mirror of
https://gitcode.com/JianFeeeee/HomeAgent.git
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931c803421
只改仓库内文件的纯展示品牌名与已随批 2 落地的安装路径；运行时契约、仓库 URL、
域名、环境变量、可执行名、插件名一律不动。
一、纯展示名（HomeAgent → HologramAgent，共 44 处 / 18 个文件）
- README.md 4、README_EN.md 4（标题、client 变体说明、Windows 安装包名、AGPL 段）
- docs/ 8 个文件 14 处（git-branching 1、ci-cd-runbook 1、commondev-design 3、
commondev-agent-runtime-design 4、deploy-runbook 1、legacy-issues 1、
prod-migration-checklist 1、triple-extract-attention 2）
- assets/docs/ 6 个文件 14 处（zh/en 的 OVERVIEW 2+2、ARCHITECTURE 2+2、
PLUGIN_DEV 3+3）
- assets/branding/mascot-xiaozhai-guide.md 2 处（身份行、标志名）
- site/index.html 6 处（title、meta description、og:title、logo alt、页头字、
插件卡片文案 "HologramAgent 接入 AgentMail"）
二、安装路径对齐批 2 产物（共 10 处）
- /usr/lib/homeagent → /usr/lib/hologram（7 处：README 2、README_EN 2、
multimodal-space 3）
- /usr/share/doc/homeagent → /usr/share/doc/hologramagent（3 处：README 1、
README_EN 1、multimodal-space 1；注意 Doc 路径带 agent 后缀）
- site/index.html 的 19 处 HomeAgent 里，13 处是 github.com/JianFeeeee/HomeAgent
仓库 URL，属红线整串保留；只改 URL 之外的展示文案（其余 13 处零改动）
三、明确不改（红线 / 一致性冲突）
- site/index.html 的 23 个 github.com/JianFeeeee/homeagentsdk 外链（21 条
example + 2 条纯仓库），SDK 仓改名属批 4-b，一个未动
- 核心仓仓库 URL、sdk./introduce.homeagent.jianfgit.xyz 域名、HOMEAGENT_ 前缀、
homed/waiter/hmapdev、.hmap、homeagent-mail-bridge 等插件名、third_party/、
internal/ 与 cmd/ 全部零改动
- ~/.homeagent 路径本批保持原样：cmd/waiter/main.go:109、daemon.go:290 硬编码
仍为 .homeagent，全仓无 .hologram 命中；照规格改文档会指向程序不用的目录，
需与代码侧那条线一起改（遗留项，已单独上报）
判据：文档区展示名残留归零（44 文件）、红线计数与 base e3584bb 逐项相同、
三条变异均被咬住且还原后复绿。
116 lines
8.5 KiB
Markdown
116 lines
8.5 KiB
Markdown
**中文** | [English](../en/OVERVIEW.md)
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# HologramAgent — Project Overview
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## What Is This
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HologramAgent is a continuously-running personal intelligent Agent framework.
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Core architecture: a long-running kernel process (`homed`) that connects to various IO channels (QQ, Web, CLI, etc.) through a plugin system. The kernel handles LLM orchestration, memory management, and knowledge retrieval; plugins handle all external IO — sending/receiving messages, file operations, web search, etc.
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### Design Highlights
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**Separation of Core Domain and Application Domain** — The kernel (core domain) performs no IO operations; all IO capabilities belong to plugins (application domain). The boundary is defined through PluginSDK:
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- Plugins register tools (Tool) with the kernel for LLM invocation
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- Plugins hook into the processing pipeline (Stage) to intercept/rewrite message flow at various phases
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- Plugins subscribe/publish events (Event) for loosely-coupled communication
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- Plugins queue or interrupt input delivery through IO API
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The significance lies in clear responsibility boundaries: the kernel focuses on orchestration and memory management, while plugins handle IO implementation — the two are not coupled.
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**Business semantics stay in plugins** — the kernel does not define business
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objects: terminals, command history, device state and media libraries all belong to
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the plugin domain. Take terminals: the PTY, session lifecycle and output buffering
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all live in `agentcli`; when WebUI or CLI needs a terminal panel it **subscribes to
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the event bus** (`EventTerminalOutput` + `EventToolCall`) and builds its own view
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instead of reading a state component inside the kernel. The kernel offers only the
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event bus and the plugin registration surface — adding a new capability does not
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require touching the kernel, and no second version of business truth accumulates there.
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**Three-Layer Memory Architecture** — Manages information retention across long agent runtimes through a tiered storage strategy:
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- **Context Layer**: In-memory pretrained word embedding scored event window (StaticEmbedder word vectors → CosineSimilarity, TF-IDF fallback), maintains recent context in real-time, low-relevance events automatically sink to the next layer
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- **Document Layer**: JSON files + TF-IDF vector-indexed temporary memory, supports explicit submission and implicit archival, cold data distills to Graph
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- **Graph Layer**: SQLite graph database, persists entities and relations, BFS traversal recall, distillation pipeline extracts triples from conversations
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Three progressive layers — context, cold archive, long-term graph memory — form an information decay and consolidation pipeline from short-term to persistent storage.
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**Media Memory (first-class memory blocks since v1.2.0)** — Images and audio are not attachments; they are first-class nodes of memory:
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- **Content-addressed store (CAS)**: addressed by digest, metadata in SQLite and blobs on disk (two-level
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bucketing), identical bytes stored once. Every `Get` re-verifies the digest (silently returning corrupt
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data is worse than an error).
