chore(ops): restore all source files in newwhats.clube67.com

newwhats.clube67.com/newwhats.local/plugins/secretaria/brain.js

@@ -0,0 +1,730 @@
+"use strict";
+/**
+ * ProtocolEngine — Cérebro Stateful da Secretária IA
+ *
+ * Princípios de economia de tokens:
+ * 1. Lê o ESTADO atual do protocolo (summary), não o histórico completo
+ * 2. Carrega apenas as últimas N mensagens (context_window)
+ * 3. Sumarização ativa a cada 10 trocas para manter o resumo atualizado
+ * 4. Nós do cérebro são compostos apenas com os ativos
+ */
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.ProtocolEngine = void 0;
+const tools_1 = require("./tools");
+class ProtocolEngine {
+    constructor(db, config) {
+        this.db = db;
+        this.config = config;
+    }
+    // ── Chat ─────────────────────────────────────────────────────────────────
+    async chat(conversationId, userMessage, opts) {
+        const conversation = await this.db('sec_conversations').where({ id: conversationId }).first();
+        if (!conversation)
+            throw new Error('Conversa não encontrada');
+        const agent = await this.db('sec_agents').where({ id: conversation.agent_id }).first();
+        if (!agent)
+            throw new Error('Agente não encontrado');
+        // Monta system prompt a partir dos nós ativos + contexto externo
+        const systemPrompt = await this.buildSystemPrompt(agent, conversation, opts);
+        // Carrega apenas as últimas N mensagens (não o histórico completo)
+        const contextWindow = agent.context_window ?? 8;
+        const recentMessages = await this.db('sec_messages')
+            .where({ conversation_id: conversationId })
+            .orderBy('created_at', 'desc')
+            .limit(contextWindow)
+            .then((rows) => rows.reverse());
+        // Salva a mensagem do usuário
+        await this.db('sec_messages').insert({
+            id: this.uuid(),
+            conversation_id: conversationId,
+            role: 'user',
+            content: userMessage,
+            created_at: new Date(),
+        });
+        // Chama a IA — usa node_model do nó persona se definido (sobrepõe o agente)
+        const personaNode = await this.db('sec_brain_nodes')
+            .where({ agent_id: conversation.agent_id, type: 'persona', active: true })
+            .orderBy('sort_order')
+            .first();
+        const agentOverride = personaNode?.node_model
+            ? { ...agent, model: personaNode.node_model }
+            : agent;
+        const messages = [
+            ...recentMessages.map((m) => ({ role: m.role, content: m.content })),
+            { role: 'user', content: userMessage },
+        ];
+        // Resolve tools: usa lista passada por opts, ou todas as builtins por padrão
+        const toolNames = opts?.tools ?? tools_1.ALL_TOOL_NAMES;
+        const toolDefs = (0, tools_1.resolveTools)(toolNames);
+        const toolCtx = {
+            db: this.db,
+            conversationId,
+            extChatId: conversation.ext_chat_id ?? undefined,
+            tenantId: opts?.tenantId,
+            hooks: opts?.hooks,
+        };
+        let response;
+        let usageInfo = null;
+        let providerUsed = null;
+        let modelUsed = null;
+        try {
+            if (toolDefs.length > 0) {
+                response = await this.callAIWithTools(agentOverride, systemPrompt, messages, toolDefs, toolCtx);
+                // Telemetria escrita pelos tool loops via side channel (toolCtx._telemetry)
+                if (toolCtx._telemetry) {
+                    usageInfo = toolCtx._telemetry.usage;
+                    providerUsed = toolCtx._telemetry.provider;
+                    modelUsed = toolCtx._telemetry.model;
+                }
+            }
+            else {
+                const result = await this.callAI(agentOverride, systemPrompt, messages);
+                response = result.text;
+                usageInfo = result.usage;
+                providerUsed = result.provider;
+                modelUsed = result.model;
+            }
+        }
+        catch (err) {
+            response = `[Erro ao chamar IA: ${err.message}. Verifique a API Key nas configurações do plugin.]`;
+        }
+        // Salva resposta da IA com telemetria de tokens
+        await this.db('sec_messages').insert({
+            id: this.uuid(),
+            conversation_id: conversationId,
+            role: 'assistant',
+            content: response,
+            usage_tokens: usageInfo ? JSON.stringify(usageInfo) : null,
+            provider_used: providerUsed,
+            model_used: modelUsed,
+            created_at: new Date(),
