AgentOptimizer: An Agentic Way to Train Your LLM Agent
AgentOptimizer is able to prompt LLMs to iteratively optimize function/skills of AutoGen agents according to the historical conversation and performance.
AgentOptimizer is able to prompt LLMs to iteratively optimize function/skills of AutoGen agents according to the historical conversation and performance.
Use planning agent in a function call.
Involve multiple human users via function calls and nested chat.
Function Inception: Enable AutoGen agents to update/remove functions during conversations.
Use Langchain tools.
Register function calls using AssistantAgent and UserProxyAgent to execute python or shell code in customized ways. Demonstrating two ways of registering functions.
This Jupyter Notebook demonstrates how to leverage OSS Insight (Open Source Software Insight) for advanced GitHub data analysis by defining `Function calls` in AutoGen for the OpenAI Assistant.
LLM-backed agents playing chess with each other using nested chats.
Learn how to register function calls using AssistantAgent and UserProxyAgent.
Use tools in a GPTAssistantAgent Multi-Agent System by utilizing functions such as calling an API and writing to a file.
LLM-backed agents playing chess with each other using nested chats.
Natural language text to SQL query using the Spider text-to-SQL benchmark.
Learn how to implement both synchronous and asynchronous function calls using AssistantAgent and UserProxyAgent in AutoGen, with examples of their application in individual and group chat settings for task execution with language models.
Use tools to extract and translate the transcript of a video file.
Scrapping web pages and summarizing the content using agents with tools.
Browse the web with agents.
Equip your agent with functions that can efficiently implement features into your software application.