diff --git a/.github/workflows/build-project.yml b/.github/workflows/build-project.yml
index 605f5e84..26d8e45a 100644
--- a/.github/workflows/build-project.yml
+++ b/.github/workflows/build-project.yml
@@ -12,22 +12,22 @@ jobs:
 
     steps:
     - name: Checkout code
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
 
     - name: Set up Python
       uses: actions/setup-python@v4
       with:
         python-version: '3.11'
 
-    - name: Install Poetry
-      run: |
-        curl -sSL https://install.python-poetry.org | python3
-
-    - name: Poetry lock
-      run: poetry lock
+    - name: Install and configure Poetry
+      uses: snok/install-poetry@v1
+      with:
+        version: 1.8.3
+        virtualenvs-create: true
+        virtualenvs-in-project: true
 
     - name: Install dependencies
-      run: poetry install
+      run: poetry lock && poetry install
 
     - name: Run build
       run: poetry build
diff --git a/examples/agent_of_flo_ai.ipynb b/examples/agent_of_flo_ai.ipynb
index 78979e23..9197b808 100644
--- a/examples/agent_of_flo_ai.ipynb
+++ b/examples/agent_of_flo_ai.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -32,7 +32,7 @@
        "True"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -58,16 +58,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<flo_ai.state.flo_session.FloSession at 0x115a0b490>"
+       "<flo_ai.state.flo_session.FloSession at 0x114923110>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,7 +111,7 @@
     "kind: FloAgent\n",
     "name: weather-assistant\n",
     "agent:\n",
-    "    name: Weather Assistant\n",
+    "    name: WeatherAssistant\n",
     "    kind: agentic\n",
     "    job: >\n",
     "      Given the city name you are capable of answering the latest whether this time of the year by searching the internet\n",
@@ -122,17 +122,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-09-22 13:22:31,840 - BUILDER - INFO - Building Flo instance from YAML\n",
-      "2024-09-22 13:22:31,846 - COMMON - INFO - Flo instance created for session e7f22b4d-7380-4d00-97c9-5267a468468a\n"
-     ]
-    },
     {
      "data": {
       "image/jpeg": 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TonqCCPQar03hdi9w4dowR+1IViiIjcFNvDriQGW+XkAWFBexypPNzc2xve6kSb2EPMec3nIciy/LbaMiytjiBHyuLFtOO22RIZgOWguMEOLDWkcimS8tbpUFJUNbHcZB5XFHi/k3EObj856C/ZL5Ks1sLeVO29mD2ASG1uwkxHEPhew4S4o8wXyjlAqwZ58nPLr/AJtd7hj0m0423OlokNX6He7s3NjkBAUtURLng7rhCdbJAPTYPp1XJeAWBZdkrt/ulgS9dXw2JDzMp9hMrk+p2zba0od1oAc6VdAB3VhdbBkFzgZJleU8Xm7rlt9tczHbLbpUWPY7m7HjMTFQVrcWkDRUnnbHmK807JKSTuutjtrHErjVwoyC7XK7R59z4eC6PeL7o/FQp7tYaykJbWkchLiipH1VaTzA8o16J8gLD4xySd4B/wBVkTLce6Ods5/1Dbbam0DXNpOkrUNp0evXrUHeuBuE5Bacbt02zKMfHGBFtamJkhl6MyEJR2YdQ4laklKEghSiDyjezWV1gy3ghCzV7iHxQes91skbGUZ3J8NhzIDrst0+Dxefs3UupSjaeUDaFaIJ670IriRkl1tXHxrh5GzSZCxzK5EWZPk+Ev8AhFnc0vUJh8dGPC+xHInmBTyu8o89NafefkwcOb7fbneJVonpuFykKlS3It8nx0uuqABWUNvpSCQB3AdwqelcFsMnWjIbbJswkRcglJmXIuyXlOvPJ5QhYdK+dHJyI5AhSQjXm6pddKAwS4K4lcW824i+IpkiEceuq7PbUs5W7bEw+RltSHnIqYrqZHOpZXzOqIUPNATy7NnsFvyXi1xMyfH8syu7WN3FLbamjCxa4LgokSn43avyStIC3EdptCEnzQEHadmtHyn5P+A5ne1Xe72Hwm4uNIYffRMkM+FIQNJS+EOJD+h0+kCuldrN+CWFcRLqxc77ZfCLkyyYyZcaU/FdUzvfZrUytBWjZJ5VbHU9OtLrBUsUu1wi8XuLFqfu86TBtlksy4iJUlSg0S1L7RxI7kqUUJKlJA2UjfcKx7AbArP8q+T5cr1fL+7cJ2FS3n5TN6ksuOuNqiq2VJWCSrnPP/fATzb5Rr0Xf+BmD5NLiSbhYw47GhJtqexlPMpcip+qw6lC0h5sbOkuBQ6n11x3PgLgt3xvHbFJsh8XY832VrDM2Q09FQUhJSl5DgcIKQAQVEK0N71RwsGI5Nc7/fsR4y8QV5pe7Jd8Putwi2i3w5pagsohoSppDsf6rxfPUlwE6cSE60KteCIuvEbjrlkm536+wLda7fYLgxY4lwdYjofdbccXzpSRtO0cqm/qr2eYK0NaFkHAHAcpyRV9umOty7i4ppx/ch5LMlTeuzU8yFht4p0NFxKj0Hqq027EbTackvF/iROyu13Qw3Nkdos9qlkKDQ5SeVPKFq+qBvfXfSihYJioDNP6hbP84tv/ADGan6gM0/qFs/zi2/8AMZrpk/Mh7yrajQqUpXjkIvKv0YvH7G9/Iar2Nfo5av2Rr+QVcZDDcphxl1PO04koUk+kEaIqhsxb/jMdm3Jsj18jx0JaZmQ5DKVLQBpPaJdWjS9DrokHv6b5R6FnacDgrR1rm6fkyWaoTtKhPG1+9jLr71C+PTxtfvYy6+9Qvj1vudpeJcxQm6VCeNr97GXX3qF8enja/exl196hfHpc7S8S5ihN0qE8bX72MuvvUL49PG1+9jLr71C+PS52l4lzFCbpUJ42v3sZdfeoXx6jombz598uNnYxS6ruNvbZdks9vEHZpd5+zOy9o77NfcTrXXXSlztLxLmKFspUJ42v3sZdfeoXx6eNr97GXX3qF8elztLxLmKE3SoTxtfvYy6+9Qvj08bX72MuvvUL49LnaXiXMUJulQnja/exl196hfHp42v3sZdfeoXx6XO0vEuYoTdQGaf1C2f5xbf+YzXJ42v3sZdfeoXx654tpuuRzoarhb1We3xXkSS288hx55xBCkJ+jUpKUhXUnZJ5QNdd1lDSXEo4mqLen+GEqOpd6UpXjGIpSlAKUpQClKUApSlAKz/FR/3lz4674Nq66/CT6df/AE/u9OgVnuKJ1xp4gK0esG09ddO6T6aA0KlKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQCs8xMj56+IPXr4BadjX4SvTWh1n2KBXzz5/sr5fAbToEeb3Se7/+9VAaDSlKAUpSgFKUoBSlKAUpSgFKUoBSlcUmSzDYW/IdQwygbU44oJSkesk91NoOWlVhzihh7SiFZTZtgkHU5o6I7x9bvr4+dXDfaqz++t/nXRh53UfBlo9C1Uqq/OrhvtVZ/fW/zp86uG+1Vn99b/OmGn9R8GW69Cxzp0a2QpEyZIaiRI7annpD6whtpCRtSlKPQAAEknoAKxvC+KuETONeZJj5hYH3J8a0x4gaubCjJd/6hPZt6X56tqSOUDe1D11ep/EbBLpBkQpeSWSTEkNqZeZcmNlLiFAhSSN9QQSK/n98nz5N2O4P8rm8XK63u2+RmMveH2aW7LR2ctxZ3HSlWxtTXUq13KQn10w0/qPgxdeh/TKlVX51cN9qrP763+dPnVw32qs/vrf50w0/qPgxdehaqVVfnVw32qs/vrf50+dXDfaqz++t/nTDT+o+DF16FqpVci8SMTnPIZYya0OvL+q2mc1zK/UObZqxAggEHYPprXHLjl5RprvJSh+0pStZBSlKAUpSgK9m+ZxsKtAlOoMiU8rso0VKtF1ff3+hIGyVegDoCSAfP16mzMpmeF3uR4xfB5kNrGmGfwbbJIT6t9VEd6jVj4q3Rd14iTGVKJatbDcVtB7krWkOuKH6wpoH/AKo+S31jF8dul5lBSo1viuy3QnvKUIKiB+OhX732ZZILNIU6L+zVa6L/A3TJEiAEjQGh6hSszwGDneQx7Pkt4ypuJHmoTLXj8a3NFltpY5ktdqfpCoAjat9++lQ+EcblIafZyGLdXkG/SrWm8phoTDZPhK22G1KBB7uRPMEkbI5lb3Xo4iHK8mq6+t5ibJSqRkHFeHjF7MK42S9x4CX2oyr0Yg8BStwpCNr5ubRUpKeYJ0CdE1FXTiqzjmU5ku5G5ottgtseUuEYjPKpKluAvNOBzmXza1yqCdcmx31m58uHa/WfIGmUrP3OM9sjovImWq62t+32p28oauDCWjKjN/WW3pStdeUFKglQ5htNU6ffOIuO4A3xCl5BGmNojt3KXjIgNoZRHVpSm23h9JzpQd8xJBI7tVhFaYIdmetOgG40qqRuJVqloylbTUpSMebQ7JVyJ06lUdMgdn53XzFAedy9fw61V43GGdcOI9mtMLHrhKstxsjVzS62hkOI7VxADiyXhptCVaUACrm7goVnFPlw0z2/wCENTpWbzeOlqgOXhx2yX02yzzlwJ91RGbVHjrRrmUdOc5QOYEkJOh3gVPo4i2x1WWBDchYxtCVylpSkpdCo6XwWjzdfMUB111/DrVU6W9j9Z8mCzutIeQUuIS4k96VDYqXxLKrhg8hBgLW7btjtbYpX0RT6ezB6Nq9WtA+kekYvG4wzrhxHs1phY9cJVluNkauaXW0MhxHauIAcWS8NNoSrSgAVc3cFCtTrB+6tUEUESqiptHpix3qJkVpjXKA720WQjmQrWiPQQR6CCCCPQQRXerIuBF0cRLv1oUrbCeymsp/ulfMlYHqG20q/Wo1rtfPbZZ8LPildC/DzM2KUpXEQUpSgPPfE6C5buJF1K98k5piY0T3aCA0oD9RaBP+IeuqVlFhZynGrtZZCihi4xHYjik96UuIKSR+I3XoziLgqc1trJYcRHusMqXFeWPNO9c7a9deVWh1HcQk6OtHBXg5DnOwJjK4Vwa/pIj+gtP4+pSfUoEg+g19A9m2mC02dS3thVGt2wPPMy7Bp/ESxQrNjdyxOLIRB7OI9f03NsMOsI0ntEtaLnOUj6pAG/SBUPI4d5CvhFeLKm37ub+SKntMds35zHjNL/Pzc2h9GCrRO/RrfStqpXbh04bsUTeTXRsf2MTzpxA4W5NkEjKwvFkX67SbimXbb7IuDaUMRULbWiO0hR5kL0lSO4JJUSVVYeJ3D7IcllcQ3bfbS6LvjkWDDCn2klx9DjylI6q6aC09TodehNbVSsMHBnm8+7fu3sGa5tw9lZfnEUvMEWR/HLhaZUlK07bW8pkJATvZOkrOwNdO/uqqTbBxGyLh+zw7nWGNCZUy3bpeTpntraXGRoKW2z/SdopCdaUAASetbrSs4rNDE26tV28vWYMZvuKZPYrjxCi2XHhd4WSxGkxJCZrTSIy0xBHKHAshX9gEFIO96JHfXLbMYyfEb9hl4jWI3cMYy1Y50VqW005FdSptfPtagladpUDyknpsbrYaUw0NapvyyzroDzpDbynILRxRxex48mUxd7/PiLvD8xtDMVLiEIWVNk86iEnY5Qdkju1U/cMMyjF3M6t1msJvkTIIDDMSZ4Y0yllbcMRil0LIV/YCgUgg70SO+tjhWyHbTIMSIxF8IdU+92LYR2ritcy1aHVR0Nk9eldmtcNkVP5ROv237t4MetmMZPiN+wy8RrEbuGMZasc6K1LaaciupU2vn2tQStO0qB5ST02N1sNK7dgs83K7mbfamw86hQD75G2owP8AaWfX6kb5j+A2RvUMFnhcTeW/oCVS88CIK3rvkNx19A2hiEhXrWOZxY/cFt/+a2KonFcai4lYo1ricym2QSp1f13Vk7UtX4kkn1DuGgAKlq+e220K02iKatj2dyyM2KUpXCQUpSgFRl9xm05PGSxdrdGuDaCSgPthRQT6UnvSfxGjUnSsoYnA70LowUNfBDEFd0KY2Nk6bucpI/2cr5+Y3Efss/8Ai0v4tX6ldeOtX1YuLLVlB+Y3Efss/wDi0v4tPmNxH7LP/i0v4tX6lXHWr6sXFirKD8xuI/ZZ/wDFpfxafMbiP2Wf/Fpfxav1KY61fVi4sVZQfmNxH7LP/i0v4tPmNxH7LP8A4tL+LV+pTHWr6sXFirKD8xuI/ZZ/8Wl/Fp8xuI/ZZ/8AFpfxav1KY61fVi4sVZRWeCeHtLSpVtfkcv8AZk3CQ6g/rSpwg/vFXG3W2HaIbcSBFYhRWxpDEdsNoSPwSBoV2aVomT5075kbfe2xVsUpStBBSlKA/9k=",
