Auto Generated Agent Chat: Solving Tasks Requiring Web Info
Solve tasks requiring web info.
Solve tasks requiring web info.
Code generation, execution, debugging and human feedback.
Register function calls using AssistantAgent and UserProxyAgent to execute python or shell code in customized ways. Demonstrating two ways of registering functions.
Explore the utilization of large language models in automated group chat scenarios, where agents perform tasks collectively, demonstrating how they can be configured, interact with each other, and retrieve specific information from external resources.
Explore a group chat example using agents such as a coder and visualization agent.
This is a nested chat re-implementation of OptiGuide which is an LLM-based supply chain optimization framework.
Perform research using a group chat with a number of specialized agents.
Use conversable language learning model agents to solve tasks and provide automatic feedback through a comprehensive example of writing, executing, and debugging Python code to compare stock price changes.
Use Databricks DBRX and Foundation Model APIs to build AutoGen applications backed by open-source LLMs.
Equip your agent with functions that can efficiently implement features into your software application.