🧠Mem0
Universal memory layer for AI Agents
⭐ Hugely popular: 63k stars, gaining about 497 a week
View on GitHub ↗repo profile
momentum
durability
bus factor = how many people it takes to cover more than half the commits (6 months). 1 is a solo project; higher means the work is spread across a team. top-author share is the single busiest author's slice of those commits.
since we covered it
why it's a big deal
- Multi-Level Memory Retention: Captures and organizes user, session, and agent-specific memories for nuanced personalization.
- Hybrid Datastore Architecture: Integrates graph, vector, and key-value stores to manage diverse memory types effectively.
- Performance Gains: Achieves 26% higher accuracy over OpenAI Memory on the LOCOMO benchmark, with 91% faster responses and 90% lower token usage.
under the hood
- Python & TypeScript SDKs: Provides flexible integration options for various development environments.
- LLM Compatibility: Supports multiple large language models, including OpenAI’s GPT-4o-mini.
- Deployment Flexibility: Available as a self-hosted solution via pip/npm or as a managed service with enterprise-grade features.
our take from PR#9, 2025-05-28
star history
- PR#9 33k 2025-05-28
- now 63k + 31k since first covered
curve is sampled from GitHub's star history, plus our own daily readings since we covered it; the dashed stretch is before we first covered it, the solid line since. figures at coverage are the numbers we printed then (approx.), current count is live.
understory
Output and attention are roughly in balance.
- output, commits & releases
- clout, star velocity
output = commits & releases; clout = star velocity, both 0 to 100 monthly indices; the gap where output runs above clout is the understory. The understory →
covered in
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Open-Source Memory Layer for Personalized AI Agents
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