+        });
+        // Atualiza conversa + sumariza a cada 10 trocas
+        const totalMsgs = await this.db('sec_messages')
+            .where({ conversation_id: conversationId })
+            .count('id as c')
+            .first()
+            .then((r) => Number(r?.c ?? 0));
+        let summary = conversation.summary;
+        if (totalMsgs > 0 && (totalMsgs % 10 === 0 || totalMsgs === 5)) {
+            summary = await this.summarize(agent, recentMessages, userMessage, response);
+        }
+        await this.db('sec_conversations')
+            .where({ id: conversationId })
+            .update({ updated_at: new Date(), summary });
+        return response;
+    }
+    // ── Protocol Number ───────────────────────────────────────────────────────
+    static generateProtocolNumber() {
+        const now = new Date();
+        const p = (n, d = 2) => String(n).padStart(d, '0');
+        return `${p(now.getDate())}${p(now.getMonth() + 1)}${String(now.getFullYear()).slice(-2)}${p(now.getHours())}${p(now.getMinutes())}${p(now.getSeconds())}`;
+    }
+    // ── System Prompt Builder ─────────────────────────────────────────────────
+    async buildSystemPrompt(agent, conversation, opts) {
+        const nodes = await this.db('sec_brain_nodes')
+            .where({ agent_id: agent.id, active: true })
+            .orderBy('sort_order');
+        let prompt = '';
+        // Data/hora real — impede o modelo de alucinar a data
+        const nowReal = new Date();
+        const dtStr = nowReal.toLocaleString('pt-BR', {
+            timeZone: 'America/Sao_Paulo',
+            weekday: 'long', day: '2-digit', month: 'long', year: 'numeric',
+            hour: '2-digit', minute: '2-digit',
+        });
+        prompt += `=== DATA E HORA ATUAL ===\n${dtStr} (horário de Brasília)\n\n`;
+        // Cabeçalho do protocolo — sempre presente, leve (3 linhas)
+        const protocolHeader = [
+            `=== PROTOCOLO ATIVO ===`,
+            `Número: ${conversation.protocol_number || '—'}`,
+            `Contato: ${conversation.contact_name}`,
+            `Status: ${conversation.status}`,
+            ``,
+        ].join('\n');
+        prompt += protocolHeader;
+        for (const node of nodes) {
+            switch (node.type) {
+                case 'persona':
+                    prompt += `${node.content}\n\n`;
+                    break;
+                case 'knowledge':
+                    prompt += `=== BASE DE CONHECIMENTO ===\n${node.content}\n\n`;
+                    break;
+                case 'rules':
+                    prompt += `=== REGRAS ===\n${node.content}\n\n`;
+                    break;
+                case 'calendar': {
+                    const calCtx = await this.getCalendarContext();
+                    prompt += `=== AGENDA DISPONÍVEL (próximos 7 dias) ===\n${calCtx}\n\nInstruções: ${node.content}\n\n`;
+                    break;
+                }
+                case 'escalation':
+                    prompt += `=== REGRAS DE ESCALADA ===\n${node.content}\n\n`;
+                    break;
+                default:
+                    prompt += `${node.content}\n\n`;
+            }
+        }
+        // Injeta contexto local do projeto (enviado pelo plugin satélite)
+        if (opts?.contextData && Object.keys(opts.contextData).length > 0) {
+            const ctx = JSON.stringify(opts.contextData, null, 2);
+            prompt += `=== CONTEXTO DO CLIENTE (dados reais do projeto) ===\n${ctx}\n\n`;
+        }
+        // Prompt extra do plugin (instruções específicas da chamada)
+        if (opts?.systemExtra?.trim()) {
+            prompt += `=== INSTRUÇÕES ADICIONAIS ===\n${opts.systemExtra.trim()}\n\n`;
+        }
+        // Injeta resumo do estado atual (economia de tokens — evita reler o histórico)
+        if (conversation.summary) {
+            prompt += `=== ESTADO ATUAL DA CONVERSA ===\n${conversation.summary}\n\n`;
+        }
+        return prompt.trim();
+    }
+    // ── Finalize Protocol ─────────────────────────────────────────────────────
+    async finalizeProtocol(conversationId) {
+        const conversation = await this.db('sec_conversations').where({ id: conversationId }).first();
+        if (!conversation)
+            throw new Error('Conversa não encontrada');
+        if (conversation.status === 'closed') {
+            return { summary: conversation.summary ?? '', protocol_number: conversation.protocol_number };
+        }
+        const agent = await this.db('sec_agents').where({ id: conversation.agent_id }).first();
+        if (!agent)
+            throw new Error('Agente não encontrado');
