Merge pull request #611 from MervinPraison/develop

Update PraisonAI version to 2.2.30 across Dockerfiles and related files
2026-11-09 03:39:37 -10:00 · 2025-06-05 18:18:04 +01:00 · 2025-06-05 18:17:47 +01:00 · 2025-06-05 18:16:11 +01:00 · 2025-06-05 18:10:43 +01:00 · 2025-06-05 18:10:25 +01:00
33 changed files with 7351 additions and 4852 deletions
@@ -74,4 +74,5 @@ agents/praisonaiagents/praisonaiagents.egg-info
 .praison
 # Local Netlify folder
 .netlify
-.qodo
+.qodo
+CopilotKit*
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    flask \
-    "praisonai>=2.2.27" \
+    "praisonai>=2.2.30" \
    "praisonai[api]" \
    gunicorn \
    markdown
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=2.2.27" \
+    "praisonai>=2.2.30" \
    "praisonai[chat]" \
    "embedchain[github,youtube]"

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=2.2.27" \
+    "praisonai>=2.2.30" \
    "praisonai[ui]" \
    "praisonai[chat]" \
    "praisonai[realtime]" \
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
    praisonai_tools \
-    "praisonai>=2.2.27" \
+    "praisonai>=2.2.30" \
    "praisonai[ui]" \
    "praisonai[crewai]"

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions

 All Docker images use consistent, up-to-date versions:
- PraisonAI: `>=2.2.27`
+- PraisonAI: `>=2.2.30`
 - PraisonAI Agents: `>=0.0.92`
 - Python: `3.11-slim`

@@ -218,7 +218,7 @@ docker-compose up -d
 ### Version Pinning
 To use specific versions, update the Dockerfile:
 ```dockerfile
-RUN pip install "praisonai==2.2.27" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.30" "praisonaiagents==0.0.92"
 ```

