feat: AutoZero 通用 Agent 框架（从 AutoGo 五阶段拆分完成）
llm（三协议 Provider）/ session（JSONL 持久化）/ tools（Registry、围栏、 builtin、computer）/ cdp（browser）/ skills / mcp / agent（循环、两级压缩、 编排、审批门控）+ 底座 jsonl/textx/execx/diag/scratch/spill/imagecache。 产品装配代码（config 桥接、schedule 工具、memory/plan/retro/prompt） 留在 AutoGo，宿主接入方式见 MIGRATION.md。
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@@ -0,0 +1,394 @@
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package llm
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import (
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"bufio"
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"bytes"
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"context"
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"encoding/json"
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"fmt"
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"io"
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"net/http"
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"strings"
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)
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// AnthropicProvider 走 Anthropic Messages API（SSE 流式 + 工具调用）。
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type AnthropicProvider struct {
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BaseURL string
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APIKey string
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Model string
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MaxTokens int // 单次回复上限；0 = 8192
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HTTP *http.Client
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}
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// SetModel 运行时切换模型（ModelSwitcher）。
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func (p *AnthropicProvider) SetModel(model string) {
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p.Model = model
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}
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// CurrentModel 报告当前模型（CurrentModeler）。
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func (p *AnthropicProvider) CurrentModel() string { return p.Model }
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// Identity 自报协议与当前模型（Identifier）。
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func (p *AnthropicProvider) Identity() (string, string) { return FormatAnthropic, p.Model }
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type anBlock struct {
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Type string `json:"type"`
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Text string `json:"text,omitempty"`
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ID string `json:"id,omitempty"`
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Name string `json:"name,omitempty"`
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Input json.RawMessage `json:"input,omitempty"`
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ToolUseID string `json:"tool_use_id,omitempty"`
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Content string `json:"content,omitempty"` // tool_result 内容
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// Signature / Data 用于 thinking 回传签名与 redacted_thinking 块。
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Signature string `json:"signature,omitempty"`
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Data string `json:"data,omitempty"`
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}
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type anStreamEvent struct {
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Type string `json:"type"`
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Delta struct {
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Type string `json:"type"`
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Text string `json:"text"`
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Thinking string `json:"thinking"` // thinking_delta 的推理文本
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Signature string `json:"signature"` // signature_delta：thinking 块签名
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PartialJSON string `json:"partial_json"` // input_json_delta
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// StopReason 只在 message_delta 里出现（end_turn / max_tokens / tool_use …）。
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// max_tokens 是"模型想写却写不下"，见 Message.OutputLimited。
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StopReason string `json:"stop_reason"`
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} `json:"delta"`
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ContentBlock anBlock `json:"content_block"`
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Usage *struct {
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InputTokens int `json:"input_tokens"`
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OutputTokens int `json:"output_tokens"`
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// 缓存明细：Anthropic 把命中/写入单列，**不计入** input_tokens。
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CacheRead int `json:"cache_read_input_tokens"`
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CacheWrite int `json:"cache_creation_input_tokens"`
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} `json:"usage"`
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Message *struct {
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Usage struct {
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InputTokens int `json:"input_tokens"`
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CacheRead int `json:"cache_read_input_tokens"`
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CacheWrite int `json:"cache_creation_input_tokens"`
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} `json:"usage"`
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} `json:"message"`
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}
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// anUsage 把 Anthropic 的分段计数归一成 Usage——口径见 llm.Usage 的注释：
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// InputTokens 必须含缓存段。本文件确实给 system+tools 前缀打了 cache_control，
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// 照抄 input_tokens 会把命中缓存的那一大段整个漏掉，终端计数与 stats 的
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// TokensIn 因此低报一个数量级（压缩走字符估算，不受影响）。
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func anUsage(input, cacheRead, cacheWrite, output int) *Usage {
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return &Usage{
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InputTokens: input + cacheRead + cacheWrite,
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OutputTokens: output,
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CacheReadTokens: cacheRead,
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CacheCreationTokens: cacheWrite,
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}
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}
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// thinkingBudget 返回 Anthropic 扩展思考的 token 预算；off/零值返回 ok=false。
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func thinkingBudget(l ThinkingLevel) (int, bool) {
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switch l {
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case ThinkingLow:
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return 2048, true
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case ThinkingMedium:
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return 8192, true
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case ThinkingHigh:
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return 16384, true
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default:
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return 0, false
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}
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}
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func (p *AnthropicProvider) Stream(ctx context.Context, system string, msgs []Message, tools []ToolDef, opts StreamOptions, onDelta func(StreamDelta)) (Message, error) {
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blocks := anBuildMessages(msgs, opts.Thinking.Enabled())
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maxTokens := p.MaxTokens
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if maxTokens <= 0 {
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maxTokens = 8192
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}
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// 扩展思考：Anthropic 要求 max_tokens > budget_tokens，故按预算抬高上限，
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// 并额外预留 8192 作为正文回答预算。
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budget, thinking := thinkingBudget(opts.Thinking)
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if thinking && maxTokens < budget+8192 {
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maxTokens = budget + 8192
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}
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body := map[string]any{
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"model": p.Model,
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"max_tokens": maxTokens,
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"messages": blocks,
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}
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if thinking {
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body["thinking"] = map[string]any{"type": "enabled", "budget_tokens": budget}
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}
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if system != "" || opts.SystemDynamic != "" {
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// 稳定 system 单独成块并标记 cache_control：工具表 + 稳定前缀在同一
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// 会话内逐字节不变，可命中 Anthropic prompt cache。动态部分（计划/记忆）
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// 放在断点之后的第二块，它的变化不会让缓存前缀失效。
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blocks := []map[string]any{}
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if system != "" {
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blocks = append(blocks, map[string]any{
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"type": "text",
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"text": system,
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"cache_control": map[string]any{"type": "ephemeral"},
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})
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}
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if opts.SystemDynamic != "" {
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blocks = append(blocks, map[string]any{"type": "text", "text": opts.SystemDynamic})
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}
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body["system"] = blocks
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}
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if len(tools) > 0 {
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ts := make([]map[string]any, 0, len(tools))
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for _, t := range tools {
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ts = append(ts, map[string]any{
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"name": t.Name,
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"description": t.Description,
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"input_schema": t.Parameters,
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})
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}
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// 最后一个工具上标记 cache_control，覆盖整个 tools 前缀。
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ts[len(ts)-1]["cache_control"] = map[string]any{"type": "ephemeral"}
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body["tools"] = ts
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}
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if opts.DisableTools && len(tools) > 0 {
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body["tool_choice"] = map[string]any{"type": "none"}
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}
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payload, err := json.Marshal(body)
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if err != nil {
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return Message{}, err
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}
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req, err := http.NewRequestWithContext(ctx, http.MethodPost,
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strings.TrimRight(p.BaseURL, "/")+"/v1/messages", bytes.NewReader(payload))
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if err != nil {
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return Message{}, err
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}
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req.Header.Set("Content-Type", "application/json")
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req.Header.Set("x-api-key", p.APIKey)
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req.Header.Set("anthropic-version", "2023-06-01")
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resp, err := doWithRetry(ctx, p.http(), req, payload)
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if err != nil {
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return Message{}, err
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}
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defer resp.Body.Close()
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if resp.StatusCode != http.StatusOK {
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var buf bytes.Buffer
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_, _ = buf.ReadFrom(io.LimitReader(resp.Body, 4096))
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return Message{}, fmt.Errorf("anthropic: HTTP %d: %s", resp.StatusCode, buf.String())
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}
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final := Message{Role: RoleAssistant}
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var st streamState
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var curTool *ToolCall
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// 分段计数在事件之间是分散给的：message_start 给提示侧（含缓存明细），
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// message_delta 给输出侧，且常常**不重复**缓存字段。所以要学下来存着，
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// 谁非零谁覆盖，否则归一出来的总量会漏掉 message_start 那一份。
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var baseIn, cachedRead, cachedWrite int
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sg := guardStream(resp.Body, streamIdleTimeout)
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defer sg.Stop()
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sc := bufio.NewScanner(sg)
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sc.Buffer(make([]byte, 1<<20), 1<<20)
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for sc.Scan() {
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line := sc.Text()
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if !strings.HasPrefix(line, "data: ") {
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continue
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}
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var ev anStreamEvent
