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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## AutoGen Studio Agent Workflow API Example\n",
"\n",
"This notebook focuses on demonstrating capabilities of the autogen studio workflow python api. \n",
"\n",
"- Declarative Specification of an Agent Team\n",
"- Loading the specification and running the resulting agent\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"task_result=TaskResult(messages=[TextMessage(source='user', models_usage=None, content='What is the weather in New York?'), ToolCallMessage(source='writing_agent', models_usage=RequestUsage(prompt_tokens=65, completion_tokens=15), content=[FunctionCall(id='call_x8C5nib1PJkMZGQ6zrNUlfa0', arguments='{\"city\":\"New York\"}', name='get_weather')]), ToolCallResultMessage(source='writing_agent', models_usage=None, content=[FunctionExecutionResult(content='The weather in New York is 73 degrees and Sunny.', call_id='call_x8C5nib1PJkMZGQ6zrNUlfa0')]), TextMessage(source='writing_agent', models_usage=RequestUsage(prompt_tokens=97, completion_tokens=14), content='The weather in New York is currently 73 degrees and sunny.'), TextMessage(source='writing_agent', models_usage=RequestUsage(prompt_tokens=123, completion_tokens=13), content='Would you like to know the weather in any other city?')], stop_reason='Maximum number of messages 5 reached, current message count: 5') usage='' duration=1.9984567165374756\n"
]
}
],
"source": [
"from autogenstudio.teammanager import TeamManager \n",
" \n",
"wm = TeamManager() \n",
"result = await wm.run(task=\"What is the weather in New York?\", team_config=\"team.json\") \n",
"print(result)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"source='user' models_usage=None content='What is the weather in New York?'\n",
"source='writing_agent' models_usage=RequestUsage(prompt_tokens=65, completion_tokens=15) content=[FunctionCall(id='call_Gwnfsa8ndnOsXTvRECTr92hr', arguments='{\"city\":\"New York\"}', name='get_weather')]\n",
"source='writing_agent' models_usage=None content=[FunctionExecutionResult(content='The weather in New York is 73 degrees and Sunny.', call_id='call_Gwnfsa8ndnOsXTvRECTr92hr')]\n",
"source='writing_agent' models_usage=RequestUsage(prompt_tokens=97, completion_tokens=14) content='The weather in New York is currently 73 degrees and sunny.'\n",
"source='writing_agent' models_usage=RequestUsage(prompt_tokens=123, completion_tokens=14) content='The weather in New York is currently 73 degrees and sunny.'\n",
"task_result=TaskResult(messages=[TextMessage(source='user', models_usage=None, content='What is the weather in New York?'), ToolCallMessage(source='writing_agent', models_usage=RequestUsage(prompt_tokens=65, completion_tokens=15), content=[FunctionCall(id='call_Gwnfsa8ndnOsXTvRECTr92hr', arguments='{\"city\":\"New York\"}', name='get_weather')]), ToolCallResultMessage(source='writing_agent', models_usage=None, content=[FunctionExecutionResult(content='The weather in New York is 73 degrees and Sunny.', call_id='call_Gwnfsa8ndnOsXTvRECTr92hr')]), TextMessage(source='writing_agent', models_usage=RequestUsage(prompt_tokens=97, completion_tokens=14), content='The weather in New York is currently 73 degrees and sunny.'), TextMessage(source='writing_agent', models_usage=RequestUsage(prompt_tokens=123, completion_tokens=14), content='The weather in New York is currently 73 degrees and sunny.')], stop_reason='Maximum number of messages 5 reached, current message count: 5') usage='' duration=2.363379955291748\n"
]
}
],
"source": [
"result_stream = wm.run_stream(task=\"What is the weather in New York?\", team_config=\"team.json\") \n",
"async for response in result_stream:\n",
" print(response)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## AutoGen Studio Database API\n",
"\n",
"Api for creating objects and serializing to a database."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO [alembic.runtime.migration] Context impl SQLiteImpl.\n",
"INFO [alembic.runtime.migration] Will assume non-transactional DDL.\n",
"\u001b[32m2024-11-14 09:06:25.242\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mautogenstudio.database.schema_manager\u001b[0m:\u001b[36mupgrade_schema\u001b[0m:\u001b[36m390\u001b[0m - \u001b[1mSchema upgraded successfully to head\u001b[0m\n",