@@ -146,23 +138,16 @@
    ],
    "source": [
     "from IPython.display import Image, display\n",
-    "flo = Flo.build(session, simple_weather_checking_agent,log_level=\"DEBUG\")\n",
+    "flo = Flo.build(session, simple_weather_checking_agent, log_level=\"ERROR\")\n",
     "\n",
     "flo.draw()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-09-22 13:22:34,352 - COMMON - INFO - Invoking query for session e7f22b4d-7380-4d00-97c9-5267a468468a: Whats the whether in New Delhi, India ?\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -171,25 +156,28 @@
       "\n",
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3m\n",
-      "Invoking: `tavily_search_results_json` with `{'query': 'current weather in New Delhi, India'}`\n",
+      "Invoking: `tavily_search_results_json` with `{'query': 'New Delhi India weather October 2023'}`\n",
       "\n",
       "\n",
-      "\u001b[0m\u001b[36;1m\u001b[1;3m[{'url': 'https://www.weatherapi.com/', 'content': \"{'location': {'name': 'New Delhi', 'region': 'Delhi', 'country': 'India', 'lat': 28.6, 'lon': 77.2, 'tz_id': 'Asia/Kolkata', 'localtime_epoch': 1726991371, 'localtime': '2024-09-22 13:19'}, 'current': {'last_updated_epoch': 1726991100, 'last_updated': '2024-09-22 13:15', 'temp_c': 35.3, 'temp_f': 95.5, 'is_day': 1, 'condition': {'text': 'Mist', 'icon': '//cdn.weatherapi.com/weather/64x64/day/143.png', 'code': 1030}, 'wind_mph': 3.4, 'wind_kph': 5.4, 'wind_degree': 350, 'wind_dir': 'N', 'pressure_mb': 1007.0, 'pressure_in': 29.74, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 56, 'cloud': 50, 'feelslike_c': 38.1, 'feelslike_f': 100.6, 'windchill_c': 36.6, 'windchill_f': 98.0, 'heatindex_c': 40.7, 'heatindex_f': 105.3, 'dewpoint_c': 20.3, 'dewpoint_f': 68.5, 'vis_km': 3.5, 'vis_miles': 2.0, 'uv': 9.0, 'gust_mph': 3.9, 'gust_kph': 6.2}}\"}, {'url': 'https://mausam.imd.gov.in/delhiums/', 'content': 'Urban Meteorological Services for Delhi - NCR India Meteorological Department Ministry of Earth Sciences Government of India. ... Sep 22, 2024. Sep 23, 2024. ... Sep 25, 2024. Interactive Display of weather and Thunderstorm warnings. Current Weather Across Delhi - NCR. Delhi; Gurugram; Faridabad; Gautam Budh Nagar; Ghaziabad; New Delhi. 33.0 ...'}, {'url': 'https://www.msn.com/en-in/news/India/delhi-weather-and-aqi-today-warm-start-at-2705-c-check-weather-forecast-for-september-22-2024/ar-AA1qYihM', 'content': \"The temperature in Delhi today, on September 22, 2024, is 35.08 °C. The day's forecast indicates a minimum and maximum temperature of 27.05 °C and 37.13 °C, respectively. The relative humidity ...\"}, {'url': 'https://weatherspark.com/h/m/109174/2024/9/Historical-Weather-in-September-2024-in-New-Delhi-NCT-India', 'content': '°F  September 2024 Weather History in New Delhi NCT, India Latest Report — 3:00 PM °F Dew Pt. 73°F\\xa0\\xa0oppressive No Report Overcast 8,000 ft Mostly Clear 1,500 ft Mostly Clear 3,000 ft This report shows the past weather for New Delhi, providing a weather history for September 2024. It features all historical weather data series we have available, including the New Delhi temperature history for September 2024. New Delhi Temperature History September 2024 Sep 65°F 65°F 70°F 70°F 75°F 75°F 80°F 80°F 85°F 85°F 90°F 90°F 95°F 95°F 100°F 100°F Hourly Temperature in September 2024 in New Delhi Sep'}, {'url': 'https://www.timeanddate.com/weather/india/new-delhi/hourly', 'content': 'Hour-by-Hour Forecast for New Delhi, Delhi, India. Weather Today Weather Hourly 14 Day Forecast Yesterday/Past Weather Climate (Averages) Currently: 84 °F. Passing clouds. (Weather station: New Delhi / Safdarjung, India). See more current weather.'}]\u001b[0m\u001b[32;1m\u001b[1;3mThe current weather in New Delhi, India, is as follows:\n",
+      "\u001b[0m\u001b[36;1m\u001b[1;3m[{'url': 'https://www.weatherapi.com/', 'content': \"{'location': {'name': 'New Delhi', 'region': 'Delhi', 'country': 'India', 'lat': 28.6, 'lon': 77.2, 'tz_id': 'Asia/Kolkata', 'localtime_epoch': 1727343896, 'localtime': '2024-09-26 15:14'}, 'current': {'last_updated_epoch': 1727343000, 'last_updated': '2024-09-26 15:00', 'temp_c': 33.4, 'temp_f': 92.1, 'is_day': 1, 'condition': {'text': 'Mist', 'icon': '//cdn.weatherapi.com/weather/64x64/day/143.png', 'code': 1030}, 'wind_mph': 6.7, 'wind_kph': 10.8, 'wind_degree': 107, 'wind_dir': 'ESE', 'pressure_mb': 1004.0, 'pressure_in': 29.65, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 59, 'cloud': 75, 'feelslike_c': 36.5, 'feelslike_f': 97.7, 'windchill_c': 34.7, 'windchill_f': 94.5, 'heatindex_c': 39.2, 'heatindex_f': 102.5, 'dewpoint_c': 21.3, 'dewpoint_f': 70.3, 'vis_km': 4.0, 'vis_miles': 2.0, 'uv': 8.0, 'gust_mph': 7.7, 'gust_kph': 12.4}}\"}, {'url': 'https://weatherspark.com/h/m/109174/2023/10/Historical-Weather-in-October-2023-in-New-Delhi-India', 'content': 'New Delhi Temperature History October 2023. The daily range of reported temperatures (gray bars) and 24-hour highs (red ticks) and lows (blue ticks), placed over the daily average high (faint red line) and low (faint blue line) temperature, with 25th to 75th and 10th to 90th percentile bands.'}, {'url': 'https://www.meteoprog.com/weather/Delhi/month/october/', 'content': 'Delhi (India) weather in October 2023 ☀️ Accurate weather forecast for Delhi in October ⛅ Detailed forecast By month Current temperature \"near me\" Weather news ⊳ Widget of weather ⊳ Water temperature | METEOPROG'}, {'url': 'https://world-weather.info/forecast/india/delhi/october-2023/', 'content': 'Extended weather forecast in Delhi. Hourly Week 10 days 14 days 30 days Year. Detailed ⚡ Delhi Weather Forecast for October 2023 - day/night 🌡️ temperatures, precipitations - World-Weather.info.'}, {'url': 'https://weather-and-climate.com/New-Delhi-October-averages', 'content': 'New Delhi, monthly averages in October. Min Temperature 20°C. Max Temperature 34°C. Chance of Rain 3%. Precipitation 14 mm. Rainy days 1 day. Humidity 44%. Windspeed 4 km/h. Sunshine 262 hours.'}]\u001b[0m\u001b[32;1m\u001b[1;3mThe current weather in New Delhi, India is as follows:\n",
