Building Memory-Aware AI Agents with AdalFlow | Persistent Memory, JSON Store & Multi-Session

Описание: Welcome back to Bazai — today we’re diving deep into how to build persistent-memory AI agents using AdalFlow.

In this hands-on walkthrough, we’ll explore how AdalFlow gives your agents true cognitive continuity — remembering user preferences, summarizing long conversations, and managing context across multiple sessions.

🚀 What you’ll learn:

What AdalFlow is and how it enables persistent, file-backed memory

The difference between short-term and long-term agent memory

How JSONMemoryStore safely preserves history and facts

History Compaction — automatic summarization that prevents prompt bloat

Global memory sharing for multi-agent collaboration

Tool-based design: remember, recall, jot, counter

Real Colab demo using OpenAI GPT models

Debugging and fixing common errors in AdalFlow agents

💡 Key Takeaway:
Persistent memory turns LLM agents from reactive responders into adaptive collaborators that learn, recall, and evolve with you.

📂 Colab Notebook & Code:
🔗 https://colab.research.google.com/dri...

https://chat.z.ai/space/x0a8q9k90d70-ppt

📣 Subscribe to Bazai for more deep-tech walkthroughs on Agentic AI, LangGraph, CrewAI, and AdalFlow-powered workflows.
#Bazai #AdalFlow #AIagents #PersistentMemory #LLM
https://colab.research.google.com/dri...

https://chat.z.ai/space/x0a8q9k90d70-ppt