"\u001b[32m2024-11-14 09:06:25.243\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mautogenstudio.database.db_manager\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m34\u001b[0m - \u001b[1mDatabase schema was upgraded automatically\u001b[0m\n",
"\u001b[32m2024-11-14 09:06:25.244\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mautogenstudio.database.db_manager\u001b[0m:\u001b[36mcreate_db_and_tables\u001b[0m:\u001b[36m108\u001b[0m - \u001b[1mDatabase tables created successfully\u001b[0m\n"
]
}
],
"source": [
"from autogenstudio.database import DatabaseManager \n",
"\n",
"# create a database\n",
"dbmanager = DatabaseManager(engine_uri=\"sqlite:///test.db\")\n",
"dbmanager.create_db_and_tables() "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"\n",
"from sqlmodel import Session, text, select\n",
"from autogenstudio.datamodel import Model, ModelConfig, ModelTypes, Team, TeamConfig, TeamTypes, Agent, AgentConfig, AgentTypes, Tool, ToolConfig, LinkTypes,ToolTypes\n",
"\n",
"user_id = \"guestuser@gmail.com\"\n",
"from autogenstudio.datamodel import ModelConfig, Model, TeamConfig, Team, Tool, Agent, AgentConfig, TerminationConfig, TerminationTypes, ModelTypes, TeamTypes, AgentTypes, ToolConfig, LinkTypes, TerminationTypes\n",
"\n",
"gpt4_model = Model(user_id=user_id, config= ModelConfig(model=\"gpt-4o-2024-08-06\", model_type=ModelTypes.OPENAI).model_dump() )\n",
"\n",
"weather_tool = Tool(user_id=user_id, config=ToolConfig(name=\"get_weather\", description=\"Get the weather for a city\", content=\"async def get_weather(city: str) -> str:\\n return f\\\"The weather in {city} is 73 degrees and Sunny.\\\"\",tool_type=ToolTypes.PYTHON_FUNCTION).model_dump() )\n",
"\n",
"adding_tool = Tool(user_id=user_id, config=ToolConfig(name=\"add\", description=\"Add two numbers\", content=\"async def add(a: int, b: int) -> int:\\n return a + b\", tool_type=ToolTypes.PYTHON_FUNCTION).model_dump() )\n",
"\n",
"writing_agent = Agent(user_id=user_id, \n",
" config=AgentConfig(\n",
" name=\"writing_agent\", \n",
" tools=[weather_tool.config], \n",
" agent_type=AgentTypes.ASSISTANT,\n",
" model_client=gpt4_model.config\n",
" ).model_dump()\n",
" )\n",
"\n",
"team = Team(user_id=user_id, config=TeamConfig(\n",
" name=\"weather_team\",\n",
" participants=[writing_agent.config],\n",
" termination_condition=TerminationConfig(termination_type=TerminationTypes.MAX_MESSAGES, max_messages=5).model_dump(),\n",
" team_type=TeamTypes.ROUND_ROBIN\n",
" ).model_dump()\n",
")\n",
"\n",
"with Session(dbmanager.engine) as session:\n",
" session.add(gpt4_model)\n",
" session.add(weather_tool)\n",
" session.add(adding_tool)\n",
" session.add(writing_agent)\n",
" session.add(team)\n",
" session.commit()\n",
"\n",
" dbmanager.link(LinkTypes.AGENT_MODEL, writing_agent.id, gpt4_model.id)\n",
" dbmanager.link(LinkTypes.AGENT_TOOL, writing_agent.id, weather_tool.id)\n",
" dbmanager.link(LinkTypes.AGENT_TOOL, writing_agent.id, adding_tool.id)\n",
" dbmanager.link(LinkTypes.TEAM_AGENT, team.id, writing_agent.id)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"53 teams in database\n"
]
}
],
"source": [
"all_teams = dbmanager.get(Team)\n",
"print(len(all_teams.data), \"teams in database\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configuration Manager\n",
"\n",
"Helper class to mostly import teams/agents/models/tools etc into a database."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from autogenstudio.database import ConfigurationManager \n",
"\n",
"config_manager = ConfigurationManager(dbmanager)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"result = await config_manager.import_component(\"team.json\", user_id=\"user_id\", check_exists=True)\n",
"print(result)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"message='Directory import complete' status=True data=[{'component': 'team', 'status': True, 'message': 'Team Created Successfully', 'id': 54}]\n"
]
}
],
"source": [
"result = await config_manager.import_directory(\".\", user_id=\"user_id\", check_exists=False)\n",
"print(result)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"54 teams in database\n"
]
}
],
"source": [
"all_teams = dbmanager.get(Team)\n",
"print(len(all_teams.data), \"teams in database\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sample AgentChat Example (Python)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from autogen_agentchat.agents import AssistantAgent\n",