 ## 🌐 Production Deployment
@@ -14,7 +14,11 @@
    {
      "cell_type": "markdown",
      "source": [
+
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Code_Analysis_Agent.ipynb)\n"
+=======
        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/code_analysis_agent.ipynb)\n"
+
      ],
      "metadata": {
        "id": "P3X8PXPyojnL"
@@ -456,4 +460,8 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+
+}
+=======
+}
+
@@ -0,0 +1,605 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "PnWDOzZelHlE",
+   "metadata": {
+    "id": "PnWDOzZelHlE"
+   },
+   "source": [
+    "# 🌱 Gemma 2B Instruction Agent\n",
+    "\n",
+    "**Goal:** You will learn how to do data prep, how to train, how to run the model, and how to save it using Google’s `gemma-2b-it` open-source model.\n",
+    "\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4qumJb5qs9CF",
+   "metadata": {
+    "id": "4qumJb5qs9CF"
+   },
+   "source": [
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Gemma2B_Instruction_Agent.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "qdsqiQDosXmA",
+   "metadata": {
+    "id": "qdsqiQDosXmA"
+   },
+   "source": [
+    "#  Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6G4RPDe3lHlH",
+   "metadata": {
+    "id": "6G4RPDe3lHlH"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install transformers accelerate datasets bitsandbytes -q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "mop0KNO7sdEl",
+   "metadata": {
+    "id": "mop0KNO7sdEl"
+   },
+   "source": [
+    "# Tools & Model Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "spOeRX2UlHlI",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 369,
+     "referenced_widgets": [
+      "a2db67e368d94f579c59b36c6ce7af33",
+      "5816fa4288cd42d6b15094e2c79f760f",
+      "5d6fe9b464f944cdbb00b9936669e737",
+      "19920e5b8157423ba5d173dd376e52e6",
+      "bd7157974bc5485ab5b6db54facb9721",
+      "0063e7320c98440d91ae475596c5cd90",
+      "a0f045bc54754d5a85ed6bf7b6b33c1a",
+      "9174c5aff19b4caab8a38fbf3acb2449",
+      "3de0f0b7ecb34c488145b24e30418e4b",
+      "d4457543819d468eb916f126382184b0",
+      "08f90c04f0de4b1db57624db4b43e17d",
+      "269373e971704b9ea2e10e261b1c56d1",
+      "d54d35c945ce4f75af6f83f37dd6af6c",
+      "b828857fecdc40fa91f6a8633f0f20b4",
+      "b5e70949aea24ae9a75e45aa291cc3aa",
+      "9c556838b24246a8b39b1f3f9babfe4a",
+      "062b64d17b7043c1bccc419280c9db0a",
+      "138ae14327184f65896c29a424bad18d",
+      "cb65fa660df54218ad804815dfba4071",
+      "93d2454f1b564896bef50906e859c660",
+      "64e7861e6afc4b57b28063096533aac8",
+      "13fe0b8a8fd14a3684ae5d9beefb3f76",
+      "d1ec61e51d4c4b07b301959c4919f2a2",
+      "a45e9ed3d46645a18ca1f6b06f875ec9",
+      "a364770555cf40fdab5ff7bb85997127",
+      "12b3225aeb8c4f61b23b588738d48683",
+      "dca4952bae4c49ddb2fec2c36726a34d",
+      "163df8921f04425caed240465d8bd8b7",
+      "1a09718a80cd4bb5a9e985a3f599c614",
+      "71c719d57e4f4e23bd520f80e2e07e33",
+      "5e8c8eb7eba948fc8a39b601e9ac658e",
+      "db4fa2c97b7247c4826a1a9f69c1bed3",
+      "42a6f9c136584cc98fc2be973d9edaec",
+      "c1bc8cf2ed6542c49c1940673a662b1d",
+      "365b69634e2846a09b91de6b61d0719b",
+      "13eb69bc1c3c432db6c504856f3a0f7a",
+      "56444404d97549848ed729ee8bc14ec8",
+      "f0303454544d42a1ada2213a085a4ac0",
+      "c717969ed1b145a9af67d74482d49eab",
+      "9e9a826dfb264e44aa69c553d978729d",
+      "f2b4ff56a96c4d7e9f56668f6b7b5213",
+      "f71f0a3ccc654df288d7489abca0ffc0",
+      "5ab0574ed1db47e9b6f43e27acd6c2b2",
+      "426fae423dbb43ef9252e6fcfb099c6f",
+      "2e1c3ca06b2948f0bcf53a975dd67aec",
+      "3685a74a466a4ca0b5f05e7aee631d74",
+      "7bda5a4e803c4cbc8da67da8a565725b",
+      "5450fde9a54d450d90d14b009047b522",
+      "e827839bf4d34d2193776d1ee90d27ad",
+      "3d9854af016a4c3c8a582fc7f0f0b0bf",
+      "7045a11d5f0f4b2d8ff224bb76a9c943",
+      "32e568b25c2d447a8fa63055cb44c75c",
+      "ebd9d55db82542e5ae6bb9e715e36a6a",
+      "798a57883792465ab024dc19a58a54be",
+      "c9d1133770b44b22bda89f3d0eecd4f8",
+      "c040fa1bbd9f442884776fdf33efb4a2",
+      "c57e497d4fd546ff950eb2c898b94c88",
+      "5ae22935b7374219a16272cef31d6d9e",
+      "aae2686ee64740218d046ec37e50b584",
+      "87b4c953163a4cff8a88b76f40da9de8",
+      "30ee0e766fae44bfb570ae8adfba1a54",
+      "1c2db67dfc904e30a4013d9fe0253a4c",
+      "f6c5380bba0f40a4bea9b78d205eb726",
+      "0de9f765500b45428f828ad173dd279e",
+      "0663e5cfc20c496993b7c0054ce3d1a1",
+      "7ce5d369129b461681f424e52415e534",
+      "dfa90d76c670436a80f1a4fd0971b171",
+      "0694216654ac41909d567a8c4af247de",
+      "658048f25b4a4ad6a3ee66e97a542cc9",
+      "0edccfd243f940cfad9723b1d3f5a97c",
+      "32d473040a6744718b1b35543f2b3a0f",
+      "eadbfe846763427e98a40597560d8519",
+      "3cec8231bccb4bcf8d657ee8d2120f79",
+      "8bd43207fa8f4b309590f605ad81354f",
+      "8821912860f04105959ceb42e53de948",
+      "3666b044c757439f948f6942d7682c2f",
+      "d47e7c7504374fee82eef9f8cbe24eff",
+      "9c6d577f113f4f4baaa1f0e001549862",
+      "b0c88d34b0df46b7a3da8db42ff3274e",
+      "e4e4a2d0422b45c09c8691ce14ba9b9a",
+      "d9b1de1b37f14d70bc56a5f6cb468c9b",
+      "cb95b11368984236937fcec7cb689b04",
+      "69e70bfdafd4459491bad75651028626",
+      "a3b9f03c1f014691b5959fb9012c0bab",
+      "088da12bcff64297ad82dc7b2d01709e",
+      "8a69dc8aa84e49b1aabb58eddca1eee3",
+      "263e458b8a5f405bb1ee365fe561309d",
+      "aa3039e03447492da11f86185384470b",
+      "3c22264e11c0402fb07f56a0763dc055",
+      "db2c5746b8cc48d9a059bcef3b55217d",
+      "ad3ab2ab81f6469485a3072bd1ba6a4a",
+      "74ab1c79c43041c7ac314d62a6da04bf",
+      "ad1a3719b1814f6b88202e131ae923c7",
+      "d1cb72251682425fa6ca68341a084f95",
+      "895914fca4204593b1a4314e50b8e5a1",
+      "a8374dc19b294b6d8f5f92bee5196a1a",
+      "b7bef910950949fbac65fd996ad058e2",
+      "ce6b7aadbd904b3caa93d927ced6beb3",
+      "ba6a9e31ddf7419db317a5d27a1567bf",
+      "f271e9c78dcf4ab686caac37f18bd6f7",
+      "9ed57fe81783476986d6cb98d07e9aa1",
+      "8da75a35ab184675b0164682d8217406",
+      "f411c4485b844ceb9b3116f209a6fc86",
+      "60ac9cd6505141348323b39db7314554",
+      "59b2d494f11344879463a3471716bc3f",
+      "46abda4b52224874a93b729f5d4d195b",
+      "37bc81b97d48474aa0fd7e7739654d86",
+      "73da5101d99440abac30dc8c084439d4",
+      "ef790742325743fdb8f7cc6822388185",
+      "5eccfbe5715f4df9a96cffb2573f916f",
+      "3bf293367470430f84f341ef8711460a",
+      "317281ca2af54a148fa4cfd8f6e0f382",
+      "d655ef37643448aa95253143f8552f58",
+      "158e61376dee400e820f47c87d59bac5",
+      "d1fbbe8010e340119bf556c75284e1c2",
+      "eea1731926554e56a85867de15153f82",
+      "766fb34d23b941b2a9e0de153f4cb031",
+      "beb52b95db564105bbd8bc2b2d21c1ce",
+      "2effd1c45bee4278bd9172310f58fed4",
+      "91d6488876284748932b664953c02146",
+      "9157d82cca3f45c5b5a666a3d35d6abe"
+     ]
+    },
+    "id": "spOeRX2UlHlI",
+    "outputId": "093b74d1-9ece-4db5-d177-3aff6e2d6e0b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2db67e368d94f579c59b36c6ce7af33",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/47.0k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "269373e971704b9ea2e10e261b1c56d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.model:   0%|          | 0.00/4.24M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1ec61e51d4c4b07b301959c4919f2a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/17.5M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1bc8cf2ed6542c49c1940673a662b1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/636 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e1c3ca06b2948f0bcf53a975dd67aec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/838 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c040fa1bbd9f442884776fdf33efb4a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors.index.json:   0%|          | 0.00/24.2k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfa90d76c670436a80f1a4fd0971b171",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Fetching 2 files:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c6d577f113f4f4baaa1f0e001549862",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00001-of-00002.safetensors:   0%|          | 0.00/4.99G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c22264e11c0402fb07f56a0763dc055",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00002-of-00002.safetensors:   0%|          | 0.00/241M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f271e9c78dcf4ab686caac37f18bd6f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bf293367470430f84f341ef8711460a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/187 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from huggingface_hub import login\n",
+    "from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer\n",
+    "from datasets import load_dataset\n",
+    "import torch\n",
+    "\n",
+    "login(\"Enter your token here\")\n",
+    "\n",
+    "model_id = \"google/gemma-2-2b-it\"\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    model_id,\n",
+    "    device_map=\"auto\",              # Automatically selects GPU if available\n",
+    "    torch_dtype=torch.float16       # Optimized for performance\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "GaykKFKYspMd",
+   "metadata": {
+    "id": "GaykKFKYspMd"
+   },
+   "source": [
+    "# Yaml Prompt Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "iLBZ7WPAlHlJ",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "iLBZ7WPAlHlJ",
+    "outputId": "a99fc3f2-68e0-4af8-cfbd-3c48a865538f"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You are Qwen, a helpful assistant.\n",
+      "User: What is the capital of France?\n",
+      "Assistant: The capital of France is **Paris**. \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = \"You are Qwen, a helpful assistant.\\nUser: What is the capital of France?\\nAssistant:\"\n",
+    "inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
+    "outputs = model.generate(**inputs, max_new_tokens=100)\n",
+    "response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
+    "print(response)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "XPqtCExSsvhn",
+   "metadata": {
+    "id": "XPqtCExSsvhn"
+   },
+   "source": [
+    "# Use a small sample dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ActfjFx0lHlK",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 49,
+     "referenced_widgets": [
+      "f1028b5dcab4460c81b09802ca7ec89c",
+      "ff33dcccaad945e9bf136d0f72e38d74",
+      "eec3a11b0605435d8d1e55092f5e5b85",
+      "d997b7bc993b4aa58cfafa7c59829ad3",
+      "62e2dc508746419ba3c0405a7199c6dc",
+      "2a7fc59019c749aa98d0e970a9b6b168",
+      "78067f66c8544020aa262c0012aa019b",
+      "f5e511b4279747c0a33b10f499a4f718",
+      "10e248a5730c4c09834b4065b6dd7843",
+      "bbd5a106c1de43e497987648899569e0",
+      "118faf3d1ed142038af4783fb3ba86cc"
+     ]
+    },
+    "id": "ActfjFx0lHlK",
+    "outputId": "f257e553-7baa-4f1e-b531-a6b7dc358665"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1028b5dcab4460c81b09802ca7ec89c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/4 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from datasets import Dataset\n",
+    "\n",
+    "sample_data = {\n",
+    "    'text': [\n",
+    "        'The sun is a star at the center of our solar system.',\n",
+    "        'Photosynthesis is the process by which green plants make food.',\n",
+    "        'Water freezes at 0 degrees Celsius.',\n",
+    "        'The Earth revolves around the sun in 365 days.'\n",
+    "    ]\n",
+    "}\n",
+    "\n",
+    "dataset = Dataset.from_dict(sample_data)\n",
+    "\n",
+    "def tokenize_function(example):\n",
+    "    return tokenizer(example['text'], padding='max_length', truncation=True, max_length=64)\n",
+    "\n",
+    "tokenized_dataset = dataset.map(tokenize_function)\n",
+    "tokenized_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "x7icWpOnszjj",
+   "metadata": {
+    "id": "x7icWpOnszjj"
+   },
+   "source": [
+    "# Inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8mM4PAhRlHlL",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8mM4PAhRlHlL",
+    "outputId": "1ba90639-b847-4cb1-df42-3fdd644e4cb8"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "user\n",
+      "Explain photosynthesis to a child.\n",
+      "model\n",
+      "Imagine plants are like tiny chefs, and they cook their own food! \n",
+      "\n",
+      "They use sunlight, air, and water to make yummy food called sugar.  \n",
+      "\n",
+      "Here's how it works:\n",
+      "\n",
+      "1. **Sunlight:** Plants have special green stuff called chlorophyll that acts like a solar panel, soaking up the sun's energy.\n",
+      "2. **Air:** Plants take in air through tiny holes in their leaves called stomata.  The air has a gas called carbon dioxide.\n",
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_text = \"Explain photosynthesis to a child.\"\n",
+    "chat = tokenizer.apply_chat_template(\n",
+    "    [{\"role\": \"user\", \"content\": input_text}],\n",
+    "    tokenize=False,\n",
+    "    add_generation_prompt=True\n",
+    ")\n",
+    "inputs = tokenizer(chat, return_tensors=\"pt\").to(model.device)\n",
+    "output = model.generate(**inputs, max_new_tokens=100)\n",
+    "print(tokenizer.decode(output[0], skip_special_tokens=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "Fhkfhbjns2oq",
+   "metadata": {
+    "id": "Fhkfhbjns2oq"
+   },
+   "source": [
+    "# Save Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "OuJEt2-jlHlL",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "OuJEt2-jlHlL",
+    "outputId": "f076e950-cce7-4850-edd9-ff1d84a096d6"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('gemma-finetuned-demo/tokenizer_config.json',\n",
+       " 'gemma-finetuned-demo/special_tokens_map.json',\n",
+       " 'gemma-finetuned-demo/chat_template.jinja',\n",
+       " 'gemma-finetuned-demo/tokenizer.model',\n",
+       " 'gemma-finetuned-demo/added_tokens.json',\n",
+       " 'gemma-finetuned-demo/tokenizer.json')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.save_pretrained(\"gemma-finetuned-demo\")\n",
+    "tokenizer.save_pretrained(\"gemma-finetuned-demo\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "uFK1sCzblHlM",
+   "metadata": {
+    "id": "uFK1sCzblHlM"
+   },
+   "source": [
+    "# Output\n",
+    "`Photosynthesis is how plants eat sunlight! 🌞 They use air, water, and sunlight to make food and grow.`"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
@@ -0,0 +1,401 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l7oSysosTfJj"
+      },
+      "source": [
+        "# Predictive Maintenance Workflow using PraisonAIAgents\n",
+        "\n",
+        "This notebook demonstrates how to build a predictive maintenance workflow using multiple AI agents."
+      ],
+      "id": "l7oSysosTfJj"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Predictive_Maintenance_Multi-Agent_Workflow.ipynb)"
+      ],
+      "metadata": {
+        "id": "LH0_JjO7Y1G2"
+      },
+      "id": "LH0_JjO7Y1G2"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Dependencies"
+      ],
+      "metadata": {
+        "id": "ei-yNZSKXgtJ"
+      },
+      "id": "ei-yNZSKXgtJ"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fnkuN8sTTfJq"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install praisonaiagents"
+      ],
+      "id": "fnkuN8sTTfJq"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Set your OpenAI API key"
+      ],
+      "metadata": {
+        "id": "7PUSTQHwX5ia"
+      },
+      "id": "7PUSTQHwX5ia"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "33HivD-LTfJu"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "os.environ['OPENAI_API_KEY'] = 'enter your api key'"
+      ],
+      "id": "33HivD-LTfJu"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Import required modules"
+      ],
+      "metadata": {
+        "id": "D6we7t4nYESB"
+      },
+      "id": "D6we7t4nYESB"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "03z7Pi22TfJw"
+      },
+      "outputs": [],
+      "source": [
+        "from praisonaiagents import Agent, Task, PraisonAIAgents\n",
+        "import time\n",
+        "from typing import Dict, List\n",
+        "import asyncio"
+      ],
+      "id": "03z7Pi22TfJw"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Define helper functions"
+      ],
+      "metadata": {
+        "id": "TzZt0jbjYGgw"
+      },
+      "id": "TzZt0jbjYGgw"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2oMzDsp3TfJw"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "def collect_sensor_data():\n",
+        "    return {\n",
+        "        \"temperature\": 75 + (int(time.time()) % 20),\n",
+        "        \"vibration\": 0.5 + (int(time.time()) % 10) / 10,\n",
+        "        \"pressure\": 100 + (int(time.time()) % 50),\n",
+        "        \"noise_level\": 60 + (int(time.time()) % 30)\n",
+        "    }\n",
+        "\n",
+        "def analyze_performance():\n",
+        "    return {\n",
+        "        \"efficiency\": 0.8 + (int(time.time()) % 20) / 100,\n",
+        "        \"uptime\": 0.95 + (int(time.time()) % 5) / 100,\n",
+        "        \"output_quality\": 0.9 + (int(time.time()) % 10) / 100\n",
+        "    }\n",
+        "\n",
+        "def detect_anomalies(sensor_data: Dict, performance: Dict):\n",
+        "    anomalies = []\n",
+        "    if sensor_data[\"temperature\"] > 90:\n",
+        "        anomalies.append({\"type\": \"temperature_high\", \"severity\": \"critical\"})\n",
+        "    if sensor_data[\"vibration\"] > 1.2:\n",
+        "        anomalies.append({\"type\": \"vibration_excess\", \"severity\": \"warning\"})\n",
+        "    if performance[\"efficiency\"] < 0.85:\n",
+        "        anomalies.append({\"type\": \"efficiency_low\", \"severity\": \"warning\"})\n",
+        "    return anomalies\n",
+        "\n",
+        "def predict_failures(anomalies: List[Dict]):\n",
+        "    predictions = []\n",
+        "    severity_scores = {\"critical\": 0.9, \"warning\": 0.6}\n",
+        "    for anomaly in anomalies:\n",
+        "        predictions.append({\n",
+        "            \"component\": anomaly[\"type\"].split(\"_\")[0],\n",
+        "            \"probability\": severity_scores[anomaly[\"severity\"]],\n",
+        "            \"timeframe\": \"24_hours\" if anomaly[\"severity\"] == \"critical\" else \"7_days\"\n",
+        "        })\n",
+        "    return predictions\n",
+        "\n",
+        "def schedule_maintenance(predictions: List[Dict]):\n",
+        "    schedule = []\n",
+        "    for pred in predictions:\n",
+        "        schedule.append({\n",
+        "            \"component\": pred[\"component\"],\n",
+        "            \"priority\": \"immediate\" if pred[\"timeframe\"] == \"24_hours\" else \"planned\",\n",
+        "            \"estimated_duration\": \"2_hours\",\n",
+        "            \"required_parts\": [\"replacement_\" + pred[\"component\"]]\n",
+        "        })\n",
