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.
Build an LLM Wiki for Your AI Agents
Build an LLM Wiki for Your AI Agents with myKG and Obsidian How to turn a folder of mixed format documents into a typed, interlinked knowledge graph your agents can actually read — using myKG and …
Karpathy's LLM Wiki as Agent Memory - Agentic AI Foundation (AAIF)
At work, I’m building agents to handle various operational tasks and have found Karpathy’s LLM Wiki design to be an excellent solution for implementing most ty…
Introducing Markdown for Agents
The way content is discovered online is shifting, from traditional search engines to AI agents that need structured data from a Web built for humans. It’s time to consider not just human visitors, but start to treat agents as first-class citizens. Markdown for Agents automatically converts any HTML page requested from our network to markdown.
AI Agents: Key Concepts and How They Overcome LLM Limitations
An AI agent is an autonomous software entity that is often used to augment a large language model. Here's what developers need to know.
Supporting Our AI Overlords: Redesigning Data Systems to be Agent-First
Large Language Model (LLM) agents, acting on their users' behalf to manipulate and analyze data, are likely to become the dominant workload for data systems in the future. When working with data,...
A-MEM: Agentic Memory for LLM Agents
While large language model (LLM) agents can effectively use external tools for complex real-world tasks, they require memory systems to leverage historical experiences. Current memory systems enable basic storage and retrieval but lack sophisticated memory organization, despite recent attempts to incorporate graph databases. Moreover, these systems' fixed operations and structures limit their adaptability across diverse tasks. To address this limitation, this paper proposes a novel agentic memory system for LLM agents that can dynamically organize memories in an agentic way. Following the basic principles of the Zettelkasten method, we designed our memory system to create interconnected knowledge networks through dynamic indexing and linking. When a new memory is added, we generate a comprehensive note containing multiple structured attributes, including contextual descriptions, keywords, and tags. The system then analyzes historical memories to identify relevant connections, establishing links where meaningful similarities exist. Additionally, this process enables memory evolution - as new memories are integrated, they can trigger updates to the contextual representations and attributes of existing historical memories, allowing the memory network to continuously refine its understanding. Our approach combines the structured organization principles of Zettelkasten with the flexibility of agent-driven decision making, allowing for more adaptive and context-aware memory management. Empirical experiments on six foundation models show superior improvement against existing SOTA baselines. The source code for evaluating performance is available at https://github.com/WujiangXu/A-mem, while the source code of the agentic memory system is available at https://github.com/WujiangXu/A-mem-sys.
Agent Plugins
A portable package format for reusable components that extend AI agents.
Supermemory
The memory layer for AI agents. Context engineering platform powering enterprise APIs, developer plugins, and a personal app that remembers everything.
Turn 10,994 Notes Into Memory - Paul Iusztin, Decoding AI & Louis-François Bouchard, Towards AI
Writing effective tools for AI agents—using AI agents
Writing effective tools for AI agents—using AI agents
browser-use/browser-use
🌐 Make websites accessible for AI agents. Automate tasks online with ease.
Monitor | Parallel | Parallel Web Systems | Infrastructure for intelligence on the web
Monitor API for AI agents.
How to Build an LLM Knowledge Base
A hands-on guide to building a simple LLM knowledge base with raw markdown sources, a compiled wiki, reusable prompts, and an agent that keeps improving the knowledge base over time.
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.