How to implement AI agents with memory and tool use?

Building AI agents is one of the most exciting areas right now! Here's a practical approach:

Framework Recommendation: For production systems, I recommend LangChain or LlamaIndex over AutoGPT. They're more stable and have better community support.

Architecture:

1. Memory System:

   • Short-term memory: Store conversation history in context window
   • Long-term memory: Use vector database (Pinecone, Weaviate) for semantic search of past conversations
   • Structured memory: Store facts in a knowledge graph

2. Tool Integration: Use LangChain's agent framework to define tools and let the agent decide which to use.

3. Decision Making (ReAct Pattern): The agent follows: Thought → Action → Observation loop

   • Thought: "I need current weather data"
   • Action: Call weather API
   • Observation: "Temperature is 72°F"
   • Thought: "Now I can answer the user"

Production Considerations:

• Error handling: Tools can fail; implement retries and fallbacks
• Cost control: Limit agent iterations (max 5-10 steps)
• Safety: Validate tool outputs before using them
• Monitoring: Log all agent decisions for debugging
• Rate limiting: Prevent infinite loops

Alternative Approach: If LangChain feels too complex, consider function calling with OpenAI's native API. This gives you more control and is easier to debug.

My recommendation: Start simple with function calling, then graduate to LangChain when you need more complex agent behaviors.