+        "    return schedule"
+      ],
+      "id": "2oMzDsp3TfJw"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Define agents and tasks"
+      ],
+      "metadata": {
+        "id": "-7qMHyY5YIph"
+      },
+      "id": "-7qMHyY5YIph"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4ZYbYzjNTfJy"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "sensor_monitor = Agent(\"Sensor Monitor\", \"Data Collection\", \"Collect sensor data\", \"Monitor and collect sensor readings\", [collect_sensor_data])\n",
+        "performance_analyzer = Agent(\"Performance Analyzer\", \"Performance Analysis\", \"Analyze equipment performance\", \"Analyze operational metrics\", [analyze_performance])\n",
+        "anomaly_detector = Agent(\"Anomaly Detector\", \"Anomaly Detection\", \"Detect operational anomalies\", \"Identify abnormal patterns\", [detect_anomalies])\n",
+        "failure_predictor = Agent(\"Failure Predictor\", \"Failure Prediction\", \"Predict potential failures\", \"Predict equipment failures\", [predict_failures])\n",
+        "maintenance_scheduler = Agent(\"Maintenance Scheduler\", \"Maintenance Planning\", \"Schedule maintenance activities\", \"Plan and schedule maintenance\", [schedule_maintenance])\n",
+        "\n",
+        "sensor_task = Task(\"collect_data\", \"Collect sensor data\", \"Sensor readings\", sensor_monitor, is_start=True, next_tasks=[\"analyze_performance\"], async_execution=True)\n",
+        "performance_task = Task(\"analyze_performance\", \"Analyze performance metrics\", \"Performance analysis\", performance_analyzer, next_tasks=[\"detect_anomalies\"], async_execution=True)\n",
+        "anomaly_task = Task(\"detect_anomalies\", \"Detect operational anomalies\", \"Detected anomalies\", anomaly_detector, next_tasks=[\"predict_failures\"], context=[sensor_task, performance_task])\n",
+        "prediction_task = Task(\"predict_failures\", \"Predict potential failures\", \"Failure predictions\", failure_predictor, next_tasks=[\"schedule_maintenance\"], task_type=\"decision\", condition={\"critical\": [\"schedule_maintenance\"], \"warning\": [\"schedule_maintenance\"], \"normal\": \"\"})\n",
+        "scheduling_task = Task(\"schedule_maintenance\", \"Schedule maintenance activities\", \"Maintenance schedule\", maintenance_scheduler, context=[prediction_task])"
+      ],
+      "id": "4ZYbYzjNTfJy"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Create and run the workflow"
+      ],
+      "metadata": {
+        "id": "XOdIxJKiYKuD"
+      },
+      "id": "XOdIxJKiYKuD"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KYu_YTVDTfJz"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "workflow = PraisonAIAgents(\n",
+        "    agents=[sensor_monitor, performance_analyzer, anomaly_detector, failure_predictor, maintenance_scheduler],\n",
+        "    tasks=[sensor_task, performance_task, anomaly_task, prediction_task, scheduling_task],\n",
+        "    process=\"workflow\",\n",
+        "    verbose=True\n",
+        ")\n",
+        "\n",
+        "async def main():\n",
+        "    print(\"\\nStarting Predictive Maintenance Workflow...\")\n",
+        "    print(\"=\" * 50)\n",
+        "    results = await workflow.astart()\n",
+        "    print(\"\\nMaintenance Planning Results:\")\n",
+        "    print(\"=\" * 50)\n",
+        "    for task_id, result in results[\"task_results\"].items():\n",
+        "        if result:\n",
+        "            print(f\"\\nTask: {task_id}\\nResult: {result.raw}\\n{'-'*50}\")\n",
+        "\n",
+        "await main()"
+      ],
+      "id": "KYu_YTVDTfJz"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"\"\"\n",
+        "[Starting Predictive Maintenance Workflow...\n",
+        "==================================================\n",
+        "╭─ Agent Info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+        "│                                                                                                                                              │\n",
+        "│  👤 Agent: Sensor Monitor                                                                                                                    │\n",
+        "│  Role: Data Collection                                                                                                                       │\n",
+        "│  Tools: collect_sensor_data                                                                                                                  │\n",
+        "│                                                                                                                                              │\n",
+        "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+        "\n",
+        "╭─ Agent Info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+        "│                                                                                                                                              │\n",
+        "│  👤 Agent: Performance Analyzer                                                                                                              │\n",
+        "│  Role: Performance Analysis                                                                                                                  │\n",
+        "│  Tools: analyze_performance                                                                                                                  │\n",
+        "│                                                                                                                                              │\n",
+        "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+        "\n",
+        "[20:01:26] INFO     [20:01:26] process.py:429 INFO Task schedule_maintenance has no next tasks, ending workflow                   process.py:429\n",
+        "\n",
+        "Maintenance Planning Results:\n",
+        "==================================================\n",
+        "\n",
+        "Task: 0\n",
+        "Result: The sensor readings you have collected are as follows:\n",
+        "\n",
+        "- **Temperature**: 86°F\n",
+        "- **Vibration**: 0.6 (units not specified, but typically measured in g-forces or mm/s)\n",
+        "- **Pressure**: 101 (units not specified, but typically measured in kPa or psi)\n",
+        "- **Noise Level**: 81 dB\n",
+        "\n",
+        "Here's a brief analysis of these readings:\n",
+        "\n",
+        "1. **Temperature**: At 86°F, the temperature is relatively warm. Depending on the context (e.g., industrial equipment, environmental monitoring), this could be within normal operating conditions or might require cooling measures if it's above the optimal range.\n",
+        "\n",
+        "2. **Vibration**: A vibration level of 0.6 is generally low, but the significance depends on the type of equipment being monitored. For precision machinery, even small vibrations can be critical, whereas for more robust equipment, this might be negligible.\n",
+        "\n",
+        "3. **Pressure**: A pressure reading of 101 is often within normal ranges for many systems, but without specific units or context, it's hard to determine if this is optimal or requires adjustment.\n",
+        "\n",
+        "4. **Noise Level**: At 81 dB, the noise level is relatively high. Prolonged exposure to noise levels above 85 dB can be harmful to hearing, so if this is a workplace environment, it might be necessary to implement noise reduction measures or provide hearing protection.\n",
+        "\n",
+        "Overall, these readings should be compared against the specific operational thresholds and safety standards relevant to the equipment or environment being monitored. If any values are outside of acceptable ranges, further investigation or corrective actions may be needed.\n",
+        "--------------------------------------------------\n",
+        "\n",
+        "Task: 1\n",
+        "Result: Based on the provided operational metrics, here's an analysis of the equipment performance:\n",
+        "\n",
+        "1. **Efficiency (94%)**:\n",
+        "   - The equipment is operating at a high efficiency level, with 94% of the input being effectively converted into useful output. This suggests\n",
+        "that the equipment is well-maintained and optimized for performance. However, there is still a 6% margin for improvement, which could be addressed by identifying and minimizing any inefficiencies in the process.\n",
+        "\n",
+        "2. **Uptime (99%)**:\n",
+        "   - The equipment has an excellent uptime rate of 99%, indicating that it is available and operational almost all the time. This is a strong indicator of reliability and suggests that downtime due to maintenance or unexpected failures is minimal. Maintaining this level of uptime should\n",
+        "be a priority, as it directly impacts productivity and operational continuity.\n",
+        "\n",
+        "3. **Output Quality (94%)**:\n",
+        "   - The output quality is also at 94%, which is a positive sign that the equipment is producing high-quality products or results. However, similar to efficiency, there is room for improvement. Efforts could be made to identify any factors that might be affecting quality, such as calibration issues, material inconsistencies, or process deviations.\n",
+        "\n",
+        "**Overall Assessment**:\n",
+        "The equipment is performing well across all key metrics, with high efficiency, uptime, and output quality. To further enhance performance, focus should be placed on fine-tuning processes to close the small gaps in efficiency and quality. Regular maintenance, monitoring, and process optimization can help sustain and potentially improve these metrics.\n",
+        "--------------------------------------------------]\n",
+        "\"\"\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4hrEJ5S6XpJ7",
+        "outputId": "899e677d-19d5-4e0a-d9ab-ebc945aeee1b"
+      },
+      "id": "4hrEJ5S6XpJ7",
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "[Starting Predictive Maintenance Workflow...\n",
+            "==================================================\n",
+            "╭─ Agent Info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+            "│                                                                                                                                              │\n",
+            "│  👤 Agent: Sensor Monitor                                                                                                                    │\n",
+            "│  Role: Data Collection                                                                                                                       │\n",
+            "│  Tools: collect_sensor_data                                                                                                                  │\n",
+            "│                                                                                                                                              │\n",
+            "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+            "\n",
+            "╭─ Agent Info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+            "│                                                                                                                                              │\n",
+            "│  👤 Agent: Performance Analyzer                                                                                                              │\n",
+            "│  Role: Performance Analysis                                                                                                                  │\n",
+            "│  Tools: analyze_performance                                                                                                                  │\n",
+            "│                                                                                                                                              │\n",
+            "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+            "\n",
+            "[20:01:26] INFO     [20:01:26] process.py:429 INFO Task schedule_maintenance has no next tasks, ending workflow                   process.py:429\n",
+            "\n",
+            "Maintenance Planning Results:\n",
+            "==================================================\n",
+            "\n",
+            "Task: 0\n",
+            "Result: The sensor readings you have collected are as follows:\n",
+            "\n",
+            "- **Temperature**: 86°F\n",
+            "- **Vibration**: 0.6 (units not specified, but typically measured in g-forces or mm/s)\n",
+            "- **Pressure**: 101 (units not specified, but typically measured in kPa or psi)\n",
+            "- **Noise Level**: 81 dB\n",
+            "\n",
+            "Here's a brief analysis of these readings:\n",
+            "\n",
+            "1. **Temperature**: At 86°F, the temperature is relatively warm. Depending on the context (e.g., industrial equipment, environmental monitoring), this could be within normal operating conditions or might require cooling measures if it's above the optimal range.\n",
+            "\n",
+            "2. **Vibration**: A vibration level of 0.6 is generally low, but the significance depends on the type of equipment being monitored. For precision machinery, even small vibrations can be critical, whereas for more robust equipment, this might be negligible.\n",
+            "\n",
+            "3. **Pressure**: A pressure reading of 101 is often within normal ranges for many systems, but without specific units or context, it's hard to determine if this is optimal or requires adjustment.\n",
+            "\n",
+            "4. **Noise Level**: At 81 dB, the noise level is relatively high. Prolonged exposure to noise levels above 85 dB can be harmful to hearing, so if this is a workplace environment, it might be necessary to implement noise reduction measures or provide hearing protection.\n",
+            "\n",
+            "Overall, these readings should be compared against the specific operational thresholds and safety standards relevant to the equipment or environment being monitored. If any values are outside of acceptable ranges, further investigation or corrective actions may be needed.\n",
+            "--------------------------------------------------\n",
+            "\n",
+            "Task: 1\n",
+            "Result: Based on the provided operational metrics, here's an analysis of the equipment performance:\n",
+            "\n",
+            "1. **Efficiency (94%)**:\n",
+            "   - The equipment is operating at a high efficiency level, with 94% of the input being effectively converted into useful output. This suggests \n",
+            "that the equipment is well-maintained and optimized for performance. However, there is still a 6% margin for improvement, which could be addressed by identifying and minimizing any inefficiencies in the process.\n",
+            "\n",
+            "2. **Uptime (99%)**:\n",
+            "   - The equipment has an excellent uptime rate of 99%, indicating that it is available and operational almost all the time. This is a strong indicator of reliability and suggests that downtime due to maintenance or unexpected failures is minimal. Maintaining this level of uptime should \n",
+            "be a priority, as it directly impacts productivity and operational continuity.\n",
+            "\n",
+            "3. **Output Quality (94%)**:\n",
+            "   - The output quality is also at 94%, which is a positive sign that the equipment is producing high-quality products or results. However, similar to efficiency, there is room for improvement. Efforts could be made to identify any factors that might be affecting quality, such as calibration issues, material inconsistencies, or process deviations.\n",
+            "\n",
+            "**Overall Assessment**:\n",
+            "The equipment is performing well across all key metrics, with high efficiency, uptime, and output quality. To further enhance performance, focus should be placed on fine-tuning processes to close the small gaps in efficiency and quality. Regular maintenance, monitoring, and process optimization can help sustain and potentially improve these metrics.\n",
+            "--------------------------------------------------]\n",
+            "\n"
+          ]
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.9"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
@@ -0,0 +1,420 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "MLutIRWTYLqq",
+   "metadata": {
+    "id": "MLutIRWTYLqq"
+   },
+   "source": [
+    "# Simple Chat with Qwen2.5 (0.5B) using Hugging Face Transformers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2LoeTPnYQOW",
+   "metadata": {
+    "id": "a2LoeTPnYQOW"
+   },
+   "source": [
+    "This beginner-friendly notebook demonstrates how to use the Qwen2.5-0.5B-Instruct model for simple chat-based generation. It includes installing dependencies, setting up authentication via Hugging Face token, generating a response using a chat prompt, and displaying the output. The notebook is organized into 5 clean sections for easy understanding."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "WlfJBFucY9gi",
+   "metadata": {
+    "id": "WlfJBFucY9gi"
+   },
+   "source": [
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Qwen2_5_InstructionAgent.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57059c85",
+   "metadata": {
+    "id": "57059c85"
+   },
+   "source": [
+    "# 🔧 Dependencies\n",
+    "Install necessary Python packages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1cdaae47",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1cdaae47",
+    "outputId": "5b140b09-5c16-4849-f370-f8b5abcc127a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m58.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m58.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m39.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m64.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25h"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install transformers --quiet\n",
+    "!pip install accelerate --quiet\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a65a2fa",
+   "metadata": {
+    "id": "9a65a2fa"
+   },
+   "source": [
+    "# 🛠️ Tools\n",
+    "Import required tools and libraries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a8a4bc30",
+   "metadata": {
+    "id": "a8a4bc30"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "import torch\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ad2bec4",
+   "metadata": {
+    "id": "1ad2bec4"
+   },
+   "source": [
+    "# ⚙️ YAML Prompt (Token Authentication)\n",
+    "Use Hugging Face token to access the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cd030b94",
+   "metadata": {
+    "id": "cd030b94"
+   },
+   "outputs": [],
+   "source": [
+    "from huggingface_hub import login\n",
