Files
HomeAgent/internal/agent/core/process.go
JianFeeeee 28a6d3f09c feat(agent): token-level streaming in core process loop
Replace the blocking Chat() call in process() with
chatStreamWithFallback: ChatStream first, accumulate chunks, fall back
to non-stream Chat on connect failure or empty-stream failure.

Why: the non-streaming path blocked for the ENTIRE LLM generation (up
to the 180s HTTP timeout). Reasoning models thinking 60-120s plus AUTO
chain failover regularly exceeded it -> context canceled -> full turn
wasted. With streaming the first chunk arrives in ~1-3s and any
flowing token keeps the connection alive; total generation time is no
longer bounded by an overall timeout.

Compatibility (external behavior unchanged):
  - process() signature/return values unchanged
  - Aggregated events (EventReasoning / EventAgentLLMChain) still fire
    once per turn with full text after stream completion - existing
    plugin subscribers see identical payloads as before
  - New incremental events EventReasoningDelta / EventContentDelta are
    additive; old subscribers ignore unknown event types
  - Tool execution loop, memory pipeline, stage pipeline untouched

Streaming details:
  - Tool call fragments accumulated per OpenAI streaming convention:
    id/name arrive on the first fragment, arguments as raw JSON string
    shards across fragments; merged and parsed once at stream end
  - normalizeStreamToolCalls keeps nameless argument shards (the
    non-stream normalizer drops them); ToolCall gains RawArguments to
    carry shard text
  - Interrupt mid-stream returns partial content instead of discarding
    the whole generation

Verified end-to-end against llmsproxy: plain chat streams correctly;
curl confirms tool-call shard wire format ({" + command" + :"date"}
-> {"command":"date"}); unit tests cover shard merging and
content/reasoning accumulation.
2026-08-25 09:30:49 +08:00

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package core
import (
"context"
"encoding/json"
"errors"
"fmt"
"log"
"strings"
agentAPI "gitcode.com/JianFeeeee/HomeAgent/internal/agent/api"
"gitcode.com/JianFeeeee/HomeAgent/internal/events"
sdk "gitcode.com/JianFeeeee/HomeAgent/internal/sdk"
)
func (a *Agent) process(input string, stageCtx *sdk.StageContext) (response string, toolsUsed []string, toolResults []ToolResultItem, err error) {
a.mu.Lock()
defer a.mu.Unlock()
if a.provider == nil {
return "", nil, nil, fmt.Errorf("agent: no LLM provider configured")
}
budget := ComputeTokenBudget(a.provider, a.systemPrompt)
memContext := a.buildMemoryContext(input, budget.MemoryTokens)
sysPrompt := a.buildSystemPrompt(memContext, input)
tools := a.buildToolDefs()
msgs := a.buildMessages(sysPrompt, input, budget.ContextTokens)
// 工具提醒interrupt以 system 角色注入,不让模型误认为用户发言
if a.interruptInput {
last := msgs[len(msgs)-1]
last.Role = "system"
last.Content = "[中断消息] " + last.Content
msgs[len(msgs)-1] = last
a.interruptInput = false
}
if blocks, ok := stageCtx.Extra["media_blocks"].([]agentAPI.ContentBlock); ok && len(blocks) > 0 {
if len(msgs) > 0 {
msgs[len(msgs)-1].Blocks = blocks
}
}
log.Printf("[agent] tool call loop start, max_ctx=%d target=%d fixed=%d mem=%d ctx=%d %d tools, %d events, personality=%t, docs=%d",
budget.MaxContext, budget.TargetUsage, budget.FixedTokens, budget.MemoryTokens, budget.ContextTokens,
