diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml
new file mode 100644
index 00000000..45bb9615
--- /dev/null
+++ b/.github/workflows/lint.yaml
@@ -0,0 +1,21 @@
+name: Python Linting
+
+on: [push, pull_request]
+
+jobs:
+  PythonLinting:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.11'
+    - name: Install dependencies
+      run: |
+        pip install poetry
+        poetry install
+    - name: Run black
+      run: |
+        poetry run black --check .
diff --git a/bespoke-dataset-viewer/app/globals.css b/bespoke-dataset-viewer/app/globals.css
index f488d136..e64dcc52 100644
--- a/bespoke-dataset-viewer/app/globals.css
+++ b/bespoke-dataset-viewer/app/globals.css
@@ -34,6 +34,9 @@ body {
     --chart-5: 27 87% 67%;
     --radius: 0.5rem;
     --success: 142 40% 75%;
+    --tooltip-bg: white;
+    --tooltip-text-bg: rgb(255, 255, 255);
+    --tooltip-text: black;
   }
   .dark {
     --background: 0 0% 3.9%;
@@ -61,6 +64,10 @@ body {
     --chart-4: 280 65% 60%;
     --chart-5: 340 75% 55%;
     --success: 142 72% 40%; /* Slightly brighter green for dark mode */
+    --tooltip-bg: rgb(90, 90, 90);
+    --tooltip-text-bg: rgb(0, 0, 0);
+    --tooltip-text: rgb(250, 250, 250);
+    --border: rgb(39, 39, 42);
   }
 }
 
diff --git a/bespoke-dataset-viewer/components/dataset-viewer/DatasetViewer.tsx b/bespoke-dataset-viewer/components/dataset-viewer/DatasetViewer.tsx
index 87872ddf..8fbe3a73 100644
--- a/bespoke-dataset-viewer/components/dataset-viewer/DatasetViewer.tsx
+++ b/bespoke-dataset-viewer/components/dataset-viewer/DatasetViewer.tsx
@@ -18,12 +18,14 @@ import { FileText, Loader2, RefreshCcw } from "lucide-react"
 import { useCallback, useEffect, useState } from "react"
 import { DetailsSidebar } from "./DetailsSidebar"
 import { DistributionChart } from "./DistributionChart"
+import { TimeSeriesChart } from './TimeSeriesChart'
 
 const COLUMNS: Column[] = [
   { key: "user_message", label: "User Message" },
   { key: "assistant_message", label: "Assistant Message" },
   { key: "prompt_tokens", label: "Prompt Tokens" },
-  { key: "completion_tokens", label: "Completion Tokens" }
+  { key: "completion_tokens", label: "Completion Tokens" },
+  { key: "generation_time", label: "Generation Time (s)" }
 ]
 
 interface DatasetViewerProps {
@@ -61,7 +63,7 @@ export function DatasetViewer({ runHash, batchMode }: DatasetViewerProps) {
   const [data, setData] = useState<DataItem[]>([])
   const [theme, setTheme] = useState<"light" | "dark">("light")
   const [mounted, setMounted] = useState(false)
-  const [selectedDistribution, setSelectedDistribution] = useState<string | null>("total_tokens")
+  const [selectedDistribution, setSelectedDistribution] = useState<string | null>("requests")
   const [selectedItem, setSelectedItem] = useState<DataItem | null>(null)
   const { toast } = useToast()
   const [isPolling, setIsPolling] = useState(false)
@@ -224,29 +226,37 @@ export function DatasetViewer({ runHash, batchMode }: DatasetViewerProps) {
               <p className="text-sm text-muted-foreground">View and analyze your dataset responses</p>
             </div>
             {data.length > 0 && (
-              <DropdownMenu>
-                <DropdownMenuTrigger asChild>
-                  <Button variant="outline">
-                    {selectedDistribution
-                      ? selectedDistribution.split('_').map(word =>
-                        word.charAt(0).toUpperCase() + word.slice(1)
-                      ).join(' ')
-                      : 'Select Metric'}
-                  </Button>
-                </DropdownMenuTrigger>
-                <DropdownMenuContent align="end">
-                  <DropdownMenuItem onClick={() => setSelectedDistribution(null)}>
-                    None
-                  </DropdownMenuItem>
-                  {["total_tokens", "prompt_tokens", "completion_tokens"].map((column) => (
-                    <DropdownMenuItem key={column} onClick={() => setSelectedDistribution(column)}>
-                      {column === "total_tokens" ? "Total Tokens" :
-                        column === "prompt_tokens" ? "Prompt Tokens" :
-                          "Completion Tokens"}
+              <div className="flex gap-2">
+                <DropdownMenu>
+                  <DropdownMenuTrigger asChild>
+                    <Button variant="outline">
+                      {selectedDistribution
+                        ? selectedDistribution === "requests" 
+                          ? "Requests & Responses"
+                          : selectedDistribution.split('_').map(word =>
+                              word.charAt(0).toUpperCase() + word.slice(1)
+                            ).join(' ')
+                        : 'Select Metric'}
+                    </Button>
+                  </DropdownMenuTrigger>
+                  <DropdownMenuContent align="end">
+                    <DropdownMenuItem onClick={() => setSelectedDistribution(null)}>
+                      None
                     </DropdownMenuItem>
-                  ))}
-                </DropdownMenuContent>
-              </DropdownMenu>
+                    <DropdownMenuItem onClick={() => setSelectedDistribution("requests")}>
+                      Requests & Responses
+                    </DropdownMenuItem>
+                    {["total_tokens", "prompt_tokens", "completion_tokens", "generation_time"].map((column) => (
+                      <DropdownMenuItem key={column} onClick={() => setSelectedDistribution(column)}>
+                        {column === "total_tokens" ? "Total Tokens" :
+                          column === "prompt_tokens" ? "Prompt Tokens" :
+                            column === "completion_tokens" ? "Completion Tokens" :
+                              "Generation Time (s)"}
+                      </DropdownMenuItem>
+                    ))}
+                  </DropdownMenuContent>
+                </DropdownMenu>
+              </div>
             )}
           </div>
 
@@ -264,10 +274,14 @@ export function DatasetViewer({ runHash, batchMode }: DatasetViewerProps) {
               <div className="mb-8 space-y-4">
                 {selectedDistribution && (
                   <div className="rounded-lg border bg-card p-4">
-                    <DistributionChart
-                      data={data}
-                      column={selectedDistribution}
-                    />
+                    {selectedDistribution === "requests" ? (
+                      <TimeSeriesChart data={data} />
+                    ) : (
+                      <DistributionChart
+                        data={data}
+                        column={selectedDistribution}
+                      />
+                    )}
                   </div>
                 )}
               </div>
diff --git a/bespoke-dataset-viewer/components/dataset-viewer/DetailsSidebar.tsx b/bespoke-dataset-viewer/components/dataset-viewer/DetailsSidebar.tsx
index 02d5fcd5..c3f673cf 100644
--- a/bespoke-dataset-viewer/components/dataset-viewer/DetailsSidebar.tsx
+++ b/bespoke-dataset-viewer/components/dataset-viewer/DetailsSidebar.tsx
@@ -120,6 +120,13 @@ export function DetailsSidebar({ item, onClose }: DetailsSidebarProps) {
                   </div>
                 </div>
               </div>
+              <Separator />
+              <div className="space-y-2">
+                <h3 className="text-lg font-semibold">Generation Time</h3>
+                <p className="text-2xl font-bold">
+                  {((new Date(item.finished_at).getTime() - new Date(item.created_at).getTime()) / 1000).toFixed(2)}s
+                </p>
+              </div>
             </CardContent>
           </div>
         </div>
diff --git a/bespoke-dataset-viewer/components/dataset-viewer/DistributionChart.tsx b/bespoke-dataset-viewer/components/dataset-viewer/DistributionChart.tsx
index f6bdb7d6..aa611db3 100644
--- a/bespoke-dataset-viewer/components/dataset-viewer/DistributionChart.tsx
+++ b/bespoke-dataset-viewer/components/dataset-viewer/DistributionChart.tsx
@@ -1,6 +1,7 @@
 "use client"
 
 import { BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer } from 'recharts'
+import { bin } from 'd3-array'
 import { DataItem } from '@/types/dataset'
 
 interface DistributionChartProps {
@@ -11,43 +12,8 @@ interface DistributionChartProps {
 const COLUMN_DISPLAY_NAMES: Record<string, string> = {
   total_tokens: "Total Tokens",
   prompt_tokens: "Prompt Tokens",
-  completion_tokens: "Completion Tokens"
-}
-
-function calculateNiceBinSize(min: number, max: number): { binSize: number, start: number, end: number } {
-  // Calculate the range
-  const range = max - min;
-  
-  // Get the magnitude of the range (in powers of 10)
-  const magnitude = Math.floor(Math.log10(range));
-  
-  // Possible bin sizes (1, 2, 5, 10) * 10^n
-  const base = Math.pow(10, magnitude);
-  const possibleBinSizes = [
-    0.1 * base,
-    0.2 * base,
-    0.5 * base,
-    base,
-    2 * base,
-    5 * base,
-    10 * base
-  ];
-
-  // Aim for between 5-10 bins
-  const targetBins = 7;
-  
-  // Find the bin size that gives us closest to our target number of bins
-  const binSize = possibleBinSizes.reduce((prev, curr) => {
-    const prevBins = range / prev;
-    const currBins = range / curr;
-    return Math.abs(currBins - targetBins) < Math.abs(prevBins - targetBins) ? curr : prev;
-  });
-
-  // Calculate nice start and end points
-  const start = Math.floor(min / binSize) * binSize;
-  const end = Math.ceil(max / binSize) * binSize;
-
-  return { binSize, start, end };
+  completion_tokens: "Completion Tokens",
+  generation_time: "Generation Time (s)"
 }
 
 function getDistributionData(data: DataItem[], column: string) {
@@ -59,53 +25,25 @@ function getDistributionData(data: DataItem[], column: string) {
         return item.raw_response.usage?.prompt_tokens || item.raw_response.response?.body?.usage?.prompt_tokens || 0;
       case "completion_tokens":
         return item.raw_response.usage?.completion_tokens || item.raw_response.response?.body?.usage?.completion_tokens || 0;
+      case "generation_time":
+        return (new Date(item.finished_at).getTime() - new Date(item.created_at).getTime()) / 1000;
       default:
         return 0;
     }
   });
 
-  const min = Math.min(...values);
-  const max = Math.max(...values);
+  const binner = bin()
+    .value(d => d)
+    .thresholds(10);
 
-  // If min equals max, all values are the same
-  if (min === max) {
-    return [{
-      range: `${min}`,
-      count: values.length
-    }];
-  }
-
-  const { binSize, start, end } = calculateNiceBinSize(min, max);
-  const numBins = Math.ceil((end - start) / binSize);
-  
-  // Initialize buckets
-  const buckets: { range: string; start: number; count: number }[] = [];
-  for (let i = 0; i < numBins; i++) {
-    const bucketStart = start + (i * binSize);
-    const bucketEnd = bucketStart + binSize;
-    buckets.push({
-      range: `${Math.round(bucketStart)}-${Math.round(bucketEnd)}`,
-      start: bucketStart,
-      count: 0
-    });
-  }
-
-  // Count values in buckets
-  values.forEach(value => {
-    const bucketIndex = Math.floor((value - start) / binSize);
-    if (bucketIndex >= 0 && bucketIndex < buckets.length) {
-      buckets[bucketIndex].count++;
-    }
-  });
+  const bins = binner(values);
 