+    "login(token=\"Enter your huggingface token\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7b1a32d",
+   "metadata": {
+    "id": "b7b1a32d"
+   },
+   "source": [
+    "# 🚀 Main\n",
+    "Load model, prepare prompt and generate response."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ef73b9ea",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 365,
+     "referenced_widgets": [
+      "a175e2b1fa8c4733afe1ddc78095be58",
+      "f5aaf63d7be04927a33e5cdd3655a003",
+      "7af74b8c184b4e72a2dba769b9caa40f",
+      "22b1c604d91648479cf01198db25a863",
+      "faaeb1c3ab914a6db9ff9b8ea06a440f",
+      "138860a12c6347fba11437eb098d31b5",
+      "d2f5fe459d544fa6adc92debab823ee6",
+      "468e8e7bee5c4198b121ac1aa677153d",
+      "068991c9ac44496bab06ccaa7d030722",
+      "44436151662f41ef81f6a4005add51de",
+      "c774c78c4fce4825872a76bb1e5fde33",
+      "4a7d0a84994c4b628974f631ecb95425",
+      "4adb654ea143456eb3b4244c3030b39f",
+      "d8dbdbc9327f4799a07767b59db59c47",
+      "669ea95c4cb642b580aa176ae2e89f9f",
+      "0bc8529b03364f83adf1110b5c1378ec",
+      "2eb8007145ef4c3caf7f5a9d1fd299e3",
+      "3fa4c368cea4474e8b97a215a20fd8d8",
+      "c750b36cfda24e24ae44a615e426de4b",
+      "e3001e1e3ba74c7ba28702fa5b2a6ed8",
+      "7ac0f7770ad341f68f194705f59ccb27",
+      "f9140e1979c6412993eee36c3f79c0d9",
+      "1ad8e2e2200c4277ba10dd8e9ac83b90",
+      "f193e4fb00b64e86b6f40f10f514739d",
+      "ae3aa36b649a43eb928ed843d2cfc239",
+      "c346fba7badb4c4596f6d6e8fa62224b",
+      "39dd29002d7b46848a29dfeb7405d9f9",
+      "166e17a0be5a4aa8831101eeb07a8645",
+      "60f9503b404945a3b99b5207190474f5",
+      "c480a676b82b48a0aae3dc438fa02ba8",
+      "d97b7d0765e84926b974b2c19156ae20",
+      "b22ca5c038f7419aa8e03f5f9a54accc",
+      "0387fba7ed044fbfb2a78c8d593c7b38",
+      "d6a31c3cf026450088596db5917c3e4a",
+      "4948c0014a95413b81848ae2b4a5fe7e",
+      "377cbca7363a495ea131a1e49a702168",
+      "a5eaad3284034beab09d212765d7ff78",
+      "7ed393b7c7564ab1897446eba875a4da",
+      "66d773bc8c7a40bc95dcba35052675cb",
+      "ad196b0deb2144b083cf8fddcbcfd147",
+      "09a99df922154a528f327bba0a47cb9a",
+      "bc9918e686244eb5b5db78932600a9d6",
+      "4fef54176f4b4a2d8d543a4bf04b416a",
+      "48551fb8dd5140cbb2d792e72b5fbb0d",
+      "955225e4206a4ee5a4284579935db920",
+      "e2faef80a0034c5384daf8ba66c1b75d",
+      "f27c3ec92bc74bacb3f86672b689370a",
+      "ec03dfb79a26474e812606b2664c931b",
+      "4705d2f502a74c3bb8eb8a06c3ba6889",
+      "7d8c99f622c34229894024c9772aed84",
+      "1e6e892b153c4c05b7de806ec674215f",
+      "85ac6a67bb0944b5bb8713fcf302da45",
+      "796771b887ce49119307725382f73bfd",
+      "c686586c431f4c22b0f0c698a9b213b6",
+      "239180fd12624e3a90188270b55ee218",
+      "e04653090c7e454d9c5e520e7f313588",
+      "992c7a4b44aa4feb8e13714fa922d705",
+      "a0a238c78d2842d680be448a2cb20157",
+      "6ab5ebed63674b5e87327a1623eba7f0",
+      "a49887aa0fcd4b91a0d5d978b29c1e0a",
+      "d9b2e4142dd14264b90829a5a4116613",
+      "154b27bb2ba44d10bde5c96929b65cc9",
+      "f0bf6206c31f49f2ad1ce97b99963011",
+      "1762735cfbcf4706b1c0686d3c2926a0",
+      "d5e3440785794f0f9ad4fd0650c2f682",
+      "08019bdd70364fabbc222dca66db0bb1",
+      "6f0b3a92d47c42dd815a670bb1c0f42c",
+      "54554545ad5a4f39b649f3acff6d5cba",
+      "bfd8f4ee8c414349a3695975e79c332e",
+      "332fc944eef14a9c82f71a68a3857dea",
+      "3b0b899fcfae451ba8ec3d8da176fcec",
+      "8383df3f07bd427287ad05726737cc67",
+      "67c267d7d1ec424593775bf8129e8ac9",
+      "85a50e2cb406403988639e6d6ecf9dc1",
+      "7251ed20382c4a60bae8549f68971043",
+      "44fbc377fd6a4c5a910c00dcceb143af",
+      "2d6d273941f04c13af598c53e067aae7"
+     ]
+    },
+    "id": "ef73b9ea",
+    "outputId": "34512fa7-527c-45cb-f3c7-b42d37e74980"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+      "You will be able to reuse this secret in all of your notebooks.\n",
+      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a175e2b1fa8c4733afe1ddc78095be58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/7.30k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a7d0a84994c4b628974f631ecb95425",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.json:   0%|          | 0.00/2.78M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ad8e2e2200c4277ba10dd8e9ac83b90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "merges.txt:   0%|          | 0.00/1.67M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6a31c3cf026450088596db5917c3e4a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/7.03M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "955225e4206a4ee5a4284579935db920",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/659 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e04653090c7e454d9c5e520e7f313588",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/988M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f0b3a92d47c42dd815a670bb1c0f42c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/242 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model_name = \"Qwen/Qwen2.5-0.5B-Instruct\"\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    model_name,\n",
+    "    torch_dtype=\"auto\",\n",
+    "    device_map=\"auto\"\n",
+    ")\n",
+    "\n",
+    "messages = [\n",
+    "    {\"role\": \"system\", \"content\": \"You are Qwen, a helpful assistant.\"},\n",
+    "    {\"role\": \"user\", \"content\": \"Explain photosynthesis in simple words.\"}\n",
+    "]\n",
+    "\n",
+    "text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
+    "inputs = tokenizer(text, return_tensors=\"pt\").to(model.device)\n",
+    "\n",
+    "outputs = model.generate(**inputs, max_new_tokens=100)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf70c765",
+   "metadata": {
+    "id": "bf70c765"
+   },
+   "source": [
+    "# 📤 Output\n",
+    "Print the model response."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d8f70a7b",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "d8f70a7b",
+    "outputId": "559e9358-c2b7-4410-c1a6-1624640dfeae"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "system\n",
+      "You are Qwen, a helpful assistant.\n",
+      "user\n",
+      "Explain photosynthesis in simple words.\n",
+      "assistant\n",
+      "Photosynthesis is the process by which plants, algae, and some bacteria convert light energy from the sun into chemical energy stored in glucose molecules, using carbon dioxide and water as reactants. This process occurs in chloroplasts within plant cells and involves two main stages: the light-dependent reactions and the light-independent reactions (also known as the Calvin cycle).\n",
+      "\n",
+      "In the light-dependent reactions, light energy is captured by pigments like chlorophyll, causing electrons to be excited and transferred to oxygen through a series\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
+    "print(response)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
@@ -0,0 +1,364 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3622c575",
+   "metadata": {
+    "id": "3622c575"
+   },
+   "source": [
+    "# TinyLlama Agent Demo\n",
+    "\n",
+    "This notebook demonstrates how to create a simple AI agent using the TinyLlama (1.1B) model.\n",
+    "\n",
+    "**Requirements:**\n",
+    "- `transformers`\n",
+    "- `accelerate`\n",
+    "- `torch`\n",
+    "\n",
+    "We will load the model, prepare a basic conversational interface, and test it with a simple prompt."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "qS0M9pRhTGpf",
+   "metadata": {
+    "id": "qS0M9pRhTGpf"
+   },
+   "source": [
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tuw0IDpBS0ei",
+   "metadata": {
+    "id": "tuw0IDpBS0ei"
+   },
+   "source": [
+    "# Install necessary packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0d06cae9",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0d06cae9",
+    "outputId": "3e1d8fb7-19be-455b-dc35-d0ab19146669"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m29.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m29.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m45.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25h"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install -q transformers accelerate torch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "RFhYLkO1S5F9",
+   "metadata": {
+    "id": "RFhYLkO1S5F9"
+   },
+   "source": [
+    "# Load TinyLlama model and tokenizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "415af398",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 241,
+     "referenced_widgets": [
+      "dae7d635257948149c74e65956f614e3",
+      "17ec6fcc9f624cdbb62fe2b8b55230d0",
+      "58474504488044138a0252bb75258d86",
+      "04fe4518fac04b9f8b29c62ed70f1dfd",
+      "c6a9d3ea25594170977243d2048d3408",
+      "730bebf00be142d3979c6d12b55d6757",
+      "03ad1674c0884e7b9c4a21af0a1e66c3",
+      "ebac47c887f04b79a60bd0918002bf0d",
+      "ece9bce5d2ca4dd7b019040175513118",
+      "458f11dbadb045f1aa8c72780e7aa2a0",
+      "ec28a20d0a14445489723684d2d69c0a",
+      "d347fa0c0fc849559b66c8f116c86317",
+      "117f649fa62b4f5d9add0160e6cd80a4",
+      "66f702418fa348eea27778282dc443aa",
+      "44413339df5b4ae688f8101faf9ed3b5",
+      "00cc576df95441c8916ce38a7c6decc2",
+      "22e9ea6d191841bf9a5a5072326aa6d8",
+      "bd94a291b3164113b5ea460b38b8c167",
+      "9d132a297f924a3d867605df81e70d8e",
+      "5345feddca204db784ea0452e739eda6",
+      "ace33e6e88d842c89efd180b3a83f674",
+      "61dd85a7edf544f196c60a259a91fa0d",
+      "7451ec9bd091441c852d075b47bd1ca5",
+      "05e3446e7c9243819a25d1ca8e8fa38e",
+      "73141c4c9218462da97999d3afa459bc",
+      "1cffc33252b94fbc861edb0953f13153",
+      "de917c0fc49040c8a9254333c34c884a",
+      "50962ececc0b42c28170eb0c23fd6171",
+      "856cc834a33a4f9fb59c77f748006b36",
+      "17fd1d476c1f4891b8cbeb8cacdeb866",
+      "c97dc50f42cb44b8a71aeff5272d5337",
+      "2e3a66f622d445c4be544a9306798cb6",
+      "92920b1d428a4ed68a9e0329e2034cad",
+      "7e282c1fcae842cc87c93ecfc1763654",
+      "f9ed70f887204e2fb336e38a35096eec",
+      "a4d99f9beca44e4a8fadff65b27aa78f",
+      "b4f77b96d00445eea77ac78baa8c1e99",
+      "695898e4a4c8476cb318446a6da592ba",
+      "d70bcacbe77c41f685a4f8020f767889",
+      "af49afc49ecf47fb9ebf2d299954eff2",
+      "1bd2a253348f4b45928c875940f72481",
+      "b6c5f55fc85b4331a2e749d27ce15f89",
+      "81a87638c71046d7aeb1c642558602f7",
+      "e750883103444391bd64872344c651d7",
+      "e78dca0cf1314d01a5d5126e448e9d87",
+      "d0c53dffc0c34d7090febf78231972b2",
+      "152a118177914c6692e7ee4987b92b9b",
+      "88fd71fd6dbf43ebb0a3b8471fd35f8d",
+      "ddb91266f8834e4eaaf136cc15f4a831",
+      "9f8179d135054eb292c9adc7fd66a91a",
+      "297d5bc8b3a04f26b80e2cc5ee3f7bbd",
+      "f49f72679cb944cd8fce5557bbd68b1b",
+      "2ee28c8ef4f74690b80a89050da4ebb5",
+      "c29924d6561a4feaa9238d1b34394941",
+      "03636146b7c14f5db80b8f105be55303",
+      "b391081de4444859ac3213aac99cc33a",
+      "2babb83499fc46dc8422473f7b98403c",
+      "a32f6cc2777941bca5b6250e36624ead",
+      "90cd9996184a452ab651f9b8cc590f47",
+      "dc04209c92764096ba9bd4aefa81c189",
+      "16e5b93c3c964a8a8ca460d9077d0638",
+      "794f22ded07d46c5a916325109618987",
+      "dce367f1a5a74ebf94aea37209aa0a40",
+      "5e355e3f340f4a7590e4a88d44b03383",
+      "4e725e19c877462aa3c10726a335bbde",
+      "d60ab6c255d244098a459459016a2089",
+      "f1b07183320d42af88f985bd1abf054f",
+      "d23103bb4b354e29a2c3e7dc93867b36",
+      "ea3e0ae0aef54e5da113c9686ebbbab9",
+      "4d3365c1bb7740dc8f36f4b419af135a",
+      "c06ccbc051264027bd2c6521fec69abe",
+      "597dbf5b28134cc0bf9d613443243455",
+      "faa4f285eb224bbcbed649bd479e571f",
+      "6e684d35885a4706b8add6834cd521ad",
+      "f9c55ada3386460183e02559defec8a4",
+      "9d64cbe1605f4f358923754c5f8b6424",
+      "6008ed7eca09485195eb37c36acfbe79"
+     ]
+    },
+    "id": "415af398",
+    "outputId": "28de1886-e83f-49bf-b05f-cd19439605c5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dae7d635257948149c74e65956f614e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/1.29k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d347fa0c0fc849559b66c8f116c86317",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7451ec9bd091441c852d075b47bd1ca5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e282c1fcae842cc87c93ecfc1763654",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/551 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e78dca0cf1314d01a5d5126e448e9d87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/608 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b391081de4444859ac3213aac99cc33a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/2.20G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1b07183320d42af88f985bd1abf054f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+    "import torch\n",
+    "\n",
+    "model_name = \"TinyLlama/TinyLlama-1.1B-Chat-v1.0\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map=\"auto\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "N0bRTd_DS8aL",
+   "metadata": {
+    "id": "N0bRTd_DS8aL"
+   },
+   "source": [
+    "# Define a function to generate responses from the agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1b0483e1",
+   "metadata": {
+    "id": "1b0483e1"
+   },
+   "outputs": [],
+   "source": [
+    "def generate_response(prompt, max_length=256):\n",
+    "    inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
+    "    outputs = model.generate(**inputs, max_new_tokens=max_length)\n",
+    "    return tokenizer.decode(outputs[0], skip_special_tokens=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "vbHKNxc-S_Bk",
+   "metadata": {
+    "id": "vbHKNxc-S_Bk"
+   },
+   "source": [
+    "# Example usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "298c9811",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "298c9811",
+    "outputId": "6e412608-735c-4604-8a5d-a66b98dd9df0"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You are a helpful assistant. What is the capital of France?\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = \"You are a helpful assistant. What is the capital of France?\"\n",
+    "response = generate_response(prompt)\n",
+    "print(response)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
@@ -0,0 +1,96 @@
+#!/usr/bin/env python3
+
+import sys
+import traceback
+from praisonaiagents import Agent, MCP
+
+def test_agent_direct():
+    """Test gpt-4o-mini (agent.py path)"""
+    print("=" * 50)
+    print("TESTING: gpt-4o-mini (agent.py direct calls)")
+    print("=" * 50)
+    
+    try:
+        agent = Agent(
+            instructions="""You are a helpful assistant that can break down complex problems.
+            Use the available tools when relevant to perform step-by-step analysis.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
+        )
+        
+        print("✅ Agent created successfully")
+        print(f"✅ Agent LLM: {getattr(agent, 'llm', 'Not set')}")
+        print(f"✅ Agent using custom LLM: {getattr(agent, '_using_custom_llm', False)}")
+        
+        result = agent.start("What are 3 steps to make coffee?")
+        print("✅ Agent execution completed successfully")
+        return True, result
+        
+    except Exception as e:
+        print(f"❌ Error in agent direct: {e}")
+        traceback.print_exc()
+        return False, str(e)
+
+def test_llm_class():
+    """Test openai/gpt-4o-mini (llm.py path)"""
+    print("\n" + "=" * 50)
+    print("TESTING: openai/gpt-4o-mini (llm.py LiteLLM)")
+    print("=" * 50)
+    
+    try:
+        agent = Agent(
+            instructions="""You are a helpful assistant that can break down complex problems.
+            Use the available tools when relevant to perform step-by-step analysis.""",
+            llm="openai/gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
+        )
+        
+        print("✅ Agent created successfully")
+        print(f"✅ Agent LLM instance: {getattr(agent, 'llm_instance', 'Not set')}")
+        print(f"✅ Agent using custom LLM: {getattr(agent, '_using_custom_llm', False)}")
+        
+        result = agent.start("What are 3 steps to make coffee?")
+        print("✅ Agent execution completed successfully")
+        return True, result
+        
+    except Exception as e:
+        print(f"❌ Error in llm class: {e}")
+        traceback.print_exc()
+        return False, str(e)
+
+if __name__ == "__main__":
+    print("🔍 DEBUGGING: Comparing both LLM approaches\n")
+    
+    # Test agent direct
+    success1, result1 = test_agent_direct()
+    
+    # Test LLM class
+    success2, result2 = test_llm_class()
+    
+    print("\n" + "=" * 50)
+    print("FINAL RESULTS")
+    print("=" * 50)
+    
+    if success1:
+        print("✅ gpt-4o-mini (agent.py) - SUCCESS")
+    else:
+        print("❌ gpt-4o-mini (agent.py) - FAILED")
+        print(f"   Error: {result1}")
+    
+    if success2:
+        print("✅ openai/gpt-4o-mini (llm.py) - SUCCESS")
+    else:
+        print("❌ openai/gpt-4o-mini (llm.py) - FAILED")
+        print(f"   Error: {result2}")
+    
+    if success1 and success2:
+        print("\n🎉 BOTH FORMATS WORK CORRECTLY!")
+        print("📝 The issue mentioned might be resolved or was a different problem.")
+    elif success1 and not success2:
+        print("\n⚠️  CONFIRMED: LLM class path has issues")
+        print("📝 Need to debug the LLM class implementation")
+    elif success2 and not success1:
+        print("\n⚠️  CONFIRMED: Agent direct path has issues")
+        print("📝 Need to debug the agent direct implementation")
+    else:
+        print("\n💥 BOTH PATHS FAILED - Something is fundamentally wrong")
@@ -0,0 +1,14 @@
+from praisonaiagents import Agent
+
+def validate_content(data):
+    if len(str(data)) < 50:
+        return False, "Content too short"
+    return True, data
+
+agent = Agent(
+    instructions="You are a writer",
+    guardrail=validate_content,
+    max_guardrail_retries=1
+)
+
+agent.start("Write a welcome message with 5 words")
@@ -12,6 +12,7 @@ from .knowledge.knowledge import Knowledge
 from .knowledge.chunking import Chunking
 from .mcp.mcp import MCP
 from .session import Session
+from .memory.memory import Memory
 from .guardrails import GuardrailResult, LLMGuardrail
 from .main import (
    TaskOutput,
@@ -43,6 +44,7 @@ __all__ = [
    'ReflectionOutput',
    'AutoAgents',
    'Session',
+    'Memory',
    'display_interaction',
    'display_self_reflection',
    'display_instruction',
@@ -619,12 +619,9 @@ Your Goal: {self.goal}
        while retry_count <= self.max_guardrail_retries:
            # Create TaskOutput object
            task_output = TaskOutput(
+                description="Agent response output",
                raw=current_response,
-                output=current_response,
-                pydantic=None,
-                json_dict=None,
-                name=f"{self.name}_output",
-                description="Agent response output"
+                agent=self.name
            )
            
