  3096→                            if (thread.emails && thread.emails.length > 0) {
  3097→                                fullContent += '\n\n--- ADDITIONAL THREAD ' + (i + 1) + ' OF ' + allAdditionalThreads.length + ' ---\n';
  3098→                                fullContent += 'Subject: ' + (thread.emails[0].subject || '(No Subject)') + '\n\n';
  3099→                                // Bodies already stripped by thread_for_ai endpoint
  3100→                                fullContent += processAdditionalThreadContent(thread.emails, 'full');
  3101→                            }
  3102→                        } catch (fetchErr) {
  3103→                            console.warn('Error fetching thread ' + tid + ':', fetchErr);
  3104→                            fullContent += '\n\n--- THREAD ' + (i + 1) + ': FETCH ERROR ---\n';
  3105→                        }
  3106→                    }
  3107→                }
  3108→
  3109→                // Enforce content budget: truncate to fit smallest model limit
  3110→                var maxChars = Math.floor(modelLimit * 3.5 * 0.85); // 85% of model limit in chars
  3111→                var wasTruncated = false;
  3112→                if (fullContent.length > maxChars) {
  3113→                    wasTruncated = true;
  3114→                    fullContent = fullContent.substring(0, maxChars) + '\n\n...(Content truncated to fit model context limit of ' + modelLimit.toLocaleString() + ' tokens)';
  3115→                }
  3116→
  3117→                var finalTokens = Math.ceil(fullContent.length / 3.5);
  3118→
  3119→                // Log to debug file BEFORE sending
  3120→                logAiDebug({
  3121→                    type: 'request',
  3122→                    thread_id: currentThreadId,
  3123→                    agent: selectedAgent,
  3124→                    providers: selectedProvidersList,
  3125→                    models: modelsMap,
  3126→                    content_length: fullContent.length,
  3127→                    estimated_tokens: finalTokens,
  3128→                    question: actualQuestion,
  3129→                    context_threads: allAdditionalThreads,
  3130→                    truncated: wasTruncated,
  3131→                    full_content: fullContent.substring(0, 10000) + (fullContent.length > 10000 ? '\n...(truncated in log, full was ' + fullContent.length + ' chars)' : '')
  3132→                });
  3133→
  3134→                // Console log
  3135→                console.log('=== AI REQUEST LOG ===');
  3136→                console.log('Agent:', selectedAgent);
  3137→                console.log('Providers:', selectedProvidersList);
  3138→                console.log('Models:', modelsMap);
  3139→                console.log('Content Mode:', contentMode);
  3140→                console.log('Context Threads:', allAdditionalThreads.length);
  3141→                console.log('Conversation History:', conversationHistory.length);
  3142→                console.log('Content Length:', fullContent.length, 'chars');
  3143→                console.log('Estimated Tokens:', finalTokens);
  3144→                console.log('======================');
  3145→
  3146→                document.getElementById('loading-message').textContent = 'Analyzing with ' + selectedProvidersList.length + ' AI provider(s)...';
  3147→
  3148→                // Build request body with all selected providers and their models
  3149→                var requestBody = {
  3150→                    user_content: fullContent,
  3151→                    providers: selectedProvidersList
  3152→                };
  3153→
  3154→                // Add model for each provider
  3155→                for (var provider in modelsMap) {
  3156→                    requestBody['model_' + provider] = modelsMap[provider];
  3157→                }
  3158→
  3159→                // Show truncation notice if applicable
  3160→                if (wasTruncated) {
  3161→                    document.getElementById('loading-message').textContent = 'Content truncated to ' + finalTokens.toLocaleString() + ' tokens. Sending to ' + selectedProvidersList.length + ' AI(s)...';
  3162→                } else {
  3163→                    document.getElementById('loading-message').textContent = 'Sending ~' + finalTokens.toLocaleString() + ' tokens to ' + selectedProvidersList.length + ' AI provider(s)...';
  3164→                }
  3165→
  3166→                // Send to AI API with timeout
  3167→                var timeoutSec = parseInt(document.getElementById('provider-timeout').value) || 120;
  3168→                var response = await fetchWithTimeout('../aiapi/api_opus/api/query.php', {
  3169→                    method: 'POST',
  3170→                    headers: { 'Content-Type': 'application/json' },
  3171→                    body: JSON.stringify(requestBody)
  3172→                }, timeoutSec * 1000);
  3173→
  3174→                if (!response.ok) {
  3175→                    var errText = '';
  3176→                    try { errText = await response.text(); } catch(e) {}
  3177→                    throw new Error('AI API returned HTTP ' + response.status + (errText ? ': ' + errText.substring(0, 200) : ''));
  3178→                }
  3179→
  3180→                var responseText = await response.text();
  3181→                var data;
  3182→                try {
  3183→                    data = JSON.parse(responseText);
  3184→                } catch (parseErr) {
  3185→                    throw new Error('AI API returned invalid JSON: ' + responseText.substring(0, 200));
  3186→                }
  3187→
  3188→                // Log response to debug file
  3189→                var responsePreview = '';
  3190→                if (data.responses) {
  3191→                    for (var rk in data.responses) {
  3192→                        if (data.responses[rk] && data.responses[rk].response) {
  3193→                            responsePreview = data.responses[rk].response.substring(0, 500);
  3194→                            break;
  3195→                        }