-  // Only return buckets that have counts
-  return buckets
-    .filter(bucket => bucket.count > 0)
-    .sort((a, b) => a.start - b.start)
-    .map(({ range, count }) => ({
-      range,
-      count
-    }));
+  return bins.map(b => ({
+    range: Number.isInteger(b.x0) ? `${b.x0}` : `${b.x0?.toFixed(2)}`,
+    count: b.length,
+    start: b.x0,
+    end: b.x1
+  }));
 }
 
 export function DistributionChart({ data, column }: DistributionChartProps) {
@@ -133,6 +71,18 @@ export function DistributionChart({ data, column }: DistributionChartProps) {
           <YAxis />
           <Tooltip 
             formatter={(value: number) => [`Count: ${value}`, displayName]}
+            contentStyle={{
+              backgroundColor: 'var(--tooltip-text-bg)',
+              border: '1px solid var(--border)',
+              borderRadius: '4px',
+              color: 'var(--tooltip-text)',
+              boxShadow: '0 2px 4px rgba(0,0,0,0.1)'
+            }}
+            labelStyle={{
+              color: 'var(--tooltip-text)',
+              marginBottom: '4px'
+            }}
+            cursor={{ fill: 'var(--tooltip-bg)', opacity: 0.2 }}
           />
           <Bar 
             dataKey="count" 
diff --git a/bespoke-dataset-viewer/components/dataset-viewer/TimeSeriesChart.tsx b/bespoke-dataset-viewer/components/dataset-viewer/TimeSeriesChart.tsx
new file mode 100644
index 00000000..3cbc05b1
--- /dev/null
+++ b/bespoke-dataset-viewer/components/dataset-viewer/TimeSeriesChart.tsx
@@ -0,0 +1,186 @@
+"use client"
+
+import { DataItem } from '@/types/dataset'
+import { XAxis, YAxis, Tooltip, ResponsiveContainer, Legend, CartesianGrid, Area, ComposedChart } from 'recharts'
+import { useState } from 'react'
+import { Button } from '@/components/ui/button'
+
+interface TimeSeriesChartProps {
+  data: DataItem[]
+}
+
+type TimeUnit = 'second' | 'minute' | 'hour'
+
+interface TimePoint {
+  timestamp: number
+  requestsSent: number
+  responsesReceived: number
+  timeLabel: string
+}
+
+function aggregateByTimeUnit(data: DataItem[], unit: TimeUnit): TimePoint[] {
+  const timePoints = new Map<number, TimePoint>()
+  
+  // Convert to milliseconds
+  const interval = unit === 'second' ? 1000 : 
+                  unit === 'minute' ? 60 * 1000 : 
+                  60 * 60 * 1000
+
+  // Find min and max timestamps
+  const timestamps = data.flatMap(item => [
+    new Date(item.created_at).getTime(),
+    new Date(item.finished_at).getTime()
+  ])
+  const minTime = Math.floor(Math.min(...timestamps) / interval) * interval
+  const maxTime = Math.ceil(Math.max(...timestamps) / interval) * interval
+
+  // Initialize all time points
+  for (let t = minTime; t <= maxTime; t += interval) {
+    timePoints.set(t, {
+      timestamp: t,
+      requestsSent: 0,
+      responsesReceived: 0,
+      timeLabel: formatTimeLabel(t, unit)
+    })
+  }
+
+  // Count requests sent and responses received in each interval
+  data.forEach(item => {
+    const createdTime = Math.floor(new Date(item.created_at).getTime() / interval) * interval
+    const finishedTime = Math.floor(new Date(item.finished_at).getTime() / interval) * interval
+    
+    const createdPoint = timePoints.get(createdTime)
+    if (createdPoint) {
+      createdPoint.requestsSent++
+    }
+    
+    const finishedPoint = timePoints.get(finishedTime)
+    if (finishedPoint) {
+      finishedPoint.responsesReceived++
+    }
+  })
+
+  return Array.from(timePoints.values())
+    .sort((a, b) => a.timestamp - b.timestamp)
+}
+
+function formatTimeLabel(timestamp: number, unit: TimeUnit): string {
+  const date = new Date(timestamp)
+  if (unit === 'second') {
+    return date.toLocaleTimeString([], { 
+      hour: '2-digit',
+      minute: '2-digit',
+      second: '2-digit'
+    })
+  } else if (unit === 'minute') {
+    return date.toLocaleTimeString([], { 
+      hour: '2-digit',
+      minute: '2-digit'
+    })
+  } else {
+    return date.toLocaleTimeString([], { 
+      hour: '2-digit'
+    })
+  }
+}
+
+export function TimeSeriesChart({ data }: TimeSeriesChartProps) {
+  const [timeUnit, setTimeUnit] = useState<TimeUnit>('second')
+  const timeSeriesData = aggregateByTimeUnit(data, timeUnit)
+
+  return (
+    <div className="w-full">
+      <div className="flex justify-between items-center mb-4">
+        <h2 className="text-xl font-semibold">Requests and Responses Over Time</h2>
+        <div className="flex gap-2">
+          <Button 
+            variant={timeUnit === 'second' ? 'default' : 'outline'}
+            onClick={() => setTimeUnit('second')}
+            size="sm"
+          >
+            Per Second
+          </Button>
+          <Button 
+            variant={timeUnit === 'minute' ? 'default' : 'outline'}
+            onClick={() => setTimeUnit('minute')}
+            size="sm"
+          >
+            Per Minute
+          </Button>
+          <Button 
+            variant={timeUnit === 'hour' ? 'default' : 'outline'}
+            onClick={() => setTimeUnit('hour')}
+            size="sm"
+          >
+            Per Hour
+          </Button>
+        </div>
+      </div>
+      
+      <div className="h-80">
+        <ResponsiveContainer width="100%" height="100%">
+          <ComposedChart
+            data={timeSeriesData}
+            margin={{ top: 10, right: 30, left: 20, bottom: 15 }}
+          >
+            <defs>
+              <linearGradient id="requestsGradient" x1="0" y1="0" x2="0" y2="1">
+                <stop offset="5%" stopColor="#8884d8" stopOpacity={0.2}/>
+                <stop offset="95%" stopColor="#8884d8" stopOpacity={0.05}/>
+              </linearGradient>
+              <linearGradient id="responsesGradient" x1="0" y1="0" x2="0" y2="1">
+                <stop offset="5%" stopColor="#82ca9d" stopOpacity={0.2}/>
+                <stop offset="95%" stopColor="#82ca9d" stopOpacity={0.05}/>
+              </linearGradient>
+            </defs>
+            <CartesianGrid strokeDasharray="3 3" stroke="#888" opacity={0.2} />
+            <XAxis
+              dataKey="timeLabel"
+              fontSize={12}
+              angle={-45}
+              textAnchor="end"
+              height={70}
+              stroke="#888"
+            />
+            <YAxis stroke="#888" />
+            <Tooltip
+              labelFormatter={(label) => `Time: ${label}`}
+              formatter={(value: number, name: string) => [
+                value,
+                name
+              ]}
+              contentStyle={{
+                backgroundColor: 'var(--tooltip-text-bg)',
+                border: '1px solid var(--border)',
+                borderRadius: '4px',
+                color: 'var(--tooltip-text)'
+              }}
+              labelStyle={{
+                color: 'var(--tooltip-text)'
+              }}
+            />
+            <Legend />
+            <Area
+              type="monotone"
+              dataKey="requestsSent"
+              fill="url(#requestsGradient)"
+              stroke="#8884d8"
+              strokeWidth={2}
+              name="Requests Sent"
+              dot={false}
+            />
+            <Area
+              type="monotone"
+              dataKey="responsesReceived"
+              fill="url(#responsesGradient)"
+              stroke="#82ca9d"
+              strokeWidth={2}
+              name="Responses Received"
+              dot={false}
+            />
+          </ComposedChart>
+        </ResponsiveContainer>
+      </div>
+    </div>
+  );
+} 
\ No newline at end of file
diff --git a/bespoke-dataset-viewer/components/ui/sortable-table.tsx b/bespoke-dataset-viewer/components/ui/sortable-table.tsx
index 8b868a2b..7b64fd2f 100644
--- a/bespoke-dataset-viewer/components/ui/sortable-table.tsx
+++ b/bespoke-dataset-viewer/components/ui/sortable-table.tsx
@@ -220,9 +220,9 @@ export function SortableTable({
             </TableRow>
           </TableHeader>
           <TableBody>
-            {paginatedData.map(row => (
+            {paginatedData.map((row, rowIndex) => (
               <motion.tr
-                key={getRowKey(row)}
+                key={`${rowIndex}-${getRowKey(row)}`}
                 className={cn(
                   onRowClick ? "cursor-pointer hover:bg-muted/50" : "",
                   rowProps?.(row)?.className
@@ -230,8 +230,10 @@ export function SortableTable({
                 onClick={() => onRowClick?.(row)}
                 {...(rowProps?.(row) || {})}
               >
-                {orderedColumns.map(column => (
-                  <TableCell key={`${getRowKey(row)}-${column.key}`}>
+                {orderedColumns.map((column, colIndex) => (
+                  <TableCell 
+                    key={`${rowIndex}-${colIndex}-${getRowKey(row)}-${column.key}`}
+                  >
                     {renderCell(getCellContent(row, column.key), truncateConfig.enabled)}
                   </TableCell>
                 ))}
diff --git a/bespoke-dataset-viewer/lib/utils.ts b/bespoke-dataset-viewer/lib/utils.ts
index 9dc3a6a0..2019e8ab 100644
--- a/bespoke-dataset-viewer/lib/utils.ts
+++ b/bespoke-dataset-viewer/lib/utils.ts
@@ -30,6 +30,8 @@ export const getColumnValue = (item: DataItem, column: string): string => {
       return item.raw_response.usage?.total_tokens?.toString() ||
         item.raw_response.response?.body?.usage?.total_tokens?.toString() ||
         "N/A";
+    case "generation_time":
+      return ((new Date(item.finished_at).getTime() - new Date(item.created_at).getTime()) / 1000).toString();
     default:
       return "N/A";
   }
diff --git a/bespoke-dataset-viewer/package-lock.json b/bespoke-dataset-viewer/package-lock.json
index 13432d19..97c34801 100644
--- a/bespoke-dataset-viewer/package-lock.json
+++ b/bespoke-dataset-viewer/package-lock.json
@@ -26,6 +26,7 @@
         "fs": "^0.0.1-security",
         "lucide-react": "^0.454.0",
         "next": "^15.0.2",
+        "prismjs": "^1.29.0",
         "react": "^18.2.0",
         "react-dom": "^18.2.0",
         "recharts": "^2.13.3",
@@ -2944,9 +2945,9 @@
       "optional": true
     },
     "node_modules/cross-spawn": {
-      "version": "7.0.3",
-      "resolved": "https://registry.npmjs.org/cross-spawn/-/cross-spawn-7.0.3.tgz",
-      "integrity": "sha512-iRDPJKUPVEND7dHPO8rkbOnPpyDygcDFtWjpeWNCgy8WP2rXcxXL8TskReQl6OrB2G7+UJrags1q15Fudc7G6w==",
+      "version": "7.0.6",
+      "resolved": "https://registry.npmjs.org/cross-spawn/-/cross-spawn-7.0.6.tgz",
+      "integrity": "sha512-uV2QOWP2nWzsy2aMp8aRibhi9dlzF5Hgh5SHaB9OiTGEyDTiJJyx0uy51QXdyWbtAHNua4XJzUKca3OzKUd3vA==",
       "license": "MIT",
       "dependencies": {
         "path-key": "^3.1.0",
@@ -6524,6 +6525,15 @@
         "node": ">= 0.8.0"
       }
     },
+    "node_modules/prismjs": {
+      "version": "1.29.0",
+      "resolved": "https://registry.npmjs.org/prismjs/-/prismjs-1.29.0.tgz",
+      "integrity": "sha512-Kx/1w86q/epKcmte75LNrEoT+lX8pBpavuAbvJWRXar7Hz8jrtF+e3vY751p0R8H9HdArwaCTNDDzHg/ScJK1Q==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=6"
+      }
+    },
     "node_modules/promise-inflight": {
       "version": "1.0.1",
       "resolved": "https://registry.npmjs.org/promise-inflight/-/promise-inflight-1.0.1.tgz",
diff --git a/bespoke-dataset-viewer/package.json b/bespoke-dataset-viewer/package.json
index 96df9b67..62643150 100644
--- a/bespoke-dataset-viewer/package.json
+++ b/bespoke-dataset-viewer/package.json
@@ -27,6 +27,7 @@
     "fs": "^0.0.1-security",
     "lucide-react": "^0.454.0",
     "next": "^15.0.2",
+    "prismjs": "^1.29.0",
     "react": "^18.2.0",
     "react-dom": "^18.2.0",
     "recharts": "^2.13.3",
diff --git a/bespoke-dataset-viewer/types/dataset.ts b/bespoke-dataset-viewer/types/dataset.ts
index f4c4bbd7..9a94efba 100644
--- a/bespoke-dataset-viewer/types/dataset.ts
+++ b/bespoke-dataset-viewer/types/dataset.ts
@@ -15,7 +15,8 @@ export type DataItem = {
     original_row: Record<string, any>
     original_row_idx: number
   }
-  
+  created_at: string
+  finished_at: string
 }
 