+        // Carrega todas as mensagens para gerar resumo completo
+        const messages = await this.db('sec_messages')
+            .where({ conversation_id: conversationId })
+            .orderBy('created_at');
+        let summary = conversation.summary ?? '';
+        if (messages.length > 0) {
+            const transcript = messages
+                .map((m) => `${m.role === 'user' ? 'Cliente' : 'Ana'}: ${m.content}`)
+                .join('\n');
+            const summaryPrompt = `Gere um resumo estruturado desta conversa de atendimento para uso futuro como contexto rápido.\nInclua: motivo do contato, o que foi resolvido, próximos passos pendentes (se houver).\nMáximo 5 linhas. Seja objetivo.\n\nProtocolo: ${conversation.protocol_number}\nContato: ${conversation.contact_name}\n\n${transcript}`;
+            const cheapModel = {
+                openai: 'gpt-4o-mini', anthropic: 'claude-3-5-haiku-20241022',
+                gemini: 'gemini-2.0-flash', ollama: agent.model ?? 'llama3',
+            };
+            const finalAgent = {
+                ...agent, temperature: 0.2, max_tokens: 200,
+                model: cheapModel[agent.provider] ?? agent.model,
+            };
+            try {
+                const result = await this.callAI(finalAgent, '', [{ role: 'user', content: summaryPrompt }]);
+                summary = result.text;
+            }
+            catch {
+                summary = conversation.summary ?? `Protocolo ${conversation.protocol_number} encerrado.`;
+            }
+        }
+        // Apaga mensagens — contexto comprimido no resumo (economia de tokens)
+        await this.db('sec_messages').where({ conversation_id: conversationId }).delete();
+        // Fecha o protocolo com resumo persistido
+        await this.db('sec_conversations')
+            .where({ id: conversationId })
+            .update({ status: 'closed', summary, updated_at: new Date() });
+        return { summary, protocol_number: conversation.protocol_number };
+    }
+    // ── AI Call ───────────────────────────────────────────────────────────────
+    buildFallbackChain(agent, cfg) {
+        const chainStr = cfg.fallback_chain ?? 'openai,gemini,anthropic,ollama';
+        const order = chainStr.split(',').map((s) => s.trim()).filter(Boolean);
+        const defaults = {
+            openai: 'gpt-4o-mini',
+            anthropic: 'claude-3-5-haiku-20241022',
+            gemini: 'gemini-2.0-flash',
+            ollama: 'llama3',
+        };
+        const hasKey = (p) => {
+            if (p === 'openai')
+                return !!cfg.openai_key;
+            if (p === 'anthropic')
+                return !!cfg.anthropic_key;
+            if (p === 'gemini')
+                return !!cfg.gemini_key;
+            if (p === 'ollama')
+                return true; // local, sempre disponível
+            return false;
+        };
+        const agentProvider = agent.provider ?? 'openai';
+        const agentModel = agent.model ?? defaults[agentProvider] ?? 'gpt-4o-mini';
+        const chain = [{ provider: agentProvider, model: agentModel }];
+        for (const p of order) {
+            if (p === agentProvider)
+                continue;
+            if (!hasKey(p))
+                continue;
+            chain.push({ provider: p, model: defaults[p] ?? p });
+        }
+        return chain;
+    }
+    isRecoverableError(err) {
+        const msg = err.message.toLowerCase();
+        return (msg.includes('quota') ||
+            msg.includes('rate limit') ||
+            msg.includes('limite da api') ||
+            msg.includes('exceeded') ||
+            msg.includes('billing') ||
+            msg.includes('insufficient') ||
+            msg.includes('invalid_api_key') ||
+            msg.includes('econnrefused') ||
+            msg.includes('enotfound') ||
+            msg.includes('não configurada'));
+    }
+    async callAI(agent, systemPrompt, messages) {
+        const cfg = await this.config.get('secretaria');
+        const chain = this.buildFallbackChain(agent, cfg);
+        let lastError = new Error('Nenhum provider disponível');
+        for (const entry of chain) {
+            try {
+                return await this.callProvider(entry.provider, entry.model, agent, cfg, systemPrompt, messages);
+            }
+            catch (err) {
+                lastError = err;
+                if (this.isRecoverableError(err))
+                    continue;
+                throw err;
+            }
+        }
+        throw new Error(`Todos os providers falharam. Último erro: ${lastError.message}`);
+    }
+    /**
+     * Chama o provider e retorna { text, usage, provider, model }.