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- **Blocks carry their own multimodal vector and fingerprint**, and are retrieved directly — there is no
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media marker in the body text any more.
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- **No separate GC, no reference counting, no keep-set**: blocks follow the single-layer invariant —
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Context → Document → Graph is a **migration** of a block, not a copy, and nothing is kept alive by
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references; **deleting a block deletes its content.**
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- **Reaches the plugin boundary since v1.1.1**: plugins read and write media through `InsertWithMedia` /
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`InjectInputMedia`; the model attaches media via the `media_digests` argument of `memory_commit` / `doc_commit`.
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- **The old mechanism was removed entirely**: the **descriptive index** — writing the vision model's
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description into the body text as a `[<mime> <short digest>] <description>` marker and retrieving by it —
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together with `media_refs` reference counting, was deleted in v1.2.0. A description is second-hand
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information produced by a model; retrieving "someone else's rendering of the image" is worse than
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retrieving the image's own vector.
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## What It Actually Does
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Code is in the project root, implemented in Go.
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**Kernel** (`internal/agent/core/`):
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- `eventloop.go` — Message loop (`eventLoop`), queuing input from the IO layer
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- `process.go` / `stages.go` — Processing pipeline: memory recall → persona injection → LLM call → tool execution → output delivery, 7 stage hooks
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- `toolcall.go` — Tool scheduling and execution
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- `context.go` — Context management (pretrained word embedding scoring StaticEmbedder → CosineSimilarity, TF-IDF fallback), automatic pruning of low-relevance events
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- LLM calls abstracted through the Provider interface, with multi-source automatic fallback
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**Memory System** (`internal/memory/`):
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- **GraphDB** (`graph.go`) — SQLite, entities + relations tables, BFS traversal
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- **Document Store** (`document/document.go`) — Temporary memory, JSON files + TF-IDF vector index, consume-on-read
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- **Text Memory** (`text/text.go`) — Raw conversation logs, JSONL file rotation
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- **Social Store** (`social/social.go`) — Persona traits + relationship network, wraps GraphDB
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- **Memory Indexer** (`indexer.go`) — Auto-vectorizes GraphDB entities, recalls and injects into system prompt on user input
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**Knowledge Base** (`internal/knowledge/knowledge.go`):
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- File system directory `knowledge/<name>/content.md`
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- TF-IDF vector search, independent index instance from the memory system
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- LLM operates via three tools: `knowledge_search` / `knowledge_create` / `knowledge_list`
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**Plugin System** (`internal/plugin/`):
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- Built-in plugins: Go `init()` self-registration, compiled into kernel
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- External plugins (since v1.0.0): compiled to an ordinary Go binary `plugin.bin`, spawned by the
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kernel as an **independent subprocess**, communicating over stdio JSON-RPC (control plane) +
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a shared memory segment (data plane) + an event ring (notification plane); Lua script plugins are
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also supported (the C ABI shared-library channel, `-buildmode=c-shared`, was removed entirely in v1.0.0)
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- PluginSDK (`internal/sdk/`) defines four channels: RegisterTool / RegisterStage / Subscribe / RegisterOutputChannel
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- 7 stage hooks: on_input → pre_action → post_action → before_toolcall → after_toolcall → before_output → after_output
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**LLM Provider** (`internal/agent/api/provider.go`):
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- Provider interface: Name / Chat / ChatStream / MaxContextTokens
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- Single implementation, `LuaAdaptedProvider`: every source goes through a Lua adapter script that
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transforms requests/responses; adapter scripts live in `internal/lua/adapters/`, one `.lua` per protocol
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- 10 built-in adapters: deepseek / openai / anthropic / gemini / mistral / groq / github / kimicode / ollama / server
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- Sources (`core.llm.sources.<name>.*`) are configured at deploy time — the count is deployment-specific
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and is not tied to the number of built-in adapters
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**WebUI** (`internal/plugins/webui/`):
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- Embedded SPA dashboard (`dashboard.html` packaged via `//go:embed`)
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- REST API: status query, configuration management, memory operations, knowledge management, plugin management
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- OpenAI API-compatible `/v1/chat/completions` endpoint
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- SSE event stream `/api/v1/chat/events`
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**ClawHub Adapter** (`internal/plugins/clawhubadapter/`):
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- Unified loader for OC plugins (Node.js), Python sidecar, JS sidecar, and SKILL plugins
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- RegistryDispatcher pattern: routes registration notifications to Tool/Provider/Channel/Stage registries
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- ClawHub marketplace search and install: `clawhubadapter_search` / `clawhubadapter_npm_install`
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- 9 provider types mapped to LLM-accessible tools (image generation, web search, speech, etc.)
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- OC channels auto-registered as IO devices with text/file/image/audio capability flags
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## Project Status
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Core functionality is operational. Plugin system and SDK are ready for independent external plugin development.
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- Built-in plugins (13): webui / cli / agentcli / cfgmgr / healthcheck / kbtree / multimodal / pluginmgr / clawhubadapter / remotedevice / skillmgr / seemedia / seq
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- Criterion: **a plugin stays built-in only if it requires internal-SDK-only capabilities**; everything else ships as an .hmap external plugin (see ARCHITECTURE)
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- External plugin examples ([homeagent-sdk](https://github.com/JianFeeeee/homeagentsdk) repo `example/`, both Go and Lua types): qq / files / a2a / ai_image / bili / browser / calendar / editdoc / memo / music / ocr / rss / sanitizer / weather / luademo
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- Distribution: `.hmap` plugin package format, installable via WebUI
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