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thedotmack / claude-mem
Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions.
View thedotmack/claude-memTrending open-source projects · updated daily
AI Agent Memory Projects
An LLM call is stateless: everything the model knows about your session has to travel inside the prompt. Agent memory is the layer in between — it decides what gets extracted from a conversation, where that state is stored, and what is pulled back into the context window on the next turn. The projects on this board attack that problem from different angles: embedded stores that run in the same process as the agent, graph memory that keeps entities and relations, vector-backed recall, and systems that summarise a transcript instead of storing it verbatim. Entries come from GitHub topic pages for agent memory, long-term memory, LLM memory and memory management, ranked by stars, so a heavyweight framework and a small single-file library land in the same list. Every card carries language, stars and forks; every page adds description, license, last push date, README and related projects. Stars measure attention, not maintenance — read the activity dates before you give an agent the keys to real user data.
Trending AI Agent Memory Projects
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mem0ai / mem0
The Memory Layer for AI Agents - Drop-in memory infrastructure for AI agents and apps. Context that persists. Built for production.
View mem0ai/mem03
volcengine / OpenViking
Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills.
View volcengine/OpenViking4
topoteretes / cognee
Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine.
View topoteretes/cognee5
supermemoryai / supermemory
Memory and context engine + app that is extremely fast, scalable, and can be run fully locally. The Memory API for the AI era.
View supermemoryai/supermemory6
TencentCloud / TencentDB-Agent-Memory
TencentDB Agent Memory is a team-level memory hub for AI Agents — turning conversations, docs, and code into four reusable memory assets (Chat Memory, Skill, LLM-Wiki, Code-Graph) that are governed
View TencentCloud/TencentDB-Agent-Memory7
MemoriLabs / Memori
Memori is agent-native memory infrastructure. A LLM-agnostic layer that turns agent execution and conversation into structured, persistent state for production systems.
View MemoriLabs/Memori8
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EverMind-AI / EverOS
One portable memory layer for every AI agent: local-first, Markdown-native, user-owned, and self-evolving across apps, tools, and workflows.
View EverMind-AI/EverOS11
MemTensor / MemOS
Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse, with 35.24% token savings and DeepSeek Harness support.
View MemTensor/MemOS12
henrypp / memreduct
Lightweight real-time memory management application to monitor and clean system memory on your computer.
View henrypp/memreduct13
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IgorMundstein / WinMemoryCleaner
This free RAM cleaner uses native Windows features to optimize memory areas. It's a compact, portable, and smart application.
View IgorMundstein/WinMemoryCleaner15
CaviraOSS / LongMemory
Local persistent memory store for LLM applications including claude desktop, github copilot, codex, antigravity, etc.
View CaviraOSS/LongMemory16
FlowElement-xinliuyuansu / m_flow
A bio-inspired cognitive memory engine — a new paradigm for Graph RAG.
View FlowElement-xinliuyuansu/m_flow17
GPUOpen-LibrariesAndSDKs / VulkanMemoryAllocator
Easy to integrate Vulkan memory allocation library
View GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator18
pashpashpash / vault-ai
OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (OpenAI + Pinecone Vector Database). Upload your own custom knowledge base files (PDF, txt, epub, etc) using a simple React frontend.
View pashpashpash/vault-ai19
letta-ai / letta-code
Stateful agents that are like people, with memory, identity, and the ability to learn and adapt
View letta-ai/letta-code20
MemMachine / MemMachine
Universal memory layer for AI Agents. It provides scalable, extensible, and interoperable memory storage and retrieval to streamline AI agent state management for next-generation autonomous systems.
View MemMachine/MemMachine21
memodb-io / memobase
User Profile-Based Long-Term Memory for AI Chatbot Applications.
View memodb-io/memobase22
zilliztech / memsearch
A persistent, unified memory layer for all your AI agents (e.g. Claude Code, Codex, DSH), backed by Markdown and Milvus.
View zilliztech/memsearch23
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cortexkit / magic-context
Unbounded context. Memory that manages itself. One session, for life. The hippocampus for coding agents, part of CortexKit.
View cortexkit/magic-context25
agentset-ai / agentset
The open-source RAG platform: built-in citations, deep research, 22+ file formats, partitions, MCP server, and more.
View agentset-ai/agentset26
xzf-thu / VoiceMem
Infrastructure for the next generation of voice agents, designed to provide universal memory. It is divided into a left brain and a right brain, storing information and emotions respectively
View xzf-thu/VoiceMem27
lucidrains / titans-pytorch
Unofficial implementation of Titans, SOTA memory for transformers, in Pytorch
View lucidrains/titans-pytorch28
MemTensor / memmy-agent
🍙 A personal AI agent & local memory hub for all AI agents, gives every AI one shared, fully controlled memory and persistent context — all AI remember the same you.