len(tools), a.context.Len(),
a.personality != nil && a.personality.Content != "",
a.docStoreSize())
if a.runStage(sdk.StagePreAction, stageCtx) {
return *stageCtx.Response, toolsUsed, toolResults, nil
}
if len(stageCtx.ContextMsgs) > 0 {
for _, m := range stageCtx.ContextMsgs {
role, _ := m["role"].(string)
content, _ := m["content"].(string)
if role != "" {
msgs = append(msgs, agentAPI.Message{Role: role, Content: content})
}
}
}
for turn := 0; ; turn++ {
for _, interrupt := range a.drainInterrupts() {
msgs = append(msgs, agentAPI.Message{
Role: "system",
Content: "[中断消息] " + interrupt,
})
}
// zen 兼容网关要求请求的最后一条消息必须是 user(thinking 续写模式校验),
// 工具轮产出的 tool/assistant 消息作结尾会被 400 拒绝,故补一条 user 占位。
// 注意:仅当尾部确为工具轮产物(assistant/tool)时才补位;首轮 system 上下文结尾不补,
// 否则会错误覆盖实际用户输入(如 injectSourceContext 追加的 system 说明)。
if last := msgs[len(msgs)-1]; last.Role == "assistant" || last.Role == "tool" {
msgs = append(msgs, agentAPI.Message{
Role: "user",
Content: "请根据以上工具结果继续。",
})
}
req := &agentAPI.CompletionRequest{
Messages: msgs,
MaxTokens: 4096,
Tools: tools,
ToolChoice: "auto",
DisableThinking: !a.thinkingEnabled,
}
var providers []agentAPI.Provider
if a.providerManager != nil {
// 精确模型名走 byModel 路由AUTO/空走优先级链
var allProviders []agentAPI.Provider
if req.Model != "" && !strings.EqualFold(req.Model, "AUTO") {
allProviders = a.providerManager.ResolveForModel(req.Model)
} else {
allProviders = a.providerManager.OrderedProviders()
}
providers = make([]agentAPI.Provider, 0, len(allProviders))
for _, p := range allProviders {
if a.providerManager.IsAvailable(p.Name()) {
providers = append(providers, p)
}
}
}
if len(providers) == 0 {
providers = []agentAPI.Provider{a.provider}
}
var resp *agentAPI.CompletionResponse
var llmErr error
for pi, fbProvider := range providers {
if pi > 0 {
log.Printf("[agent] LLM fallback: trying provider %q (fallback #%d/%d)",
fbProvider.Name(), pi, len(providers)-1)
}
fCtx, fCancel := context.WithCancel(a.ctx)
a.llmMu.Lock()
a.cancelLLM = fCancel
a.llmMu.Unlock()
resp, llmErr = chatStreamWithFallback(fCtx, fbProvider, req, a)
a.llmMu.Lock()
a.cancelLLM = nil
a.llmMu.Unlock()
fCancel()
if llmErr == nil {
a.providerManager.ResetAvailability(fbProvider.Name())
if fbProvider != a.provider {
a.provider = fbProvider
log.Printf("[agent] switched active provider to %q after fallback",
fbProvider.Name())
}
break
}
if errors.Is(llmErr, context.Canceled) {
break
}
var pe *agentAPI.ProviderError
if errors.As(llmErr, &pe) && (pe.StatusCode == 401 || pe.StatusCode == 403) {
a.providerManager.ReportStatus(fbProvider.Name(), pe.StatusCode)
log.Printf("[agent] provider %q marked unavailable (HTTP %d)", fbProvider.Name(), pe.StatusCode)
} else {
a.providerManager.MarkUnavailable(fbProvider.Name())
}
log.Printf("[agent] provider %q failed: %v", fbProvider.Name(), llmErr)
}
if llmErr != nil {
if errors.Is(llmErr, context.Canceled) && a.ctx.Err() == nil {
if a.currentOutputChannel == "_consolidation_" {
return "", toolsUsed, toolResults, fmt.Errorf("interrupted by user input")
}
continue
}
return "", toolsUsed, toolResults, fmt.Errorf("all %d providers failed, last error: %w",
len(providers), llmErr)
}
stageCtx.LLMText = resp.Content
stageCtx.ReasoningContent = resp.ReasoningContent