            # Process guardrail
@@ -413,24 +413,30 @@ class LLM:
            start_time = time.time()
            reflection_count = 0

-            while True:
-                try:
-                    if verbose:
-                        display_text = prompt
-                        if isinstance(prompt, list):
-                            display_text = next((item["text"] for item in prompt if item["type"] == "text"), "")
-                        
-                        if display_text and str(display_text).strip():
-                            display_instruction(
-                                f"Agent {agent_name} is processing prompt: {display_text}",
-                                console=console,
-                                agent_name=agent_name,
-                                agent_role=agent_role,
-                                agent_tools=agent_tools
-                            )
+            # Display initial instruction once
+            if verbose:
+                display_text = prompt
+                if isinstance(prompt, list):
+                    display_text = next((item["text"] for item in prompt if item["type"] == "text"), "")
+                
+                if display_text and str(display_text).strip():
+                    display_instruction(
+                        f"Agent {agent_name} is processing prompt: {display_text}",
+                        console=console,
+                        agent_name=agent_name,
+                        agent_role=agent_role,
+                        agent_tools=agent_tools
+                    )

+            # Sequential tool calling loop - similar to agent.py
+            max_iterations = 10  # Prevent infinite loops
+            iteration_count = 0
+            final_response_text = ""
+
+            while iteration_count < max_iterations:
+                try:
                    # Get response from LiteLLM
-                    start_time = time.time()
+                    current_time = time.time()

                    # If reasoning_steps is True, do a single non-streaming call
                    if reasoning_steps:
@@ -445,6 +451,7 @@ class LLM:
                        )
                        reasoning_content = resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
                        response_text = resp["choices"][0]["message"]["content"]
+                        final_response = resp
                        
                        # Optionally display reasoning if present
                        if verbose and reasoning_content:
@@ -452,7 +459,7 @@ class LLM:
                                original_prompt,
                                f"Reasoning:\n{reasoning_content}\n\nAnswer:\n{response_text}",
                                markdown=markdown,
-                                generation_time=time.time() - start_time,
+                                generation_time=time.time() - current_time,
                                console=console
                            )
                        else:
@@ -460,14 +467,14 @@ class LLM:
                                original_prompt,
                                response_text,
                                markdown=markdown,
-                                generation_time=time.time() - start_time,
+                                generation_time=time.time() - current_time,
                                console=console
                            )
                    
                    # Otherwise do the existing streaming approach
                    else:
                        if verbose:
-                            with Live(display_generating("", start_time), console=console, refresh_per_second=4) as live:
+                            with Live(display_generating("", current_time), console=console, refresh_per_second=4) as live:
                                response_text = ""
                                for chunk in litellm.completion(
                                    **self._build_completion_params(
@@ -481,7 +488,7 @@ class LLM:
                                    if chunk and chunk.choices and chunk.choices[0].delta.content:
                                        content = chunk.choices[0].delta.content
                                        response_text += content
-                                        live.update(display_generating(response_text, start_time))
+                                        live.update(display_generating(response_text, current_time))
                        else:
                            # Non-verbose mode, just collect the response
                            response_text = ""
@@ -499,20 +506,20 @@ class LLM:

                        response_text = response_text.strip()

-                    # Get final completion to check for tool calls
-                    final_response = litellm.completion(
-                        **self._build_completion_params(
-                            messages=messages,
-                            tools=formatted_tools,
-                            temperature=temperature,
-                            stream=False,  # No streaming for tool call check
-                            **kwargs
+                        # Get final completion to check for tool calls
+                        final_response = litellm.completion(
+                            **self._build_completion_params(
+                                messages=messages,
+                                tools=formatted_tools,
+                                temperature=temperature,
+                                stream=False,  # No streaming for tool call check
+                                **kwargs
+                            )
                        )
-                    )
                    
                    tool_calls = final_response["choices"][0]["message"].get("tool_calls")
                    
-                    # Handle tool calls
+                    # Handle tool calls - Sequential tool calling logic
                    if tool_calls and execute_tool_fn:
                        # Convert tool_calls to a serializable format for all providers
                        serializable_tool_calls = []
@@ -535,6 +542,7 @@ class LLM:
                            "tool_calls": serializable_tool_calls
                        })
                        
+                        should_continue = False
                        for tool_call in tool_calls:
                            # Handle both object and dict access patterns
                            if isinstance(tool_call, dict):
@@ -574,6 +582,18 @@ class LLM:
                                "content": json.dumps(tool_result) if tool_result is not None else "Function returned an empty output"
                            })

+                            # Check if we should continue (for tools like sequential thinking)
+                            # This mimics the logic from agent.py lines 1004-1007
+                            if function_name == "sequentialthinking" and arguments.get("nextThoughtNeeded", False):
+                                should_continue = True
+                        
+                        # If we should continue, increment iteration and continue loop
+                        if should_continue:
+                            iteration_count += 1
+                            continue
+
+                        # If we reach here, no more tool calls needed - get final response
+                        # Make one more call to get the final summary response
                        # Special handling for Ollama models that don't automatically process tool results
                        if self.model and self.model.startswith("ollama/") and tool_result:
                            # For Ollama models, we need to explicitly ask the model to process the tool results
@@ -666,115 +686,141 @@ class LLM:
                        else:
                            # Get response after tool calls with streaming
                            if verbose:
-                                with Live(display_generating("", start_time), console=console, refresh_per_second=4) as live:
-                                    response_text = ""
+                                with Live(display_generating("", current_time), console=console, refresh_per_second=4) as live:
+                                    final_response_text = ""
                                    for chunk in litellm.completion(
                                        **self._build_completion_params(
                                            messages=messages,
+                                            tools=formatted_tools,
                                            temperature=temperature,
-                                            stream=True
+                                            stream=True,
+                                            **kwargs
                                        )
                                    ):
                                        if chunk and chunk.choices and chunk.choices[0].delta.content:
                                            content = chunk.choices[0].delta.content
-                                            response_text += content
-                                            live.update(display_generating(response_text, start_time))
+                                            final_response_text += content
+                                            live.update(display_generating(final_response_text, current_time))
                            else:
-                                response_text = ""
+                                final_response_text = ""
                                for chunk in litellm.completion(
                                    **self._build_completion_params(
                                        messages=messages,
+                                        tools=formatted_tools,
                                        temperature=temperature,
-                                        stream=True
+                                        stream=True,
+                                        **kwargs
                                    )
                                ):
                                    if chunk and chunk.choices and chunk.choices[0].delta.content:
-                                        response_text += chunk.choices[0].delta.content
-
-                            response_text = response_text.strip()
-
-                    # Handle output formatting
-                    if output_json or output_pydantic:
-                        self.chat_history.append({"role": "user", "content": original_prompt})
-                        self.chat_history.append({"role": "assistant", "content": response_text})
+                                        final_response_text += chunk.choices[0].delta.content
+                            
+                            final_response_text = final_response_text.strip()
+                        
+                        # Display final response
                        if verbose:
-                            display_interaction(original_prompt, response_text, markdown=markdown,
-                                             generation_time=time.time() - start_time, console=console)
-                        return response_text
+                            display_interaction(
+                                original_prompt,
+                                final_response_text,
+                                markdown=markdown,
+                                generation_time=time.time() - start_time,
+                                console=console
+                            )
+                        
+                        return final_response_text
+                    else:
+                        # No tool calls, we're done with this iteration
+                        break
+                        
+                except Exception as e:
+                    logging.error(f"Error in LLM iteration {iteration_count}: {e}")
+                    break
+                    
+            # End of while loop - return final response
+            if final_response_text:
+                return final_response_text
+            
+            # No tool calls were made in this iteration, return the response
+            if verbose:
+                display_interaction(
+                    original_prompt,
+                    response_text,
+                    markdown=markdown,
+                    generation_time=time.time() - start_time,
+                    console=console
+                )
+            
+            response_text = response_text.strip()
+            
+            # Handle output formatting
+            if output_json or output_pydantic:
+                self.chat_history.append({"role": "user", "content": original_prompt})
+                self.chat_history.append({"role": "assistant", "content": response_text})
+                if verbose:
+                    display_interaction(original_prompt, response_text, markdown=markdown,
+                                     generation_time=time.time() - start_time, console=console)
+                return response_text