 export interface Run {
diff --git a/examples/distill.py b/examples/distill.py
index 7fc785cb..20cfd53b 100644
--- a/examples/distill.py
+++ b/examples/distill.py
@@ -1,7 +1,9 @@
-from bespokelabs import curator
-from datasets import load_dataset
 import logging
 
+from datasets import load_dataset
+
+from bespokelabs import curator
+
 dataset = load_dataset("allenai/WildChat", split="train")
 dataset = dataset.select(range(3_000))
 
diff --git a/examples/poem.py b/examples/poem.py
index 5697e5e2..ffb8c5a5 100644
--- a/examples/poem.py
+++ b/examples/poem.py
@@ -2,10 +2,12 @@
 
 We generate 10 diverse topics and then generate 2 poems for each topic."""
 
-from bespokelabs import curator
+from typing import List
+
 from datasets import Dataset
 from pydantic import BaseModel, Field
-from typing import List
+
+from bespokelabs import curator
 
 
 # We use Pydantic and structured outputs to define the format of the response.
@@ -41,9 +43,7 @@ class Poems(BaseModel):
     model_name="gpt-4o-mini",
     response_format=Poems,
     # `row` is the input row, and `poems` is the Poems class which is parsed from the structured output from the LLM.
-    parse_func=lambda row, poems: [
-        {"topic": row["topic"], "poem": p} for p in poems.poems_list
-    ],
+    parse_func=lambda row, poems: [{"topic": row["topic"], "poem": p} for p in poems.poems_list],
 )
 
 # We apply the prompter to the topics dataset.
diff --git a/poetry.lock b/poetry.lock
index 805672b6..0af8eb19 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -24,112 +24,98 @@ files = [
 
 [[package]]
 name = "aiohttp"
-version = "3.10.10"
+version = "3.11.2"
 description = "Async http client/server framework (asyncio)"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
 aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
-async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
+async-timeout = {version = ">=4.0,<6.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
-yarl = ">=1.12.0,<2.0"
+propcache = ">=0.2.0"
+yarl = ">=1.17.0,<2.0"
 
 [package.extras]
 speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
@@ -183,13 +169,13 @@ trio = ["trio (>=0.26.1)"]
 
 [[package]]
 name = "async-timeout"
-version = "4.0.3"
+version = "5.0.1"
 description = "Timeout context manager for asyncio programs"
 optional = false
-python-versions = ">=3.7"
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 files = [
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 ]
 
 [[package]]
@@ -503,76 +489,65 @@ files = [
 
 [[package]]
 name = "contourpy"
-version = "1.3.0"
+version = "1.3.1"
 description = "Python library for calculating contours of 2D quadrilateral grids"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
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 [package.extras]
@@ -996,13 +973,13 @@ files = [
 
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 name = "httpcore"
-version = "1.0.6"
+version = "1.0.7"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -1124,39 +1101,54 @@ files = [
 
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+version = "1.6.4"
 description = "structured outputs for llm"
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 python-versions = "<4.0,>=3.9"
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 pydantic = ">=2.8.0,<3.0.0"
 pydantic-core = ">=2.18.0,<3.0.0"
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 rich = ">=13.7.0,<14.0.0"
 tenacity = ">=9.0.0,<10.0.0"
 typer = ">=0.9.0,<1.0.0"
 
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+name = "isort"
+version = "5.13.2"
+description = "A Python utility / library to sort Python imports."
+optional = false
+python-versions = ">=3.8.0"
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 [[package]]
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 version = "3.4.0"
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 description = "The official Python library for the openai API"
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-python-versions = ">=3.7.1"
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 [[package]]
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 [[package]]
@@ -2519,6 +2588,20 @@ files = [
 [package.dependencies]
 six = ">=1.5"
 
+[[package]]
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+version = "1.0.1"
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+
 [[package]]
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 version = "2024.2"
@@ -2622,6 +2705,21 @@ Pygments = ">=2.5.1"
 [package.extras]
 md = ["cmarkgfm (>=0.8.0)"]
 
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+version = "0.35.1"
+description = "JSON Referencing + Python"
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+rpds-py = ">=0.7.0"
+
 [[package]]
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 version = "2024.11.6"
@@ -2776,13 +2874,13 @@ idna2008 = ["idna"]
 
 [[package]]
 name = "rich"
-version = "13.9.3"
+version = "13.9.4"
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 [package.extras]
 jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
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+description = "Python bindings to Rust's persistent data structures (rpds)"
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+python-versions = ">=3.9"
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+    {file = "yarl-1.17.1-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:d8a8b74d843c2638f3864a17d97a4acda58e40d3e44b6303b8cc3d3c44ae2d29"},
+    {file = "yarl-1.17.1-cp39-cp39-musllinux_1_2_armv7l.whl", hash = "sha256:7fac95714b09da9278a0b52e492466f773cfe37651cf467a83a1b659be24bf71"},
+    {file = "yarl-1.17.1-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:c180ac742a083e109c1a18151f4dd8675f32679985a1c750d2ff806796165b55"},
+    {file = "yarl-1.17.1-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:578d00c9b7fccfa1745a44f4eddfdc99d723d157dad26764538fbdda37209857"},
+    {file = "yarl-1.17.1-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:1a3b91c44efa29e6c8ef8a9a2b583347998e2ba52c5d8280dbd5919c02dfc3b5"},
+    {file = "yarl-1.17.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:a7ac5b4984c468ce4f4a553df281450df0a34aefae02e58d77a0847be8d1e11f"},
+    {file = "yarl-1.17.1-cp39-cp39-win32.whl", hash = "sha256:7294e38f9aa2e9f05f765b28ffdc5d81378508ce6dadbe93f6d464a8c9594473"},
+    {file = "yarl-1.17.1-cp39-cp39-win_amd64.whl", hash = "sha256:eb6dce402734575e1a8cc0bb1509afca508a400a57ce13d306ea2c663bad1138"},
+    {file = "yarl-1.17.1-py3-none-any.whl", hash = "sha256:f1790a4b1e8e8e028c391175433b9c8122c39b46e1663228158e61e6f915bf06"},
+    {file = "yarl-1.17.1.tar.gz", hash = "sha256:067a63fcfda82da6b198fa73079b1ca40b7c9b7994995b6ee38acda728b64d47"},
 ]
 
 [package.dependencies]
@@ -3243,13 +3569,13 @@ propcache = ">=0.2.0"
 
 [[package]]
 name = "zipp"
-version = "3.20.2"
+version = "3.21.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
-    {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
+    {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
+    {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
 ]
 
 [package.extras]
@@ -3263,4 +3589,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "62ec40513d9b430b34af88d05d5320835934380519ba8d8c8992a08c5400693f"
+content-hash = "3604f19ac9d9dd28454528f2623f2b638bbd985d12810f4d99934d2bd11a3294"
diff --git a/pyproject.toml b/pyproject.toml
index d7262c11..0e622361 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,6 +32,9 @@ matplotlib = "^3.9.2"
 aiofiles = "^24.1.0"
 tiktoken = "^0.8.0"
 nest-asyncio = "^1.6.0"
+rich = "^13.7.0"
+litellm = "^1.52.11"
+isort = "^5.13.2"
 
 [tool.poetry.group.dev.dependencies]
 black = "^24.2.0"
@@ -45,4 +48,4 @@ build-backend = "poetry.core.masonry.api"
 curator-viewer = "bespokelabs.curator.viewer.__main__:main"
 
 [tool.black]
-line-length = 80
+line-length = 100
diff --git a/src/bespokelabs/__init__.py b/src/bespokelabs/__init__.py
index e89e45ee..f7b99017 100644
--- a/src/bespokelabs/__init__.py
+++ b/src/bespokelabs/__init__.py
@@ -3,9 +3,7 @@
 logger = logging.getLogger("bespokelabs.curator")
 
 handler = logging.StreamHandler()
-formatter = logging.Formatter(
-    "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-)
+formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
 handler.setFormatter(formatter)
 logger.addHandler(handler)
 logger.setLevel(logging.WARNING)
diff --git a/src/bespokelabs/curator/__init__.py b/src/bespokelabs/curator/__init__.py
index 37ec5dbb..bb0b7aa2 100644
--- a/src/bespokelabs/curator/__init__.py
+++ b/src/bespokelabs/curator/__init__.py
@@ -1,2 +1,2 @@
-from .prompter.prompter import Prompter
 from .dataset import Dataset
+from .prompter.prompter import Prompter
diff --git a/src/bespokelabs/curator/dataset.py b/src/bespokelabs/curator/dataset.py
index 56b6c63d..b0abece0 100644
--- a/src/bespokelabs/curator/dataset.py
+++ b/src/bespokelabs/curator/dataset.py
@@ -1,19 +1,16 @@
+import glob
 import json
 import logging
 import os
-import glob
+from typing import Any, Dict, Iterable, Iterator, List, TypeVar
 
 import pandas as pd
-
-from pydantic import BaseModel
 from datasets import Dataset as HFDataset
 from datasets.arrow_writer import ArrowWriter, SchemaInferenceError
-from typing import Any, Dict, Iterable, Iterator, List, TypeVar
+from pydantic import BaseModel
 
 from bespokelabs.curator.prompter.prompt_formatter import PromptFormatter
-from bespokelabs.curator.request_processor.generic_response import (
-    GenericResponse,
-)
+from bespokelabs.curator.request_processor.generic_response import GenericResponse
 