                            if (thread.emails && thread.emails.length > 0) {
                                fullContent += '\n\n--- ADDITIONAL THREAD ' + (i + 1) + ' OF ' + allAdditionalThreads.length + ' ---\n';
                                fullContent += 'Subject: ' + (thread.emails[0].subject || '(No Subject)') + '\n\n';
                                // Bodies already stripped by thread_for_ai endpoint
                                fullContent += processAdditionalThreadContent(thread.emails, 'full');
                            }
                        } catch (fetchErr) {
                            console.warn('Error fetching thread ' + tid + ':', fetchErr);
                            fullContent += '\n\n--- THREAD ' + (i + 1) + ': FETCH ERROR ---\n';
                        }
                    }
                }

                // Enforce content budget: truncate to fit smallest model limit
                var maxChars = Math.floor(modelLimit * 3.5 * 0.85); // 85% of model limit in chars
                var wasTruncated = false;
                if (fullContent.length > maxChars) {
                    wasTruncated = true;
                    fullContent = fullContent.substring(0, maxChars) + '\n\n...(Content truncated to fit model context limit of ' + modelLimit.toLocaleString() + ' tokens)';
                }

                var finalTokens = Math.ceil(fullContent.length / 3.5);

                // Log to debug file BEFORE sending
                logAiDebug({
                    type: 'request',
                    thread_id: currentThreadId,
                    agent: selectedAgent,
                    providers: selectedProvidersList,
                    models: modelsMap,
                    content_length: fullContent.length,
                    estimated_tokens: finalTokens,
                    question: actualQuestion,
                    context_threads: allAdditionalThreads,
                    truncated: wasTruncated,
                    full_content: fullContent.substring(0, 10000) + (fullContent.length > 10000 ? '\n...(truncated in log, full was ' + fullContent.length + ' chars)' : '')
                });

                // Console log
                console.log('=== AI REQUEST LOG ===');
                console.log('Agent:', selectedAgent);
                console.log('Providers:', selectedProvidersList);
                console.log('Models:', modelsMap);
                console.log('Content Mode:', contentMode);
                console.log('Context Threads:', allAdditionalThreads.length);
                console.log('Conversation History:', conversationHistory.length);
                console.log('Content Length:', fullContent.length, 'chars');
                console.log('Estimated Tokens:', finalTokens);
                console.log('======================');

                document.getElementById('loading-message').textContent = 'Analyzing with ' + selectedProvidersList.length + ' AI provider(s)...';

                // Build request body with all selected providers and their models
                var requestBody = {
                    user_content: fullContent,
                    providers: selectedProvidersList
                };

                // Add model for each provider
                for (var provider in modelsMap) {
                    requestBody['model_' + provider] = modelsMap[provider];
                }

                // Show truncation notice if applicable
                if (wasTruncated) {
                    document.getElementById('loading-message').textContent = 'Content truncated to ' + finalTokens.toLocaleString() + ' tokens. Sending to ' + selectedProvidersList.length + ' AI(s)...';
                } else {
                    document.getElementById('loading-message').textContent = 'Sending ~' + finalTokens.toLocaleString() + ' tokens to ' + selectedProvidersList.length + ' AI provider(s)...';
                }

                // Send to AI API with timeout
                var timeoutSec = parseInt(document.getElementById('provider-timeout').value) || 120;
                var response = await fetchWithTimeout('../aiapi/api_opus/api/query.php', {
                    method: 'POST',
                    headers: { 'Content-Type': 'application/json' },
                    body: JSON.stringify(requestBody)
                }, timeoutSec * 1000);

                if (!response.ok) {
                    var errText = '';
                    try { errText = await response.text(); } catch(e) {}
                    throw new Error('AI API returned HTTP ' + response.status + (errText ? ': ' + errText.substring(0, 200) : ''));
                }

                var responseText = await response.text();
                var data;
                try {
                    data = JSON.parse(responseText);
                } catch (parseErr) {
                    throw new Error('AI API returned invalid JSON: ' + responseText.substring(0, 200));
                }

                // Log response to debug file
                var responsePreview = '';
                if (data.responses) {
                    for (var rk in data.responses) {
                        if (data.responses[rk] && data.responses[rk].response) {
                            responsePreview = data.responses[rk].response.substring(0, 500);
                            break;
                        }