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if err := json.Unmarshal([]byte(strings.TrimPrefix(line, "data: ")), &ev); err != nil {
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continue
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}
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switch ev.Type {
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case "message_start":
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if ev.Message != nil {
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u := ev.Message.Usage
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if u.InputTokens > 0 {
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baseIn = u.InputTokens
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}
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if u.CacheRead > 0 {
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cachedRead = u.CacheRead
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}
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if u.CacheWrite > 0 {
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cachedWrite = u.CacheWrite
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}
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final.Usage = anUsage(baseIn, cachedRead, cachedWrite, 0)
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}
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case "content_block_start":
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b := ev.ContentBlock
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if b.Type == "tool_use" {
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curTool = &ToolCall{ID: b.ID, Name: b.Name}
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}
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// 安全策略触发时返回脱敏思考块，需原样回传。
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if b.Type == "redacted_thinking" && b.Data != "" {
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final.RedactedThinking = b.Data
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}
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case "content_block_delta":
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// 扩展思考：thinking_delta 携带推理文本，signature_delta 携带回传签名。
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if ev.Delta.Type == "signature_delta" {
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final.ReasoningSignature = ev.Delta.Signature
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} else if ev.Delta.Type == "thinking_delta" && ev.Delta.Thinking != "" {
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final.Reasoning += ev.Delta.Thinking
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if onDelta != nil {
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onDelta(StreamDelta{Reasoning: ev.Delta.Thinking})
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}
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} else if ev.Delta.Text != "" {
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final.Content += ev.Delta.Text
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if onDelta != nil {
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onDelta(StreamDelta{Text: ev.Delta.Text})
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}
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} else if curTool != nil && ev.Delta.Type == "input_json_delta" {
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curTool.Input += ev.Delta.PartialJSON
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}
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case "content_block_stop":
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if curTool != nil {
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final.ToolCalls = append(final.ToolCalls, *curTool)
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// 参数到 content_block_stop 才算完整：提前告诉调用方，只读工具
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// 可以趁模型继续生成后面的块先跑。
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if onDelta != nil {
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tc := *curTool
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onDelta(StreamDelta{ToolCall: &tc})
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}
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curTool = nil
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}
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case "message_delta":
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if marksOutputLimit(ev.Delta.StopReason) {
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final.OutputLimited = true
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}
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if ev.Usage != nil {
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st.usageSeen = true
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if ev.Usage.InputTokens > 0 {
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baseIn = ev.Usage.InputTokens
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}
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if ev.Usage.CacheRead > 0 {
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cachedRead = ev.Usage.CacheRead
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}
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if ev.Usage.CacheWrite > 0 {
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cachedWrite = ev.Usage.CacheWrite
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}
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final.Usage = anUsage(baseIn, cachedRead, cachedWrite, ev.Usage.OutputTokens)
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}
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case "message_stop":
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st.doneSeen = true
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return completion("anthropic", final, st)
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}
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}
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if err := sc.Err(); err != nil {
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if stall := sg.Err(); stall != nil {
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return Message{}, fmt.Errorf("anthropic: %w", stall)
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}
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return Message{}, fmt.Errorf("anthropic: stream: %w", err)
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}
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// 没收到 message_stop 就结束，且是看门狗掐断的：绝不能把半截内容当成品返回。
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if stall := sg.Err(); stall != nil {
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return Message{}, fmt.Errorf("anthropic: %w", stall)
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}
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return completion("anthropic", final, st)
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}
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// anThinkingBlocks 构造需要回传的 thinking / redacted_thinking 块。
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// 只在本次请求开启思考时回传：thinking 关闭时必须剥离，否则 Anthropic 会拒绝。
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// 工具调用场景下不回传 thinking 块同样会被拒（要求完整原文 + 签名）。
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func anThinkingBlocks(m Message) []map[string]any {
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var out []map[string]any
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if m.RedactedThinking != "" {
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out = append(out, map[string]any{"type": "redacted_thinking", "data": m.RedactedThinking})
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}
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if m.Reasoning != "" {
