How Mem0 is Building the Memory Layer for AI

Mem0, a universal, self‑improving memory layer for LLM applications, led by Taranjeet Singh and Deshraj Yadav, has secured $24M raise across Seed and Series A, the seed round led by Kindred Ventures, and Series A led by Basis Set Ventures, with participation from Peak XV Partners, GitHub Fund, and Y Combinator. Mem0 is also backed by an incredible group of angel investors, including Scott Belsky, Dharmesh Shah, and the CEOs of companies that have built core infrastructure at scale, Olivier Pomel (Datadog), Paul Copplestone (Supabase), James Hawkins (PostHog), Thomas Dohmke (ex-GitHub), and Lukas Biewald (Weights & Biases).

AI now matches human capability across many domains, but lacks true memory, each interaction starts from scratch, limiting personalization and learning.

Mem0 fixes this with just three lines of code, giving AI lasting context and adaptive intelligence. Thousands of developers and enterprises already use Mem0 to build agents that remember and evolve.

Developers often start building AI memory themselves, storing interactions, retrieving context, it seems simple at first. But as scale grows, subtle challenges emerge: semantic search misses nuance, preferences conflict, data decays, and duplicates pile up. What looked like a weekend task turns into months of complex engineering.

Mem0 eliminates that burden, handling memory infrastructure so developers can focus on creating exceptional user experiences.

Mem0 manages memory at scale through a simple API, extracting, categorizing, and updating information intelligently while retrieving only what’s relevant. It handles the complexity so developers can focus on building.

With 41,000+ GitHub stars, 14M Python downloads, and API calls soaring from 35M to 186M this year, Mem0 is trusted by thousands of teams worldwide. Integrated into CrewAI, Flowise, and Langflow, and chosen by AWS as the exclusive memory provider for its Agent SDK, Mem0 has become the leading memory layer for AI.

Big AI labs are using memory to lock users into their ecosystems, a pattern likely to extend to APIs. But agentic applications rely on multiple models and frameworks, demanding flexibility. Mem0 stays neutral, a universal memory layer that works across all models, frameworks, and platforms.

Memories today are trapped in silos, each AI application builds its own isolated context, forcing users to start over every time. As expectations move toward seamless experiences, memory will become portable, just like contacts once did. Developers will shift from asking how to learn about users to how to integrate what’s already known. Mem0 is building the infrastructure for that future, your memory, wherever it’s needed.