View MemTensor/memmy-agent29
doobidoo / mcp-memory-service
Open-source persistent memory for AI agent pipelines (LangGraph, CrewAI, AutoGen) and Claude. REST API + knowledge graph + autonomous consolidation.
View doobidoo/mcp-memory-service30
tapwork / HeapInspector-for-iOS
Find memory issues & leaks in your iOS app without instruments
View tapwork/HeapInspector-for-iOS31
plasma-umass / Mesh
A memory allocator that automatically reduces the memory footprint of C/C++ applications.
View plasma-umass/Mesh32
mex-memory / mex
Team memory for engineers and their AI agents. Lives in your repo. Shared through Git.
View mex-memory/mex33
BAI-LAB / MemoryOS
[EMNLP 2025 Oral] MemoryOS is designed to provide a memory operating system for personalized AI agents.
View BAI-LAB/MemoryOS34
Dataojitori / nocturne_memory
A lightweight, rollbackable, and visual Long-Term Memory Server for MCP Agents. Say goodbye to Vector RAG and amnesia.
View Dataojitori/nocturne_memory35
0voice / kernel_memory_management
总结整理linux内核的内存管理的资料，包含论文，文章，视频，以及应用程序的内存泄露，内存池相关
View 0voice/kernel_memory_management36
zjunlp / LightMem
[ICLR 2026] LightMem: Lightweight and Efficient Memory-Augmented Generation
View zjunlp/LightMem37
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NirDiamant / Agent_Memory_Techniques
Agent memory for LLMs: 30 runnable Jupyter notebooks covering conversation buffers, vector stores, knowledge graphs, episodic and semantic memory, MemGPT, Mem0, Letta, Zep, Graphiti
View NirDiamant/Agent_Memory_Techniques40
GPUOpen-LibrariesAndSDKs / D3D12MemoryAllocator
Easy to integrate memory allocation library for Direct3D 12
View GPUOpen-LibrariesAndSDKs/D3D12MemoryAllocator41
Captainarash / The_Holy_Book_of_X86
A simple guide to x86 architecture, assembly, memory management, paging, segmentation, SMM, BIOS....
View Captainarash/The_Holy_Book_of_X8642
tigerless-labs / agent-memory
Long-term memory runtime for AI agents — plain Markdown as the source of truth, local ranked retrieval, and an independent sleep-time Manage layer. Claude Code and Codex share one store. No API key.
View tigerless-labs/agent-memory43
Shy2593666979 / AgentChat
AgentChat 是一个基于 LLM 的智能体交流平台，内置默认 Agent 并支持用户自定义 Agent。通过多轮对话和任务协作，Agent 可以理解并协助完成复杂任务。项目集成 LangChain、Function Call、MCP 协议、RAG、Memory、HITL、Skill、Milvus 和 ElasticSearch 等技术，实现高效的知识检索与工具调用，使用 FastAPI
View Shy2593666979/AgentChat44
vshulcz / deja-vu
One memory shared by Claude Code, Codex, Cursor, Copilot CLI, OpenClaw and 28 more coding agents, built from the session history already on disk.
View vshulcz/deja-vu45
CodeAbra / iai-personal-memory-engine
A cyber brain for your AI. It never forgets a detail, remembers exactly what you said, and learns how you work over time.
View CodeAbra/iai-personal-memory-engine46
riponcm / projectmem
Open-source coding agents memory. Records issues, attempts, fixes and decisions, then warns your agent before it repeats an approach that already failed.
View riponcm/projectmem47
Victorwz / LongMem
Official implementation of our NeurIPS 2023 paper "Augmenting Language Models with Long-Term Memory".
View Victorwz/LongMem48
AVIDS2 / memorix
Open-source cross-agent memory layer for coding agents via MCP. Compatible with Claude Code, Codex, Cursor, Windsurf, Gemini CLI, Antigravity, OpenClaw, Hermes Agent, Oh-my-Pi, Pi, Copilot, Kiro
View AVIDS2/memorix49
gamenet / redis-memory-analyzer
Redis memory profiler to find the RAM bottlenecks throw scaning key space in real time and aggregate RAM usage statistic by patterns.
View gamenet/redis-memory-analyzer50
rapidsai / rmm
NVIDIA RMM is a library for allocating and managing GPU memory in C++ and Python.