-                    if not self_reflect:
-                        if verbose:
-                            display_interaction(original_prompt, response_text, markdown=markdown,
-                                             generation_time=time.time() - start_time, console=console)
-                        # Return reasoning content if reasoning_steps is True
-                        if reasoning_steps and reasoning_content:
-                            return reasoning_content
-                        return response_text
+            if not self_reflect:
+                if verbose:
+                    display_interaction(original_prompt, response_text, markdown=markdown,
+                                     generation_time=time.time() - start_time, console=console)
+                # Return reasoning content if reasoning_steps is True
+                if reasoning_steps and reasoning_content:
+                    return reasoning_content
+                return response_text

-                    # Handle self-reflection
-                    reflection_prompt = f"""
+            # Handle self-reflection loop
+            while reflection_count < max_reflect:
+                # Handle self-reflection
+                reflection_prompt = f"""
 Reflect on your previous response: '{response_text}'.
 Identify any flaws, improvements, or actions.
 Provide a "satisfactory" status ('yes' or 'no').
 Output MUST be JSON with 'reflection' and 'satisfactory'.
-                    """
-                    
-                    reflection_messages = messages + [
-                        {"role": "assistant", "content": response_text},
-                        {"role": "user", "content": reflection_prompt}
-                    ]
+                """
+                
+                reflection_messages = messages + [
+                    {"role": "assistant", "content": response_text},
+                    {"role": "user", "content": reflection_prompt}
+                ]

-                    # If reasoning_steps is True, do a single non-streaming call to capture reasoning
-                    if reasoning_steps:
-                        reflection_resp = litellm.completion(
-                            **self._build_completion_params(
-                                messages=reflection_messages,
-                                temperature=temperature,
-                                stream=False,  # Force non-streaming
-                                response_format={"type": "json_object"},
-                                **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
-                            )
+                # If reasoning_steps is True, do a single non-streaming call to capture reasoning
+                if reasoning_steps:
+                    reflection_resp = litellm.completion(
+                        **self._build_completion_params(
+                            messages=reflection_messages,
+                            temperature=temperature,
+                            stream=False,  # Force non-streaming
+                            response_format={"type": "json_object"},
+                            **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
                        )
-                        # Grab reflection text and optional reasoning
-                        reasoning_content = reflection_resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
-                        reflection_text = reflection_resp["choices"][0]["message"]["content"]
+                    )
+                    # Grab reflection text and optional reasoning
+                    reasoning_content = reflection_resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
+                    reflection_text = reflection_resp["choices"][0]["message"]["content"]

-                        # Optionally display reasoning if present
-                        if verbose and reasoning_content:
-                            display_interaction(
-                                "Reflection reasoning:",
-                                f"{reasoning_content}\n\nReflection result:\n{reflection_text}",
-                                markdown=markdown,
-                                generation_time=time.time() - start_time,
-                                console=console
-                            )
-                        elif verbose:
-                            display_interaction(
-                                "Self-reflection (non-streaming):",
-                                reflection_text,
-                                markdown=markdown,
-                                generation_time=time.time() - start_time,
-                                console=console
-                            )
-                    else:
-                        # Existing streaming approach
-                        if verbose:
-                            with Live(display_generating("", start_time), console=console, refresh_per_second=4) as live:
-                                reflection_text = ""
-                                for chunk in litellm.completion(
-                                    **self._build_completion_params(
-                                        messages=reflection_messages,
-                                        temperature=temperature,
-                                        stream=True,
-                                        response_format={"type": "json_object"},
-                                        **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
-                                    )
-                                ):
-                                    if chunk and chunk.choices and chunk.choices[0].delta.content:
-                                        content = chunk.choices[0].delta.content
-                                        reflection_text += content
-                                        live.update(display_generating(reflection_text, start_time))
-                        else:
+                    # Optionally display reasoning if present
+                    if verbose and reasoning_content:
+                        display_interaction(
+                            "Reflection reasoning:",
+                            f"{reasoning_content}\n\nReflection result:\n{reflection_text}",
+                            markdown=markdown,
+                            generation_time=time.time() - start_time,
+                            console=console
+                        )
+                    elif verbose:
+                        display_interaction(
+                            "Self-reflection (non-streaming):",
+                            reflection_text,
+                            markdown=markdown,
+                            generation_time=time.time() - start_time,
+                            console=console
+                        )
+                else:
+                    # Existing streaming approach
+                    if verbose:
+                        with Live(display_generating("", start_time), console=console, refresh_per_second=4) as live:
                            reflection_text = ""
                            for chunk in litellm.completion(
                                **self._build_completion_params(
@@ -786,48 +832,102 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                )
                            ):
                                if chunk and chunk.choices and chunk.choices[0].delta.content:
-                                    reflection_text += chunk.choices[0].delta.content
-
-                    try:
-                        reflection_data = json.loads(reflection_text)
-                        satisfactory = reflection_data.get("satisfactory", "no").lower() == "yes"
-
-                        if verbose:
-                            display_self_reflection(
-                                f"Agent {agent_name} self reflection: reflection='{reflection_data['reflection']}' satisfactory='{reflection_data['satisfactory']}'",
-                                console=console
+                                    content = chunk.choices[0].delta.content
+                                    reflection_text += content
+                                    live.update(display_generating(reflection_text, start_time))
+                    else:
+                        reflection_text = ""
+                        for chunk in litellm.completion(
+                            **self._build_completion_params(
+                                messages=reflection_messages,
+                                temperature=temperature,
+                                stream=True,
+                                response_format={"type": "json_object"},
+                                **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
                            )
+                        ):
+                            if chunk and chunk.choices and chunk.choices[0].delta.content:
+                                reflection_text += chunk.choices[0].delta.content

-                        if satisfactory and reflection_count >= min_reflect - 1:
-                            if verbose:
-                                display_interaction(prompt, response_text, markdown=markdown,
-                                                 generation_time=time.time() - start_time, console=console)
-                            return response_text
+                try:
+                    reflection_data = json.loads(reflection_text)
+                    satisfactory = reflection_data.get("satisfactory", "no").lower() == "yes"

-                        if reflection_count >= max_reflect - 1:
-                            if verbose:
-                                display_interaction(prompt, response_text, markdown=markdown,
-                                                 generation_time=time.time() - start_time, console=console)
-                            return response_text
+                    if verbose:
+                        display_self_reflection(
+                            f"Agent {agent_name} self reflection: reflection='{reflection_data['reflection']}' satisfactory='{reflection_data['satisfactory']}'",
+                            console=console
+                        )

-                        reflection_count += 1
-                        messages.extend([
-                            {"role": "assistant", "content": response_text},
-                            {"role": "user", "content": reflection_prompt},
-                            {"role": "assistant", "content": reflection_text},
-                            {"role": "user", "content": "Now regenerate your response using the reflection you made"}
-                        ])
-                        continue
+                    if satisfactory and reflection_count >= min_reflect - 1:
+                        if verbose:
+                            display_interaction(prompt, response_text, markdown=markdown,
+                                             generation_time=time.time() - start_time, console=console)
+                        return response_text

-                    except json.JSONDecodeError:
-                        reflection_count += 1
-                        if reflection_count >= max_reflect:
-                            return response_text
-                        continue
+                    if reflection_count >= max_reflect - 1:
+                        if verbose:
+                            display_interaction(prompt, response_text, markdown=markdown,
+                                             generation_time=time.time() - start_time, console=console)
+                        return response_text

+                    reflection_count += 1
+                    messages.extend([
+                        {"role": "assistant", "content": response_text},
+                        {"role": "user", "content": reflection_prompt},
+                        {"role": "assistant", "content": reflection_text},
+                        {"role": "user", "content": "Now regenerate your response using the reflection you made"}
+                    ])
+                    
+                    # Get new response after reflection
+                    if verbose:
+                        with Live(display_generating("", time.time()), console=console, refresh_per_second=4) as live:
+                            response_text = ""
+                            for chunk in litellm.completion(
+                                **self._build_completion_params(
+                                    messages=messages,
+                                    temperature=temperature,
+                                    stream=True,
+                                    **kwargs
+                                )
+                            ):
+                                if chunk and chunk.choices and chunk.choices[0].delta.content:
+                                    content = chunk.choices[0].delta.content
+                                    response_text += content
+                                    live.update(display_generating(response_text, time.time()))
+                    else:
+                        response_text = ""
+                        for chunk in litellm.completion(
+                            **self._build_completion_params(
+                                messages=messages,
+                                temperature=temperature,
+                                stream=True,
+                                **kwargs
+                            )
+                        ):
+                            if chunk and chunk.choices and chunk.choices[0].delta.content:
+                                response_text += chunk.choices[0].delta.content
+                    
+                    response_text = response_text.strip()
+                    continue
+
+                except json.JSONDecodeError:
+                    reflection_count += 1
+                    if reflection_count >= max_reflect:
+                        if verbose:
+                            display_interaction(prompt, response_text, markdown=markdown,
+                                             generation_time=time.time() - start_time, console=console)
+                        return response_text
+                    continue
                except Exception as e:
                    display_error(f"Error in LLM response: {str(e)}")
                    return None
+            
+            # If we've exhausted reflection attempts
+            if verbose:
+                display_interaction(prompt, response_text, markdown=markdown,
+                                 generation_time=time.time() - start_time, console=console)
+            return response_text

        except Exception as error:
            display_error(f"Error in get_response: {str(error)}")
@@ -29,6 +29,12 @@ try:
 except ImportError:
    OPENAI_AVAILABLE = False

+try:
+    import litellm
+    LITELLM_AVAILABLE = True
+except ImportError:
+    LITELLM_AVAILABLE = False
+



@@ -340,14 +346,28 @@ class Memory:
            
        elif self.use_rag and hasattr(self, "chroma_col"):
            try:
-                from openai import OpenAI
-                client = OpenAI()
-                
-                response = client.embeddings.create(
-                    input=query,
-                    model="text-embedding-3-small"
-                )
-                query_embedding = response.data[0].embedding
+                if LITELLM_AVAILABLE:
+                    # Use LiteLLM for consistency with the rest of the codebase
+                    import litellm
+                    
+                    response = litellm.embedding(
+                        model="text-embedding-3-small",
+                        input=query
+                    )
+                    query_embedding = response.data[0]["embedding"]
+                elif OPENAI_AVAILABLE:
+                    # Fallback to OpenAI client
+                    from openai import OpenAI
+                    client = OpenAI()
+                    
+                    response = client.embeddings.create(
+                        input=query,
+                        model="text-embedding-3-small"
+                    )
+                    query_embedding = response.data[0].embedding
+                else:
+                    self._log_verbose("Neither litellm nor openai available for embeddings", logging.WARNING)
+                    return []
                
                resp = self.chroma_col.query(
                    query_embeddings=[query_embedding],
@@ -464,19 +484,39 @@ class Memory:
        # Store in vector database if enabled
        if self.use_rag and hasattr(self, "chroma_col"):
            try:
-                from openai import OpenAI
-                client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))  # Ensure API key is correctly set
-                
-                logger.info("Getting embeddings from OpenAI...")
-                logger.debug(f"Embedding input text: {text}")  # Log the input text
-                
-                response = client.embeddings.create(
-                    input=text,
-                    model="text-embedding-3-small"
-                )
-                embedding = response.data[0].embedding
-                logger.info("Successfully got embeddings")
-                logger.debug(f"Received embedding of length: {len(embedding)}")  # Log embedding details
+                if LITELLM_AVAILABLE:
+                    # Use LiteLLM for consistency with the rest of the codebase
+                    import litellm
+                    
+                    logger.info("Getting embeddings from LiteLLM...")
+                    logger.debug(f"Embedding input text: {text}")
+                    
+                    response = litellm.embedding(
+                        model="text-embedding-3-small",
+                        input=text
+                    )
+                    embedding = response.data[0]["embedding"]
+                    logger.info("Successfully got embeddings from LiteLLM")
+                    logger.debug(f"Received embedding of length: {len(embedding)}")
+                    
+                elif OPENAI_AVAILABLE:
+                    # Fallback to OpenAI client
+                    from openai import OpenAI
+                    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+                    
+                    logger.info("Getting embeddings from OpenAI...")
+                    logger.debug(f"Embedding input text: {text}")
+                    
+                    response = client.embeddings.create(
+                        input=text,
+                        model="text-embedding-3-small"
+                    )
+                    embedding = response.data[0].embedding
+                    logger.info("Successfully got embeddings from OpenAI")
+                    logger.debug(f"Received embedding of length: {len(embedding)}")
+                else:
+                    logger.warning("Neither litellm nor openai available for embeddings")
+                    return
                
