JianFeeeee 41926e770d fix(lua): 适配器永久落后 —— 修死代码 + 改为显式 opt-in 同步
## 缺陷:适配器会永久卡在旧版

writeBundledAdapters 用清单里的 prevHash 判断「盘上有没有被人动过」,
被动的那个就保留。但清单只存 hash ——「内置旧版本的 hash」与
「用户改过的 hash」在清单里长得一模一样。于是:

    记了盘上那份当新基线 ⇒ 下次 onDisk == prevHash
      ⇒ 被误判成「没跟上新版本」⇒ 覆盖掉用户改动
    而保留分支里的 cur[name] = onDisk 又会

## 被循环末尾无条件 `cur[name] = sum` 冲掉

⇒ 判「用户改过」后清单仍记内置 hash ⇒ 下次再判一遍「用户改过」
⇒ **永久**停在旧版。日志只有一句「保留用户版本」,
看起来像用户自己的选择,完全看不出是卡住。

本机实测 8 个适配器中招(openai/anthropic/github/groq/mistral/ollama/
kimicode/server),落后内置版 3000~6500 字节,缺的是用量归一化
(cache_read / reasoning_tokens)、流式 tool_calls 等真实修复。
症状最终表现为「模型不听话」「工具调不动」——指不到适配器身上。

## 为什么不能靠「自动覆盖」解决

「旧发行版残留」与「用户手工改过」在文件内容上无法区分(都只是
hash 不等于内置版)。擅自改用户盘上的文件,比落后一个版本更糟。

所以改成**显式 opt-in**:

    core.lua.adapter_sync = off(默认)  不碰文件,但告警必须可操作
    core.lua.adapter_sync = auto         差异即备份 .bak-<hash8> 后覆盖

零值 = off:忘记注入配置时宁可不动用户文件,也不能默默全量覆盖。

## 告警要可操作,不是「保留用户版本」一句话

教训(2026-10-26 + 2026-11-11 两次):这类静默落后最难查,因为症状
指向模型而不是适配器。现在会给出:数量、名单、影响、三条处理路径
(跟随内核 / 长期定制改 adapter_path / 删掉旧文件)。

长期定制的正道是 `core.llm.sources.<name>.adapter_path`(每源独立可配),
不需要改内置位置的同名文件 —— 这一点也写进告警里。

## 判据 7 条,均经变异验证

    TestAdapterSyncDefaultsToOff            零值/未注入必须是 off
      (变异:return "off" → "auto" ⇒ 红)
    TestOffModeLeavesDiskUntouched          off 不碰文件、不产生备份
      (变异:case mode=="auto" → case mode!="" ⇒ 红)
    TestAutoModeUpdatesWithBackup           auto 更新且备份可回滚
    TestAutoModeNoRedundantBackup           一致时不重复堆 .bak
    TestAdapterStateStableAcrossRestarts    两种模式启停 3 次不漂移
    TestManifestCoversAllBundledAdapters    清单不漏项
    TestFirstInstallHappensInBothModes      off 只「不动已存在文件」,≠不安装

    go build / vet / gofmt / go test -race  全绿

## 生产现状(已按用户确认保留更新)

    适配器 10/10 与内置一致,8 个 .bak-<hash8> 可回滚
    服务 active,NRestarts=0
2026-11-11 21:39:03 +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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