       "\n",
-      "- **Temperature**: 35.3°C (95.5°F)\n",
+      "- **Temperature**: 33.4°C (92.1°F)\n",
       "- **Condition**: Mist\n",
-      "- **Feels Like**: 38.1°C (100.6°F)\n",
-      "- **Humidity**: 56%\n",
-      "- **Wind**: 3.4 mph (5.4 kph) from the North\n",
-      "- **Visibility**: 3.5 km\n",
-      "- **Pressure**: 1007 mb\n",
-      "\n",
-      "The forecast for today indicates a minimum temperature of 27.05°C and a maximum of 37.13°C.\n",
+      "- **Feels Like**: 36.5°C (97.7°F)\n",
+      "- **Humidity**: 59%\n",
+      "- **Wind**: 10.8 kph (6.7 mph) from the East-Southeast\n",
+      "- **Visibility**: 4 km\n",
+      "- **Pressure**: 1004 mb\n",
       "\n",
-      "For more details, you can check the [India Meteorological Department](https://mausam.imd.gov.in/delhiums/) or other weather services.\u001b[0m\n",
+      "For more detailed forecasts and historical data, you can check the following links:\n",
+      "- [Weather API](https://www.weatherapi.com/)\n",
+      "- [Weather Spark](https://weatherspark.com/h/m/109174/2023/10/Historical-Weather-in-October-2023-in-New-Delhi-India)\n",
+      "- [Meteoprog](https://www.meteoprog.com/weather/Delhi/month/october/)\n",
+      "- [World Weather Info](https://world-weather.info/forecast/india/delhi/october-2023/)\n",
+      "- [Weather and Climate](https://weather-and-climate.com/New-Delhi-October-averages)\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
-      "{'messages': [HumanMessage(content='Whats the whether in New Delhi, India ?')], 'output': 'The current weather in New Delhi, India, is as follows:\\n\\n- **Temperature**: 35.3°C (95.5°F)\\n- **Condition**: Mist\\n- **Feels Like**: 38.1°C (100.6°F)\\n- **Humidity**: 56%\\n- **Wind**: 3.4 mph (5.4 kph) from the North\\n- **Visibility**: 3.5 km\\n- **Pressure**: 1007 mb\\n\\nThe forecast for today indicates a minimum temperature of 27.05°C and a maximum of 37.13°C.\\n\\nFor more details, you can check the [India Meteorological Department](https://mausam.imd.gov.in/delhiums/) or other weather services.'}\n"
+      "{'messages': [HumanMessage(content='Whats the whether in New Delhi, India ?')], 'output': 'The current weather in New Delhi, India is as follows:\\n\\n- **Temperature**: 33.4°C (92.1°F)\\n- **Condition**: Mist\\n- **Feels Like**: 36.5°C (97.7°F)\\n- **Humidity**: 59%\\n- **Wind**: 10.8 kph (6.7 mph) from the East-Southeast\\n- **Visibility**: 4 km\\n- **Pressure**: 1004 mb\\n\\nFor more detailed forecasts and historical data, you can check the following links:\\n- [Weather API](https://www.weatherapi.com/)\\n- [Weather Spark](https://weatherspark.com/h/m/109174/2023/10/Historical-Weather-in-October-2023-in-New-Delhi-India)\\n- [Meteoprog](https://www.meteoprog.com/weather/Delhi/month/october/)\\n- [World Weather Info](https://world-weather.info/forecast/india/delhi/october-2023/)\\n- [Weather and Climate](https://weather-and-climate.com/New-Delhi-October-averages)'}\n"
      ]
     }
    ],
@@ -393,20 +381,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-09-22 13:22:57,657 - BUILDER - INFO - Building Flo instance from YAML\n",
-      "2024-09-22 13:22:57,664 - COMMON - INFO - Flo instance created for session 71b7747f-6530-45d7-b78d-54b8cc8bbb02\n"
+      "2024-09-26 15:54:42,618 - BUILDER - INFO - Building Flo instance from YAML\n",
+      "2024-09-26 15:54:42,624 - COMMON - INFO - Flo instance created for session 61LQSb1Ceb4ieNvy\n"
      ]
     },
     {
      "data": {
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -470,20 +458,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-09-22 13:25:01,619 - BUILDER - INFO - Building Flo instance from YAML\n",
-      "2024-09-22 13:25:01,627 - COMMON - INFO - Flo instance created for session 11351ff8-1110-4724-9e15-042eeccd99b4\n"
-     ]
-    },
     {
      "data": {
-      "image/jpeg": 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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
diff --git a/examples/hierarchical_blogging_team.py b/examples/hierarchical_blogging_team.py
index 9a2e78c4..6651fbc3 100644
--- a/examples/hierarchical_blogging_team.py
+++ b/examples/hierarchical_blogging_team.py
@@ -27,7 +27,7 @@
               job: From the documents provider by the researcher write a blog of 300 words with can be readily published, make in engaging and add reference links to original blogs
               tools:
                 - name: TavilySearchResults
-        - name: Writing Team
+        - name: WritingTeam
           supervisor:
             name: supervisor
           agents: 
diff --git a/examples/planned_blogging_team.py b/examples/planned_blogging_team.py
deleted file mode 100644
index e7011c51..00000000
--- a/examples/planned_blogging_team.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from flo_ai.core import Flo
-from langchain_openai import ChatOpenAI
-
-from flo_ai import FloSession
-from langchain_community.tools.tavily_search.tool import TavilySearchResults
-from dotenv import load_dotenv
-load_dotenv()
-
-yaml_data = """
-apiVersion: flo/alpha-v1
-kind: FloRoutedTeam
-name: blogging-team
-team:
-    planner:
-        name: blog-planner
-    name: BloggingTeam
-    agents:
-      - name: Reasercher
-        job: Do a research on the internet and find articles of relevent to the topic asked by the user
-        tools:
-          - name: TavilySearchResults
-      - name: BlogWriter
-        job: Able to write a blog using information provided
-        tools:
-          - name: TavilySearchResults
-"""
-
-llm = ChatOpenAI(temperature=0, model_name='gpt-4o')
-session = FloSession(llm).register_tool(
-    name="TavilySearchResults", 
-    tool=TavilySearchResults()
-)
-flo: Flo = Flo.build(llm, yaml=yaml_data)
-image_data = flo.draw_to_file("examples/images/agent-planned-graph.png")
-
-config = {"recursion_limit": 50}
-inputs = "Write a interesting blog about Sachin Tendulkar vs Brian Lara?"
-for event in flo.stream(inputs, config=config):
-    for k, v in event.items():
-        if k != "__end__":
-            print(v)
\ No newline at end of file
diff --git a/examples/tool_agent.py b/examples/tool_agent.py
index abfda1b0..3f35d94a 100644
--- a/examples/tool_agent.py
+++ b/examples/tool_agent.py
@@ -49,6 +49,6 @@ def _run(
           - name: printStateTool
 """
 
