Evr

io.github.BrienBounded由 GitHub 账号 BrienBounded 发布v0.1.2更新于 Oct 24, 2026

Checks agent traces for evidence-readiness: verifies evidence is present, not that it is true.

已验证STDIO仅桌面开发者工具安全与监控

概览

AI 生成的概览

检查智能体执行轨迹是否包含事后重建四类故障所需的证据。

可以做什么
通过 stdio 暴露证据就绪度检查器,提供三个工具:check_trace_readiness(文件、目录或内联 JSON)、get_requirement 和 list_class_requirements。它依据公开规范评估轨迹,覆盖 config_drift、session_isolation_failure、cascade_contamination 和 prompt_injection 四类故障,并报告缺失的具体字段及其所属的 span 类型。它只检查证据是否存在,不检查证据是否真实,也从不判定故障是否发生。
适用场景
适用于为智能体做埋点、希望事后能从遥测数据重建事故的场景,也适用于评估某智能体系统的轨迹是否足以支持事后损失裁定。还可用于 CI,在每个拉取请求上检查轨迹。
运行要求
作为本地 stdio 进程运行,无网络调用,未声明认证或环境变量。需安装 PyPI 包 evidence-readiness(Python 3.10 或更高版本,无运行时依赖)并运行 evr-mcp 控制台脚本,或从克隆仓库按路径运行该模块。检查器 CLI 是另一个名为 evr 的脚本。
安装前请注意
通过只表示证据存在,不表示证据真实:伪造但内部一致的轨迹也会通过,干净的运行结果不得转述为“该智能体是安全的”。该服务器没有提交工具,不传输任何内容;向任何地方提交轨迹属于人工决定,需要先做脱敏。注意名为 evidence-readiness 的控制台脚本启动的是 MCP 服务器,而不是检查器。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Evr,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

evidence-readiness

Checks whether an agent execution trace contains the evidence a post-hoc causal reconstruction would require.

When an AI agent causes a loss — a bad config change, a cross-session data leak, contaminated output cascading between agents, a prompt injection — someone eventually has to reconstruct why from the trace. Traces from general-purpose agent instrumentation cannot support that reconstruction: the evidence was never recorded. This repository contains the evidence-readiness specification (SPEC.md), a checker that evaluates a trace against it, and reference fixtures showing what conformant instrumentation looks like.

What this is — and is not

  • It checks that evidence is present, not that it is true. A fabricated-but-internally-consistent trace passes. This tool authenticates nothing, and its result must never be relayed as "this agent is safe."
  • It never determines whether a failure occurred. It answers "does this trace contain what a reconstruction of class X would require," never "did class X occur." It contains no detection or scoring logic.
  • This repository does not contain the reconstruction engine. The specification is published so the method is assessable; the engine is available for inspection under the terms in SOURCE_AVAILABILITY.md.
  • Four failure classes are covered: config_drift, session_isolation_failure, cascade_contamination, prompt_injection. A fifth (tool_misuse) was investigated and declined for stated structural reasons — a documented negative result, not a gap (SPEC.md §8).
  • The spec is a profile of the OTel GenAI semantic conventions: core OTel plus documented extensions (SPEC.md §2). It carries no standards-body endorsement.

Who it is for

  • Engineers instrumenting agents who want incidents to be reconstructable after the fact — diff your trace against a conformant fixture and see what to add.
  • Reviewers (insurers, auditors, counterparties) evaluating whether an agent system's telemetry would support retrospective loss adjudication.
  • Coding agents: see llms.txt, spec.json (the machine-readable spec export, generated — never hand-maintained), and the MCP server in src/evidence_readiness/mcp_server.py (configuration below).

Quickstart (60 seconds)

bash
# from a clone of this repositorypip install .evr check path/to/trace.json        # or a directory of tracesevr check --json path/to/traces/    # machine-readable reportevr check --require-class prompt_injection path/to/trace.json

The package has zero runtime dependencies; pip install . in any Python ≥3.10 environment is the whole setup. Equivalent uvx forms, both exercised: uvx --from . evr check ./traces from a clone, and uvx --from evidence-readiness evr check ./traces from the package index.

Both need --from, and the trailing evr is not optional. A bare uvx evidence-readiness ... runs the console script named for the distribution, and that script is the MCP server (below), not the checker — it reads standard input, ignores the arguments, and exits successfully without checking anything.

