JianFeeeee c01f4d6350 test: 两个零测试包补黑箱判据 —— files(沙箱)与 tfidf
覆盖率分析显示这两个包**零测试文件**,而它们各自守着一条不能错的边界:

    internal/plugins/files   581 行  含沙箱逃逸防护
    internal/tfidf           306 行  记忆检索的 fallback 路径

## files:沙箱边界

581 行、0 判据。它是文件读写插件,`pathWithinSandbox` 写错一个字符就是
任意文件读写。没有判据意味着没人知道它现在是否还挡得住。

6 条判据全部走**真实装配**(调 `Start()` 而不是手搓 Plugin 结构),
通过注入的 registrar 捕获 handler,然后以「输入 {path, content} → 输出」断言:

    TestToolsAreRegistered           四个工具都在
    TestReadWriteRoundTrip           写进去能原样读回
    TestSandboxEscapeIsRejected      ★ 沙箱外路径必须被拒(3 种逃逸形态)
    TestSandboxAllowsInside          反向:沙箱内必须能读
    TestEmptyPathIsRejected          read/write 拒绝空 path
    TestLsEmptyPathMeansCurrentDir   ls 把空 path 当 "."
    TestLsListsDirectory             ls 列出目录内容

**沙箱那条的判据设计**:沙箱外造一个**真实存在**的文件。若只拦不存在的
路径,判据会假绿;文件真实存在而仍被拒,才证明是沙箱在起作用。

变异(`pathWithinSandbox` 加 `if true { return true }`)⇒ 实测红:

    ★ 沙箱外路径 ".../secret.txt" 被接受了,返回 {"content":"SECRET",...}
      这是任意文件读取。沙箱根: ...

## tfidf:检索判别力

306 行、0 判据。它是没有神经网络 embedder 时的文本相似度来源 ——
判据错 = 检索错,而检索错是静默的(不报错,只是答非所问)。

判据**只断言契约,不断言数值**:TF-IDF 的权重取决于语料统计,写死数值
等于把训练集也当成契约。6 条钉的是换了实现也必须成立的性质:

    TestVectorizeIsDeterministic            同输入同输出(map 遍历浮点累加
                                            会产生末位差异,让缓存/去重失效)
    TestVectorizeHasFixedDimAndNonNegative  维度恒定、非 NaN/Inf、非负
    TestEmptyTextGivesZeroVector            空文本不 panic 且给零向量
    TestSearchRanksRelatedAboveUnrelated    ★ 核心质量判据:只排序,不看数值
    TestAddRemoveChangesResults             增删后检索结果随之变化
    TestFingerprintAndDim                   元信息非空

`TestSearchRanksRelatedAboveUnrelated` 直接对治 engineering-lessons §1.1
记的那个坑:16 条判据全绿而检索结果完全无关(分数全挤在 0.75~0.79)。
所以它只问"billing invoice 的查询里,auth 文档有没有排到相关文档前面"。

变异(`Search` 提前 `return nil`)⇒ 实测红两条。

## 一处判据写错了(已按实际契约修正)

第一版把 `files_ls` 也纳入"空 path 必须拒绝",实测红:

    ★ files_ls 接受了空 path,返回 {"content":"(empty directory)"}

查代码(plugin.go:488)发现那是**明确设计**:`if path == "" { path = "." }`。
不是缺陷,是我把三条工具的输入契约当成同一条了。改成两条判据分开钉:

    TestEmptyPathIsRejected          read/write 拒绝空 path
    TestLsEmptyPathMeansCurrentDir   ls 把空 path 当 "."

把差异显式写下来,以后有人想"统一都拒绝空 path"时能看到这是有意的。

## 覆盖率

    internal/plugins/files   0.0% → 54.1%
    internal/tfidf           0.0% → 有判据(契约全覆盖)

## 验证

    go build ./...            PASS
    go vet ./...              PASS
    gofmt -l                  干净
    go test ./... -count=1    67 包全过,0 FAIL(新增 2 个 test 包)
2026-11-04 20:41:43 +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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