  3195→                        }
  3196→                    }
  3197→                }
  3198→                logAiDebug({
  3199→                    type: 'response',
  3200→                    thread_id: currentThreadId,
  3201→                    agent: selectedAgent,
  3202→                    providers: selectedProvidersList,
  3203→                    models: modelsMap,
  3204→                    estimated_tokens: finalTokens,
  3205→                    question: actualQuestion,
  3206→                    response: responsePreview
  3207→                });
  3208→
  3209→                console.log('=== AI RESPONSE LOG ===');
  3210→                console.log('Success:', data.success);
  3211→                console.log('Query ID:', data.query_id);
  3212→                console.log('Total Cost:', data.total_cost);
  3213→                console.log('=======================');
  3214→
  3215→                if (data.success) {
  3216→                    var firstSuccessfulProvider = null;
  3217→                    var firstSuccessfulResponse = null;
  3218→
  3219→                    // Save to MongoDB for each provider
  3220→                    for (var pi = 0; pi < selectedProvidersList.length; pi++) {
  3221→                        var prov = selectedProvidersList[pi];
  3222→                        var providerResponse = data.responses[prov + '_response'];
  3223→                        if (providerResponse && providerResponse.success) {
  3224→                            if (!firstSuccessfulProvider) {
  3225→                                firstSuccessfulProvider = prov;
  3226→                                firstSuccessfulResponse = providerResponse;
  3227→                            }
  3228→
  3229→                            // Save to MongoDB via our API
  3230→                            await saveAIResponse({
  3231→                                thread_ids: [currentThreadId].concat(allAdditionalThreads),
  3232→                                contact_id: primaryContactId,
  3233→                                question: actualQuestion,
  3234→                                context: context,
  3235→                                agent: selectedAgent,
  3236→                                content_mode: contentMode,
  3237→                                content_sent: fullContent,
  3238→                                content_length: fullContent.length,
  3239→                                estimated_tokens: finalTokens,
  3240→                                provider: prov,
  3241→                                model: modelsMap[prov],
  3242→                                query_id: data.query_id,
  3243→                                response: providerResponse,
  3244→                                total_cost: providerResponse.cost || 0,
  3245→                                all_providers: selectedProvidersList,
  3246→                                conversation_history_count: conversationHistory.length
  3247→                            });
  3248→                        }
  3249→                    }
  3250→
  3251→                    // ADD TO CONVERSATION HISTORY (first successful response)
  3252→                    if (firstSuccessfulProvider && firstSuccessfulResponse && firstSuccessfulResponse.response) {
  3253→                        addToConversation(
  3254→                            actualQuestion,
  3255→                            firstSuccessfulResponse.response,
  3256→                            firstSuccessfulProvider,
  3257→                            modelsMap[firstSuccessfulProvider],
  3258→                            context
  3259→                        );
  3260→                    }
  3261→
  3262→                    // Clear the question field for next question
  3263→                    document.getElementById('ai-question').value = '';
  3264→                    updateTokenEstimate();
  3265→
  3266→                    // Store Round 1 info for potential Round 2 / follow-ups
  3267→                    lastRound1Providers = selectedProvidersList.slice();
  3268→                    lastRound1Models = Object.assign({}, modelsMap);
  3269→                    lastRound1Question = actualQuestion;
  3270→                    lastFullContent = fullContent;
  3271→
  3272→                    // Display Round 1 response
  3273→                    displayAIResponse(data, 'Round 1');
  3274→

                        }
                    }
                }
                logAiDebug({
                    type: 'response',
                    thread_id: currentThreadId,
                    agent: selectedAgent,
                    providers: selectedProvidersList,
                    models: modelsMap,
                    estimated_tokens: finalTokens,
                    question: actualQuestion,
                    response: responsePreview
                });

                console.log('=== AI RESPONSE LOG ===');
                console.log('Success:', data.success);
                console.log('Query ID:', data.query_id);
                console.log('Total Cost:', data.total_cost);
                console.log('=======================');

                if (data.success) {
                    var firstSuccessfulProvider = null;
                    var firstSuccessfulResponse = null;

                    // Save to MongoDB for each provider
                    for (var pi = 0; pi < selectedProvidersList.length; pi++) {
                        var prov = selectedProvidersList[pi];
                        var providerResponse = data.responses[prov + '_response'];
                        if (providerResponse && providerResponse.success) {
                            if (!firstSuccessfulProvider) {
                                firstSuccessfulProvider = prov;
                                firstSuccessfulResponse = providerResponse;
                            }

                            // Save to MongoDB via our API
                            await saveAIResponse({
                                thread_ids: [currentThreadId].concat(allAdditionalThreads),
                                contact_id: primaryContactId,
                                question: actualQuestion,
                                context: context,
                                agent: selectedAgent,
                                content_mode: contentMode,
                                content_sent: fullContent,
                                content_length: fullContent.length,
                                estimated_tokens: finalTokens,
                                provider: prov,
                                model: modelsMap[prov],
                                query_id: data.query_id,
                                response: providerResponse,
                                total_cost: providerResponse.cost || 0,
                                all_providers: selectedProvidersList,
                                conversation_history_count: conversationHistory.length
                            });
                        }
                    }

                    // ADD TO CONVERSATION HISTORY (first successful response)
                    if (firstSuccessfulProvider && firstSuccessfulResponse && firstSuccessfulResponse.response) {
                        addToConversation(
                            actualQuestion,
                            firstSuccessfulResponse.response,
                            firstSuccessfulProvider,
                            modelsMap[firstSuccessfulProvider],
                            context
                        );
                    }

                    // Clear the question field for next question
                    document.getElementById('ai-question').value = '';
                    updateTokenEstimate();

                    // Store Round 1 info for potential Round 2 / follow-ups
                    lastRound1Providers = selectedProvidersList.slice();
                    lastRound1Models = Object.assign({}, modelsMap);
                    lastRound1Question = actualQuestion;
                    lastFullContent = fullContent;

                    // Display Round 1 response
                    displayAIResponse(data, 'Round 1');


Now let me check the `displayAIResponse` and `sendRound2` functions:


3316:        function displayAIResponse(data, roundLabel) {


3316→        function displayAIResponse(data, roundLabel) {
  3317→            var container = document.getElementById('ai-response-container');
  3318→            var tabsContainer = document.getElementById('response-tabs');
  3319→            var contentContainer = document.getElementById('ai-responses-content');
  3320→            var badge = document.getElementById('response-round-badge');
  3321→
  3322→            container.style.display = 'block';
  3323→            tabsContainer.innerHTML = '';
  3324→            contentContainer.innerHTML = '';
  3325→
  3326→            // Update badge
  3327→            if (badge) {
  3328→                badge.textContent = roundLabel || 'Round 1';
  3329→                badge.className = 'round-badge round-1';
  3330→            }
  3331→
  3332→            // Hide Round 2 container (fresh start)
  3333→            document.getElementById('ai-response-round2-container').style.display = 'none';
  3334→
  3335→            // Store responses for follow-up
  3336→            lastRound1Responses = {};
  3337→
  3338→            var tabIndex = 0;
  3339→            var successCount = 0;
  3340→            var failCount = 0;
  3341→
  3342→            for (var key in data.responses) {
  3343→                var response = data.responses[key];
  3344→                var providerName = key.replace('_response', '');
  3345→                var providerConfig = providers[providerName] || { name: providerName };
  3346→
  3347→                // Store for Round 2 / follow-up
  3348→                lastRound1Responses[providerName] = response;
  3349→
  3350→                // Create tab
  3351→                var tab = document.createElement('button');
  3352→                tab.className = 'response-tab' + (tabIndex === 0 ? ' active' : '');
  3353→                tab.textContent = providerConfig.name + (response.success ? '' : ' (fail)');
  3354→                tab.dataset.index = tabIndex;
  3355→                (function(idx) {
  3356→                    tab.onclick = function() { switchResponseTab(idx); };
  3357→                })(tabIndex);
  3358→                tabsContainer.appendChild(tab);
  3359→
  3360→                // Create content with selection checkbox
  3361→                var contentDiv = document.createElement('div');
  3362→                contentDiv.className = 'response-content' + (tabIndex === 0 ? ' active' : '');
  3363→                contentDiv.id = 'response-tab-' + tabIndex;
  3364→
  3365→                if (response.success) {
  3366→                    successCount++;
  3367→                    var cbId = 'resp-cb-' + providerName;
  3368→                    contentDiv.innerHTML = '
' +
  3369→                        '<input type="checkbox" class="response-select-cb" id="' + cbId + '" checked ' +
  3370→                        'data-provider="' + escapeHtml(providerName) + '" ' +
  3371→                        'data-model="' + escapeHtml(response.model || '') + '" ' +
  3372→                        'data-response-text="' + escapeHtml(response.response || '').replace(/"/g, '"') + '">' +
  3373→                        'Include in follow-up' +
  3374→                        '
' +
  3375→                        '
' +
  3376→                        '
' +
  3377→                        '' + escapeHtml(providerConfig.name) + '' +
  3378→                        '' +
  3379→                        'Model: ' + escapeHtml(response.model || 'Unknown') + ' | ' +
  3380→                        'Time: ' + (response.request_time ? response.request_time.toFixed(2) : '?') + 's | ' +
  3381→                        'Cost: $' + (response.cost ? response.cost.toFixed(6) : '0') +
  3382→                        '
' +
  3383→                        '
' + escapeHtml(response.response) + '
' +
  3384→                        '
';
  3385→                } else {
  3386→                    failCount++;
  3387→                    contentDiv.innerHTML = '
' +
  3388→                        'Error (' + escapeHtml(providerConfig.name) + '):' +
  3389→                        '
' + escapeHtml(response.error || 'Unknown error / timeout') + '

';
  3390→                }
  3391→
  3392→                contentContainer.appendChild(contentDiv);
  3393→                tabIndex++;
  3394→            }
  3395→

function displayAIResponse(data, roundLabel) {
            var container = document.getElementById('ai-response-container');
            var tabsContainer = document.getElementById('response-tabs');
            var contentContainer = document.getElementById('ai-responses-content');
            var badge = document.getElementById('response-round-badge');

            container.style.display = 'block';
            tabsContainer.innerHTML = '';
            contentContainer.innerHTML = '';

            // Update badge
            if (badge) {
                badge.textContent = roundLabel || 'Round 1';
                badge.className = 'round-badge round-1';
            }

            // Hide Round 2 container (fresh start)
            document.getElementById('ai-response-round2-container').style.display = 'none';

            // Store responses for follow-up
            lastRound1Responses = {};

            var tabIndex = 0;
            var successCount = 0;
            var failCount = 0;

            for (var key in data.responses) {
                var response = data.responses[key];
                var providerName = key.replace('_response', '');
                var providerConfig = providers[providerName] || { name: providerName };

                // Store for Round 2 / follow-up
                lastRound1Responses[providerName] = response;

                // Create tab
                var tab = document.createElement('button');
                tab.className = 'response-tab' + (tabIndex === 0 ? ' active' : '');
                tab.textContent = providerConfig.name + (response.success ? '' : ' (fail)');
                tab.dataset.index = tabIndex;
                (function(idx) {
                    tab.onclick = function() { switchResponseTab(idx); };
                })(tabIndex);
                tabsContainer.appendChild(tab);

                // Create content with selection checkbox
                var contentDiv = document.createElement('div');
                contentDiv.className = 'response-content' + (tabIndex === 0 ? ' active' : '');
                contentDiv.id = 'response-tab-' + tabIndex;

                if (response.success) {
                    successCount++;
                    var cbId = 'resp-cb-' + providerName;
                    contentDiv.innerHTML = '
' +
                        '<input type="checkbox" class="response-select-cb" id="' + cbId + '" checked ' +
                        'data-provider="' + escapeHtml(providerName) + '" ' +
                        'data-model="' + escapeHtml(response.model || '') + '" ' +
                        'data-response-text="' + escapeHtml(response.response || '').replace(/"/g, '"') + '">' +
                        'Include in follow-up' +
                        '
' +
                        '
' +
                        '
' +
                        '' + escapeHtml(providerConfig.name) + '' +
                        '' +
                        'Model: ' + escapeHtml(response.model || 'Unknown') + ' | ' +
                        'Time: ' + (response.request_time ? response.request_time.toFixed(2) : '?') + 's | ' +
                        'Cost: $' + (response.cost ? response.cost.toFixed(6) : '0') +
                        '
' +
                        '
' + escapeHtml(response.response) + '
' +
                        '
';
                } else {
                    failCount++;
                    contentDiv.innerHTML = '
' +
                        'Error (' + escapeHtml(providerConfig.name) + '):' +
                        '
' + escapeHtml(response.error || 'Unknown error / timeout') + '