 T = TypeVar("T")
 
@@ -33,9 +30,7 @@ def from_iterable(iterable: Iterable[Dict[str, Any] | BaseModel]):
         return Dataset(iterable=iterable)
 
     def from_working_dir(working_dir: str, prompt_formatter: PromptFormatter):
-        return Dataset(
-            working_dir=working_dir, prompt_formatter=prompt_formatter
-        )
+        return Dataset(working_dir=working_dir, prompt_formatter=prompt_formatter)
 
     def __iter__(self) -> Iterator[Dict[str, Any] | BaseModel]:
         if self.iterable is not None:
@@ -48,13 +43,9 @@ def __iter__(self) -> Iterator[Dict[str, Any] | BaseModel]:
             for line in open(response_file, "r"):
                 response = GenericResponse.model_validate_json(line)
                 if self.prompt_formatter.response_format:
-                    response.response = self.prompt_formatter.response_format(
-                        **response.response
-                    )
+                    response.response = self.prompt_formatter.response_format(**response.response)
                 if self.prompt_formatter.parse_func:
-                    response = self.prompt_formatter.parse_func(
-                        response.row, response.response
-                    )
+                    response = self.prompt_formatter.parse_func(response.row, response.response)
                 else:
                     response = [response.response]
 
@@ -97,10 +88,8 @@ def to_huggingface(self, in_memory: bool = False) -> None:
                         total_responses_count += 1
                         response = GenericResponse.model_validate_json(line)
                         if self.prompt_formatter.response_format:
-                            response.response = (
-                                self.prompt_formatter.response_format(
-                                    **response.response
-                                )
+                            response.response = self.prompt_formatter.response_format(
+                                **response.response
                             )
 
                         if response is None:
@@ -119,9 +108,7 @@ def to_huggingface(self, in_memory: bool = False) -> None:
                                 row = row.model_dump()
                             writer.write(row)
 
-            logging.info(
-                f"Read {total_responses_count} responses, {failed_responses_count} failed"
-            )
+            logging.info(f"Read {total_responses_count} responses, {failed_responses_count} failed")
             logging.info("Finalizing writer")
 
             if failed_responses_count == total_responses_count:
diff --git a/src/bespokelabs/curator/install_ui.py b/src/bespokelabs/curator/install_ui.py
new file mode 100644
index 00000000..746e67f7
--- /dev/null
+++ b/src/bespokelabs/curator/install_ui.py
@@ -0,0 +1,184 @@
+"""Enhanced installation UI for bespokelabs-curator.
+
+This module provides a rich, interactive UI for the installation process of the Curator package.
+It includes progress tracking, status updates, and a polished success message.
+"""
+
+import subprocess
+import sys
+from dataclasses import dataclass
+from enum import Enum
+from typing import Optional, Tuple
+
+from rich.console import Console
+from rich.live import Live
+from rich.panel import Panel
+from rich.progress import ProgressBar
+from rich.spinner import Spinner
+from rich.text import Text
+
+
+class InstallationStage(Enum):
+    """Enum representing different stages of the installation process."""
+
+    PREPARING = ("Preparing your environment...", 0.0)
+    COLLECTING = ("Downloading packages...", 0.2)
+    DOWNLOADING = ("Downloading packages...", 0.4)
+    INSTALLING = ("Installing...", 0.7)
+    FINALIZING = ("Finalizing...", 0.9)
+    COMPLETE = ("Installation complete!", 1.0)
+
+    def __init__(self, message: str, progress: float):
+        self.message = message
+        self.progress = progress
+
+
+@dataclass
+class InstallationUI:
+    """Class to manage the installation UI components and styling."""
+
+    package_name: str
+    console: Console = Console()
+
+    def create_progress_bar(self, completed: float = 0) -> Text:
+        """Create a stylish progress bar with the given completion percentage."""
+        width = 40
+        filled = int(width * completed)
+        bar = Text()
+        bar.append("\n╭", style="dim white")
+        bar.append("─" * (width + 2), style="dim white")
+        bar.append("╮\n", style="dim white")
+        bar.append("│ ", style="dim white")
+        bar.append("█" * filled, style="green")
+        bar.append("▒" * (width - filled), style="dim white")
+        bar.append(" │", style="dim white")
+        bar.append(f"\n╰", style="dim white")
+        bar.append("─" * (width + 2), style="dim white")
+        bar.append("╯", style="dim white")
+        return bar
+
+    def create_loading_text(self, stage: InstallationStage, progress: float) -> Text:
+        """Create the loading text with current stage and progress."""
+        return Text.assemble(
+            ("✨ Installing ", "bold"),
+            (self.package_name, "bold cyan"),
+            "\n",
+            ("↳ ", "dim white"),
+            ("Your synthetic data journey begins in moments", "dim white"),
+            self.create_progress_bar(progress),
+            ("\n ", ""),
+            (stage.message, "italic dim white"),
+        )
+
+    def create_success_text(self) -> Text:
+        """Create the success message with links."""
+        text = Text()
+        text.append("✨ Curator installed successfully!\n\n", style="bold green")
+        text.append(
+            "Start building production-ready synthetic data pipelines:\n\n", style="dim white"
+        )
+        text.append("   📚 ", style="")
+        text.append("docs.bespokelabs.ai", style="dim cyan link https://docs.bespokelabs.ai")
+        text.append("\n   📦 ", style="")
+        text.append(
+            "github.com/bespokelabsai/curator",
+            style="dim cyan link https://github.com/bespokelabsai/curator",
+        )
+        text.append("\n   💬 ", style="")
+        text.append(
+            "discord.gg/KqpXvpzVBS", style="dim cyan link https://discord.com/invite/KqpXvpzVBS"
+        )
+        return text
+
+
+class PackageInstaller:
+    """Class to handle the package installation process."""
+
+    def __init__(self, package_name: str, version: Optional[str] = None):
+        self.package_spec = f"{package_name}=={version}" if version else package_name
+        self.ui = InstallationUI(package_name)
+
+    def run_pip_install(self) -> subprocess.Popen:
+        """Run pip install and capture output."""
+        return subprocess.Popen(
+            [sys.executable, "-m", "pip", "install", self.package_spec],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=True,
+            bufsize=1,
+            universal_newlines=True,
+        )
+
+    def parse_pip_output(self, line: str) -> Tuple[InstallationStage, float]:
+        """Parse pip output to determine installation stage and progress."""
+        line = line.strip().lower()
+
+        if "collecting" in line:
+            return InstallationStage.COLLECTING, InstallationStage.COLLECTING.progress
+        elif "downloading" in line:
+            if "%" in line:
+                try:
+                    percent = float(line.split("%")[0].split()[-1])
+                    # Scale download progress between 20% and 60%
+                    return InstallationStage.DOWNLOADING, 0.2 + (percent / 100.0 * 0.4)
+                except:
+                    pass
+            return InstallationStage.DOWNLOADING, InstallationStage.DOWNLOADING.progress
+        elif "installing" in line:
+            return InstallationStage.INSTALLING, InstallationStage.INSTALLING.progress
+        elif "successfully installed" in line:
+            return InstallationStage.FINALIZING, InstallationStage.FINALIZING.progress
+
+        return InstallationStage.PREPARING, InstallationStage.PREPARING.progress
+
+    def install(self) -> None:
+        """Execute the installation with progress tracking and UI updates."""
+        spinner = Spinner(
+            "dots2", text=self.ui.create_loading_text(InstallationStage.PREPARING, 0), style="green"
+        )
+
+        with Live(spinner, console=self.ui.console, refresh_per_second=30) as live:
+            try:
+                process = self.run_pip_install()
+
+                while True:
+                    output_line = process.stdout.readline()
+                    if output_line == "" and process.poll() is not None:
+                        break
+
+                    stage, progress = self.parse_pip_output(output_line)
+                    spinner.text = self.ui.create_loading_text(stage, progress)
+
+                # Show completion
+                spinner.text = self.ui.create_loading_text(InstallationStage.COMPLETE, 1.0)
+
+                if process.poll() == 0:
+                    live.update(self.ui.create_success_text())
+                else:
+                    error = process.stderr.read()
+                    error_text = Text(error, style="red")
+                    live.update(error_text)
+                    sys.exit(1)
+
+            except Exception as e:
+                error_text = Text(f"Error: {str(e)}", style="red")
+                live.update(error_text)
+                sys.exit(1)
+
+        self.ui.console.print()
+
+
+def enhanced_install(package_name: str, version: Optional[str] = None) -> None:
+    """
+    Enhance pip installation with a professional progress UI.
+
+    Args:
+        package_name: Name of the package to install
+        version: Optional specific version to install
+    """
+    installer = PackageInstaller(package_name, version)
+    installer.install()
+
+
+if __name__ == "__main__":
+    enhanced_install("bespokelabs-curator")
diff --git a/src/bespokelabs/curator/prompter/prompt_formatter.py b/src/bespokelabs/curator/prompter/prompt_formatter.py
index 40b26e2a..5682c978 100644
--- a/src/bespokelabs/curator/prompter/prompt_formatter.py
+++ b/src/bespokelabs/curator/prompter/prompt_formatter.py
@@ -25,9 +25,7 @@ class PromptFormatter:
     def __init__(
         self,
         model_name: str,
-        prompt_func: Callable[
-            [Union[Dict[str, Any], BaseModel]], Dict[str, str]
-        ],
+        prompt_func: Callable[[Union[Dict[str, Any], BaseModel]], Dict[str, str]],
         parse_func: Optional[
             Callable[
                 [
@@ -44,9 +42,7 @@ def __init__(
         self.parse_func = parse_func
         self.response_format = response_format
 
-    def create_generic_request(
-        self, row: Dict[str, Any] | BaseModel, idx: int
-    ) -> GenericRequest:
+    def create_generic_request(self, row: Dict[str, Any] | BaseModel, idx: int) -> GenericRequest:
         """Format the request object based off Prompter attributes."""
         sig = inspect.signature(self.prompt_func)
         if len(sig.parameters) == 0:
@@ -54,9 +50,7 @@ def create_generic_request(
         elif len(sig.parameters) == 1:
             prompts = self.prompt_func(row)
         else:
-            raise ValueError(
-                f"Prompting function {self.prompt_func} must have 0 or 1 arguments."
-            )
+            raise ValueError(f"Prompting function {self.prompt_func} must have 0 or 1 arguments.")
 
         if isinstance(prompts, str):
             messages = [{"role": "user", "content": prompts}]
@@ -74,8 +68,6 @@ def create_generic_request(
             original_row=row,
             original_row_idx=idx,
             response_format=(
-                self.response_format.model_json_schema()
-                if self.response_format
-                else None
+                self.response_format.model_json_schema() if self.response_format else None
             ),
         )
diff --git a/src/bespokelabs/curator/prompter/prompter.py b/src/bespokelabs/curator/prompter/prompter.py
index a8afa2da..f0b5e9c9 100644
--- a/src/bespokelabs/curator/prompter/prompter.py
+++ b/src/bespokelabs/curator/prompter/prompter.py
@@ -1,20 +1,20 @@
 """Curator: Bespoke Labs Synthetic Data Generation Library."""
 
 import inspect
+import logging
 import os
 from datetime import datetime
+from io import BytesIO
 from typing import Any, Callable, Dict, Iterable, Optional, Type, TypeVar, Union
 
+import dill
 from datasets import Dataset
 from pydantic import BaseModel
 from xxhash import xxh64
-import logging
 
 from bespokelabs.curator.db import MetadataDB
 from bespokelabs.curator.prompter.prompt_formatter import PromptFormatter
-from bespokelabs.curator.request_processor.base_request_processor import (
-    BaseRequestProcessor,
-)
+from bespokelabs.curator.request_processor.base_request_processor import BaseRequestProcessor
 from bespokelabs.curator.request_processor.openai_batch_request_processor import (
     OpenAIBatchRequestProcessor,
 )
@@ -34,9 +34,7 @@ class Prompter:
     def __init__(
         self,
         model_name: str,
-        prompt_func: Callable[
-            [Union[Dict[str, Any], BaseModel]], Dict[str, str]
-        ],
+        prompt_func: Callable[[Union[Dict[str, Any], BaseModel]], Dict[str, str]],
         parse_func: Optional[
             Callable[
                 [
@@ -115,9 +113,7 @@ def __init__(
                 frequency_penalty=frequency_penalty,
             )
 
-    def __call__(
-        self, dataset: Optional[Iterable] = None, working_dir: str = None
-    ) -> Dataset:
+    def __call__(self, dataset: Optional[Iterable] = None, working_dir: str = None) -> Dataset:
         """
         Run completions on a dataset.
 
