JianFeeeee 06f3161e1d docs(en): 建立英文文档目录 —— 索引 + 教训全文 + 设计摘要
## 问题

仓库是公开的,README / README_EN 已经双语,但 `docs/` 只有 `docs/zh/`:
英文读者读完首页就撞墙。

## 做法:摘要而非全译,并写明为什么

`docs/zh/` 有 27 份文档、约 7000 行设计。**全译会烂得比审得还快**,
而一份陈旧的译文比没有更糟 —— 读者不知道它落后于原文多少。

所以按"内容变化速度"分级:

| 类型 | 译法 | 理由 |
| --- | --- | --- |
| 教训 | ✅ 全文 | 复用价值最高,失败形态会反复出现 |
| 索引 | ✅ 全文 | 没有它其余都不可发现 |
| 设计文档 | ⚠️ 一页摘要 + 状态行 | 7000 行全译跟不上演进 |
| 历史快照/证据 | ❌ 不译 | 时点产物,翻译无增益 |
| 未实施计划 | ❌ 不译 | 会变;且按 §4.3 本就不该进仓 |

判据写在 `docs/en/README.md` 里:

> 如果一份文档的内容变化速度快过译文能被审阅的速度,就译它的**摘要**而不是正文。

## 三份文件

**docs/en/README.md** —— 索引。含分类表(哪些已译、哪些只有中文摘要、
哪些不译)、状态行约定、以及"本索引必须在同一提交里更新"这条硬规则
(滞后的索引比没有索引更糟:它教读者信任错的地图)。

**docs/en/engineering-lessons.md** —— §1(判据会骗人)+ §5(判据形态)
全文翻译,§2–§4 摘要。§5 是本轮新增的核心内容:

    5.1    判据必须把被测模块当黑箱(含三个正当例外)
    5.2    禁止读源码做断言(附"想保证什么 → 错误做法 → 正确做法"表)
    5.2.1  判据不得以文档为依据
    5.3    测试与业务代码分离
    5.3.1  三种测试位置的实测边界
    5.3.2  覆盖率必须用 -coverpkg=./...
    5.4    CI 必须覆盖每个独立 module
    5.5    CI 与本地必须跑同一套门

每个小节都带实测数据与变异验证结果,不是原则罗列。

**docs/en/design-summaries.md** —— 6 份核心设计的一页摘要:

    kernel-modularization   6 层 20 模块;依赖图在构建期算好并嵌进二进制;
                            depgraph.bin 必须与二进制同目录,放数据目录会崩溃循环
    secret-firewall         security 是外围到核心之间的防火墙,自持数据结构、
                            只接受事实不接受策略;出入向都要洗
    input-scheduler         L1–L4 + 现场保存;工具执行是临界区不可抢占
    resident-subagent       与 rtchild 语义相反;单数/复数共用 Cleanup
    toolcall-contract       assistant(tool_calls) 后必须紧跟每个 id 的 tool
                            消息;批内不得插入任何东西
    context-overflow-l4     下沉到冷上下文而非截断;InactivePages 是本次渲染
                            的临时标记(json:"-")

另有 13 份(插件相关 / 数据报告型)列为 "Chinese only" 并说明理由。

## 中文索引加了英文入口

`docs/zh/README.md` 顶部增加指向 `../en/README.md` 的一行,并明确:

> 本目录是**中文原文,也是唯一权威**;英文侧只做摘要,与本文冲突时以本文为准。

这一条很重要 —— 否则两份文档会逐渐分叉,而读者不知道该信哪个。

## 验证

    go build ./...        PASS
    go vet ./...          PASS
    gofmt -l              干净
    go test ./...         65 包全过,0 FAIL
2026-11-04 20:05:10 +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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