JianFeeeee 47afd67ca1 refactor(security): 删除三份包级单例,共享数据统一挂 kstate
## 用户裁定

> secutiry 的实现也是错误的,所有共享数据统一挂载一个 kernel 提供的
> 挂载点上暴露。

> store也应该调用security的加密能力!

## 缺陷

security 包原有**三份**独立包级单例,各自被 InitXxx/SetXxx 装入、
各自有访问器,三份都绕开了内核统一挂载点 kstate.State:

    global      *Store    变量库(凭据别名)    Global()
    runtime     *Runtime  安全防火墙            RuntimeGlobal()
    globalCodec *Codec    配置落盘加密          ConfigCodec()

## 为什么单例是错的(不是"多一个全局变量")

本 agent 的 a.security 与 security.Global() 是**两个独立变量**。
正常装配下它们恰好指向同一份数据,但"恰好"不是保证:

  · 测试替身:测试装单例、被测代码用 agent 字段 ⇒ 测的与跑的不是同一份
  · 多 Runtime:两个内核实例/热重载时单例只有一份,另一方被覆盖
  · 装配顺序:单例装晚了,先注册的 provider 就没被装饰

第三条**曾真实存在**:ProviderManager.Register 在注册时读单例套装饰器,
而 llm 模块原本不声明 FacSecurity 依赖 —— 顺序正确纯属巧合。
改动过程中判据如实报红 640 次泄漏。

## 本次做的(T3.1 + T3.3)

### 删 global *Store → 注入

变量库的唯一来源是 security 模块产出的 Runtime,经注入面分发:

    AgentConfig.Security        → a.security.Store() → a.secretStore()
    ProviderManager.SetSecretStore
    plugin.Registry.SetSecretStore  → sdk.NewSecretsAPI(store)

core 的 4 处 security.Global()(output/toolcall/tooldefs/secret_tools)
改为 a.secretStore();context 与 stageHost 改为注入字段;
provider 装饰器改为读注入的 Store。

### 删 runtime *Runtime → 注入

    plugin.Registry.SetSecurityRuntime → sdk.NewSecurityAPI(name, rt)
                                       → sdk.newEventsAPI(name, bus, rt)

Prepare 的幂等缓存改为 kernelmod/security 私有变量 ——
装配器私有,不再是任何包都能取的公开访问器。

### 顺带删死代码

security.FillToolArgs / FillPayload 生产调用者为 0(StageHost 已自己
用注入的 Store 做同一件事),连同只测它们的判据一起删除。

### llm 模块补 FacSecurity 依赖

这是**真实存在**的依赖:注册 provider 时要套出向装饰器,而装饰器需要
变量库。此前靠"单例先装了"掩盖了顺序错误。

## 判据(均经变异验证)

  TestNoPackageLevelSingletons          源码扫描;加回 var global ⇒ 红(实测)
  TestMustStoreDoesNotRegisterGlobally  两次 MustStore 必须得到不同实例
  TestStoreEncryptsWithOwnKey           落盘无明文 + 重载可解

## 验证

  go build ./...              PASS
  go vet ./...                PASS
  gofmt -l                    干净
  go test ./... -count=1      63 包全过,0 FAIL
  干净检出(git worktree)     build + 3 包测试全绿

## 未做(见 kernelplan.md T3.2)

  globalCodec 收敛 + Store 复用 Codec
  还牵涉 config 的 6 处调用点与**存量密文惰性迁移**,单独一步做。
  TestNoPackageLevelSingletons 的禁表暂不含 globalCodec ——
  半成品状态长期红的判据会被人习惯性忽略,那时它就等于不存在。
2026-11-04 11:57:48 +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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