                # Sanitize metadata for ChromaDB
                sanitized_metadata = self._sanitize_metadata(metadata)
@@ -527,15 +567,28 @@ class Memory:

        elif self.use_rag and hasattr(self, "chroma_col"):
            try:
-                from openai import OpenAI
-                client = OpenAI()
-                
-                # Get query embedding
-                response = client.embeddings.create(
-                    input=query,
-                    model="text-embedding-3-small"  # Using consistent model
-                )
-                query_embedding = response.data[0].embedding
+                if LITELLM_AVAILABLE:
+                    # Use LiteLLM for consistency with the rest of the codebase
+                    import litellm
+                    
+                    response = litellm.embedding(
+                        model="text-embedding-3-small",
+                        input=query
+                    )
+                    query_embedding = response.data[0]["embedding"]
+                elif OPENAI_AVAILABLE:
+                    # Fallback to OpenAI client
+                    from openai import OpenAI
+                    client = OpenAI()
+                    
+                    response = client.embeddings.create(
+                        input=query,
+                        model="text-embedding-3-small"
+                    )
+                    query_embedding = response.data[0].embedding
+                else:
+                    self._log_verbose("Neither litellm nor openai available for embeddings", logging.WARNING)
+                    return []
                
                # Search ChromaDB with embedding
                resp = self.chroma_col.query(
@@ -910,18 +963,44 @@ class Memory:
        """

        try:
-            # Use OpenAI client from main.py
-            from ..main import client
-            
-            response = client.chat.completions.create(
-                model=llm or "gpt-4o",
-                messages=[{
-                    "role": "user", 
-                    "content": custom_prompt or default_prompt
-                }],
-                response_format={"type": "json_object"},
-                temperature=0.3
-            )
+            if LITELLM_AVAILABLE:
+                # Use LiteLLM for consistency with the rest of the codebase
+                import litellm
+                
+                # Convert model name if it's in litellm format
+                model_name = llm or "gpt-4o-mini"
+                
+                response = litellm.completion(
+                    model=model_name,
+                    messages=[{
+                        "role": "user", 
+                        "content": custom_prompt or default_prompt
+                    }],
+                    response_format={"type": "json_object"},
+                    temperature=0.3
+                )
+            elif OPENAI_AVAILABLE:
+                # Fallback to OpenAI client
+                from openai import OpenAI
+                client = OpenAI()
+                
+                response = client.chat.completions.create(
+                    model=llm or "gpt-4o-mini",
+                    messages=[{
+                        "role": "user", 
+                        "content": custom_prompt or default_prompt
+                    }],
+                    response_format={"type": "json_object"},
+                    temperature=0.3
+                )
+            else:
+                logger.error("Neither litellm nor openai available for quality calculation")
+                return {
+                    "completeness": 0.0,
+                    "relevance": 0.0,
+                    "clarity": 0.0,
+                    "accuracy": 0.0
+                }
            
            metrics = json.loads(response.choices[0].message.content)
            
@@ -308,7 +308,11 @@ class Task:
                if self.agent:
                    if getattr(self.agent, '_using_custom_llm', False) and hasattr(self.agent, 'llm_instance'):
                        # For custom LLM instances (like Ollama)
-                        llm_model = self.agent.llm_instance
+                        # Extract the model name from the LLM instance
+                        if hasattr(self.agent.llm_instance, 'model'):
+                            llm_model = self.agent.llm_instance.model
+                        else:
+                            llm_model = "gpt-4o-mini"  # Default fallback
                    elif hasattr(self.agent, 'llm') and self.agent.llm:
                        # For standard model strings
                        llm_model = self.agent.llm
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "praisonaiagents"
-version = "0.0.98"
+version = "0.0.102"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 requires-python = ">=3.10"
 authors = [
@@ -25,7 +25,8 @@ mcp = [
 ]

 memory = [
-    "chromadb>=1.0.0"
+    "chromadb>=1.0.0",
+    "litellm>=1.50.0",
 ]

 knowledge = [
@@ -63,5 +64,6 @@ all = [
    "praisonaiagents[api]"
 ]

-[tool.setuptools]
-packages = ["praisonaiagents"]
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["praisonaiagents*"]
@@ -4,7 +4,7 @@ import os
 sequential_agent = Agent(
    instructions="""You are a helpful assistant that can break down complex problems.
    Use the available tools when relevant to perform step-by-step analysis.""",
-    llm="openai/gpt-4o-mini",
+    llm="gpt-4o-mini",
    tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
 )

@@ -0,0 +1,11 @@
+from praisonaiagents import Agent, MCP
+import os
+
+sequential_agent = Agent(
+    instructions="""You are a helpful assistant that can break down complex problems.
+    Use the available tools when relevant to perform step-by-step analysis.""",
+    llm="openai/gpt-4o-mini",
+    tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
+)
+
+sequential_agent.start("Break down the process of making a cup of tea")
@@ -0,0 +1,33 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+llm_config = {
+    "model": "openai/gpt-4o-mini",
+    "api_key": os.getenv('OPENAI_API_KEY'),
+    "temperature": 0.7,
+    "max_tokens": 2000
+}
+
+blog_agent = Agent(
+    role="Blog Writer",
+    goal="Write a blog post about AI",
+    backstory="Expert at writing blog posts",
+    llm="gpt-4o-mini",
+)
+
+blog_task = Task(
+    description="Write a blog post about AI trends in 1 paragraph",
+    expected_output="Well-written blog post about AI trends",
+    agent=blog_agent
+)
+
+agents = PraisonAIAgents(
+    agents=[blog_agent],
+    tasks=[blog_task],
+    memory=True
+)
+
+result = agents.start()
@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+
+from praisonaiagents import Agent, MCP
+import os
+
+def test_agent_direct():
+    """Test using gpt-4o-mini directly (agent.py path)"""
+    print("="*50)
+    print("Testing gpt-4o-mini (agent.py path)")
+    print("="*50)
+    
+    agent = Agent(
+        instructions="You are a helpful assistant that can break down complex problems.",
+        llm="gpt-4o-mini",
+        tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
+    )
+    
+    result = agent.start("What are 5 steps to make coffee?")
+    print("✅ Agent direct path completed successfully")
+    return result
+
+def test_llm_class():
+    """Test using openai/gpt-4o-mini (llm.py path)"""
+    print("\n" + "="*50)
+    print("Testing openai/gpt-4o-mini (llm.py path)")
+    print("="*50)
+    
+    agent = Agent(
+        instructions="You are a helpful assistant that can break down complex problems.",
+        llm="openai/gpt-4o-mini",
+        tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
+    )
+    
+    result = agent.start("What are 5 steps to make coffee?")
+    print("✅ LLM class path completed successfully")
+    return result
+
+if __name__ == "__main__":
+    try:
+        # Test both approaches
+        result1 = test_agent_direct()
+        result2 = test_llm_class()
+        
+        print("\n" + "="*50)
+        print("SUMMARY")
+        print("="*50)
+        print("✅ Both formats work correctly!")
+        print("✅ gpt-4o-mini uses agent.py direct OpenAI calls")
+        print("✅ openai/gpt-4o-mini uses llm.py LiteLLM wrapper")
+        print("✅ Both support tool calling and MCP integration")
+        
+    except Exception as e:
+        print(f"❌ Error occurred: {e}")
+        import traceback
+        traceback.print_exc()
@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+"""Test self-reflection functionality in both Agent and LLM classes"""
+
+from praisonaiagents import Agent
+from praisonaiagents.llm import LLM
+
+def test_agent_self_reflection():
+    """Test self-reflection in Agent class"""
+    print("=== Testing Agent Self-Reflection ===")
+    agent = Agent(
+        name="ReflectiveAgent",
+        instructions="You are a helpful assistant.",
+        llm="gpt-4o-mini",
+        self_reflect=True,
+        min_reflect=1,
+        max_reflect=3
+    )
+    
+    response = agent.start("What is 2+2? Be brief.")
+    print(f"Agent Response: {response}")
+    print()
+
+def test_llm_self_reflection():
+    """Test self-reflection in LLM class directly"""
+    print("=== Testing LLM Self-Reflection ===")
+    llm = LLM(model="gpt-4o-mini")
+    
+    response = llm.get_response(
+        prompt="What is 2+2? Be brief.",
+        system_prompt="You are a helpful assistant.",
+        self_reflect=True,
+        min_reflect=1,
+        max_reflect=3,
+        verbose=True
+    )
+    print(f"LLM Response: {response}")
+    print()
+
+def test_llm_no_reflection():
+    """Test LLM without self-reflection for comparison"""
+    print("=== Testing LLM Without Self-Reflection ===")
+    llm = LLM(model="gpt-4o-mini")
+    
+    response = llm.get_response(
+        prompt="What is 2+2? Be brief.",
+        system_prompt="You are a helpful assistant.",
+        self_reflect=False,
+        verbose=True
+    )
+    print(f"LLM Response: {response}")
+    print()
+
+if __name__ == "__main__":
+    # Test without reflection first
+    test_llm_no_reflection()
+    
+    # Test with reflection
+    test_llm_self_reflection()
+    
+    # Test agent reflection
+    test_agent_self_reflection()
@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+
+from praisonaiagents import Agent, MCP
+import os
+
+def test_agent_direct():
+    """Test using gpt-4o-mini directly (agent.py path)"""
+    print("=" * 50)
+    print("Testing gpt-4o-mini (agent.py path)")
+    print("=" * 50)
+    
+    agent = Agent(
+        instructions="You are a helpful assistant that can break down complex problems.",
+        llm="gpt-4o-mini",
+        tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
+    )
+    
+    result = agent.start("What are 3 steps to make coffee?")
+    print("✅ Agent direct path completed successfully")
+    return result
+
+def test_llm_class():
+    """Test using openai/gpt-4o-mini (llm.py path)"""
+    print("\n" + "=" * 50)
+    print("Testing openai/gpt-4o-mini (llm.py path)")
+    print("=" * 50)
+    
+    agent = Agent(
+        instructions="You are a helpful assistant that can break down complex problems.",
+        llm="openai/gpt-4o-mini",
+        tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
+    )
+    
+    result = agent.start("What are 3 steps to make coffee?")
+    print("✅ LLM class path completed successfully")
+    return result
+
+if __name__ == "__main__":
+    try:
+        # Test both approaches
+        result1 = test_agent_direct()
+        result2 = test_llm_class()
+        
+        print("\n" + "=" * 50)
+        print("SUMMARY")
+        print("=" * 50)
+        print("✅ Both formats work correctly!")
+        print("✅ gpt-4o-mini uses agent.py direct OpenAI calls")
+        print("✅ openai/gpt-4o-mini uses llm.py LiteLLM wrapper")
+        print("✅ Both support sequential tool calling and MCP integration")
+        print("✅ Sequential thinking tool works properly in both modes")
+        
+    except Exception as e:
+        print(f"❌ Error occurred: {e}")
+        import traceback
+        traceback.print_exc()
@@ -3,8 +3,8 @@ class Praisonai < Formula
  
    desc "AI tools for various AI applications"
    homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.27.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.27.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.30.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.30.tar.gz | shasum -a 256`.split.first
    license "MIT"
  
    depends_on "[email protected]"
@@ -56,7 +56,7 @@ class CloudDeployer:
            file.write("FROM python:3.11-slim\n")
            file.write("WORKDIR /app\n")
            file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.2.27 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.2.30 gunicorn markdown\n")
            file.write("EXPOSE 8080\n")
            file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
            
@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.2.27"
+version = "2.2.30"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 readme = "README.md"
 license = ""
@@ -12,7 +12,7 @@ dependencies = [
    "rich>=13.7",
    "markdown>=3.5",
    "pyparsing>=3.0.0",
-    "praisonaiagents>=0.0.97",
+    "praisonaiagents>=0.0.102",
    "python-dotenv>=0.19.0",
    "instructor>=1.3.3",
    "PyYAML>=6.0",
@@ -95,7 +95,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.15", "crewai"]

