JianFeeeee 217e32b494 ci: 补 staticcheck + 覆盖率采集 —— 把"看不见"变成可见
## 背景

本轮判据治理的起点是「覆盖率/判据形状两者都看不清」。前几个提交补了
gofmt 门和 SDK 判据步骤;这个补最后两项。

## staticcheck

### 为什么选它

`go vet` 只跑标准检查器。实测今天撞到的那类缺陷它抓不到:

    pluginHealthTracker 标记 Unhealthy=true 后,isHealthy 立刻用
    LastReload(**从未被赋值**,恒为零值)判"冷却已过"而自愈

那是 SA4xxx 家族(无用赋值 / 永真条件)的领域。它不替代判据,
但能在"写完了但逻辑自相矛盾"处提前报警。

只查四族,不查风格:

    SA4xxx 无用赋值 / 永真条件
    SA5xxx 可疑代码
    SA6xxx 低效写法
    SA9xxx 必错分支

### 踩到的坑:版本不匹配会造出一道永远绿的门

第一版钉 staticcheck@2025.1.1,在 Go 1.27.1 上实测:

    internal error in importing "image/color"
    (cannot decode "image/color", export data version 4 is
     greater than maximum supported version 2)

然后**静默返回退出码 0** —— 一道永远绿的门,比没有门更危险。
改用 2026.2.2 后正常解析。步骤里加了两道判:

  · 退出码非 0 但输出里没有任何 `file.go:line` ⇒ 判定"未能运行",直接红
  · 版本变化导致的静默退化由上面这条兜住

### 存量基线:24 条 / 13 个文件(2026-11-04 实测)

按分布:

    SA9003  empty branch          10
    SA4006  value never used       4
    SA4000  identical expressions  3
    SA4010  value never checked    2
    SA4008  1    SA4023 1    SA6001 1    SA6003 1

处理方式是**基线 + 只许减不许增**:新增告警让这一步红,存量可见但
暂不阻塞(否则合并即长红,所有人学会忽略这道门 —— 正是本仓
ci.yml 里那段"长红 ⇒ 整道门失效"的教训)。低于基线时会提示把
BASE 调低,否则门会越来越松。

抽样确认这些是真问题,不是误报。例如:

    internal/agent/api/codec_chunkfast_golden_test.go:68:9:
        this value of fastOK is never used (SA4006)
    internal/agent/api/codec_chunkfast_golden_test.go:69:2:
        empty branch (SA9003)

`fastOK` 赋值后从未使用,且紧跟一个空分支 —— 那里本意要区分
「回退」与「快速路径给出失败」,实际什么都没做。

## 覆盖率采集

仓里 521 个测试文件、11.2 万行测试代码,但**没有任何覆盖率数据** ——
无法回答"哪些模块其实没被测到"。

补的步骤只报数,不设阈值:

  · 打总覆盖率
  · 打覆盖率最低的 15 个包

**为什么不设阈值**:阈值会促成"为凑数写空判据",那比没覆盖率更糟
(本仓 engineering-lessons §1.1 记的就是这个)。这里只把数字
摆出来,让人做模块级决策时有参照。

顺带:这一步同时兜住了 `go test` 的失败(`|| { ... exit 1; }`),
所以它不只是统计,也是一道执行力门。

## 验证

    YAML 语法             OK
    CI 门禁自检           通过(动态枚举 / 排除表 / 启动图 / 交叉枚举 / run 步骤语法)
    本地复跑 staticcheck  rc=1、24 条 = 基线 ⇒ 通过
    go build ./...        PASS
    go test(core/webui) 全绿

## 后续

staticcheck 存量 24 条已登记方向(future-plan.md「staticcheck 存量」)。
它不是判据问题,是代码问题,单独收敛。
2026-11-04 18:20:04 +08:00

⚠️ AI-Assisted Programming Notice: Parts of this project's code, documentation, and commit history were generated or modified with AI assistance. Key changes have been human-reviewed, but please evaluate and verify before use.

HologramAgent

中文: README.md

Self-hosted agent kernel. Runs on your own hardware; plugins attach as separate processes.

make build build-cli
./build/homed -data /tmp/ha

API keys go in the web UI at http://localhost:8080. Requires Linux.

The main difference from typical agent frameworks is context handling: instead of truncating on overflow or asking the model to summarize, it scores history by relevance and sinks the low-scoring events into cold context, then distills those into a graph. Two other things differ: startup order is resolved at build time, and plugins launch as soon as their facilities are ready.

Architecture

                    homed (kernel)
        LLM orchestration · memory · retrieval
                       │
        ┌──────────────┼──────────────┐
        │              │              │
   stdio RPC       shared mem     event ring
     control       bulk data      notify
        │              │              │
        └──────────────┼──────────────┘
                       │
              plugins (separate processes)
   web UI · QQ · CLI · files · browser

The kernel process handles orchestration, memory and retrieval; HTTP, files and devices belong to plugins (internal/plugin). A crashed plugin is restarted with backoff 1s / 2s / 3s; the 4th crash within 5 minutes stops automatic restarting pending human intervention (cmd/homed/kmod_runtime.go).