@@ -161,11 +157,7 @@ def _completions(
         else:
             curator_cache_dir = working_dir
 
-        dataset_hash = (
-            dataset._fingerprint
-            if dataset is not None
-            else xxh64("").hexdigest()
-        )
+        dataset_hash = dataset._fingerprint if dataset is not None else xxh64("").hexdigest()
 
         prompt_func_hash = _get_function_hash(self.prompt_formatter.prompt_func)
 
@@ -192,13 +184,9 @@ def _completions(
         metadata_db = MetadataDB(metadata_db_path)
 
         # Get the source code of the prompt function
-        prompt_func_source = _get_function_source(
-            self.prompt_formatter.prompt_func
-        )
+        prompt_func_source = _get_function_source(self.prompt_formatter.prompt_func)
         if self.prompt_formatter.parse_func is not None:
-            parse_func_source = _get_function_source(
-                self.prompt_formatter.parse_func
-            )
+            parse_func_source = _get_function_source(self.prompt_formatter.parse_func)
         else:
             parse_func_source = ""
 
@@ -233,7 +221,9 @@ def _get_function_hash(func) -> str:
     if func is None:
         return xxh64("").hexdigest()
 
-    return xxh64(_get_function_source(func)).hexdigest()
+    file = BytesIO()
+    dill.Pickler(file, recurse=True).dump(func)
+    return xxh64(file.getvalue()).hexdigest()
 
 
 def _get_function_source(func) -> str:
diff --git a/src/bespokelabs/curator/request_processor/base_request_processor.py b/src/bespokelabs/curator/request_processor/base_request_processor.py
index dcc344b7..d1f0b4e9 100644
--- a/src/bespokelabs/curator/request_processor/base_request_processor.py
+++ b/src/bespokelabs/curator/request_processor/base_request_processor.py
@@ -16,9 +16,7 @@
 from bespokelabs.curator.prompter.prompt_formatter import PromptFormatter
 from bespokelabs.curator.request_processor.event_loop import run_in_event_loop
 from bespokelabs.curator.request_processor.generic_request import GenericRequest
-from bespokelabs.curator.request_processor.generic_response import (
-    GenericResponse,
-)
+from bespokelabs.curator.request_processor.generic_response import GenericResponse
 
 logger = logging.getLogger(__name__)
 
@@ -42,9 +40,7 @@ def get_rate_limits(self) -> dict:
         pass
 
     @abstractmethod
-    def create_api_specific_request(
-        self, generic_request: GenericRequest
-    ) -> dict:
+    def create_api_specific_request(self, generic_request: GenericRequest) -> dict:
         """
         Creates a API-specific request body from a GenericRequest.
 
@@ -115,9 +111,7 @@ def create_request_files(
                     num_jobs = i + 1
 
                 if num_jobs > 0:
-                    logger.info(
-                        f"There are {num_jobs} existing requests in {requests_files[0]}"
-                    )
+                    logger.info(f"There are {num_jobs} existing requests in {requests_files[0]}")
                     logger.info(
                         f"Example request in {requests_files[0]}:\n{json.dumps(first_job, default=str, indent=2)}"
                     )
@@ -129,19 +123,13 @@ def create_request_files(
 
         if dataset is None:
             with open(requests_file, "w") as f:
-                generic_request = prompt_formatter.create_generic_request(
-                    dict(), 0
-                )
-                f.write(
-                    json.dumps(generic_request.model_dump(), default=str) + "\n"
-                )
+                generic_request = prompt_formatter.create_generic_request(dict(), 0)
+                f.write(json.dumps(generic_request.model_dump(), default=str) + "\n")
             return requests_files
 
         if self.batch_size:
             num_batches = ceil(len(dataset) / self.batch_size)
-            requests_files = [
-                f"{working_dir}/requests_{i}.jsonl" for i in range(num_batches)
-            ]
+            requests_files = [f"{working_dir}/requests_{i}.jsonl" for i in range(num_batches)]
 
             async def create_all_request_files():
                 tasks = [
@@ -157,11 +145,7 @@ async def create_all_request_files():
 
             run_in_event_loop(create_all_request_files())
         else:
-            run_in_event_loop(
-                self.acreate_request_file(
-                    dataset, prompt_formatter, requests_file
-                )
-            )
+            run_in_event_loop(self.acreate_request_file(dataset, prompt_formatter, requests_file))
 
         return requests_files
 
@@ -184,12 +168,8 @@ async def acreate_request_file(
             for idx, dataset_row in enumerate(dataset):
                 dataset_row_idx = idx + start_idx
                 # Get the generic request from the map function
-                request = prompt_formatter.create_generic_request(
-                    dataset_row, dataset_row_idx
-                )
-                await f.write(
-                    json.dumps(request.model_dump(), default=str) + "\n"
-                )
+                request = prompt_formatter.create_generic_request(dataset_row, dataset_row_idx)
+                await f.write(json.dumps(request.model_dump(), default=str) + "\n")
         logger.info(f"Wrote {end_idx - start_idx} requests to {request_file}.")
 
     def create_dataset_files(
@@ -248,9 +228,7 @@ def create_dataset_files(
                 with open(responses_file, "r") as f_in:
                     for generic_response_string in f_in:
                         total_responses_count += 1
-                        response = GenericResponse.model_validate_json(
-                            generic_response_string
-                        )
+                        response = GenericResponse.model_validate_json(generic_response_string)
 
                         # response.response_errors is not None IFF response.response_message is None
                         if response.response_errors is not None:
@@ -261,10 +239,8 @@ def create_dataset_files(
                             # Response message is a string, which is converted to a dict
                             # The dict is then used to construct the response_format Pydantic model
                             try:
-                                response.response_message = (
-                                    prompt_formatter.response_format(
-                                        **response.response_message
-                                    )
+                                response.response_message = prompt_formatter.response_format(
+                                    **response.response_message
                                 )
                             except ValidationError as e:
                                 schema_str = json.dumps(
@@ -287,17 +263,13 @@ def create_dataset_files(
                                     response.response_message,
                                 )
                             except Exception as e:
-                                logger.error(
-                                    f"Exception raised in your `parse_func`. {error_help}"
-                                )
+                                logger.error(f"Exception raised in your `parse_func`. {error_help}")
                                 os.remove(dataset_file)
                                 raise e
                             if not isinstance(dataset_rows, list):
                                 dataset_rows = [dataset_rows]
                         else:
-                            dataset_rows = [
-                                {"response": response.response_message}
-                            ]
+                            dataset_rows = [{"response": response.response_message}]
 
                         for row in dataset_rows:
                             if isinstance(row, BaseModel):
@@ -317,9 +289,7 @@ def create_dataset_files(
 
                             writer.write(row)
 
-            logger.info(
-                f"Read {total_responses_count} responses, {failed_responses_count} failed"
-            )
+            logger.info(f"Read {total_responses_count} responses, {failed_responses_count} failed")
             if failed_responses_count == total_responses_count:
                 os.remove(dataset_file)
                 raise ValueError("All requests failed")
@@ -345,7 +315,5 @@ def parse_response_message(
                 f"Failed to parse response as JSON: {response_message}, skipping this response."
             )
             response_message = None
-            response_errors = [
-                f"Failed to parse response as JSON: {response_message}"
-            ]
+            response_errors = [f"Failed to parse response as JSON: {response_message}"]
     return response_message, response_errors
diff --git a/src/bespokelabs/curator/request_processor/generic_request.py b/src/bespokelabs/curator/request_processor/generic_request.py
index a407a12c..1fa23327 100644
--- a/src/bespokelabs/curator/request_processor/generic_request.py
+++ b/src/bespokelabs/curator/request_processor/generic_request.py
@@ -1,4 +1,5 @@
 from typing import Any, Dict, List, Optional, Type
+
 from pydantic import BaseModel
 
 """A generic request model for LLM API requests.
diff --git a/src/bespokelabs/curator/request_processor/generic_response.py b/src/bespokelabs/curator/request_processor/generic_response.py
index dc261270..58471370 100644
--- a/src/bespokelabs/curator/request_processor/generic_response.py
+++ b/src/bespokelabs/curator/request_processor/generic_response.py
@@ -1,5 +1,8 @@
+import datetime
 from typing import Any, Dict, List, Optional
-from pydantic import BaseModel
+
+from pydantic import BaseModel, Field
+
 from .generic_request import GenericRequest
 
 """A generic response model for LLM API requests.
@@ -13,12 +16,34 @@
     raw_response: The raw response data from the API.
     raw_request: The raw request data. Will be None for BatchAPI requests.
     generic_request: The associated GenericRequest object.
+    created_at: The datetime when the request was created.
+    finished_at: The datetime when the request was finished.
+    token_usage: Token usage information for the request.
+    response_cost: The cost of the request in USD.
 """
 
 
+class TokenUsage(BaseModel):
+    """Token usage information for an API request.
+
+    Attributes:
+        prompt_tokens: Number of tokens in the prompt
+        completion_tokens: Number of tokens in the completion
+        total_tokens: Total number of tokens used
+    """
+
+    prompt_tokens: int
+    completion_tokens: int
+    total_tokens: int
+
+
 class GenericResponse(BaseModel):
     response_message: Optional[Dict[str, Any]] | str = None
     response_errors: Optional[List[str]] = None
     raw_response: Optional[Dict[str, Any]]
     raw_request: Optional[Dict[str, Any]] = None
     generic_request: GenericRequest
+    created_at: datetime.datetime
+    finished_at: datetime.datetime
+    token_usage: Optional[TokenUsage] = None
+    response_cost: Optional[float] = None
diff --git a/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py b/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py
index ca1520ed..e6289ed2 100644
--- a/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py
+++ b/src/bespokelabs/curator/request_processor/openai_batch_request_processor.py
@@ -1,10 +1,12 @@
 import asyncio
+import datetime
 import json
 import logging
 import os
 from dataclasses import dataclass
 
 import aiofiles
+import litellm
 from openai import AsyncOpenAI
 from openai.types import Batch
 from tqdm import tqdm
@@ -18,6 +20,7 @@
     parse_response_message,
 )
 from bespokelabs.curator.request_processor.event_loop import run_in_event_loop
+from bespokelabs.curator.request_processor.generic_response import TokenUsage
 
 logger = logging.getLogger(__name__)
 
@@ -89,17 +92,13 @@ def get_rate_limits(self) -> dict:
         else:
             tpd = model_tpd[self.model]
 
-        logger.info(
-            f"Automatically set max_tokens_per_day to {tpd}, model: {self.model} "
-        )
+        logger.info(f"Automatically set max_tokens_per_day to {tpd}, model: {self.model} ")
 
         rate_limits = {"max_tokens_per_day": tpd}
 
         return rate_limits
 
-    def create_api_specific_request(
-        self, generic_request: GenericRequest
-    ) -> dict:
+    def create_api_specific_request(self, generic_request: GenericRequest) -> dict:
         """
         Creates a API-specific request body from a generic request body.
 