+     * usage: { input, output, cached?, total } — chars/tokens consumidos.
+     * Reads agent.max_tokens (default 250 — adequado a WhatsApp).
+     */
+    async callProvider(provider, model, agent, cfg, systemPrompt, messages) {
+        const maxTokens = agent.max_tokens ?? 250;
+        const temperature = agent.temperature ?? 0.7;
+        // ── OpenAI ────────────────────────────────────────────────────────────────
+        if (provider === 'openai') {
+            const apiKey = cfg.openai_key ?? '';
+            if (!apiKey)
+                throw new Error('OpenAI API Key não configurada. Acesse Admin → Plugins → Secretária IA.');
+            const res = await fetch('https://api.openai.com/v1/chat/completions', {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json', Authorization: `Bearer ${apiKey}` },
+                signal: AbortSignal.timeout(25000),
+                body: JSON.stringify({
+                    model,
+                    temperature,
+                    max_tokens: maxTokens,
+                    messages: [{ role: 'system', content: systemPrompt }, ...messages],
+                }),
+            });
+            const data = (await res.json());
+            if (!res.ok)
+                throw new Error(data.error?.message ?? `OpenAI ${res.status}`);
+            const text = data.choices[0].message.content;
+            const usage = {
+                input: data.usage?.prompt_tokens ?? 0,
+                output: data.usage?.completion_tokens ?? 0,
+                cached: data.usage?.prompt_tokens_details?.cached_tokens ?? 0,
+                total: data.usage?.total_tokens ?? 0,
+            };
+            return { text, usage, provider, model };
+        }
+        // ── Anthropic (com prompt caching ephemeral no system) ───────────────────
+        if (provider === 'anthropic') {
+            const apiKey = cfg.anthropic_key ?? '';
+            if (!apiKey)
+                throw new Error('Anthropic API Key não configurada. Acesse Admin → Plugins → Secretária IA.');
+            const res = await fetch('https://api.anthropic.com/v1/messages', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                    'x-api-key': apiKey,
+                    'anthropic-version': '2023-06-01',
+                    // Header necessário até GA do prompt caching
+                    'anthropic-beta': 'prompt-caching-2024-07-31',
+                },
+                signal: AbortSignal.timeout(25000),
+                body: JSON.stringify({
+                    model,
+                    max_tokens: maxTokens,
+                    // System como array com cache_control: trecho fica em cache 5min
+                    // Próximas chamadas com mesmo systemPrompt pagam ~10% pelo trecho cacheado.