Context window

internal/agent/core/context.go

Each event gets two vectors on entry: a dense vector sharing one space with media and cold context, and a TF-IDF word vector as fallback. Pruning scores history against the current input and keeps topK, with the most recent N events exempt from scoring.

N defaults to 10, bounded by budget: min(configured, ContextTokens / avg tokens per event).

That bound came from a measurement. At a 50k window with 5800 tokens per event and a 40000 budget, pinning 10 events needed 58k, more than the budget itself. Every prune met its target without freeing space; the budget was gone after two rounds and the task terminated, with recall falling from 44% to 22%. Small windows now tighten automatically; a 1M window uses the configured value.

Pruned events move to the cold-context layer together with the media blocks they hold. Block IDs stay the same; only the holding layer changes. Retrieval uses coldcontext_query: Peek to confirm, then Remove the source copy. There is no fetch-and-delete path, since deletion follows confirmation.

Three layers

internal/agent/core/context.go · internal/memory/coldcontext/ · internal/memory/graph.go

Layer Holds Reaches the model Cooling
L1 Context events + blocks directly, in the system prompt low relevance sinks
L2 Cold Context events + blocks (same shape as L1) inserted back into L1 order on recall ≤2 accesses and 72h idle, then distilled
L3 Stable triples, nodes are blocks on recall never leaves

A block has exactly one holder at any layer, with no reference counting. Images and audio are blocks in L2/L3 with their own vectors, searched in the same space as text.

Scenes

internal/memory/scene_emerge.go

A scene is built from five weighted features: channel 1.0, conversation partner 1.0, tools 0.8, topic 0.4, time of day 0.2. A scene is created on the second occurrence of the same fingerprint (minSceneEvidence = 2); one-off interactions don't create one. Join threshold 0.5, recall 0.35. Recurrence adds 1 to strength; scenes that stop recurring decay by half-life.

Two entry points run in parallel: a plugin declares the scene at an injection point (e.g. chan:qq/peer:group_1), or fingerprints cluster on their own. A declared scene derives features only from its own key and does not absorb the full fingerprint. Absorbing it would make the declared scene match nearly every later turn of that kind at close to 1.0 similarity, and the clustering path could never grow narrower scenes.

Seven stages

internal/agent/core/stages.go

Stage When Plugins can
on_input message arrives blocklist, rate limit, short-circuit reply
pre_action context ready, before LLM inject external data
post_action LLM returns text and tool calls filter, force redirect
before_toolcall before one tool runs audit, deny, rewrite args
after_toolcall after one tool runs redact, sort results
before_output final text ready format adaptation
after_output after sending statistics

The inner loop is post_action → [before_toolcall → run → after_toolcall] → post_action. It exits when the model stops calling tools, all calls are denied, or the limit is reached.

Everything else

  • Channels: web UI, CLI, QQ groups and DMs, A2A, ACP in your IDE
  • Tools: files, shell, code, browser (real Chrome), calendar, notes, RSS, music
  • Modalities: images, video, voice, screenshots, web pages

28 built-in plugins, 89 model-callable tools. External plugins live one per repo under github.com/HologramAgent.

Downloads

Releases ship three variants:

Variant Contents For
full homed + waiter + desktop GUI + systemd unit Single-machine, everything
server homed + waiter + systemd unit Servers (no desktop environment)
client waiter + desktop GUI Connecting to a remote HologramAgent
  • Linux: .deb (amd64/arm64), .rpm (x86_64), .tar.gz
  • Windows: HologramAgent_v1.2.0_{Full,Server,Client}_win64.exe (NSIS installer, includes the AGPL license page). Since v1.2.0 homed no longer supports native Windows (it relies on fd inheritance and in-segment offset dereferencing), so the installer bootstraps WSL2 and installs the Linux packages inside the distribution the same way a Linux host would.
  • Portable: homeagent-bin-<os>_<arch>.tar.gz (homed/waiter/initconfig)
  • Verification: SHA256SUMS

The macOS homed requires a native macOS build (CGO + sqlite3), so release packages ship only waiter/initconfig.

Build

make build build-cli    # Build daemon + CLI
make test               # go test ./...
make install            # Install to system

Dependencies: Go 1.25+, CGo (go-sqlite3), Linux.

homed requires Linux (it relies on fd inheritance and intra-segment offset dereferencing of the shared memory region; see cmd/homed/platform_windows.go). On Windows only waiter.exe is built and homed runs under WSL2 (see Downloads). macOS can build waiter/initconfig; homed must be built on native macOS.

License

AGPL-3.0-only. See LICENSE.

Bundled third-party components keep their own licenses; full texts ship in /usr/share/doc/hologramagent/licenses/.

Third-party components shipped with the packages

Component License Location
Chinese-CLIP ViT-B/16 (ONNX artifacts) Apache-2.0 /usr/lib/hologram/models/chinese-clip-vit-b16-onnx/
ONNX Runtime (libonnxruntime.so) MIT /usr/lib/hologram/onnxruntime/
jieba dictionary (embedded in the binary) MIT internal/memory/jiebadict/
Go dependencies (go-sqlite3, gojieba, bubbletea, …) MIT / BSD-3 / Apache-2.0 permissive, AGPL-3.0-compatible

The package metadata declares this package as AGPL-3.0-only.

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