View rapidsai/rmm51
agentic-box / memora
Give your AI agents persistent, collective memory — with deduplicating absorb, supersession lineage, semantic search, and a graph UI. Speaks MCP.
View agentic-box/memora52
neo4j-labs / create-context-graph
AI agents with graph based reasoning memory, scaffolded in seconds
View neo4j-labs/create-context-graph53
okf-memory / okf-agent-memory
Git-native persistent memory for AI coding agents. Implements Google OKF v0.2 with sub-300µs in-memory BM25 search, embedded MCP server, and progressive disclosure.
View okf-memory/okf-agent-memory54
swarmclawai / swarmvault
The local-first LLM Wiki: open-source knowledge graph builder, RAG knowledge base, and agent memory store. Built on Andrej Karpathy's pattern.
View swarmclawai/swarmvault55
caspianmoon / memoripy
Evidence-first local memory for AI agents with temporal versions, admission policies, citations, explainable recall, MCP, and audit tooling.
View caspianmoon/memoripy56
swarmclawai / swarmclaw
Open-source self-hosted AI agent runtime and multi-agent framework for autonomous agent swarms. Agent memory, MCP tools, schedules, delegation, and 23+ LLM providers (Claude, GPT, Gemini, OpenRouter
View swarmclawai/swarmclaw57
jmerelnyc / Photo-agents
Autonomous self-evolving agents. Vision-grounded layered memory and self-written skills for LLM agents that operate your computer.
View jmerelnyc/Photo-agents58
framerslab / agentos
TypeScript AI agent framework: cognitive memory, runtime tool forging, multi-agent orchestration, 11 LLM providers.
View framerslab/agentos59
jaredrhod / ai-memory-vault
Give your AI a real, persistent memory. The open-source system plus templates that turn an Obsidian vault into your AI's working memory. No vector database, just markdown.
View jaredrhod/ai-memory-vault60
samvallad33 / vestige
Cognitive Deterministic Memory Security OS for Agentic AI that reaches backward through time to find the quiet change behind today's failure, not the lookalike.
View samvallad33/vestige61
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24kchengYe / MemoMind
Give your AI agent a brain that remembers. Local memory system for Claude Code — 100% private, GPU-accelerated, zero cloud dependency.
View 24kchengYe/MemoMind63
mnemon-dev / mnemon
LLM-supervised persistent memory for AI agents — graph-based recall, cross-session knowledge, single binary. Works with DeepSeek Harness, Claude Code, OpenClaw, and any agent runtime.
View mnemon-dev/mnemon64
MaxFreedomPollard / Compartment
Encrypted, fully offline agentic memory. One click install, GUI w/ memory map, all OS and agents. Superior memory creation, storage and retrieval.
View MaxFreedomPollard/Compartment65
memvid / claude-brain
Give Claude Code photographic memory in ONE portable file. No database, no SQLite, no ChromaDB - just a single .mv2 file you can git commit, scp, or share. Native Rust core with sub-ms operations.
View memvid/claude-brain66
neo4j-labs / agent-memory
A graph-native memory system for AI agents and context graphs. Store conversations, build knowledge graphs, and let your agents learn from their own reasoning — all backed by Neo4j.
View neo4j-labs/agent-memory67
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LeoYeAI / openclaw-auto-dream
Automatic memory consolidation for OpenClaw agents — like sleep for your AI. Powered by MyClaw.ai
View LeoYeAI/openclaw-auto-dream69
leandrosa81 / taskbar-monitor
monitoring tool with graphs (CPU, memory, disk and network) for Windows taskbar.
View leandrosa81/taskbar-monitor70
caura-ai / caura
Caura (formerly MemClaw) — governed shared memory for AI agent fleets. Multi-agent, multi-tenant, MCP-native. Trust tiers, keystone policies, audit trails, knowledge graph, self-improving retrieval.
View caura-ai/caura71
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TeleAI-UAGI / telemem
TeleMem is a high-performance drop-in replacement for Mem0, featuring semantic deduplication, long-term dialogue memory, and multimodal video reasoning.
View TeleAI-UAGI/telemem73
Viladoman / StructLayout
Visual Studio Extension for C++ struct memory layout visualization
View Viladoman/StructLayout74
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syncable-dev / memtrace-public
Structural memory for AI coding agents. Bi-temporal graph, MCP-native, zero LLM calls. Cursor · Claude Code · Codex · DeepSeek Harness · Hermes · VS Code · Windsurf.
View syncable-dev/memtrace-public76
angelnicolasc / graymatter
30 sec to give your AI agents persistent memory. Reduce 90% token consumption while also maintaining quality.