+                    system: [
+                        { type: 'text', text: systemPrompt, cache_control: { type: 'ephemeral' } },
+                    ],
+                    messages,
+                }),
+            });
+            const data = (await res.json());
+            if (!res.ok)
+                throw new Error(data.error?.message ?? `Anthropic ${res.status}`);
+            const text = data.content[0].text;
+            const usage = {
+                input: data.usage?.input_tokens ?? 0,
+                output: data.usage?.output_tokens ?? 0,
+                cache_create: data.usage?.cache_creation_input_tokens ?? 0,
+                cache_read: data.usage?.cache_read_input_tokens ?? 0,
+                total: (data.usage?.input_tokens ?? 0) + (data.usage?.output_tokens ?? 0),
+            };
+            return { text, usage, provider, model };
+        }
+        // ── Google Gemini ─────────────────────────────────────────────────────────
+        if (provider === 'gemini') {
+            const apiKey = cfg.gemini_key ?? '';
+            if (!apiKey)
+                throw new Error('Google Gemini API Key não configurada. Acesse Admin → Plugins → Secretária IA.');
+            const geminiModel = model.startsWith('gemini') ? model : 'gemini-2.0-flash';
+            const geminiMessages = messages.map((m) => ({
+                role: m.role === 'assistant' ? 'model' : 'user',
+                parts: [{ text: m.content }],
+            }));
+            const geminiBody = JSON.stringify({
+                systemInstruction: { parts: [{ text: systemPrompt }] },
+                contents: geminiMessages,
+                generationConfig: { temperature, maxOutputTokens: maxTokens },
+            });
+            const geminiUrl = `https://generativelanguage.googleapis.com/v1beta/models/${geminiModel}:generateContent?key=${apiKey}`;
+            const doGeminiCall = async () => fetch(geminiUrl, {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json' },
+                signal: AbortSignal.timeout(25000),
+                body: geminiBody,
+            });
+            let res = await doGeminiCall();
+            let data = (await res.json());
+            if (!res.ok && (res.status === 429 || String(data.error?.message ?? '').toLowerCase().includes('quota'))) {
+                const msg = data.error?.message ?? '';
+                const match = msg.match(/retry in ([\d.]+)s/i);
+                // Espera no máximo 8s (era 30s) e mínimo 1.5s (era 5s) — limita impacto de quota no tempo total
+                const waitMs = match ? Math.min(Math.ceil(parseFloat(match[1])) * 1000, 8000) : 1500;
+                await new Promise((r) => setTimeout(r, waitMs));
+                res = await doGeminiCall();
+                data = (await res.json());
+            }
+            if (!res.ok) {
+                const errMsg = data.error?.message ?? `Gemini ${res.status}`;
+                if (errMsg.toLowerCase().includes('quota') || res.status === 429) {
+                    throw new Error('Limite da API Gemini atingido. Aguarde alguns instantes e tente novamente.');
+                }
+                throw new Error(errMsg);
+            }
+            const text = data.candidates[0].content.parts[0].text;
+            const usage = {
+                input: data.usageMetadata?.promptTokenCount ?? 0,
+                output: data.usageMetadata?.candidatesTokenCount ?? 0,
+                total: data.usageMetadata?.totalTokenCount ?? 0,
+            };
+            return { text, usage, provider, model: geminiModel };
+        }
+        // ── Ollama (local) ────────────────────────────────────────────────────────
+        if (provider === 'ollama') {
+            const baseUrl = cfg.ollama_url ?? 'http://localhost:11434';
+            const ollamaModel = model || 'llama3';
+            const res = await fetch(`${baseUrl}/api/chat`, {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json' },
+                signal: AbortSignal.timeout(25000),
+                body: JSON.stringify({
+                    model: ollamaModel,
+                    stream: false,
+                    messages: [{ role: 'system', content: systemPrompt }, ...messages],
+                    options: { temperature, num_predict: maxTokens },
+                }),
+            });
+            const data = (await res.json());
+            if (!res.ok)
+                throw new Error(data.error ?? `Ollama ${res.status}`);
+            const text = data.message.content;
+            const usage = {
+                input: data.prompt_eval_count ?? 0,
+                output: data.eval_count ?? 0,
+                total: (data.prompt_eval_count ?? 0) + (data.eval_count ?? 0),
+            };
+            return { text, usage, provider, model: ollamaModel };
+        }
+        throw new Error(`Provider "${provider}" não suportado. Use: openai, anthropic, gemini, ollama`);
+    }
+    // ── Calendar Context ──────────────────────────────────────────────────────
+    async getCalendarContext() {
+        const today = new Date().toISOString().split('T')[0];
+        const nextWeek = new Date();
+        nextWeek.setDate(nextWeek.getDate() + 7);
+        const nextWeekStr = nextWeek.toISOString().split('T')[0];
+        const slots = await this.db('sec_calendar')
+            .whereIn('status', ['available', 'booked'])
+            .whereBetween('date', [today, nextWeekStr])
+            .orderBy('date')
+            .orderBy('time_start')
+            .limit(30);
+        if (slots.length === 0)
+            return 'Nenhum horário nos próximos 7 dias.';
+        const lines = slots.map((s) => {
+            const time = `${s.date} ${s.time_start.slice(0, 5)}–${s.time_end.slice(0, 5)}`;
+            if (s.status === 'booked') {
+                const who = s.attendee_name ? ` | Paciente: ${s.attendee_name}` : '';
+                const phone = s.attendee_phone ? ` (${s.attendee_phone})` : '';
+                return `• [AGENDADO] ${time}: ${s.title}${who}${phone}`;
+            }
+            return `• [DISPONÍVEL] ${time}: ${s.title}`;
+        });
+        return lines.join('\n');
+    }
+    // ── Summarization (token economy) ────────────────────────────────────────
+    /**
+     * Sumarização com modelo barato (M1.5).