stageCtx.TokenUsage = map[string]int{
"prompt_tokens": resp.TokenUsage.Prompt,
"completion_tokens": resp.TokenUsage.Completion,
"total_tokens": resp.TokenUsage.Total,
}
stageCtx.ToolCalls = convertToolCalls(resp.ToolCalls)
for i := range stageCtx.ToolCalls {
if stageCtx.ToolCalls[i].Plugin == "" {
stageCtx.ToolCalls[i].Plugin = a.resolveToolPlugin(stageCtx.ToolCalls[i].Name)
}
}
if a.runStage(sdk.StagePostAction, stageCtx) {
return *stageCtx.Response, toolsUsed, toolResults, nil
}
resp.Content = stageCtx.LLMText
resp.ToolCalls = convertBackToolCalls(stageCtx.ToolCalls)
chainPayload := map[string]interface{}{
"content": resp.Content,
"reasoning": resp.ReasoningContent,
"tool_calls": resp.ToolCalls,
"phase": "intermediate",
"turn": turn,
}
if resp.TokenUsage.Total > 0 {
chainPayload["usage"] = map[string]int{
"prompt": resp.TokenUsage.Prompt,
"completion": resp.TokenUsage.Completion,
"total": resp.TokenUsage.Total,
}
}
a.publishEvent(events.EventAgentLLMChain, chainPayload)
if resp.ReasoningContent != "" {
a.publishEvent(events.EventReasoning, map[string]interface{}{
"content": resp.ReasoningContent,
"channel": a.currentOutputChannel,
})
}
if len(resp.ToolCalls) == 0 {
return resp.Content, toolsUsed, toolResults, nil
}
contentOnce := true
for _, tc := range resp.ToolCalls {
if len(a.interceptCh) > 0 {
for _, interrupt := range a.drainInterrupts() {
msgs = append(msgs, agentAPI.Message{Role: "system", Content: "[中断消息] " + interrupt})
}
a.publishEvent(events.EventToolCall, map[string]interface{}{
"tool": tc.Name,
"plugin": a.resolveToolPlugin(tc.Name),
"args": tc.Arguments,
"status": "interrupted",
"reason": "user interrupt before execution",
"channel": a.currentOutputChannel,
})
break
}
toolsUsed = append(toolsUsed, tc.Name)
pluginName := a.resolveToolPlugin(tc.Name)
log.Printf("[agent] executing tool: %s (plugin=%s, id=%s)", tc.Name, pluginName, tc.ID)
sdkTC := sdk.ToolCall{ID: tc.ID, Name: tc.Name, Plugin: pluginName, Arguments: tc.Arguments}
stageCtx.ToolCalls = []sdk.ToolCall{sdkTC}
stageCtx.ToolResults = nil
if a.runStage(sdk.StageBeforeToolcall, stageCtx) {
result := fmt.Sprintf("工具 %s 已被插件拒绝", tc.Name)
msgs = append(msgs, agentAPI.Message{Role: "assistant", ToolCalls: []agentAPI.ToolCall{tc}})
msgs = append(msgs, agentAPI.Message{Role: "tool", ToolCallID: tc.ID, Content: result})
a.publishEvent(events.EventToolCall, map[string]interface{}{
"tool": tc.Name,
"plugin": pluginName,
"args": tc.Arguments,
"result": result,
"status": "denied",
"channel": a.currentOutputChannel,
})
continue
}
tc.Arguments = stageCtx.ToolCalls[0].Arguments
if pluginName != "" && !a.pluginHealth.isHealthy(pluginName) {
result := fmt.Sprintf("插件 %s 处于崩溃状态,已跳过执行,等待自动恢复重载", pluginName)
log.Printf("[agent] skip tool %s: plugin %s unhealthy", tc.Name, pluginName)
msgs = append(msgs, agentAPI.Message{Role: "assistant", ToolCalls: []agentAPI.ToolCall{tc}})
msgs = append(msgs, agentAPI.Message{Role: "tool", ToolCallID: tc.ID, Content: result})
continue
}
result := a.executeToolCall(tc)
toolResults = append(toolResults, ToolResultItem{Name: tc.Name, Output: result})
log.Printf("[agent] tool %s result: %s", tc.Name, truncateStr(result, 100))
stageCtx.ToolResults = []sdk.ToolResult{{CallID: tc.ID, Name: tc.Name, Plugin: pluginName, Success: true, Result: result}}
