问题一:提示词说反话,导致 qq 回复大量丢失。
v2 架构曾有「回复自动回投来源通道」的能力(IOManager.routes + RegisterOutputRoute),
但 304c3ae 'remove IO route mapping' 删掉了整套路由映射。此后:
- outputCh 唯一消费者(cmd/homed/main.go)只处理 memory_candidate,其余静默丢弃,
output.go 末尾的 EmitTextTo 兜底成为死路
- ResponseCh 只剩 webui /api/v1/chat、cli、clawhubadapter 三处同步 HTTP 用途,
不再承担渠道投递;qq 走 InjectInterruptText → interruptCh,ResponseCh 恒为 nil
- emitResponse 从不调 GetDevice(),纯文本对异步通道 = 丢弃
提示词却仍写着「直接返回纯文本即可送达,无需额外工具」「output_send 不是回复的必要步骤」。
实测近 12h 6 次 qq 输入仅 1 次送达,规律是 tools 含 output_send__qq 才到,否则全丢,
且 agent 自认为已回复。现改为明确区分同步/异步通道并要求显式 output_send__。
问题二:无事实性约束,工具失败时模型编造内容。
qq_get_message 30 次调用有 9 次返回 not_found:true(NapCat 响应解析失败),
模型未如实说明,转而虚构消息正文——包括一条不存在的 message_id=1321159191
(全 journal 零命中、不在任何调用记录里)配上完全虚构的正文
「我想搭一个 Dify 工作流,想做一个人脸识别系统 demo」,用户从未说过。
虚构内容经 formatMergedTimeline 回灌【对话时序】后被当作既有事实反复复述放大
(两条消息 reasoning_content 达 180KB / 220KB)。
现补充:工具返回 not_found/空结果必须如实说明不得猜测;【对话时序】是历史事实摘要
不是当前任务;无依据的人名/需求/数字/路径直接说不知道。
注:set() 用 INSERT OR IGNORE,改此默认值只影响新部署;
本机运行实例的 config.db core.agent.system_prompt 已同步更新(改前备份)。
验证:重新部署后实测 agent 明确回答 qq 需 output_send__qq 且纯文本会被丢弃;
对 message_id=1321159191 如实报告 not_found 并声明「正文完全不知道,绝不猜」。
⚠️ AI-Assisted Programming Notice: Parts of this project's code, documentation, and commit history were generated or modified with AI assistance. Key changes have been human-reviewed, but please evaluate and verify before use.
HomeAgent
中文: README.md
An Agent framework designed around separation of core domain and application domain. The kernel enforces a zero-IO policy — all external interaction (WebUI, QQ, CLI, file operations, web search, memos, etc.) is handled by the plugin layer; the kernel performs no direct IO operations.
Combined with a three-layer memory architecture (Context → Document → Graph), it maintains contextual coherence across long-running single-conversation sessions through tiered storage and automated archival.
homed (kernel, zero IO) ← PluginSDK → plugins (all IO capabilities)
Design Principles
Separation of Core Domain and Application Domain — The kernel's responsibilities are limited to LLM orchestration, memory management, and knowledge retrieval; all IO capabilities (message send/receive, file read/write, network requests, hardware interaction, etc.) are implemented by plugins. This separation defines domain boundaries at the Agent framework level, with distinct responsibility scopes for the kernel and plugins.
Three-Layer Memory Architecture — Manages information retention in long-running agents through a tiered storage strategy:
- Context Layer: Pretrained word embedding / TF-IDF fallback relevance-scored event window, protects last 10 entries, maintains topK context entries
- Document Layer: Temporary memory with automatic cold data sinking, also supports user-initiated submissions
- Graph Layer: SQLite graph database, persists entity relationships and semantic memory, supports distillation pipelines to extract triples from conversations
Architecture Diagrams
1. Message Processing Sequence
sequenceDiagram
participant U as User/Plugin
participant IO as IOManager
participant EV as eventLoop
participant CTX as RelevanceContext
participant LLM as LLM+Tool Loop
participant ST as StageHost
participant MEM as Three-Layer Memory
U->>IO: InjectInput(type, payload)
IO->>EV: inputCh
rect lavender
Note over EV: processTextInput
EV->>ST: StageOnInput Plugin can rewrite/short-circuit
EV->>CTX: Prune(input,topK) StaticEmbedder/TF-IDF cosine pruning
CTX->>MEM: Low-score events archived to Document (original timestamp)
EV->>CTX: Append(input) CleanTemplateText→three-branch vector→5s write
end
rect lightgreen
Note over EV,LLM: process()
EV->>MEM: buildMemoryContext Indexer recalls from Graph (vector+jieba→BFS depth=2)
EV->>MEM: buildSystemPrompt DocQuery summary+Graph memory index+Persona+Skills
EV->>ST: StagePreAction Plugin can pre-intercept
loop Tool loop
LLM->>LLM: drainInterrupts
LLM->>LLM: LLM Chat
LLM->>ST: StagePostAction Plugin can modify/short-circuit
alt No tool call
LLM-->>EV: Returns response
else
loop Each tool
ST->>ST: StageBeforeToolcall Plugin can reject
