HomeAgent/assets/docs/en/OVERVIEW.md

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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 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