The author introduces Tiro, an open-source agentic memory and retrieval framework designed to solve long-term context drift in LLM agents by providing modular, inspectable memory lanes for sessions, documents, and operational state.
A year ago, when I first got into LLMs, I started by using them to play D&D. ChatGPT 4o was surprisingly good at narration, improvisation, and keeping the game moving. The problem was memory. Context would drift, old facts would get flattened, and anything long-running eventually turned into a fight against the model’s forgetting. As I got more interested in LLMs and started learning to code, that became the problem I wanted to solve for myself. So I started building my own memory framework for agents. My goal was not “just add RAG” and call it a day. I had read a paper on Machine Learning that talked about agentic sorted RAG. A small agentic shard that exists only in a few lines of code, using a miniscule amount of tokensI to assist in more accurate memory retrieval and packing before passing it on to the real agent. I wanted something modular, inspectable, and reusable across different personal agents and projects. Something that could store documents, retrieve facts, track sessions, remember operational state, distinguish between active and stale information, and package all of that back into a clean context packet instead of a blob of vibes. The result is **Tiro**. Tiro is a standalone memory and retrieval substrate I’m building for my personal agent ecosystem. It is designed to be plugged into by different agents rather than belonging to just one of them. Right now it has separate memory lanes for corpus/archive memory, session/state memory, and structured operational memory like decisions, TODOs, unknowns, warnings, and lifecycle-aware facts. In plain English, I’m trying to build agentic memory in a box of code. Not consciousness. Not magic. Just a serious memory engine that can support multiple agents and keep long-running context from collapsing into soup. It’s still early, but it’s gotten to the point that it's scary good. I can upload entire PDFs and it just ingests, parses, and breaks up the data across a SQLite databases sorted for their function. When I'm finished. It'll be extensible to any provider API and you can simply drop it into your own agent repo, point your favorite coding aagent at it and incorporate into your agent and give it an advanced memory and RAG system.
agentmemory is an open-source persistent memory layer for AI coding agents (Claude Code, Cursor, Gemini CLI, Codex CLI, etc.) that uses knowledge graphs, confidence scoring, and hybrid search to give agents long-term memory across sessions via MCP, hooks, or REST API. Built on the iii engine, it requires no external databases and exposes 51 MCP tools.
T-Mem is a new long-term conversational memory architecture that enables both descriptive and associative recall, covering scenarios where query and memory share surface features and those where they are connected by latent semantic arcs. It reaches state-of-the-art on the LoCoMo and LoCoMo-Plus benchmarks.
This paper introduces MemToolAgent, a framework that enhances LLM agents' tool-using capabilities by integrating a memory system that stores and retrieves past experiences, achieving significant improvements on multiple benchmarks without requiring model fine-tuning.
Proposes Multi-Agent Transactive Memory (MATM), a framework for population-level storage and retrieval of agent-generated trajectories to improve task performance and reduce interaction steps in interactive environments like ALFWorld and WebArena.
An exploration of the components and design decisions behind agent memory libraries, clarifying the gap between cognitive science terminology and engineering implementation.