 [tool.poetry]
 name = "PraisonAI"
-version = "2.2.27"
+version = "2.2.30"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 authors = ["Mervin Praison"]
 license = ""
@@ -113,7 +113,7 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.97"
+praisonaiagents = ">=0.0.102"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"
@@ -0,0 +1,135 @@
+#!/usr/bin/env python3
+"""
+Comprehensive test script to verify all import scenarios work correctly
+Tests the original failing import from the GitHub issue
+"""
+
+import sys
+import os
+
+# Add the praisonai-agents source to Python path
+sys.path.insert(0, '/home/runner/work/PraisonAI/PraisonAI/src/praisonai-agents')
+
+def test_original_failing_import():
+    """Test the exact import that was failing in the GitHub issue"""
+    print("=== Testing Original Failing Import ===")
+    try:
+        from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
+        print('✅ SUCCESS: from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents')
+        return True
+    except ImportError as e:
+        print(f'❌ ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+    except Exception as e:
+        print(f'❌ UNEXPECTED ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_memory_direct_import():
+    """Test direct Memory import"""
+    print("\n=== Testing Direct Memory Import ===")
+    try:
+        from praisonaiagents.memory import Memory
+        print('✅ SUCCESS: from praisonaiagents.memory import Memory')
+        return True
+    except ImportError as e:
+        print(f'❌ ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+    except Exception as e:
+        print(f'❌ UNEXPECTED ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_memory_from_package_root():
+    """Test Memory import from package root"""
+    print("\n=== Testing Memory Import from Package Root ===")
+    try:
+        from praisonaiagents import Memory
+        print('✅ SUCCESS: from praisonaiagents import Memory')
+        return True
+    except ImportError as e:
+        print(f'❌ ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+    except Exception as e:
+        print(f'❌ UNEXPECTED ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_session_import():
+    """Test Session import which depends on Memory"""
+    print("\n=== Testing Session Import ===")
+    try:
+        from praisonaiagents.session import Session
+        print('✅ SUCCESS: from praisonaiagents.session import Session')
+        return True
+    except ImportError as e:
+        print(f'❌ ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+    except Exception as e:
+        print(f'❌ UNEXPECTED ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_memory_instantiation():
+    """Test that Memory can be instantiated without errors"""
+    print("\n=== Testing Memory Instantiation ===")
+    try:
+        from praisonaiagents.memory import Memory
+        
+        # Test with minimal config (no external dependencies)
+        config = {"provider": "none"}
+        memory = Memory(config=config)
+        print('✅ SUCCESS: Memory instance created with provider="none"')
+        
+        # Test basic methods don't fail immediately
+        memory.store_short_term("test content", metadata={"test": True})
+        results = memory.search_short_term("test", limit=1)
+        print('✅ SUCCESS: Basic memory operations work')
+        
+        return True
+    except Exception as e:
+        print(f'❌ ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+def run_all_tests():
+    """Run all tests and report results"""
+    print("🔍 Running comprehensive import tests...")
+    
+    tests = [
+        test_original_failing_import,
+        test_memory_direct_import, 
+        test_memory_from_package_root,
+        test_session_import,
+        test_memory_instantiation
+    ]
+    
+    results = []
+    for test in tests:
+        results.append(test())
+    
+    print(f"\n📊 Test Results: {sum(results)}/{len(results)} tests passed")
+    
+    if all(results):
+        print("🎉 ALL TESTS PASSED! The Memory import issue has been resolved.")
+    else:
+        print("❌ Some tests failed. The issue may not be fully resolved.")
+    
+    return all(results)
+
+if __name__ == "__main__":
+    success = run_all_tests()
+    sys.exit(0 if success else 1)
@@ -0,0 +1,29 @@
+#!/usr/bin/env python3
+"""
+Test script to verify Memory import works correctly
+"""
+
+import sys
+import os
+
+# Add the praisonai-agents source to Python path
+sys.path.insert(0, '/home/runner/work/PraisonAI/PraisonAI/src/praisonai-agents')
+
+try:
+    from praisonaiagents.memory import Memory
+    print('SUCCESS: Memory import works correctly')
+    print('Memory class found:', Memory)
+    
+    # Try to create a minimal instance to ensure it doesn't fail immediately
+    config = {"provider": "none"}  # Use the simplest provider
+    memory = Memory(config=config)
+    print('SUCCESS: Memory instance created successfully')
+    
+except ImportError as e:
+    print('ERROR:', e)
+    import traceback
+    traceback.print_exc()
+except Exception as e:
+    print('UNEXPECTED ERROR:', e)
+    import traceback
+    traceback.print_exc()
@@ -0,0 +1,28 @@
+#!/usr/bin/env python3
+"""
+Test script to verify session.py import works correctly (which imports Memory)
+"""
+
+import sys
+import os
+
+# Add the praisonai-agents source to Python path
+sys.path.insert(0, '/home/runner/work/PraisonAI/PraisonAI/src/praisonai-agents')
+
+try:
+    from praisonaiagents.session import Session
+    print('SUCCESS: Session import works correctly')
+    print('Session class found:', Session)
+    
+    # This was the failing import chain
+    from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
+    print('SUCCESS: Agent, Task, PraisonAIAgents import works correctly')
+    
+except ImportError as e:
+    print('ERROR:', e)
+    import traceback
+    traceback.print_exc()
+except Exception as e:
+    print('UNEXPECTED ERROR:', e)
+    import traceback
+    traceback.print_exc()
Author	SHA1	Message	Date
Mervin Praison	dc5b10fba8	Merge pull request #611 from MervinPraison/develop Update PraisonAI version to 2.2.30 across Dockerfiles and related files	2025-06-05 18:18:04 +01:00
MervinPraison	f0172e8339	Update PraisonAI version to 2.2.30 across Dockerfiles and related files - Incremented the version of PraisonAI from 2.2.29 to 2.2.30 in Dockerfiles (Dockerfile, Dockerfile.chat, Dockerfile.dev, Dockerfile.ui). - Updated the version in the README.md and pyproject.toml files to reflect the new version. - Adjusted the deploy.py script to install the updated version of PraisonAI. - Ensured consistency across all relevant files for seamless integration.	2025-06-05 18:17:47 +01:00
Mervin Praison	5c498a12de	Merge pull request #610 from MervinPraison/claude/issue-605-20250605_165941 fix: resolve Memory import error with litellm compatibility	2025-06-05 18:16:11 +01:00
Mervin Praison	88410881e1	Rename test_session_import.py to src/praisonai/tests/unit/test_session_import.py	2025-06-05 18:10:43 +01:00
Mervin Praison	791d59f96e	Rename test_memory_import.py to src/praisonai/tests/unit/test_memory_import.py	2025-06-05 18:10:25 +01:00
Mervin Praison	cdbe1303ad	Rename test_comprehensive_import.py to src/praisonai/tests/unit/test_comprehensive_import.py	2025-06-05 18:10:10 +01:00
claude[bot]andMervinPraison	b63f931bea	feat: add Memory to main package exports and comprehensive tests - Export Memory class from main praisonaiagents package - Add Memory to __all__ list for proper package API - Create comprehensive test suite to verify all import scenarios - Tests original failing import from GitHub issue - Tests direct Memory imports and instantiation Co-authored-by: MervinPraison <[email protected]>	2025-06-05 17:02:47 +00:00
claude[bot]andMervinPraison	ff25b96f91	fix: resolve Memory import error by implementing litellm compatibility - Add LITELLM_AVAILABLE flag for proper dependency detection - Implement litellm → openai fallback pattern for all embedding operations - Update search_short_term, store_long_term, search_long_term embedding calls - Update calculate_quality_metrics to use litellm → openai fallback - Add graceful degradation when neither litellm nor openai is available - Fixes ImportError: cannot import name 'Memory' from 'praisonaiagents.memory' Co-authored-by: MervinPraison <[email protected]>	2025-06-05 17:02:03 +00:00
Mervin Praison	603f0ea008	Merge pull request #609 from MervinPraison/develop Develop	2025-06-05 17:55:15 +01:00
MervinPraison	db38b55174	Update PraisonAI version to 2.2.29 across Dockerfiles and related files - Incremented the version of PraisonAI from 2.2.28 to 2.2.29 in Dockerfiles (Dockerfile, Dockerfile.chat, Dockerfile.dev, Dockerfile.ui). - Updated the version in the README.md and pyproject.toml files to reflect the new version. - Adjusted the deploy.py script to install the updated version of PraisonAI. - Ensured consistency across all relevant files for seamless integration.	2025-06-05 17:54:57 +01:00
MervinPraison	cebd175d61	Update versioning and dependencies in project files - Incremented version of PraisonAI from 0.0.99 to 0.0.101 in pyproject.toml and uv.lock. - Added 'litellm' dependency to memory requirements for enhanced functionality. - Updated .gitignore to include 'CopilotKit*' for better file management. - Optimised TaskOutput instantiation in agent.py for clarity. - Refined memory handling in memory.py to utilise LiteLLM for consistency. - Improved model extraction logic in task.py for better fallback handling.	2025-06-05 17:53:38 +01:00
Mervin Praison	01ff75e573	Merge pull request #606 from DhivyaBharathy-web/add-qwen2-5-agent Add Qwen2.5 Instruction Agent notebook using Hugging Face Transformers	2025-06-05 17:43:36 +01:00
Mervin Praison	28629f2203	Merge pull request #607 from DhivyaBharathy-web/add-gemma2b-instruction-agent Add Gemma2B_Instruction_Agent to Cookbooks	2025-06-05 17:43:19 +01:00
Mervin Praison	ac5c463362	Merge pull request #602 from DhivyaBharathy-web/add-predictive-maintenance-notebook Add predictive maintenance notebook	2025-06-05 17:43:05 +01:00
Mervin Praison	84dc57583e	Merge pull request #608 from DhivyaBharathy-web/add-tinyllama-model-agent Add tinyllama model agent	2025-06-05 17:42:50 +01:00
DhivyaBharathy	460e8b45a7	Merge branch 'main' into add-tinyllama-model-agent	2025-06-05 19:30:02 +05:30
DhivyaBharathy	f6bbbd7cd6	Update TinyLlama_1_1B_model_SimpleAIAgent.ipynb	2025-06-05 19:29:06 +05:30
DhivyaBharathy	8a435571f4	Add files via upload	2025-06-05 19:10:44 +05:30
DhivyaBharathy	204106daa8	Merge branch 'main' into add-gemma2b-instruction-agent	2025-06-05 16:47:50 +05:30
DhivyaBharathy	6a1e14f97a	Merge branch 'main' into add-qwen2-5-agent	2025-06-05 16:47:02 +05:30
DhivyaBharathy	333777b991	Merge branch 'main' into add-predictive-maintenance-notebook	2025-06-05 16:46:13 +05:30
DhivyaBharathy	b02cd85290	Update Gemma2B_Instruction_Agent.ipynb	2025-06-05 16:27:38 +05:30
DhivyaBharathy	3bc0c2b533	Update Gemma2B_Instruction_Agent.ipynb	2025-06-05 16:26:56 +05:30
DhivyaBharathy	68d4f34bb7	Update Gemma2B_Instruction_Agent.ipynb	2025-06-05 16:26:37 +05:30
DhivyaBharathy	a24d900a8c	Add files via upload	2025-06-05 16:22:33 +05:30
DhivyaBharathy-web	552a10df9e	Add Qwen2.5 Instruction Agent notebook	2025-06-05 10:49:32 +00:00
DhivyaBharathy	9c57aa3165	Update Qwen2_5_InstructionAgent.ipynb	2025-06-05 15:16:04 +05:30
DhivyaBharathy	40fe92f1d0	Update Qwen2_5_InstructionAgent.ipynb	2025-06-05 15:14:56 +05:30
DhivyaBharathy	179cc9566d	Update Qwen2_5_InstructionAgent.ipynb	2025-06-05 15:09:09 +05:30
DhivyaBharathy	d9cf9ba534	Add files via upload	2025-06-05 15:07:47 +05:30
DhivyaBharathy-web	4ec0366e79	Add Qwen2.5 Instruction Agent notebook	2025-06-05 09:26:46 +00:00
Mervin Praison	d6a3bd1240	Merge pull request #604 from MervinPraison/develop Update PraisonAI version to 2.2.28 across Dockerfiles and related files	2025-06-04 22:59:48 +01:00
MervinPraison	0a18e27f54	Update PraisonAI version to 2.2.28 across Dockerfiles and related files - Updated the version of PraisonAI from 2.2.27 to 2.2.28 in Dockerfiles (Dockerfile, Dockerfile.chat, Dockerfile.dev, Dockerfile.ui). - Modified the version in the README.md and pyproject.toml files to reflect the new version. - Adjusted the deploy.py script to install the updated version of PraisonAI. - Ensured consistency across all relevant files for seamless integration.	2025-06-04 22:59:30 +01:00
DhivyaBharathy	b824d950b5	Add files via upload	2025-06-04 20:32:10 +05:30
DhivyaBharathy	b31edc90d5	Update Code_Analysis_Agent.ipynb	2025-06-04 16:58:52 +05:30
DhivyaBharathy	d2841ccb3a	Merge branch 'MervinPraison:main' into main	2025-06-04 16:53:54 +05:30