JianFeeeee 24b03e56e9 fix(memory): L1 恢复为 provider 原生消息序列
## 设计口径(用户裁定,2026-11-03)

	「我的设计是 l1 渲染出来就是传统式的上下文排列,
	  难怪指代消解能力这么差」

## 之前错在哪

buildMessages 把**全部历史事件拼成一条 system 消息**:

	system: 系统提示词
	system: [Conversation timeline]
	        [15:04:05] 用户: 帮我找月租<400的房子
	        [15:04:05] 助手: ...
	        [15:04:05] 工具(qq_get_message): ...
	user:   当前输入

说话人身份靠**文本前缀**表达。模型对它拿到的是"背景资料"式的注意力
结构,而不是"我说过的对话"——指代消解因此失效(用户回"新乡"接不上
上一句"帮我找月租<400…")。

★ 0f98f36 那次只改了**文本渲染格式**(把日志样式换成对话样式),
  底层"一条 system 承载全部历史"的结构一动没动,所以问题还在。

## 现在的形态

	system     系统提示词
	user       帮我找一套月租<400 水电常价带独卫的单间
	assistant  好的,我去找。
	user       新乡
	assistant  新乡这边有几套。
	user       当前输入

工具轮按协议展开:

	user       帮我看看天气
	assistant  (content + tool_calls=[c1,c2])
	tool       (tool_call_id=c1) 晴 26 度
	tool       (tool_call_id=c2) 他说新乡
	user       继续

实现要点:
- 新增 renderL1Messages:一个 ContextEvent 展开为
  user → assistant(calls) → tool... 消息;
- tool_call 与 tool 按 CallID 严格配对(CallID 优先,缺失退化到工具名);
- InactivePages 剔除**完整的工具页**:assistant 上对应的 tool_call 与
  紧随的 tool 消息一起消失,绝不留下"有 call 无 result"(上游必 400);
- 超长工具结果仍渲染为句柄(toolresult_read 取回);
- 保留 timelineUserText 的中断轮处理:从聊天工具回执里取真实原话,
  不让注入样板冒充用户发言;
- 保留 token 预算裁剪,事件级按消息粒度计入。

## 清理(无生产调用方的旧渲染路径)

删除:
- formatMergedTimeline(旧 system-timeline 唯一生产者)
- renderedToolResultsOf(旧截断风格渲染,已被原生序列取代)
- timelineToolLines(仅上面两者使用)
- timelineUserLabel / timelineAssistantLabel
- toolResultTimelineChars / toolResultTimelineCharsChat /
  toolResultTimelineMax / isChatCorpusTool
删除对应已失效判据:timeline_contract_test.go、timeline_toolresult_test.go

保留 toolResultPageIndex:overflow.go 的 eventTokens 仍在用,
且统计侧与渲染侧必须共用同一份配对规则(否则"渲染少了、统计没少"
会让裁剪永远裁不动,2026-11-02 实测事故)。

## 判据(新增,均已变异验证)

l1_native_sequence_test.go:
- TestL1RendersNativeMessageSequence
    断言 role 序列 system/user/assistant/user/assistant/user,
    且 system 消息中不得再出现 "[Conversation timeline]" 或 "用户:"
- TestL1ExpandsToolCallsAsPairedMessages
    断言 assistant.ToolCalls[i].ID 与紧随 tool.ToolCallID 一一配对
- TestL1InactivePageDropsWholePair
    剔除页时 call 与 result 必须同时消失

变异验证:把实现退回旧 system-timeline ⇒ 三条全红,恢复后全绿。

## 验证

	go build ./...                 → 0
	go vet ./...                   → 0
	gofmt -l internal/             → 干净
	go test ./... -count=1 -race   → 62 ok / 0 FAIL / 0 DATA RACE
2026-11-03 14:37:53 +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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