JianFeeeee ba8a33dad8 fix(agentcli): 终端默认不推送 + input/key 可同传 + 失效终端结构化答复
## 一、默认不主动推送(用户裁定)

	「agentcli 没必要进行直接推送,因为本质它就类似人类开启的终端窗口」

终端窗口的语义是:人敲命令 → 自己去看输出。输出不该主动跳到眼前。
主动注入虽然走 inputCh(排队、不抢占),但**每次都会唤醒 agent 跑一轮
推理**;`tail -f` / `npm install` 这类长输出命令会让它被反复唤醒。

改动:
- terminal_create 默认 watch 从 `{onExit:true}` 改为 `{onExit:false, quiet:true}`
- 结束/退出/超时清理一律静默,输出只由 terminal_read 主动读
- 需要推送必须**显式**声明 notify=exit / interval=Ns / buffer=N
- terminal_watch 的 clear 语义改为「回到默认(不推送)」,
  不再自动恢复 exit 提醒
- 删掉「没配 interval/buffer/quiet 就自动 onExit」的兜底(它会让默认
  静默被悄悄推翻)

## 二、input 与 key 可以同时提交(真 bug)

旧实现是互斥结构:

	if key != "" { ... } else if input != "" { ... }

于是 `{"input":"ls","key":"enter"}` 只发出 `\r`,**input 被静默丢弃**。
模型收到 status=ok / bytes=1 以为成功,去读输出却是空的,于是反复重试
——这就是「终端没反应」的直接成因。

现在按声明顺序发送:先 input,再 key。整对写入在同一把会话锁
(新增 TerminalSession.writeMu)内完成,避免同终端并发写插进中间把命令拆坏。

## 三、失效终端给结构化答复

「终端不存在或已关闭」从一句文本改为:

	{"error":"terminal_gone","terminal":"...","next_action":"调用 terminal_create 重建..."}

原因与 browser 插件同形态:只给普通失败时,模型会拿同一个 id 反复重试。
覆盖 terminal_write/read/resize/close/watch 五处。

## 四、通道注册不再吞错

	- _ = s.RegisterInputChannel("agentcli", sdk.ChannelDef{})
	+ if err := s.RegisterInputChannel(...); err != nil { return fmt.Errorf(...) }

注册失败时该通道根本不存在,之后所有 InjectText 都投进虚空 ——
通知静默消失且日志无痕。宁可启动失败也不静默降级。

## 判据(均已变异验证可红)

- TestWriteSendsInputThenKey        断言 input 与 \r 两段都真的写进终端
- TestWriteInputOnlyUnchanged       只传 input 时不额外发键
- TestTerminalCreateDefaultsToNoPush 默认 watch 必须不推送
- TestExplicitExitNotifyStillWorks  显式 notify=exit 仍然生效
- TestWatchClearRestoresNoPush      clear 回到默认而非 exit
- TestTerminalGoneIsStructured      五个工具都返回 terminal_gone + next_action

变异验证:
- 默认改回 onExit:true        → TestTerminalCreateDefaultsToNoPush 红
- input 分支不发送            → TestWriteSendsInputThenKey 红(实际 1 次: ["\r"])
- 错误码改成其它文本          → TestTerminalGoneIsStructured 红

## 验证

	go build ./...                 → 0
	go vet ./...                   → 0
	gofmt -l internal/plugins/agentcli/ → 干净
	go test ./... -count=1 -race   → 62 ok / 0 FAIL / 0 DATA RACE
2026-11-04 09:51:22 +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%