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## 补上真正的差异点 之前架构节只有一张图和七阶段表,把最核心的上下文管理省略了。 用 codegraph 查了 `RelevanceContext` / `PruneWithProtected` / `coldcontext.Store` / `block_recall`,确认实际做法,逐条对照常见 agent: ### 上下文:按相关度取舍,不是截断或摘要 常见做法是超窗裁掉旧对话,或让模型自己摘要 —— 共同问题是**丢什么由位置决定**。 这里是给每条历史事件按当前输入打分,低分下沉到文档层。 打分主路径是稠密余弦(与媒体、文档同一向量空间),模型未就绪退到 TF-IDF。 关键在 `effectiveProtectedCount`:保护条数受预算约束。 ```go budgetCap = ContextTokens / avgEventTokens pCount = min(配置值, budgetCap) ``` 源码里记着这条约束的来历:50k 窗口下单条事件约 5800 token(工具回灌型), 预算 40000,「钉住 10 条」本身就装不下 —— 每轮裁剪都撞保护下限, 两轮后预算耗尽,任务直接终止,召回率从 44% 掉到 22%。 ### 三层是迁移,不是复制 `ColdDoc` 的注释写得很清楚:「块的真实归属在 Events[i].Blocks 里」, 块在任一层只有一个持有者,不靠引用计数保活。 回灌用 `Peek` + 确认 + `Remove` 两阶段,源码明确标注「**禁止先删后灌** —— 删除必须排在确认之后,回灌失败即永久丢数据」。 ### 场面:两条路并行,且互不吞并 声明场景**不吸收整轮指纹**(`EnsureScene` 的注释):否则它会以接近 1.0 的相似度 吃掉后续所有同类轮次,被动聚类再也长不出更细的场面。 这条边界是踩出来的,不是设计时想到的。 ## 开头相应收缩 原来开头四段「**粗体断言** + 三行解释」与架构节重复。改成一句介绍 + 安装 + 三段话点出差异,细节留给架构节。 两版 163 / 191 行,9 个标题对齐,代码块 10/10 配对,无死链。
192 lines
9.5 KiB
Markdown
192 lines
9.5 KiB
Markdown
> ⚠️ **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.
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# HologramAgent
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> **中文**: [README.md](./README.md)
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Self-hosted agent kernel. Runs on your own hardware; plugins attach as separate processes.
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```bash
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make build build-cli
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./build/homed -data /tmp/ha
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```
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API keys go in the web UI at `http://localhost:8080`. Requires Linux.
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The main difference from typical agent frameworks is context handling: instead of truncating
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on overflow or asking the model to summarize, it scores history by relevance and sinks the
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low-scoring events into documents, then distills those into a graph. Two other things differ:
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startup order is resolved at build time, and plugins launch as soon as their facilities are ready.
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## Architecture
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```
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homed (kernel)
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LLM orchestration · memory · retrieval
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│
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┌──────────────┼──────────────┐
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│ │ │
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stdio RPC shared mem event ring
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control bulk data notify
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│ │ │
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└──────────────┼──────────────┘
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│
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plugins (separate processes)
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web UI · QQ · CLI · files · browser
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```
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The kernel makes no HTTP calls and reads no files. Everything touching the outside world is a
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plugin. A plugin crash leaves the kernel running; restarts back off 1s / 2s / 3s, and the 4th
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crash within 5 minutes stops automatic restarting for human intervention.
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### Context is not "truncate and summarize"
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The usual approach drops old turns or asks the model to summarize them. Both share a flaw: what
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gets lost is decided by position. Here each historical event is scored against the current input,
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and low scorers sink into the document layer.
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Scoring uses dense cosine similarity in the same vector space as media and documents, falling
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back to TF-IDF when the model isn't loaded. The most recent N events are always kept, but N is
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itself bounded by budget:
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```go
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// protected count cannot exceed what the budget can hold
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budgetCap = ContextTokens / avgEventTokens
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pCount = min(configured, budgetCap)
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```
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That bound came from a measurement: with a 50k window, one event averaged 5800 tokens (tool
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results), while the budget was 40000, so "pin 10 events" didn't fit. Every prune hit the
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protection floor, the budget was exhausted after two rounds, and the task terminated. Recall
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dropped from 44% to 22%. Smaller windows now tighten automatically, and a 1M window can use the
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full configured value.
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Pruned events aren't discarded, they migrate: the event and the media blocks it holds move into
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the document layer with block IDs unchanged, only the holding layer changes. Retrieval uses
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`coldcontext_query` in two phases (Peek to confirm, then Remove) rather than delete-then-refill,
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which would lose data permanently if the refill failed.
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### Three layers migrate, they don't copy
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```
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L0 context relevance pruning, last N always protected
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↓ low scorers sink (with media blocks)
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L1 documents dense retrieval; access count ≤2 and stale means sink further
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↓ distilled into triples
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L2 graph entities, relations, scene index; recall by scene
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```
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A block has exactly one holder at any layer, with no reference counting to keep it alive. Media
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are not attachments: images and audio are first-class graph nodes with their own vectors,
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searchable in the same space as text.
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### Memory is partitioned by scene, not by session
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Typical agents keep one history per session, so switching topics loses context or crams
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everything into one window. Here memory is partitioned by observable scene fingerprint: channel,
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conversation partner, tools, topic, time of day, weighted 1.0/1.0/0.8/0.4/0.2.