';
                }

                contentContainer.appendChild(contentDiv);
                tabIndex++;
            }


Now let me check where the `fetchWithTimeout` and `saveAIResponse` functions are, and also the `buildEmailContent` and `processAdditionalThreadContent`:


Grep

function fetchWithTimeout|function saveAIResponse|function processAdditionalThreadContent|function buildConversationContext|function addToConversation


2332:        function fetchWithTimeout(url, options, timeoutMs) {
2601:        function addToConversation(question, answer, provider, model, context = '') {
2632:        function buildConversationContext() {
2974:        function processAdditionalThreadContent(emails, mode) {
3303:        async function saveAIResponse(responseData) {


  2332→        function fetchWithTimeout(url, options, timeoutMs) {
  2333→            var controller = new AbortController();
  2334→            options.signal = controller.signal;
  2335→            var timeoutId = setTimeout(function() { controller.abort(); }, timeoutMs);
  2336→
  2337→            return fetch(url, options).then(function(response) {
  2338→                clearTimeout(timeoutId);
  2339→                return response;
  2340→            }).catch(function(err) {
  2341→                clearTimeout(timeoutId);
  2342→                if (err.name === 'AbortError') {
  2343→                    throw new Error('Request timed out after ' + (timeoutMs / 1000) + 's');
  2344→                }
  2345→                throw err;
  2346→            });
  2347→        }
  2348→
  2349→        // ============================================
  2350→        // D3.JS CHART GENERATION
  2351→        // ============================================
  2352→        async function generateChart() {
  2353→            // Collect AI response text from the displayed response
  2354→            var responseEls = document.querySelectorAll('#ai-responses-content .ai-response-content, #ai-responses-content-round2 .ai-response-content');
  2355→            var responseText = '';
  2356→            responseEls.forEach(function(el) {
  2357→                if (el.innerText) responseText += el.innerText + '\n\n';
  2358→            });
  2359→            if (!responseText.trim()) {
  2360→                alert('No AI response to chart. Submit a query first.');
  2361→                return;

        function fetchWithTimeout(url, options, timeoutMs) {
            var controller = new AbortController();
            options.signal = controller.signal;
            var timeoutId = setTimeout(function() { controller.abort(); }, timeoutMs);

            return fetch(url, options).then(function(response) {
                clearTimeout(timeoutId);
                return response;
            }).catch(function(err) {
                clearTimeout(timeoutId);
                if (err.name === 'AbortError') {
                    throw new Error('Request timed out after ' + (timeoutMs / 1000) + 's');
                }
                throw err;
            });
        }

        // ============================================
        // D3.JS CHART GENERATION
        // ============================================
        async function generateChart() {
            // Collect AI response text from the displayed response
            var responseEls = document.querySelectorAll('#ai-responses-content .ai-response-content, #ai-responses-content-round2 .ai-response-content');
            var responseText = '';
            responseEls.forEach(function(el) {
                if (el.innerText) responseText += el.innerText + '\n\n';
            });
            if (!responseText.trim()) {
                alert('No AI response to chart. Submit a query first.');
                return;


Now let me see the `getSmallestModelLimit` function and how `AI_AGENTS` is defined:


1559:        function getSmallestModelLimit() {


1466:        const AI_AGENTS = {
1575:            var agent = AI_AGENTS[selectedAgent];
1921:            for (var id in AI_AGENTS) {
1922:                var agent = AI_AGENTS[id];
1944:            var agent = AI_AGENTS[selectedAgent];

[Showing results with pagination = limit: 5, offset: 0]


