Uploaded January 2026 | Updated September 2026, 2 weeks ago
While LLMs continue to evolve, they still struggle with memory. The startup Mem0 is working to change that by building the memory layer for AI agents. In this episode of Founder Firesides, YC’s Nicolas Dessaigne sat down with co-founders Taranjeet Singh and Deshraj Yadav to discuss why agents need persistent memory to improve over time, how Mem0 reduces cost and latency compared to native context stuffing, and why memory must remain neutral across models as AI becomes more agent-driven.
Chapters:
00:05 What Is Mem0?
00:49 Traction & Open Source Adoption
01:24 Why Memory Improves AI Agents
02:01 Saving Cost and Latency
02:31 Founder Origins & YC Pivot
05:13 How Mem0 Works Under the Hood
06:04 Hybrid Memory Architecture
07:10 Custom Memory Rules & Expectations
08:00 Real-World Use Cases
10:05 Competing With Model-Native Memory
11:48 Fundraising & What’s Next
While LLMs continue to evolve, they still struggle with memory. The startup Mem0 is working to change that by building the memory layer for AI agents. In this episode of Founder Firesides, YC’s Nicolas Dessaigne sat down with co-founders Taranjeet Singh and Deshraj Yadav to discuss why agents need persistent memory to improve over time, how Mem0 reduces cost and latency compared to native context stuffing, and why memory must remain neutral across models as AI becomes more agent-driven.
Chapters:
00:05 What Is Mem0?
00:49 Traction & Open Source Adoption
01:24 Why Memory Improves AI Agents
02:01 Saving Cost and Latency
02:31 Founder Origins & YC Pivot
05:13 How Mem0 Works Under the Hood
06:04 Hybrid Memory Architecture
07:10 Custom Memory Rules & Expectations
08:00 Real-World Use Cases
10:05 Competing With Model-Native Memory
11:48 Fundraising & What’s Next
