Memory

OpenClaw v2026.4.10 ships with a new Active Memory plugin that fundamentally changes how your agent handles context and recall. Instead of relying on you to manually tell it what to remember, the plugin runs a background memory sub-agent that automatically pulls in relevant history before each reply. This guide walks you through installation, configuration, and the key things to know before you turn it on. Prerequisites OpenClaw v2026.4.10 or later (check with openclaw --version) An existing OpenClaw workspace configured Basic familiarity with OpenClaw plugins Step 1: Install the Active Memory Plugin The plugin ships as an optional module in v2026.4.10+. To enable it: ...

OpenClaw had a busy Friday. Two releases shipped within hours of each other — first a genuinely exciting new capability, then a same-day polish pass to smooth the rough edges. If you’re an OpenClaw user, there’s a lot to unpack here. v2026.4.10: Active Memory Plugin The headline feature is the Active Memory plugin — a new optional plugin that gives OpenClaw a dedicated memory sub-agent. Rather than waiting for users to manually say “remember this,” the plugin automatically pulls in relevant preferences, past decisions, and contextual history before each reply. ...

If you’ve ever wished your AI agent could dream — not literally, but in the sense of consolidating fragmented memories into coherent long-term knowledge — OpenClaw v2026.4.9 just made that real. Released today, version 2026.4.9 introduces Dreaming, a REM backfill pipeline that fundamentally changes how OpenClaw agents handle memory continuity. It’s one of the most conceptually interesting updates the project has shipped, and the implications for persistent agentic systems are significant. ...

If you want to understand how a complete agentic AI system actually fits together — not from a marketing diagram, but from working Python code — nanobot is one of the best educational repositories available right now. Built by HKUDS and actively maintained (last commit March 2026), it’s an ultralight OpenClaw-inspired personal agent framework that clocks in at roughly 4,000 lines of Python. No heavy dependencies, no framework magic — just the core subsystems laid bare. ...

Building one AI agent is easy in 2026. Managing a fleet of them — keeping track of who they are, what they have access to, and whether they can be trusted to act without supervision — is the hard problem nobody talked about during the hype cycle. LangChain just shipped their answer. LangSmith Fleet launched on March 19, 2026 as an enterprise workspace for creating, deploying, and governing AI agents at scale. ...

If you’ve built anything serious with OpenClaw agents, you’ve hit the memory wall: agents that forget everything between sessions, multi-agent pipelines that can’t share context, and the perpetual workaround of dumping state into handoff files or external databases. Memori Labs has just shipped a direct answer to that problem. The Memori Labs OpenClaw Plugin adds automatic, persistent memory recall and capture to agents running through OpenClaw gateways. Agents can now access shared context across sessions and across multiple agents in the same deployment — without custom database integrations or session-state hacks. ...

Two significant moves from Anthropic landed today, and they’re clearly designed to work together: Claude memory is now available on the free tier, and a new import tool lets users bring their ChatGPT and Gemini conversation history into Claude. Both features went live March 2, 2026 — confirmed across Engadget, 9to5Mac, MacRumors, AndroidHeadlines, and Bloomberg. If you’ve been evaluating Claude as a reasoning backbone for agentic workflows, this changes the accessibility calculus significantly. ...

One of the hardest unsolved problems in agentic AI is not “can the agent do one thing well” — it’s “can the agent juggle dozens of interdependent tasks across hours or days without losing track of where it is.” That’s the problem CORPGEN is built to solve. Microsoft Research published the CORPGEN framework today — a benchmark and execution architecture for managing multi-horizon task completion in autonomous agents. The results are substantial: CORPGEN achieves up to 3.5x improvement over baseline approaches, reaching a 15.2% task completion rate compared to 4.3% for standalone UFO2. ...