+     * Força modelo "mini/haiku/flash" mesmo que o agente principal use modelo caro.
+     * Sumário é tarefa simples — não precisa do modelo de produção.
+     */
+    async summarize(agent, recentMsgs, lastUser, lastAssistant) {
+        const excerpt = [
+            ...recentMsgs.slice(-6).map((m) => `${m.role}: ${m.content}`),
+            `user: ${lastUser}`,
+            `assistant: ${lastAssistant}`,
+        ].join('\n');
+        const prompt = `Resuma em no máximo 2 frases curtas o estado atual desta conversa de atendimento, focando no tema e próximo passo:\n\n${excerpt}`;
+        // Modelo barato por provider
+        const cheapModel = {
+            openai: 'gpt-4o-mini',
+            anthropic: 'claude-3-5-haiku-20241022',
+            gemini: 'gemini-2.0-flash',
+            ollama: agent.model ?? 'llama3',
+        };
+        const summaryAgent = {
+            ...agent,
+            temperature: 0.3,
+            max_tokens: 120,
+            model: cheapModel[agent.provider] ?? agent.model,
+        };
+        try {
+            const result = await this.callAI(summaryAgent, '', [{ role: 'user', content: prompt }]);
+            return result.text;
+        }
+        catch {
+            return '';
+        }
+    }
+    // ── Tool Calling ──────────────────────────────────────────────────────────
+    async callAIWithTools(agent, systemPrompt, inputMessages, tools, toolCtx) {
+        const cfg = await this.config.get('secretaria');
+        const chain = this.buildFallbackChain(agent, cfg);
+        // Prefere provider com suporte a tool calling; Ollama cai em modo texto
+        const TOOL_PROVIDERS = ['openai', 'anthropic', 'gemini'];
+        const entry = chain.find(e => TOOL_PROVIDERS.includes(e.provider));
+        if (!entry) {
+            // Nenhum provider com tool calling disponível — usa modo texto normal
+            return (await this.callAI(agent, systemPrompt, inputMessages)).text;
+        }
+        try {
+            switch (entry.provider) {
+                case 'openai':
+                    return await this.openAIToolLoop(entry.model, agent, cfg, systemPrompt, inputMessages, tools, toolCtx);
+                case 'anthropic':
+                    return await this.anthropicToolLoop(entry.model, agent, cfg, systemPrompt, inputMessages, tools, toolCtx);
+                case 'gemini':
+                    return await this.geminiToolLoop(entry.model, agent, cfg, systemPrompt, inputMessages, tools, toolCtx);
+                default:
+                    return (await this.callAI(agent, systemPrompt, inputMessages)).text;
+            }
+        }
+        catch (err) {
+            if (this.isRecoverableError(err)) {
+                // Provider com tools falhou — tenta sem tools no próximo da chain
+                return (await this.callAI(agent, systemPrompt, inputMessages)).text;
+            }
+            throw err;
+        }
+    }
+    async executeTool(name, rawArgs, tools, toolCtx) {
+        const tool = tools.find(t => t.name === name);
+        if (!tool)
+            return { error: `Tool "${name}" não encontrada.` };
+        const args = typeof rawArgs === 'string' ? JSON.parse(rawArgs || '{}') : rawArgs;
+        try {
+            return await tool.execute(args, toolCtx);
+        }
+        catch (e) {
+            return { error: e.message };
+        }
+    }
+    // ── Telemetria helper para tool loops ─────────────────────────────────────
+    accumTelemetry(toolCtx, provider, model, incremental) {
+        if (!toolCtx._telemetry) {
+            toolCtx._telemetry = {
+                usage: { input: 0, output: 0, total: 0, cache_read: 0, cached: 0 },
+                provider, model, iterations: 0,
+            };
+        }
+        toolCtx._telemetry.iterations += 1;
+        toolCtx._telemetry.usage.input += incremental.input;
+        toolCtx._telemetry.usage.output += incremental.output;
+        toolCtx._telemetry.usage.total += incremental.input + incremental.output;
+        if (incremental.cache_read)
+            toolCtx._telemetry.usage.cache_read = (toolCtx._telemetry.usage.cache_read ?? 0) + incremental.cache_read;
+        if (incremental.cached)
