JianFeeeee 443110506c test: 按模块导出接口重写黑盒判据(批次 3:memory / agent-core)
延续批次 1/2 的形态:只调模块**本来就有**的导出接口,输入 → 可观察输出。
2 个文件全部在 `<module>/test/`,源码目录仍零 `_test.go`。
共 17 条判据。

## internal/memory(10 条)

纯函数(输入文本 → 输出):

    TestCleanText                     空白/「,」前缀(实现剥两次)
    TestChannelScene                  "qq" → "chan:qq",空 → 空
    TestFilterNoiseTriples            噪音主语/宾语被过滤,且不改输入切片
    TestSentenceBlockIDIsStable       同文本同 ID
    TestNewSentenceBlockFields        Trim 后的 Text 与 ID
    TestBuildCentroid                 中心向量均值 = 输入算术均值

GraphDB 真实构造 → 写入 → 可观察输出:

    TestCommitThenGraphData           Commit → GraphData 给出 nodes 结构
    TestReadOnlyHandleRejectsWrite    OpenGraphDBReadOnly 在 SQLite 层拒写

`TestReadOnlyHandleRejectsWrite` 守的是「子 agent 改不了主记忆」的
**结构性**保证(PRAGMA query_only=1),不是靠调用方自觉。

**判据写错过两次,按真实契约修正**:
① `CleanText` 连剥两次「,」,第一版按"只剥一次"写 ⇒ 红。
② 只读句柄的第一版三元组用了单字符实体名,被 `validEntityName`
   提前拒掉、根本走不到 SQL 层 ⇒ 假绿。换成合法实体名后才真正验到
   query_only。这个坑值得记:**判据的输入必须能走到被测路径**。

## internal/agent/core(7 条)

    TestAgentID                       New(AgentConfig{ID}) → ID()
    TestOutputAllowlist               空白名单=完整授权;非空=白名单
    TestDuplicateInputWindow          同源同内容第二次判重;异源/异内容否
    TestRealtimeFlag                  普通 agent=false;nil receiver 安全
    TestEmptyCollectionsOnFreshAgent  Residents/RTChildren/DumpScheduler 为空
    TestResolveOutputTargetWithoutRegistry  无注册表 → ok=false,不 panic
    TestGetKernelStatusNeverNil       状态查询返回可用对象

只用 `New(AgentConfig)` 装配,不启动事件循环(已核实 `New` 里没有
`go a.xxx` / `Start()`),不构造 TaskFrame、不调 stepLLM/runStage、
不碰 a.sched、不读源码。

## 变异验证(改回旧实现必须红)

    memory  CleanText 不剥前缀        → ★ CleanText(",,你好")=",你好"
    memory  只读句柄不设 query_only  → ★ 只读句柄接受了写入
    core    白名单恒放行             → ★ 白名单外的通道被放行
    core    去重恒 false             → ★ 同源同内容第二次未判重

全部还原后复跑全绿。

## 验证

    go build ./...                            PASS
    go vet ./...                              PASS
    gofmt -l                                  干净
    python3 .github/scripts/check-ci-gates.py PASS
    go test ./... -count=1                    14 个测试包 / 100 包无测试 / 0 FAIL
    2 个新包 -race                            全绿
    源码目录 _test.go                         0
2026-11-09 12:37:52 +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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