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- TESIGN / RADAR
- REPOSITORY CARD
- mem0ai/mem0
mem0ai/mem0
The Memory Layer for AI Agents - Drop-in memory infrastructure for AI agents and apps. Context that persists. Built for production.
RANKS All-time #88
AT A GLANCE
- LANGUAGE
- Python
- LICENSE
- Apache-2.0
- USAGE
- Commercial use, redistribution OK. Keep notices; mark changes.
- ACTIVITY
- last commit 5 days ago ()
- TOPICS
- agentic-memory
- agentic-memory-system
- agents
- ai
- ai-agents
- chatgpt
- genai
- llm
- long-term-memory
- memory
- memory-management
- python
- HOMEPAGE
- https://mem0.ai
- REPOSITORY
- GitHub ↗
- CATEGORY
- AI
A summary, not legal advice.
README EXCERPT
Learn more · Join Discord · Demo 📄 Benchmarking Mem0's token-efficient memory algorithm → New Memory Algorithm (April 2026) Benchmark Old New Tokens Latency p50 --- --- --- --- --- LoCoMo 71.4 92.5 7.0K 0.88s LongMemEval 67.8 94.4 6.8K 1.09s BEAM (1M) — 64.1 6.7K 1.00s BEAM (10M) — 48.6 6.9K 1.05s All benchmarks run on the same production-representative model stack. Single-pass retrieval (one call, no agentic loops) at a top 200 retrieval budget. Scores reflect Mem0's managed platform, which includes proprietary optimizations not available in the open-source SDK; open-source users should expect directionally similar gains but not identical numbers. What changed: - Single-pass ADD-only extraction -- one LLM call, no UPDATE/DELETE. Memories accumulate; nothing is overwritten. - Agent-generated facts are first-class -- when an agent confirms an action, that information is now stored with equal weight. - Entity linking -- entities are extracted, embedded, and linked across memories for retrieval boosting. - Multi-signal retrieval -- semantic, BM25 keyword, and entity matching scored in parallel and fused. - Temporal Reasoning -- time-aware retrieval that ranks the right dated instance…
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TIMELINE
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- Last push
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