View angelnicolasc/graymatter77
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aoci-spec / aoci-code
A persistent, Git-versioned map of your whole codebase and database schema that coding agents read before they touch anything.
View aoci-spec/aoci-code79
atomicstrata / atomicmemory
Portable semantic memory for AI agents: core engine, TypeScript SDK, framework adapters, MCP server, CLI, and host plugins.
View atomicstrata/atomicmemory80
llnl / Umpire
An application-focused API for memory management on NUMA & GPU architectures
View llnl/Umpire81
pax-beehive / paxm
Persistent, provider-neutral memory for Codex, Claude Code, OpenCode, Pi, and MCP coding agents.
View pax-beehive/paxm82
emeryberger / Heap-Layers
Heap Layers: An Extensible Memory Allocation Infrastructure
View emeryberger/Heap-Layers83
grpcer / ownmem
Open-source, Git-native memory for AI coding agents — deterministic local recall for Claude Code, Codex, Cursor, Gemini CLI, and compatible tools.
View grpcer/ownmem84
NeoLi00 / memX
memX: self-learning, self-maintaining memory plugin for AI agents; native support for claude code, codex, and openclaw
View NeoLi00/memX85
Lyellr88 / marm-memory
Local-first 3-in-1 AI memory layer & MCP server for Claude Code, Codex, Grok, Gemini, VS Code and Cursor. Fuses session history, codebase indexing & concept graphs in SQLite.
View Lyellr88/marm-memory86
omdsh-dev / dsh-mnemon
Composable, view-based memory for DeepSeek Harness. Pluggable sources and strategies, with three-tier memory out of the box.
View omdsh-dev/dsh-mnemon87
struct / isoalloc
A general purpose memory allocator that implements an isolation security strategy to mitigate memory safety issues while maintaining good performance
View struct/isoalloc88
OnlyTerp / openclaw-optimization-guide
Make your OpenClaw AI agent faster, smarter, and cheaper. Speed optimization, memory architecture, context management, model selection, and one-shot development guide.
View OnlyTerp/openclaw-optimization-guide89
buildingjoshbetter / TrueMemory
The memory your AI should have had from the start. Automatic capture, automatic recall, 100% local. One SQLite file, zero cloud. Works with Claude Code, Claude CLI, Cursor, Codex CLI, Gemini CLI.
View buildingjoshbetter/TrueMemory90
databrickslabs / ontobricks
Transforms Databricks Unity Catalog tables into a materialized knowledge graph, with ontology design and reasoning exposed as tools via MCP.
View databrickslabs/ontobricks91
Goldentrii / AgentRecall-X
Correction-first persistent memory for AI agents. MCP server + SDK + CLI. Compounds across sessions.
View Goldentrii/AgentRecall-X92
prigoyal / pytorch_memonger
Experimental ground for optimizing memory of pytorch models
View prigoyal/pytorch_memonger93
frank-tan / SinsOfMemoryLeaks
Some common patterns of memory leaks in Android development and how to fix/avoid them
View frank-tan/SinsOfMemoryLeaks94
smaramwbc / statewave
Open-source memory runtime for AI agents — reproducible, provenance-tagged context bundles instead of query-time retrieval. Apache-2.0, self-hosted on Postgres + pgvector, Python + TypeScript SDKs.
View smaramwbc/statewave95
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aayoawoyemi / Ori-Mnemos
Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). Open source must win.
View aayoawoyemi/Ori-Mnemos97
rush-db / rushdb
RushDB is a graph + vector database and memory layer for AI agents. Push any JSON, get typed, searchable, relationship-aware records back — no schema, no migrations. Built on Neo4j.
View rush-db/rushdb98
JordyZomer / lemmalog
A Datalog engine for LLM agent memory: stratified rules, provenance-tracked facts, incremental derivation, and an MCP server that lets your harness use it as a shared brain.
