topic dsh-plugin;
3. npm- and topic-backed community marketplaces discover it automatically;
4. curated marketplaces backed by awesome-dsh-plugin require a separate registry pull request.

Publishing prebuilt/plain JavaScript avoids pnpm's Git prepare/allowBuilds permission flow. This package intentionally has no install script.

This package lives in the repository's integrations/ directory. Registry automation that only scans packages/, plugins/, or apps/ may not detect the monorepo subpackage; npm and GitHub-topic discovery remain unaffected.

Official references:

• Plugin packaging
• Contributing policy
• DSH Plugin Marketplace discussion

Development

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powershell
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pnpm install
$env:PYTHONPATH = "C:\path\to\AgentDebugX\src"
pnpm test
pnpm test:bridge
pnpm pack

The tests treat AgentDebugX as a read-only dependency. Compatibility changes belong in this adapter unless a generally reusable AgentDebugX public API is independently justified.