Memory retrieval tuned to your knowledge base | Enzyme

Supermemory

The memory layer for AI agents. Context engineering platform powering enterprise APIs, developer plugins, and a personal app that remembers everything.

solstone: a memory your agents can work from

an open source, local-first journal of what you see and hear, on your device. a memory the agents you already use can work from. always private, only yours.

OpenMemory - AI Memory MCP Server for Coding Agents | Mem0

With OpenMemory, add persistent, project-aware memory to Cursor, Windsurf, and VS Code agents. Store preferences, patterns, and context that get retrieved automatically.

Hermes Agent — Open-Source AI Agent with Memory, Skills, and Cron

The open-source AI agent from Nous Research with persistent memory, reusable skills, tools, cron jobs, GitHub workflows, and multi-platform messaging.

Semble: Fast and Accurate Code Search for Agents

Fast and Accurate Code Search for Agents. Uses ~98% fewer tokens than grep+read

How to Fix OpenClaw's Memory Search with QMD | Jose Casanova

Upgrade OpenClaw's memory search from basic SQLite to QMD — a local hybrid search engine combining BM25, vector search, and LLM re-ranking for better AI memory.

Building napkin - a memory system for agents

A decade of information retrieval and three years of agent harness engineering, poured into a local-first knowledge system that avoids vector search entirely.

Cognition | Agent Trace: Capturing the Context Graph of Code

We’re excited to join in Cursor, Cloudflare, Vercel, git-ai, OpenCode and others in support of [Agent Trace](https://agent-trace.dev/). As described in the spec, Agent Trace is an open, vendor-neutral spec for recording AI contributions alongside human authorship in version-controlled codebases.

Syke — Cross-harness agentic memory

Open-source agentic memory for users and their agents. Syke acts as a live cache across every AI tool you use — Claude Code, Cursor, ChatGPT, Hermes — so what one harness learns, the next one already knows. Local-first.

Syke — Cross-harness agentic memory

Open-source agentic memory for users and their agents. Syke acts as a live cache across every AI tool you use — Claude Code, Cursor, ChatGPT, Hermes — so what one harness learns, the next one already knows. Local-first.

nanomem: An Extremely Simple, Inference-Time Memory Module

nanomem is an extremely simple, user-owned memory module that casts memory management as LLM calls / agent loops on a markdown file tree. You interact with the tree with natural language commands like nanomem add <fact>, nanomem retrieve <query>, and nanomem import <chatgpt>. As such, nanomem is by design interpretable, partitionable, portable, and versioned.

Learning to Continually Learn via Meta-learning Agentic Memory Designs

The statelessness of foundation models bottlenecks agentic systems' ability to continually learn, a core capability for long-horizon reasoning and adaptation. To address this limitation, agentic systems commonly incorporate memory modules to retain and reuse past experience, aiming for continual learning during test time. However, most existing memory designs are human-crafted and fixed, which limits their ability to adapt to the diversity and non-stationarity of real-world tasks. In this paper, we introduce ALMA (Automated meta-Learning of Memory designs for Agentic systems), a framework that meta-learns memory designs to replace hand-engineered memory designs, therefore minimizing human effort and enabling agentic systems to be continual learners across diverse domains. Our approach employs a Meta Agent that searches over memory designs expressed as executable code in an open-ended manner, theoretically allowing the discovery of arbitrary memory designs, including database schemas as well as their retrieval and update mechanisms. Extensive experiments across four sequential decision-making domains demonstrate that the learned memory designs enable more effective and efficient learning from experience than state-of-the-art human-crafted memory designs on all benchmarks. When developed and deployed safely, ALMA represents a step toward self-improving AI systems that learn to be adaptive, continual learners.

Prime Intellect - The Open Stack for Self-Improving Agents

The compute and infrastructure platform for you to train, evaluate, and deploy your own agentic models.

Prime Intellect - The Open Stack for Self-Improving Agents

The compute and infrastructure platform for you to train, evaluate, and deploy your own agentic models.

IWE - Agent Memory in Plain Markdown

A local-first knowledge graph for you and your AI agents. Query markdown like a database, edit it with guarded operations, enforce structure with schemas.