@ -1,56 +1,39 @@
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"machine_shape": "hm",
"gpuType": "L4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "Qf8eZIT71wba"
},
"source": [
"# Entry for SwarmsHackathon 2024\n",
"\n"
],
"metadata": {
"id": "Qf8eZIT71wba"
}
]
},
{
"cell_type": "markdown",
"source": [
"## Install Swarms"
],
"metadata": {
"id": "-rBXNMWV4EWN"
}
},
"source": [
"## Install Swarms"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "w4FoSEyP1q_x",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "w4FoSEyP1q_x",
"outputId": "ea6b15e7-c53c-47aa-86c6-b24d4aff041b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting swarms\n",
" Downloading swarms-5.1.4-py3-none-any.whl (338 kB)\n",
@ -214,19 +197,19 @@
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"id": "43b664ed28b2464da4f7c30cb0f343ce",
"pip_warning": {
"packages": [
"PIL",
"asyncio"
]
},
"id": "43b664ed28b2464da4f7c30cb0f343ce"
}
}
},
"metadata": {}
"metadata": {},
"output_type": "display_data"
}
],
"source": [
@ -235,60 +218,57 @@
},
{
"cell_type": "markdown",
"source": [
"Import keys"
],
"metadata": {
"id": "QTMXxRxw7yR5"
}
},
"source": [
"Import keys"
]
},
{
"cell_type": "code",
"source": [
"from google.colab import userdata\n",
"anthropic_api_key = userdata.get('ANTHROPIC_API_KEY')"
],
"execution_count": 1,
"metadata": {
"id": "lzSnwHw-7z8B"
},
"execution_count": 1,
"outputs": []
"outputs": [],
"source": [
"from google.colab import userdata\n",
"anthropic_api_key = userdata.get('ANTHROPIC_API_KEY')"
]
},
{
"cell_type": "markdown",
"source": [
"## Devin like"
],
"metadata": {
"id": "eD0PkNm25SVT"
}
},
"source": [
"## Devin like"
]
},
{
"cell_type": "markdown",
"source": [
"This example requires the anthropic library which is not installed by default."
],
"metadata": {
"id": "0Shm1vrS-YFZ"
}
},
"source": [
"This example requires the anthropic library which is not installed by default."
]
},
{
"cell_type": "code",
"source": [
"!pip install anthropic"
],
"execution_count": 2,
"metadata": {
"id": "aZG6eSjr-U7J",
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aZG6eSjr-U7J",
"outputId": "b5460b70-5db9-45d7-d66a-d2eb596b86b7"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting anthropic\n",
" Using cached anthropic-0.28.0-py3-none-any.whl (862 kB)\n",
@ -324,23 +304,26 @@
"Successfully installed anthropic-0.28.0 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 jiter-0.4.1\n"
]
}
],
"source": [
"!pip install anthropic"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "NyroG92H1m2G",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "NyroG92H1m2G",
"outputId": "69f4ff8b-39c7-41db-c876-4694336d812e"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2024-06-02T20:32:00.407576+0000\u001b[0m \u001b[1mNumber of tools: 4\u001b[0m\n",
"\u001b[32m2024-06-02T20:32:00.407998+0000\u001b[0m \u001b[1mTools provided, Automatically converting to OpenAI function\u001b[0m\n",
@ -351,8 +334,8 @@
]
},
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing Autonomous Agent Devin...\n",
"Autonomous Agent Activated.\n",
@ -506,9 +489,9 @@
]
},
{
"output_type": "error",
"ename": "KeyboardInterrupt",
"evalue": "Interrupted by user",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
@ -613,7 +596,6 @@
" max_loops=\"auto\",\n",
" autosave=True,\n",
" dashboard=False,\n",
" streaming_on=True,\n",
" verbose=True,\n",
" stopping_token=\"<DONE>\",\n",
" interactive=True,\n",
@ -629,71 +611,7 @@
},
{
"cell_type": "code",
"source": [
"from swarms import Agent, AgentRearrange, rearrange\n",
"from typing import List\n",
"\n",
"llm = Anthropic(\n",
" temperature=0.1,\n",
" anthropic_api_key = anthropic_api_key\n",
")\n",
"# Initialize the director agent\n",
"director = Agent(\n",
" agent_name=\"Director\",\n",
" system_prompt=\"Directs the tasks for the workers\",\n",
" llm=llm,\n",
" max_loops=1,\n",
" dashboard=False,\n",
" streaming_on=True,\n",
" verbose=True,\n",
" stopping_token=\"<DONE>\",\n",
" state_save_file_type=\"json\",\n",
" saved_state_path=\"director.json\",\n",
")\n",
"\n",
"# Initialize worker 1\n",
"worker1 = Agent(\n",
" agent_name=\"Worker1\",\n",
" system_prompt=\"Generates a transcript for a youtube video on what swarms are\",\n",
" llm=llm,\n",
" max_loops=1,\n",
" dashboard=False,\n",
" streaming_on=True,\n",
" verbose=True,\n",
" stopping_token=\"<DONE>\",\n",
" state_save_file_type=\"json\",\n",
" saved_state_path=\"worker1.json\",\n",
")\n",
"\n",
"# Initialize worker 2\n",
"worker2 = Agent(\n",
" agent_name=\"Worker2\",\n",
" system_prompt=\"Summarizes the transcript generated by Worker1\",\n",
" llm=llm,\n",
" max_loops=1,\n",
" dashboard=False,\n",
" streaming_on=True,\n",
" verbose=True,\n",
" stopping_token=\"<DONE>\",\n",
" state_save_file_type=\"json\",\n",
" saved_state_path=\"worker2.json\",\n",
")\n",
"\n",
"# Create a list of agents\n",
"agents = [director, worker1, worker2]\n",
"\n",
"# Define the flow pattern\n",
