4GitHub Trending (AI/LLM filtered)·
memtrace: Structural memory layer for AI coding agents using bi-temporal graph and MCP
memtrace-public is an open-source Python library providing structural memory for AI coding agents via a bi-temporal graph architecture. It is MCP-native and operates without LLM calls, targeting integrations with Cursor, Claude Code, Codex, and VS Code. The project is early-stage with 407 stars and modest daily traction.
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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.
basic-memory: persistent memory layer for AI conversations via MCP
basic-memory is an open-source Python project providing persistent memory for AI conversations, allowing users to avoid re-explaining context across sessions. The project has accumulated 3,212 GitHub stars with modest daily growth. It appears to implement a local knowledge-graph or note-based memory store exposed via MCP or similar tooling.
AutoMem: Automated framework trains LLMs to manage memory as a learnable cognitive skill
AutoMem is a new framework that treats memory management in LLMs as a trainable skill, using two optimization loops: one that iteratively revises memory structure via trajectory review by a strong LLM, and one that distills good memory decisions into direct training signal for the agent model. Evaluated on three long-horizon procedurally generated games (Crafter, MiniHack, NetHack), optimizing memory alone yielded 2x-4x performance improvements, bringing a 32B open-weight model competitive with frontier systems like Claude Opus 4.5 and Gemini 3.1 Pro Thinking. The work draws on cognitive science concepts of metamemory and demonstrates that memory management is an independently learnable, high-leverage capability for long-horizon agentic tasks.
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.
MemTrace: Framework for Tracing and Attributing Errors in LLM Memory Systems
MemTrace introduces a framework that converts LLM memory pipelines into executable memory evolution graphs to enable fine-grained error tracing and root-cause attribution. The authors construct MemTraceBench, a benchmark covering Long-Context, RAG, Mem0, and EverMemOS memory systems, to systematically characterize memory failure modes such as information loss and retrieval misalignment. An automatic attribution method iteratively traces operation subgraphs to pinpoint failures, and the resulting signals are used to guide prompt optimization in a closed-loop system that improves end-task performance by up to 7.62%.
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.
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.
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.