Files
HomeAgent/assets/docs/en/OVERVIEW.md
JianFeeeee 6d38a164eb chore(module): Go module 路径随托管地迁移 gitcode → github
## 背景

仓库早已迁移到 GitHub(`github.com/JianFeeeee/HomeAgent`,CI/CD 都在那边跑),
gitcode 保留为**国内只读镜像**(`ci.yml` 的 sync-gitcode-main 快进推送)。
但 go.mod 的 module 路径一直是 `gitcode.com/JianFeeeee/...` —— 既与权威源
不符,`meta.SourceURL`(AGPL §13 的源码提供入口)也指向镜像而非实际运行的代码。

## 关键区分(这是本次改动安全的前提)

两个 module 是**不同**的,影响面差一个数量级:

| module | 谁依赖 | 改它的后果 |
|---|---|---|
| `.../HomeAgent`(主仓) | **只有主仓自己** | 内部重构,零外部影响 |
| `.../homeagent-sdk`(SDK 仓) | 主仓 49 文件 + 所有外部插件 | 需重编插件 |

★ 实测确认:**插件不 import 主仓 module**(`strings qq/plugin.bin | grep HomeAgent` = 0),
只依赖 SDK 仓。而插件与内核的边界是 **RPC 方法名字符串**
(`plugin.init` / `tool.invoke`…,见 proc/protocol.go),**与 Go module 名无关**。
⇒ module 改名**不改变任何 wire 协议**,现网已装的插件照常运行。

SDK 的 GitHub 仓名是 `homeagentsdk`(无连字符),module 路径必须与之精确匹配,
否则 `go get` 会因路径不符失败,故取 `github.com/JianFeeeee/homeagentsdk`。

## 改动

- `go.mod` / `go.sum`:两个 module 路径 + require/replace
- 277 个 Go 文件 + Makefile + 打包脚本的 import 与 ldflags 路径
- SDK 仓 `meta.CoreModule`、`tools/hmapdev` 的 module 声明与工程模板
- 对外文档/链接的仓库地址(README、PLUGIN_DEV、OVERVIEW 等)

## 明确**保留** gitcode 的地方(镜像机制,不能动)

- `GITCODE_TOKEN`、`GC_URL="…@gitcode.com"`(CI 同步 job)
- `gitcode.com/api/v5/...`(release 附件上传)
- git remote `gitcode`(镜像拉取/推送)
- `docs/zh/ci-cd-runbook.md`(整篇讲镜像运维)

## 验证

- `go build ./...` / `go vet ./...` 绿
- `go test ./... -count=1` → 48 包 ok / 0 FAIL
- hmapdev 工具链编译通过(`third_party/homeagent-sdk/tools/hmapdev`)
- 全仓残留检查:module 形态的 `gitcode.com/JianFeeeee` = 0
- 镜像机制抽验:GC_URL / @gitcode / api/v5 / remote 均完好

## 顺带修正

`hmapdev/templates.go` 的注释此前写「gitcode 的模块不在 proxy.golang.org 上」——
module 已迁 GitHub,该论断不再成立。改为如实说明**为何仍默认 replace 到本机 SDK**:
离线可构建、避免版本漂移、内网可用。理由与托管地无关,故 replace 保留。
2026-10-08 10:40:17 +08:00

106 lines
7.6 KiB
Markdown

**中文** | [English](../en/OVERVIEW.md)
# HomeAgent — Project Overview
## What Is This
HomeAgent 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.
**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
- 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: webui / cli / timer / cmd / mcp / agentcli / healthcheck / pluginmgr / clawhubadapter / files / cfgmgr
- 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
- Distribution: `.hmap` plugin package format, installable via WebUI