MemOS: Self-Evolving Memory OS for LLM Agents with Hybrid Retrieval and Token Savings
MemOS is an open-source TypeScript project providing a memory operating system layer for LLM and AI agents, featuring ultra-persistent memory, hybrid retrieval, and cross-task skill reuse. The project claims 35.24% token savings through its memory management approach. It has accumulated 9,329 GitHub stars with moderate daily momentum (+67). The system targets agent memory persistence and efficiency as a foundational infrastructure component.
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Mem-π: Adaptive Memory for LLM Agents via On-Demand Generation and Decoupled RL
Mem-π introduces a framework where a dedicated language or vision-language model generates context-specific guidance for LLM agents on demand, rather than retrieving static entries from episodic memory banks. The system is trained with a decision-content decoupled reinforcement learning objective that jointly learns when to generate guidance and what to generate, enabling abstention when generation would not help. Evaluated across web navigation, terminal-based tool use, and text-based embodied interaction benchmarks, Mem-π achieves over 30% relative improvement on web navigation tasks compared to retrieval-based and prior RL-optimized memory baselines.
agentmemory: Persistent Memory for AI Coding Agents
agentmemory is an open-source TypeScript library providing persistent memory for AI coding agents, designed based on real-world benchmarks. The repository has accumulated 13,772 total stars with a notable single-day gain of 1,626 stars, indicating strong community traction. It targets the agent tool ecosystem by addressing memory continuity across coding agent sessions.
FluxMem: Connectivity-Evolving Memory Framework for LLM Agents
FluxMem proposes a heterogeneous graph-based memory framework for LLM agents that continuously evolves its topology through three stages: initial connection formation, feedback-driven refinement, and long-term consolidation. Unlike static memory repositories, FluxMem repairs missing links, prunes interference, aligns abstraction granularity, and distills successful trajectories into reusable procedural circuits. The system is guided by a single metric for memory generalizability and evolutionary maturity, achieving state-of-the-art results on LoCoMo, Mind2Web, and GAIA benchmarks.
claude-mem: Persistent Cross-Session Memory Layer for AI Coding Agents
claude-mem is an open-source TypeScript library that provides persistent context across sessions for AI coding agents. It captures agent activity during sessions, compresses it using AI, and injects relevant context into future sessions. The tool claims compatibility with Claude Code, OpenAI Codex, Gemini, GitHub Copilot, and other coding agents. The repository has accumulated 78,579 stars with 319 added today, indicating strong community traction.
supermemoryai/supermemory: Fast Scalable Memory Engine and API for AI Applications
supermemory is an open-source TypeScript project providing a memory engine and API designed for AI-era applications, emphasizing speed and scalability. It has accumulated 23,282 GitHub stars with 236 added today, indicating active community interest. The project targets the growing need for persistent, retrievable memory layers in AI agents and applications.
MemPalace: open-source AI memory system trending on GitHub
MemPalace is an open-source Python library positioning itself as a highly-benchmarked AI memory system, currently trending on GitHub with over 53,000 stars. The project claims benchmark leadership among open-source memory systems for AI agents. High star count suggests significant community adoption or interest in persistent memory tooling for AI applications.
Infini Memory: Topic-structured persistent memory architecture for long-term LLM agents
Researchers propose Infini Memory, a persistent memory architecture for LLM agents that organizes memory as topic-structured documents rather than isolated records or summaries. New observations are staged in a buffer and periodically consolidated, while retrieval uses iterative agentic tool calls instead of a single lookup step. The system achieves 64.7% on MemoryAgentBench, with ablations showing complementary gains from topic-structured maintenance and iterative evidence inspection.
LongMINT: Benchmark for Evaluating Memory Under Multi-Target Interference in Long-Horizon Agent Systems
LongMINT is a new benchmark designed to evaluate memory-augmented agents in realistic long-horizon settings where information is repeatedly updated and interferes across memories. It contains 15.6k QA pairs over contexts averaging 138.8k tokens (up to 1.8M tokens), spanning domains including state tracking, multi-turn dialogue, Wikipedia revisions, and GitHub commits. Evaluation of 7 representative systems—including vanilla long-context LLMs, RAG, and memory-augmented agent frameworks—reveals consistently low average accuracy of 27.9%, with performance particularly degraded on multi-target aggregation tasks and when earlier facts are revised by subsequent context. The analysis identifies retrieval and memory construction as the primary bottlenecks.