  1466→        const AI_AGENTS = {
  1467→            general: {
  1468→                name: 'General',
  1469→                icon: '\uD83D\uDCAC',
  1470→                systemPrompt: 'You are a highly capable business intelligence AI assistant. Analyze the provided email conversations thoroughly. Provide clear, actionable insights. Be direct and specific.',
  1471→                quickPrompts: [
  1472→                    'Summarize the key points',
  1473→                    'What are the action items?',
  1474→                    'What is the overall sentiment?',
  1475→                    'What should I reply?'
  1476→                ]
  1477→            },
  1478→            quote_analyst: {
  1479→                name: 'Quotes',
  1480→                icon: '\uD83D\uDCB0',
  1481→                systemPrompt: 'You are a pricing and quotation analyst. Focus on:\n- All quoted prices, amounts, and financial figures\n- Price changes over time and negotiation patterns\n- Scope of work vs pricing alignment\n- Competitor pricing if mentioned\n- Negotiation leverage points\n- Budget signals and willingness to pay\nAlways present findings with specific numbers and dates.',
  1482→                quickPrompts: [
  1483→                    'What prices have been quoted?',
  1484→                    'Show the negotiation history',
  1485→                    'What is the pricing trend?',
  1486→                    'What should we quote next?'
  1487→                ]
  1488→            },
  1489→            deception_detector: {
  1490→                name: 'Deception',
  1491→                icon: '\uD83D\uDD0D',
  1492→                systemPrompt: 'You are a deception and inconsistency detection specialist. Analyze the emails for:\n- Contradictions between different messages\n- Promises made but not kept\n- Vague or evasive responses to direct questions\n- Sudden changes in tone or position\n- Pressure tactics or urgency manipulation\n- Information that doesn\'t add up\n- Omissions of relevant details\nRate the overall deception risk as LOW/MEDIUM/HIGH with evidence.',
  1493→                quickPrompts: [
  1494→                    'Any contradictions detected?',
  1495→                    'Rate the deception risk level',
  1496→                    'Are there broken promises?',
  1497→                    'Any pressure tactics used?'
  1498→                ]
  1499→            },
  1500→            contact_profiler: {
  1501→                name: 'Profiler',
  1502→                icon: '\uD83D\uDC64',
  1503→                systemPrompt: 'You are a contact profiling specialist. Build a psychological and business profile of the contact based on their emails:\n- Communication style (formal/casual, verbose/concise)\n- Decision-making patterns\n- Negotiation style and tendencies\n- Responsiveness and reliability\n- Priorities and motivations\n- Relationship health indicators\n- Loyalty signals\nProvide specific examples from the emails to support your assessment.',
  1504→                quickPrompts: [
  1505→                    'Build a full profile',
  1506→                    'Communication style analysis',
  1507→                    'How loyal is this contact?',
  1508→                    'What motivates them?'
  1509→                ]
  1510→            },
  1511→            email_composer: {
  1512→                name: 'Composer',
  1513→                icon: '\u270D\uFE0F',
  1514→                systemPrompt: 'You are a professional email composer. Based on the conversation context:\n- Draft a professional, well-structured reply\n- Match the appropriate tone (formal/casual based on the thread)\n- Address all open questions or action items\n- Be concise but thorough\n- Include appropriate greetings and sign-offs\nOutput ONLY the email draft, ready to send.',
  1515→                quickPrompts: [
  1516→                    'Draft a reply',
  1517→                    'Write a follow-up email',
  1518→                    'Compose a polite decline',
  1519→                    'Draft a meeting request'
  1520→                ]
  1521→            }
  1522→        };
  1523→
  1524→        let selectedAgent = 'general';
  1525→
  1526→        // ============================================
  1527→        // TOKEN ESTIMATION
  1528→        // ============================================
  1529→        const MODEL_LIMITS = {
  1530→            'claude-opus-4-6': 200000,
  1531→            'claude-sonnet-4-6': 200000,
  1532→            'claude-sonnet-4-5-20250929': 200000,
  1533→            'claude-3-5-sonnet-20241022': 200000,
  1534→            'claude-opus-4-5-20250929': 200000,
  1535→            'claude-3-opus-20240229': 200000,
  1536→            'claude-3-5-haiku-20241022': 200000,
  1537→            'gpt-4o': 128000,
  1538→            'gpt-4o-mini': 128000,
  1539→            'gpt-4-turbo': 128000,
  1540→            'gpt-4.1': 1000000,
  1541→            'gpt-4.1-mini': 1000000,
  1542→            'o3': 200000,
  1543→            'o3-mini': 200000,
  1544→            'o4-mini': 200000,
  1545→            'gemini-2.0-flash': 1048576,
  1546→            'gemini-2.5-pro-preview-05-06': 1048576,
  1547→            'gemini-2.5-flash-preview-04-17': 1048576,
  1548→            'grok-3': 131072,
  1549→            'grok-3-mini': 131072,
  1550→            'deepseek-chat': 64000,
  1551→            'deepseek-reasoner': 64000,
  1552→            'qwen-max': 32000,
  1553→            'qwen-plus': 131000,
  1554→            'mistral-large-latest': 128000,
  1555→            'command-a-03-2025': 256000,
  1556→            'command-r-plus': 128000
  1557→        };
  1558→
  1559→        function getSmallestModelLimit() {
  1560→            var selectedProviders = getSelectedProviders();
  1561→            var minLimit = Infinity;
  1562→            selectedProviders.forEach(function(prov) {
  1563→                var modelSelect = document.getElementById('model_' + prov);
  1564→                var model = modelSelect ? modelSelect.value : '';
  1565→                var limit = MODEL_LIMITS[model] || 128000;

        const AI_AGENTS = {
            general: {
                name: 'General',
                icon: '\uD83D\uDCAC',
                systemPrompt: 'You are a highly capable business intelligence AI assistant. Analyze the provided email conversations thoroughly. Provide clear, actionable insights. Be direct and specific.',
                quickPrompts: [
                    'Summarize the key points',
                    'What are the action items?',
                    'What is the overall sentiment?',
                    'What should I reply?'
                ]
            },
            quote_analyst: {
                name: 'Quotes',
                icon: '\uD83D\uDCB0',
                systemPrompt: 'You are a pricing and quotation analyst. Focus on:\n- All quoted prices, amounts, and financial figures\n- Price changes over time and negotiation patterns\n- Scope of work vs pricing alignment\n- Competitor pricing if mentioned\n- Negotiation leverage points\n- Budget signals and willingness to pay\nAlways present findings with specific numbers and dates.',
                quickPrompts: [
                    'What prices have been quoted?',
                    'Show the negotiation history',
                    'What is the pricing trend?',
                    'What should we quote next?'
                ]
            },
            deception_detector: {
                name: 'Deception',
                icon: '\uD83D\uDD0D',
                systemPrompt: 'You are a deception and inconsistency detection specialist. Analyze the emails for:\n- Contradictions between different messages\n- Promises made but not kept\n- Vague or evasive responses to direct questions\n- Sudden changes in tone or position\n- Pressure tactics or urgency manipulation\n- Information that doesn\'t add up\n- Omissions of relevant details\nRate the overall deception risk as LOW/MEDIUM/HIGH with evidence.',
                quickPrompts: [
                    'Any contradictions detected?',
                    'Rate the deception risk level',
                    'Are there broken promises?',
                    'Any pressure tactics used?'
                ]
            },
            contact_profiler: {
                name: 'Profiler',
                icon: '\uD83D\uDC64',
                systemPrompt: 'You are a contact profiling specialist. Build a psychological and business profile of the contact based on their emails:\n- Communication style (formal/casual, verbose/concise)\n- Decision-making patterns\n- Negotiation style and tendencies\n- Responsiveness and reliability\n- Priorities and motivations\n- Relationship health indicators\n- Loyalty signals\nProvide specific examples from the emails to support your assessment.',
                quickPrompts: [
                    'Build a full profile',
                    'Communication style analysis',
                    'How loyal is this contact?',
                    'What motivates them?'
                ]
            },
            email_composer: {
                name: 'Composer',
                icon: '\u270D\uFE0F',
                systemPrompt: 'You are a professional email composer. Based on the conversation context:\n- Draft a professional, well-structured reply\n- Match the appropriate tone (formal/casual based on the thread)\n- Address all open questions or action items\n- Be concise but thorough\n- Include appropriate greetings and sign-offs\nOutput ONLY the email draft, ready to send.',
                quickPrompts: [
                    'Draft a reply',
                    'Write a follow-up email',
                    'Compose a polite decline',
                    'Draft a meeting request'
                ]
            }
        };

