How I Build an Agent with Long-Term, Personalized Memory
Build low-latency voice agents powered by memory via mem0
os.environ["MEM0_API_KEY"] = "<your-mem0-api-key>"
from llama_index.memory.mem0 import Mem0Memory
context = {"user_id": "david"}
memory_from_client = Mem0Memory.from_client(
context=context,
api_key=os.environ["MEM0_API_KEY"],
search_msg_limit=4, # optional, default is 5
)
memobase Memobase is a user profile-based memory system designed to bring long-term user memory to your Generative AI (GenAI) applications. Whether you're building virtual companions, educational tools, or personalized assistants, Memobase empowers your AI to remember, understand, and evolve with your users.
Memory: The secret sauce of AI agents
Turn PDFs into structured, queryable memory—built for LLMs.
Large Language Models struggle with memory. PdfToMem makes it effortless. By combining reasoning-powered ingestion, structured retrieval, and a multi-agent architecture, it transforms unstructured PDFs into rich memory representations.