Long-term memory layers for AI agents — vector stores, episodic recall, semantic compression, and persistent context across sessions.
Agent Memory tools are AI-powered software designed to help developers and teams tackle agent memory-related tasks more efficiently. These tools are typically published as open-source projects on GitHub and can be integrated into existing workflows via MCP (Model Context Protocol), Claude Skills, or standalone agent frameworks. On Agent Skills Hub, we index 30 quality-scored agent memory tools across languages including Python, Go, JavaScript.
In 2026, the AI agent ecosystem is maturing rapidly. Agent Memory tools can significantly boost development efficiency by automating repetitive tasks, reducing human error, and providing intelligent suggestions. The top 3 tools — mem0, letta, mcp-memory-service — have earned an average of 4,023 GitHub stars, reflecting strong community validation. 27 of the listed tools come with clear open-source licenses, ensuring freedom to use and modify.
When choosing a agent memory tool, consider these factors: 1) Community activity — GitHub stars and recent commit frequency indicate reliability; 2) Integration method — check if it supports MCP, Claude, or your preferred agent framework; 3) Language compatibility — the most common language in this list is Python; 4) Quality score — Agent Skills Hub's composite score evaluates code quality, documentation completeness, and maintenance activity. Our recommendation: start with mem0 — it ranks highest in both star count and quality score.
Platform for stateful agents: AI with advanced memory that can learn and self-improve over time.
Open-source persistent memory for AI agent pipelines (LangGraph, CrewAI, AutoGen) and Claude. REST API + knowledge graph + autonomous consolidation.
Coding agent skill for Tensorlake. Routes Claude Code, OpenAI Codex, and other AI agents to live Tensorlake docs for sandboxes, orchestration, and SDK usage.
A lightweight, rollbackable, and visual Long-Term Memory Server for MCP Agents. Say goodbye to Vector RAG and amnesia. Empower your AI with persistent, graph-like structured memory across any model, session, or tool. Drop-in replacement for OpenClaw.
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.
Structural memory for AI coding agents. Bi-temporal graph, MCP-native, zero LLM calls. Cursor · Claude Code · Codex · DeepSeek Harness · Hermes · VS Code · Windsurf.
Self-hosted AI agent harness in a single Go binary — writes, sandbox-tests and repairs its own tools, and lets Claude Code, Codex and any MCP client build and share them.
Open-source cognitive coprocessor with active memory for AI agents — persistent recall, semantic search, overnight dreaming, verified facts, encrypted USB sync. MCP server; works with Claude, ChatGPT, and any local LLM. Built by one maker and his agents.
Xmem is a India's First open source multi-modal, multi-agentic long‑term memory layer for AI agents.
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.
AI-powered second brain for Claude Code that builds itself. Extract knowledge from every session—past and present—into auto-organized Markdown. Local-first, queryable, learns your patterns.
MCP server with persistent memory + FTS5 search for Claude Code conversation history. Index your ~/.claude/projects/, expose 10 MCP tools, browse via web UI. MIT-licensed.
Noema long-term memory plugin for DSH: durable, inspectable agent memory with recall tools and a settings page.
Human-like memory for AI agents — semantic, episodic & procedural. Experience-driven procedures that learn from failures. Free API, Python & JS SDKs, LangChain, CrewAI & OpenClaw integrations.
Persistent long-term memory for Claude Code via MCP — captures coding decisions, bugfixes, and context across sessions. Hybrid FTS5 + TF-IDF search with episode batching. Single SQLite DB, no external services. Alternative to claude-mem with 600x lower cost.
One portable memory layer for every AI agent: local-first, Markdown-native, user-owned, and self-evolving across apps, tools, and workflows.
TeleMem is a high-performance drop-in replacement for Mem0, featuring semantic deduplication, long-term dialogue memory, and multimodal video reasoning.
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. Enables zero-cloud, privacy-first context & instant recall, supports multi-agent swarms.
✨ mem0 MCP Server: A memory system using mem0 for AI applications with model context protocl (MCP) integration. Enables long-term memory for AI agents as a drop-in MCP server.
Local-first AI memory you can see, edit, and override — portable across Claude Code, Codex, Cursor, Windsurf, and other MCP coding tools.
TencentDB Agent Memory delivers fully local long-term memory for AI Agents via a 4-tier progressive pipeline, with zero external API dependencies.
A cyber brain for your AI. It never forgets a detail, remembers exactly what you said, and learns how you work over time. Free, local, works with Cursor, Claude Code, Codex, OpenClaw, Hermes and more. MIT.
Agentic AI memory with Ebbinghaus forgetting curve decay. +16pp better recall than Mem0 on LoCoMo.
Three-tier memory control plane for DeepSeek Harness: persistent runtime context, searchable project documents, pluggable long-term memory, smart routing, supervised agent workflows, WebUI, and headless tools.