-flo = Flo.build(session, simple_tool_agent)
+flo = Flo.build(session, simple_tool_agent, log_level="ERROR")
 
 print(flo.invoke("Testing ...."))
\ No newline at end of file
diff --git a/features/flo_name.feature b/features/flo_name.feature
new file mode 100644
index 00000000..b4b94689
--- /dev/null
+++ b/features/flo_name.feature
@@ -0,0 +1,13 @@
+Feature: While building a flo
+
+  Scenario Outline: Flo Name Validation
+     When use the name, <flo_name>
+     Then it should be <validity>
+
+  Examples: Flo Names
+   | flo_name        | validity |
+   | CorrectName     | True     |
+   | Wrong Name      | False    |
+   | correct_name    | True     |
+   | correct-name    | True     |
+   | wrong/name      | False    |
\ No newline at end of file
diff --git a/features/steps/flo_name_steps.py b/features/steps/flo_name_steps.py
new file mode 100644
index 00000000..81f74ea2
--- /dev/null
+++ b/features/steps/flo_name_steps.py
@@ -0,0 +1,15 @@
+from behave import given, when, then, step
+from flo_ai.yaml.validators import raise_for_name_error
+
+@when('use the name, {flo_name}')
+def step_impl(context, flo_name):
+    isException = "False"
+    try:
+        raise_for_name_error(flo_name)
+    except Exception as e:
+        isException = "True"
+    context.isException = isException
+
+@then('it should be {validity}')
+def step_impl(context, validity):
+    assert context.isException != validity
\ No newline at end of file
diff --git a/flo_ai/builders/yaml_builder.py b/flo_ai/builders/yaml_builder.py
index 86ad74b2..e9838a64 100644
--- a/flo_ai/builders/yaml_builder.py
+++ b/flo_ai/builders/yaml_builder.py
@@ -1,24 +1,32 @@
 from flo_ai.models.flo_team import FloTeam
-from flo_ai.models.flo_agent import FloAgent
 from flo_ai.yaml.config import (FloRoutedTeamConfig, TeamConfig, AgentConfig, FloAgentConfig)
 from flo_ai.models.flo_executable import ExecutableFlo
 from flo_ai.state.flo_session import FloSession
 from flo_ai.router.flo_router_factory import FloRouterFactory
 from flo_ai.factory.agent_factory import AgentFactory
+from flo_ai.yaml.validators import raise_for_name_error, DuplicateStringError
+from flo_ai.common.flo_logger import builder_logger
 
 def build_supervised_team(session: FloSession) -> ExecutableFlo:
+    name_set = set()
     flo_config = session.config
     if isinstance(flo_config, FloRoutedTeamConfig):
         team_config: TeamConfig = flo_config.team
-        team = parse_and_build_subteams(session, team_config)    
+        team = parse_and_build_subteams(session, team_config, name_set)
         return team
     elif isinstance(flo_config, FloAgentConfig):
         agent_config: AgentConfig = flo_config.agent
+        validate_names(name_set, agent_config.name)
         agent = AgentFactory.create(session, agent_config)
         return agent
+    
+def validate_team(name_set: set, team_config: TeamConfig):
+    validate_names(name_set, team_config.name)
+    [validate_names(name_set, agent.name) for agent in team_config.agents]
 
-def parse_and_build_subteams(session: FloSession, team_config: TeamConfig) -> ExecutableFlo:
+def parse_and_build_subteams(session: FloSession, team_config: TeamConfig, name_set = set()) -> ExecutableFlo:
     flo_team = None
+    validate_team(name_set, team_config)
     if team_config.agents:
         members = [AgentFactory.create(session, agent) for agent in team_config.agents]
         flo_team = FloTeam.Builder(team_config, members=members).build()
@@ -27,10 +35,16 @@ def parse_and_build_subteams(session: FloSession, team_config: TeamConfig) -> Ex
     else:
         flo_teams = []
         for subteam in team_config.subteams:
-            flo_subteam = parse_and_build_subteams(session, subteam)
+            flo_subteam = parse_and_build_subteams(session, subteam, name_set)
             flo_teams.append(flo_subteam)
         flo_team = FloTeam.Builder(team_config, members=flo_teams).build()
         router = FloRouterFactory.create(session, team_config, flo_team)
         flo_routed_team = router.build_routed_team()
     return flo_routed_team
 
+def validate_names(name_set: set, name):
+    raise_for_name_error(name)
+    if name in name_set:
+        builder_logger.error(f"Duplicate name found: '{name}'")
+        raise DuplicateStringError(f"The name '{name}' is already in the set.")
+    name_set.add(name)
\ No newline at end of file
diff --git a/flo_ai/core.py b/flo_ai/core.py
index f857bae9..801d1c9d 100644
--- a/flo_ai/core.py
+++ b/flo_ai/core.py
@@ -34,6 +34,7 @@ def invoke(self, query, config = None) -> Iterator[Union[dict[str, Any], Any]]:
     