        let selectedAgent = 'general';

        // ============================================
        // TOKEN ESTIMATION
        // ============================================
        const MODEL_LIMITS = {
            'claude-opus-4-6': 200000,
            'claude-sonnet-4-6': 200000,
            'claude-sonnet-4-5-20250929': 200000,
            'claude-3-5-sonnet-20241022': 200000,
            'claude-opus-4-5-20250929': 200000,
            'claude-3-opus-20240229': 200000,
            'claude-3-5-haiku-20241022': 200000,
            'gpt-4o': 128000,
            'gpt-4o-mini': 128000,
            'gpt-4-turbo': 128000,
            'gpt-4.1': 1000000,
            'gpt-4.1-mini': 1000000,
            'o3': 200000,
            'o3-mini': 200000,
            'o4-mini': 200000,
            'gemini-2.0-flash': 1048576,
            'gemini-2.5-pro-preview-05-06': 1048576,
            'gemini-2.5-flash-preview-04-17': 1048576,
            'grok-3': 131072,
            'grok-3-mini': 131072,
            'deepseek-chat': 64000,
            'deepseek-reasoner': 64000,
            'qwen-max': 32000,
            'qwen-plus': 131000,
            'mistral-large-latest': 128000,
            'command-a-03-2025': 256000,
            'command-r-plus': 128000
        };

        function getSmallestModelLimit() {
            var selectedProviders = getSelectedProviders();
            var minLimit = Infinity;
            selectedProviders.forEach(function(prov) {
                var modelSelect = document.getElementById('model_' + prov);
                var model = modelSelect ? modelSelect.value : '';
                var limit = MODEL_LIMITS[model] || 128000;


Now let me find where the UI buttons/controls are in the HTML to know where to add the Deep Analysis button:


804:        .agent-buttons {
1336:                        <div class="agent-buttons" id="agent-buttons">
1383:                            <input type="checkbox" id="two-round-mode">
1405:                    <button class="btn btn-primary" id="submit-ai-btn" onclick="submitToAI()" style="width: 100%;">
1918:            var container = document.getElementById('agent-buttons');
2113:                document.getElementById('submit-ai-btn').disabled = false;
2231:            document.getElementById('submit-ai-btn').disabled = true;
2306:                document.getElementById('submit-ai-btn').disabled = false;
3002:        // Submit to AI
3037:            document.getElementById('submit-ai-btn').disabled = true;
3276:                    var twoRoundCheckbox = document.getElementById('two-round-mode');
3298:                document.getElementById('submit-ai-btn').disabled = false;


1380→
  1381→

  1382→
  1383→                            <input type="checkbox" id="two-round-mode">
  1384→

  1385→                                Auto Two-Round AI
  1386→
Round 1: all providers answer independently. Round 2: all providers receive everyone's Round 1 answers and produce a unified summary.

  1387→

  1388→
  1389→

  1390→
  1391→                                Provider timeout:
  1392→                                <select id="provider-timeout" style="padding:3px 6px;border-radius:4px;border:1px solid #ccc;font-size:0.9em;">
  1393→                                    <option value="60">60s</option>
  1394→                                    <option value="90">90s</option>
  1395→                                    <option value="120" selected>120s</option>
  1396→                                    <option value="180">180s</option>
  1397→                                    <option value="300">300s</option>
  1398→                                </select>
  1399→                                (skip slow providers after this)
  1400→
  1401→

  1402→

  1403→
  1404→
  1405→                    <button class="btn btn-primary" id="submit-ai-btn" onclick="submitToAI()" style="width: 100%;">
  1406→                        Send to AI
  1407→                    </button>
  1408→

  1409→

  1410→

  1411→

  1412→
  1413→
  1414→

                            <input type="checkbox" id="two-round-mode">

                                Auto Two-Round AI

Round 1: all providers answer independently. Round 2: all providers receive everyone's Round 1 answers and produce a unified summary.

