Building Memory-Augmented Conversational Agents with Mem0 and Qdrant in Python (2026 Tutorial)

Giving LLMs Long-Term Personalized Memory Across Sessions

Standard LLM chats suffer from immediate amnesia once a session ends: user preferences, past project details, and personalized coding styles are lost. Mem0 introduces a continuous, adaptive memory layer that extracts user facts, updates preferences over time, and stores high-dimensional embeddings inside a vector database like Qdrant, giving autonomous agents human-like persistent recall.

Written by Saizul Amin for AI engineers and conversational software developers.

Python Implementation: Persistent Memory Loop

from mem0 import Memory

# Initialize Mem0 with Qdrant Vector Storage
config = {
    "vector_store": {
        "provider": "qdrant",
        "config": {
            "host": "localhost",
            "port": 6333,
        }
    }
}
m = Memory.from_config(config)

# Store memory from conversation
m.add(
    "User prefers TypeScript and Next.js 15 for frontend and FastAPI for ML services.",
    user_id="developer_saizul"
)

# Search relevant user context before calling LLM
memories = m.search("What tech stack does the user recommend?", user_id="developer_saizul")
for mem in memories:
    print(f"[RECALLED MEMORY]: {mem['\''memory'\'']}")