Was just sitting here working away and saw a text from Atom, looks like I just sold AgentMemory(.)io for $7,500. I don't sell a lot of domains these days, so always a pleasant surprise when it happens. Congrats to the new owner, excited to see what they do with it!

🧠 Maybe an AI agent's memory shouldn't be organized the moment it's stored, but the moment it's recalled. Title: Just-in-Time Memory: Learning to Curate Task-Adaptive Memory for LLM Agents (JitMem) URL: ❓ What's the core idea behind JitMem? Traditional agent memory summarizes an experience at write time, right after a task finishes. JitMem skips that: it stores raw trajectories as-is and only synthesizes a task-specific summary at read time, once the new task is actually known. ❓ Why isn't write-time summarization good enough? You have to decide what matters before you know which future task will need it, so valuable details often get discarded for good. Worse, the same experience can teach different lessons depending on the task, but a write-time summary can only be one generic version. ❓ How is it trained? A curator model that writes summaries from the current task and past trajectories is trained directly on the success reward the executor gets from using that summary. No need to wait for future queries, which makes optimization much simpler. ❓ How much does it help? Across ALFWorld, WebShop, and τ²-bench, JitMem beats the strongest baseline by 16.2, 16.3, and 3.9 points respectively. Even the untrained version is already strong, showing that read-time curation itself is a major source of the gain. #AIAgents# #AgentMemory#

Agent "memory" that only learns from past trajectories can quietly bake in wrong knowledge. Here's a smart fix for that. Title: Grounding Agent Memory: Environment-Probing Curation for Enterprise Agents URL: ❓ What was wrong with traditional agent memory? 💡 Curating memory purely from completed trajectories means over-generalizing from a single lucky observation, and memories going stale the moment something like a database schema changes. ❓ How does this paper fix it? 💡 Before committing anything to memory, it lets the agent probe the live environment with read-only tools to verify the knowledge is actually correct, using a "propose-probe-commit" pipeline. ❓ How much does it actually help? 💡 On a database exploration benchmark, pass rate jumped from 39% to 73%, reward roughly 2.6x higher, while tool calls dropped 47% and cost fell about 50%. ❓ Is it practical for real deployments? 💡 Yes — it needs no model retraining and doesn't change the task-time interface, so it can be dropped into long-running enterprise agents as-is. #AIAgents# #AgentMemory#

Most agent memory benches score recall. StateMemBench scores whether the answer uses the current fact or a superseded one. 234 multi-session scenarios. 322 graded probes. Closed-pool grading for state drift. StateMem lifts current-state accuracy about 1.8x over the strongest same-backbone memory baseline on DeepSeek-V4-Flash (0.199 to 0.363).

Our agent memory system just hit #1# on GitHub's TypeScript Trending! Now we are rolling out 2.0.0 beta: • Solo builders: One place to manage memory across all your agents and AI tools, chat, code, tasks. Built for the one-person company. • Teams: A shared memory hub that turns conversations, docs and code into four reusable assets: Chat Memory, Skill, LLM-Wiki, Code-Graph, governed and shared across agents and frameworks. Check out the new release & source 👉 #TencentDB# #TencentDBAgentMemory#

For agent memory, the real question isn't "how to store" — it's "what to remember" 🧠 A fresh take that learns what to memorize via reinforcement learning. Title: Task-Focused Memorization for Multimodal Agents URL: 🧠 Overview This work proposes TaskMem, which treats long-term memory for multimodal agents as a learnable policy optimized with reinforcement learning, focused on deciding what to memorize. From an unbounded stream of observations, it selectively retains only the content relevant to the agent's role and task. ❓ Challenges Solved A multimodal agent operating in the real world continuously receives an unbounded stream of observations. ・Most prior work focused on how to store memories (designing memory modules) ・But the essential problem is what to memorize — without a principled way to select role-relevant content from an endless stream, memory simply fails This work starts from that shift in perspective. 💡 Methodology & Proposed Approach TaskMem treats memorization as a learnable policy, optimized in two phases. ・Phase 1: learn high-quality memorization under fidelity requirements ・Phase 2: post-deployment fine-tuning that uses task rewards to align memorization with the environment's demands ・It builds on the MLLM Qwen3-VL-30B-A3B and optimizes the policy lightly via adapter tuning ・Reward models derived from real tasks steer the policy toward selecting relevant content 🌍 Use Cases / Experimental Results On reformulated streaming benchmarks, it delivered clear accuracy gains. ・VideoMME: 67.9% VQA accuracy (+6.3%) ・EgoLife: 45.4% VQA accuracy (+7.0%) ・EgoTempo: 27.6% VQA accuracy (+5.3%) ・Strong precision across all benchmarks (80.5-85.6%) It charts a practical path for long-running, always-on agents to selectively remember the right things while keeping context bloat in check. #AIAgents# #Memory#

I think agent memory is like a kind of shitty glue over the work you _should_ be doing to build a software factory manually. Building new skills, loops, bots, etc. It _kind of_ works but ultimately falls down because agents aren't as good as you are.

lots of coding agent memory systems are unhelpful - at best i have said this a lot and some folks think I'm against agent memory in entirety which is not true - I just think that users should be on the loop that generates memories and should be able to enable / disable the system or individual memories at will and should be able to dismiss disruptive or unhelpful memories putting a user on the loop also lets you build a better data flywheel over time to improve the quality of recommendations live in @humanlayer_dev btw lmk if you want beta access