     @staticmethod
     def build(session: FloSession, yaml: str, log_level: str = "INFO"):
+        set_global_log_level(log_level)
         builder_logger.info("Building Flo instance from YAML")
         return Flo(session, to_supervised_team(yaml), log_level)
 
diff --git a/flo_ai/helpers/utils.py b/flo_ai/helpers/utils.py
index 1b97bd0a..c457fa0c 100644
--- a/flo_ai/helpers/utils.py
+++ b/flo_ai/helpers/utils.py
@@ -4,10 +4,4 @@
 def random_str(length: int = 5):
     letters = string.ascii_letters + string.digits
     result_str = ''.join(random.choice(letters) for i in range(length))
-    return result_str
-
-def randomize_name(name: str):
-    return "{}-{}".format(name, random_str(5))
-
-def agent_name_from_randomized_name(name: str):
-    return name.split("-")[0]
\ No newline at end of file
+    return result_str
\ No newline at end of file
diff --git a/flo_ai/models/flo_agent.py b/flo_ai/models/flo_agent.py
index abdd47d4..f1bfa3e9 100644
--- a/flo_ai/models/flo_agent.py
+++ b/flo_ai/models/flo_agent.py
@@ -4,7 +4,6 @@
 from langchain_core.runnables import Runnable
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from flo_ai.helpers.utils import randomize_name
 from flo_ai.models.flo_executable import ExecutableFlo
 from flo_ai.state.flo_session import FloSession
 from typing import Union, Optional
@@ -33,7 +32,7 @@ def __init__(self,
                     handle_parsing_errors: bool = True) -> None:
             prompt: Union[ChatPromptTemplate, str] = config.job
             
-            self.name: str = randomize_name(config.name)
+            self.name: str = config.name
             self.llm = llm if llm is not None else session.llm
             self.config = config
             # TODO improve to add more context of what other agents are available
diff --git a/flo_ai/models/flo_llm_agent.py b/flo_ai/models/flo_llm_agent.py
index d753dd47..814db9fc 100644
--- a/flo_ai/models/flo_llm_agent.py
+++ b/flo_ai/models/flo_llm_agent.py
@@ -1,7 +1,6 @@
 from langchain_core.runnables import Runnable
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from flo_ai.helpers.utils import randomize_name
 from flo_ai.models.flo_executable import ExecutableFlo
 from flo_ai.state.flo_session import FloSession
 from typing import Union
@@ -25,7 +24,7 @@ def __init__(self,
                     llm: Union[BaseLanguageModel, None] =  None) -> None:
             prompt: Union[ChatPromptTemplate, str] = config.job
         
-            self.name: str = randomize_name(config.name)
+            self.name: str = config.name
             self.llm = llm if llm is not None else session.llm
             # TODO improve to add more context of what other agents are available
             system_prompts = [("system", "You are a {}".format(config.role)), ("system", prompt)] if config.role is not None else [("system", prompt)]
diff --git a/flo_ai/models/flo_planner.py b/flo_ai/models/flo_planner.py
deleted file mode 100644
index f7acb418..00000000
--- a/flo_ai/models/flo_planner.py
+++ /dev/null
@@ -1,157 +0,0 @@
-from langchain_core.language_models import BaseLanguageModel
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from flo_ai.models.flo_executable import ExecutableFlo
-from flo_ai.state.flo_state import TeamFloAgentStateWithPlan
-from langchain_core.pydantic_v1 import BaseModel, Field
-from typing import Union
-from langgraph.graph.graph import CompiledGraph
-from flo_ai.models.flo_routed_team import FloRoutedTeam
-from langgraph.graph import StateGraph
-from flo_ai.state.flo_session import FloSession
-from flo_ai.helpers.utils import randomize_name
-
-
-class Response(BaseModel):
-    """Response to user."""
-    response: str
-
-class Plan(BaseModel):
-    """Plan to follow in future"""
-
-    steps: list[str] = Field(
-        description="different steps to follow, should be in sorted order"
-    )
-
-class Act(BaseModel):
-    """Action to perform."""
-    action: Union[Response, Plan] = Field(
-        description="Action to perform. If you want to respond to user, use Response. "
-        "If you need to further use tools to get the answer, use Plan."
-    )
-
-class FloPlanner(ExecutableFlo):
-    def __init__(self, name: str, graph: CompiledGraph) -> None:
-        super().__init__(name, graph)
-
-    def draw(self, xray=True):
-        return self.graph.get_graph(xray=xray).draw_mermaid_png()
-    
-    def stream(self, work, config = None):
-        return self.graph.stream({
-             "input": work
-        }, config)
-    
-    def invoke(self, work):
-        return self.graph.invoke({
-             "input": work
-        })
-
-class FloPlannerBuilder:
-
-    def __init__(self, 
-                 session: FloSession,
-                 name: str,
-                 team: FloRoutedTeam,
-                 planner_prompt: Union[ChatPromptTemplate, None] = None,
-                 replanner_prompt: Union[ChatPromptTemplate, None] = None,
-                 llm: Union[BaseLanguageModel, None] = None) -> None:
-        self.planner_name = randomize_name(name)
-        self.re_planner_name = randomize_name("{}-{}".format("replan", name))
-        self.session = session
-        self.llm = llm if llm is not None else session.llm
-        self.team = team
-        planner_prompt = ChatPromptTemplate.from_messages(
-            [
-                (
-                    "system",
-                    """For the given objective, come up with a simple step by step plan. \
-        This plan should involve individual tasks, that if executed correctly will yield the correct answer. Do not add any superfluous steps. \
-        The result of the final step should be the final answer. Make sure that each step has all the information needed - do not skip steps.""",
-                ),
-                MessagesPlaceholder(variable_name="messages"),
-            ]
-        ) if planner_prompt is None else planner_prompt
-        self.planner =  planner_prompt | self.llm.with_structured_output(Plan)
-
-        self.replanner_prompt = ChatPromptTemplate.from_template(
-                """For the given objective, come up with a simple step by step plan. \
-            This plan should involve individual tasks, that if executed correctly will yield the correct answer. Do not add any superfluous steps. \
-            The result of the final step should be the final answer. Make sure that each step has all the information needed - do not skip steps.
-
-            Your objective was this:
-            {input}
-
-            Your original plan was this:
-            {plan}
-
-            You have currently done the follow steps:
-            {past_steps}
-
-            Update your plan accordingly. If no more steps are needed and you can return to the user, then do that. 
-            Otherwise if there are still steps to complete, fill out the plan. Only add steps to the plan that still NEED to be done. 
-            Do not return previously done steps as part of the plan."""
-        ) if replanner_prompt is None else replanner_prompt
-        self.replanner =  self.replanner_prompt | self.llm.with_structured_output(Act)
-
-    def execute_step(self, state: TeamFloAgentStateWithPlan):
-        plan = state["plan"]
-        plan_str = "\n".join(f"{i+1}. {step}" for i, step in enumerate(plan))
-        task = plan[0]
-        task_formatted = f"""For the following plan:
-            {plan_str}\n\nYou are tasked with executing step {1}, {task}."""
-        agent_response = self.team.invoke(task_formatted)
-        return {
-            "past_steps": (task, agent_response["messages"][-1].content),
-        }
-
-    def plan_step(self, state: TeamFloAgentStateWithPlan):
-        plan =  self.planner.invoke({"messages": [("user", state["input"])]})
-        if self.session is not None:
-            self.session.append(self.planner_name)
-        return {"plan": plan.steps}
-    
-    def replan_step(self, state: TeamFloAgentStateWithPlan):
-        output = self.replanner.invoke(state)
-        if self.session is not None:
-            self.session.append(self.re_planner_name)
-        if isinstance(output.action, Response):
-            return { "response": output.action.response }
-        else:
-            return { "plan": output.action.steps }
-        
-    def should_end(self, state: TeamFloAgentStateWithPlan):
-        if "response" in state and state["response"]:
-            return "__end__"
-        else:
-            return self.team.name
-
-    def build(self):
-        workflow = StateGraph(TeamFloAgentStateWithPlan)
-
-        # Add the plan node
-        workflow.add_node(self.planner_name, self.plan_step)
-
-        # Add the execution step
-        workflow.add_node(self.team.name, self.execute_step)
-
-        # Add a replan node
-        workflow.add_node(self.re_planner_name, self.replan_step)
-
-        workflow.set_entry_point(self.planner_name)
-
-        # From plan we go to agent
-        workflow.add_edge(self.planner_name, self.team.name)
-
-        # From agent, we replan
-        workflow.add_edge(self.team.name, self.re_planner_name)
-
-        workflow.add_conditional_edges(
-            self.re_planner_name,
-            # Next, we pass in the function that will determine which node is called next.
-            self.should_end,
-        )
-
-        # Finally, we compile it!
-        # This compiles it into a LangChain Runnable,
-        # meaning you can use it as you would any other runnable
-        return FloPlanner(self.planner_name, workflow.compile())
\ No newline at end of file
diff --git a/flo_ai/models/flo_rag.py b/flo_ai/models/flo_rag.py
index 7c73b261..a66576d7 100644
--- a/flo_ai/models/flo_rag.py
+++ b/flo_ai/models/flo_rag.py
@@ -9,7 +9,6 @@
 from flo_ai.models.flo_executable import ExecutableFlo
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain import hub
-from flo_ai.helpers.utils import randomize_name
 
