Example implementations of a basic conversational AI agent using different frameworks and approaches:
- Langchain Agent
- PydanticAI Agent
This project includes implementations of a basic conversational AI agent which acts as a waiter at a restaurant, asking about dietary requirements and taking your order. They aim to demonstrate how the following features can be achieved with each approach:
- Dynamic model choice
- Human-in-the-loop input
- Tool calling with dependencies
- Conversation history/memory
- Dynamic system prompt
- Structured output
AI Waiter: Ah, bonjour madame et monsieur! Welcome to Le Bistro, where the food is as delightful as the company! Before we embark on a culinary adventure, might you have any dietary restrictions or preferences? We wouldn't want any rogue ingredients crashing the party!
You: I only eat yellow coloured food
AI Waiter: Magnifique! A connoisseur of the golden hue, I see! Let's explore the menu with your sunshine-filled preference in mind. For appetizers, we have the Quinoa Stuffed Bell Peppers, which are a vibrant yellow. For the main course, the Chickpea and Sweet Potato Curry is a golden delight! And for dessert, the Fresh Fruit Sorbet might have some yellow options depending on the day! What do you think?
You: I'll take the Bell Peppers and fruit sorbet thanks
AI Waiter: Excellent choices! One order of the Quinoa Stuffed Bell Peppers and the Fresh Fruit Sorbet for the discerning palate at table number 1! Your order has been placed, and I shall return with your delectable delights shortly. Au revoir!
Order placed: [Order(menu_items=['Quinoa Stuffed Bell Peppers', 'Fresh Fruit Sorbet'], table_number=1)]
These approaches use PydanticAI's Agent and LangChain's legacy AgentExecutor classes to build the agent,
implementing the AgentRunner common interface.
- PydanticAI implementation: pydanticai/agent.py
- LangChain implementation: langchain/agent.py
TBC
- Python 3.11+
- API key for LLM provider (e.g., OpenAI, Anthropic, Gemini)
- Install from the repository:
pip install git@https://github.com/Finndersen/ai-framework-comparison.git
- Run via CLI:
python -m ai_framework_comparison <framework> --model=<provider:model_name> --api_key=<api_key>
Run python -m ai_framework_comparison -h for details about the available options.
This project is licensed under the MIT License - see the LICENSE file for details.