Files
HomeAgent/assets/docs/en/OVERVIEW.md
xiao-zhai 8040586daa docs(rename-batch7): SDK 仓名引用统一为 hologramagentsdk,module 路径按发布 tag 保持旧名
SDK 仓已在 GitHub 改名为 JianFeeeee/hologramagentsdk(旧名 homeagentsdk
301 跳转,仓 id 与提交未变)。本批把仓内**指向该仓的引用**跟上:

- 仓库链接 / 克隆地址 / 示例目录链接:homeagentsdk → hologramagentsdk
  (README zh+en、PLUGIN_DEV zh+en、OVERVIEW zh+en、site/index.html)
- package-linux.sh 的 fallback clone URL 同步新名(仅 URL 与注释)
- ci-cd-runbook:仓库表与 secret 配置命令用当前名,并注明历史改名点

**刻意保留旧名的三处**,都是事实而非遗漏:
1. Go import / go.mod replace 与文档里的 import 示例——已发布 tag
   (v1.3.0 及更早)的 go.mod 仍是 module github.com/JianFeeeee/homeagentsdk,
   改了直接编译不过。PLUGIN_DEV 里加了显式注记说明这条边界。
2. gitcode 的 homeagentsdk → 404 实测行(那是实测事实表)
3. runbook 里「配置时还叫 homeagentsdk」的历史说明

实测(2026-10-22):github.com/JianFeeeee/hologramagentsdk 的 /tree/v1.1.0、
/tree/v1.3.0、/blob/main/LICENSE 均 200;example/* 为 301→tree 200
(GitHub 对目录自动从 blob 跳 tree,非死链);ls-remote 取到
refs/tags/v1.1.0 = 49f9957。

零代码改动:.go/.lua/go.mod/go.sum/third_party 变更数 0。
2026-10-22 07:25:53 +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 (hologramagentsdk 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