a.runStage(sdk.StageAfterToolcall, stageCtx)
if len(stageCtx.ToolResults) > 0 {
if r, ok := stageCtx.ToolResults[0].Result.(string); ok {
result = r
}
}
msgContent := ""
if contentOnce {
msgContent = resp.Content
contentOnce = false
}
msgs = append(msgs, agentAPI.Message{Role: "assistant", Content: msgContent, ReasoningContent: resp.ReasoningContent, ToolCalls: []agentAPI.ToolCall{tc}})
msgs = append(msgs, agentAPI.Message{Role: "tool", ToolCallID: tc.ID, Content: result})
a.publishEvent(events.EventToolCall, map[string]interface{}{
"tool": tc.Name,
"plugin": pluginName,
"args": tc.Arguments,
"result": result,
"status": "ok",
"channel": a.currentOutputChannel,
})
if len(a.interceptCh) > 0 {
for _, interrupt := range a.drainInterrupts() {
msgs = append(msgs, agentAPI.Message{Role: "system", Content: "[中断消息] " + interrupt})
}
break
}
}
}
}
// chatStreamWithFallback 优先流式调用 provider失败时回退非流式 Chat()。
//
// 流式路径ChatStream 拿到 chunk channel逐块累积 content/reasoning_content
// 并发布 EventReasoningDelta / EventContentDelta 增量事件(新订阅者可选订,
// 旧订阅者不认识自然忽略)。流结束后拼出与 Chat() 等价的 CompletionResponse
// 返回——process() 的后续逻辑stageCtx/聚合事件/工具循环)完全不变。
//
// 回退条件ChatStream 返回错误连接失败、provider 不支持流式)。
// 已收到部分 chunk 后出错则不回退(避免重复生成),直接返回已累积内容。
//
// 超时收益:首包 ~1-3s 到达即建立活性,后续只要 token 在流动就不会触发
// 空闲超时;总生成时长不再受限於 180s 整体超时。
func chatStreamWithFallback(ctx context.Context, p agentAPI.Provider, req *agentAPI.CompletionRequest, a *Agent) (*agentAPI.CompletionResponse, error) {
ch, err := p.ChatStream(ctx, req)
if err != nil {
log.Printf("[agent] stream connect failed (%v), falling back to non-stream chat", err)
return p.Chat(ctx, req)
}
resp, accErr := accumulateStream(ctx, ch, a)
if accErr == nil {
return resp, nil
}
// 流中途错误:若已累积到内容则返回部分结果,否则回退非流式
if resp != nil && (resp.Content != "" || len(resp.ToolCalls) > 0) {
log.Printf("[agent] stream interrupted mid-way (%v), returning partial result", accErr)
return resp, nil
}
log.Printf("[agent] stream failed before content (%v), falling back to non-stream chat", accErr)
return p.Chat(ctx, req)
}
// toolCallAcc 累积流式 tool call 的各个分片。OpenAI 风格:每个 index 的
// id/name/arguments 跨多个 chunk 增量到达arguments 是 JSON 字符串分片。
type toolCallAcc struct {
id string
name string
argsRaw strings.Builder
}
// accumulateStream 消费 chunk channel累积为完整 CompletionResponse
// 同时发布增量事件。返回的 response 与非流式 Chat() 的返回等价。
func accumulateStream(ctx context.Context, ch <-chan agentAPI.StreamChunk, a *Agent) (*agentAPI.CompletionResponse, error) {
resp := &agentAPI.CompletionResponse{
ToolCalls: make([]agentAPI.ToolCall, 0),
}
accs := make(map[int]*toolCallAcc) // index → 累积中的 tool call
var lastFinish string
flushToolCall := func(idx int) {
acc := accs[idx]
if acc == nil {
return
}
if acc.name == "" {
delete(accs, idx)
return
}
tc := agentAPI.ToolCall{
ID: acc.id,
Name: acc.name,
Arguments: parseToolArgsJSON(acc.argsRaw.String()),
}
resp.ToolCalls = append(resp.ToolCalls, tc)
delete(accs, idx)
}
for {
select {
case ck, ok := <-ch:
if !ok {
for idx := range accs {
flushToolCall(idx)
}
if lastFinish != "" {
resp.FinishReason = lastFinish
}
return resp, nil
}
if ck.ReasoningContent != "" {
resp.ReasoningContent += ck.ReasoningContent
if a != nil {
a.publishEvent(events.EventReasoningDelta, map[string]interface{}{
"content": ck.ReasoningContent,
"channel": a.currentOutputChannel,
})
}
}
