生产现象:单轮内 output_send__qq 被调用 34 次、持续 514 秒,直到 QQ 插件
自己的循环保险拒绝发送才停下(problem.md)。根因是多环节叠加,核心侧修四处:
1. 工具轮补位文案(process.go)
通用占位「请根据以上工具结果继续。」对纯输出通道调用是错的:异步通道
(qq/wechat)的回复只能经 output_send__* 交付,所以模型「已完成回复」的
表达形式就是一个工具调用,紧随其后的「请继续」会被读成「还要再做一步」,
而能做的「一步」恰好还是再发一条消息。
改为按上一批工具的性质选文案:全部是 output_send__* 时补
「若你的回复已完成,直接返回纯文本即可结束本轮,无需再调用任何工具。」
同时每轮先移除旧占位再补一条,避免占位在 prompt 前缀里线性累积。
(该占位是 zen 网关「最后一条必须是 user」的传输层附加物,HEAD 版本是
无条件内联追加、从不移除。)
2. 输出成功回执(output.go)
「已通过 [qq] 通道发送: map[status:sent]」这类富回执会被读成「这步成功,
继续下一步」。成功改为只回极简标记。
3. proc 桥标量透传(internal/plugin/proc/plugin.go)
插件返回 "ok" 时不再伪造 {status:sent} 覆盖插件真实返回值,否则只改
output.go 不生效。
4. 子任务结果幂等(spawn.go)
child_result 原先读到即删,而完成通知长期留在持久上下文里
(formatMergedTimeline 每轮重新注入),第二次查询必然得到
「不存在或已过期」这个永久失败信号,模型据此认为任务未完成而反复重试。
改为保留结果 + delivered 标记,重复查询返回明确提示;结果按上限有界淘汰。
顺带:agent.go 去掉文档层显式向量器注入(TF-IDF 已内置为 fallback),
cmd/homed/main.go 同步 document.NewStore 的 tokenizer 参数。
测试:internal/agent/core/tooloop_test.go(5 例)、spawn_test.go(3 例)。
⚠️ 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)
Since v1.1.1 media reaches the plugin boundary: plugins and the model can both read and
write images/audio in memory (InsertWithMedia, InjectInputMedia). Media lives in plain-text
memory as a [<mime> <short digest>] <description> marker — the description is the searchable
semantic memory, the digest is the key back to the bytes.
Since v1.0.0 external plugins are independent subprocesses, communicating with the kernel over
stdio JSON-RPC (control plane) + a shared memory segment (data plane) + an event ring (notification
plane). A plugin crash cannot take down the kernel and it restarts automatically; swapping
plugin.bin gives true hot-reload.
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 + subprocess loader (stdio RPC + shared memory segment + event ring)
├── 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
v1.1.1 — Multimodal reaches the plugin boundary. v1.1.0 gave the memory system binary
multimedia nodes, but that path was open only to the kernel itself; this release opens it to
plugins and the model. The public SDK gains media fields and three media injection methods
(paired with SDK v1.1.0,
shared by the whole 1.1.x line), and the kernel implements the four matching RPCs. The bridge
layer had been silently dropping fields: Confidence/types/SentenceText handed in by a
plugin were discarded, Doc kept only three fields, and Remove never released references
(media stayed "referenced" forever, so GC could never reclaim it). processTextInput and
processMediaInput were unified into a single processInput, which finally gives the media
path the dedup, no_memory, channel Cleaner, interrupt semantics and correct EventRawInput
it had always lacked. Three real defects fixed: user-sent images never appeared in the WebUI
chat log (the media path published a map while the subscriber asserted a string),
memory_commit's sentence_text had never been exposed to the model (though it is the
mandatory link in the media binding chain), and two data races in PluginSDK (11 reported
by -race; in production this showed up as sporadic nil-dereference crashes during plugin reload).
v1.1.0 — Memory system supports binary multimedia nodes. Content-addressed media store
(CAS + SQLite metadata + on-disk blobs, Get always re-verifies the digest) wired through L0
(context events) / L2 (documents) / L3 (graph sentences), with reference-counted GC (referenced
items are never deleted). The description text produced by the vision model is the durable
semantic memory; the blob is only a cache that capacity GC may evict.
v1.0.0 — External plugins moved from C ABI shared libraries to subprocess + shared memory. The first release that no longer loads .so/.dll, and it is incompatible with 0.9.x (existing plugins must be rebuilt into plugin.bin with the new plugindev, though business code needs zero changes). Eliminates 6 classes of defects that had caused production incidents: hot-reload silently failing (DF_1_NODELETE making dlclose a no-op), no crash isolation (a plugin panic took down homed), stage lost updates (35.8~36.8% loss under the copy model), uncancellable cgo timeouts (linear OS-thread leaks), output_send reporting false success (the model was told "sent" while the message never went out), and Windows capability degradation (only 3 stage fields visible, no write-back). Three communication planes: stdio JSON-RPC (control) + shared memory segment (data) + event ring (notification); the privilege gradient is now enforced by three explicit gates. RPC round-trip p50 24.1µs; crash-to-recovery under 1s.
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). This ABI retired with v1.0.0.
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
Downloads
Releases ship three variants:
| Variant | Contents | For |
|---|---|---|
| full | homed + waiter + desktop GUI + systemd unit | Single-machine, everything |
| server | homed + waiter + systemd unit | Servers (no desktop environment) |
| client | waiter + desktop GUI | Connecting to a remote HomeAgent |
- Linux:
.deb(amd64/arm64),.rpm(x86_64),.tar.gz - Windows:
HomeAgent_v1.1.1_{Full,Server,Client}_win64.exe(NSIS installer) - Portable:
homeagent-bin-<os>_<arch>.tar.gz(homed/waiter/initconfig) - Verification:
SHA256SUMS
The macOS homed requires a native macOS build (CGO + sqlite3), so release packages ship only waiter/initconfig.
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.
