fatihfurkan46/SignalWeave

选用指南

AI 根据 README 整理 · 2026/10/29 02:01:35

适用场景

适用于将 LangChain、OpenTelemetry、图运行器等异源遥测事件整合为统一格式，并观察运行可靠性、质量和成本指标。

查看 README 依据

<p align="center"> <img src="docs/assets/logo.svg" alt="SignalWeave logo: indigo warp threads and amber weft threads interlacing into one central junction" width="150"/> </p> # SignalWeave Every AI framework emits telemetry in its own shape. LangChain calls it `lc_run_id` and `latency_ms`; an OpenTelemetry span calls it `trace_id` and `start_time`; a graph runner calls it `session` and `elapsed_ms`. SignalWeave reads those heterogeneous events and weaves them onto one canonical `Signal`, then groups signals by run and reports reliability, quality, and cost indicators. Nothing is dropped:

`examples/events.jsonl` carries four runs across four vendor shapes: `run-alpha` (LangChain), `run-beta` (agent toolkit), `run-gamma` (graph runner, including a critical guardrail error), and `run-delta` (generic evaluation and lifecycle events). `examples/events_array.json` holds the same style of data as a JSON array, exercising the array input path. ## Make targets | Target | Action | |--------|--------| | `make check` | Byte-compile the `signalweave` package | | `make normalize` | Normalize the fixture | | `make aggregate` | Aggregate the fixture |

使用方式

通过命令行使用，支持从文件或标准输入读取事件，输出 JSON 或文本报告，可嵌入 shell 管道。

查看 README 依据

an unrecognized shape falls through to a permissive generic adapter. It runs on the Python standard library only, reads from a file or stdin, and writes to stdout, so it drops into a shell pipeline without ceremony. ## Pipeline  Four stages, each a subcommand: ```bash # Canonical signals, one JSON object per line. python -m signalweave normalize examples/events.jsonl # Group into runs and emit the aggregate JSON.

python -m signalweave aggregate examples/events.jsonl # Full weave report (text by default, or --format json). python -m signalweave report examples/events.jsonl # Everything for one run, in detail. python -m signalweave inspect examples/events.jsonl --run-id run-gamma ``` Input is either a JSON array of event objects or newline-delimited JSON (JSONL). Pass `-` (or omit the path) to read stdin, which lets you chain stages or pipe from another tool. ## How normalization works

部署要求

仅依赖 Python 标准库，可选构建 C++17 原生聚合核心。

查看 README 依据

an unrecognized shape falls through to a permissive generic adapter. It runs on the Python standard library only, reads from a file or stdin, and writes to stdout, so it drops into a shell pipeline without ceremony. ## Pipeline  Four stages, each a subcommand: ```bash # Canonical signals, one JSON object per line. python -m signalweave normalize examples/events.jsonl # Group into runs and emit the aggregate JSON.

## Native aggregation core `engine/signal_aggregate.cpp` is a dependency-free C++17 companion. It reads canonical signals as JSONL on stdin (the exact output of `signalweave normalize`) and emits a per-run reliability aggregate, mirroring `signalweave.aggregate` so the native and Python paths agree. ```bash make engine # build engine/signal_aggregate make parity # normalize | ./signal_aggregate ``` ## Example data