改名 HomeAgent → HologramAgent 后,介绍站与仓内文档仍指向旧仓地址、旧站点
域名,点击即 404 / 落回旧站。本批只动文档与静态站,代码零改动。
映射(严格按主管给定的唯一权威口径):
- github.com/JianFeeeee/HomeAgent → .../HologramAgent(含 /blob /releases /tree)
- github.com/JianFeeeee/homeagentsdk → .../HologramAgent-sdk
- introduce.homeagent.jianfgit.xyz → introduce.hologramagent.jianfgit.xyz
- sdk.homeagent.jianfgit.xyz → sdk.hologramagent.jianfgit.xyz
- 品牌名/链接文本 homeagent-sdk → HologramAgent-sdk(含 `cd homeagent-sdk`
克隆后的本地目录名,跟随新仓名)
改动面(12 个文件,138 行,纯替换无逻辑变更):
- site/index.html 36 处:13 处主仓链接 + 23 处 SDK 仓链接(22 个 example/
与 LICENSE、仓库首页)
- README.md / README_EN.md 各 12 处(9 处 URL + 3 处链接文本)
- assets/docs/{zh,en}/PLUGIN_DEV.md 各 34 处、OVERVIEW.md 各 2 处
- docs/zh/ci-cd-runbook.md 9 处、docs/zh/deploy-runbook.md 2 处(站点域名)
- deploy/scripts/deploy-sdk-site.sh 5 处(注释里的 server_name + 3 处 curl
探活地址,否则改完站脚本仍在验旧域名)
- docs/git-branching.md 2 处、assets/skills/commondev-discipline/SKILL.md 1 处
(gitcode module 路径)
刻意不动(动了会坏):
- 真实目录 third_party/homeagent-sdk(只改 URL 里的 homeagentsdk)
- go.mod 的 module / replace、所有 .go import(module 仍是
github.com/JianFeeeee/HologramAgent,SDK 仍 homeagentsdk)
- deploy/packaging/package-linux.sh 里的 `replace github.com/JianFeeeee/
homeagentsdk` 与 clone URL —— 它必须与未改动的 go.mod 逐字一致,属代码非文档
- 数据目录 ~/.homeagent、二进制 homed、产物名 hologramagent_*、
插件名 homeagent-mail-bridge
- systemd 相关行(homeagent.service / systemctl / journalctl)—— 待上级确认,
本批不动,清单见汇报
- docs/api/*.md(首行「请勿手改」,生成型)
判据(含变异验证):
- 三条旧串 grep 全仓清零;third_party/homeagent-sdk 目录仍在
- site/index.html 标签配平(HEAD/WORK 双跑)+ section 数 13→13 未减
- 变异:分别还原 deploy-sdk-site.sh:206、site/index.html:3041、README.md:243、
ci-cd-runbook.md:11 各一处,以及删一个 </footer>,判据均按预期报红
- go build ./... 通过(仅把 TMPDIR 指到 /var/tmp 规避 /tmp tmpfs 满,仓内文件无关)
未执行任何线上动作:不 push、不开 PR、不重命名 GitHub 仓。注意 SDK 仓在
GitHub 侧目前仍叫 homeagentsdk,本批把文档指向 HologramAgent-sdk 是「改名
在路上」,该仓改名完成前这批 SDK 链接会 404。
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
Getre-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 themedia_digestsargument ofmemory_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 withmedia_refsreference 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 layerprocess.go/stages.go— Processing pipeline: memory recall → persona injection → LLM call → tool execution → output delivery, 7 stage hookstoolcall.go— Tool scheduling and executioncontext.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 ininternal/lua/adapters/, one.luaper 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.htmlpackaged via//go:embed) - REST API: status query, configuration management, memory operations, knowledge management, plugin management
- OpenAI API-compatible
/v1/chat/completionsendpoint - 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 (HologramAgent-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:
.hmapplugin package format, installable via WebUI