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| from smolagents import LiteLLMModel, ToolCallingAgent | |
| def main() -> None: | |
| model = LiteLLMModel( | |
| model_id="ollama_chat/qwen2:7b", | |
| api_base="http://127.0.0.1:11434", | |
| temperature=0.3, | |
| ) | |
| # ToolCallingAgent is more reliable than CodeAgent for general chat-like prompts | |
| # with local Ollama models, because it avoids strict code-block parsing. | |
| agent = ToolCallingAgent( | |
| tools=[], | |
| model=model, | |
| max_steps=3, | |
| ) | |
| prompt = "Explain how AI agents work in 3 bullet points." | |
| try: | |
| answer = agent.run(prompt) | |
| print(answer) | |
| except Exception: | |
| # Some Ollama models intermittently skip tool-call JSON. | |
| # Fallback to direct model generation while still using smolagents. | |
| response = model.generate( | |
| messages=[ | |
| {"role": "system", "content": "You are a concise and helpful assistant."}, | |
| {"role": "user", "content": prompt}, | |
| ] | |
| ) | |
| print(response.content) | |
| if __name__ == "__main__": | |
| main() |