 class FloRag(ExecutableFlo):
     def __init__(self, 
@@ -28,7 +27,7 @@ def __init__(self,
                  tools: list[Tool], 
                  llm: BaseLanguageModel,
                  prompt: Optional[ChatPromptTemplate] = None) -> None:
-        self.name = randomize_name(name)
+        self.name = name
         self.llm = llm
         self.tools = tools
         self.prompt = hub.pull("rlm/rag-prompt") if prompt is None else prompt
diff --git a/flo_ai/models/flo_reflection_agent.py b/flo_ai/models/flo_reflection_agent.py
index 2d531581..97b5a287 100644
--- a/flo_ai/models/flo_reflection_agent.py
+++ b/flo_ai/models/flo_reflection_agent.py
@@ -4,7 +4,6 @@
 from flo_ai.state.flo_session import FloSession
 from flo_ai.models.flo_executable import ExecutableFlo
 from langchain_core.language_models import BaseLanguageModel
-from flo_ai.helpers.utils import randomize_name
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from flo_ai.models.flo_executable import ExecutableType
 from langchain_core.output_parsers import StrOutputParser
@@ -23,7 +22,7 @@ def __init__(self,
                     llm: Union[BaseLanguageModel, None] =  None) -> None:
             
             prompt_message: Union[ChatPromptTemplate, str] = config.job
-            self.name: str = randomize_name(config.name)
+            self.name: str = config.name
             self.llm = llm if llm is not None else session.llm
             self.config = config
 
diff --git a/flo_ai/models/flo_team.py b/flo_ai/models/flo_team.py
index e2a7313a..feb56442 100644
--- a/flo_ai/models/flo_team.py
+++ b/flo_ai/models/flo_team.py
@@ -1,10 +1,9 @@
 from flo_ai.models.flo_member import FloMember
-from flo_ai.helpers.utils import randomize_name
 from flo_ai.yaml.config import TeamConfig
 
 class FloTeam():
     def __init__(self, team_config: TeamConfig, members: list[FloMember]) -> None:
-        self.name = randomize_name(team_config.name)
+        self.name = team_config.name
         self.config = team_config
         self.members = members
 
diff --git a/flo_ai/models/flo_tool_agent.py b/flo_ai/models/flo_tool_agent.py
index 365e2452..50ac9650 100644
--- a/flo_ai/models/flo_tool_agent.py
+++ b/flo_ai/models/flo_tool_agent.py
@@ -1,5 +1,4 @@
 from langchain_core.runnables import Runnable
-from flo_ai.helpers.utils import randomize_name
 from flo_ai.models.flo_executable import ExecutableFlo
 from flo_ai.state.flo_session import FloSession
 from flo_ai.yaml.config import AgentConfig
diff --git a/flo_ai/router/flo_custom_router.py b/flo_ai/router/flo_custom_router.py
index bea0fffa..05b5072e 100644
--- a/flo_ai/router/flo_custom_router.py
+++ b/flo_ai/router/flo_custom_router.py
@@ -5,7 +5,6 @@
 from flo_ai.models.flo_routed_team import FloRoutedTeam
 from flo_ai.models.flo_team import FloTeam
 from flo_ai.state.flo_session import FloSession
-from flo_ai.helpers.utils import agent_name_from_randomized_name, randomize_name
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain_core.output_parsers.openai_functions import JsonOutputFunctionsParser
 
@@ -13,7 +12,7 @@ class FloCustomRouter(FloRouter):
 
     def __init__(self, session: FloSession, config: TeamConfig, flo_team: FloTeam):
         self.llm = session.llm
-        super().__init__(session=session, name=randomize_name(config.name),
+        super().__init__(session=session, name=config.name,
                           flo_team=flo_team, executor=None, config=config)
         self.router_config = config.router
     
@@ -66,7 +65,7 @@ def build_agent_graph(self):
         workflow = StateGraph(TeamFloAgentState)
         
         for flo_agent_node in flo_agent_nodes:
-            agent_name = agent_name_from_randomized_name(flo_agent_node.name)
+            agent_name = flo_agent_node.name
             workflow.add_node(agent_name, flo_agent_node.func)
 
         router_config = self.router_config
@@ -97,7 +96,7 @@ def build_team_graph(self):
         super_graph = StateGraph(TeamFloAgentState)
 
         for flo_team_chain in flo_team_entry_chains:
-            agent_name = agent_name_from_randomized_name(flo_team_chain.name)
+            agent_name = flo_team_chain.name
             super_graph.add_node(agent_name, flo_team_chain.func)
 
         router_config = self.router_config
diff --git a/flo_ai/router/flo_linear.py b/flo_ai/router/flo_linear.py
index 49add530..1d40addd 100644
--- a/flo_ai/router/flo_linear.py
+++ b/flo_ai/router/flo_linear.py
@@ -6,12 +6,11 @@
 from flo_ai.models.flo_team import FloTeam
 from flo_ai.state.flo_session import FloSession
 from flo_ai.models.flo_executable import ExecutableType
-from flo_ai.helpers.utils import agent_name_from_randomized_name, randomize_name
 
 class FloLinear(FloRouter):
 
     def __init__(self, session: FloSession, config: TeamConfig, flo_team: FloTeam):
-        super().__init__(session=session, name=randomize_name(config.name),
+        super().__init__(session=session, name=config.name,
                           flo_team=flo_team, executor=None, config=config)
         self.router_config = config.router
     
@@ -21,29 +20,32 @@ def build_agent_graph(self):
         workflow = StateGraph(TeamFloAgentState)
         
         for flo_node in flo_agent_nodes:
-            agent_name = agent_name_from_randomized_name(flo_node.name)
+            agent_name = flo_node.name
             workflow.add_node(agent_name, flo_node.func)
             
         if self.router_config.edges is None:
-            start_node_name = agent_name_from_randomized_name(flo_agent_nodes[0].name)
-            end_node_name = agent_name_from_randomized_name(flo_agent_nodes[-1].name)
-            workflow.add_edge(START, start_node_name)
+            start_node = flo_agent_nodes[0]
+            end_node = flo_agent_nodes[-1]
+            workflow.add_edge(START, start_node.name)
             for i in range(len(flo_agent_nodes) - 1):
-                agent1_name = agent_name_from_randomized_name(flo_agent_nodes[i].name)
-                agent2_name = agent_name_from_randomized_name(flo_agent_nodes[i+1].name)
-                if (flo_agent_nodes[i].kind == ExecutableType.reflection):
-                    self.add_reflection_edge(workflow, flo_agent_nodes[i], flo_agent_nodes[i+1])
-                else:
-                    if (flo_agent_nodes[i+1].kind != ExecutableType.reflection and flo_agent_nodes[i].kind != ExecutableType.delegator):
-                        if flo_agent_nodes[i+1].kind == ExecutableType.delegator:
-                            self.add_delegation_edge(workflow, flo_agent_nodes[i], flo_agent_nodes[i+1], flo_agent_nodes[i+2] if  (i+2)<len(flo_agent_nodes) else END)
-                        else:
-                            workflow.add_edge(agent1_name, agent2_name)
-            if (flo_agent_nodes[-1].kind == ExecutableType.reflection):
-                self.add_reflection_edge(workflow, flo_agent_nodes[-1], END)
-            else: 
-                if (flo_agent_nodes[-1].kind != ExecutableType.delegator):
-                    workflow.add_edge(end_node_name, END)
+                parent_node = flo_agent_nodes[i]
+                child_node = flo_agent_nodes[i+1]
+                next_node = flo_agent_nodes[i+2] if (i+2) < len(flo_agent_nodes) else END
+
+                if (parent_node.kind == ExecutableType.reflection):
+                    self.add_reflection_edge(workflow, parent_node, child_node)
+                    continue
+                if (child_node.kind == ExecutableType.delegator):
+                    self.add_delegation_edge(workflow, parent_node, child_node, next_node)
+                    continue
+
+                if (child_node.kind != ExecutableType.reflection and parent_node.kind != ExecutableType.delegator):
+                    workflow.add_edge(parent_node.name, child_node.name)
+                    
+            if (end_node.kind == ExecutableType.reflection):
+                self.add_reflection_edge(workflow, end_node, END)
+            elif (end_node.kind != ExecutableType.delegator):
+                    workflow.add_edge(end_node.name, END)
         else:
             workflow.add_edge(START, self.router_config.start_node)
             for edge in self.router_config.edges:
@@ -60,16 +62,16 @@ def build_team_graph(self):
         super_graph = StateGraph(TeamFloAgentState)
         # First add the nodes, which will do the work
         for flo_team_chain in flo_team_entry_chains:
-            agent_name = agent_name_from_randomized_name(flo_team_chain.name)
+            agent_name = flo_team_chain.name
             super_graph.add_node(agent_name, flo_team_chain.func)
 