The best-benchmarked open-source memory system for AI coding assistants
Durable, file-based long-term memory for AI agents. Five-package plugin family: SDK, CLI, MCP server, Hermes adapter, and a LangGraph BaseStore. No vector database, no embeddings.
A traceable personal memory layer that carries verified state across apps, models, and AI agents.
A memory OS that makes your OpenClaw agents more personal while saving tokens.
Neo.mjs is a self-evolving software organism: a professional end-to-end AI engineering team whose cross-model swarm inhabits live apps via Neural Link, Active Hybrid GraphRAG, DreamService, and self-healing loops.
| Tool | Stars | Language | License | Score |
|---|---|---|---|---|
| mem0 | ★ 64.0k | Python | Apache-2.0 | 77 |
| letta | ★ 24.4k | — | Apache-2.0 | 75 |
| mcp-memory-service | ★ 1.9k | Python | Apache-2.0 | 71 |
| tensorlake-skills | ★ 174 | Python | MIT | 73 |
| nocturne_memory | ★ 1.3k | Python | MIT | 71 |
| mnemon | ★ 511 | Go | Apache-2.0 | 67 |
| memtrace-public | ★ 452 | Python | — | 64 |
| Agenvoy | ★ 464 | Go | Apache-2.0 | 69 |
| mnemo-cortex | ★ 150 | Python | MIT | 64 |
| XMem | ★ 233 | Python | BSD-3-Clause | 52 |
| TrueMemory | ★ 372 | Python | AGPL-3.0 | 66 |
| remember | ★ 58 | JavaScript | MIT | 61 |
| claudex | ★ 92 | JavaScript | MIT | 63 |
| dsh-noema | ★ 120 | TypeScript | MIT | 60 |
| mengram | ★ 189 | Python | Apache-2.0 | 66 |
| claude-mem-lite | ★ 54 | JavaScript | MIT | 65 |
| EverOS | ★ 12.4k | Python | Apache-2.0 | 77 |
| telemem | ★ 484 | Python | Apache-2.0 | 65 |
| marm-memory | ★ 336 | Python | Apache-2.0 | 67 |
| mem0-mcp | ★ 100 | TypeScript | MIT | 76 |
| piia-engram | ★ 157 | Python | AGPL-3.0 | 68 |
| TencentDB-Agent-Memory | ★ 4.5k | TypeScript | — | 69 |
| iai-personal-memory-engine | ★ 797 | Python | MIT | 68 |
| YourMemory | ★ 262 | Python | — | 63 |
| dsh-mnemon | ★ 196 | TypeScript | MIT | 66 |
| iai-mcp | ★ 119 | Python | MIT | 69 |
| Sibyl-Memory | ★ 103 | Python | MIT | 64 |
| memexa | ★ 55 | Python | Apache-2.0 | 57 |
| EverMemOS | ★ 3.5k | Python | Apache-2.0 | 69 |
| neo | ★ 3.3k | JavaScript | MIT | 68 |
The top agent memory tools in 2026 are mem0, letta, mcp-memory-service. Agent Skills Hub ranks 30 options by GitHub stars, quality score (6 dimensions including completeness, examples, and agent readiness), and recent activity. The list is rebuilt every 8 hours from live GitHub data.
mem0 (64.0k stars) is the most adopted choice for general agent memory workflows, written in Python. letta (24.4k stars) is a strong alternative. Pick by your existing stack: match the language and runtime your team already uses to minimize integration cost. If unsure, start with mem0 — it has the deepest community and the most examples online.
Avoid pre-built agent memory tools when (1) your use case requires deep customization that the tool's plugin system doesn't support, (2) you have strict compliance requirements that ban third-party dependencies, (3) the tool's maintenance is inactive (last commit >6 months ago), or (4) your data volume is small enough that a 50-line custom script is cheaper than learning the tool. For most production workflows above 100 requests/day, the time savings from a maintained tool outweigh the customization loss.
Agent Memory focuses specifically on long-term memory layers for ai agents — vector stores, episodic recall, semantic compression, and persistent context across sessions. Knowledge Base & RAG is a related but distinct category — see https://agentskillshub.top/best/knowledge-base/ for those tools. The two often appear in the same agent pipeline but solve different problems: choose agent memory when your primary goal is the specific task, and knowledge base & rag when the workflow is broader.
For most teams, yes. mem0 has 64.0k stars worth of community testing, handles edge cases you haven't thought of, and ships with documentation. Build your own only when (1) your requirements are deeply non-standard, (2) you have a security/compliance reason to avoid OSS dependencies, or (3) the maintenance burden is small enough (<200 lines of code) that you'll save time long-term. The break-even point is usually around 2-3 weeks of dev time saved.
Most agent memory tools listed are open source under permissive licenses (MIT, Apache 2.0). A handful offer paid managed/cloud versions on top of free self-hosted core. Always check the LICENSE file on each tool's GitHub repository before commercial use — some use AGPL or non-commercial restrictions that may not fit your deployment model.