@@ -186,9 +185,7 @@ async def asubmit_batch(self, batch_file: str) -> dict:
             )
 
         # this let's you upload a file that is larger than 200MB and won't error, so we catch it above
-        batch_file_upload = await async_client.files.create(
-            file=file_content, purpose="batch"
-        )
+        batch_file_upload = await async_client.files.create(file=file_content, purpose="batch")
 
         logger.info(f"File uploaded: {batch_file_upload}")
 
@@ -200,9 +197,7 @@ async def asubmit_batch(self, batch_file: str) -> dict:
                 "request_file_name": batch_file
             },  # for downloading the batch to similarly named responses file
         )
-        logger.info(
-            f"Batch request submitted, received batch object: {batch_object}"
-        )
+        logger.info(f"Batch request submitted, received batch object: {batch_object}")
         # Explicitly close the client. Otherwise we get something like
         # future: <Task finished name='Task-46' coro=<AsyncClient.aclose() done ... >>
         await async_client.close()
@@ -228,9 +223,7 @@ def run(
         Returns:
             Dataset: Completed dataset
         """
-        requests_files = self.create_request_files(
-            dataset, working_dir, prompt_formatter
-        )
+        requests_files = self.create_request_files(dataset, working_dir, prompt_formatter)
         batch_objects_file = f"{working_dir}/batch_objects.jsonl"
 
         # TODO(Ryan): we should have an easy way to cancel all batches in batch_objects.jsonl if the user realized they made a mistake
@@ -242,10 +235,7 @@ def run(
             # upload requests files and submit batches
             # asyncio gather preserves order
             async def submit_all_batches():
-                tasks = [
-                    self.asubmit_batch(requests_files[i])
-                    for i in range(len(requests_files))
-                ]
+                tasks = [self.asubmit_batch(requests_files[i]) for i in range(len(requests_files))]
                 return await asyncio.gather(*tasks)
 
             batch_objects = run_in_event_loop(submit_all_batches())
@@ -283,9 +273,7 @@ async def watch_batches():
 
         run_in_event_loop(watch_batches())
 
-        dataset = self.create_dataset_files(
-            working_dir, parse_func_hash, prompt_formatter
-        )
+        dataset = self.create_dataset_files(working_dir, parse_func_hash, prompt_formatter)
 
         return dataset
 
@@ -331,8 +319,7 @@ def __init__(
             self.batch_objects = [json.loads(line) for line in f]
         self.batch_ids = [obj["id"] for obj in self.batch_objects]
         self.batch_id_to_request_file_name = {
-            obj["id"]: obj["metadata"]["request_file_name"]
-            for obj in self.batch_objects
+            obj["id"]: obj["metadata"]["request_file_name"] for obj in self.batch_objects
         }
         self.check_interval = check_interval
         self.working_dir = working_dir
@@ -390,18 +377,14 @@ async def check_batch_status(self, batch_id: str) -> Batch | None:
                 logger.warning(f"Unknown batch status: {batch.status}")
 
         if batch_returned:
-            logger.info(
-                f"Batch {batch.id} returned with status: {batch.status}"
-            )
+            logger.info(f"Batch {batch.id} returned with status: {batch.status}")
             self.tracker.n_returned_batches += 1
             self.tracker.n_completed_returned_requests += n_completed_requests
             self.tracker.n_failed_returned_requests += n_failed_requests
             self.remaining_batch_ids.remove(batch.id)
             return batch
         else:
-            self.tracker.n_completed_in_progress_requests += (
-                n_completed_requests
-            )
+            self.tracker.n_completed_in_progress_requests += n_completed_requests
             self.tracker.n_failed_in_progress_requests += n_failed_requests
             return None
 
@@ -424,8 +407,7 @@ async def watch(self) -> None:
 
             # check batch status also updates the tracker
             status_tasks = [
-                self.check_batch_status(batch_id)
-                for batch_id in self.remaining_batch_ids
+                self.check_batch_status(batch_id) for batch_id in self.remaining_batch_ids
             ]
             batches_to_download = await asyncio.gather(*status_tasks)
             batches_to_download = filter(None, batches_to_download)
@@ -445,10 +427,7 @@ async def watch(self) -> None:
             # Failed downloads return None and print any errors that occurred
             all_response_files.extend(await asyncio.gather(*download_tasks))
 
-            if (
-                self.tracker.n_returned_batches
-                < self.tracker.n_submitted_batches
-            ):
+            if self.tracker.n_returned_batches < self.tracker.n_submitted_batches:
                 logger.debug(
                     f"Batches returned: {self.tracker.n_returned_batches}/{self.tracker.n_submitted_batches} "
                     f"Requests completed: {pbar.n}/{self.tracker.n_submitted_requests}"
@@ -464,9 +443,7 @@ async def watch(self) -> None:
                 "Please check the logs above and https://platform.openai.com/batches for errors."
             )
 
-    async def download_batch_to_generic_responses_file(
-        self, batch: Batch
-    ) -> str | None:
+    async def download_batch_to_generic_responses_file(self, batch: Batch) -> str | None:
         """Download the result of a completed batch to file.
 
         Args:
@@ -479,9 +456,7 @@ async def download_batch_to_generic_responses_file(
             file_content = await self.client.files.content(batch.output_file_id)
         elif batch.status == "failed" and batch.error_file_id:
             file_content = await self.client.files.content(batch.error_file_id)
-            logger.warning(
-                f"Batch {batch.id} failed\n. Errors will be parsed below."
-            )
+            logger.warning(f"Batch {batch.id} failed\n. Errors will be parsed below.")
         elif batch.status == "failed" and not batch.error_file_id:
             errors = "\n".join([str(error) for error in batch.errors.data])
             logger.error(
@@ -500,47 +475,72 @@ async def download_batch_to_generic_responses_file(
         response_file = f"{self.working_dir}/responses_{request_file_idx}"
 
         generic_request_map = {}
+        request_creation_times = {}  # Track creation times for requests
         with open(request_file, "r") as f:
             for line in f:
                 generic_request = GenericRequest.model_validate_json(line)
-                generic_request_map[generic_request.original_row_idx] = (
-                    generic_request
-                )
+                generic_request_map[generic_request.original_row_idx] = generic_request
+                request_creation_times[generic_request.original_row_idx] = datetime.datetime.now()
 
         with open(response_file, "w") as f:
             for raw_response in file_content.text.splitlines():
                 raw_response = json.loads(raw_response)
-                generic_request = generic_request_map[
-                    int(raw_response["custom_id"])
-                ]
-
-                generic_response = GenericResponse(
-                    raw_response=raw_response,
-                    raw_request=None,
-                    generic_request=generic_request,
-                )
+                request_idx = int(raw_response["custom_id"])
+                generic_request = generic_request_map[request_idx]
 
                 # TODO(Ryan): Add more specific error handling
-                status_code = raw_response["response"]["status_code"]
-                if status_code != 200:
+                if raw_response["response"]["status_code"] != 200:
                     logger.warning(
-                        f"Request {generic_request} failed with status code {status_code}"
+                        f"Request {generic_request} failed with status code {raw_response['response']['status_code']}"
+                    )
+                    generic_response = GenericResponse(
+                        response_message=None,
+                        response_errors=[
+                            f"Request {generic_request} failed with status code {raw_response['response']['status_code']}"
+                        ],
+                        raw_response=raw_response,
+                        raw_request=None,
+                        generic_request=generic_request,
+                        created_at=request_creation_times[request_idx],
+                        finished_at=datetime.datetime.now(),
+                        token_usage=None,
+                        response_cost=None,
                     )
-                    generic_response.response_errors = [
-                        f"Request {generic_request} failed with status code {status_code}"
-                    ]
                 else:
-                    choices = raw_response["response"]["body"]["choices"]
-                    response_message = choices[0]["message"][
-                        "content"
-                    ]  # Assuming N = 1
+                    response_body = raw_response["response"]["body"]
+                    choices = response_body["choices"]
+                    usage = response_body.get("usage", {})
+
+                    token_usage = TokenUsage(
+                        prompt_tokens=usage.get("prompt_tokens", 0),
+                        completion_tokens=usage.get("completion_tokens", 0),
+                        total_tokens=usage.get("total_tokens", 0),
+                    )
+
+                    # Calculate cost using litellm
+                    cost = litellm.completion_cost(
+                        model=generic_request.model,
+                        prompt=str(
+                            generic_request.messages
+                        ),  # Convert messages to string for cost calculation
+                        completion=choices[0]["message"]["content"],
+                    )
+
+                    response_message = choices[0]["message"]["content"]
                     response_message, response_errors = parse_response_message(
                         response_message, self.prompt_formatter.response_format
                     )
-                    generic_response.response_message = response_message
-                    generic_response.response_errors = response_errors
-                f.write(
-                    json.dumps(generic_response.model_dump(), default=str)
-                    + "\n"
-                )
+
+                    generic_response = GenericResponse(
+                        response_message=response_message,
+                        response_errors=response_errors,
+                        raw_response=raw_response,
+                        raw_request=None,
+                        generic_request=generic_request,
+                        created_at=request_creation_times[request_idx],
+                        finished_at=datetime.datetime.now(),
+                        token_usage=token_usage,
+                        response_cost=cost,
+                    )
+                f.write(json.dumps(generic_response.model_dump(), default=str) + "\n")
         return response_file
diff --git a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py
index 1a199f1c..132ae01a 100644
--- a/src/bespokelabs/curator/request_processor/openai_online_request_processor.py
+++ b/src/bespokelabs/curator/request_processor/openai_online_request_processor.py
@@ -1,15 +1,17 @@
 import asyncio
+import datetime
 import json
 import logging
 import os
 import re
+import resource
 import time
 from dataclasses import dataclass, field
 from functools import partial
 from typing import Any, Callable, Dict, Optional, Set, Tuple, TypeVar
-import resource
 
 import aiohttp
+import litellm
 import requests
 import tiktoken
 from tqdm import tqdm
@@ -23,6 +25,7 @@
     parse_response_message,
 )
 from bespokelabs.curator.request_processor.event_loop import run_in_event_loop
+from bespokelabs.curator.request_processor.generic_response import TokenUsage
 
 T = TypeVar("T")
 logger = logging.getLogger(__name__)
@@ -74,9 +77,7 @@ def get_rate_limits(self) -> dict:
         tpm = int(response.headers.get("x-ratelimit-limit-tokens", 0))
 
         if not rpm or not tpm:
-            logger.warning(
-                "Failed to get rate limits from OpenAI API, using default values"
-            )
+            logger.warning("Failed to get rate limits from OpenAI API, using default values")
             rpm = 30_000
             tpm = 150_000_000
 
@@ -90,9 +91,7 @@ def get_rate_limits(self) -> dict:
 
         return rate_limits
 
-    def create_api_specific_request(
-        self, generic_request: GenericRequest
-    ) -> dict:
+    def create_api_specific_request(self, generic_request: GenericRequest) -> dict:
         """
         Creates a API-specific request body from a generic request body.
 
