🤖 Core Concepts: Agents

Agents are the central orchestrators in the NexAU framework. They combine an LLM, a system prompt, and a set of tools to accomplish tasks. You can define agents either programmatically in Python or declaratively using YAML files.

Programmatic Agent Creation

Creating an agent in Python gives you maximum flexibility. This is ideal for dynamic setups or when integrating into existing applications.

Example from examples/deep_research/quickstart.py:

import os
from datetime import datetime
from nexau import Agent, AgentConfig, Tool, LLMConfig
from nexau.archs.tool.builtin.web_tools import google_web_search, web_fetch

def main():
    # Create tools from YAML configurations
    web_search_tool = Tool.from_yaml("tools/WebSearch.yaml", binding=google_web_search)
    web_read_tool = Tool.from_yaml("tools/WebRead.yaml", binding=web_fetch)

    # Configure the LLM
    llm_config = LLMConfig(
        model=os.getenv("LLM_MODEL"),
        base_url=os.getenv("LLM_BASE_URL"),
        api_key=os.getenv("LLM_API_KEY")
    )

    # Create the agent instance
    agent_config = AgentConfig(
        name="research_agent",
        tools=[web_search_tool, web_read_tool],
        llm_config=llm_config,
        system_prompt="You are a research agent. Use web_search and web_read tools to find information.",
    )
    research_agent = Agent(config=agent_config)

    # Run the agent
    response = research_agent.run(
        "What's the latest news about AI developments?"
    )
    print(response)

if __name__ == "__main__":
    main()

YAML-Based Agent Configuration

For a more declarative approach, you can define an agent's entire configuration in a YAML file. This makes it easy to manage and version different agent personalities and capabilities.

1. Create an agent configuration file:

   File: agents/my_agent.yaml

   type: agent
   name: my_research_agent
   max_context_tokens: 100000
   system_prompt: |
     Date: {{date}}. You are a research agent specialized in finding and analyzing information.
     Use web_search to find relevant information, then web_read to get detailed content.
   system_prompt_type: string
   llm_config:
     temperature: 0.7
     max_tokens: 4096
   tools:
     - name: web_search
         yaml_path: ./tools/WebSearch.yaml
       binding: nexau.archs.tool.builtin.web_tools:google_web_search
     - name: web_read
         yaml_path: ./tools/WebRead.yaml
       binding: nexau.archs.tool.builtin.web_tools:web_fetch
   sub_agents: []

2. Load and use the agent in Python:

   
   import os
   from datetime import datetime
   from nexau import Agent, AgentConfig, LLMConfig
   from pathlib import Path
   
   def main():
   
       agent_config = AgentConfig.from_yaml(
           Path("agent/my_agent.yaml")
       )
       agent_config.llm_config = LLMConfig(
           model = os.getenv("LLM_MODEL"),
           base_url = os.getenv("LLM_BASE_URL"),
           api_key = os.getenv("LLM_API_KEY"),
       )
   
       # Load the agent from agent_config
       agent = Agent(
           config=agent_config
       )
   
       # Use the agent
       response = agent.run(
           "Research the latest developments in quantum computing",
           context=\{"date": datetime.now().strftime("%Y-%m-%d %H:%M:%S")\}
       )
       print(response)
   
   if __name__ == "__main__":
       main()