Notebooks
OpenAI Assistants in AutoGen
Use Cases
- Use cases
- Reference Agents
- Notebooks
- All Notebooks
- Group Chat with Customized Speaker Selection Method
- RAG OpenAI Assistants in AutoGen
- Using RetrieveChat with Qdrant for Retrieve Augmented Code Generation and Question Answering
- Auto Generated Agent Chat: Function Inception
- Task Solving with Provided Tools as Functions (Asynchronous Function Calls)
- Using Guidance with AutoGen
- Solving Complex Tasks with A Sequence of Nested Chats
- Group Chat
- Solving Multiple Tasks in a Sequence of Async Chats
- Auto Generated Agent Chat: Task Solving with Provided Tools as Functions
- Conversational Chess using non-OpenAI clients
- RealtimeAgent with local websocket connection
- Web Scraping using Apify Tools
- DeepSeek: Adding Browsing Capabilities to AG2
- Interactive LLM Agent Dealing with Data Stream
- Generate Dalle Images With Conversable Agents
- Supercharging Web Crawling with Crawl4AI
- RealtimeAgent in a Swarm Orchestration
- Perform Research with Multi-Agent Group Chat
- Agent Tracking with AgentOps
- Translating Video audio using Whisper and GPT-3.5-turbo
- Automatically Build Multi-agent System from Agent Library
- Auto Generated Agent Chat: Collaborative Task Solving with Multiple Agents and Human Users
- Structured output
- CaptainAgent
- Group Chat with Coder and Visualization Critic
- Cross-Framework LLM Tool Integration with AG2
- Using FalkorGraphRagCapability with agents for GraphRAG Question & Answering
- Demonstrating the `AgentEval` framework using the task of solving math problems as an example
- RealtimeAgent in a Swarm Orchestration using WebRTC
- A Uniform interface to call different LLMs
- From Dad Jokes To Sad Jokes: Function Calling with GPTAssistantAgent
- Solving Complex Tasks with Nested Chats
- Usage tracking with AutoGen
- Agent with memory using Mem0
- Using RetrieveChat Powered by PGVector for Retrieve Augmented Code Generation and Question Answering
- Tools with Dependency Injection
- Solving Multiple Tasks in a Sequence of Chats with Different Conversable Agent Pairs
- WebSurferAgent
- Using RetrieveChat Powered by MongoDB Atlas for Retrieve Augmented Code Generation and Question Answering
- Assistants with Azure Cognitive Search and Azure Identity
- ReasoningAgent - Advanced LLM Reasoning with Multiple Search Strategies
- Agentic RAG workflow on tabular data from a PDF file
- Making OpenAI Assistants Teachable
- Run a standalone AssistantAgent
- AutoBuild
- Solving Multiple Tasks in a Sequence of Chats
- Currency Calculator: Task Solving with Provided Tools as Functions
- Swarm Orchestration with AG2
- Use AutoGen in Databricks with DBRX
- Using a local Telemetry server to monitor a GraphRAG agent
- Auto Generated Agent Chat: Solving Tasks Requiring Web Info
- StateFlow: Build Workflows through State-Oriented Actions
- Groupchat with Llamaindex agents
- Using Neo4j's native GraphRAG SDK with AG2 agents for Question & Answering
- Agent Chat with Multimodal Models: LLaVA
- Group Chat with Retrieval Augmented Generation
- Runtime Logging with AutoGen
- SocietyOfMindAgent
- Agent Chat with Multimodal Models: DALLE and GPT-4V
- Agent Observability with OpenLIT
- Mitigating Prompt hacking with JSON Mode in Autogen
- Trip planning with a FalkorDB GraphRAG agent using a Swarm
- Language Agent Tree Search
- Auto Generated Agent Chat: Collaborative Task Solving with Coding and Planning Agent
- OptiGuide with Nested Chats in AutoGen
- Auto Generated Agent Chat: Task Solving with Langchain Provided Tools as Functions
- Writing a software application using function calls
- Auto Generated Agent Chat: GPTAssistant with Code Interpreter
- Adding Browsing Capabilities to AG2
- Agentchat MathChat
- Chatting with a teachable agent
- RealtimeAgent with gemini client
- Preprocessing Chat History with `TransformMessages`
- Chat with OpenAI Assistant using function call in AutoGen: OSS Insights for Advanced GitHub Data Analysis
- Websockets: Streaming input and output using websockets
- Task Solving with Code Generation, Execution and Debugging
- Agent Chat with Async Human Inputs
- Agent Chat with custom model loading
- Chat Context Dependency Injection
- Nested Chats for Tool Use in Conversational Chess
- Auto Generated Agent Chat: Group Chat with GPTAssistantAgent
- Cross-Framework LLM Tool for CaptainAgent
- Auto Generated Agent Chat: Teaching AI New Skills via Natural Language Interaction
- SQL Agent for Spider text-to-SQL benchmark
- Auto Generated Agent Chat: Task Solving with Code Generation, Execution, Debugging & Human Feedback
- OpenAI Assistants in AutoGen
- (Legacy) Implement Swarm-style orchestration with GroupChat
- Enhanced Swarm Orchestration with AG2
- Using RetrieveChat for Retrieve Augmented Code Generation and Question Answering
- Using Neo4j's graph database with AG2 agents for Question & Answering
- AgentOptimizer: An Agentic Way to Train Your LLM Agent
- Engaging with Multimodal Models: GPT-4V in AutoGen
- RealtimeAgent with WebRTC connection
- FSM - User can input speaker transition constraints
- Config loader utility functions
- Community Gallery
Notebooks
OpenAI Assistants in AutoGen
Two-agent chat with OpenAI assistants.
