JianFeeeee 387ae945df fix(agent): 子 agent 继承技能索引 —— 修 agent 把终端当命令执行器用
## 缺陷(生产实测)

`SetSkillIndexProvider` 只在 cmd/homed/bootstrap.go 里对**根 agent** 调了一次。
rtchild / 驻留子各自 `New(AgentConfig{...})` 构造载体,都没传这个字段 ⇒
子的 system prompt 里**完全没有 skill 索引**。

症状(日志实录):A2A 会话由「临时分诊助手」(驻留子)接手,它手上没有
`cmd_run`(cmd 插件 `auto_start=false`),而 `cmd-tools` skill 正文里写着
「先 plugin_start cmd」。它看不到这条 skill,于是把 `terminal_create` 当成
命令执行器:每条 `cd … && echo … && grep …` 开一个新终端,终端随命令结束退出,
`terminal_read` 立刻撞 `read /dev/ptmx: input/output error`,然后为下一条命令
再开一个 —— 终端列表被填满,每一条都死在半路。

与本仓 Security 那次同型:父有的能力,子没继承。

## 修法

rtchild 与驻留子的 AgentConfig 各补一行 `SkillIndexProvider: a.skillIndex`,
与 `SystemPrompt` / `StageHost` / `Security` 同一组「必须继承父」的字段。

## 顺带两件事

### 1. 内置 skill 的更新永远到不了已部署机器

原落盘规则是「目标目录已存在就跳过」,本意是保住用户改动。但它把两件事混成一件:

    用户改过的版本     ⇒ 应该保留
    内核自己写过的旧版 ⇒ 应该随内核更新

两者都表现为「目录已存在」,于是 b850fea 把 `memory_recall` 改成
`stablemem_recall` 后,生产机的 kernel-memory 一直停在旧名,模型照着
旧名调工具必然失败,而日志里一个字都没有。

改为用 `.builtin-sha256` 摘要区分:
  当前内容 == stamp  ⇒ 没人动过 ⇒ 可安全覆盖成新版
  当前内容 != stamp  ⇒ 用户改过 ⇒ 保留
  没有 stamp(老目录)⇒ 保守视为用户版本,不覆盖

### 2. 新增 kernel-terminal skill

工具描述只说「适用于交互式程序」,没说何时**不该**用;`command` 参数又鼓励
传整条命令。补一条内置 skill 讲清 cmd_run 与 terminal_create 的边界、
以及终端的 input/key/notify 用法。描述控制在 80 字符内(进索引不截断)。

## 判据(均经变异验证)

  TestRTChild_InheritsSkillIndex        删掉接线 ⇒ 红(实测确认)
  TestResident_InheritsSkillIndex       同上(生产实际中招的那一类)
  TestRootAgent_SkillIndexStillInjected 确认没改坏根 agent 路径
  TestChildWithoutSkillIndexIsNotBroken 父无索引时子不 panic
  TestMaterializeBuiltinSkills_*        5 条:覆盖语义四条成组断言
                                        (改回旧行为 ⇒ OverwritesUnmodified 红,实测确认)

判据测的是**生成的 prompt**,不是 `skillIndex != nil`:
后者接上了但注入那段被删照样绿。

## 验证

  go build ./...                       PASS
  go vet ./...                         PASS
  gofmt -l                             干净
  go test ./... -count=1               62 包全过,0 FAIL
  干净检出(git worktree)单独验证      PASS
2026-11-04 10:17:45 +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.

Description
No description provided
Readme AGPL-3.0 190 MiB
Languages
Go 80.7%
JavaScript 9.1%
Python 2.9%
HTML 1.8%
CSS 1.4%
Other 4%