"flow = \"Director -> Worker1 -> Worker2\"\n",
"\n",
"# Using AgentRearrange class\n",
"agent_system = AgentRearrange(agents=agents, flow=flow)\n",
"output = agent_system.run(\"Create a format to express and communicate swarms of llms in a structured manner for youtube\")\n",
"print(output)\n",
"\n",
"# Using rearrange function\n",
"output = rearrange(agents, flow, \"Create a format to express and communicate swarms of llms in a structured manner for youtube\")\n",
"print(output)"
],
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@ -701,19 +619,18 @@
"id": "1j3RgVk1ol6G",
"outputId": "a365266e-7c11-4c2d-9e31-19842483b165"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2024-06-02T20:34:54.149688+0000\u001b[0m \u001b[1mAgentRearrange initialized with agents: ['Director', 'Worker1', 'Worker2']\u001b[0m\n",
"\u001b[32m2024-06-02T20:34:54.151361+0000\u001b[0m \u001b[1mRunning agents sequentially: ['Director']\u001b[0m\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"Flow is valid.\n",
"Initializing Autonomous Agent Director...\n",
@ -728,15 +645,15 @@
]
},
{
"output_type": "stream",
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2024-06-02T20:35:02.526464+0000\u001b[0m \u001b[1mRunning agents sequentially: ['Worker1']\u001b[0m\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Llm Swarm Video Format\n",
@ -771,15 +688,15 @@
]
},
{
"output_type": "stream",
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2024-06-02T20:35:07.814536+0000\u001b[0m \u001b[1mRunning agents sequentially: ['Worker2']\u001b[0m\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"[Swarm Name] Llm Swarm\n",
@ -810,16 +727,16 @@
]
},
{
"output_type": "stream",
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2024-06-02T20:35:11.887014+0000\u001b[0m \u001b[1mAgentRearrange initialized with agents: ['Director', 'Worker1', 'Worker2']\u001b[0m\n",
"\u001b[32m2024-06-02T20:35:11.889429+0000\u001b[0m \u001b[1mRunning agents sequentially: ['Director']\u001b[0m\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"[Swarm Name] Llm Swarm\n",
@ -868,15 +785,15 @@
]
},
{
"output_type": "stream",
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2024-06-02T20:35:18.085897+0000\u001b[0m \u001b[1mRunning agents sequentially: ['Worker1']\u001b[0m\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Llm Swarm Video Format\n",
@ -912,15 +829,15 @@
]
},
{
"output_type": "stream",
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2024-06-02T20:35:23.508710+0000\u001b[0m \u001b[1mRunning agents sequentially: ['Worker2']\u001b[0m\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"[Swarm Name] Llm Swarm\n",
@ -993,7 +910,86 @@
"I think focusing on presenting uplifting dialogue between AI systems is a thoughtful idea. This script outlines a respectful approach. Please let me know if you would like me to modify or expand on anything! I'm happy to help further.\n"
]
}
],
"source": [
"from swarms import Agent, AgentRearrange, rearrange\n",
"from typing import List\n",
"\n",
"llm = Anthropic(\n",
" temperature=0.1,\n",
" anthropic_api_key = anthropic_api_key\n",
")\n",
"# Initialize the director agent\n",
"director = Agent(\n",
" agent_name=\"Director\",\n",
" system_prompt=\"Directs the tasks for the workers\",\n",
" llm=llm,\n",
" max_loops=1,\n",
" dashboard=False,\n",
" verbose=True,\n",
" stopping_token=\"<DONE>\",\n",
" state_save_file_type=\"json\",\n",
" saved_state_path=\"director.json\",\n",
")\n",
"\n",
"# Initialize worker 1\n",
"worker1 = Agent(\n",
" agent_name=\"Worker1\",\n",
" system_prompt=\"Generates a transcript for a youtube video on what swarms are\",\n",
" llm=llm,\n",
" max_loops=1,\n",
" dashboard=False,\n",
" verbose=True,\n",
" stopping_token=\"<DONE>\",\n",
" state_save_file_type=\"json\",\n",
" saved_state_path=\"worker1.json\",\n",
")\n",
"\n",
"# Initialize worker 2\n",
"worker2 = Agent(\n",
" agent_name=\"Worker2\",\n",
" system_prompt=\"Summarizes the transcript generated by Worker1\",\n",
" llm=llm,\n",
" max_loops=1,\n",
" dashboard=False,\n",
" verbose=True,\n",
" stopping_token=\"<DONE>\",\n",
" state_save_file_type=\"json\",\n",
" saved_state_path=\"worker2.json\",\n",
")\n",
"\n",
"# Create a list of agents\n",
"agents = [director, worker1, worker2]\n",
"\n",
"# Define the flow pattern\n",
"flow = \"Director -> Worker1 -> Worker2\"\n",
"\n",
"# Using AgentRearrange class\n",
"agent_system = AgentRearrange(agents=agents, flow=flow)\n",
"output = agent_system.run(\"Create a format to express and communicate swarms of llms in a structured manner for youtube\")\n",
"print(output)\n",
"\n",
"# Using rearrange function\n",
"output = rearrange(agents, flow, \"Create a format to express and communicate swarms of llms in a structured manner for youtube\")\n",
"print(output)"
]
}
]
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "L4",
"machine_shape": "hm",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
Nicolas Nahas
9 months ago