+            toolCtx._telemetry.usage.cached = (toolCtx._telemetry.usage.cached ?? 0) + incremental.cached;
+    }
+    // ── OpenAI tool loop ───────────────────────────────────────────────────────
+    async openAIToolLoop(model, agent, cfg, systemPrompt, inputMessages, tools, toolCtx) {
+        const apiKey = cfg.openai_key ?? '';
+        if (!apiKey)
+            throw new Error('OpenAI API Key não configurada');
+        const oaiTools = tools.map(t => ({
+            type: 'function',
+            function: { name: t.name, description: t.description, parameters: t.parameters },
+        }));
+        let msgs = [...inputMessages];
+        const MAX_ITER = 5;
+        for (let i = 0; i < MAX_ITER; i++) {
+            const res = await fetch('https://api.openai.com/v1/chat/completions', {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json', Authorization: `Bearer ${apiKey}` },
+                signal: AbortSignal.timeout(25000),
+                body: JSON.stringify({
+                    model,
+                    temperature: agent.temperature ?? 0.7,
+                    max_tokens: agent.max_tokens ?? 250,
+                    messages: [{ role: 'system', content: systemPrompt }, ...msgs],
+                    tools: oaiTools,
+                    tool_choice: 'auto',
+                }),
+            });
+            const data = (await res.json());
+            if (!res.ok)
+                throw new Error(data.error?.message ?? `OpenAI ${res.status}`);
+            // Telemetria (M1.4)
+            this.accumTelemetry(toolCtx, 'openai', model, {
+                input: data.usage?.prompt_tokens ?? 0,
+                output: data.usage?.completion_tokens ?? 0,
+                cached: data.usage?.prompt_tokens_details?.cached_tokens ?? 0,
+            });
+            const choice = data.choices[0];
+            const assistantMsg = choice.message;
+            if (choice.finish_reason !== 'tool_calls' || !assistantMsg.tool_calls?.length) {
+                return (assistantMsg.content ?? '');
+            }
+            // Execute tools in parallel
+            msgs.push(assistantMsg);
+            const toolResults = await Promise.all(assistantMsg.tool_calls.map(async (tc) => {
+                const result = await this.executeTool(tc.function.name, tc.function.arguments, tools, toolCtx);
+                return { role: 'tool', tool_call_id: tc.id, content: JSON.stringify(result) };
+            }));
+            msgs.push(...toolResults);
+        }
+        throw new Error('Tool calling: limite de iterações atingido');
+    }
+    // ── Anthropic tool loop ────────────────────────────────────────────────────
+    async anthropicToolLoop(model, agent, cfg, systemPrompt, inputMessages, tools, toolCtx) {
+        const apiKey = cfg.anthropic_key ?? '';
+        if (!apiKey)
+            throw new Error('Anthropic API Key não configurada');
+        const anthropicTools = tools.map(t => ({
+            name: t.name, description: t.description, input_schema: t.parameters,
+        }));
+        let msgs = [...inputMessages];
+        const MAX_ITER = 5;
+        for (let i = 0; i < MAX_ITER; i++) {
+            const res = await fetch('https://api.anthropic.com/v1/messages', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                    'x-api-key': apiKey,
+                    'anthropic-version': '2023-06-01',
+                },
+                signal: AbortSignal.timeout(25000),
+                body: JSON.stringify({
+                    model, max_tokens: agent.max_tokens ?? 250, system: systemPrompt,
+                    messages: msgs, tools: anthropicTools,
+                }),
+            });
+            const data = (await res.json());
+            if (!res.ok)
+                throw new Error(data.error?.message ?? `Anthropic ${res.status}`);
+            // Telemetria (M1.4)
+            this.accumTelemetry(toolCtx, 'anthropic', model, {
+                input: data.usage?.input_tokens ?? 0,
+                output: data.usage?.output_tokens ?? 0,
+                cache_read: data.usage?.cache_read_input_tokens ?? 0,
+            });
+            // Texto puro