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block := map[string]any{"type": "thinking", "thinking": m.Reasoning}
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if m.ReasoningSignature != "" {
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block["signature"] = m.ReasoningSignature
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}
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out = append(out, block)
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}
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return out
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}
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func anBuildMessages(msgs []Message, thinking bool) []map[string]any {
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out := []map[string]any{}
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for _, m := range msgs {
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switch {
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case m.Role == RoleTool:
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tr := map[string]any{"type": "tool_result", "tool_use_id": m.ToolCallID}
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if len(m.Images) == 0 {
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tr["content"] = m.Content
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} else {
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// tool_result.content 支持块数组：文本 + 图片（Anthropic 原生支持）。
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content := []map[string]any{}
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if m.Content != "" {
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content = append(content, map[string]any{"type": "text", "text": m.Content})
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}
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content = append(content, anImageBlocks(m.Images)...)
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tr["content"] = content
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}
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out = append(out, map[string]any{"role": "user", "content": []any{tr}})
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case len(m.ToolCalls) > 0:
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blocks := []any{}
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// thinking 块必须是 assistant content 的第一个块。
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if thinking {
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for _, b := range anThinkingBlocks(m) {
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blocks = append(blocks, b)
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}
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}
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if m.Content != "" {
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blocks = append(blocks, anBlock{Type: "text", Text: m.Content})
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}
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for _, tc := range m.ToolCalls {
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input := json.RawMessage(tc.Input)
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if len(input) == 0 {
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input = json.RawMessage("{}")
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}
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blocks = append(blocks, anBlock{Type: "tool_use", ID: tc.ID, Name: tc.Name, Input: input})
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}
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out = append(out, map[string]any{"role": "assistant", "content": blocks})
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case len(m.Images) > 0:
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content := []map[string]any{}
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if m.Content != "" {
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content = append(content, map[string]any{"type": "text", "text": m.Content})
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}
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content = append(content, anImageBlocks(m.Images)...)
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out = append(out, map[string]any{"role": string(m.Role), "content": content})
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default:
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// 纯文本 assistant 消息也可能带思考，同样需要回传（块数组形式）。
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if thinking && (m.Reasoning != "" || m.RedactedThinking != "") {
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content := []any{}
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for _, b := range anThinkingBlocks(m) {
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content = append(content, b)
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}
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if m.Content != "" {
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content = append(content, map[string]any{"type": "text", "text": m.Content})
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}
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out = append(out, map[string]any{"role": string(m.Role), "content": content})
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continue
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}
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out = append(out, map[string]any{"role": string(m.Role), "content": m.Content})
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}
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}
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return out
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}
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// anImageBlocks 构造 Anthropic image 块；读取失败的图降级为文本说明。
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// 支持两种图片引用方式：
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// 1. Filename: 从缓存目录加载（优先）
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// 2. Path: 直接文件路径（兼容旧格式）
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func anImageBlocks(imgs []Image) []map[string]any {
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blocks := []map[string]any{}
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for _, img := range imgs {
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b64, mt, err := loadImageRef(img)
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if err != nil {
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blocks = append(blocks, map[string]any{"type": "text", "text": imageUnavailable(img, err)})
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continue
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}
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blocks = append(blocks, map[string]any{
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"type": "image",
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"source": map[string]any{"type": "base64", "media_type": mt, "data": b64},
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})
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}
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return blocks
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}
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func (p *AnthropicProvider) http() *http.Client {
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if p.HTTP != nil {
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return p.HTTP
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}
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return defaultHTTPClient
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}
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