@@ -148,21 +147,16 @@ def run(
         Returns:
             Dataset: Completed dataset
         """
-        generic_requests_files = self.create_request_files(
-            dataset, working_dir, prompt_formatter
-        )
+        generic_requests_files = self.create_request_files(dataset, working_dir, prompt_formatter)
         generic_responses_files = [
-            f"{working_dir}/responses_{i}.jsonl"
-            for i in range(len(generic_requests_files))
+            f"{working_dir}/responses_{i}.jsonl" for i in range(len(generic_requests_files))
         ]
 
         rate_limits = self.get_rate_limits()
         rpm = rate_limits["max_requests_per_minute"]
         tpm = rate_limits["max_tokens_per_minute"]
 
-        token_encoding_name = get_token_encoding_name(
-            prompt_formatter.model_name
-        )
+        token_encoding_name = get_token_encoding_name(prompt_formatter.model_name)
 
         # NOTE(Ryan): If you wanted to do this on batches, you could run a for loop here about request_files. Although I don't recommend it because you are waiting for straggler requests to finish for each batch.
         # NOTE(Ryan): And if you wanted to do batches in parallel, you would have to divide rpm and tpm by the number of parallel batches.
@@ -183,9 +177,7 @@ def run(
                 )
             )
 
-        dataset = self.create_dataset_files(
-            working_dir, parse_func_hash, prompt_formatter
-        )
+        dataset = self.create_dataset_files(working_dir, parse_func_hash, prompt_formatter)
         return dataset
 
     async def process_generic_requests_from_file(
@@ -224,12 +216,8 @@ async def process_generic_requests_from_file(
 
         # initialize trackers
         queue_of_requests_to_retry = asyncio.Queue()
-        task_id_generator = (
-            task_id_generator_function()
-        )  # generates integer IDs of 0, 1, 2, ...
-        status_tracker = (
-            StatusTracker()
-        )  # single instance to track a collection of variables
+        task_id_generator = task_id_generator_function()  # generates integer IDs of 0, 1, 2, ...
+        status_tracker = StatusTracker()  # single instance to track a collection of variables
         next_request = None  # variable to hold the next request to call
 
         # initialize available capacity counts
@@ -245,9 +233,7 @@ async def process_generic_requests_from_file(
         if os.path.exists(save_filepath):
             if resume:
                 # save all successfully completed requests to a temporary file, then overwrite the original file with the temporary file
-                logger.debug(
-                    f"Resuming progress from existing file: {save_filepath}"
-                )
+                logger.debug(f"Resuming progress from existing file: {save_filepath}")
                 logger.debug(
                     f"Removing all failed requests from {save_filepath} so they can be retried"
                 )
@@ -265,16 +251,12 @@ async def process_generic_requests_from_file(
                             )
                             num_previously_failed_requests += 1
                         else:
-                            completed_request_ids.add(
-                                response.generic_request.original_row_idx
-                            )
+                            completed_request_ids.add(response.generic_request.original_row_idx)
                             output_file.write(line)
                 logger.info(
                     f"Found {len(completed_request_ids)} completed requests and {num_previously_failed_requests} previously failed requests"
                 )
-                logger.info(
-                    "Failed requests and remaining requests will now be processed."
-                )
+                logger.info("Failed requests and remaining requests will now be processed.")
                 os.replace(temp_filepath, save_filepath)
             elif resume_no_retry:
                 logger.warning(
@@ -284,9 +266,7 @@ async def process_generic_requests_from_file(
                 with open(save_filepath, "r") as input_file, open(
                     temp_filepath, "w"
                 ) as output_file:
-                    for line in tqdm(
-                        input_file, desc="Processing existing requests"
-                    ):
+                    for line in tqdm(input_file, desc="Processing existing requests"):
                         data = json.loads(line)
                         if isinstance(data[1], list):
                             # this means that the request failed and we have a list of errors
@@ -316,9 +296,7 @@ async def process_generic_requests_from_file(
             # Count total number of requests
             total_requests = sum(1 for _ in open(generic_requests_filepath))
             if total_requests == len(completed_request_ids):
-                logger.debug(
-                    "All requests have already been completed so will just reuse cache."
-                )
+                logger.debug("All requests have already been completed so will just reuse cache.")
                 return
 
             # Create progress bar
@@ -335,41 +313,28 @@ async def process_generic_requests_from_file(
                     # get next request (if one is not already waiting for capacity)
                     if next_request is None:
                         if not queue_of_requests_to_retry.empty():
-                            next_request = (
-                                queue_of_requests_to_retry.get_nowait()
-                            )
-                            logger.debug(
-                                f"Retrying request {next_request.task_id}: {next_request}"
-                            )
+                            next_request = queue_of_requests_to_retry.get_nowait()
+                            logger.debug(f"Retrying request {next_request.task_id}: {next_request}")
                         elif file_not_finished:
                             try:
                                 # get new generic request
-                                generic_request_json = json.loads(
-                                    next(generic_requests)
-                                )
+                                generic_request_json = json.loads(next(generic_requests))
                                 generic_request = GenericRequest.model_validate(
                                     generic_request_json
                                 )
                                 request_idx = generic_request.original_row_idx
 
                                 # Skip requests we already have responses for
-                                if (
-                                    resume
-                                    and request_idx in completed_request_ids
-                                ):
+                                if resume and request_idx in completed_request_ids:
                                     logger.debug(
                                         f"Skipping already completed request {request_idx}"
                                     )
-                                    status_tracker.num_tasks_already_completed += (
-                                        1
-                                    )
+                                    status_tracker.num_tasks_already_completed += 1
                                     continue
 
                                 # Create API-specific request
-                                api_specific_request_json = (
-                                    self.create_api_specific_request(
-                                        generic_request
-                                    )
+                                api_specific_request_json = self.create_api_specific_request(
+                                    generic_request
                                 )
                                 next_request = APIRequest(
                                     task_id=next(task_id_generator),
@@ -454,16 +419,11 @@ async def process_generic_requests_from_file(
 
                     # if a rate limit error was hit recently, pause to cool down
                     seconds_since_rate_limit_error = (
-                        time.time()
-                        - status_tracker.time_of_last_rate_limit_error
+                        time.time() - status_tracker.time_of_last_rate_limit_error
                     )
-                    if (
-                        seconds_since_rate_limit_error
-                        < seconds_to_pause_after_rate_limit_error
-                    ):
+                    if seconds_since_rate_limit_error < seconds_to_pause_after_rate_limit_error:
                         remaining_seconds_to_pause = (
-                            seconds_to_pause_after_rate_limit_error
-                            - seconds_since_rate_limit_error
+                            seconds_to_pause_after_rate_limit_error - seconds_since_rate_limit_error
                         )
                         await asyncio.sleep(remaining_seconds_to_pause)
                         # ^e.g., if pause is 15 seconds and final limit was hit 5 seconds ago
@@ -475,9 +435,7 @@ async def process_generic_requests_from_file(
             pbar.close()
 
             # after finishing, log final status
-            logger.info(
-                f"""Parallel processing complete. Results saved to {save_filepath}"""
-            )
+            logger.info(f"""Parallel processing complete. Results saved to {save_filepath}""")
 
             logger.info(f"Status tracker: {status_tracker}")
 
@@ -503,9 +461,7 @@ class StatusTracker:
     num_rate_limit_errors: int = 0
     num_api_errors: int = 0  # excluding rate limit errors, counted above
     num_other_errors: int = 0
-    time_of_last_rate_limit_error: int = (
-        0  # used to cool off after hitting rate limits
-    )
+    time_of_last_rate_limit_error: int = 0  # used to cool off after hitting rate limits
 
 
 @dataclass
@@ -518,6 +474,7 @@ class APIRequest:
     token_consumption: int
     attempts_left: int
     result: list = field(default_factory=list)
+    created_at: datetime.datetime = field(default_factory=datetime.datetime.now)
 
     async def call_api(
         self,
@@ -539,17 +496,13 @@ async def call_api(
             ) as response:
                 response = await response.json()
             if "error" in response:
-                logger.warning(
-                    f"Request {self.task_id} failed with error {response['error']}"
-                )
+                logger.warning(f"Request {self.task_id} failed with error {response['error']}")
                 status_tracker.num_api_errors += 1
                 error = response
                 if "rate limit" in response["error"].get("message", "").lower():
                     status_tracker.time_of_last_rate_limit_error = time.time()
                     status_tracker.num_rate_limit_errors += 1
-                    status_tracker.num_api_errors -= (
-                        1  # rate limit errors are counted separately
-                    )
+                    status_tracker.num_api_errors -= 1  # rate limit errors are counted separately
 
         except (
             Exception
@@ -570,6 +523,8 @@ async def call_api(
                     raw_request=self.api_specific_request_json,
                     raw_response=None,
                     generic_request=self.generic_request,
+                    created_at=self.created_at,
+                    finished_at=datetime.datetime.now(),
                 )
                 append_generic_response(generic_response, save_filepath)
                 status_tracker.num_tasks_in_progress -= 1
@@ -583,12 +538,26 @@ async def call_api(
             response_message, response_errors = parse_response_message(
                 response_message, self.generic_request.response_format
             )
+            usage = response.get("usage", {})
+            token_usage = TokenUsage(
+                prompt_tokens=usage.get("prompt_tokens", 0),
+                completion_tokens=usage.get("completion_tokens", 0),
+                total_tokens=usage.get("total_tokens", 0),
+            )
+
+            # Calculate cost using litellm
+            cost = litellm.completion_cost(completion_response=response)
+
             generic_response = GenericResponse(
                 response_message=response_message,
                 response_errors=response_errors,
                 raw_request=self.api_specific_request_json,
                 raw_response=response,
                 generic_request=self.generic_request,
+                created_at=self.created_at,
+                finished_at=datetime.datetime.now(),
+                token_usage=token_usage,
+                response_cost=cost,
             )
             append_generic_response(generic_response, save_filepath)
             status_tracker.num_tasks_in_progress -= 1
@@ -607,9 +576,7 @@ def get_token_encoding_name(model: str) -> str:
         return "cl100k_base"
 
