Files
HomeAgent/assets/docs/en/OVERVIEW.md
JianFeeeee 931c803421 refactor(docs): 核心仓文档与介绍站展示名改名为 HologramAgent
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。
2026-10-21 17:00:48 +08:00

8.5 KiB

中文 | English

HologramAgent — Project Overview

What Is This

HologramAgent is a continuously-running personal intelligent Agent framework.

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.

Design Highlights

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:

  • Plugins register tools (Tool) with the kernel for LLM invocation
  • Plugins hook into the processing pipeline (Stage) to intercept/rewrite message flow at various phases
  • Plugins subscribe/publish events (Event) for loosely-coupled communication
  • Plugins queue or interrupt input delivery through IO API

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.

Business semantics stay in plugins — the kernel does not define business objects: terminals, command history, device state and media libraries all belong to the plugin domain. Take terminals: the PTY, session lifecycle and output buffering all live in agentcli; when WebUI or CLI needs a terminal panel it subscribes to the event bus (EventTerminalOutput + EventToolCall) and builds its own view instead of reading a state component inside the kernel. The kernel offers only the event bus and the plugin registration surface — adding a new capability does not require touching the kernel, and no second version of business truth accumulates there.

Three-Layer Memory Architecture — Manages information retention across long agent runtimes through a tiered storage strategy:

  • 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
  • Document Layer: JSON files + TF-IDF vector-indexed temporary memory, supports explicit submission and implicit archival, cold data distills to Graph
  • Graph Layer: SQLite graph database, persists entities and relations, BFS traversal recall, distillation pipeline extracts triples from conversations

Three progressive layers — context, cold archive, long-term graph memory — form an information decay and consolidation pipeline from short-term to persistent storage.

Media Memory (first-class memory blocks since v1.2.0) — Images and audio are not attachments; they are first-class nodes of memory:

  • Content-addressed store (CAS): addressed by digest, metadata in SQLite and blobs on disk (two-level bucketing), identical bytes stored once. Every Get re-verifies the digest (silently returning corrupt data is worse than an error).
  • Blocks carry their own multimodal vector and fingerprint, and are retrieved directly — there is no media marker in the body text any more.
  • No separate GC, no reference counting, no keep-set: blocks follow the single-layer invariant — Context → Document → Graph is a migration of a block, not a copy, and nothing is kept alive by references; deleting a block deletes its content.
  • Reaches the plugin boundary since v1.1.1: plugins read and write media through InsertWithMedia / InjectInputMedia; the model attaches media via the media_digests argument of memory_commit / doc_commit.
  • The old mechanism was removed entirely: the descriptive index — writing the vision model's description into the body text as a [<mime> <short digest>] <description> marker and retrieving by it — together with media_refs reference counting, was deleted in v1.2.0. A description is second-hand information produced by a model; retrieving "someone else's rendering of the image" is worse than retrieving the image's own vector.

What It Actually Does

Code is in the project root, implemented in Go.

Kernel (internal/agent/core/):

  • eventloop.go — Message loop (eventLoop), queuing input from the IO layer
  • process.go / stages.go — Processing pipeline: memory recall → persona injection → LLM call → tool execution → output delivery, 7 stage hooks
  • toolcall.go — Tool scheduling and execution
  • context.go — Context management (pretrained word embedding scoring StaticEmbedder → CosineSimilarity, TF-IDF fallback), automatic pruning of low-relevance events
  • LLM calls abstracted through the Provider interface, with multi-source automatic fallback

Memory System (internal/memory/):

  • GraphDB (graph.go) — SQLite, entities + relations tables, BFS traversal
  • Document Store (document/document.go) — Temporary memory, JSON files + TF-IDF vector index, consume-on-read
  • Text Memory (text/text.go) — Raw conversation logs, JSONL file rotation
  • Social Store (social/social.go) — Persona traits + relationship network, wraps GraphDB
  • Memory Indexer (indexer.go) — Auto-vectorizes GraphDB entities, recalls and injects into system prompt on user input

Knowledge Base (internal/knowledge/knowledge.go):

  • File system directory knowledge/<name>/content.md
  • TF-IDF vector search, independent index instance from the memory system
  • LLM operates via three tools: knowledge_search / knowledge_create / knowledge_list

Plugin System (internal/plugin/):

  • Built-in plugins: Go init() self-registration, compiled into kernel
  • External plugins (since v1.0.0): compiled to an ordinary Go binary plugin.bin, spawned by the kernel as an independent subprocess, communicating over stdio JSON-RPC (control plane) + a shared memory segment (data plane) + an event ring (notification plane); Lua script plugins are also supported (the C ABI shared-library channel, -buildmode=c-shared, was removed entirely in v1.0.0)
  • PluginSDK (internal/sdk/) defines four channels: RegisterTool / RegisterStage / Subscribe / RegisterOutputChannel
  • 7 stage hooks: on_input → pre_action → post_action → before_toolcall → after_toolcall → before_output → after_output

LLM Provider (internal/agent/api/provider.go):

  • Provider interface: Name / Chat / ChatStream / MaxContextTokens
  • Single implementation, LuaAdaptedProvider: every source goes through a Lua adapter script that transforms requests/responses; adapter scripts live in internal/lua/adapters/, one .lua per protocol
  • 10 built-in adapters: deepseek / openai / anthropic / gemini / mistral / groq / github / kimicode / ollama / server
  • Sources (core.llm.sources.<name>.*) are configured at deploy time — the count is deployment-specific and is not tied to the number of built-in adapters

WebUI (internal/plugins/webui/):

  • Embedded SPA dashboard (dashboard.html packaged via //go:embed)
  • REST API: status query, configuration management, memory operations, knowledge management, plugin management
  • OpenAI API-compatible /v1/chat/completions endpoint
  • SSE event stream /api/v1/chat/events

ClawHub Adapter (internal/plugins/clawhubadapter/):

  • Unified loader for OC plugins (Node.js), Python sidecar, JS sidecar, and SKILL plugins
  • RegistryDispatcher pattern: routes registration notifications to Tool/Provider/Channel/Stage registries
  • ClawHub marketplace search and install: clawhubadapter_search / clawhubadapter_npm_install
  • 9 provider types mapped to LLM-accessible tools (image generation, web search, speech, etc.)
  • OC channels auto-registered as IO devices with text/file/image/audio capability flags

Project Status

Core functionality is operational. Plugin system and SDK are ready for independent external plugin development.

  • Built-in plugins (13): webui / cli / agentcli / cfgmgr / healthcheck / kbtree / multimodal / pluginmgr / clawhubadapter / remotedevice / skillmgr / seemedia / seq
  • Criterion: a plugin stays built-in only if it requires internal-SDK-only capabilities; everything else ships as an .hmap external plugin (see ARCHITECTURE)
  • External plugin examples (homeagent-sdk repo example/, both Go and Lua types): qq / files / a2a / ai_image / bili / browser / calendar / editdoc / memo / music / ocr / rss / sanitizer / weather / luademo
  • Distribution: .hmap plugin package format, installable via WebUI