         if self.router_config.edges is None:
-            start_node_name = agent_name_from_randomized_name(flo_team_entry_chains[0].name)
-            end_node_name = agent_name_from_randomized_name(flo_team_entry_chains[-1].name)
+            start_node_name = flo_team_entry_chains[0].name
+            end_node_name = flo_team_entry_chains[-1].name
             super_graph.add_edge(START, start_node_name)
             for i in range(len(flo_team_entry_chains) - 1):
-                agent1_name = agent_name_from_randomized_name(flo_team_entry_chains[i].name)
-                agent2_name = agent_name_from_randomized_name(flo_team_entry_chains[i+1].name)
+                agent1_name = flo_team_entry_chains[i].name
+                agent2_name = flo_team_entry_chains[i+1].name
                 super_graph.add_edge(agent1_name, agent2_name)
             super_graph.add_edge(end_node_name, END)
         else:
diff --git a/flo_ai/router/flo_llm_router.py b/flo_ai/router/flo_llm_router.py
index 1d1c06a1..c06945bf 100644
--- a/flo_ai/router/flo_llm_router.py
+++ b/flo_ai/router/flo_llm_router.py
@@ -5,7 +5,6 @@
 from langchain_core.runnables import Runnable
 from flo_ai.state.flo_session import FloSession
 from flo_ai.constants.prompt_constants import FLO_FINISH
-from flo_ai.helpers.utils import randomize_name
 from flo_ai.router.flo_router import FloRouter
 from flo_ai.models.flo_team import FloTeam
 from flo_ai.models.flo_routed_team import FloRoutedTeam
@@ -75,7 +74,7 @@ def __init__(self,
                     router_prompt: ChatPromptTemplate = None,
                     llm: Union[BaseLanguageModel, None] = None) -> None:
     
-            self.name = randomize_name(team_config.router.name)
+            self.name = team_config.router.name
             self.session = session
             self.llm = llm if llm is not None else session.llm
             self.flo_team = flo_team
diff --git a/flo_ai/router/flo_router.py b/flo_ai/router/flo_router.py
index d2634fd7..4dce706d 100644
--- a/flo_ai/router/flo_router.py
+++ b/flo_ai/router/flo_router.py
@@ -127,6 +127,7 @@ def add_reflection_edge(self, workflow: StateGraph, reflection_node: FloNode, ne
         next = nextNode if isinstance(nextNode, str) else nextNode.name
         
         workflow.add_node(INTERNAL_NODE_REFLECTION_MANAGER, functools.partial(self.update_reflection_state, reflection_agent_name=reflection_agent_name))
+        
         workflow.add_edge(to_agent_name, INTERNAL_NODE_REFLECTION_MANAGER)
         workflow.add_conditional_edges(
             INTERNAL_NODE_REFLECTION_MANAGER, 
diff --git a/flo_ai/router/flo_supervisor.py b/flo_ai/router/flo_supervisor.py
index f1fc5577..a888f75b 100644
--- a/flo_ai/router/flo_supervisor.py
+++ b/flo_ai/router/flo_supervisor.py
@@ -5,7 +5,6 @@
 from langchain_core.runnables import Runnable
 from flo_ai.state.flo_session import FloSession
 from flo_ai.constants.prompt_constants import FLO_FINISH
-from flo_ai.helpers.utils import randomize_name
 from flo_ai.router.flo_llm_router import FloLLMRouter, StateUpdateComponent
 from flo_ai.models.flo_team import FloTeam
 from flo_ai.yaml.config import TeamConfig
@@ -39,8 +38,7 @@ def __init__(self,
                     team_config: TeamConfig,
                     flo_team: FloTeam,
                     llm: Union[BaseLanguageModel, None] = None) -> None:
-            # TODO add validation for reporteess
-            self.name = randomize_name(team_config.router.name)
+            self.name = team_config.router.name
             self.session = session
             self.llm = llm if llm is not None else session.llm
             self.flo_team = flo_team
diff --git a/flo_ai/state/flo_session.py b/flo_ai/state/flo_session.py
index 17c98dc0..280270cc 100644
--- a/flo_ai/state/flo_session.py
+++ b/flo_ai/state/flo_session.py
@@ -5,6 +5,7 @@
 from flo_ai.common.flo_logger import session_logger, FloLogger
 from flo_ai.common.flo_langchain_logger import FloLangchainLogger
 from flo_ai.yaml.config import FloRoutedTeamConfig, FloAgentConfig
+from flo_ai.helpers.utils import random_str
 
 from typing import Optional
 
@@ -16,7 +17,8 @@ def __init__(self,
                  max_loop: int = 3, 
                  log_level: Optional[str] = "INFO",
                  custom_langchainlog_handler: Optional[FloLangchainLogger] = None) -> None:
-        self.session_id = str(uuid.uuid4())
+        
+        self.session_id = str(random_str(16))
         self.llm = llm
         self.tools = dict()
         self.counter = dict()
diff --git a/flo_ai/yaml/validators.py b/flo_ai/yaml/validators.py
new file mode 100644
index 00000000..9974b5f3
--- /dev/null
+++ b/flo_ai/yaml/validators.py
@@ -0,0 +1,13 @@
+import re
+
+name_regex = r'^[a-zA-Z0-9-_]+$'
+
+class DuplicateStringError(Exception):
+    pass
+
+class InvalidStringError(Exception):
+    pass
+
+def raise_for_name_error(string):
+    if not re.match(name_regex, string):
+        raise InvalidStringError("Name must contain only alphanumeric characters and hyphens.")
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index 043e95d0..9d3051d4 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
@@ -205,17 +205,6 @@ doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphin
 test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
 trio = ["trio (>=0.23)"]
 
-[[package]]
-name = "appdirs"
-version = "1.4.4"
-description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
-optional = false
-python-versions = "*"
-files = [
-    {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"},
-    {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"},
-]
-
 [[package]]
 name = "appnope"
 version = "0.1.4"
@@ -343,6 +332,26 @@ files = [
 tests = ["pytest (>=3.2.1,!=3.3.0)"]
 typecheck = ["mypy"]
 
+[[package]]
+name = "behave"
+version = "1.2.6"
+description = "behave is behaviour-driven development, Python style"
+optional = false
+python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
+files = [
+    {file = "behave-1.2.6-py2.py3-none-any.whl", hash = "sha256:ebda1a6c9e5bfe95c5f9f0a2794e01c7098b3dde86c10a95d8621c5907ff6f1c"},
+    {file = "behave-1.2.6.tar.gz", hash = "sha256:b9662327aa53294c1351b0a9c369093ccec1d21026f050c3bd9b3e5cccf81a86"},
+]
+
+[package.dependencies]
+parse = ">=1.8.2"
+parse-type = ">=0.4.2"
+six = ">=1.11"
+
+[package.extras]
+develop = ["coverage", "invoke (>=0.21.0)", "modernize (>=0.5)", "path.py (>=8.1.2)", "pathlib", "pycmd", "pylint", "pytest (>=3.0)", "pytest-cov", "tox"]
+docs = ["sphinx (>=1.6)", "sphinx-bootstrap-theme (>=0.6)"]
+
 [[package]]
 name = "build"
 version = "1.2.1"
@@ -660,35 +669,6 @@ files = [
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
-[[package]]
-name = "cmake"
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-description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
-optional = false
-python-versions = ">=3.7"
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-
-[package.extras]
-test = ["coverage (>=4.2)", "pytest (>=3.0.3)", "pytest-cov (>=2.4.0)"]
-
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -749,50 +729,6 @@ files = [
 marshmallow = ">=3.18.0,<4.0.0"
 typing-inspect = ">=0.4.0,<1"
 