 
-def get_rate_limits(
-    model: str, request_url: str, api_key: str
-) -> Tuple[int, int]:
+def get_rate_limits(model: str, request_url: str, api_key: str) -> Tuple[int, int]:
     """
     Function to get rate limits for a given annotator. Makes a single request to openAI API
     and gets the rate limits from the response headers. These rate limits vary per model
@@ -632,20 +599,14 @@ def get_rate_limits(
             json={"model": model, "messages": []},
         )
         # Extract rate limit information from headers
-        max_requests = int(
-            response.headers.get("x-ratelimit-limit-requests", 30_000)
-        )
-        max_tokens = int(
-            response.headers.get("x-ratelimit-limit-tokens", 150_000_000)
-        )
+        max_requests = int(response.headers.get("x-ratelimit-limit-requests", 30_000))
+        max_tokens = int(response.headers.get("x-ratelimit-limit-tokens", 150_000_000))
     elif "api.sambanova.ai" in request_url:
         # Send a dummy request to get rate limit information
         max_requests = 50
         max_tokens = 100_000_000
     else:
-        raise NotImplementedError(
-            f'Rate limits for API endpoint "{request_url}" not implemented'
-        )
+        raise NotImplementedError(f'Rate limits for API endpoint "{request_url}" not implemented')
 
     return max_requests, max_tokens
 
@@ -673,9 +634,7 @@ def api_endpoint_from_url(request_url: str) -> str:
         return match[1]
 
     # for Azure OpenAI deployment urls
-    match = re.search(
-        r"^https://[^/]+/openai/deployments/[^/]+/(.+?)(\?|$)", request_url
-    )
+    match = re.search(r"^https://[^/]+/openai/deployments/[^/]+/(.+?)(\?|$)", request_url)
     if match:
         return match[1]
 
@@ -685,9 +644,7 @@ def api_endpoint_from_url(request_url: str) -> str:
     elif "completions" in request_url:
         return "completions"
     else:
-        raise NotImplementedError(
-            f'API endpoint "{request_url}" not implemented in this script'
-        )
+        raise NotImplementedError(f'API endpoint "{request_url}" not implemented in this script')
 
 
 def append_generic_response(data: GenericResponse, filename: str) -> None:
@@ -724,9 +681,7 @@ def num_tokens_consumed_from_request(
                         )
                         num_tokens += len(str(value)) // 4
                     if key == "name":  # if there's a name, the role is omitted
-                        num_tokens -= (
-                            1  # role is always required and always 1 token
-                        )
+                        num_tokens -= 1  # role is always required and always 1 token
             num_tokens += 2  # every reply is primed with <im_start>assistant
             return num_tokens + completion_tokens
         # normal completions
@@ -759,9 +714,7 @@ def num_tokens_consumed_from_request(
             )
     # more logic needed to support other API calls (e.g., edits, inserts, DALL-E)
     else:
-        raise NotImplementedError(
-            f'API endpoint "{api_endpoint}" not implemented in this script'
-        )
+        raise NotImplementedError(f'API endpoint "{api_endpoint}" not implemented in this script')
 
 
 def task_id_generator_function():
diff --git a/src/bespokelabs/curator/viewer/__main__.py b/src/bespokelabs/curator/viewer/__main__.py
index e57c63bd..062454a2 100644
--- a/src/bespokelabs/curator/viewer/__main__.py
+++ b/src/bespokelabs/curator/viewer/__main__.py
@@ -1,16 +1,16 @@
+import logging
 import os
+import platform
+import shutil
+import socket
 import subprocess
 import sys
-from pathlib import Path
-from argparse import ArgumentParser
+import tempfile
+import time
 import webbrowser
+from argparse import ArgumentParser
 from contextlib import closing
-import socket
-import logging
-import time
-import platform
-import tempfile
-import shutil
+from pathlib import Path
 
 
 def get_viewer_path():
@@ -32,9 +32,7 @@ def ensure_dependencies():
             print(f"Error installing dependencies: {e}")
             sys.exit(1)
         except FileNotFoundError:
-            print(
-                "Error: Node.js is not installed. Please install Node.js to run the viewer."
-            )
+            print("Error: Node.js is not installed. Please install Node.js to run the viewer.")
             sys.exit(1)
 
 
@@ -49,9 +47,7 @@ def _setup_logging(level):
 def check_node_installed():
     """Check if Node.js is installed and return version if found"""
     try:
-        result = subprocess.run(
-            ["node", "--version"], capture_output=True, text=True, check=True
-        )
+        result = subprocess.run(["node", "--version"], capture_output=True, text=True, check=True)
         return result.stdout.strip()
     except (subprocess.CalledProcessError, FileNotFoundError):
         return None
@@ -105,22 +101,16 @@ def main():
     server_file = os.path.join(viewer_path, "server.js")
 
     if not os.path.exists(os.path.join(static_dir, ".next")):
-        print(
-            "Error: Next.js build artifacts not found. The package may not be built correctly."
-        )
+        print("Error: Next.js build artifacts not found. The package may not be built correctly.")
         sys.exit(1)
 
     try:
-        subprocess.run(
-            ["node", server_file], cwd=viewer_path, env=env, check=True
-        )
+        subprocess.run(["node", server_file], cwd=viewer_path, env=env, check=True)
     except subprocess.CalledProcessError as e:
         print(f"Error starting Next.js server: {e}")
         sys.exit(1)
     except FileNotFoundError:
-        print(
-            "Error: Node.js is not installed. Please install Node.js to run the viewer."
-        )
+        print("Error: Node.js is not installed. Please install Node.js to run the viewer.")
         sys.exit(1)
 
 
diff --git a/tests/test_caching.py b/tests/test_caching.py
new file mode 100644
index 00000000..73803465
--- /dev/null
+++ b/tests/test_caching.py
@@ -0,0 +1,115 @@
+from datasets import Dataset
+
+from bespokelabs.curator import Prompter
+
+
+def test_same_value_caching(tmp_path):
+    """Test that using the same value multiple times uses cache."""
+    values = []
+
+    # Test with same value multiple times
+    for _ in range(3):
+
+        def prompt_func():
+            return f"Say '1'. Do not explain."
+
+        prompter = Prompter(
+            prompt_func=prompt_func,
+            model_name="gpt-4o-mini",
+        )
+        result = prompter(working_dir=str(tmp_path))
+        values.append(result.to_pandas().iloc[0]["response"])
+
+    # Count cache directories, excluding metadata.db
+    cache_dirs = [d for d in tmp_path.glob("*") if d.name != "metadata.db"]
+    assert len(cache_dirs) == 1, f"Expected 1 cache directory but found {len(cache_dirs)}"
+    assert values == ["1", "1", "1"], "Same value should produce same results"
+
+
+def test_different_values_caching(tmp_path):
+    """Test that using different values creates different cache entries."""
+    values = []
+
+    # Test with different values
+    for x in [1, 2, 3]:
+
+        def prompt_func():
+            return f"Say '{x}'. Do not explain."
+
+        prompter = Prompter(
+            prompt_func=prompt_func,
+            model_name="gpt-4o-mini",
+        )
+        result = prompter(working_dir=str(tmp_path))
+        values.append(result.to_pandas().iloc[0]["response"])
+
+    # Count cache directories, excluding metadata.db
+    cache_dirs = [d for d in tmp_path.glob("*") if d.name != "metadata.db"]
+    assert len(cache_dirs) == 3, f"Expected 3 cache directories but found {len(cache_dirs)}"
+    assert values == ["1", "2", "3"], "Different values should produce different results"
+
+
+def test_same_dataset_caching(tmp_path):
+    """Test that using the same dataset multiple times uses cache."""
+    dataset = Dataset.from_list([{"instruction": "Say '1'. Do not explain."}])
+    prompter = Prompter(
+        prompt_func=lambda x: x["instruction"],
+        model_name="gpt-4o-mini",
+    )
+
+    result = prompter(dataset=dataset, working_dir=str(tmp_path))
+    assert result.to_pandas().iloc[0]["response"] == "1"
+
+    result = prompter(dataset=dataset, working_dir=str(tmp_path))
+    assert result.to_pandas().iloc[0]["response"] == "1"
+
+    # Count cache directories, excluding metadata.db
+    cache_dirs = [d for d in tmp_path.glob("*") if d.name != "metadata.db"]
+    assert len(cache_dirs) == 1, f"Expected 1 cache directory but found {len(cache_dirs)}"
+
+
+def test_different_dataset_caching(tmp_path):
+    """Test that using different datasets creates different cache entries."""
+    dataset1 = Dataset.from_list([{"instruction": "Say '1'. Do not explain."}])
+    dataset2 = Dataset.from_list([{"instruction": "Say '2'. Do not explain."}])
+    prompter = Prompter(
+        prompt_func=lambda x: x["instruction"],
+        model_name="gpt-4o-mini",
+    )
+
+    result = prompter(dataset=dataset1, working_dir=str(tmp_path))
+    assert result.to_pandas().iloc[0]["response"] == "1"
+
+    result = prompter(dataset=dataset2, working_dir=str(tmp_path))
+    assert result.to_pandas().iloc[0]["response"] == "2"
+
+    # Count cache directories, excluding metadata.db
+    cache_dirs = [d for d in tmp_path.glob("*") if d.name != "metadata.db"]
+    assert len(cache_dirs) == 2, f"Expected 2 cache directory but found {len(cache_dirs)}"
+
+
+def test_nested_call_caching(tmp_path):
+    """Test that changing a nested upstream function invalidates the cache."""
+
+    def value_generator():
+        return 1
+
+    def prompt_func():
+        return f"Say '{value_generator()}'. Do not explain."
+
+    prompter = Prompter(
+        prompt_func=prompt_func,
+        model_name="gpt-4o-mini",
+    )
+    result = prompter(working_dir=str(tmp_path))
+    assert result.to_pandas().iloc[0]["response"] == "1"
+
+    def value_generator():
+        return 2
+
+    result = prompter(working_dir=str(tmp_path))
+    assert result.to_pandas().iloc[0]["response"] == "2"
+
+    # Count cache directories, excluding metadata.db
+    cache_dirs = [d for d in tmp_path.glob("*") if d.name != "metadata.db"]
+    assert len(cache_dirs) == 2, f"Expected 2 cache directory but found {len(cache_dirs)}"
diff --git a/tests/test_install_ui.py b/tests/test_install_ui.py
new file mode 100644
index 00000000..2ef39b29
--- /dev/null
+++ b/tests/test_install_ui.py
@@ -0,0 +1,47 @@
+"""Test script for installation UI."""
+
+import argparse
+import importlib.util
+import os
+import sys
+
+
+def import_install_ui():
+    """Import just the install_ui module without importing the whole package."""
+    # Get the absolute path to install_ui.py
+    install_ui_path = os.path.join(
+        os.path.dirname(os.path.dirname(__file__)),  # Go up one level since we're in tests/
+        "src/bespokelabs/curator/install_ui.py",
+    )
+
+    # Import the module directly from file
+    spec = importlib.util.spec_from_file_location("install_ui", install_ui_path)
+    module = importlib.util.module_from_spec(spec)
+    sys.modules["install_ui"] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+def main():
+    """Run the test script with command line arguments."""
+    parser = argparse.ArgumentParser(description="Test the installation UI.")
+    parser.add_argument(
+        "--scenario",
+        choices=["success", "error"],
+        default="success",
+        help="Which scenario to test (success or error)",
+    )
+    args = parser.parse_args()
+
+    # Import just the install_ui module
+    install_ui = import_install_ui()
+
+    # Run the enhanced install based on scenario
+    if args.scenario == "success":
+        install_ui.enhanced_install("bespokelabs-curator")
+    else:
+        install_ui.enhanced_install("nonexistent-package-12345")
+
+
+if __name__ == "__main__":
+    main()