agent-framework-ollama

import asyncio import os from agent_framework.agent import Agent from agent_framework.user import User from agent_framework_ollama import OllamaAdapter async def main(): # Pre-requisites: # 1. Ollama server must be running (e.g., `ollama serve` in your terminal) # 2. The desired model must be pulled (e.g., `ollama pull llama3`) # Configure Ollama connection using environment variables or defaults ollama_base_url = os.environ.get("OLLAMA_BASE_URL", "http://localhost:11434") ollama_model = os.environ.get("OLLAMA_MODEL", "llama3") if not ollama_model: print("Error: OLLAMA_MODEL environment variable not set, or no default provided.") print("Please specify an Ollama model, e.g., 'llama3'.") return print(f"Attempting to connect to Ollama at {ollama_base_url} with model: {ollama_model}") try: # Initialize the OllamaAdapter ollama_adapter = OllamaAdapter( model=ollama_model, base_url=ollama_base_url, ) # Create an Agent using the OllamaAdapter agent = Agent( name="OllamaAgent", adapter=ollama_adapter, # Optional: Add hooks to log messages as they are processed pre_process_messages=lambda msgs: print(f"\n[OllamaAgent received]: {[m.content for m in msgs if m.content]}"), post_process_messages=lambda msgs: print(f"[OllamaAgent sent]: {[m.content for m in msgs if m.content]}") ) # Create a User to interact with the agent user = User( name="User", connect_to=agent ) print("\n--- Starting conversation with OllamaAgent ---") await user.send("Hello, OllamaAgent! Please introduce yourself and tell me what you can do.") # In a more complex application, you'd manage message history or await a specific response. # For this quickstart, we observe the interaction via the print hooks. print("\n--- Conversation complete ---") except Exception as e: print(f"\nAn error occurred during agent execution: {e}") print("Please ensure your Ollama server is running, the model is pulled, and the base_url is correct.") if __name__ == "__main__": asyncio.run(main())