LLM->>LLM: executeToolCall
ST->>ST: StageAfterToolcall
end
end
end
end
rect lightpink
Note over EV: emitResponse
CTX->>CTX: Append(response)
ST->>ST: StageBeforeOutput Plugin can rewrite
EV-->>U: ResponseCh CLI sync
EV-->>EV: Event bus WebUI SSE
ST->>ST: StageAfterOutput Read-only
EV->>MEM: emitMemoryCandidate
end
2. Stage Pipeline
flowchart LR
S1[① on_input] --> S2[② pre_action]
S2 --> S3[③ post_action]
S3 --> Q{Has tool?}
Q -->|Yes| S4[④ before_toolcall]
S4 --> T[executeToolCall]
T --> S5[⑤ after_toolcall]
S5 --> S3
Q -->|No| S6[⑥ before_output]
S6 --> S7[⑦ after_output]
style S1 fill:#e1f5fe
style S3 fill:#fff3e0
style S6 fill:#e8f5e9
3. Three-Layer Memory
flowchart TB
subgraph C[① Context Working Window]
RC[RelevanceContext]
A[Append] -->|CleanTemplateText→three-branch vector| RC
P[Prune StaticEmbedder/TF-IDF Cosine] -->|Low score original timestamp| D
P -->|Keep| TL[timeline→chronological→system prompt]
end
subgraph D[② Document File Memory]
DS[DocStore JSON+TF-IDF]
Q1[Query summary auto-inject] -->|[Related Memory Docs]| SP
Q2[doc_query LLM active recall] -->|Consume+delete source| DS
Q2 -->|Original timestamp write to context| RC
CD[FindColdDocs 72h] -->|docToTriples| G
end
subgraph G[③ Graph Database]
DB[(SQLite)]
IDX[Indexer vector+jieba→BFS depth=2] -->|[Memory Index]| SP
MEM[memory_recall/commit/merge/purge/edit]
SOC[person_query/set_trait]
end
subgraph H[④ Heartbeat Distillation]
REORG -->|Step3 Cold docs| CD
REORG -->|Step4 Bigram Jaccard| CONS[consolidation]
PIPE[Pipeline regex] -->|Name/Address/Likes/Age/Job| DB
end
SP[System Prompt] -->|Sequential assembly| LLM
LLM[LLM] -->|doc_query| Q2
LLM -->|memory_recall| MEM
See assets/docs/en/ARCHITECTURE.md for details.
Web Mascot
Quick Start
make build build-cli
./build/homed -data /tmp/ha
# Interactive mode
./build/waiter
# Or single message
echo "Hello, remember that I like coffee" | ./build/waiter
API keys are configured via WebUI http://localhost:8080 settings page, persisted in SQLite.
Code Structure
cmd/homed/ Daemon entry, assembles all subsystems
cmd/waiter/ CLI client (Unix socket)
internal/
├── agent/core/ Agent core: event loop, LLM tool loop, 7-stage pipeline
├── agent/api/ LLM Provider + 8 Lua adapters
├── memory/ Three-layer memory: Graph(SQLite) / Document(JSON+TF-IDF) / Text(JSONL) + StaticEmbedder(pretrained word embedding/TF-IDF fallback) + CleanTemplateText(de-template)
├── knowledge/ Knowledge base (filesystem + TF-IDF)
├── plugin/ Plugin registry + .so/.dll dynamic loader
├── plugins/ 11 built-in plugins (webui/cli/timer/cmd/mcp/clawhubadapter/agentcli/healthcheck/pluginmgr/files/cfgmgr)
├── sdk/ PluginSDK (Tool/Stage/Event three channels)
├── config/ SQLite config center
├── events/ Event bus
└── internal/lua/adapters/ 8 LLM protocol adapter scripts
External plugin development: see [homeagent-sdk](https://gitcode.com/JianFeeeee/homeagent-sdk) repo, use `plugindev` toolchain, refer to Go and Lua examples in `example/`
Project Status
v0.9.0 — C ABI v2: external plugin Stage callbacks can now write back (invoke_stage gained a result out-param; plugins may mutate RawMessage/LLMText/ToolResults etc. in OnInput/AfterToolcall/PostAction and have them synced to the core). ABI version now tracks core minor releases (v0.9.x → ABIVersion=2, version_min=1 keeps old plugins loadable). Also fixes the tool-loop zen-compat placeholder that wrongly fired on first-turn system context tail. The SDK ships an enhanced sanitizer example (bad-UTF-8 / U+FFFD / ANSI-escape scrub across the whole pipeline).
v0.8.0 — Core is functional, plugin system enhanced. 20+ built-in plugins. External plugin development via homeagent-sdk repo. Added input channel NoMemory/Cleaner, ChannelDef, plugin disable/enable system (CLI + WebUI), plugindev toolchain C ABI ChannelDef support.
Documentation
- Project Overview | 中文
- Technical Architecture | 中文
- Plugin Development Guide | 中文
- Lua Adapter | 中文
- Knowledge Base Demo
Build
make build build-cli # Build daemon + CLI
make test # go test ./...
make install # Install to system
Dependencies: Go 1.25+, CGo (go-sqlite3), Linux/Windows.
