---
title: "Integrating Long-Term Memory with Gemini 2.5"
url: https://daily.dev/posts/integrating-long-term-memory-with-gemini-2-5-r9vvlf1ht
source_url: https://www.philschmid.de/gemini-with-memory
type: article
source: "Philipp Schmid"
published: 2026-05-31T07:41:06.851Z
updated: 2026-05-31T08:28:02.683Z
tags: ["python", "llm", "google-gemini"]
reading_time: 4
upvotes: 0
comments: 0
language: en
---

> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Integrating Long-Term Memory with Gemini 2.5

**[Philipp Schmid](https://daily.dev/sources/philschmid)** · 4 min read · 0 upvotes · 0 comments

## Summary

LLMs are stateless by default, forgetting past conversations. This guide demonstrates how to add long-term memory to a Gemini 2.5 chatbot using the Gemini API and Mem0, an open-source memory layer for AI agents. Mem0 extracts salient information from conversations, stores it as vector embeddings in Qdrant, and retrieves relevant memories to personalize future responses. The tutorial covers configuring Gemini 2.5 Flash as the LLM, text-embedding-004 as the embedding model, and building an interactive chatbot that persists context across sessions.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.philschmid.de/gemini-with-memory>

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---

Tags: [#python](https://daily.dev/tags/python), [#llm](https://daily.dev/tags/llm), [#google-gemini](https://daily.dev/tags/google-gemini)

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