How to build your own long-term Agentic Memory System for LLMs | Mem0 from scratch in DSPy
In this video we are using DSPy and QDrant Vector Database to create our own memory system from scratch! We are building the core components of Mem0 from the ground up, one step at a time. You'll learn about the Mem0 API, the basics of DSPy signatures and modules, generating embeddings, inserting and searching with vector databases (QDrant), and tool calling with dspy.React.
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Check the publicly accessible repo here:
https://github.com/avbiswas/mem0-dspy
Patreon members get access to additional code materials.
Videos to learn about DSPy:
Learn about RAG Systems: https://youtu.be/OHh_SByRYmQ
Learn about DSPy: https://youtu.be/_ROckQHGHsU
Learn more Context Engineering: https://youtu.be/5Bym0ffALaU
Check out Mem0 here: t.co/h3U8W1Lr5u
Get Mem0 API Key - t.co/lmFqnI5CkV
Mem0 Github - https://mem0.dev/github/avb
Mem0 Paper: https://arxiv.org/abs/2504.19413
QDrant: https://qdrant.tech/
Self hosting Qdrant: https://qdrant.tech/documentation/quickstart/
DSPy: https://dspy.ai/
Timestamps:
0:00 - Intro
2:56 - The Basic Chatbot
6:16 - What the original Mem0 API looks like
10:04 - The Roadmap
12:11 - Extracting Memories from Conversations
21:17 - Embeddings & Vector DBs
27:03 - Evaluation Dataset
34:39 - Tool Calling
39:13 - Memory Upkeep CRUD Agent
45:46 - Testing
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Chapters (10)
Intro
2:56
The Basic Chatbot
6:16
What the original Mem0 API looks like
10:04
The Roadmap
12:11
Extracting Memories from Conversations
21:17
Embeddings & Vector DBs
27:03
Evaluation Dataset
34:39
Tool Calling
39:13
Memory Upkeep CRUD Agent
45:46
Testing
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Tutor Explanation
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