Exit codes carry no reconstruction vocabulary: 0 all requirements met · 1 one or more requirements partially met · 2 one or more requirements unmet · 3 malformed input (SPEC.md §9 is the source of record). Universal requirements (EVR-R1..R6) drive the exit code; per-class evidence readiness is reported separately, and --require-class folds a named class's readiness into the exit code. For every failed requirement the output names the specific missing field and the span kind it belongs on.

Every run ends with the same statement this README opened with: presence, not truth — evidence being present does not make it authentic, and a passing trace is not a safe agent.

CI: check every PR (GitHub Action)

A spec is read once; a check that runs on every PR changes instrumentation behavior. Add this to a workflow in the repository that produces your traces (evidence-readiness is this repository's owner once published; from within this repository itself, uses: ./.github/actions/readiness-check):

yaml
steps:  - uses: actions/checkout@v4  - uses: evidence-readiness/spec/.github/actions/readiness-check@main    with:      trace-path: traces/

That is the whole setup — the default GitHub runner's python3 is sufficient, and the Action installs the (zero-dependency) checker itself. The step fails when the checker's exit code exceeds minimum-passing-exit-level (default 0; exit-code semantics as in the Quickstart above). Optional required-classes folds named classes' evidence requirements into the exit code. Outputs: exit-code, exit-meaning, report-path (full JSON report), and per-class-readiness. PR annotations name each incomplete requirement's specific missing field and span kind. Every annotation is a statement about the trace telemetry, never about the agent: an annotated PR has incomplete evidence, not a detected failure — and a clean run is not a safe agent. This repository's own CI (.github/workflows/ci.yml) dogfoods the Action against fixtures/, asserting the expected — deliberately nonzero — exit codes per bucket.

MCP server (agent surface)

src/evidence_readiness/mcp_server.py exposes the checker and the requirement data to agents over the Model Context Protocol (stdio). Three tools: check_trace_readiness (file, directory, or inline JSON — inline content is processed in memory and never written anywhere), get_requirement, and list_class_requirements.

Installing the package delivers three console scripts, and it is worth being precise about which is which before you copy anything below:

scriptwhat it runs
evrthe checker CLI — this is the one a human wants
evr-mcpthe MCP server, on stdio
evidence-readinessthe MCP server, on stdio — same target as evr-mcp, named for the distribution so that per-invocation runners which resolve <package> to a like-named script find it

So evidence-readiness is not the checker. Typing it at a shell gets a server waiting for JSON-RPC on stdin, which looks like a hang. Use evr.

If the package is installed, the evr-mcp console script is the whole configuration:

json
{  "mcpServers": {    "evidence-readiness": {      "command": "evr-mcp"    }  }}

From a clone, with nothing installed, run the module by path instead:

json
{  "mcpServers": {    "evidence-readiness": {      "command": "python3",      "args": [        "/absolute/path/to/evidence-readiness/src/evidence_readiness/mcp_server.py"      ]    }  }}

Either way it needs no dependencies, and the second form needs no installation. It is local-only: no network calls, no phone-home, nothing persisted. It deliberately has no submission tool — the server never transmits anything; submitting a trace anywhere is a human decision gated on the CONTRIBUTING.md disclaimer and a redaction judgment an agent cannot make. Every tool description carries the presence-not-truth statement, so a readiness result cannot reasonably be relayed as "this agent is safe."

Status

The specification is versioned (SPEC.md carries its current version and correction log) and the checker is implemented against it, with reference fixtures (fixtures/), the machine-readable export (spec.json), the agent surface (llms.txt, the MCP server), and the CI action (.github/actions/readiness-check/) in place. The methodology and limitations paper is drafted (PAPER.md); its front-matter banner records which sections have had an adversarial read and which have not. The standing disclosure policy is SOURCE_AVAILABILITY.md; reporting and trace-submission terms are CONTRIBUTING.md, with redaction guidance in REDACTION.md and release verification in RELEASING.md. Corrections are logged in CHANGELOG.md. The spec is descriptive: every requirement is backed by an observed outcome in a validation record of 240 reconstruction attempts, 222 of them synthetic — backing is per-requirement, from the specific cases that exercised it, not a property measured across all 240 (18 real-world Who&When cases validate the abstention direction only; the split is stated wherever the count appears — SPEC.md §1).

Attribution

Brien Buckman

Licensed under Apache-2.0 (see LICENSE).

The substance of this repository's development record is public: corrections that affect published content are logged in CHANGELOG.md, and the development defect record, as it bears on the method, is summarized in PAPER.md §8.6. The commit history in which these files were drafted and revised is not published.

来源:README.md,提交 725dbba

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v0.1.2最新Sep 16, 2026

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