-[[package]]
-name = "datasets"
-version = "2.21.0"
-description = "HuggingFace community-driven open-source library of datasets"
-optional = false
-python-versions = ">=3.8.0"
-files = [
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-
-[package.dependencies]
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-dill = ">=0.3.0,<0.3.9"
-filelock = "*"
-fsspec = {version = ">=2023.1.0,<=2024.6.1", extras = ["http"]}
-huggingface-hub = ">=0.21.2"
-multiprocess = "*"
-numpy = ">=1.17"
-packaging = "*"
-pandas = "*"
-pyarrow = ">=15.0.0"
-pyyaml = ">=5.1"
-requests = ">=2.32.2"
-tqdm = ">=4.66.3"
-xxhash = "*"
-
-[package.extras]
-apache-beam = ["apache-beam (>=2.26.0)"]
-audio = ["librosa", "soundfile (>=0.12.1)", "soxr (>=0.4.0)"]
-benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
-dev = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0)", "sqlalchemy", "tensorflow (>=2.16.0)", "tensorflow (>=2.6.0)", "tensorflow (>=2.6.0)", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "transformers (>=4.42.0)", "typing-extensions (>=4.6.1)", "zstandard"]
-docs = ["s3fs", "tensorflow (>=2.6.0)", "torch", "transformers"]
-jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
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-quality = ["ruff (>=0.3.0)"]
-s3 = ["s3fs"]
-tensorflow = ["tensorflow (>=2.6.0)"]
-tensorflow-gpu = ["tensorflow (>=2.6.0)"]
-tests = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0)", "sqlalchemy", "tensorflow (>=2.16.0)", "tensorflow (>=2.6.0)", "tiktoken", "torch (>=2.0.0)", "transformers (>=4.42.0)", "typing-extensions (>=4.6.1)", "zstandard"]
-tests-numpy2 = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (<8.0.0)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0)", "sqlalchemy", "tiktoken", "torch (>=2.0.0)", "typing-extensions (>=4.6.1)", "zstandard"]
-torch = ["torch"]
-vision = ["Pillow (>=9.4.0)"]
-
 [[package]]
 name = "db"
 version = "0.1.1"
@@ -861,45 +797,6 @@ files = [
     {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
 ]
 
-[[package]]
-name = "deepeval"
-version = "0.21.78"
-description = "The open-source evaluation framework for LLMs."
-optional = false
-python-versions = "*"
-files = [
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-[package.dependencies]
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-grpcio = "1.63.0"
-importlib-metadata = ">=6.0.2"
-langchain = "*"
-langchain-core = "*"
-langchain-openai = "*"
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-opentelemetry-exporter-otlp-proto-grpc = "1.24.0"
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-protobuf = "4.25.1"
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-requests = "*"
-rich = "*"
-sentry-sdk = "*"
-tabulate = "*"
-tenacity = ">=8.4.1,<8.5.0"
-tqdm = "*"
-typer = "*"
-
-[package.extras]
-dev = ["black"]
-
 [[package]]
 name = "defusedxml"
 version = "0.7.1"
@@ -928,21 +825,6 @@ wrapt = ">=1.10,<2"
 [package.extras]
 dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
 
-[[package]]
-name = "dill"
-version = "0.3.8"
-description = "serialize all of Python"
-optional = false
-python-versions = ">=3.8"
-files = [
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-
-[package.extras]
-graph = ["objgraph (>=1.7.2)"]
-profile = ["gprof2dot (>=2022.7.29)"]
-
 [[package]]
 name = "distro"
 version = "1.9.0"
@@ -974,16 +856,6 @@ idna = ["idna (>=3.6)"]
 trio = ["trio (>=0.23)"]
 wmi = ["wmi (>=1.5.1)"]
 
-[[package]]
-name = "docx2txt"
-version = "0.8"
-description = "A pure python-based utility to extract text and images from docx files."
-optional = false
-python-versions = "*"
-files = [
-    {file = "docx2txt-0.8.tar.gz", hash = "sha256:2c06d98d7cfe2d3947e5760a57d924e3ff07745b379c8737723922e7009236e5"},
-]
-
 [[package]]
 name = "exceptiongroup"
 version = "1.2.2"
@@ -998,20 +870,6 @@ files = [
 [package.extras]
 test = ["pytest (>=6)"]
 
-[[package]]
-name = "execnet"
-version = "2.1.1"
-description = "execnet: rapid multi-Python deployment"
-optional = false
-python-versions = ">=3.8"
-files = [
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-]
-
-[package.extras]
-testing = ["hatch", "pre-commit", "pytest", "tox"]
-
 [[package]]
 name = "executing"
 version = "2.0.1"
@@ -1170,9 +1028,6 @@ files = [
     {file = "fsspec-2024.6.1.tar.gz", hash = "sha256:fad7d7e209dd4c1208e3bbfda706620e0da5142bebbd9c384afb95b07e798e49"},
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-[package.dependencies]
-aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
-
 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
@@ -1571,17 +1426,6 @@ enabler = ["pytest-enabler (>=2.2)"]
 test = ["jaraco.test (>=5.4)", "pytest (>=6,!=8.1.*)", "zipp (>=3.17)"]
 type = ["pytest-mypy"]
 
-[[package]]
-name = "iniconfig"
-version = "2.0.0"
-description = "brain-dead simple config-ini parsing"
-optional = false
-python-versions = ">=3.7"
-files = [
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-
 [[package]]
 name = "ipykernel"
 version = "6.29.5"
@@ -1672,23 +1516,6 @@ docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alab
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
 testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
-[[package]]
-name = "jinja2"
-version = "3.1.4"
-description = "A very fast and expressive template engine."
-optional = false
-python-versions = ">=3.7"
-files = [
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-
-[package.dependencies]
-MarkupSafe = ">=2.0"
-
-[package.extras]
-i18n = ["Babel (>=2.7)"]
-
 [[package]]
 name = "jiter"
 version = "0.5.0"
@@ -1759,17 +1586,6 @@ files = [
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 ]
 
-[[package]]
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-description = "Lightweight pipelining with Python functions"
-optional = false
-python-versions = ">=3.8"
-files = [
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-
 [[package]]
 name = "jsonpatch"
 version = "1.33"
@@ -1795,41 +1611,6 @@ files = [
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-[[package]]
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-version = "4.23.0"
-description = "An implementation of JSON Schema validation for Python"
-optional = false
-python-versions = ">=3.8"
-files = [
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-
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-jsonschema-specifications = ">=2023.03.6"
-referencing = ">=0.28.4"
-rpds-py = ">=0.7.1"
-
-[package.extras]
-format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
-format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=24.6.0)"]
-
-[[package]]
-name = "jsonschema-specifications"
-version = "2023.12.1"
-description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
-optional = false
-python-versions = ">=3.8"
-files = [
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-
 [[package]]
 name = "jupyter-client"
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@@ -2129,34 +1910,6 @@ pydantic = [
 ]
 requests = ">=2,<3"
 
-[[package]]
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-description = "a modern parsing library"
-optional = false
-python-versions = ">=3.8"
-files = [
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-atomic-cache = ["atomicwrites"]
-interegular = ["interegular (>=0.3.1,<0.4.0)"]
-nearley = ["js2py"]
-regex = ["regex"]
-
-[[package]]
-name = "lit"
-version = "18.1.8"
-description = "A Software Testing Tool"
-optional = false
-python-versions = "*"
-files = [
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-]
-
 [[package]]
 name = "markdown-it-py"
 version = "3.0.0"
@@ -2181,75 +1934,6 @@ profiling = ["gprof2dot"]
 rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
 testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
 
-[[package]]
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-version = "2.1.5"
-description = "Safely add untrusted strings to HTML/XML markup."
-optional = false
-python-versions = ">=3.7"
-files = [
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 tests = ["pytest (>=4.6)"]
 
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-
 [[package]]
 name = "yarl"
 version = "1.9.6"
@@ -6267,4 +4634,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10, <3.13"
-content-hash = "075546648313f58b4aff34614023fecf8b88554814494216e30772b09837cd92"
+content-hash = "b59070159c750fac77ee0236f0f42c8ebeecdeb42933952cf567062fab076566"
diff --git a/pyproject.toml b/pyproject.toml
index a6ef2896..239b32e7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,6 +15,7 @@ httpx = "0.27.0"
 langchain-community = "0.2.0"
 langchain-experimental = "0.0.59"
 pillow = "^10.3.0"
+pydantic = "^2.9.2"
 
 
 [tool.poetry.group.dev.dependencies]
@@ -29,6 +30,7 @@ langchain-anthropic = "^0.1.22"
 db-sqlite3 = "^0.0.1"
 peewee = "^3.17.6"
 python-dotenv = "^1.0.1"
+behave = "^1.2.6"
 
 [build-system]
 requires = ["poetry-core"]