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A scene comes into being only on the second matching fingerprint (`minSceneEvidence = 2`).
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Join threshold 0.5, recall 0.35 — recall is looser because failing to recall loses information
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while an extra scene costs a few lines of context. Each recurrence adds to scene strength;
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scenes that stop recurring decay by half-life.
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Two paths run in parallel: **declared** (a plugin states at the injection point which scene this
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is) and **emergent** (fingerprints cluster on their own). Declared scenes deliberately don't
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absorb the full fingerprint, otherwise they'd score near 1.0 against every later turn and the
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emergent path could never grow finer-grained scenes.
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### Seven stages
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`internal/agent/core/stages.go`
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| Stage | When | Plugins can |
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|---|---|---|
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| `on_input` | message arrives | blocklist, rate limit, short-circuit reply |
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| `pre_action` | context ready, before LLM | inject external data |
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| `post_action` | LLM returns text and tool calls | filter, force redirect |
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| `before_toolcall` | before one tool runs | audit, deny, rewrite args |
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| `after_toolcall` | after one tool runs | redact, sort results |
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| `before_output` | final text ready | format adaptation |
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| `after_output` | after sending | statistics |
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`post_action → [before_toolcall → run → after_toolcall] → post_action` is the inner loop. It exits
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when the model stops calling tools, all calls are denied, or the limit is reached.
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## Everything else
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- **Channels**: web UI, CLI, QQ groups and DMs, A2A, ACP in your IDE
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- **Tools**: files, shell, code, browser (real Chrome), calendar, notes, RSS, music
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- **Modalities**: images, video, voice, screenshots, web pages
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28 built-in plugins, 89 model-callable tools. External plugins live one per repo under [github.com/HologramAgent](https://github.com/HologramAgent).
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## Downloads
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[Releases](https://github.com/JianFeeeee/HologramAgent/releases) ship three variants:
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| Variant | Contents | For |
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| **full** | homed + waiter + desktop GUI + systemd unit | Single-machine, everything |
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| **server** | homed + waiter + systemd unit | Servers (no desktop environment) |
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| **client** | waiter + desktop GUI | Connecting to a remote HologramAgent |
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- Linux: `.deb` (amd64/arm64), `.rpm` (x86_64), `.tar.gz`
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- 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.
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- Portable: `homeagent-bin-<os>_<arch>.tar.gz` (homed/waiter/initconfig)
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- Verification: `SHA256SUMS`
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The macOS `homed` requires a native macOS build (CGO + sqlite3), so release packages ship only `waiter`/`initconfig`.
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## Build
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```bash
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make build build-cli # Build daemon + CLI
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make test # go test ./...
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make install # Install to system
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```
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Dependencies: Go 1.25+, CGo (go-sqlite3), Linux.
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> `homed` requires Linux (it relies on fd inheritance and intra-segment offset
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dereferencing of the shared memory region; see `cmd/homed/platform_windows.go`).
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On Windows only `waiter.exe` is built and `homed` runs under WSL2 (see Downloads).
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macOS can build `waiter`/`initconfig`; `homed` must be built on native macOS.
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## License
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This project is released under the **GNU Affero General Public License, version 3
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(AGPL-3.0-only)** — see [LICENSE](LICENSE).
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This is the strongest copyleft in the GPL family: besides shipping the complete corresponding
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source when you distribute the software, **you must also offer the source to users who interact
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with it over a network** (§13, Remote Network Interaction). Anyone running a modified HologramAgent
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as a network service therefore has to make the modified source available to that service's users.
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Plugins are **statically linked** against this project through the public
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[hologramagentsdk](https://github.com/JianFeeeee/hologramagentsdk) (the SDK source ends up inside the
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plugin binary), but that repository is released under **MIT**. That is a permissive license, so code
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received under it does **not** inherit this project's AGPL. External plugins are therefore **not
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derivative works of this project**: authors pick their own license (closed-source, commercial or
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private included), with no same-license obligation and no §13 network clause. The safety and
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vitality of the third-party plugin ecosystem rest on this.
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The boundary is clean: **AGPL covers the kernel and the bundled plugins** (`homed`, `internal/`,
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the 13 built-in plugins under `internal/plugins/`); **MIT covers the public SDK** (`sdk/`, whose
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`go.mod` has zero external dependencies and imports only the Go standard library, never
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any kernel code). Process isolation is beside the point here. What decides the
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license is the linked SDK code itself, and that code is MIT.
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### Third-party components shipped with the packages
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| Component | License | Location |
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| Chinese-CLIP ViT-B/16 (ONNX artifacts) | Apache-2.0 | `/usr/lib/hologram/models/chinese-clip-vit-b16-onnx/` |
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| ONNX Runtime (`libonnxruntime.so`) | MIT | `/usr/lib/hologram/onnxruntime/` |
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| jieba dictionary (embedded in the binary) | MIT | `internal/memory/jiebadict/` |
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| Go dependencies (go-sqlite3, gojieba, bubbletea, …) | MIT / BSD-3 / Apache-2.0 | permissive, AGPL-3.0-compatible |
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These components keep their own licenses and are not relicensed by this project. Full texts are
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shipped in `/usr/share/doc/hologramagent/licenses/`, and the package metadata declares this package
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as `AGPL-3.0-only`.
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