                                Provider timeout:
                                <select id="provider-timeout" style="padding:3px 6px;border-radius:4px;border:1px solid #ccc;font-size:0.9em;">
                                    <option value="60">60s</option>
                                    <option value="90">90s</option>
                                    <option value="120" selected>120s</option>
                                    <option value="180">180s</option>
                                    <option value="300">300s</option>
                                </select>
                                (skip slow providers after this)

                    <button class="btn btn-primary" id="submit-ai-btn" onclick="submitToAI()" style="width: 100%;">
                        Send to AI
                    </button>


Now let me see the response containers to understand where results go:


1169:

1187:

1190:                        <button class="btn btn-secondary" onclick="document.getElementById('ai-response-round2-container').style.display='none'">Close</button>
2119:            var container = document.getElementById('ai-response-round2-container');
3317:            var container = document.getElementById('ai-response-container');
3333:            document.getElementById('ai-response-round2-container').style.display = 'none';
3441:            document.getElementById('ai-response-container').style.display = 'none';
3473:                    const container = document.getElementById('ai-response-container');


1160→                                <?= date('M j, Y g:i A', strtotime($email['date'])) ?>
  1161→

  1162→
<?= htmlspecialchars($displayBody) ?>

  1163→

  1164→                        <?php endforeach; ?>
  1165→

  1166→

  1167→
  1168→
  1169→

  1170→

  1171→
AI Response Round 1

  1172→                        <button class="btn btn-secondary" onclick="closeResponse()">Close</button>
  1173→

  1174→

  1175→

  1176→
  1177→

  1178→                        Use responses as context:
  1179→                        <button class="btn btn-small" onclick="selectAllResponses()">All</button>
  1180→                        <button class="btn btn-small" onclick="deselectAllResponses()">None</button>
  1181→                        <button class="btn btn-primary btn-small" onclick="askFollowUp()" style="margin-left:auto;">Ask Follow-up</button>
  1182→                        <button class="btn btn-small" onclick="generateChart()" style="background:#8b5cf6;color:white;">Generate Chart</button>
  1183→

  1184→

  1185→
  1186→
  1187→

  1188→

  1189→
AI Response Round 2 - Summary

  1190→                        <button class="btn btn-secondary" onclick="document.getElementById('ai-response-round2-container').style.display='none'">Close</button>
  1191→

  1192→

  1193→

  1194→

  1195→
  1196→
  1197→

  1198→

  1199→

  1200→
Relationship Chart

  1201→                            <button class="btn btn-secondary" onclick="document.getElementById('d3-chart-container').style.display='none'">Close</button>
  1202→

  1203→

  1204→

  1205→

  1206→

  1207→
  1208→
  1209→                <?php if (!empty($savedResponses)): ?>

<?= date('M j, Y g:i A', strtotime($email['date'])) ?>

<?= htmlspecialchars($displayBody) ?>

                        <?php endforeach; ?>

AI Response Round 1

                        <button class="btn btn-secondary" onclick="closeResponse()">Close</button>

                        Use responses as context:
                        <button class="btn btn-small" onclick="selectAllResponses()">All</button>
                        <button class="btn btn-small" onclick="deselectAllResponses()">None</button>
                        <button class="btn btn-primary btn-small" onclick="askFollowUp()" style="margin-left:auto;">Ask Follow-up</button>
                        <button class="btn btn-small" onclick="generateChart()" style="background:#8b5cf6;color:white;">Generate Chart</button>

AI Response Round 2 - Summary

                        <button class="btn btn-secondary" onclick="document.getElementById('ai-response-round2-container').style.display='none'">Close</button>

Relationship Chart

                            <button class="btn btn-secondary" onclick="document.getElementById('d3-chart-container').style.display='none'">Close</button>

                <?php if (!empty($savedResponses)): ?>


Now I have a complete understanding. Let me implement the automated Deep Analysis flow. The approach:

1. A "Deep Analysis" button next to "Send to AI"
2. When clicked, it builds content once (including all context threads), then runs 5 passes sequentially
3. Each pass uses the appropriate agent prompt, sends to AI, displays results in a dedicated panel
4. Conversation history builds up between passes naturally
5. Progress shown in a dedicated overlay


  1402→                    </div>
  1403→
  1404→                    <!-- Submit Button -->
  1405→                    <button class="btn btn-primary" id="submit-ai-btn" onclick="submitToAI()" style="width: 100%;">
  1406→                        Send to AI
  1407→                    </button>
  1408→                </div>
  1409→            </div>
  1410→        </div>
  1411→    </div>
  1412→
  1413→    <!-- Loading Overlay -->

                    </div>

                    <!-- Submit Button -->
                    <button class="btn btn-primary" id="submit-ai-btn" onclick="submitToAI()" style="width: 100%;">
                        Send to AI
                    </button>
                </div>
            </div>
        </div>
    </div>

    <!-- Loading Overlay -->


Now let me also check the loading overlay structure:


1413→
  1414→

  1415→

  1416→

  1417→
Analyzing with AI...

  1418→

  1419→

  1420→
  1421→
  1422→

  1423→

  1424→

  1425→
AI Response

  1426→                <button class="quick-view-close" onclick="closeQuickView()">✕ Close (Esc)</button>
  1427→

Analyzing with AI...

AI Response

                <button class="quick-view-close" onclick="closeQuickView()">✕ Close (Esc)</button>


Good. Now I have enough context. Let me implement the entire Deep Analysis feature. I'll add:
1. The button next to "Send to AI"
2. A Deep Analysis results container in the HTML
3. The `DEEP_ANALYSIS_PASSES` constant and `runDeepAnalysis()` function


Edit

                    <!-- Submit Button -->
                    <button class="btn btn-primary" id="submit-ai-btn" onclick="submitToAI()" style="width: 100%;">
                        Send to AI
                    </button>

                    <!-- Submit Buttons -->
                    <div style="display:flex;gap:8px;">
                        <button class="btn btn-primary" id="submit-ai-btn" onclick="submitToAI()" style="flex:1;">
                            Send to AI
                        </button>
                        <button class="btn" id="deep-analysis-btn" onclick="runDeepAnalysis()" style="flex:1;background:linear-gradient(135deg,#8b5cf6,#3b82f6);color:white;font-weight:600;">
                            Deep Analysis (5-Pass)
                        </button>
                    </div>