dsh-logtimeline

🐳 dsh-logtimeline

Query local log files with Chinese natural-language time expressions — LogTimeline for DeepSeek Harness.

简体中文 | English

Ask 「昨天下午」「3小时前」「凌晨12点」「上周三 14:00-15:00」 — get the matching log lines, the resolved time range, and ERROR/WARN/INFO level stats. Fully offline. Logs never leave the machine.

Why

Coding agents constantly need to answer *"what happened around 3pm yesterday?"*. Generic log tools make you hand-write grep pipelines and timestamp math. log_query resolves the fuzzy Chinese time expression for you and returns structured, machine-readable matches the agent can reason over directly — no parsing prose out of terminal output.

Features

Install

Prerequisites: a dsh profile (web or headless) + Python 3.9+ on the machine (on Windows, the py launcher is auto-detected when python is missing).

Local / development (from a checkout):

git clone https://github.com/anyuer678/dsh-logtimeline.git
dsh plugin --profile web add file:/path/to/dsh-logtimeline

Community install — once listed in awesome-dsh-plugin, install via the plugin market (GitHub-only plugins are supported):

dsh plugin --profile web add dshmarket
# then: Settings → Plugin Market → search "dsh-logtimeline" → one-click install

Compatibility: tested against @deepseek-ai/[email protected] (current harness release line, e.g. [email protected]).

Usage

Tell the agent to use the tool — no special syntax needed:

用 log_query 查一下 demo.log 里 2026-07-03 的 ERROR 情况

// log_query(time_text: "2026-07-03", files: ["demo.log"])
{
  "time_range": { "start": "2026-07-03 00:00:00", "end": "2026-07-04 00:00:00", "confidence": 1.0 },
  "filter": {
    "total_matched": 25,
    "stats": { "errors": 13, "warns": 5, "infos": 7, "debugs": 0 },
    "lines": [ /* up to max_lines rows: {lineno, timestamp, raw} */ ]
  }
}

Parameters

The tool runs in offline mode by default: it filters precisely (its unique value), and leaves root-cause reasoning to the agent itself.

Architecture

src/query.ts      Core logic: subprocess → vendored lq.py --json → parse (framework-free, testable)
src/runtime.ts    DSH assembly: registers log_query via defineTool (ctx.tools)
src/config.ts     Plugin config (pythonBin, timeoutMs)
python/           Vendored LogTimeline CLI (GPL-3.0, see python/VENDORED.md)
tests/            Vitest integration tests running the real vendored Python

The plugin shells out to a vendored copy of the LogTimeline Python CLI (python/lq.py --json --no-llm), so the battle-tested parsing/filtering logic stays untouched. Vendoring changes are minimal and documented in python/VENDORED.md.

Development

npm install --legacy-peer-deps   # peer deps are provided by the DSH host runtime
npm run typecheck
npm run test                     # integration tests: real vendored Python + host-assembly smoke
npm run build
python scripts/run-upstream-tests.py   # upstream LogTimeline pytest suite against the vendored code

tests/query.test.ts runs the real vendored Python CLI against fixture logs; tests/host.test.ts loads the built plugin (lib/) into a minimal Cordis container, applies it, and validates the canonical value against the output.schema contract. Peer packages (@deepseek-ai/dsh-*, pinned to the 0.1.0-rc.6 line) are installed as devDependencies so the runtime import chain resolves outside a DSH host.

Security & Privacy

• No key handling: the plugin reads no API keys, stores no credentials, and makes no outbound calls — log analysis is 100% local
• No log exfiltration: log lines are returned to the agent (the model) as tool results, never sent anywhere else
• Path scoping: only the paths the model passes (via files/dir) are read; the tool is read-only, it never writes or deletes log files
• Output hygiene: control characters / ANSI escapes in log lines are stripped from rendered output

Disclaimer

This is a community plugin for the DeepSeek Harness ecosystem and is not affiliated with DeepSeek. It is provided for learning and demonstration purposes and does not constitute any form of commercial service or technical commitment. The software is provided "as is", without warranty of any kind, express or implied, including but not limited to merchantability, fitness for a particular purpose, and non-infringement.

By using this project you acknowledge that you assume all risk. If you find defects or issues, please report them via GitHub Issues; however, the author shall not be liable for any direct or indirect losses arising from the use of this software (including but not limited to data loss, business interruption, or third-party claims).

This project has not been validated or hardened to production-grade standards (architecture, security baseline, fault tolerance, or performance) and is not suitable for production or critical environments. Deploying it to production systems, exposing it as a service, or integrating it into real business workflows is your own decision, and any resulting adverse outcomes (service interruption, data corruption or leakage, business loss, compliance risks, third-party disputes) are your sole responsibility. Log analysis results are for reference only — always verify against the original log files before acting on them. If you need production-grade usage, harden it yourself (security audit, load testing, code review) and bear the associated risk.

License

GPL-3.0 — derived from LogTimeline (GPL-3.0). Copyright (C) 2026 anyuer678.