This notebook shows a very basic example of the
GPTAssistantAgent
,
which is an experimental AutoGen agent class that leverages the OpenAI
Assistant API for
conversational capabilities, working with UserProxyAgent
in AutoGen.
import logging
import os
from autogen import AssistantAgent, UserProxyAgent, config_list_from_json
from autogen.agentchat.contrib.gpt_assistant_agent import GPTAssistantAgent
logger = logging.getLogger(__name__)
logger.setLevel(logging.WARNING)
assistant_id = os.environ.get("ASSISTANT_ID", None)
config_list = config_list_from_json("OAI_CONFIG_LIST")
llm_config = {"config_list": config_list}
assistant_config = {"assistant_id": assistant_id}
gpt_assistant = GPTAssistantAgent(
name="assistant",
instructions=AssistantAgent.DEFAULT_SYSTEM_MESSAGE,
llm_config=llm_config,
assistant_config=assistant_config,
)
user_proxy = UserProxyAgent(
name="user_proxy",
code_execution_config={
"work_dir": "coding",
"use_docker": False,
}, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
is_termination_msg=lambda msg: "TERMINATE" in msg["content"],
human_input_mode="NEVER",
max_consecutive_auto_reply=1,
)
user_proxy.initiate_chat(gpt_assistant, message="Print hello world")
OpenAI client config of GPTAssistantAgent(assistant) - model: gpt-4-turbo-preview
GPT Assistant only supports one OpenAI client. Using the first client in the list.
No matching assistant found, creating a new assistant
user_proxy (to assistant):
Print hello world
--------------------------------------------------------------------------------
assistant (to user_proxy):
```python
print("Hello, world!")
```
--------------------------------------------------------------------------------
>>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...
user_proxy (to assistant):
exitcode: 0 (execution succeeded)
Code output:
Hello, world!
--------------------------------------------------------------------------------
assistant (to user_proxy):
TERMINATE
--------------------------------------------------------------------------------
ChatResult(chat_id=None, chat_history=[{'content': 'Print hello world', 'role': 'assistant'}, {'content': '```python\nprint("Hello, world!")\n```\n', 'role': 'user'}, {'content': 'exitcode: 0 (execution succeeded)\nCode output: \nHello, world!\n', 'role': 'assistant'}, {'content': 'TERMINATE\n', 'role': 'user'}], summary='\n', cost=({'total_cost': 0}, {'total_cost': 0}), human_input=[])
user_proxy.initiate_chat(gpt_assistant, message="Write py code to eval 2 + 2", clear_history=True)
user_proxy (to assistant):
Write py code to eval 2 + 2
--------------------------------------------------------------------------------
assistant (to user_proxy):
```python
# Calculate 2+2 and print the result
result = 2 + 2
print(result)
```
--------------------------------------------------------------------------------
>>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...
user_proxy (to assistant):
exitcode: 0 (execution succeeded)
Code output:
4
--------------------------------------------------------------------------------
assistant (to user_proxy):
The Python code successfully calculated \(2 + 2\) and printed the result, which is \(4\).
TERMINATE
--------------------------------------------------------------------------------
ChatResult(chat_id=None, chat_history=[{'content': 'Write py code to eval 2 + 2', 'role': 'assistant'}, {'content': '```python\n# Calculate 2+2 and print the result\nresult = 2 + 2\nprint(result)\n```\n', 'role': 'user'}, {'content': 'exitcode: 0 (execution succeeded)\nCode output: \n4\n', 'role': 'assistant'}, {'content': 'The Python code successfully calculated \\(2 + 2\\) and printed the result, which is \\(4\\).\n\nTERMINATE\n', 'role': 'user'}], summary='The Python code successfully calculated \\(2 + 2\\) and printed the result, which is \\(4\\).\n\n\n', cost=({'total_cost': 0}, {'total_cost': 0}), human_input=[])
gpt_assistant.delete_assistant()
Permanently deleting assistant...