"from autogen_agentchat.task import TextMentionTermination\n",
"from autogen_agentchat.teams import RoundRobinGroupChat, SelectorGroupChat\n",
"from autogen_ext.models import OpenAIChatCompletionClient \n",
"\n",
"planner_agent = AssistantAgent(\n",
" \"planner_agent\",\n",
" model_client=OpenAIChatCompletionClient(model=\"gpt-4\"),\n",
" description=\"A helpful assistant that can plan trips.\",\n",
" system_message=\"You are a helpful assistant that can suggest a travel plan for a user based on their request. Respond with a single sentence\",\n",
")\n",
"\n",
"local_agent = AssistantAgent(\n",
" \"local_agent\",\n",
" model_client=OpenAIChatCompletionClient(model=\"gpt-4\"),\n",
" description=\"A local assistant that can suggest local activities or places to visit.\",\n",
" system_message=\"You are a helpful assistant that can suggest authentic and interesting local activities or places to visit for a user and can utilize any context information provided. Respond with a single sentence\",\n",
")\n",
"\n",
"language_agent = AssistantAgent(\n",
" \"language_agent\",\n",
" model_client=OpenAIChatCompletionClient(model=\"gpt-4\"),\n",
" description=\"A helpful assistant that can provide language tips for a given destination.\",\n",
" system_message=\"You are a helpful assistant that can review travel plans, providing feedback on important/critical tips about how best to address language or communication challenges for the given destination. If the plan already includes language tips, you can mention that the plan is satisfactory, with rationale.Respond with a single sentence\",\n",
")\n",
"\n",
"travel_summary_agent = AssistantAgent(\n",
" \"travel_summary_agent\",\n",
" model_client=OpenAIChatCompletionClient(model=\"gpt-4\"),\n",
" description=\"A helpful assistant that can summarize the travel plan.\",\n",
" system_message=\"You are a helpful assistant that can take in all of the suggestions and advice from the other agents and provide a detailed tfinal travel plan. You must ensure th b at the final plan is integrated and complete. YOUR FINAL RESPONSE MUST BE THE COMPLETE PLAN. When the plan is complete and all perspectives are integrated, you can respond with TERMINATE.Respond with a single sentence\",\n",
")\n",
"\n",
"termination = TextMentionTermination(\"TERMINATE\")\n",
"group_chat = RoundRobinGroupChat(\n",
" [planner_agent, local_agent, language_agent, travel_summary_agent], termination_condition=termination\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"source='user' models_usage=None content='Plan a 3 day trip to Nepal.'\n",
"source='planner_agent' models_usage=RequestUsage(prompt_tokens=45, completion_tokens=54) content=\"Consider starting your 3-day trip to Nepal with a cultural tour in Kathmandu Valley, followed by an exploration of Pokhara's natural beauty on the second day, and finally, indulge in a thrilling safari at Chitwan National Park on the third day.\"\n",
"source='local_agent' models_usage=RequestUsage(prompt_tokens=116, completion_tokens=54) content=\"Consider starting your 3-day trip to Nepal with a cultural tour in Kathmandu Valley, followed by an exploration of Pokhara's natural beauty on the second day, and finally, indulge in a thrilling safari at Chitwan National Park on the third day.\"\n",
"source='language_agent' models_usage=RequestUsage(prompt_tokens=201, completion_tokens=45) content=\"Your travel plan lacks a mention of dealing with potential language barriers; it might be useful to learn some phrases in Nepali, as it's the official language of Nepal, or have a local translation app handy during your trip.\"\n",
"source='travel_summary_agent' models_usage=RequestUsage(prompt_tokens=270, completion_tokens=237) content='Day 1: Start your adventure in the capital city, Kathmandu. Take a guided tour of Kathmandu Valley to explore its UNESCO World Heritage Sites, such as the Durbar Squares, Swayambhunath Stupa, and Boudhanath Stupa. Engage with the locals and sample some traditional Nepalese cuisine.\\n\\nDay 2: Proceed to Pokhara, known for its stunning natural beauty. Visit the iconic Phewa Lake and enjoy a boat ride, then trek to the Peace Pagoda for a panoramic view of the city. Round off the day with a visit to the fascinating Pokhara Mountain Museum.\\n\\nDay 3: Travel to Chitwan National Park for a memorable safari. Explore the diverse wildlife and lush vegetation that make the park a UNESCO World Heritage site. Be on the lookout for rhinos, Bengal tigers, and a multitude of bird species.\\n\\nNote: Communication is key to enjoying your trip. The official language of Nepal is Nepali. It can be helpful to learn a few basic phrases or carry a translation app to help you interact with the local people and enrich your cultural experience.\\n\\nTERMINATE.'\n",
"TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Plan a 3 day trip to Nepal.'), TextMessage(source='planner_agent', models_usage=RequestUsage(prompt_tokens=45, completion_tokens=54), content=\"Consider starting your 3-day trip to Nepal with a cultural tour in Kathmandu Valley, followed by an exploration of Pokhara's natural beauty on the second day, and finally, indulge in a thrilling safari at Chitwan National Park on the third day.\"), TextMessage(source='local_agent', models_usage=RequestUsage(prompt_tokens=116, completion_tokens=54), content=\"Consider starting your 3-day trip to Nepal with a cultural tour in Kathmandu Valley, followed by an exploration of Pokhara's natural beauty on the second day, and finally, indulge in a thrilling safari at Chitwan National Park on the third day.\"), TextMessage(source='language_agent', models_usage=RequestUsage(prompt_tokens=201, completion_tokens=45), content=\"Your travel plan lacks a mention of dealing with potential language barriers; it might be useful to learn some phrases in Nepali, as it's the official language of Nepal, or have a local translation app handy during your trip.\"), TextMessage(source='travel_summary_agent', models_usage=RequestUsage(prompt_tokens=270, completion_tokens=237), content='Day 1: Start your adventure in the capital city, Kathmandu. Take a guided tour of Kathmandu Valley to explore its UNESCO World Heritage Sites, such as the Durbar Squares, Swayambhunath Stupa, and Boudhanath Stupa. Engage with the locals and sample some traditional Nepalese cuisine.\\n\\nDay 2: Proceed to Pokhara, known for its stunning natural beauty. Visit the iconic Phewa Lake and enjoy a boat ride, then trek to the Peace Pagoda for a panoramic view of the city. Round off the day with a visit to the fascinating Pokhara Mountain Museum.\\n\\nDay 3: Travel to Chitwan National Park for a memorable safari. Explore the diverse wildlife and lush vegetation that make the park a UNESCO World Heritage site. Be on the lookout for rhinos, Bengal tigers, and a multitude of bird species.\\n\\nNote: Communication is key to enjoying your trip. The official language of Nepal is Nepali. It can be helpful to learn a few basic phrases or carry a translation app to help you interact with the local people and enrich your cultural experience.\\n\\nTERMINATE.')], stop_reason=\"Text 'TERMINATE' mentioned\")\n"
]
}
],
"source": [
"\n",
"result = group_chat.run_stream(task=\"Plan a 3 day trip to Nepal.\")\n",
"async for response in result:\n",
" print(response) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Human in the Loop with a UserProxy Agent\n",
"\n",
"AutoGen studio provides a custom agent allows a human interact as part of the agent team.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"from autogenstudio.components import UserProxyAgent \n",
"\n",
"def input_func(prompt: str) -> str: \n",
" return \"Hello World there\" + str(prompt)\n",
"user_agent = UserProxyAgent(name=\"user_agent\", description=\"a human user\", input_func=input_func)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"source='user' models_usage=None content='hello there'\n",
"source='user_agent' models_usage=None content='Hello World thereEnter your response: '\n",
"TaskResult(messages=[TextMessage(source='user', models_usage=None, content='hello there'), TextMessage(source='user_agent', models_usage=None, content='Hello World thereEnter your response: ')], stop_reason=None)\n"
]
}
],
"source": [
"from autogen_core.base import CancellationToken \n",
"cancellation_token = CancellationToken()\n",
"stream = user_agent.run_stream(task=\"hello there\", cancellation_token=cancellation_token)\n",
"\n",
"async for response in stream:\n",
" print(response)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "agnext",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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