+            if (data.stop_reason !== 'tool_use') {
+                const textBlock = data.content.find(b => b.type === 'text');
+                return (textBlock?.text ?? '');
+            }
+            // Tool calls
+            msgs.push({ role: 'assistant', content: data.content });
+            const toolResults = await Promise.all(data.content
+                .filter(b => b.type === 'tool_use')
+                .map(async (b) => {
+                const result = await this.executeTool(b.name, b.input, tools, toolCtx);
+                return { type: 'tool_result', tool_use_id: b.id, content: JSON.stringify(result) };
+            }));
+            msgs.push({ role: 'user', content: toolResults });
+        }
+        throw new Error('Tool calling (Anthropic): limite de iterações atingido');
+    }
+    // ── Gemini tool loop ───────────────────────────────────────────────────────
+    async geminiToolLoop(model, agent, cfg, systemPrompt, inputMessages, tools, toolCtx) {
+        const apiKey = cfg.gemini_key ?? '';
+        if (!apiKey)
+            throw new Error('Gemini API Key não configurada');
+        const geminiModel = model.startsWith('gemini') ? model : 'gemini-2.0-flash';
+        const url = `https://generativelanguage.googleapis.com/v1beta/models/${geminiModel}:generateContent?key=${apiKey}`;
+        const geminiTools = [{
+                functionDeclarations: tools.map(t => ({
+                    name: t.name, description: t.description, parameters: t.parameters,
+                })),
+            }];
+        // Converte msgs para formato Gemini
+        let contents = inputMessages.map(m => ({
+            role: m.role === 'assistant' ? 'model' : 'user',
+            parts: [{ text: m.content }],
+        }));
+        const MAX_ITER = 5;
+        for (let i = 0; i < MAX_ITER; i++) {
+            const res = await fetch(url, {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json' },
+                signal: AbortSignal.timeout(25000),
+                body: JSON.stringify({
+                    systemInstruction: { parts: [{ text: systemPrompt }] },
+                    contents,
+                    tools: geminiTools,
+                    generationConfig: { temperature: agent.temperature ?? 0.7, maxOutputTokens: agent.max_tokens ?? 250 },
+                }),
+            });
+            const data = (await res.json());
+            if (!res.ok)
+                throw new Error(data.error?.message ?? `Gemini ${res.status}`);
+            // Telemetria (M1.4)
+            this.accumTelemetry(toolCtx, 'gemini', geminiModel, {
+                input: data.usageMetadata?.promptTokenCount ?? 0,
+                output: data.usageMetadata?.candidatesTokenCount ?? 0,
+            });
+            const candidate = data.candidates?.[0];
+            const parts = candidate?.content?.parts ?? [];
+            // Verifica se há function calls
+            const fnCalls = parts.filter(p => p.functionCall);
+            if (!fnCalls.length) {
+                const textPart = parts.find(p => p.text);
+                return (textPart?.text ?? '');
+            }
+            // Adiciona resposta do modelo ao histórico
+            contents.push({ role: 'model', parts });
+            // Executa tools e injeta resultados
+            const resultParts = await Promise.all(fnCalls.map(async (p) => {
+                const result = await this.executeTool(p.functionCall.name, p.functionCall.args ?? {}, tools, toolCtx);
+                return { functionResponse: { name: p.functionCall.name, response: result } };
+            }));
+            contents.push({ role: 'user', parts: resultParts });
+        }
+        throw new Error('Tool calling (Gemini): limite de iterações atingido');
+    }
+    // ── Utils ─────────────────────────────────────────────────────────────────
+    uuid() {
+        // Node 14.17+ tem crypto.randomUUID globalmente; fallback para Date-based
+        try {
+            return crypto.randomUUID();
+        }
+        catch {
+            return `${Date.now()}-${Math.random().toString(36).slice(2)}`;
+        }
+    }
+}
+exports.ProtocolEngine = ProtocolEngine;
+//# sourceMappingURL=brain.js.map