View JordyZomer/lemmalog99
mcncarl / agent-memory-vault
Markdown-first shared memory vault for Claude Code and Codex with SQLite, Zvec, Git, closeout, and audit
View mcncarl/agent-memory-vault100
LucieEveille / kiwi-mem
🥝 Self-hosted memory gateway for AI companions — OpenAI-compatible proxy with vector search, memory heat
View LucieEveille/kiwi-memLong-Term & Persistent Agent Memory
- thedotmack/claude-mem — ★ 94,340
- mem0ai/mem0 — ★ 65,719
- volcengine/OpenViking — ★ 38,212
- topoteretes/cognee — ★ 30,864
- supermemoryai/supermemory — ★ 30,687
- TencentCloud/TencentDB-Agent-Memory — ★ 27,043
- MemoriLabs/Memori — ★ 16,866
- NevaMind-AI/memU — ★ 14,418
Vector, Graph & Retrieval Memory
- alibaba/zvec — ★ 15,976
- MemTensor/MemOS — ★ 11,489
- FlowElement-xinliuyuansu/m_flow — ★ 4,507
- pashpashpash/vault-ai — ★ 3,391
- MemMachine/MemMachine — ★ 3,222
- agentset-ai/agentset — ★ 2,093
- doobidoo/mcp-memory-service — ★ 1,952
- Dataojitori/nocturne_memory — ★ 1,360
Agent Memory Frameworks
- EverMind-AI/EverOS — ★ 13,094
- henrypp/memreduct — ★ 10,567
- plastic-labs/honcho — ★ 7,274
- IgorMundstein/WinMemoryCleaner — ★ 5,052
- CaviraOSS/LongMemory — ★ 4,510
- GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator — ★ 3,505
- letta-ai/letta-code — ★ 3,386
- memodb-io/memobase — ★ 2,907
On-Device & Embedded Memory Stores
- MemTensor/memmy-agent — ★ 1,958
- NirDiamant/Agent_Memory_Techniques — ★ 1,069
- tigerless-labs/agent-memory — ★ 959
- vshulcz/deja-vu — ★ 898
- CodeAbra/iai-personal-memory-engine — ★ 891
- riponcm/projectmem — ★ 828
- okf-memory/okf-agent-memory — ★ 707
- swarmclawai/swarmvault — ★ 695
More AI Trending Projects
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→What agent memory actually means
A language model has no memory of its own. Every call starts blank, so anything an agent needs to know — a user preference, a decision taken three sessions ago, the contents of a repository it just read — has to be reconstructed and placed into the prompt. Agent memory is the machinery that does the reconstruction. It is usually three jobs glued together: extraction (deciding which part of a conversation is worth keeping), storage (a vector index, a graph, a relational table, or plain files on disk) and retrieval (choosing what goes back into the context window on the next call). Almost every project on this board is an opinionated answer to one question: where does state live, and how is it fetched.
The distinction that matters in production is not the short-term versus long-term label, but the prompt-token budget. A system that stores every turn and replays all of it drowns the context window and pays for it on every request; a system that retrieves three relevant facts costs almost nothing. That trade-off — how much to remember versus how much to re-send — separates a weekend demo from something you can run for a month.
Long-term memory, summarisation and graph recall
Long-term memory is whatever survives the end of a session: facts about a person, decisions taken on a project, the state of a task. Three shapes dominate the projects below. Summarisation compresses the running transcript so recent turns stay cheap to send. Vector recall embeds each memory as text plus an embedding and fetches the nearest by similarity — quick to build, weak at questions that need structure. Graph memory extracts people, projects and relations, then walks them at query time; it answers questions like which library did we drop and why far better than similarity search, at the cost of an extraction step you must maintain.
The three are not exclusive. The stronger systems here write to two stores at once — a cheap summary for the live conversation, and a structured, searchable store for everything older.
On-device and self-hosted memory
On-device memory means the memory layer runs beside the model — same process, same container, same laptop — instead of posting user data to a hosted service. Two families dominate. Embedded stores are essentially a database file with vector search bolted on: no server, no network, no per-query bill. Self-hosted servers buy you a retention policy you can actually explain to a user and a deletion path that does not depend on someone else’s API. If data residency or offline operation is the requirement, start with the embedded tools — they are the ones that keep working with the network cable unplugged.
Agent memory questions
What is an AI agent memory system? The component that persists information between model calls and decides what goes back into the context window: normally an extraction step, a store, and a retrieval policy.
Does agent memory need a vector database? No. Vector search is one retrieval strategy and the easiest to start with. Graph stores, SQLite tables and flat files are all used in production, especially when the questions you ask are structural rather than semantic.
What is on-device memory for AI agents? A memory store running on the same machine or in the same process as the agent, so nothing is sent to a third party and recall keeps working offline. Embedded databases and local vector indexes are the usual building blocks.
Is agent memory the same as fine-tuning? No. Fine-tuning changes the weights and is expensive to update. Memory changes what is in the prompt, which means you can edit, delete and audit it per user — usually exactly what privacy rules demand.
How do I stop memory from growing forever? Give it a write policy and a decay rule: score memories as they are written, expire the low-value ones, and cap the retrieved set. Every mature project on this board exposes some version of that policy.