if ck.Content != "" {
resp.Content += ck.Content
if a != nil {
a.publishEvent(events.EventContentDelta, map[string]interface{}{
"content": ck.Content,
"channel": a.currentOutputChannel,
})
}
}
// 增量 tool call 分片OpenAI 风格按 index 拼接 id/name/arguments
for i, tc := range ck.ToolCalls {
idx := i
acc := accs[idx]
if acc == nil {
acc = &toolCallAcc{}
accs[idx] = acc
}
if tc.ID != "" {
acc.id = tc.ID
}
if tc.Name != "" {
acc.name = tc.Name
}
// arguments 以 JSON 字符串分片到达OpenAI 标准),拼接后最终解析
if tc.RawArguments != "" {
acc.argsRaw.WriteString(tc.RawArguments)
}
}
if ck.Done && ck.FinishReason != "" {
lastFinish = ck.FinishReason
}
if ck.Usage != nil {
resp.TokenUsage = *ck.Usage
}
case <-ctx.Done():
for idx := range accs {
flushToolCall(idx)
}
return resp, ctx.Err()
}
}
}
// parseToolArgsJSON 将经过完整拼接的 tool call arguments JSON 字符串解析为 map。
// 空字符串返回空 map。
func parseToolArgsJSON(s string) map[string]interface{} {
if s == "" {
return map[string]interface{}{}
}
var m map[string]interface{}
if err := json.Unmarshal([]byte(s), &m); err == nil && m != nil {
return m
}
return map[string]interface{}{}
}
func convertToolCalls(tcs []agentAPI.ToolCall) []sdk.ToolCall {
if tcs == nil {
return nil
}
result := make([]sdk.ToolCall, len(tcs))
for i, tc := range tcs {
result[i] = sdk.ToolCall{ID: tc.ID, Name: tc.Name, Arguments: tc.Arguments}
}
return result
}
func convertBackToolCalls(tcs []sdk.ToolCall) []agentAPI.ToolCall {
if tcs == nil {
return nil
}
result := make([]agentAPI.ToolCall, len(tcs))
for i, tc := range tcs {
result[i] = agentAPI.ToolCall{ID: tc.ID, Name: tc.Name, Arguments: tc.Arguments}
}
return result
}
func (a *Agent) docStoreSize() int {
if a.docStore == nil {
return 0
}
s := a.docStore.Stats()
if n, ok := s["doc_count"]; ok {
if ni, ok := n.(int); ok {
return ni
}
}
return 0
}
func (a *Agent) formatMergedTimeline(maxTokens int) string {
a.context.mu.Lock()
events := make([]*ContextEvent, len(a.context.events))
copy(events, a.context.events)
a.context.mu.Unlock()
if len(events) == 0 {
return ""
}
// 第一轮:从最新到最旧,计算在预算内能放多少条
headerTokens := EstimateTokens("【对话时序】\n")
remaining := maxTokens - headerTokens
include := 0
for i := len(events) - 1; i >= 0; i-- {
e := events[i]
est := len(e.Source) + len(e.Input) + 40
if e.Response != "" {
est += 120
}
estTokens := est * 2
if remaining-estTokens < 0 && include > 0 {
break
}
remaining -= estTokens
include++
}
if include == 0 && len(events) > 0 {
include = 1
}
// 第二轮:按时间正序渲染
start := len(events) - include
if start < 0 {
start = 0
}
var sb strings.Builder
sb.WriteString("【对话时序】\n")
for _, e := range events[start:] {
sb.WriteString(fmt.Sprintf("[%s] %s: %s",
e.Timestamp.Format("15:04:05"), e.Source, e.Input))
if len(e.ToolsUsed) > 0 {
sb.WriteString(fmt.Sprintf(" → 调用工具: %s", strings.Join(e.ToolsUsed, ", ")))
}
if e.Response != "" {
sb.WriteString(fmt.Sprintf(" → %s", truncateStr(e.Response, 120)))
}
sb.WriteString("\n")
}
return sb.String()
}
func (a *Agent) buildMessages(sysPrompt, input string, ctxTokens int) []agentAPI.Message {
msgs := []agentAPI.Message{{Role: "system", Content: sysPrompt}}
if ctxTok := a.formatMergedTimeline(ctxTokens); ctxTok != "" {
msgs = append(msgs, agentAPI.Message{Role: "system", Content: ctxTok})
}
msgs = append(msgs, agentAPI.Message{Role: "user", Content: input})
return msgs
}